Data Dictionary

Appalachian Regional Commission Distressed Areas

Details:

Appalachian Regional Commission Distressed Areas

Topics:

County Economic Status and Distressed Areas in Appalachia

Source:

Appalachian Regional Commission

Years Available:

2012-2019

Geographies:

census tract

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

http://www.arc.gov/appalachian_region/countyeconomicstatusanddistressedareasinappalachia.asp

Description:

The Appalachian Regional Commission designates certain counties and tracts as “distressed areas” in order to identify and monitor the economic status of areas within Appalachia. The designations are based on an index that compares economic indicators of Appalachian counties and tracts to national averages. The index employs three-year average unemployment rates, per capita market incomes, and poverty rates to rank every county in the nation. Counties are designated as distressed, at-risk, transitional, competitive, or attainment, based on this rank, where distressed counties rank as mostly economically depressed (worst 10% economic performance in the nation) and attainment the most economically strong (best 10% economic performance in the nation). In addition to all census tracts located in distressed counties, census tracts in at-risk or transitional counties are considered “distressed areas” if they have a median family income 67% or less than the national average and a poverty rate 150% of the national average or greater.

Distressed areas are updated annually using the most recently available American Community Survey five-year estimates. Distressed areas for 2012 are presented at the 2000 census tract boundaries, and 2013-2019 distressed areas are presented at the 2010 boundaries.

< Back to list of datasets

Black Knight: Home Sales

Details:

Single family, condo, mobile home home sales and sales price

Topics:

housing

Source:

Black Knight

Years Available:

2008 to 2021 Annual and Quarterly

Geographies:

zip code

Public Edition or Subscriber-only:

Premium Subscriber Only

Download Available:

no

For more information:

https://www.blackknightinc.com/

Last updated on PolicyMap:

April 2022

Description:

Black Knight estimates number of sales of existing single-family homes, condominiums, and mobile homes for U.S. zip codes. The estimates are based on deed transaction filings from county recorders offices’ public-record filings.

Percent changes are calculated by PolicyMap.

< Back to list of datasets

Bureau of Economic Analysis: Personal Income

Details:

personal income, per capita personal income

Topics:

income

Source:

Bureau of Economic Analysis

Years Available:

2000 – 2019

Geographies:

county, state, nation

Public Edition or Subscriber-only:

Widget, API-only

Download Available:

no

For more information:

https://bea.gov/newsreleases/regional/lapi/lapi_newsrelease.htm

Last updated on PolicyMap:

May 2021

Description:

Personal income consists of income received in return for provision of labor, land, and capital used in current production, as well as other income such as personal current transfer receipts. It is the sum of wages and salaries, supplements to wages and salaries, proprietors’ income with inventory valuation and capital consumption adjustments, rental income of persons with capital consumption adjustment, personal dividend income, personal interest income, and personal current transfer receipts, less contributions for government social insurance plus the adjustment for residence.

< Back to list of datasets

Bureau of Labor Statistics Local Area Unemployment Statistics

Details:

number of unemployed workers and unemployment rate

Topics:

employment, unemployment, labor force

Source:

Bureau of Labor Statistics Local Area Unemployment Statistics Program

Years Available:

Annual and monthly for 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022 (monthly)

Geographies:

county, Metropolitan Division, CBSA, state, place

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

http://www.bls.gov/lau/lauov.htm

Last updated on PolicyMap:

April 2022

Description:

The Bureau of Labor Statistics’ Local Area Unemployment Statistics (LAUS) program produces monthly and annual employment, unemployment, and labor force data for Census regions and divisions, States, counties, metropolitan areas, and many cities, by place of residence. PolicyMap contains county and state counts of people employed, unemployed, and in the labor force, as well as the unemployment rate. The annual values presented in PolicyMap are annual averages for the years listed as provided by the BLS. The concepts and definitions used by LAUS come from the Current Population Survey (CPS), the household survey that is the official measure of the labor force for the nation. According to this definition, employed persons include people who did any paid work as employees, worked in their own business or farm, or did unpaid work of 15 or more hours in an establishment owned by a relative. Unemployed persons include people who had no employment but were available for and seeking employment. People in the labor force are all those people classified as employed or unemployed. The labor force does not include military (active duty) and institutionalized persons. Every April, the Bureau of Labor Statistics re-releases revised data for the previous five years. PolicyMap’s data reflects these revisions. In 2015, the BLS changed the methodology of their model, with changes relating to structural differences, real-time benchmarking, smoothed seasonal adjustment, and treatment of outliers. At the state-level data, this methodology was used to re-estimate all data back to 1976 (PolicyMap displays data for 2000 and later). At sub-state geographies (city, county, CBSA, metropolitan division), the new methodology was only used to re-estimate data going back to January 2010. For this reason, comparisons between data before and after this change are not advised. Certain sub-state areas have the revised methodology re-estimated back to 1990: New York City (back to 1976), the Los Angeles-Long Beach-Glendale metropolitan division, the Chicago-Naperville-Arlington Heights, IL metropolitan division; the Cleveland-Elyria, OH metropolitan area; the Detroit-Warren-Dearborn, MI metropolitan area; the Miami-Miami Beach-Kendall, FL metropolitan division; and the Seattle-Bellevue-Everett, WA metropolitan division. Note that the previous methodology disaggregated data from the state level; because the state methodology is being re-estimated for before 2010, sub-state level data is indirectly affected. This data reflects recent county geography changes not yet implemented on PolicyMap: Bedford city (county equivalent) in Virginia was added to Bedford County; Petersburg Borough in Alaska was created out of parts of the former Petersburg Census Area and part of Hoonah-Angoon Census Area; and Prince of Wales-Hyder Census Area in Alaska added a part of the former Petersburg Census Area. These removed counties still appear on the map, showing insufficient data. Data for counties with geography changes (such as Bedford County, Virginia) reflect the new geography which is not visible on PolicyMap.

The annual data contains percent change calculations on the number of people in the labor force and employed, as well as a change in percent calculation of the unemployment rate. These were calculated by PolicyMap.

< Back to list of datasets

Bureau of Labor Statistics Quarterly Census of Employment and Wages

Details:

Average annual wage by industry

Topics:

wages

Source:

Bureau of Labor Statistics Quarterly Census of Employment and Wages

Years Available:

2000 – 2019

Geographies:

county, state, CBSA

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

http://www.bls.gov/cew/

Last updated on PolicyMap:

January 2022

Description:

The Bureau of Labor Statistics’ gathers data on employment and wages from state workforce agencies and compiles it under the Quarterly Census of Employment and Wages (QCEW) program. The data are derived from quarterly tax reports submitted by employers to State workforce agencies under State Unemployment Insurance (UI) laws; and from Federal agencies under the Unemployment Compensation for Federal Employees (UCFE) program. PolicyMap displays only private sector employment and wage data.

< Back to list of datasets

Census: Business Dynamics Statistics (BDS)

Details:

Firms by size and age

Topics:

firms by size, firms by age, startups, small businesses

Source:

U.S. Census Bureau, Business Dynamics Statistics

Years Available:

2000-2019

Geographies:

state, CBSA, county

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

https://www.census.gov/programs-surveys/bds.html

Last updated on PolicyMap:

January 2022

Description:

Business Dynamics Statistics (BDS) provides data over time on openings and closings of businesses, as well as detailed information about those businesses. Data comes from the Longitudinal Business Database. Data provided on PolicyMap includes number and percent of firms of various sizes and ages. This can be used to see the activity of small businesses and startups, as well as larger and established businesses. Firms included in our calculations include all which have at least one active physical establishment in a given area. (If one firm has two establishments in two different areas, it will be counted once in each location.) Firm age is calculated by assigning an initial age according to the age of the oldest establishment that is part of the firm when it is created. (If a firm is created out of a merger or acquisition, its age is based on the age of the oldest establishment in the original firm or acquisition.) Firm size is calculated by the average employment in the current and previous year.

BDS data is available at the state and CBSA level. To view CBSA data, select “Metro Area” from the “Shaded by” menu in the map legend.

< Back to list of datasets

Census: Decennial Census and American Community Survey (ACS)

Topics:

home values, housing stock, rental units, vacancy, household turnover, school enrollment, educational attainment, per capita income, family incomes, household incomes, aggregate income by type, incomes by age for older households, income inequality, people in poverty, families in poverty, population by ethnicity, age, sex, people with disabilities, total population, foreign born population, predominant foreign born population, household characteristics, families, veterans, homeowner characteristics, renter characteristics, affordability and cost burdens, unemployment, employment, commute to work, vehicles per household, home heating fuel types, healthcare uninsurance and healthcare uninsured and insured population

Source:

2000 U.S. Census, Summary File 3; 2010 U.S. Census Summary File 1; 2020 U.S. Census Decennial Redistricting File (PL 94-171); 2006-2010 U.S. Census American Community Survey (ACS), 2011-2015 U.S. Census American Community Survey (ACS); 2016-2020 U.S. Census American Community Survey (ACS)

Years Available:

2000, 2010, 2020, 2006-2010, 2011-2015, 2016-2020

Geographies:

block group, census tract, county subdivision, Census place (city), county, state, CBSA (metro area), metropolitan division, congressional district, zip code tabulation area (ZCTA), nation

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

https://data.census.gov/cedsci/

Last updated on PolicyMap:

June 2022

Description:

Demographic data for 2000 is from the U.S. Bureau of the Census’ Summary File 3 (SF3). This dataset is derived from the longer version (“long form”) of the household survey that takes place every ten years. SF3 data include information on housing conditions as well as characteristics of the household and its members. Demographic data for 2006-2010, 2011-2015, and 2016-2020 is from the U.S. Bureau of the Census’ American Community Survey (ACS). This survey replaced the long form from the Decennial Census in 2010. Rather than distributing both a short survey and the long form in 2010, the U.S. Census Bureau instead distributed the short survey as the Decennial Census. Beginning in 2000, the U.S. Census Bureau began administering the new ACS Survey, which is comprised of many of the questions from the old Census long form. With the release of the 2005-2009 ACS data, the ACS data includes small geographic estimates. The ACS data provides demographic, social, economic and housing characteristic estimates on a rolling basis, whereas the 2010 and 2020 Decennial Census provides counts of the population and their basic characteristics (sex, age, race, Hispanic origin, and homeowner status) as a snapshot in time. The move from the long form on the Decennial Census to the ACS format allows data consumers to enjoy annually updated detailed population characteristics, rather than having to wait for the Decennial Census data release. The ACS differs from the Decennial Census in that it is not an enumeration (complete count) of the population, however. Instead, the Census Bureau collects ACS data from a sample of the population, and it provides a margin of error for every ACS estimate. Margins of error are not shown on PolicyMap, but users are encouraged to visit the Census’ website with questions about ACS estimates shown on PolicyMap. PolicyMap shows the 2000 SF3 data using the Census’ 2000 geographic file boundaries. PolicyMap displays the 2006-2010 and 2011-2015 ACS data and the 2010 SF1 data using the Census’ 2010 geographic file boundaries. PolicyMap shows the 2020 Census data using the Census’ 2020 geography file boundaries. Because TIGER file boundaries across different years are not identical, users will likely see differences in boundary areas when toggling from data at different geographic file boundaries. For places, counties and county subdivisions, PolicyMap employed a Census-provided bridge table in order to calculate percent changes. PolicyMap also employed a Census-provided bridge table to relate 2000 census tracts to 2010 census tracts for percent change calculations. Because the Census has not yet provided relationship files for 2010 to 2020 boundaries or 2000 to 2020, PolicyMap created equivalent relationship files, see the Census Geography Division section for more details. In the case of block groups, PolicyMap created a bridge table to relate the 2000 Census SF3 data to the 2010 Census boundaries. The bridge table was created by first allocating 2000 block counts to their respective 2010 blocks using a family, household, or population multiplier. Then, after employing the Census-provided 2000 block to 2010 block table, PolicyMap summed the 2000 block estimates to 2010 Census boundary block groups. For percent change calculations for medians, PolicyMap calculated 2000 medians at the 2010 Census boundaries by creating component buckets of values using the Census 2000 count data at the 2010 Census boundaries and deriving the median from those counts. Data is shown at the Zip Code Tabulation Area (ZCTA) level. ZCTAs are based on US Postal Service ZIP Codes, but they are not identical, and in many cases, may be significantly different. Though ZIP Codes change continuously, ZCTAs are set in 2010 and 2020, and remain unchanged throughout the decade. Data shown for ZCTAs represent the information in the ZCTA area, not the ZIP Code. More information on ZCTAs can be found here: https://www.census.gov/programs-surveys/geography/guidance/geo-areas/zctas.html. Demographic data for 2010 is from the U.S. Bureau of the Census’ Summary File 1 (SF1). This dataset comprises what previously was referred to as the shorter version (“short form”) of the household survey that takes place every ten years. The SF1 data represents the count of every resident in the United States, mandated by Article I, Section 2 of the Constitution. These counts determine the number of seats per state in the U.S. House of Representatives. It is also used to distribute federal funds at the sub-state level. SF1 data includes information about population, age, race, ethnicity, household composition, home ownership and housing unit occupancy. The American Community Survey (ACS) has replaced the previous Census’ long form. Census 2010 data, therefore, constitutes only a fraction of the indicators previously released as Census Decennial data. Demographic data for 2020 is from the U.S. Bureau of the Census’ Decennial Redistricting File (PL 94-171). The PL 94-171 data represents the count of every resident in the United States, mandated by Article I, Section 2 of the Constitution. These counts determine the number of seats per state in the U.S. House of Representatives. It is also used to distribute federal funds at the sub-state level. PL 94-171 data includes information about population, race, ethnicity, and housing unit occupancy. This is only a portion of the 2020 decennial data. The full release of the Demographic and Housing Characteristics File (DHC), what replaces the SF1 file, is expected in 2022. For the 2020 Census a new form of privacy protection was introduced called differential privacy. This will introduce some noise into the 2020 decennial data that may look different than the previous privacy technique called “swapping” that was employed for the 2010 decennial data. No noise was introduced to the state counts since those are used for apportionment of state representatives.

Data on specific languages spoken at home was not released at local geographies after 2011-2015, so data for that time frame is shown.

< Back to list of datasets

Census Geography Division

Details:

Boundary files for Block Group, Tract, Place, County, County Subdivisions, State, CBSA, Metropolitan Division, Congressional Boundaries, ZCTAs

Topics:

Boundary files

Source:

US Census Bureau, Geography Division

Years Available:

2000, 2010, 2020

Public Edition or Subscriber-only:

Public Edition

Download Available:

no

For more information:

https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.html

Last updated on PolicyMap:

October 2021

Description:

Most of the boundary files on PolicyMap come directly from the Geography Division of the U.S. Census. Block Group, Tract, County, State, County Subdivision, Place, Core-based Statistical Area (CBSA), Metropolitan Division, Congressional District, and Zip Code Tabulation Area (ZCTA) boundary files are all publicly available TIGER files. See: https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.html. Every ten years, following the Decennial Census, there are major updates to many of the Census boundaries based on a new population count. Additionally, there are minor updates annually reflecting changes to local geographies (for example, in 2014, the county-equivalent Bedford City, Virginia, was combined with its adjacent county). These minor updates also reflect more accurate surveying techniques by the Census. Because of the significant changes that were made to underlying geography files in 2010, PolicyMap displays different data at Census 2000, 2010, and 2020 boundaries. In some cases, a single dataset will display some years of data at the different vintages. For example, Census 2000 data is shown at the 2000 Census boundaries while the most recent American Community Survey (ACS) data is displayed at the 2010 boundaries. In cases where a single dataset relies on multiple boundary files, percent changes from 2000 to 2010 are calculated using relationship files and other materials provided by the Census. For more, see: https://www.census.gov/geographies/reference-files.html. In most cases the materials provided by the Census were adequate to make the calculations. However, the process for calculating Block Group percent changes was more complex because the Census does not provide Block Group relationship files. PolicyMap relied on the Census Block relationship files for these calculations using a family, household, or population multiplier to crosswalk the 2000 data to 2010 boundaries to make the calculation. Percent changes that involve both 2000 and 2010 vintages are all displayed using 2010 Census boundary files. For additional information on percent change calculations please see data directory entries for individual datasets. In cases where a single dataset relies on multiple boundary files, percent changes from 2010 to 2020 and 2000 to 2020 are calculated using relationship files created by PolicyMap from Census TIGER boundaries. These PolicyMap relationship files match census boundaries across different vintages (such as 2000 to 2020) as equivalent if both boundaries contain 98% or more of the area for an individual boundary that shares the same unique identification code (often called a FIPS code). Change over time was also calculated across boundaries if each boundary vintage was comprised of more than 50% of both vintages. PolicyMap has the following geographies with distinct boundaries for the 2000, 2010, and 2020 vintages: block group, tract, county, county subdivision, and place. The state boundaries do not have substantial changes from 2000 to 2010 or from 2010 to 2020 (changes are simply related to improved surveying), so only the latest 2020 state file is loaded in PolicyMap. Core-based Statistical Area (CBSAs, or “Metro Areas” on PolicyMap) and Metropolitan Division boundary files have been released on a different schedule than the other boundaries, as they are delineated by the Office of Management and Budget (OMB). Significant changes were made in 2003, 2013, and 2019. PolicyMap uses 2010 CBSAs (which are based on the 2003 OMB delineation), 2013 CBSAs, and 2019 CBSAs. CBSA encompasses both metropolitan and micropolitan areas. For additional information on metro areas and their historical and current delineations please see: https://www.census.gov/programs-surveys/metro-micro.html. Places (identified as “Cities” on PolicyMap) are defined by the Census as either incorporated places or Census Designated Places (CDPs). Incorporated places are legally established to provide governmental functions for a concentration of people. An incorporated place is usually a city, town, village, or borough, but can take other forms. CDPs are statistical entities created by the Census “to provide data for settled concentrations of population that are identifiable by name but are not legally incorporated under the laws of the state in which they are located”. For more on the difference between incorporated areas and CDPs, see: https://www.census.gov/geo/reference/gtc/gtc_place.html The vintages of Place boundaries used in PolicyMap are 2009, 2010, and 2020. The 2009 cities are labeled on PolicyMap as 2000 for the sake of consistency and ease of use, because they are used for Census 2000 data. Zip Code Tabulation Areas (ZCTAs) are based on US Postal Service ZIP Codes, but they are not identical, and in many cases, may be significantly different. Though ZIP Codes change continuously, ZCTAs are set in 2010 and 2020, and remain unchanged throughout the decade. The congressional districts from 2004 to 2020 in the boundary menu reflect the congressional boundaries changing to redistricting in the 108th, 112th, 113th, 115th, 116th, and 117th sessions. On PolicyMap, election data displays the 110th Congress boundaries for 108th, 109th, 110th, 111th, and 112th Congresses, which are years 2004 to 2012. Congressional sessions 113th is for 2014, 2016 maps to 115th, and 2018 maps to 116th Congress. The boundary menu maps the 117th congressional boundaries with updated districts in North Carolina for the 2020 election cycle. The North Carolina boundaries are from the North Carolina General Assembly, which became law in 2019. The vintage – or year – of the boundary used can be found in several places throughout the site. On the Maps page, the boundary type and boundary year is shown in the legend once data is loaded on the map. There is also an option to overlay additional 2000, 2010, and 2020 boundaries by clicking on the “Boundaries” menu. When you search for a geography using the Set Location Bar, a dropdown menu will appear allowing you to select the vintage of the boundary you would like to see. If the boundary for the area of your search has changed, you can select either the 2000, 2010, or 2020 boundary definition. If the boundary has not changed there will only be one item in the dropdown labeled “2000 and 2010 and 2020 boundary”.

PolicyMap ZIP codes are licensed from Precisely. See: Precisely ZIP Code Boundaries.

< Back to list of datasets

Census: Longitudinal Employer – Household Dynamics

Topics:

Employment By Industry; Workforce Demographics; Worker Age, Educational Attainment, Race, Ethnicity, Sex, and Earnings; Wages; Distance Traveled to Work; Live-Work

Source:

LEHD Origin-Destination Employment Statistics (LODES) data, version 7; Data used by OnTheMap; Origin-Destination (OD), Residence Area Characteristics (RAC), and Workplace Area Characteristics (WAC) files are included.

Years Available:

2002-2017

Geographies:

block group, census tract, Census place (city), county, state, CBSA (metro area), zip code tabulation area (ZCTA)

Public Edition or Subscriber-only:

Public Edition

For more information:

http://lehd.ces.census.gov/data/#lodes

Last updated on PolicyMap:

November 2019

Description:

The LEHD Origin-Destination Employment Statistics (LODES) datasets are released at the Census block level in a series of state-based files available for download here: http://lehd.ces.census.gov/data/#lodes. This is the same data as what is available on Census’ OnTheMap application: http://onthemap.ces.census.gov/. The Longitudinal Employer-Household Dynamics (LEHD) program is part of the Center for Economic Studies at the U.S. Census Bureau. The LEHD program combines federal, state and Census Bureau data on employers and employees under the Local Employment Dynamics (LED) Partnership. Under the LED partnership, states share Unemployment Insurance earnings data and the Quarterly Census of Employment and Wages (QCEW) data with the Census. Census combines these data with additional federal administrative data, Census data, and surveys. The LEHD program also creates a partially synthetic dataset on workers’ residential patterns, offering a dynamic link showing where people live and where they work. LED was built state by state, and a handful of state-year combinations are not available. These include Alaska (2017), Arizona (2002 and 2003), Arkansas (2002), the District of Columbia (2002-2009), Massachusetts (2002-2010), Mississippi (2002 and 2003), New Hampshire (2002), South Dakota (2017), Puerto Rico (all years), and the U.S. Virgin Islands (all years). Data on the resident workforce exists for these locations for all years for employees that work in states where data is available, but they are not included on PolicyMap due to their incompleteness. For more on the LED Partnership see: http://lehd.ces.census.gov/state_partners/. Federal employment is not counted in state Unemployment Insurance data, and as a result federal employment was not included in LEHD until 2010. Furthermore, it is not included in the most recent data, for 2016 and 2017. Data shown by default on PolicyMap does not contain federal employment. However, additional variables in the legend allow users to see data with federal employment included for years 2010-2015. In order to calculate percent changes in employment and workforce numbers, PolicyMap subtracted federal employment from certain indicators (as noted) from 2010 to 2015. Percent change numbers involving any year between 2002 and 2009 or 2016 and 2017 does not include federal jobs. Some demographic variables were introduced in 2009, including race, ethnicity, educational attainment, and sex. Because of the incomparability of the data between 2009 and 2010 with the introduction of Federal employment, PolicyMap chose to begin mapping these additional indicators in 2010.

LODES data are released at the 2010 Census tabulation block geographies and PolicyMap aggregated up to the larger geographies using Census provided relationship tables. Data is aggregated using LEHD’s TIGER 2018 crosswalk files. However, PolicyMap displays all LEHD data at the TIGER 2010 boundary geographies, except CBSAs, which are shown at 2013 geographies.

< Back to list of datasets

Census: Public Use Microdata sample (PUMS)

Topics:

home values, housing stock, rental units, vacancy, household turnover, school enrollment, educational attainment, per capita income, family incomes, household incomes, aggregate income by type, incomes by age for older households, income inequality, people in poverty, families in poverty, population by ethnicity, age, sex, people with disabilities, total population, foreign born population, predominant foreign born population, household characteristics, families, veterans, homeowner characteristics, renter characteristics, affordability and cost burdens, unemployment, employment, commute to work, vehicles per household, home heating fuel types, healthcare uninsured and insured population

Source:

US Census Bureau

Years Available:

2016-2020

Geographies:

Public Use Microdata Area (PUMA), State, Nation

Public Edition or Subscriber-only:

Available in custom reports only

For more information:

https://www.census.gov/programs-surveys/acs/microdata.html

Last updated on PolicyMap:

June 2022

Description:

The Census Bureau releases an anonymized sample of the American Community Survey person and household survey responses as “microdata.” This microdata can be used to calculate estimates not available in the published American Community Survey data. To preserve anonymity, the Public Use Microdata Sample (PUMS) data is only available at a distinct large geography called a Public Use Microdata Area (PUMA), as well as the state level. PolicyMap calculates estimates for custom client reports using PUMS 5-year data. Any estimates at geographies other than the PUMA- or State-level were arrived at by combining survey results from PUMAs that overlap a given area. The PUMAs used in each calculation appear in footnotes.

< Back to list of datasets

Census, Brown University’s Longitudinal Tract Database, and PolicyMap

Details:

Persistent poverty

Topics:

Persistent poverty

Source:

U.S. Census American Community Survey (ACS), Brown University’s Longitudinal Tract Database, PolicyMap

Years Available:

2014

Geographies:

tract

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

http://www.s4.brown.edu/us2010/Researcher/Bridging.htm

Description:

The tract-level persistent poverty data layer on PolicyMap was created by PolicyMap using poverty data from the 1990 and 2000 censuses and the 2010-2014 American Community Survey, as provided by Brown University’s Longitudinal Tract Database (LTDB). In determining persistent poverty tracts, PolicyMap applied the same definition that the Community Development Financial Institutions (CDFI) Fund uses in determining persistent poverty county status, which is to assume a persistent poverty tract to be any tract that has had 20 percent of more of its population living in poverty over the past 30 years.

PolicyMap downloaded the 1990, 2000 and 2010-2014 data at the 2010 tract boundaries from the LTDB. The LTDB, developed by a research team including John Logan (Brown University), Zengwang Xu (University of Wisconsin, Milwaukee), and Brian Stults (Florida State University), provides public-use tools to create estimates within 2010 tract boundaries for tract-level Census data that are available as far back as 1970. For a detailed explanation of the LTDB team’s methodology for harmonizing data over this period, please see their website. Further explanation of the methodology can also be found in the Professional Geographer’s article entitled “Interpolating US Decennial Census Tract Data from as Early as 1970 to 2010: A Longitudinal Tract Database,” which was authored by Loan, Xu, and Stults.

< Back to list of datasets

Census County Business Patterns

Details:

count and percent of jobs located in a place, by gross and detailed industry classifications; count and percent of certain health-related establishments

Topics:

industry concentrations

Source:

US Census, Census County Business Patterns

Years Available:

2003 – 2019

Geographies:

zip code, county, state, CBSA

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

http://www.census.gov/econ/cbp/

Last updated on PolicyMap:

October 2021

Description:

County Business Pattern Data (CBP) is an annual series that provides economic data by industry. The data describe the number and type of jobs that are located in any given place. This is different from describing the occupations of people living in the same area. CBP covers most of the country’s economic activity. The series excludes data on self-employed individuals, employees of private households, railroad employees, agricultural production employees, and most government employees. CBP data are extracted from the Business Register, the Census Bureau’s file of all known single and multi-establishment companies. The Company Organization Survey (annual) and Economic Censuses (every five years) provide individual establishment data for multi-location firms. Data for single-location firms are obtained from various surveys conducted by the Census Bureau, such as the Economic Censuses, the Annual Survey of Manufacturers, and Current Business Surveys, as well as from administrative records of the Internal Revenue Service, the Social Security Administration, and the Bureau of Labor Statistics. Jobs in the CBP data are reported by North American Industry Classification System (NAICS, pronounced “Nakes”) categories. NAICS is the standard for use by Federal statistical agencies in classifying business establishments for the collection, analysis, and publication of statistical data related to the national business economy. NAICS is run through the Office of Management and Budget (OMB), and, in 1997, replaced the Standard Industrial Classification (SIC) system. Business establishments self-assign their NAICS code based on the primary economic activities in which they engage. CBP data is given using two different methodologies. At the county, CBSA, state, and nation level, values are given for the number of employees in a given industry. These values are infused with noise added by the Census in order to avoid disclosing data that would be identifiable to a specific employer. In addition, some values are withheld by the Census to avoid disclosing data for individual companies. In these cases, the Census provides a range within which the value falls, but these are not included on PolicyMap, and are shown as Insufficient Data. On PolicyMap, the data is also given as an “estimate”. For each industry, at every geography, (including Zip code) CBP provides values for the number of establishments that fall in various ranges of number of employees. These ranges are 1-4 employees, 5-9, 10-19, 20-49, 50-99, 100-249, 250-499, 500-999, and 1000 or more. There are additional higher ranges at the county and CBSA level. PolicyMap takes the midpoint of each range (so, for 10-19 it would be 14.5) and multiplies that by the number of establishments. Establishments with 1,000 or more employees are given a value of 1,750. This number for each range is added together to get the estimate. Though this number is less precise, its advantages are that there are no longer suppressions (so there is better coverage on the map), and ZIP code data is included.

Number of establishments is also provided for three industries relating to health: Fitness and Recreational Sports Centers; Beer, Wine, and Liquor Stores; and Limited-Service Restaurants. Both the count of establishments and rate of establishments per 100,000 people are provided. The rate uses population data from the American Community Survey. Population rate data not available at the ZIP code level.

< Back to list of datasets

Census Manufacturing, Mining and Construction Statistics

Details:

number of building permits issued for single family, 2-unit, 3-4 unit, and 5 or more unit buildings; number of buildings authorized by building permits; total value of buildings for which permits were issued

Topics:

building permits

Source:

Census Manufacturing, Mining and Construction Statistics

Years Available:

Annual through 2020, monthly through May 2021

Geographies:

county, state, CBSA, national

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

http://www.census.gov/construction/bps/

Last updated on PolicyMap:

August 2021

Description:

The U.S. Census Bureau’s Manufacturing, Mining, and Construction Statistics Division provides annual and monthly estimates of housing units authorized by building permit officials. Data are available at the county, state, CBSA, and national geographies. These estimates are aggregated and imputed from reports submitted by local permit-issuing offices. Most permit-issuing offices are municipalities, and the rest are counties, townships or towns. 9,000 out of the 20,000 permit-issuing places submit the Form C-404 report “Report of Building or Zoning Permits Issued and Local Public Construction” on a monthly basis. The other places are surveyed only annually. The 9,000 surveyed monthly include: all permit-issuing places in the 75 Metropolitan Areas (MAs) with the largest number of permits (as of 2002); all permit-issuing places in states with limited numbers of permit-issuing places; permit-issuing places with special data reporting arrangements. The rest of the sample is stratified by state. Prior to 2005, monthly counts of building permits were based on a different sample of 8,500 out of 19,000 permit-issuing agencies. As a result, comparisons of building permit issuances between 2004 and 2005 should be made very cautiously. If a building permit report is not received for a given month or year, the missing data are either obtained from the Survey of Use of Permits (SUP) or imputed. The SUP is an annual survey of a smaller sample of permit-issuing areas that gathers data on housing construction, completion, sales, and characteristics of new housing. Monthly state and national data are estimates based on the sample data collected from the 9,000 permit-issuing agencies. For information on standard errors associated with these estimates see: http://www.census.gov/construction/bps/. Monthly county data are counts rather than estimates, and are therefore reported only for those counties where every permit office issues monthly reports. Annual data are obtained by summing monthly data reporters. If permit-issuing agencies submit both monthly and annual reports, the annual count is used. The annual building permit data on PolicyMap is unadjusted data.

Building permit data will not accurately reflect construction activity in those areas where building permits are not issued. Nationally, only roughly 2 percent of housing starts are issued in areas not requiring permits, however this varies greatly state to state and region to region.

< Back to list of datasets

Census Planning Database

Details:

Census Mail Return Rate

Topics:

Census Response Rate

Source:

U.S. Census Planning Database

Years Available:

2010, 2013-2017, 2019

Geographies:

Block Group, Census Tract

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

https://www.census.gov/topics/research/guidance/planning-databases.2012.html

Last updated on PolicyMap:

August 2019

Description:

The United States Census Bureau’s Planning Database (PDB) was designed as an aid for survey and census planning purposes. It contains information on the mail return rate for the 2010 census, self-response rate for the 2013-2017 ACS, and Low Response Score, all of which may be helpful in targeting areas for a higher response rate in future counts and surveys. In the 2010 census, households that did not return their census form were contacted through a non-response follow-up (NRFU), which utilized various methods to count these households. Non-response follow-ups require more resources than mail returns, so this data can be used to strategically increase mail returns in certain areas.

< Back to list of datasets

Census and Opportunity Insights

Details:

Incarceration rates, upward mobility rates, eventual household income, by Race and Sex

Topics:

Incarceration, Equity, Income

Source:

Census and Opportunity Insights

Years Available:

2018

Geographies:

tract, county, commuting zone

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

https://opportunityinsights.org/

Last updated on PolicyMap:

May 2019

Description:

Opportunity Insights, a team of researchers and policy analysts based at Harvard University, conducted a longitudinal study of economic and social conditions of adults based on where they were raised. One of the products of this study was a dataset published at https://opportunityinsights.org/. This dataset includes average household earnings and incarceration rates for adults who were raised in low income households in a given tract, county, or commuting zone.

The researchers used demographic data from the 2000 and 2010 Census short forms, combined with data from the 2000 Census long form and 2015 American Community Survey. They linked this Census data with tax returns from 1989, 1994, 1995, and 1998 to 2015. By combining all this data at the person level, they were able to match people who were born from 1978 to 1983 with the census tracts where they were born and raised, and with the household earnings of their parents. They used this longitudinal dataset to calculate the incarceration rate (per 100 people) for people raised in households with incomes less than the 25th percentile based on whether they were in jail or prison on April 1, 2010, the reference date of the 2010 Census for two genders—men and women—and three racial and ethnic groups—Black, Hispanic, and White. Average household income was also calculated for this age cohort and parental income group using the income data from the tax returns. Incarceration rates and average incomes were prorated based on how much time the person spent in a given tract or county in their youth, and information was suppressed for areas with fewer than 20 children. Some noise was also infused into the source data to preserve privacy.

< Back to list of datasets

Census Public Elementary-Secondary Education Finance Data

Details:

Student enrollment/number of students, school district revenue, school district expenses, federal education revenue, state education revenue, local education revenue, Child Nutrition Programs revenue, children with disabilities (IDEA) revenue, Title I revenue

Topics:

Education, Public school finance

Source:

Census Public School Finance Data

Years Available:

2002 – 2018

Geographies:

School district

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

http://www.census.gov/govs/school/

Last updated on PolicyMap:

May 2020

Description:

The U.S. Census provides annual survey data on public school finances. Data is available at school district geographies. It includes the following indicators: student enrollment, total elementary-secondary revenue, total revenue from federal sources, total revenue for Title I, total revenue for children with disabilities, total revenue for child nutrition act, total revenue from state sources, total revenue from local sources, total elementary-secondary expenditures. Rates calculated by PolicyMap (such as revenue per student) were suppressed for school districts with 0 students in the given year.

< Back to list of datasets

Census and PolicyMap: Racial and Ethnic Diversity

Details:

Diversity index, predominant race/ethnicity

Topics:

Demographics, race, ethnicity, diversity

Source:

U.S. Census American Community Survey (ACS), PolicyMap

Years Available:

2013-2017

Geographies:

Block group, tract, ZCTA, county

Public Edition

Public Edition

Download Available:

yes

For more information:

http://www.census.gov/acs/

Last updated on PolicyMap:

December 2018

Description:

PolicyMap calculated the diversity index and predominant race/ethnicity data layers using Census’ American Community Survey 2013-2017 estimates. For both data layers, PolicyMap used a total of 8 non-overlapping racial and ethnic categories provided by the US Census Bureau. These included the ethnic category Hispanic and the following 7 Non-Hispanic racial categories: White, African American, American Indian or Alaska native, Asian, Native Hawaiian or Pacific Islander, some other race, and two or more races. The diversity index reflects the probability that any two people chosen at random from a given study area (e.g., block group) are of different races or ethnicities. An index value of 0 indicates complete homogeneity (i.e., an area’s entire population belonging to one racial or ethnic group), while the maximum index value represents complete heterogeneity (i.e., each racial or ethnic group constituting an equal proportion of an area’s population). The maximum value is calculated as one minus the reciprocal of the number of racial or ethnic groups. For example, with 3 racial or ethnic groups, the index value reflecting complete diversity would be 1-(1/3) or 67%. Articulated further, with 3 racial groups of equal proportions (i.e., complete diversity), the index equation becomes 1 – (0.33^2 + 0.33^2 + 0.33^2) = 67%. Given 8 racial or ethnic categories, the maximum value of the index displayed on PolicyMap is 87.5%. With the diversity index data layer, lower index values between 0 and 20 suggest more homogeneity and higher index values above 50 suggest more heterogeneity. Racial and ethnic diversity can be indicative of economic and behavioral patterns. For example, racially and ethnically homogenous areas are sometimes representative of concentrated poverty or concentrated wealth. They could also be indicative of discriminatory housing policies or other related barriers.

The predominant racial or ethnic group is calculated as the racial or ethnic group constituting the highest proportion of the population in a given geography. A “tie” was noted in cases where the more than one racial or ethnic group shared the highest percent of population, rounded to the nearest 0.1 percent.

< Back to list of datasets

Census and PolicyMap: Racial and Ethnic Segregation

Details:

Theil’s H index

Topics:

Demographics, race, ethnicity, segregation

Source:

U.S. Census 2010 Summary File 1, PolicyMap

Years Available:

2010

Geographies:

Block group, tract, county

Public Edition or Subscriber-only

Public Edition

Download Available:

yes

For more information:

http://www.census.gov/2010census/ https://www.census.gov/housing/patterns/about/multigroup_entropy.pdf

Description:

PolicyMap calculated Theil’s H index of segregation using the U.S. Census Bureau’s 2010 Decennial Census estimates. For this index, PolicyMap used a total of 8 non-overlapping racial and ethnic categories provided by the US Census Bureau. These categories included the ethnic category Hispanic and the following 7 Non-Hispanic racial categories: White, African American, American Indian or Alaska native, Asian, Native Hawaiian or Pacific Islander, some other race, and two or more races. Theil’s H is an index ranging from 0 to 1 that estimates the extent to which racial and ethnic groups are evenly distributed in a sub-area as compared to a larger area. Values approaching 0 suggest that sub-areas have a composition similar to the larger area (i.e., even distribution, less segregation) and values approaching 1 suggest that the racial and ethnic composition of sub-areas within a larger area deviates from the larger area (i.e., non-uniform distribution, more segregation). On PolicyMap, sub-areas are defined at the level of the Census block and are compared to the following larger areas: block groups, tracts, counties and Core-Based Statistical Areas (CBSAs), which are an approximation of metropolitan areas. The calculation of Theil’s H is based on the methodology presented in the report, “The Multigroup Entropy Index” by John Iceland (2004). This methodology involves calculating the entropy, a measure of diversity, for each sub-area and larger area and calculating the population-weighted deviation in entropy values across all sub-areas within each larger area.

Geographies for which no data or limited data were provided or for which the population was less than 10 are represented as having “Insufficient Data.”

< Back to list of datasets

Census’ Small Area Income and Poverty Estimates

Details:

number of families receiving food stamps, number of school-age children in poverty

Topics:

food stamps, school-age poverty

Source:

US Census Small Area Income and Poverty Estimates

Years Available:

2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020

Geographies:

county, state, school district

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

https://www.census.gov/programs-surveys/saipe/data.html

Last updated on PolicyMap:

March 2022

Description:

The Census’ Small Area Income & Poverty Estimates (SAIPE) dataset provides more current estimates of selected income and poverty statistics than the most recent decennial census. Estimates are created for states, counties, and school districts, depending on the data. This dataset mainly serves administrators of federal programs who need current statistics on the demonstrated need of places.

< Back to list of datasets

Centers for Disease Control and Prevention (CDC) 500 Cities

Details:

BMI status, physical inactivity, chronic health conditions (arthritis, asthma, chronic kidney disease, COPD, coronary heart disease, diabetes, high blood pressure, high cholesterol, hypertension, stroke), alcohol consumption, tobacco use, routine checkup within last year, dental visits, lack of sleep, health screening (cholesterol, colorectal cancer, mammography use, Pap test use, core preventative services)

Topics:

Health-risk behavior, diabetes and related risk factors, physical inactivity, health screening, chronic health conditions

Source:

Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Division of Population Health

Years Available:

2013, 2014, 2015, 2016

Geographies:

census tract, place

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

https://www.cdc.gov/500cities/

Description:

The CDC Foundation and the Robert Wood Johnson Foundation (RWJF) funded a project that created small area estimates for the largest 500 American cities. Twenty seven chronic disease measures were estimated at the level of census tracts, using the Centers for Disease Control and Prevention’s Behavioral Risk Factor Surveillance System (BRFSS) data, Census Bureau 2010 census population data, and the American Community Survey (ACS) estimates. The chronic disease measures were selected to help implement targeted prevention programs, identify emerging health problems, and establish and monitor health objectives. The 500 cities included the top 497 largest American cities and the largest cities in Vermont (Burlington), West Virginia (Charleston), and Wyoming (Cheyenne). The range of city population spans 42,400 in Burlington, VT to 8,175,100 in New York, NY. The study includes a total population of approximately 103,020,800, which represents 33.4% of the United States population. A total of approximately 28,000 census tracts are included for all 500 cities.

Because the small area estimation model cannot capture the direct effects of local programs and initiatives, caution should be taken not to use the data to evaluate the outcome of such programs. Small area estimation models are statistical techniques used to estimate the values of indicators for sub-populations from larger surveys that include those sub-populations. The overall survey used for the small area estimation model is the CDC’s annual Behavioral Risk Factor Surveillance System (BRFSS) survey. Because some questions in the BRFSS survey rotate yearly, not every 500 Cities indicator is available for every year. The 500 Cities estimation approach allows for the estimation at multiple geographic levels, and accounts for the relationships between individual characteristics, individual health outcomes, and spatial contexts and other factors. The CDC conducted internal and external validation studies that demonstrated the consistency between the model-based estimations and direct BRFSS survey estimates. For more information on the methodology of this dataset, see https://www.cdc.gov/500cities/methodology.htm.

< Back to list of datasets

Centers for Disease Control and Prevention (CDC) COVID-19 Vaccination

Details:

COVID-19 vaccination

Topics:

Health, COVID-19, vaccination

Source:

Centers for Disease Control and Prevention

Years Available:

2021, 2022

Geographies:

county, state, nation

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

https://covid.cdc.gov/covid-data-tracker/#vaccinations

Last Updated on PolicyMap:

see data description for details

Description:

The CDC posts daily data updates on COVID-19 vaccination administration and state distribution, assembled from data reported by state public health agencies and offices. Healthcare providers report doses administered to federal, state, territorial, and local agencies within 72 hours of administration, but there could be an additional lag before data reaches the CDC. Each entity may use various reporting methods, including immunization information systems, Vaccine Administration Management System, which supports temporary, mobile, and satellite clinics, in addition to direct submissions. The CDC’s data might differ from state systems and dashboards due to unfound duplicates during the agency’s data consolidation or due to varying reporting practices. Visit https://covid.cdc.gov/covid-data-tracker/#vaccinations to see data validation and collection protocols. Doses delivered and administered for U.S. States, D.C., and Puerto Rico are cumulative counts of COVID-19 vaccine doses reported to the Federal COVID Vaccine Operation delivered since December 14, 2020. Doses delivered to the U.S. Virgin Islands, Palau, Micronesia, Marshall Islands, Guam, American Samoa, and Northern Marianas Islands include those marked as shipped in CDC’s Vaccine Tracking System (VTrckS) since December 13, 2020. Doses delivered and administered in a state or territory also include those delivered and administered in pharmacies and the Federal Pharmacy Partnership for Long-Term Care (LTC) Program in the jurisdiction as reported in VTrckS. Doses administered are attributed to the jurisdiction in which the vaccine was administered. People receiving one or more doses represent the total number of people who have received at least one vaccine dose. People receiving two doses represent the number of people who have received a second dose of the vaccine. The number of people receiving one or more doses and the number of people receiving two doses was determined based on CDC’s information by state, territorial, and local public health agencies and federal entities on dose number, administration date, recipient I.D, and date of submission. A dose number was determined for nearly all reported doses administered; some missing data for dose number resulted in people receiving one or more doses and people receiving two doses not equaling the total doses. Rates per 100,000 represent the number of total doses delivered, the number of total doses administered, the number of people receiving one or more doses, and people receiving two doses per 100,000 residents of all ages. These metrics use the U.S. Census Bureau Annual Estimates of the Resident Population for the United States and Puerto Rico, 2019. 2018 U.S. Census Bureau population estimates and estimates from the CIA World Factbook are used for American Samoa, Federated States of Micronesia, Guam, Northern Mariana Islands, Republic of Palau, Marshall Islands, and U.S. Virgin Islands. Emergency Use Authorization has been granted for using the Pfizer-BioNTech vaccine among persons aged 16 and older and using the Moderna vaccine among persons aged 18 and older. Therefore, vaccine use is limited among those under age 18, representing approximately 22% of the U.S. population. Jurisdictions may use more targeted population counts for the denominators in their rate calculations (i.e., persons over 18 or over 16 years old), resulting in values different from those reported on the CDC COVID Tracker. In some limited circumstances, people might receive vaccinations outside of their state or territory of residency. These rates currently account for vaccinations occurring in the jurisdiction where the vaccination was administered.

Occasionally, states will overreport or underreport vaccine administration and distribution causing “Insufficient Data” to appear on the map for Change in number of people who are fully COVID-19 vaccinated, and Percent change in people who are fully COVID-19 vaccinated indicators.

< Back to list of datasets

Centers for Disease Control and Prevention (CDC) Opioid Overdose Prevention

Details:

Opioid prescription rate, annual change in prescription rate

Topics:

Opioid prescriptions

Source:

Centers for Disease Control and Prevention, National Center for Injury Prevention and Control, Division of Unintentional Injury Prevention

Years Available:

2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019

Geographies:

county, state

Public Edition or Subscriber-only:

subscriber-only

Download Available:

yes

For more information:

https://www.cdc.gov/drugoverdose/maps/rxrate-maps.html

Last updated on PolicyMap:

May 2021

Description:

CDC prescription data comes from the QuintilesIMS Transactional Data Warehouse, which is based on a sample of approximately 59,000 retail (non-hospital) pharmacies that dispense roughly 88% of all retail prescriptions in the U.S. This database includes initial and refill prescriptions dispensed at retail pharmacies but does not include mail order prescriptions. Prescriptions include those purchased through commercial insurance, Medicaid, Medicare, or cash or its equivalent. Opioid prescriptions include butrans (buprenorphine), codeine, fentanyl, hydrocodone, hydromorphone, methadone, morphine, oxycodone, oxymorphone, propoxyphene, tapentadol, and tramadol. Cough and cold formulations containing opioids and buprenorphine, an opioid partial agonist used for treatment of opioid use disorder as well as for pain, are not included. In addition, methadone dispensed through methadone maintenance treatment programs is not included. Opioid prescription rates per 100 persons, reported by the CDC, are calculated using population estimates from the Population Estimates Program, U.S. Census Bureau. After a steady increase in overall opioid prescribing rates from 2006, total opioid prescriptions peaked in 2012 at 255 million and a rate of 81.3 prescriptions per 100 people. The rate does not represent the percent of the population receiving opioid prescriptions. Since an individual may receive multiple prescriptions in a year, many counties have rates that are greater than 100 prescriptions per 100 persons. Data is available for over 85% of all U.S. counties. Counties displayed as having insufficient data may indicate counties with no retail pharmacies, counties where no retail pharmacies were sampled, or counties where the prescription volume was erroneously attributed to an adjacent, more populous county according to the sampling rules used.

This data differs from the data shown in the July 2017 issue of CDC Vital Signs, which featured different facets of opioid prescribing from 2006 to 2015. For more information visit https://www.cdc.gov/vitalsigns/opioids/index.html.

< Back to list of datasets

CDC: Population Level Analysis and Community Estimates (PLACES)

Details:

Health Risk Factors, Health Outcomes

Topics:

Public Health

Source:

Centers for Disease Control and Prevention (CDC)

Years Available:

2018, 2019

Geographies:

Census tract, ZCTA, City, County

Public Edition or Subscriber-only:

Premium

Download Available:

yes

For more information:

https://www.cdc.gov/places/about/index.html

Last updated on PolicyMap:

July 2022

Description:

The CDC’s PLACES program is an expansion of the small area health estimates that they created for their 500 Cities program starting in 2015. The 500 Cities program used statistical techniques to produce estimates for various health outcomes or risk factors at small areas for the largest 500 Cities in the United States. The PLACES program expands these estimates across the country, and makes the data available for Census tracts, ZCTAs, Census places (called Cities on PolicyMap), and Counties. These estimates were created using the CDC’s Behavioral Risk Factor Surveillance System (BRFSS), the 2010 Decennial Census, and estimates from the American Community Survey.

The CDC publishes both crude and age-adjusted estimates. Many of the chronic health conditions and risk factors estimated in this dataset have a strong correlation with age. This means that areas with much older populations may have deceptively high crude rates of illness. Age-adjusted estimates correct for the different age profiles of different geographies, which makes it easier to understand whether certain health conditions are truly worse in certain areas. Crude rates are available at the Census tract, ZCTA, City, and County levels, and Age-adjusted rates are available at the City and County levels.

PLACES variables include the following chronic conditions: arthritis, asthma, heart disease, high blood pressure, cancer, high cholesterol, kidney disease, COPD (lung disease), depression, diabetes, obesity, all teeth lost, and stroke. Prevention: lack of health insurance, routine medical checkups, dental visits, high blood pressure medication, cholesterol screening, mammography, cervical cancer screening, colonoscopy, core clinical preventive services for male and female older adults. Risk behaviors: binge drinking, smoking, no physical activity, limited sleep. Health status: poor mental health, poor physical health, poor self-rated health.

< Back to list of datasets

Centers for Disease Control and Prevention (CDC) Infant Birth and Prenatal Care

Details:

count of births, number and percent of mothers by age and trimester in which prenatal care was received, number and percent of infants born with low birth weight

Topics:

infant birth, prenatal care, young mothers, low birthweight

Source:

CDC National Center for Health Statistics

Years Available:

2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020

Geographies:

county, state

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

https://wonder.cdc.gov/natality.html

Last updated on PolicyMap:

April 2022

Description:

The Centers for Disease Control (CDC) dataset provides the number of births, the number and percent of infants born with birth weight under 2,500 ounces (low birthweight), the number and percent of infants born with birth weight under 1,500 ounces (very low birthweight), the number and percent of births where prenatal care began during the first trimester and the number and percent of births where prenatal care was received in only the third trimester or not at all. The CDC only reports numbers of births for counties with populations of 100,000. The CDC also provides numbers and rates for mothers under age 20. Additionally, this dataset includes the number and percent of births to mothers under the age of 20, with break outs for mother under age 18 and mothers 18 and 19 for select years. Data on prenatal care is only available for counties with populations of 100,000 or more.

Beginning in 2007, data are reported from the 2003 U.S. standard Certificate of Live Birth, with additions of information on birth anomalies and several less variables related to maternal risk factors than the previous 2003 revision.

< Back to list of datasets

Centers for Disease Control and Prevention (CDC) Flu Activity & Surveillance

Details:

level of influenza-like-illness activity, weeks of above-average influenza-like-illness activity, geographic spread of flu

Topics:

Seasonal influenza, flu

Source:

CDC: FluView

Years Available:

Seasonal for 2008-09, 2009-10, 2010-11, 2011-12, 2012-13, 2013-14, 2014-15, 2015-16, 2016-17, 2017-18, 2018-19

Geographies:

State

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

http://www.cdc.gov/flu/weekly/fluactivitysurv.htm

Last updated on PolicyMap:

July 2019

Description:

The Centers for Disease Control (CDC) Flu Activity & Surveillance System dataset provides estimates of flu activity for states and territories of the U.S. Flu activity indicators are a measure of the proportion of visits to healthcare providers for influenza-like illness (ILI) symptoms. Estimates are collected from public health facilities participating in the Outpatient Influenza-like Illness Surveillance Network (ILINet). These data may disproportionately represent certain populations within a state; for instance, a severe flu outbreak in one city or region may cause the statewide activity level to be High, even if flu activity is low or minimal in other areas throughout the state. State health departments may have more geographically precise information available; contact information for these departments is available in FluView. Geographic spread of influenza is reported directly to CDC by state epidemiologists. This is a measure of how much of each state is affected by flu, and is not a measure of the severity of influenza activity. Weekly data and state and local surveillance information are available at the CDC Influenza Surveillance website. ILI activity and geographic spread measures are provided weekly. To obtain seasonal values, PolicyMap calculated the average of the numerical activity levels for all weeks ending in a given season. Flu season is defined as the period beginning in October and ending in March. For geographic spread, PolicyMap assigned a numerical scale where No Activity = 1 and Widespread = 5, in order to calculate the average spread for the year. Only states with at least 24 weeks of activity per season are included in these calculations.

New York City reports flu data to CDC separately from New York State; as such, New York State flu activity and geographic spread measures do not take New York City into account.

< Back to list of datasets

Centers for Disease Control and Prevention (CDC) National Center for Health Statistics

Details:

count and rate of infant deaths, count and rate of infant deaths by race of mother, count and rate of cancer deaths, count and rate of stroke deaths, count and rate of coronary heart disease deaths, count and rate of chronic lower respiratory disease deaths, count and rate of homicides, count and rate of suicides, count and rate of traffic deaths, count and rate of accidental injury deaths, drug overdose deaths, count and rate of opioid drug overdose deaths, count and rate of narcotic overdose deaths, count and rate of unspecified drug overdose deaths

Topics:

infant mortality, cancer, stroke, heart disease, chronic lower respiratory disease, cause of death, mortality, drug overdose, opioid overdose, narcotics overdose

Source:

CDC National Center for Health Statistics, National Vital Statistics System, National Vital Statistics System Multiple Cause of Death

Years Available:

Various (2000-2020)

Geographies:

county, state

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

http://wonder.cdc.gov/ https://www.cdc.gov/nchs/data-visualization/drug-poisoning-mortality/ https://wonder.cdc.gov/mcd-icd10.html

Last updated on PolicyMap:

April 2022

Description:

The Centers for Disease Control (CDC) dataset provides the number of infant deaths, and the rate of deaths to infants for every 1000 live births by maternal residents of the US. The CDC only reports numbers of births for counties with populations of 100,000 or more and number and rate of infant deaths for counties with populations of 250,000 or more. It suppresses the rate where there are fewer than 20 deaths reported. Adult mortality data are taken from the National Center for Health Statistics’ Compressed Mortality file as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. The Compressed mortality file provides the number and rate of deaths, by age group and cause of death as reported through the tenth revision of the International Statistical Classification of Diseases and Related Health Problems (ICD-10). Data on PolicyMap represent deaths from cancer, coronary heart disease, stroke, and chronic lower respiratory disease among those aged 45 or older, from 2000 through 2015; and deaths from homicide, suicide, motor vehicle traffic, and accidental injury for all age groups. These causes have topped the CDC’s list of leading causes of death since 2005. Underlying cause-of-death is indicated on the death certificate by the physician. The National Center for Health Statistics determines one cause of death when more than one cause or condition is entered by the physician. PolicyMap shows mortality data from 2000 through 2018. Adults ages 35 and older are used as a base category for deaths from disease because these age groups represent most of the deaths from the four leading causes. Rates are calculated per 100,000 population 35 and over in the source data using population estimates based on 2000 and 2010 U.S. Census counts. The CDC’s National Center for Health Statistics released an estimated model of drug overdose data in its Data Visualization Gallery. Smoothed crude death rate estimates were generated using Hierarchical Bayesian models with spatial and temporal random effects. Bayesian hierarchical modeling “borrows strength” across geographic areas and allows estimates to be generated for counties that have small populations. Updated county-level estimates now include point estimates rather than estimate ranges. The CDC adds a disclaimer to this dataset that in certain states and years, for example New Jersey (2009) and West Virginia (2005, 2009), the rates may be lower than expected due to a large number of unresolved cases or misclassification of ICD-10 codes. More information on the CDC’s methodology is available here. Opioid and narcotic poisoning data comes from the CDC’s Multiple Cause of Death files. Drug overdose deaths were classified using the Tenth Revision (ICD-10) of the International Classification of Disease underlying-cause-of-death codes for drug poisonings (overdose): X40-44 (unintentional), X60-64 (suicide), X85 (homicide), and Y10–Y14 (undetermined intent). The types of opioid involved in drug overdose deaths were classified following the ICD-10 codes: and T40.1 (heroin), T40.2 (natural and semisynthetic opioids), T40.3 (methadone), and T40.4 (synthetic opioids, other than methadone). The category for all opioid overdoses includes all these categories (T40.1, T40.2, T40.3, and T40.4). T40.0 (opium) was not included since fewer than 10 people are reported each year as having died from opium overdose in the nation. Deaths involving multiple types of opioids are recorded in each applicable category, therefore the US totals may include overcounting. Heroin is an illegally-made semi-synthetic opioid derived from morphine. “Natural and semisynthetic opioids” is a category of prescription opioids, which includes natural opioid analgesics (codeine, morphine, etc.) and semi-synthetic opioid analgesics (hydrocodone, hydromorphone, oxycodone, and oxymorphone), but excludes heroin. Methadone is a prescribed synthetic opioid used to treat moderate to severe pain, and also withdrawal symptoms in those addicted to heroin or other narcotics. “Synthetic opioids, other than methadone” is a category of opioids commonly available by prescription and includes drugs such as fentanyl and tramadol, but excludes methadone. The CDC does not differentiate between deaths from pharmaceutical fentanyl and illegally-made fentanyl, and deaths from both forms are included in the data. While medically not considered a narcotic, cocaine is legally classified as such and is included in the CDC’s definition of narcotics along with opioids. The types of narcotics involved in drug overdose deaths were classified following the ICD-10 codes: T40.6 (other and unspecified narcotics), and T40.5 (cocaine). The category for all narcotics overdoses includes T40.1, T40.2, T40.3, T40.4, T40.5 and T40.6. The methods used to classify deaths on death certificates may lead to a significant undercount of opioid-related deaths, which could inaccurately portray the severity of this public health problem. Because of reporting discrepancies and nonspecific language, it is likely that national statistics underestimate by a substantial fraction the amount of opioid analgesic- and heroin-related deaths. Additionally, the degree of underestimation varies based on states’ death certification systems. For more information undercounting opioid-related deaths visit https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4547584/. To provide context for a given area, it is helpful to also look at how many overdose deaths are recorded with no additional drug information. These were classified according to the ICD-10 code of T50.9 (other or unspecified drugs).

For more information on the data visit https://wonder.cdc.gov/mcd-icd10.html.

< Back to list of datasets

Centers for Disease Control and Prevention (CDC) National Center for Health Statistics, Small-area Life Expectancy Estimates

Details:

life expectancy at birth, remaining life expectancy by age, probability of death by age

Topics:

health, life expectancy, mortality

Source:

CDC National Center for Health Statistics, the United States Small-area Life Expectancy Estimates Project (USALEEP)

Years Available:

2010 – 2015

Geographies:

tract

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

https://www.naphsis.org/usaleep

Last updated on PolicyMap:

March 2021

Description:

The National Center for Health Statistics (NCHS) released the results of their Small-area Life Expectancy Estimates Project in September of 2018. For this project, NCHS calculated abridged life tables at the Census tract level. An “abridged life table” is a series of estimates of life expectancy for people of different ages grouped into multi-year cohorts. To create these abridged life tables, NCHS worked with the National Vital Statistics System to geocode residences recorded on death certificates from 2010 to 2015 (inclusive). The Department of Housing and Urban Development Geocode Center performed the geocoding. Maine and Wisconsin are excluded from this study because they did not have geocoded death certificates for 2010. The life table calculations also required Census tract level population estimates. NCHS worked with the Census Bureau to create a set of custom 6-year population estimates for the years 2010–2015 using data from the 2010 Census and the 2011–2015 ACS for use in this project. The NCHS used information on the location of residence and age of the deceased combined with the population estimates to build age patterns or “schedules of mortality” for 4,639 “model tracts.” Model tracts met two criteria—each had a population over 5,000 and one or more recorded deaths in each age group during the 6-year window. Using schedules of mortality from the model tracts, the NCHS developed statistical models to predict death rates based on demographic, socioeconomic, and geographic variables. They used these models to fill in data for tracts with smaller populations, and for tracts with age groups that had “missing deaths” or no recorded deaths within an age group. For the model tracts, reported values for all age ranges are calculated directly from the death certificates and population estimates. For the tracts with some missing deaths, values predicted by the statistical models are used for the age groups that had no recorded deaths, and observed values are used for all other age ranges. For some tracts with low populations, all values are based on predictions from the statistical models. See the complete documentation of the methodology here: https://www.cdc.gov/nchs/data/series/sr_02/sr02_181.pdf.

< Back to list of datasets

Centers for Disease Control and Prevention (CDC) National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention

Details:

count and rate of STD incidence – chlamydia, gonorrhea, syphilis, HIV; count and rate of persons living with HIV

Topics:

Sexually Transmitted Diseases (STDs), Notifiable Infectious Diseases, chlamydia, gonorrhea, syphilis, HIV, AIDS

Source:

National Center for HIV, STD and TB Prevention (NCHSTP), Division of STD/HIV Prevention

Years Available:

various, 2000 – 2019

Geographies:

county, state

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

http://www.cdc.gov/nchhstp/About.htm

Last updated on PolicyMap:

November 2021

Description:

The Centers for Disease Control and Prevention HIV Incidence and Case Surveillance Branch provides the number of estimated active HIV infection cases among people aged 13 and older from state and local health departments. These data are available for states and counties. These data represent the place of residence at earliest HIV diagnosis; duplicate records from different states are reconciled by the source. Some states without confidential name-based HIV infection reporting have elected not to release state and/or county-level data. CDC provides estimates on new HIV diagnoses for a given year. HIV data for a given year presented on PolicyMap represent the number of confirmed diagnoses of HIV infection or infection classified as stage 3 (AIDS) confirmed by laboratory analysis as of December 31 of that year and reported to the CDC by June 30 of the following year. These data may not be limited to new infections (i.e., incidence); rather, these data represent new diagnoses, as the date of infection may vary by individual. Estimates are statistically-adjusted values based upon actual case counts reported to CDC by state and local health departments. The CDC suppressed values in areas with fewer than 5 reported cases and/or population less than 100 as well as in the county with the lowest population in states where only one other county’s data were suppressed. Data on the number of new cases of chlamydia, gonorrhea, and syphilis reported each year, and the rate of new STD cases reported for every 100,000 residents, by state and county are available from CDC. Data are based on cases of STDs reported to state and local health departments. Data is reported by both public and private agencies, such as STD clinics, counseling/testing sites, drug treatment clinics, family planning clinics, and private physicians. The CDC collects data from regional jurisdictions, and publishes the data in an annual report, which can be downloaded here: https://www.cdc.gov/std/stats/default.htm. Syphilis is presented as a combined sum of cases classified in either primary or secondary stages of the disease. Other categories of syphilis – not included in the data – are latent (without symptoms), tertiary (late stage), and congenital (transferred from mother to child). Primary and secondary forms of the disease are the most infectious and therefore important when considering the risk of transfer and spread of disease. Some variability in the amount of in the amount of reporting may exist across the country. Chlamydia, gonorrhea, and syphilis are considered Nationally Notifiable, which means that regional jurisdictions provide information to the CDC on a voluntary basis. A nationally notifiable disease is not necessarily reportable by law within a given state. Because of incomplete diagnosis and reporting, the number of STD cases reported is less than the actual number of cases occurring. The level of consistency may vary between local jurisdictions, reporting agencies, and reporting years. In some areas, reporting from public sources is thought to be more complete than reporting from private sources.

Incidence rates were calculated by the CDC using total population as the denominator. These population values are estimates created by the National Center for Health Statistics (NCHS) using 2000 U.S. Census data as a base year. Ten-year percent change variables for incidence of STDs and five-year percent change for HIV incidence were calculated by PolicyMap.

< Back to list of datasets

Centers for Disease Control and Prevention (CDC) Social Vulnerability Index (SVI)

Details:

Social Vulnerability Index and Level

Topics:

Social Vulnerability, resilience

Source:

Centers for Disease Control and Prevention, Agency for Toxic Substances and Disease Registry

Years Available:

2018

Geographies:

tract, county

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

https://svi.cdc.gov/index.html

Last updated on PolicyMap:

June 2020

Description:

Social vulnerability refers to populations that are particularly vulnerable to disruption and health problems as a result of natural disasters, human-made disasters, climate change, and extreme weather. The Geospatial Research, Analysis, and Services Program (GRASP) within the CDC created the Social Vulnerability Index (SVI) to help flag areas where residents will be in greatest need of support and recovery assistance in the case of a disaster or extreme weather event. The index is comprised of four categories of vulnerability—socioeconomic status, household composition and disability, minority status and language, and housing and transportation. Data from the 2012-2016 ACS informs the score for each category. Socioeconomic vulnerability is comprised of population below poverty, unemployed population, low income population, and people with no high school diploma. Vulnerability due to household composition and disability is comprised of adults age 65 or older, children age 17 or younger, people over age 5 with a disability, and single parent households. The minority status and language category is comprised of minority population, defined as people of any race or ethnicity other than non-Hispanic White, and people over the age of five who speak English less than well. The housing and transportation category is a combination of housing units in multifamily buildings with ten or more units, mobile homes, crowded housing units (defined as housing units with more people than rooms), households with no available vehicle, and group quarters population (which includes dormitories, prisons, and people living in other institutions).

The GRASP program combined the ACS variables into categories by assigning each geography a percentile ranking for that variable, then summing those percentiles to create an overall score within that category. Then percentile rankings were assigned for each of the four categories, which were summed to create the overall Social Vulnerability Index. The four social vulnerability levels—Low, Low to Moderate, Moderate to High, and High—are defined by dividing all tracts or counties in the country into quantiles based on the Social Vulnerability Index. Find more information on the CDC’s Social Vulnerability Index at https://svi.cdc.gov/.

< Back to list of datasets

Centers for Disease Control and Prevention (CDC) State Cancer Profiles

Details:

Count and rate of cancer incidence – including: Total Cancer, Breast, Cervix, Colon & Rectum, Lung & Bronchus, Melanoma of the Skin, and Prostate

Topics:

Cancer

Source:

Centers for Disease Control and Prevention (CDC) and National Cancer Institute (NCI)

Years Available:

2014-2018

Geographies:

State, county

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

https://gis.cdc.gov/Cancer/USCS/#/AtAGlance/

Last updated on PolicyMap:

March 2022

Description:

The data presents the estimated Annual Incidence Rate and Average Cases of cancer by type per year in state and county level geographies. The data is available in the aggregate by race and cancer type. In some cases, data have been suppressed to ensure confidentiality and stability of rate estimates.

This data is collected by the CDC from public health surveillance systems by using either their published reports or public use files. Many state departments of health publish state-specific cancer data. This data may be more recent or may provide more detail than the data published nationally.

< Back to list of datasets

Centers for Medicare and Medicaid Services

Details:

Medicare fee-for-service beneficiaries, Medicare costs, Medicare inpatient service utilization, Medicare outpatient service utilization, Medicare FQHC/RHC service utilization, HCC risk scores, Medicare beneficiary demographics, Medicaid eligibility, chronic health conditions of Medicare beneficiaries (alcohol abuse, Alzheimer’s disease, arthritis, asthma, atrial fibrillation, autism spectrum disorder, cancer, COPD, chronic kidney disease, depression, diabetes, drug/substance abuse heart failure, hepatitis (chronic viral B & C), high cholesterol, high blood pressure, HIV/AIDS, coronary artery disease, osteoporosis, schizophrenia, and stroke)

Topics:

Medicare, Medicaid, chronic health conditions

Source:

Centers for Medicare and Medicaid Services

Years Available:

2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017

Geographies:

county, state

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

http://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/Medicare-Geographic-Variation/index

Last updated on PolicyMap:

April 2019

Description:

The Centers for Medicare and Medicaid Services’ chronic conditions data warehouse contains claims information for persons enrolled in the Medicare fee-for-service (FFS) program. Only information for beneficiaries enrolled in both Part A and Part B is included; information for beneficiaries who have died during the study year is included. Non-FFS Medicare beneficiaries are those with partial Part A and/or Part B coverage and people with more than one month of HMO coverage. Medicare Part A (hospital insurance) and Part B (medical insurance) cover individuals ages 65 and over who are receiving Social Security, people who have received disability benefits for at least two years, people who have amyotrophic lateral sclerosis (Lou Gehrig’s disease) and receive disability benefits, and people who have end-stage renal disease (permanent kidney failure) and receive maintenance dialysis or a kidney transplant. Individuals with Medicare Advantage (Part C) and Medicare Prescription Drug Plan (Part D) coverage are not represented in the data. Chronic health condition data is based on CMS administrative enrollment and claims data for Medicare fee-for-service beneficiaries. A Medicare beneficiary is considered to have a chronic condition if there is a CMS claim indicating that the beneficiary received a service or treatment for that specific condition. Beneficiaries may have more than one of the chronic conditions listed.

All dollar amounts in this data set are standardized by CMS to adjust for factors that result in different payment rates for the same service, including local variations in wages and payments Medicare makes to hospitals to advance program goals (including training doctors). The standardized values represent what Medicare would have paid in the absence of those adjustments. Because the state of Maryland is exempt from reporting special payments to Medicare, costs in Maryland were standardized using different factors than the nationwide model.

< Back to list of datasets

Centers for Medicare & Medicaid Services (CMS) and Health Resources Services Administration (HRSA) – Hospital Compare: Quality of Care

Details:

Hospital performance data for Medicare-certified hospitals

Topics:

Healthcare Quality

Source:

Centers for Medicare and Medicaid Services (CMS) and Health Resources Services Administration (HRSA)

Years Available:

2020 FY

Geographies:

Points

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

https://www.medicare.gov/hospitalcompare/

Last updated on PolicyMap:

March 2022

Description:

The Hospital Compare dataset is part of a data repository maintained by the Centers for Medicare & Medicaid Services (CMS), focusing on the quality of care at over 4,500 Medicare-certified hospitals (including acute care hospitals, critical access hospitals (CAHs), children’s hospitals, and hospital outpatient departments) across the country. The dataset was created in collaboration with organizations representing consumers, doctors, hospitals, employers, accrediting organizations, and other federal agencies, as part of an overall effort to improve patient safety and care. The Hospital Compare dataset on PolicyMap includes data on:
  • General information (Overall Rating, Mortality, Safety of Care, Readmission, Patient Experience, Effectiveness of Care, Timeliness of Care, Effective use of Medical Imaging): The overall hospital rating is given in stars from 1 to 5, while all other measures are designated as either being below, same, or above the national average. Data for these measures is compiled through the Inpatient/Outpatient Quality Reports and other programs mandatory for Medicare-certified hospitals. All comparisons on national average were standardized to ensure a common scale and direction for each measure. This implies that hospitals that perform above average on mortality or readmission comparison have a higher standardized z-score on these measures, based on lower mortality and readmission rates than the national average. The overall star rating in this section is intended primarily for acute care hospitals, due to which CMS may have omitted the measure for specialty hospitals. More details on the methodology for calculating overall ratings can be found here.
  • Structural Measures (Presence of facilities which include ability to receive lab results electronically, ability to track lab results, tests, and referrals electronically, presence of various registries): These measures are designated a Yes or No classification depending on whether a given facility is available at a hospital. Data for these measures is submitted by hospital and their vendors through an online data entry tool.
  • Survey of patients’ experiences – (Ratings on Care transition, Cleanliness, Communication, Pain Management, Staff Responsiveness, Quietness, Discharge Information, and overall hospital ratings): All of these measures are assigned a star rating from 1 to 5. Data for these measures is compiled using the Hospital Consumer Assessment of Healthcare Providers and Systems survey [HCAHPS], which is administered to a random sample of adult inpatients after discharge. In order to receive HCAHPS Star Ratings, hospitals must have at least 100 completed HCAHPS surveys over a given four-quarter period. While all of the star ratings are based on direct patient responses, the summary star rating is calculated as a weighted measure using all categories of patient responses, including overall patient rating. More details on the methodology for calculating HCAHPS star ratings can be found here.
  • Healthcare Associated Infections (HAI): HAI measures show how often patients in a particular hospital contract certain infections during the course of their medical treatment, when compared to like hospitals. These infections can often be prevented when healthcare facilities follow guidelines for safe care. Hospitals currently submit information on central line-associated bloodstream infections (CLABSIs), catheter-associated urinary tract infections (CAUTIs), colon and abdominal hysterectomy surgical site infections (SSIs), MRSA Bacteremia, and C.difficile laboratory-identified events. More details on the methodology for calculating HCAHPS star ratings can be found here.
Reasons for exclusion of certain measures for a hospital may include when number of cases/patients is too few to report, results are based on a shorter time period than required, data suppressed by CMS for one or more quarters, results are not available for this reporting period, there were discrepancies in the data collection process, this result is not based on performance data; the hospital did not submit data and did not submit a waiver, data are shown only for hospitals that participate in the Inpatient Quality Reporting (IQR) and Outpatient Quality Reporting (OQR) programs. More details on data collection and computation methodology for each dataset can be found here.

This dataset is available on PolicyMap as point data based on hospital location, and can be viewed upon clicking each respective point. The CMS Hospital Compare data was joined by PolicyMap to hospital locations using data from HRSA. HRSA hospital location data can be found here.

< Back to list of datasets

Centers for Medicare and Medicaid Services (CMS) – Opioid Prescriptions

Details:

opioid claims, opioid prescribing rates

Topics:

opioid prescriptions

Source:

Centers for Medicare & Medicaid Services (CMS)

Years Available:

2013, 2014, 2015, 2016, 2017, 2018

Geographies:

zip code, county, state, nation

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/Medicare-Provider-Charge-Data/OpioidMap.html

Last updated on PolicyMap:

November 2021

Description:

Centers for Medicare and Medicaid Services’ (CMS) opioid prescription claims include information about Medicare and Medicaid prescriptions. CMS opioid claims and prescribing rates are available for all opioids as well as extended-release and long-acting (ER/LA) opioid formulations. Prescription numbers and rates for ER/LA opioids are also counted within the “all opioids” indicators. ER/LA opioids are designed to deliver more stable dosing for chronic pain patients and reduce dosage frequency but may pose a higher risk of overdose when misused. Certain methadone types are considered ER/LA. For a list of opioids included see https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/Medicare-Provider-Charge-Data/Downloads/OpioidDrugList.zip.. Medicare Opioid Prescriptions CMS Medicare opioid prescription claims come from Medicare Part D claims prescribed by health care providers, as collected from Part D Prescriber public use files. Medicare opioid prescribing rates were calculated as the rate of opioid prescription claims per 100 total Medicare Part D prescription claims, including both prescriptions and refills. Medicare opioid prescription data includes claims for beneficiaries enrolled in Medicare Advantage Prescription Drug Plans and stand-alone Prescription Drug plans, but does not include prescriptions for patients on Medicaid, those with commercial insurance, or self-pay patients. Approximately 70% of Medicare beneficiaries have Medicare prescription drug coverage either from a Part D plan or a Medicare Advantage Plan offering Medicare prescription drug coverage. In 2017, Medicare Part D spending was $155 billion, while U.S. retail prescription drug spending was around $333 billion. Due to data redactions for geographies with 10 or fewer claims, county and Zip code Medicare opioid claim totals may not add up to state totals or may be lower than the true program totals. Medicaid Opioid Prescriptions CMS Medicaid opioid prescription claims come from claims where a portion was paid through Medicaid, as reported through the Medicaid State Drug Utilization Data. Medicaid opioid prescribing rates were calculated as the rate of opioid prescription claims per 100 total prescription claims for which Medicaid paid a portion. CMS Medicaid opioid prescription data includes claims prescribed through Fee-For-Service (FFS) programs and Manage Care Organizations (MCO). In 2017, Medicaid spending on prescription drugs was $68 billion, while U.S. retail prescription drug spending was around $333 billion.

Medicaid opioid claims are available at the state level only.

< Back to list of datasets

CDFI (Community Development Financial Institutions) Fund

Details:

Selected federal incentive program designations

Topics:

CDFI Fund Equitable Recovery Program, CDFI Fund Investment Areas, BEA Distressed Communities

Source:

Community Development Financial Institutions Fund, US Department of the Treasury

Years Available:

2022

Geographies:

Census Tract

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

https://www.cdfifund.gov/programs-training/programs/erp, https://www.cdfifund.gov/Documents/CDFI%20Investment%20Areas%20potential%20FAQs%20FINAL%20100118.pdf

Last Updated on PolicyMap:

July 2022

Description:

Equitable Recovery Program: The Community Development Financial Institutions Fund awards Community Development Financial Institutions (CDFIs) grants for economic recovery from the COVID-19 pandemic. The CDFI Fund designates census tracts as eligible for these grants based on the following criteria: (a) are census tracts that (i) demonstrate “severe impact” of the COVID-19 pandemic, and (ii) have a median income at or below 120% of the Area Median Income, and (iii) are CDFI Investment Areas; or (b) are Native Areas.

Severe COVID Impact/Low Community Resilience designation is given to census tracts that demonstrate any of the following: severe mortality (being in the highest tercile of the number of deaths per 100,000 people, according to reported cumulative mortality for the period from April 1, 2020 to March 31, 2021), severe change in unemployment or low community resilience (based on data from the US Census Bureau Community Resilience Estimates Program). CDFI Investment Areas are geographies which have a population poverty rate of at least 20% (including Persistent Poverty Counties, which are counties that have had 20% of more of their populations living in poverty over the past 30 years),or have unemployment rates of at least 1.5 times the national or other criteria listed by CDFI Fund.

The Community Development Financial Institution (CDFI) Fund, a division of the US Department of the Treasury, administers the New Markets Tax Credit (NMTC) and Bank Enterprise Award (BEA) programs, and supports and invests in Community Development Financial Institutions. For information about the NMTC, please see entry, below. The CDFI Fund maintains a list of Census Tracts and their program eligibility or designation, based on income, poverty and unemployment data provided by the Census Bureau’s 2011-2015 American Community Survey (ACS) for 2010 census tracts. During a one year transition period (until October 1, 2019), the designations based on 2006-2010 ACS data will remain in effect. For more on these programs users should consult the CDFI Fund website directly: www.cdfifund.gov.

Designations are current as of October 2018, but may be changed at any time by the CDFI Fund. For this reason, users should verify eligibility directly with the CDFI Fund. For CDFI Program Investment Areas, information in PolicyMap does not include Native American Areas.

< Back to list of datasets

CDFI (Community Development Financial Institutions) Fund and PolicyMap

Details:

New Markets Tax Credit Program Eligibility

Topics:

NMTC Program Eligibility, Severe Distress, Very Low Income, Very Low-Income, Poverty, Unemployment, HUBZone, Medically Underserved Area, Appalachian Regional Commission Distressed County, Delta Regional Authority Distressed County, AMI, Brownfield, ERS/USDA Food Desert

Source:

Community Development Financial Institutions Fund, US Department of the Treasury and PolicyMap

Years Available:

2006-2010, 2011-2015 (2019 Eligibility)

Geographies:

Census Tract

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

https://www.cdfifund.gov/programs-training/Programs/new-markets-tax-credit/Pages/apply-step.aspx#step2

Description:

The Community Development Financial Institutions (CDFI) Fund, a division of the US Department of the Treasury, administers the New Markets Tax Credit (NMTC). PolicyMap has performed calculations on various data sources in order to map eligibility and threshold requirements established by the CDFI Fund for Part II (Community Impact) of the NMTC Allocation Application. The NMTC Allocation Application data on PolicyMap is available as follows. Note that the latest eligibility criteria use Census American Community Survey (ACS) 2011-2015 estimates. In 2017, there was a one-year transition period for investments started under the 2006-2010 ACS data; that data is also available. More information is available here: https://www.cdfifund.gov/Documents/NMTC%20CIMS%20November-Census%20Transition%202011-2015%20ACS%20Oct30-2017-4.30pm.pdf CDFI Fund New Markets Tax Credit NMTC Eligibility NMTC Eligible Census tracts include those that have either (1) Median Family Income at or below 80% of Area Median Income (AMI) in the period of 2006-2010/2011-2015 or (2) Poverty Rate of 20% or greater in the period of 2006-2010/2011-2015. PolicyMap provides a map of those eligible Census tracts (“Eligible Tracts”), as well as the underlying data used to create that map in the (“Eligibility Criteria”). PolicyMap also provides the underlying data without the NMTC thresholds (“Tract Family Income as % of AMI” and “Poverty”). According to the NMTC application, an applicant generally scores more favorably that serves tracts with “Severe Distress” or in non-metropolitan counties, or qualifies for two or more other criteria. Severe Distress and Non-Metropolitan Meeting the NMTC Severe Distress or Non-Metropolitan criteria is based on whether or not a given Census tract meets basic NMTC Eligibility, plus one of the following factors: having a median family income at or below 60% of AMI in the period of 2006-2010/2011-2015; having a poverty rate at or above 30% in the period of 2006-2010/2011-2015; having an unemployment rate of at least 1.5 times the national unemployment rate in the period of 2006-2010/2011-2015; or being in a county that is not part of a metropolitan statistical area. *The median family income threshold for NMTC, more specifically, is: Census tracts with, if located within a non-Metropolitan Area, median family income at or below 60% of statewide median family income or, if located within a Metropolitan Area, median family income at or below 60% of the greater of the statewide median family income or the Metropolitan Area median family income. Other Criteria for NMTC Other criteria can include two of the following: meeting NMTC Heavy Distress requirements; being located within: an SBA Designated HUB Zone, a Medically Underserved Area (MUA), a Census tract within which a Brownfield is located, a HOPE VI Redevelopment Area, a Federal Native Area, an Appalachian Regional Commission or Delta Regional Authority Area, a Colonias Area, a State or Local Economic Zone (such as TIF or KOZ), a FEMA Disaster Area, or a ERS/USDA Food Desert. Please note that the data on PolicyMap do not take into account the following, due to unavailability of data: HOPE VI Redevelopment Areas, Federal Native Areas, Colonias Areas, State or Local Economic Zones, and FEMA Disaster Areas. Included in this submenu are the data for each of the available factors that constitute the Secondary Criteria for NMTC Severely Distressed. The data used for the NMTC Eligibility maps include numerous sources, listed below. Data for the 2018 Application:
Median Family Income Census ACS 2006-2010/2011-2015
Area Median Income Census ACS 2006-2010/2011-2015
Poverty Rate Census ACS 2006-2010/2011-2015
Unemployment Rate Census ACS 2006-2010/2011-2015
SBA HUBZones Small Business Administration HUBZones
Medically Underserved Areas US Department of Health and Human Services Health Resources and Services Administration Shortage Areas
Delta Regional Authority Distressed Counties Delta Regional Authority Distressed List (http://dra.gov/funding-programs/states-economic-development-assistance-program/distressed-counties-and-parishes/)
Appalachian Regional Commission County Economic Status and Distressed Areas in Appalachia (http://www.arc.gov/appalachian_region/CountyEconomicStatusandDistressedAreasinAppalachia.asp)
Brownfield locations EPA Brownfields
ERS/USDA Food Deserts ERS, USDA
Because any of these data sources may have been updated since the production of these calculations, users should verify eligibility directly with the CDFI Fund. Information in PolicyMap does not include HOPE VI Redevelopment Areas, Federal Native Areas, Colonias Areas, or State or Local Economic Zones. FEMA Disaster Areas are accessible under Federal Guidelines in the Data Points menu. The data for the 2019 application comes from the 2006-2010/2011-2015 American Community Survey.

NMTC Eligibility and Qualified Opportunity Zones A joint dataset that includes both NMTC eligibility and designated Qualified Opportunity Zones. Census tracts labeled as “Designated OZ” are census tracts that have been nominated and designated as a Qualified Opportunity Zone (QOZ), according to the CDFI Fund and census tracts labeled as “NMTC Eligible” are census tracts that meet the CDFI Fund’s New Markets Tax Credit (NMTC) eligibility for CY 2018. For more information see the directory entry for Qualified Opportunity Zones.

< Back to list of datasets

CDFI (Community Development Financial Institutions) Fund Capital Magnet Fund

Details:

Capital Magnet Fund Projects

Topics:

Capital Magnet Fund, affordable housing, nonprofits

Source:

Community Development Financial Institutions Fund, US Department of the Treasury

Years Available:

2012-2013

Geographies:

points

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

http://www.cdfifund.gov/what_we_do/programs_id.asp?programID=11

Description:

The Community Development Financial Institutions (CDFI) Fund, a division of the US Department of the Treasury, administers the Capital Magnet Fund (CMF). CMF, established in 2008 and appropriated in 2010, is a competitive grant program to attract private capital for affordable housing development. CMF dollars are available to CDFIs and nonprofit housing developers who are active in affordable housing development. In FY 2012-2013, CMF awardees used the funds to finance 8,049 affordable rental units and 922 homeowner-occupied homes. CMF dollars may be used for the following purposes: loan loss reserves, revolving loan funds, affordable housing funds, or risk-sharing loans; economic development activities or community service facilities (day-care centers, workforce development centers, health care clinics) that support affordable housing as part of an overall community revitalization strategy. CMF grants must be matched at least 10:1 with other funding sources. One hundred percent of housing-eligible project costs must be used to finance units for households with income below 120% of area median income (AMI); and 51% of costs must be used for households with income below 80% of AMI. Rental housing projects have more specific requirements; please see the CDFI Fund website for more information.

CMF grantees track the use of funds though periodic financial and project reports submitted to the CDFI Fund.

< Back to list of datasets

CDFI (Community Development Financial Institutions) Fund – Capital Magnet Fund

Details:

Areas of High Housing Need, Areas of Economic Distress, Underserved Rural Areas

Topics:

Capital Magnet Fund, Areas of High Housing Need, Areas of Economic Distress, Underserved Rural Areas

Source:

Community Development Financial Institutions Fund, US Department of the Treasury

Years Available:

2016, 2017, 2018, 2019, 2021

Geographies:

census tract

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

https://www.cdfifund.gov/programs-training/Programs/cmf/Pages/apply-step.aspx

Last updated on PolicyMap:

October 2021

Description:

The Community Development Financial Institutions (CDFI) Fund, a division of the US Department of the Treasury, provides funding to CDFIs and qualified non-profit housing organizations through the Capital Magnet Fund. Areas of Economic Distress and Rural Areas are among the selection criteria used to determine eligibility. Additionally, Areas of High Housing Need and Metropolitan Areas were among the past selection criteria used to determine eligibility and are also available on PolicyMap. The CDFI Fund considers a tract to be an Area of Economic Distress in 2021 if it meets at least one of the following criteria: (i) At least 20% of Very-Low Income households spend more than half of their income on housing; or (ii) the tract was a designated Qualified Opportunity Zone; or (iii) The tract is a Low-Income Housing Tax Credit qualified census tract; or (iv) Greater than 20 percent of households have incomes below the poverty rate with a rental vacancy rate of at least 10 percent; or (v) Greater than 20 percent of households have incomes below the poverty rate with a homeownership vacancy rate of at least 10 percent; or (vi) Tract is an underserved rural area as defined in the CMF Interim Rule. FHFA’s Duty to Serve regulation defines “rural area” as: (1) A census tract outside of a metropolitan statistical area, as designated by the Office of Management and Budget; or (2) A census tract in a metropolitan statistical area, as designated by the Office of Management and Budget, that is outside of the metropolitan statistical area’s Urbanized Areas as designated by the U.S. Department of Agriculture’s Rural-Urban Commuting Area Code #1, and outside of tracts with a housing density of over 64 housing units per square mile for USDA’s RUCA Code #2. FHFA’s Duty to Serve regulation defines “high opportunity areas” as: (1) tracts designated by the Department of Housing and Urban Development (HUD) as a “Difficult Development Area” (DDA) during any year covered by an Enterprise’s Underserved Markets Plan or in the year before a Plan’s effective date, whose poverty rate falls below 10% (for metropolitan areas) or 15% (for non-metropolitan areas). Or (2) an area designated by a state or local Qualified Allocation Plan (QAP) as a high opportunity area whose poverty falls below 10% (for metropolitan areas) or 15% (for non-metropolitan areas). High opportunity areas are used to determine eligibility for extra credit under Duty to Serve. Low-Income Area means a census tract in which the median income does not exceed 80 percent of the median income for the area in which such census tract or block numbering area is located. For a census tract or block numbering area located within a Metropolitan Area, the median family income shall be at or below 80 percent of the Metropolitan Area median family income or the national Metropolitan Area median family income, whichever is greater. In the case of a census tract located outside of a Metropolitan Area, the median family income shall be at or below 80 percent of the statewide Non-Metropolitan Area median family income or the national Non-Metropolitan Area median family income, whichever is greater.

For more information about the Capital Magnet Fund data, see the CDFI Fund’s website here.

< Back to list of datasets

CDFI (Community Development Financial Institutions) Fund CDFI Program

Details:

Certified CDFIs

Topics:

Community Development Financial Institutions, Native CDFIs

Source:

Community Development Financial Institutions Fund, US Department of the Treasury

Years Available:

2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2021

Geographies:

state, county, census tract, points

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

https://www.cdfifund.gov/programs-training/certification/cdfi/Pages/default.aspx

Last updated on PolicyMap:

January 2022

Description:

The Community Development Financial Institutions (CDFI) Fund, a division of the US Department of the Treasury, supports and invests in Community Development Financial Institutions through the CDFI Program and Native American CDFI Assistance Program. CDFIs are financial institutions that provide products and services in economically distressed target markets. The CDFI Fund certifies CDFIs through an application process on a rolling basis, depending on the type of institution. Not all CDFIs are certified, but certification is a requirement for some federal program funding. PolicyMap was able to locate 84% of CDFI addresses on a map. Data on certified CDFI locations are updated twice annually. Data on CDFI loans and investments comes from the Community Investment Impact System (CIIS), a database through which CDFI’s self-report their investment activity. The data on PolicyMap is from the Transaction Level Report (TLR) and Institution Level Report (ILR), and is aggregated to states, counties, and census tracts. Median and aggregate investment amounts are calculated by type of CDFI and for select transaction characteristics.

For CDFI transactions that span multiple census tracts or counties, medians are calculated using the total project cost while aggregations are calculated by dividing the total transaction cost by the number of census tracts or counties involved. Transaction or project counts at smaller geographies may not match larger geography counts given the double counting of split transactions and projects across census tracts and counties. Subcategories of transactions, such as CDFI Investments by Borrower Type, may not sum to the total amount of CDFI investments in a given area.

< Back to list of datasets

CDFI (Community Development Financial Institutions) Fund New Markets Tax Credit Projects

Details:

Low-income community investments

Topics:

CDFI Fund, low-income businesses, low-income investments, New Markets Tax Credit (NMTC) program, Qualified Equity Investments (QEIs), Qualified Active Low-Income Community Businesses (QALICBs), Qualified Low-Income Community Investments (QLICIs), Real Estate, Non-Real Estate, Community Development Entity (CDE), Low-Income Communities (LICs)

Source:

Community Development Financial Institutions Fund, US Department of the Treasury

Years Available:

2010-2014, 2015-2019

Geographies:

ZIP code, census tract, state, points

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

https://www.cdfifund.gov/Documents/Forms/DataReleases.aspx https://www.cdfifund.gov/programs-training/Programs/new-markets-tax-credit/Pages/default.aspx

Last updated on PolicyMap:

January 2022

Description:

The Community Development Financial Institutions (CDFI) Fund, a division of the U.S. Department of the Treasury, collects data from Community Development Entities (CDEs) based on information submitted through the New Markets Tax Credit (NMTC) program. NMTC awards are allocated to CDEs investing in operating businesses and real estate projects located in Low-Income Communities (LICs). This dataset is an aggregated collection of these projects, totaling the number, project type, and dollar value of investments reported from 2010 through 2019. Calculations were conducted by PolicyMap to create summary values based on geography and by project type. Total values were aggregated to state and zip code based on the address provided for the transaction. The CDFI Fund provided 2000 or 2010 census tracts associated with each transaction. Dollar values for transactions in multiple tracts were averaged across all tracts associated with the project. The total number of transactions for census tracts in an area may not be equivalent to totals by state and zip code. NMTC investments are also aggregated to CDEs, using the list of certified CDEs made available by the CDFI Fund. Only entities certified as CDEs may receive NMTC allocations. PolicyMap geocoded 276 CDEs with New Markets transactions as reported from 2005 – 2012, and was able to locate 100% of the addresses on a map.

CDE Locations are no longer updated by the source. PolicyMap will remove this data in 2020 unless the dataset is updated or another source is found.

< Back to list of datasets

CDFI (Community Development Financial Institutions) Fund Persistent Poverty Counties

Details:

persistent poverty counties

Topics:

CDFI Fund applications, poverty

Source:

Community Development Financial Institutions Fund, US Department of the Treasury, US Census

Years Available:

2017

Geographies:

County

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

https://www.cdfifund.gov/research-data/Pages/default.aspx

Description:

The Community Development Financial Institutions (CDFI) Fund is a division of the U.S. Department of Treasury. Per the Consolidated Appropriations Act of 2012, funding was provided for several CDFI Fund programs (Bank Enterprise Award Program, CDFI Program, Healthy Food Financing Initiative, and Native American CDFI Assistance Program) on condition that a minimum of 10% of the projects served must be in persistent poverty counties. The legislation defines a persistent poverty county as any county that has had 20 percent or more of its population living in poverty for the past 30 years as measured by the U.S. Census Bureau. Based on this criteria, the CDFI Fund used data from the 1990 and 2000 decennial censuses, and the 2011-2015 American Community Survey to determine qualifying counties.

< Back to list of datasets

CDFI (Community Development Financial Institutions) Fund Priority Points

Details:

Distress indicators and priority points

Topics:

CDFI Fund applications and priority point scoring system

Source:

Community Development Financial Institutions Fund, US Department of the Treasury

Years Available:

FY 2011

Geographies:

county

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

http://www.cdfifund.gov/what_we_do/priority_points_overview.asp

Description:

The Community Development Financial Institutions (CDFI) Fund, a division of the US Department of the Treasury, is now using a priority point system for scoring its applications. Applicants are awarded up to 5 “priority points” for their commitment to serve communities facing the highest levels of distress.

The score is tabulated using various distress indicators, which are also mapped on PolicyMap. These indicators are: poverty rates, median household income, unemployment rates, home foreclosures and high-cost mortgages.

< Back to list of datasets

CDFI (Community Development Financial Institutions) Fund Qualified Opportunity Zones

Details:

Qualified Opportunity Zone eligibility

Topics:

Qualified Opportunity Zone

Source:

Community Development Financial Institutions Fund, US Department of the Treasury

Years Available:

2018

Geographies:

census tract

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

https://www.cdfifund.gov/Pages/Opportunity-Zones.aspx

Description:

Once designated, Qualified Opportunity Zones (QOZ) can receive substantial tax breaks for long term investments to low-income neighborhoods. State governors can nominate up to twenty five percent or twenty five total, whichever is larger, low-income community (LIC) census tracts for QOZ designation. Census tracts are considered LICs if the tract has either (1) a median family income at or below 80% of Area Median Income (AMI) or (2) a poverty rate of 20% or greater as determined with the 2011-2015 Census American Community Survey data. Updates by the CDFI Fund on February 27th, 2018, expanded the LIC eligibility definition to also include select qualified high migration tracts, low-population tracts within Empowerment Zones, and territorial census tracts that meet the LIC qualifications. These updates resulted in an additional 168 LIC eligible census tracts, and are included here.

Up to five percent of tracts that are nominated for the QOZ can be qualified non-LIC as long as they are contiguous to a LIC QOZ and have a median family income that is not greater than 125 percent of the adjacent LIC QOZ. Eligible contiguous non-LIC tracts that are contiguous to a LIC QOZ in a different state are also eligible for nomination if the adjacent state nominates the LIC QOZ. This data includes technical corrections to the contiguity analysis that were released by the CDFI Fund on February 27th, 2018. These corrections accounted for an increase in 1,007 additional eligible non-LIC contiguous census tracts and the removal of 72 previously eligible non-LIC contiguous tracts. For more information please contact the CDFI Fund.

< Back to list of datasets

Civil Rights Data Collection

Details:

Civil rights data on public elementary, middle, and high schools

Topics:

Limited English proficient students, students with disabilities, student retention (held back), corporal punishment, student suspensions and expulsions, student referrals to law enforcement and school-related arrests, student harassment and bullying, gifted and talented programs, teacher absences

Source:

Office of Civil Rights, US Department of Education

Years Available:

2017-2018 school year

Geographies:

point

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

http://ocrdata.ed.gov/

Last updated on PolicyMap:

April 2022

Description:

The Civil Rights Data Collection (CRDC) contains wide-ranging data on equity among public schools. The data is based on school district responses to a biennial survey conducted by the Office of Civil Rights in the Department of Education. Percent and rate indicators have all been derived by PolicyMap.

School locations, names, district names, level, type, and charter school status are all from the NCES CCD.

< Back to list of datasets

Consumer Financial Protection Bureau, Rural and Underserved Counties

Details:

Rural and underserved counties

Topics:

Rural and underserved counties, Escrows Rule

Source:

Consumer Financial Protection Bureau

Years Available:

2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020

Geographies:

County

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

http://www.consumerfinance.gov/guidance/#ruralunderserved

Last updated on PolicyMap:

February 2021

Description:

The Escrow Requirements under the Truth in Lending Act rule (known as the Escrows Rule) requires that certain creditors create escrow accounts for a minimum of five years for higher-priced mortgage loans (HPMLs), except HPMLs made by certain small creditors that operate predominantly in rural or underserved counties. Rural counties are defined by using the USDA Economic Research Service’s urban influence codes, and underserved counties are defined by reference to data collected under the Home Mortgage Disclosure Act (HMDA).

< Back to list of datasets

CRA (Community Reinvestment Act) Eligibility Criteria

Details:

Tract eligibility status for Community Reinvestment Act (CRA), Census tract Median Family Income as a percent of Area Median Family Income

Topics:

CRA eligible census tracts

Source:

Federal Financial Institutions Examination Council (FFIEC), US Department of Housing and Urban Development (HUD), US Census

Years Available:

2022

Geographies:

Census Tract (2020)

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

http://www.ffiec.gov/CRA/

Last updated on PolicyMap:

July 2022

Description:

The Community Reinvestment Act (CRA), which was enacted by Congress in 1977, is intended to encourage depository institutions to help meet the credit needs of the communities in which they operate, including low- and moderate-income neighborhoods, consistent with safe and sound banking operations. CRA requires that each insured depository institution’s record in helping meet the credit needs of its entire community be evaluated periodically. These examinations are conducted by federal agencies: the Board of Governors of the Federal Reserve System (FRB), the Federal Deposit Insurance Corporation (FDIC), the Office of the Comptroller of the Currency (OCC), and the Office of Thrift Supervision (OTS). That record is taken into account in considering an institution’s application for deposit facilities, including mergers and acquisitions. In order to gauge CRA performance, the evaluation looks for bank activity in low- and moderate-income neighborhoods, nonmetropolitan distressed and underserved areas, and federally designated disaster areas. These areas are identified by calculating tract income level. This is the Median Family Income (MFI) of each tract divided by Area Median Family Income (AMFI). For tracts located outside of an MSA/MD, the MFI used in the denominator is the statewide non-MSA/MD MFI. This figure is calculated using incomes from all areas of a state that are not assigned to MSA/MDs. As of 2022, FFIEC calculates tract income level using the Census’ 2016-2020 American Community Survey estimates. For additional information on data and calculations see: http://www.ffiec.gov/geocode/help3.aspx The tract income level is defined as follows: If the Median Family Income % is < 50% then the Income Level is Low. If the Median Family Income % is >= 50% and < 80% then the Income Level is Moderate. If the Median Family Income % is >= 80% and < 120% then the Income Level is Middle. If the Median Family Income % is >=120% then the Income Level is Upper. If the Median Family Income % is 0% then the Income Level is Not Known. Tracts are CRA eligible if they are low- or moderate-income, or if they are nonmetropolitan middle income tracts designated by FFIEC as distressed or underserved. Distressed middle income tracts are those with: (1) Unemployment rate at least 1.5 times the national average or (2) Poverty rate of 20% or greater or (3) Population loss of 10% or more between the 2010 and 2020 census, or a net migration loss of 5% or more between 2010 and 2020. Underserved middle-income tract are those designated by the Economic Research Service of the United States Department of Agriculture with an “urban influence code” of 7, 10, 11 or 12. Lists of these tracts are released annually and available on the CRA website at: http://www.ffiec.gov/cra/examinations.htm. PolicyMap also provides the distressed and underserved designation for tracts that were designated as such in the previous year but not the current year because of the allowance of a one-year “lag period.” According to the source, “this lag period will be in effect for the 12 months immediately following the date when a census tract that was designated distressed or underserved is removed from the designation list. Revitalization or stabilization activities undertaken during the lag period will receive consideration as community development activities if they would have been considered to have a primary purpose of community development if the census tract in which they were located were still designated as distressed or underserved.” For more information, see page 24 of this Federal Register document: https://www.gpo.gov/fdsys/pkg/FR-2016-07-25/pdf/2016-16693.pdf

To identify tracts that are “designated disaster areas” consult the Federal Emergency Management Agency (FEMA) website: http://wwww.fema.gov. Disaster designations are also mapped on PolicyMap and can be found in the Federal Guidelines menu under FEMA Disaster Declarations.

< Back to list of datasets

Convenient Care Association

Details:

Retail-Based Healthcare

Topics:

Health, Retail-Based Healthcare, Clinics

Source:

Convenient Care Association

Years Available:

2017

Geographies:

Point

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

http://www.ccaclinics.org

Description:

Data obtained from the Convenient Care Association on March 31, 2017. Includes only members of the Convenient Care Association.

< Back to list of datasets

The COVID Tracking Project

Details:

COVID-19 testing, racial disparities

Topics:

Health, COVID-19, race, equity

Source:

The COVID Tracking Project

Years Available:

2020

Geographies:

State, Nation

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

https://covidtracking.com/

Last updated on PolicyMap:

see data description

Description:

The COVID Tracking Project is a nation-wide volunteer effort that started at The Atlantic. This project posts daily data updates on COVID-19 testing by state, assembled from data reported by state public health agencies and offices. The quality of the data reported varies by state. The COVID Tracking Project assigns letter grades to each state assessing the quality of the data reported by that state. Visit https://covidtracking.com/data to see each state’s grade. PolicyMap calculated testing rates using population estimates from the 2014-2018 ACS. PolicyMap calculated the percent of tests that were positive over the last week, percent of tests that were negative over the last week, and number of tests results reported over the last week. Inconsistencies in reporting positive test results and total test results by state authorities occasionally caused percents to be larger than 100. PolicyMap suppressed these values, and also suppressed percents where the denominator was less than 10. Sudden unexpected peaks in percents positive or negative also may have resulted from changes in reporting practices by the individual states. Visit the Covid Tracking Project documentation page to read more about the reporting history of specific states.

Data on racial disparities in COVID-19 cases and deaths was collected as part of a collaboration between The COVID Tracking Project and the Boston University Center for Anti-Racism. The researchers collected data on race and ethnicity of people diagnosed with COVID-19 and those who died of COVID-19 directly from state departments of public health. Because of variations across states in racial and ethnic categories, it is not always advisable to compare case or death rates of a given race or ethnicity with their prevalence within the local population. PolicyMap suppressed racial and ethnic population data for states flagged by the source as incomparable. Population data is from ACS 2014-2018 5-year estimates. Race and ethnicity categories are not mutually exclusive.

< Back to list of datasets

Dartmouth Atlas of Health Care

Details:

Hospital Referral Regions, Hospital Service Areas

Topics:

Health, Medicare

Source:

Dartmouth Atlas of Health Care at the Dartmouth Institute for Health Policy and Clinical Practice

Years Available:

2005

Geographies:

Hospital Referral Regions, Hospital Service Areas

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

http://www.dartmouthatlas.org/

Description:

Hospital Referral Region (HRR) and Hospital Service Area (HSA) boundaries, downloaded from the Dartmouth Atlas of Health Care, are geographic representations of access to medical care. HRRs represent regional health care markets, and were determined based on the locations of referrals for major cardiovascular surgeries and neurosurgery procedures. HSAs represent smaller, local health care markets, based on Medicare hospitalizations.

HRR and HSA boundaries were created by the Dartmouth Atlas in 2005, based on contemporary hospital and Medicare data. Hospital Service Area boundaries are available only for the contiguous United States. Hospital Referral Region boundaries include Alaska and Hawaii.

< Back to list of datasets

DHS Immigration Yearbook

Details:

number and percent of people receiving Legal Permanent Resident status, by region and selected countries

Topics:

green cards, Legal Permanent Residents (LPR), immigration and foreign born population

Source:

Department of Homeland Security Yearbook of Immigration Statistics

Years Available:

2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017

Geographies:

State, CBSA

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

http://www.dhs.gov/yearbook-immigration-statistics

Last updated on PolicyMap:

February 2019

Description:

The Department of Homeland Security’s Yearbook of Immigration Statistics is an annual publication on documented foreign nationals in the United States. PolicyMap contains state and CBSA-level data on the number of people granted Legal Permanent Resident (LPR) status by region of birth and by selected countries. If the volume of immigrants receiving green cards in any year was more than 15,000 people, the country was included.

< Back to list of datasets

Energy Information Administration (EIA)

Details:

Locations of electric generating plants, energy capacity

Topics:

Energy, renewable energy, nonrenewable energy, energy capacity

Source:

US Department of Energy, Energy Information Administration (EIA), U.S. Energy Mapping System

Years Available:

2019

Geographies:

Point, county, state

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

http://www.eia.gov/state/maps.cfm

Last updated on PolicyMap:

April 2019

Description:

The Energy Information Administration (EIA) U.S. Energy Mapping System provides the locations and capacity of operable electric generating plants which includes all plants that are operating, on standby, or short- or long-term out of service with a combined nameplate capacity of 1 MW or more. Generator-level data is collected as part of the EIA-860 Annual Electric Generator Report. Geographic coordinates are assigned to the plant locations in the source data. Thematic indicators of electricity generation capacity were determined based on a spatial join performed by PolicyMap of geocoded plant locations and standard Census geographic boundaries. Generator-level megawatt output capacity was aggregated for county and state boundaries.

References in the data to “renewable” energy sources refers to energy from biomass, hydropower, geothermal, wind, and solar. This is consistent with how the EIA classifies renewable energy sources as outlined on their renewable sources webpage.

< Back to list of datasets

Environmental Protection Agency (EPA), AirData Air Quality Index Report

Details:

Air quality index

Topics:

air quality

Source:

US EPA

Years Available:

2021

Geographies:

County, CBSA, Place, ZIP, Neighborhood

Public Edition or Subscriber-only:

API only

Download Available:

no

For more information:

http://www.epa.gov/airdata/

Last updated on PolicyMap:

February 2022

Description:

The United States’ Environmental Protection Agency (US EPA) provides a Median Air Quality Index (AQI) at the county level. The median AQI is based on the value for which half of daily AQI values during the year were less than or equal to the median value, and half equaled or exceeded it. Air quality is defined by the EPA as follows: good air quality ranges from 0-50; moderate air quality ranges from 51-100; unhealthy air quality for sensitive groups ranges from 101-150; and unhealthy air quality is 151 or higher, which includes the AQI categories of unhealthy, very unhealthy and hazardous.

< Back to list of datasets

Environmental Protection Agency (EPA), Brownfields Sites Reports

Details:

Brownfield site locations

Topics:

Brownfields

Source:

Cleanups in my Community, US EPA

Years Available:

2021

Geographies:

points

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

http://epa.gov/brownfields/

Last updated on PolicyMap:

May 2022

Description:

The EPA regularly updates its Brownfields Sites Report List. The points in PolicyMap are as of December of 2019. The coordinates used in PolicyMap are provided by the EPA. PolicyMap removes points that do not appear in the site’s listed state. The points shown on PolicyMap include brownfield sites that have received assessment, cleanup, and/or redevelopment funding from the EPA. Brownfields designated by states or local entities, sites that may qualify for but have not received EPA assessment funding, and underground storage tanks are not included on the map.

Each point represents a transfer of funds related to a known brownfield site. Multiple points for the same brownfield location indicate multiple actions over a period of time; the entity receiving funds may differ.

< Back to list of datasets

Environmental Protection Agency (EPA), Safe Drinking Water Information System

Details:

Water quality violations from reporting state agencies.

Topics:

Public health, water quality, drinking water

Source:

Environmental Protection Agency

Years Available:

2021

Geographies:

County

Public Edition or Subscriber-only:

API only

Download Available:

no

For more information:

http://www.epa.gov/enviro/html/sdwis/

Last updated on PolicyMap:

February 2022

Description:

The EPA’s Safe Drinking Water Information System provides information on public water systems, and their quantity and types of violations of drinking water regulations. Using EPA guidelines, PolicyMap categorizes each violation as a health violation or a monitoring and reporting violation. The source data comes at the agency-level; PolicyMap determines what county the water system is in and provides county-level data. Only water systems that serve 10,000 people or more are included. In counties where multiple water systems were included, the average number of violations was calculated weighted by the population size served by each system.

< Back to list of datasets

Environmental Protection Agency (EPA) Smart Location Database

Details:

Frequency of transit service per hour and per mile, job and worker accessibility by auto travel and transit commute, economic diversity, road network density, intersection density, walkability

Topics:

Distance to transit, Access to destinations, economic diversity, built environment

Source:

Environmental Protection Agency

Years Available:

2021

Geographies:

Block group

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

https://www.epa.gov/smartgrowth/smart-location-mapping#SLD

Last updated on PolicyMap:

January 2022

Description:

The Environmental Protection Agency’s (EPA) Smart Location Database provides data on the relationship between land use and transportation efficiency. The Smart Location Database (SLD) summarizes several demographic, employment, and land use variables for Census block groups. PolicyMap made an excerpt from this larger body of work available on its platform. Frequency of transit service provides a general metric of the quality of public transit options in an area. EPA calculated transit frequency through an analysis of General Transit Feed Specification (GTFS) data between 4:00 and 7:00 PM on a weekday. Then, for each block group, EPA identified transit routes with service that stops within 0.4 km (0.25 miles). Finally, EPA summed aggregate service frequency by block group. Values for this metric are expressed as service frequency per hour of service. GTFS is a transit data reporting standard that allows public transit agencies to publish data in a standard format. EPA also calculated frequency of transit service per square mile by dividing frequency of transit service per hour by total land acreage then converting to units per square mile. Where the total land acreage was zero, total block group acreage was used as the denominator.

To create the indicators on job or workforce accessibility by auto travel, the EPA joined an origin-destination matrix to employment and demographic data from the 2010 Census. Although the transit accessibility indicators were analyzed the same way as the auto accessibility, it was analyzed for evening peak travel period only, as this is normally the period of relatively intense levels of transit service.

< Back to list of datasets

Environmental Protection Agency (EPA), Superfund Enterprise Management System

Details:

Superfund site locations

Topics:

Superfund

Source:

U.S. Environmental Protection Agency

Years Available:

2021

Geographies:

points

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

http://www.epa.gov/superfund/superfund-data-and-reports

Last updated on PolicyMap:

January 2022

Description:

Locations of Superfund sites are from EPA’s Superfund Enterprise Management System. The Superfund program is an evaluation program for active and inactive hazardous waste sites. Sites on the National Priorities List (NPL) are included on PolicyMap, as well as sites proposed for the NPL or formerly on the NPL.

Human exposure and groundwater migration information are environmental indicators based on metrics set by the EPA. These indicators are used to measure progress made through site cleanup activities.

< Back to list of datasets

FBI Uniform Crime Reports

Details:

Nationwide FBI Crime Counts and Rates per 100,000 people for Violent Crimes, Property Crimes, Aggravated Assault, Burglary and Larceny, Motor Vehicle Thefts, Murder, Rape, and Robbery

Topics:

Crime Rates

Source:

FBI Uniform Crime Reports

Years Available:

2005-2020

Geographies:

selected counties and places

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

https://www.fbi.gov/services/cjis/ucr

Last updated on PolicyMap:

March 2022

Description:

The Federal Bureau of Investigation’s Uniform Crime Reporting (UCR) Program compiles standardized incident reports from local law enforcement agencies in order to produce reliable, uniform, and national crime data. The UCR Program is voluntary, and includes data for only counties and cities with population over 10,000. As a result, coverage is not universal. The UCR Program collects data on known offenses and persons arrested by law enforcement agencies. The UCR Program does not record the findings of a court, coroner, jury, or the decision of a prosecutor. As the FBI does not provide standard geographic identifiers, PolicyMap assigns the data to places and counties using the US Department of Justice’s Law Enforcement Agency Identifier Crosswalk from 2005. The Crosswalk relates originating agency identifiers to Federal Information Processing Standards Codes (FIPS codes). PolicyMap uses the matched FIPS codes to display the data on the map. Data was reported to the FBI for selected places and counties by local law enforcement agencies. The FBI compiled the data and provided PolicyMap with those statistics that met FBI reporting standards. County counts reflect the sum of all reported offenses from agencies within the county that submitted data to the FBI. The county count may not include all offenses if agencies within the county did not report or if reported figures did not comply with FBI reporting standards. Those places or counties either not reporting to the FBI or not complying with FBI reporting standards and, thus, not compiled by the FBI, are shown as having Insufficient Data in the map. Certain counties, such as those in New York City, are not shown on PolicyMap because the FBI reported no population in those counties. Data for agencies in these counties was reported by the FBI, but not included in the FBI’s county aggregation. Values for places were obtained by matching each agency with the place it is in based on the Crosswalk. Where multiple agencies reported data in one place, the values were aggregated by PolicyMap. The FBI cautions against using UCR data to rank municipalities, as many factors contribute to the number of crimes that occur and are reported. More information can be found here: https://ucr.fbi.gov/ucr-statistics-their-proper-use. PolicyMap divides the total number of aggravated assaults that were reported in a county or place by the population count provided by the FBI and multiplied that ratio by 100,000. The population count used for places in this calculation is from the FBI. The county population count is an estimate of the number of people served by the agencies within the county that report offenses. In 2015, Vermont data may not be complete due to technical issues at agencies in the state. According to the FBI, underreporting of data is highly likely.

In 2020, there is limited data for Alabama, Maryland, and Pennsylvania. Data is shown at 2010 County boundaries.

< Back to list of datasets

FBI Uniform Crime Reports – Hate Crime Statistics

Details:

Hate crime counts and rates per 100,000 people for crimes motivated by a bias against race/ethnicity/ancestry, religion, sexual orientation, gender/gender identity, or disability

Topics:

Hate Crime Counts and Rates

Source:

FBI Uniform Crime Reports

Years Available:

2006-2018

Geographies:

state

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

https://ucr.fbi.gov/hate-crime/

Last updated on PolicyMap:

December 2019

Description:

The Federal Bureau of Investigation’s Uniform Crime Reporting (UCR) Program compiles standardized incident reports from local law enforcement agencies in order to produce reliable, uniform, and national crime data. The UCR Program is voluntary, and includes data for only counties and cities with population over 10,000. As a result, coverage is not universal. The UCR Program collects data on known offenses and persons arrested by law enforcement agencies. The UCR Program does not record the findings of a court, coroner, jury, or the decision of a prosecutor.

The Hate Crime Statistics Program within the FBI’s (UCR) Program collects data regarding criminal offenses that were motivated, in whole or in part, by the offender’s bias against a race, ethnicity, ancestry, gender, gender identity, religion, disability or sexual orientation and were committed against persons, property, or society. Hate crime data is captured by including the element of bias in offenses already being reported to the UCR Program. State hate crime counts reflect the sum of all reported offenses from agencies within the state that submitted data to the FBI. The state population count used in the rate calculations is the total population of the state as reported in the Census’s Population Estimates Program. The State of Hawaii does not participate in the Hate Crime Statistics Program. Due to variation in reporting and hate crime definitions changing over time, FBI hate crime statistics should not be compared across states, and should not be compared from one year to another. An agency can report up to four bias motivation types per offense. Multiple-bias offenses are not common, but when they occur, they are double-counted in the value of the total number of hate crimes.

< Back to list of datasets

FCC Broadband Deployment Data

Details:

Broadband internet availability, speed, technology, number of providers, wired and wireless

Topics:

Broadband internet availability

Source:

Broadband Deployment Data from FCC Form 477

Years Available:

2018

Geographies:

block

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

https://www.fcc.gov/general/broadband-deployment-data-fcc-form-477

Last updated on PolicyMap:

January 2020

Description:

All facilities-based broadband internet providers are required to file Form 477 with the FCC twice a year with information on where they offer internet access at speeds of 200 kbps or more in at least one direction. The wired and wireless data is accurate as of December 31, 2018. Data are provided at the Census block level. The data shown on PolicyMap indicates service available anywhere in the block, not necessarily for the entire block. For indicators showing number of providers, this is the number of providers throughout the block, even if they are not in the same part of the block, so it is not necessarily a measure of competition.

Wireless data are also provided at the block level. However, because wireless signals often do not conform to these boundaries, the source data indicates how much of the block is covered by a particular provider. Separate data is provided for 4G LTE and 4G non-LTE service. Because these two technologies provide a similar level of service, they are shown as a single indicator on PolicyMap. The area covered by 4G service represents whichever one of the two technologies covers the most area, not necessarily the total area covered by the two technologies. Similarly, the area covered by all wireless broadband service represents whichever one of 3G, 4G, and 4G LTE covers the most area.

< Back to list of datasets

FDIC

Details:

FDIC insured bank failures, bank branches

Topics:

Bank failures, bank branches

Source:

Federal Deposit Insurance Corporation

Years Available:

2000 – 2020

Geographies:

Point

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

Failures: http://www2.fdic.gov/hsob/hsobRpt.asp Branches: https://www5.fdic.gov/sod/dynaDownload.asp?barItem=6

Last updated on PolicyMap:

December 2020

Description:

The Federal Deposit Insurance Corporation releases data on failures and assistance transactions of financial institutions in the United States and its territories. This data draws on information from two FDIC databases: Failures and Assistance Transactions United States and Other Areas (Table BF01, available here: https://banks.data.fdic.gov/explore/failures) which is updated on an ongoing basis, and the FDIC Institution Directory (https://www5.fdic.gov/idasp/warp_download_all.asp), which is updated weekly. The data includes banks that have failed since October 1, 2000. The fields in the data for the assets and deposits of the acquiring bank are from the most recent quarterly report by the FDIC at the time of the most recent data update (this may be different than the acquiring bank information at the time of the initial bank closing).

The FDIC’s Summary of Deposits (SOD) is an annual survey of branch offices for all FDIC-insured institutions, including U.S. branches of foreign banks. Data is updated by the FDIC annually.

< Back to list of datasets

FDIC National Survey of Unbanked and Underbanked Households

Details:

Count, percentage, and percent change of households that are unbanked and underbanked by age, education, household type, and race

Topics:

Unbanked Households, underbanked households

Source:

Federal Deposit Insurance Corporation

Years Available:

2015 – 2019

Geographies:

State, CBSA

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

http://www.fdic.gov/householdsurvey/

Last updated on PolicyMap:

April 2021

Description:

Every two years, the FDIC sponsors the National Survey of Unbanked and Underbanked Households to collect data on the number of U.S. households that are unbanked and underbanked; their demographic characteristics such as household type, race/ethnicity, age, and level of education; and their reasons for being unbanked or underbanked. This survey is conducted by the U.S. Census Bureau as a special supplement to the Current Population Survey (CPS). The FDIC undertakes this effort to address a gap in the availability of comprehensive data on the number of unbanked and underbanked households in the United States.These estimates are based on data aggregated from the 2015, 2017, and 2019 surveys, pulled from the multi-year microdata published by the FDIC at https://www.economicinclusion.gov/downloads/.

Like with all estimates derived from survey data, the values published in these data layers are associated with some uncertainty. In order to help users understand the reliability of the estimates, PolicyMap published margins of error at the 90% confidence level associated with each estimate. This means that there is a 90% likelihood that if every household in the given geography was interviewed, the count or percent would be within the margin of error above or below the estimate derived from the survey sample. For example, 3.9% of households in Colorado were unbanked, with a margin of error of 1.5%. This may be expressed as 3.9% ± 1.5%, which means that there is a 90% likelihood that the true percentage of unbanked households in Colorado was between 2.4% and 5.4% (expressing the margin of error in this way is known as a “confidence interval”). PolicyMap has also published a data flag to help users interpret these margins of error. The data flags are based on “coefficients of variation,” which are the ratios between the estimate and the margin of error. Estimates flagged as “Use with Caution” have coefficients of variation between 15% and 30%. Estimates flagged as “Reliable” have coefficients of variation of 15% or less. PolicyMap suppressed estimates with coefficients of variation greater than 30%; these are marked as “insufficient data” on the map.

< Back to list of datasets

Federal Housing Finance Agency (FHFA)

Details:

Change in annual housing price index

Topics:

Home sales, housing prices

Source:

Federal Housing Finance Agency (FHFA), Housing Price Index datasets

Years Available:

2000-2020

Geographies:

Census tract, ZIP code, county, CBSA, state

Public Edition or Subscriber-only:

Public Edition

Download Available:

no

For more information:

https://www.fhfa.gov/DataTools/Downloads/Pages/House-Price-Index-Datasets.aspx#qpo

Last Updated on PolicyMap:

May 2021

Description:

The housing price index (HPI) is a broad measure of the movement of single-family house prices based on transactions involving conforming, conventional mortgages purchased or securitized by Fannie Mae or Freddie Mac. The HPI is a weighted, repeat-sales index, meaning that it measures average price changes in repeat sales or refinancings on the same properties.

The annual indices for smaller geographies should be considered developmental. As with the standard FHFA HPIs, revisions to these indexes may reflect the impact of new data or technical adjustments. Indexes are calibrated using appraisal values and sales prices for mortgages bought or guaranteed by Fannie Mae and Freddie Mac. An index is not reported in cases where sample sizes on mortgage transactions are too small for a given geographic area. These indices were developed as part of FHFA Working Paper 16-01 (Bogin, A., Doerner, W. and Larson, W. (2016). Local House Price Dynamics: New Indices and Stylized Facts. Federal Housing Finance Agency, Working Paper 16-01.) The working paper may be accessed at http://www.fhfa.gov/papers/wp1601.aspx.

< Back to list of datasets

Feeding America: Map the Meal Gap

Details:

Overall food insecurity rates, child food insecurity, percent of insecure eligible for National Nutrition Assistance, and food-budget shortfall

Topics:

Food insecurity

Source:

Feeding America

Years Available:

2015, 2016, 2017, 2018, 2019

Geographies:

State, county, congressional district

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

http://map.feedingamerica.org/

Last updated on PolicyMap:

March 2022

Description:

Feeding America publishes the Map the Meal Gap project annually to look at hunger on the state and local level. The project includes overall and child food insecurity data, along with the percentages of food insecure individuals eligible for National Nutrition Assistance and other nutrition programs, their average cost per meal, and annual food-budget shortfalls. The poverty thresholds that determine SNAP and other nutrition program eligibility vary by state. Changes in state eligibility requirements limit year to year comparisons. More detail on how Feeding America obtained the data can be found in the technical brief located here: https://www.feedingamerica.org/research/map-the-meal-gap/how-we-got-the-map-data.

Gundersen, C., Strayer, M., Dewey, A., Hake, M., & Engelhard, E. (2021). Map the Meal Gap 2021: An Analysis of County and Congressional District Food Insecurity and County Food Cost in the United States in 2019. Feeding America.

< Back to list of datasets

FEMA National Flood Hazard Layer

Details:

FEMA National Flood Hazard Layer

Topics:

Flood maps

Source:

Federal Emergency Management Agency

Years Available:

2022

Geographies:

Polygon

Public Edition or Subscriber-only:

Subscriber and API only

Download Available:

no

For more information:

http://www.msc.fema.gov/

Last updated on PolicyMap:

May 2022

Description:

The National Flood Hazard Layer (NFHL), published by FEMA, is used for floodplain management, mitigation, and insurance purposes. It includes the Digital Flood Insurance Rate Map (DFIRM) and Letters of Map Revision (LOMRs). The map divides areas into three primary risk classifications: 1 percent annual chance flood event (high risk), 0.2 percent annual chance flood event (moderate risk), and areas of minimal flood risk. The maps processed by PolicyMap show areas of high risk, moderate risk, minimal risk, and undetermined risk. These categories of risk are simplified from FEMA’s more specific categories. High risk flood zones include flood zones categorized as A, A99, AE, AH, AO, V, and VE. Moderate risk includes X (shaded). Low risk includes X (unshaded).

PolicyMap receives updates annually from FEMA, and classifies areas based on the Designations of FEMA Flood Zone Designations. Not all counties are included in the NFHL. A coverage map can be found at FEMA’s website. Counties which show data available on the FEMA coverage map do not necessarily have complete coverage.

< Back to list of datasets

FEMA Disaster Declarations

Details:

FEMA Historical Disaster Declarations

Topics:

Disaster areas, fires, floods, hurricanes, severe storms, COVID-19

Source:

Federal Emergency Management Agency

Years Available:

2012 – 2021

Geographies:

Polygon

Public Edition or Subscriber-only:

Public Edition

Download Available:

no

For more information:

http://www.fema.gov/disasters/ https://www.fema.gov/openfema-data-page/disaster-declarations-summaries-v2

Last updated on PolicyMap:

December 2021

Description:

Federal disaster areas are places where the state or tribal government has requested and received federal assistance to protect the public’s health and safety in an emergency. After a governor seeks a presidential disaster declaration, FEMA conducts a preliminary damage assessment before recommending a decision to the president. Factors influencing the declaration of a federal disaster include the amount and type of damage, impact on infrastructure or critical facilities, imminent threats to health and public safety, impacts to essential government services and functions, unique capability of the Federal government to provide resources and available assistance from other sources, dispersion or concentration of damage, level of local insurance coverage, state and local resource commitments from previous events, and the frequency of recent disaster events. FEMA provides disaster funding though four programs: Individuals and Households, Individual Assistance, Public Assistance, Hazard Mitigation. For more information on the programs, visit FEMA’s website. Each declaration area is assigned a sequential disaster number. Disaster numbers are unique to states. Disasters indicate both the dates the incident itself began and ended, as well as the date of the disaster declaration and the date all financial transactions for all programs are completed (the closeout date). Disaster areas are displayed as groups of counties and/or Indian areas.

Federal disaster declarations are only displayed on PolicyMap since 2012, and do not include disasters declared before January 1, 2012 or after December 16, 2021. Please visit www.fema.gov/disasters for a comprehensive list of current and/or historical disaster declarations.

< Back to list of datasets

FFIEC: CRA (Federal Financial Institutions Examination Council: Community Reinvestment Act)

Details:

Number, average amount, and percent of small business and small farm loans by amount, borrower revenue, and leading lenders

Topics:

Small business lending, small farm lending

Source:

CRA (Community Reinvestment Act)

Years Available:

2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019

Geographies:

census tract, county

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

http://www.ffiec.gov/CRA/

Last updated on PolicyMap:

May 2021

Description:

The Community Reinvestment Act (CRA), which was enacted by Congress in 1977, is intended to encourage depository institutions to help meet the credit needs of the communities in which they operate, including low- and moderate-income neighborhoods, consistent with safe and sound banking operations. CRA requires that each insured depository institution’s record in helping meet the credit needs of its entire community be evaluated periodically. That record is taken into account in considering an institution’s application for deposit facilities, including mergers and acquisitions. CRA examinations are conducted by the federal agencies that are responsible for supervising depository institutions: the Board of Governors of the Federal Reserve System (FRB), the Federal Deposit Insurance Corporation (FDIC), the Office of the Comptroller of the Currency (OCC), and the Office of Thrift Supervision (OTS). PolicyMap extracted the database of lending activity from the Peer Small Business Data. PolicyMap aggregated the number of loans by amount of loan and by borrower revenue. PolicyMap also aggregated the number, average amount and percent of loans by top small business lenders and by top small farm lenders in order to construct categories that would be useful to policymakers and descriptive of neighborhoods and markets.

When performing aggregations and calculations on the CRA data, averages were not calculated and percents were not computed where the denominator of the calculation was less than five. These places are identified on the map as having Insufficient Data. The 2004-2011 data is at the 2000 Census boundaries and the 2012-2019 data is at the 2010 boundaries. For percent changes, PolicyMap created a bridge table between 2000 and 2010 geographies in order to calculate previous years of data at 2010 Census boundaries. These previous years of data calculations are used for comparison to the current year of data.

< Back to list of datasets

PolicyMap, Federal Financial Institutions Examination Council (FFIEC): Home Mortgage Disclosure Act (HMDA) Summaries

Topics:

Originations, Purchase Loans, Piggyback Loans, Refinance Loans, Prime Loans, High-Cost Loans, Loans By Race and Ethnicity, Government-Insured Loans, FHA Loans, VA Loans, Loan to Income “Leverage” Ratio, Manufactured Loans, Loans by Tract Income, Loans by Borrower Income, Mortgage Loan Denials, Mortgage Loan Denials by Race and Ethnicity

Source:

PolicyMap, FFIEC

Years Available:

2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020

Geographies:

tract, county, place, CBSA, Metropolitan Division, state

Public Edition or Subscriber-only:

Subscriber-only

Download Available:

yes

For more information:

http://www.ffiec.gov/hmda/

Last Updated on PolicyMap:

May 2022

PolicyMap Exclusive:

yes

Description:

The Home Mortgage Disclosure Act (HMDA), enacted by Congress in 1975, requires most mortgage lenders located in metropolitan areas to collect, report, and disseminate data about their housing-related lending activity. The public database of lending activity is called the Loan/ Application Register (LAR). PolicyMap aggregated data on home loans from the LAR into indicators useful to policymakers and descriptive of neighborhoods and housing markets into its Home Mortgage Data indicators. PolicyMap’s Home Mortgage Data is restricted to loans for owner-occupied 1-4 unit homes. The indicators include data on all home loans, which includes both home purchase loans and home refinance loans, and indicators specific to either home purchase loans or refinance loans. PolicyMap aggregated data on the number of home loans that were originated and loan applications that were denied. When performing aggregations and calculations on the LAR data, medians were not calculated where the count of loan events of that type was less than five. Percentages were not calculated where the denominator was less than five. These places are identified on the map as having Insufficient Data.
High-Cost Loans
PolicyMap classifies loans as “high cost” if they had a reported rate spread. The rate spread on a loan is the difference between the Annual Percentage Rate (APR) of the loan and the estimated average prime offer rate (APOR). The APR of a loan is the amount that a lender charges the borrower each year expressed as a percentage of the loan amount. The APOR is a benchmark established by the FFIEC based on a national survey of home loan offer prices, published weekly at https://ffiec.cfpb.gov/tools/rate-spread. Rate spreads are only reported by financial institutions if the APR is more than 1.5 percentage points higher than the APOR for a first lien loan, or more than 3.5 percentage points higher for a second lien loan. A rate spread of 1.5 or more suggests that a loan is of notably higher price than a typical loan, indicating that it is to be classified as high cost. PolicyMap previously denoted high-cost loans as “subprime,” but changed the terminology with the release of the 2008 data to reflect language used by the Federal Reserve. The “high-cost” designation is not to be confused with “HOEPA”. HOEPA loans are a subset of the high-cost loan category.
Prime Loans
Prime loans are defined as loans with no reported rate spread. PolicyMap assumes for the purpose of its calculations that a loan without a reported rate spread is of a “typical” APR and most likely prime.
HMDA’s 2009 rule change
In the fourth quarter of 2009, HMDA changed its rules for reporting rate spreads to more accurately capture the current high-cost lending activity. In order to accurately display the data according to the rule adjustments, PolicyMap divided the 2009 data into 2009Q1-2009Q3 and 2009Q4. Change calculations between previous years and 2009 to present should not be made due to the change in HMDA’s definition of “high cost.” In the 2009 data, due to the high incidence of error notations in the manufactured home loan data in 2009, medians are shown as “N/A” wherever error notations were present. Before the rule change in the fourth quarter of 2009, the rate spread on a loan was calculated as the difference between the Annual Percentage Rate (APR) and the treasury security yields as of the date of the loan’s origination. The treasury security yield is the percentage of interest that the U.S. government pays out to those who invest in treasury bonds or other securities. Rate spreads were only reported by financial institutions if the APR was 3 or more percentage points higher than the treasury security yields for a first lien loan, or 5 or more percentage points higher for a second lien loan. A rate spread of 3 or more suggested that a loan was of notably higher price than a typical loan, indicating that it could be classified as high cost.
Multiple mortgage transactions
PolicyMap’s Home Mortgage Data includes information on multiple mortgage transactions, also termed “80-20 loans,” or “piggyback loans.” A multiple mortgage transaction is when a buyer obtains at least two loans in order to purchase a home. The second loan finances that part of the purchase price not being financed by the first loan. Multiple mortgage transactions have been used to avoid underwriting standards held by most lenders that require private mortgage insurance (PMI) when less than a 20% down payment is made by the buyer. Studies suggest that these transactions have a higher risk of default and foreclosure as the homebuyers have little or no equity at risk. HMDA data does not explicitly identify 80-20 or piggyback loans. PolicyMap created an algorithm for estimating transactions involving multiple loans to purchase a property. First- and second-position loans in the same census tract, from the same lender, and to applicants with the same race, ethnicity, gender, and income were flagged as multiple loans for the same property. These loans were then combined into one record and the loan amounts were summed, thus reflecting the total loan for the property transaction.
Government-Insured Loans
The federal government has several entities through which it insures or guarantees consumer home loans, including the Federal Housing Administration (FHA), Department of Veteran Affairs (VA), and the USDA’s Farm Service Agency (FSA). Although often referred to as government insurance, a government guarantee on a loan does not take the place of private mortgage insurance (PMI). Rather, the government guarantees the value of the property to the bank that originates the loans. In the case of default on the loan or foreclosure on the property, the government entity that guaranteed the loan repays the debt to the bank in full and takes over ownership of the property. The programs that the federal government uses to guarantee loans have varied target populations, but generally are committed to expanding the opportunities for home ownership to buyers who might not otherwise qualify for a loan with favorable terms. Government-guaranteed loans generally also require banks to commitment to negotiation with the homeowner in the event of loan default, beyond what is required of banks for non-government-insured home loans. The Federal Housing Administration (FHA) is one entity through which the government guarantees consumer loans. There are several FHA programs with missions that include helping moderate income first-time homebuyers, buyers of properties that need significant rehabilitation, and the elderly. For more on FHA-insured lending, see https://www.hud.gov/buying/loans. The Department of Veterans Affairs (VA) is another entity through which the government guarantees consumer loans. The VA home loan program helps veterans finance the purchase of homes with favorable loans terms and interest rates. For more on VA-insured lending, see https://www.benefits.va.gov/homeloans/. In HMDA, loans guaranteed by the USDA Farm Service Agency (FSA) and those guaranteed by the USDA Rural Housing Service (RHS) are counted in the same category. FSA loans are intended for farmers who cannot qualify for conventional loans due to insufficient financial resources and farmers who have suffered financial setbacks due to natural disasters. RHS guarantees mostly apply to loans for essential community facilities in rural areas. For more on FSA-insured lending, see https://www.fsa.usda.gov/programs-and-services/farm-loan-programs/.
Loan to Income, or Leverage Ratios
PolicyMap used the reported applicant income from the LAR to calculate “leverage ratios,” or the ratio of the applicant’s income to the amount of the originated loan.
Census Places
Loan applications reported to HMDA do not contain Census Places identifiers. PolicyMap aggregated HMDA data to the Place level by creating a correspondence between Census tracts and Places. A tract was considered part of a Place if it was completely contained by the Place. In the event a tract was divided in two or more sections, the tract was considered to belong to the Place where the largest section of the tract was located.
Racial and ethnic categories
The LAR reports basic demographic information on the applicant and co-applicant including race and ethnicity. The FFIEC follows the conventions used by the Census Bureau, by considering Hispanic or Latino status as an ethnicity distinct from race. Additionally, the LAR classifies race according to five categories—American Indian or Alaska Native, Asian, Black or African American, Native Hawaiian or Pacific Islander, and White. PolicyMap has provided aggregations for the largest racial groups in the United States—White, Black, and Asian—regardless of ethnicity. Because of lower coverage, PolicyMap did not tabulate indicators for American Indian or Alaska Native, or Native Hawaiian or Pacific Islander racial categories. PolicyMap also provides indicators on the Hispanic or Latino ethnicity, and on race and ethnicity together. For the combined race and ethnicity indicators, PolicyMap grouped Asian, American Indian or Alaska Native, and Native Hawaiian or Pacific Islander, and those who either did not provide information or provided inapplicable information together under the category of “another race.” The loan applications were categorized according to the ethnicity and first reported race of the primary applicant.
FSA Loans

In HMDA, loans guaranteed by the USDA Farm Service Agency (FSA) and those guaranteed by the USDA Rural Housing Service (RHS) are counted in the same category. FSA loans are intended for farmers who cannot qualify for conventional loans due to insufficient financial resources and farmers who have suffered financial setbacks due to natural disasters. RHS guarantees mostly apply to loans for essential community facilities in rural areas. For more on FSA-insured lending, see http://www.fsa.usda.gov/FSA/webapp?area=home&subject=fmlp&topic=landing.

< Back to list of datasets

Glenmary Research Center and Association of Statisticians of American Religious Bodies (ASARB)

Details:

Rates of adherence by denomination, Counts of denominations, Percent Change in Adherents

Topics:

Religious adherence

Source:

Major Religious Families by Counties of the United States 2000 from “Religious Congregations and Membership in the United States, 2000, Dale E. Jones, et. Al. Nashville, TN: Glenmary Research Center. Copyright 2002 Association of Statisticians of American Religious Bodies. (all rights reserved)

Years Available:

2000, 2010

Geographies:

county, state

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

http://www.thearda.com/mapsReports/rcms_notes.asp

Description:

The Religious Congregations and Membership Study, carried out by the Association of Statisticians of American Religious Bodies (ASARB), was conducted with significantly different methodologies in 2000 and 2010. 2000 data were collected by the ASARB and include statistics for 149 religious groups, including number of churches and adherents. Dale E. Jones, Sherri Doty, Clifford Grammich, James E. Horsch, Richard Houseal, Mac Lynn, John P. Marcum, Kenneth M. Sanchagrin and Richard H. Taylor supervised the collection. These data originally appeared in Religious Congregations & Membership in the United States, 2000: An Enumeration by Region, State and County Based on Data Reported by 149 Religious Bodies, published by the Glenmary Research Center. The 2000 data excludes most of the historically African-American denominations and some other major groups. In an effort to correct for this, in 2002 the ASARB released an adjusted rate of adherence to all denominations per 1,000 people. The adjusted rate is included on PolicyMap; because of this correction some counties will have rates in excess of 1000. For more on the corrections see Roger Finke and Christopher P. Scheitle’s article Accounting for the Uncounted at http://www.thearda.com/mapsReports/Accounting%20for%20the%20Uncounted.pdf. 2010 data were also collected by ASARB and include statistics for 236 religious groups, including number of churches and adherents. In contrast to the 2000 study, researchers obtained mailing lists for the eight largest historically African-American denominations. In addition to including membership information gathered from this list, online church locators were identified and used to identify additional congregation locations. For each congregation located in this way, a membership of 100 was assigned. However, it is important to note that, while the figures for African-American denominations are more accurate than those imputed for the 2000 U.S. Religion Census, the figures are still significantly lower than those reported by the denominations in the Yearbook of American and Canadian Churches, 2010. In total, the 236 groups reported 344,894 congregations with 150,686,156 adherents, comprising 48.8 percent of the total U.S. population in 2010.

The data reported on Jews and Muslims are estimates rather than counts. For more information on how these estimates were calculated, including changes in the estimation methodologies from the 2000 to 2010 surveys, see: http://www.thearda.com/mapsReports/rcms_notes.asp.

< Back to list of datasets

GreatSchools’ School District Performance

Topics:

Public and Public Charter School District performance, test scores by district

Source:

GreatSchools

Years Available:

varied, 2012 to 2018

Geographies:

school district

Public Edition or Subscriber-only:

subscriber-only

Download Available:

no

For more information:

http://www.greatschools.org

Last updated on PolicyMap:

November 2019

Description:

GreatSchools is a national, independent nonprofit organization providing elementary, middle and high school information for public, private, and charter schools nationwide. PolicyMap licenses GreatSchools’ school district test score information for incorporation in PolicyMap. PolicyMap displays data for the following standardized tests: Alaska Alaska Measure of Progress (AMP): The Alaska Department of Education administered AMP (Alaska Measures of Progress) for the first time in spring of 2015. Previously, students in grades 3-10 took the Standards Based Assessments (SBAs). AMP is aligned to Alaska’s English Language Arts and Mathematics Standards, which are much more rigorous than the Grade Level Expectations on which the SBAs were based. Because the two assessments measure very different standards, under no circumstances should the results be compared between these two assessments. Alaska Science Assessment: The Alaska Science Assessment are designed to measure a student’s understanding of the skills and concepts outlined in the Alaska Science Grade Level Expectations (GLEs). In 2017-18, the science assessment is administered to students in grades 4, 8, and 10. Alaska Performance Evaluation for Alaska’s Schools: The Performance Evaluation for Alaska’s Schools (PEAKS) is designed to measure a student’s understanding of the skills and concepts outlined in the Alaska English Language Arts (ELA) and Mathematics Standards. The Alaska English Language Arts and Mathematics Standards are specific rigorous expectations for growth in students’ skills across grades.The Alaska English language arts (ELA) standards demonstrate the expectation that students’ skills will build across grades in reading and analyzing a variety of complex texts, writing with clarity for different purposes, and presenting and evaluating ideas and evidence. The ELA standards are designed to help students develop a logical progression of fluency, analysis, and application, moving toward college and career readiness. The Alaska mathematics standards have the expectation that students’ skills will grow across grades in mathematics content as well as mathematical practices. The mathematics standards are designed to help students develop a logical progression of mathematical fluency, conceptual understanding, and real world application. In 2017-18, the PEAKS assessments are administered to students in grades 3-9. Alaska Standards Based Assessments (SBAs): In 2014-2015 Alaska used the Standards Based Assessment (SBA) to test students in grades 4, 8 and 10 in science. The SBA is a standards-based test, which means it measures specific skills defined for each grade by the state of Alaska. The goal is for all students to score at or above the proficient level. Alabama Alabama Science Assessment (ASA): In 2013-2014 Alabama used the Alabama Science Assessment (ASA) to test students in grades 5 and 7 in science. The ASA is a standards-based test, which means it measures specific skills defined for each grade by the state of Alabama. The goal is for all students to score at or above proficiency level 3. AL ACT Aspire: In 2016-2017, students in Alabama took the ACT Aspire. The ACT Aspire is a standards-based assessment system that gauges student progression from grades 3-8, and grade 10 in english and math, and grades 5, 7, and 10 in science. The ACT Aspire is administered to grades 3-8 and grade 10 students in Alabama public schools. AL ACT PLAN: ACT Plan contains four curriculum-based assessment-English, mathematics, reading, and science. Students in grade 10 receive scores on each subject test as well as a predictor composite score for the ACT. Score reports provide information to help students identify skills and knowledge required for college success as well as areas where extra help or additional high school courses were needed Arkansas Benchmark Exam: In 2014-2015 Arkansas used the Benchmark Exam to test students in grades 5 and 7 in science. The Benchmark Exam is a standards-based test, which means it measures specific skills defined for each grade by the state of Arkansas. The goal is for all students to score at or above the proficient level. End of Course Exam: In 2014-2015 Arkansas used the End of Course Exam to test high school students in Biology. The results for End of Course Exams administered in the spring of each school year are displayed on GreatSchools profiles. The End of Course Exam is a standards-based test, which means it measures specific skills defined by the state of Arkansas. The goal is for all students to score at or above the proficient level. AR ACT Aspire: In 2016-2017, students in Arkansas took the ACT Aspire. The ACT Aspire is an end-of-year summative assessment that gauges student progression from grades 3 through 10 in English, reading, writing, math, and science. The ACT Aspire is administered to students in grades 3-10 in Arkansas public schools. The Partnership for Assessment of Readiness for College and Careers (PARCC): The PARCC assessments summarize student performance through one of five performance levels. They include: Exceeded Expectations, Met Expectations, Approached Expectations, Partially Met Expectations, or Did Not Yet Meet Expectations.The knowledge and skills students need to demonstrate at each of the performance levels were based on recommendations of educator panels representing each of the participating states in the Consortium. Arkansas teachers were strong participants on these panels. All states in the Consortium have adopted these same performance standards. Arizona Arizona’s Instrument to Measure Standards (AIMS): In 2016-2017, Arizona used the Arizona Instrument to Measure Standards (AIMS) to test students in science in grades 4, 8 and high school students in grades 9-11. AIMS is a standards-based test, which means that it measures how well students have mastered Arizona learning standards. The goal is for all students to meet or exceed state standards. on the test. AZMerit: In 2016-2017, students in Arizona took the AZMerit. Arizona’s Measurement of Educational Readiness to Inform Teaching (AzMERIT) is an annual statewide test that measures how students are performing in English language arts and math for students in grade 3-8, and grade 11. The AZ Merit is also used to assess students for End of Course assessments for Algebra I, Algebra II, and Geometry. California California Assessment of Student Performance and Progress (CAASPP): In 2017-2018, California tested students using the California Assessment of Student Performance and Progress (CAASPP), administered through the online Smarter Balanced Summative Assessments. These are comprehensive, end-of-year assessments of grade-level learning that measure progress toward college and career readiness. Each test, English language arts/literacy (ELA) and mathematics is comprised of two parts: (1) a computer adaptive test and (2) a performance task; administered within a 12-week window beginning at 66 percent of the instructional year for grades three through eight, or within in a 7-week window beginning at 80 percent of the instructional year for grade eleven. The summative assessments are aligned with the Common Core State Standards (CCSS) for ELA and mathematics. The tests capitalize on the strengths of computer adaptive testing-efficient and precise measurement across the full range of achievement and timely turnaround of results. California Standards Tests: In 2015-2016 California used the California Standards Tests (CSTs) to test students in science in grades 5, 8 and 10. The CSTs are standards-based tests, which means they measure how well students are mastering specific skills defined for each grade by the state of California. The goal is for all students to score at or above proficient on the tests. Colorado Colorado Measures of Academic Success (CMAS): In 2017-2018, students in Colorado took the CMAS assessment for English Language Arts, Math, and Science. Connecticut Connecticut Mastery Test: In 2015-2016, students in Connecticut took the CMT assessment for science in grades 5 and 8. Connecticut Academic Performance Test: In 2015-2016, students in Connecticut took the CAPT assessment for science in grade 10. Smarter Balanced Assessment: In 2015-2016, students in Connecticut took the CT SBAC for grades 3-8. Students were tested in ELA and Math. District of Columbia The Partnership for Assessment of Readiness for College and Careers (PARCC): In spring 2017, District of Columbia students took the Partnership for the Assessment of Readiness for College and Careers, or PARCC, assessments for the first time. The new assessment, which replaced the DC CAS annual assessment, is more rigorous and designed to measure students readiness for college and career. District of Columbia Comprehensive Assessment System (DC CAS): In 2013-2014 Washington, D.C. used the District of Columbia Comprehensive Assessment System (DC-CAS) to test students in reading and math in grades 3 through 8 and 10, science in grades 5, 8, and 10, and composition in grades 4, 7, and 10. Currently GreatSchools is displaying results for reading and math only. The DC-CAS is a standards-based testing program, which means it measures specific skills defined for each grade by the District of Columbia. The goal is for all students to score at or above the proficient level. Delaware Delaware Comprehensive Assessment System (DCAS): In 2014-2015 Delaware used the Delaware Comprehensive Assessment System (DCAS) to test students in social studies in grades 4 and in science in grades 5, 8 and 10. The DCAS is a standards-based test, which means it measures specific skills defined for each grade by the state of Delaware. The goal is for all students to score at or above the state standard. Smarter Balanced Assessment: The Smarter Balanced assessments are designed to measure the progress of Delaware students in ELA/Literacy and Mathematics standards in grades 3-8. The administration of the Smarter assessments in grades 3-8, and 11 occurred during spring 2017 Florida Florida Comprehensive Assessment Test 2 (FCAT 2): From 2011-2014, the FCAT 2.0 was used to measure student achievement of the Next Generation Sunshine State Standards in reading, mathematics, and writing. In spring 2015, it was replaced by the Florida Standards Assessments (FSA) in English language arts and mathematics to measure student achievement of the Florida Standards. The Statewide Science Assessment will continue to be administered, along with retakes for Grade 10 FCAT 2.0 Reading. Florida End Of Course Test (EOC): In 2017-2018, the FL End-of-Course (EOC) assessment tested students in various subjects like Algebra 1 and US History. Florida Standards Assessment: In 2017-2018, the Florida Standards Assessments (FSA) tested students in English Language Arts (ELA) and Mathematics. Georgia Georgia Milestones End-of-Course (EOC) Assessment: In 2016-2017, students in Georgia took the George Milestones Assessment. The Georgia Milestones Assessment System (Georgia Milestones) is a comprehensive summative assessment program spanning grades 3 through high school. Georgia Milestones measures how well students have learned the knowledge and skills outlined in the state-adopted content standards in language arts, mathematics, science, and social studies. Students in grades 3 through 8 will take an end-of-grade assessment in each content area, while high school students will take an end-of-course assessment for each of the eight courses designated by the State Board of Education. Georgia Milestones End-of-Grade (EOG) Assessment: In 2016-2017, students in Georgia took the George Milestones Assessment. The Georgia Milestones Assessment System (Georgia Milestones) is a comprehensive summative assessment program spanning grades 3 through high school. Georgia Milestones measures how well students have learned the knowledge and skills outlined in the state-adopted content standards in language arts, mathematics, science, and social studies. Students in grades 3 through 8 will take an end-of-grade assessment in each content area, while high school students will take an end-of-course assessment for each of the eight courses designated by the State Board of Education. Georgia End of Course Tests (EOCT): In 2013-2014 Georgia administered End-of-Course Tests (EOCT) in 9th grade math level 2, geometry, analytic geometry, coordinated algebra, biology, United States history, physical science, American literature, and economics. The EOCT is a standards-based assessment, which means it measures how well students are mastering specific skills defined by the state of Georgia. The goal is for all students to score at or above the state standard. Georgia High School Writing Test (GHSWT): In 2014-2015 Georgia administered the Georgia High School Writing Test (GHSWT) to students in grade 11. The GHSWT is a standards-based assessment, which means it measures how well students are mastering specific skills defined by the state of Georgia. Students must pass the GHSWT in order to graduate from high school. The goal is for all students to pass the test. This assessment was retired in March 2015 and will no longer be administered in Georgia. Georgia Criterion-Referenced Competency Tests: In 2013-2014 Georgia administered the Criterion-Referenced Competency Tests (CRCT) in reading, social studies, science, English language arts and math in grades 3 through 8. The CRCT is a standards-based assessment, which means it measures how well students are mastering specific skills defined for each grade by the state of Georgia. The goal is for all students to score at or above the state standard. Hawaii Hawaii State Assessments (HSA): In 2012-2013 Hawaii used the Hawaii State Assessment (HSA) to test students in grades 3 through 8 and 10 in reading and math, and grade 10 in biology. The HSA is a standards-based test that measures how well students are mastering specific skills defined for each grade by the state of Hawaii. The goal is for all students to score at or above the proficient level on the test. Smarter Balanced Assessment: In 2016-2017, students in HI took the Smarter Balanced Assessments (SBA) in mathematics and English Language Arts/Literacy (ELA). The SBA is aligned to the Hawaii Common Core Standards, and designed to measure whether students are on track for readiness in college and/or career. SBA replaced the Hawaii State Assessment in math and reading. These are mandatory assessments given to students in grades 3-8 and 11. Iowa Iowa Assessment: In 2016-2017 Iowa used the Iowa Assessments to test students in grades 3 through 8 and 11 in reading and math. The scores reflect the performance of students enrolled for the full academic year. The Iowa Assessments are standards-based tests, which measure specific skills defined for each grade by the state of Iowa. The goal is for all students to score at or above proficient on the tests. Idaho Smarter Balanced Assessment: In 2016-2017, students in grades 3-8 and once in high school take the SBAC to determine whether they have achieved the standards for their grade level and subject area. There are tests for English Language Arts/Literacy and Mathematics. Idaho Standards Achievement Tests (ISAT): In 2016-2017, students in grades 5 and 7, students took the ISAT science assessment. Illinois Illinois Standard Achievement Test (ISAT): In 2013-2014 Illinois used the Illinois Standards Achievement Test (ISAT) to test students in grades 3 through 8 in reading and math, and in grades 4 and 7 in science. The ISAT is a standards-based test, which means it measures how well students are mastering specific skills defined for each grade by the state of Illinois. The goal is for all students to score at or above the state standard. In 2013 the Illinois State Board of Education raised the performance expectations for the ISAT in reading and math. These expectations have been adjusted to better align with the Common Core State Standards, a multi-state initiative that established year-by-year guidelines outlining the grade-specific skills and content students need to stay on the path to college and career readiness. The higher expectations of the new standards will result in a downward shift of where students rank in meeting or exceeding standards. The Partnership for Assessment of Readiness for College and Careers (PARCC): In 2016-2017, students in Illinois took The Partnership for Assessment of Readiness for College and Careers (PARCC). PARCC is the state assessment and accountability measure for Illinois students enrolled in a public school district. PARCC assesses the New Illinois Learning Standards Incorporating the Common Core and will be administered to students in English Language Arts and Mathematics. Illinois Prairie State Achievement Examination (PSAE): In 2013-2014 Illinois used the Prairie State Achievement Examination (PSAE) to test students in grade 11 in reading, math and science. The PSAE is a standards-based test, which means it measures how well students are mastering specific skills defined for each grade by the state of Illinois. The goal is for all students to score at or above the state standard. Indiana Indiana Statewide Testing for Educational Progress Plus: In 2016-2017, Indiana used the Indiana Statewide Testing for Educational Progress-Plus (ISTEP+) assessment to test students in grades 3 through 8, and grade 10 in English language arts and math. The ISTEP+ is a standards-based test, which means it measures specific skills defined for each grade by the state of Indiana. The goal is for all students to score at the passing level on the test. Indiana Reading Evaluation and Determination: The IREAD-3 assesses the 2017 Indiana Academic Standards, specifically those standards which align to foundational skills in reading. Indiana End-of-Course Assessments (ECAs): In 2014-2015 Indiana used the End-of-Course (ECA) assessment to test students in middle and high school in Algebra I, Biology I, and English 10. The ECA is a criterion-referenced assessment developed specifically for students completing their instruction in Algebra I, Biology I, or English 10. The goal is for all students to score at the passing level on the test. Kansas Kansas State Assessments: In 2017-18, Kansas used the Kansas State Assessments (KSA) to test students in grades 3 though 8, and 10 in reading and math. Students were also tested in grades 5,8,11 in science, and grades, 6,8, and 11 in history and government. The tests are standards-based, which means they measure how well students are mastering specific skills defined for each grade by the state of Kansas. The goal is for all students to score at or above the state standard. Kentucky Kentucky Performance Rating for Educational Progress (K-PREP): In 2016-2017, Kentucky used the Kentucky Performance Rating for Educational Progress (K-PREP) tests to assess students in grades 3 through 8 in reading and mathematics, 5 and 8 in social studies, 5, 8, and 11 in writing, and 4 and 6 in language mechanics. The K-PREP is a standards-based test, which means it measures how well students are mastering specific skills defined for each grade by the state of Kentucky. Kentucky End-of-Course Assessments: In 2016-2017, Kentucky administered the End-of-Course (EOC) assessments. EOCs are tests given to public high school students when they complete a course to assess their knowledge of important course concepts. They are similar to a final exam, except that they are created and scored by an outside testing company, ensuring that the tests are both rigorous and aligned with state and national college readiness standards. Louisiana Louisiana End of Course Test: In 2015-2016 Louisiana used the End-Of-Course (EOC) tests to test grade high school students in English 2, English 3, U.S. history, biology 1, algebra 1 and geometry. The EOC is a standards-based test, which means it measures specific skills defined for each grade by the state of Louisiana. The EOC is a high school graduation requirement. The goal is for all students to score at or above basic on the test. Louisiana Educational Assessment Program: In 2015-2016, students took the Louisiana Educational Assessment Program (LEAP) for grades 3-8 ELA, Math and Social Studies, and grades 4 and 8 in Science. These assessments are aligned to the Louisiana Standards which were developed with significant input from Louisiana educators. Integrated Louisiana Educational Assessment Program (iLEAP): In 2014-2015 Louisiana used the integrated Louisiana Educational Assessment Program (iLEAP) to assess students in grades 3, 5, 6, and 7 in science, and social studies. The iLEAP is a standards-based test, which means it measures specific skills defined for each grade by the state of Louisiana.vIn preparation for new assessments in 2014-15, Louisiana is including more common core aligned content in LEAP and iLEAP tests in 2012-13 and in 2013-14. The percent of students earning a proficient score is expected to be lower as a result of this change. The Partnership for Assessment of Readiness for College and Careers (PARCC): In grades 3-8 for English language arts (ELA) and mathematics, Louisiana has chosen to adopt the assessments developed by The Partnership for Assessment of Readiness for College and Careers (PARCC). PARCC is a group of states working together to develop high-quality assessments driven by determining whether students are college- and career-ready or on track. PARCC assesses the full range of the Common Core State Standards (CCSS), measures the full range of student performance, including the performance of high- and low-performing students, provides data during the academic year to inform instruction, interventions and professional development, provides data for accountability, including measures of growth, and incorporating innovative approaches throughout the assessment system. Massachusetts Massachusetts Comprehensive Assessment System (MCAS): In 2016-2017 Massachusetts used the Massachusetts Comprehensive Assessment System (MCAS) to test students in grade 10 in English Language Arts and Math, and grades 5, 8 and 10 in science. The grade 10 MCAS is a high school graduation requirement. The MCAS is a standards-based test, it measures specific skills defined for each grade by the state of Massachusetts. The goal is for all students to score at or above proficient on the test. MCAS Science and Technology/Engineering (MCAS STE) Tests: In 2016-2017 Massachusetts used the Massachusetts Comprehensive Assessment System Science and Technology/Engineering Tests (MCAS STE) to test students in high school in biology, chemistry, introductory physics and technology/engineering. The MCAS STE is a standards-based test, which means it measures specific skills defined for each grade by the state of Massachusetts. The goal is for all students to score at or above proficient on the test. The Partnership for Assessment of Readiness for College and Careers (PARCC): In 2016-2017, students were tested with the PARCC assessment for grades 3-8 in English and Math. Maryland The Partnership for Assessment of Readiness for College and Careers (PARCC): In school year 2015-2016, the PARCC assessments in mathematics and English Arts(ELA)/Literacy were administered to students in Maryland. The PARCC assessments will measure the content and skills contained in the new standards. These new advanced assessments are aligned to new standards, the user experience will be totally different, the scoring is different, and for the first time, Maryland will be able to examine deeper learning, critical-thinking, problem-solving, and communication skills needed for career and college readiness. Maryland High School Assessments: In 2014-2015 Maryland used the Maryland High School Assessments (HSA) to test students in Biology upon completion of each course. The HSA is a standards-based test, which means it measures how well students are mastering specific skills defined for each grade by the state of Maryland. Students are required to pass the tests in order to graduate. The goal is for all students to pass the tests. Maryland School Assessment: In 2015-2016 Maryland used the Maryland School Assessment (MSA) to test students in grades 5 and 8 in science. The MSA is a standards-based test, which means it measures how well students are mastering specific skills defined for each grade by the state of Maryland. The goal is for all students to score at or above proficient on the test. Maine New England Common Assessment Program (NECAP): In 2012-2013 Maine used the New England Common Assessment Program (NECAP) to test students in grades 3 through 8 in reading and math and in grades 5 and 8 in writing. The NECAP is a standards-based test, which means it measures how well students are mastering specific skills defined for each grade by the state of Maine. The goal is for all students to score at or above the state standard. ME SBAC: The 2014-15 Smarter Balanced Assessment scores in English language arts and mathematics for grades 3 through 8 and high school. These results are reflective of a more rigorous assessment as the world is changing rapidly, and Maine is poised to improve in this educational shift to better prepare our students for future success. Maine Educational Assessment (MEA): In 2014-2015 Maine used the Maine Educational Assessment (MEA) to test students in grades 5, 8, and 11 in science. The MEA is a standards-based test, which means it measures how well students are mastering specific skills defined for each grade by the state of Maine. The goal is for all students to score at or above the state standard. Maine High School Assessment (MHSA): In 2012-2013 Maine used the Maine High School Assessment (MHSA) to test students in grade 11 in critical reading, math, writing and science. The results reported show how well students are mastering state standards, specific skills defined by the state of Maine. The goal is for all students to score at or above the state standard. Michigan Michigan Merit Exam (MME): In 2013-2014 Michigan used the Michigan Merit Examination (MME) to assess students in grade 11 in reading, writing, math, science and social studies. The MME is a standards-based test, which measures how well students are mastering specific skills defined for each grade by the state of Michigan. The goal is for all students to score at or above the state standard. Beginning in the 2011-2012 school year, the Michigan State Board of Education implemented new definitions of what it means to be proficient on the MME test. In addition, they have recalculated past years’ results using these new standards for proficiency, making the above year-over-year results comparable. Michigan Educational Assessment Program (MEAP): In 2013-2014 Michigan used the Michigan Educational Assessment Program (MEAP) to test students in grades 3 through 8 in math, reading and writing; in grades 5 and 8 in science; and in grades 6 and 9 in social studies. Currently, GreatSchools’ ratings reflect 2013 MEAP results; ratings will be updated after 2014 Michigan Merit Examination (MME) results are released. The MEAP is a standards-based test, which measures how well students are mastering specific skills defined for each grade by the state of Michigan. The goal is for all students to score at or above the proficient level. Beginning in the 2011-2012 school year, the Michigan State Board of Education implemented new definitions of what it means to be proficient on the MEAP test. In addition, they have recalculated past years’ results using these new standards for proficiency, making the above year-over-year results comparable. Michigan Student Test of Educational Progress (M-STEP): In 2016-2017, students in Michigan took the Michigan Student Test of Educational Progress, or M-STEP. The M-STEP includes a summative assessments designed to measure student growth effectively for today’s students. English language arts and mathematics will be assessed in grades 3-8, science in grades 4 and 7, and social studies in grades 5 and 8. It also includes the Michigan Merit Examination in 11th grade, which consists of a college entrance exam, work skills assessment, and M-STEP summative assessments in English language arts, mathematics, science, and social studies. Minnesota Minnesota Comprehensive Assessments Series III (MCA-III): In 2017-18, Minnesota used the Minnesota Comprehensive Assessment-III (MCA-III) to test in math in grades 3 through 8 and 11, in reading in grades 3 through 8 and 10, and in science for grades 5 and 8, and once in high school. The MCA-III is a standards-based test, which means it measures specific skills defined for each grade by the state of Minnesota. The goal is for all students to score at or above the state standard. Missouri Missouri Assessment Program: The 2016-2017 Missouri Assessment Program assesses students progress toward mastery of the Show-Me Standards which are the educational standards in Missouri. The Grade-Level Assessment is a yearly standards-based test that measures specific skills defined for each grade by the state of Missouri. All students in grades 3-8 in Missouri will take the grade level assessment. English Language Arts and Mathematics are administered in all grades. Science is administered in grades 5 and 8. Missouri Assessment Program (MAP) End-of-Course Assessments: The 2016-2017 Missouri Assessment Program assesses students progress toward the Missouri Learning Standards, which are Missouri content standards. End-of-Course assessments are taken when a student has received instruction on the Missouri Learning Standards for an assessment, regardless of grade level. Missouri suite of available End-of-Course assessments includes: English I, English II, Algebra II, Geometry, American History, Government, Biology and Physical Science. Smarter Balanced Assessment: In 2014-2015, Missouri used the Smarter Balanced Assessment to test students in grade 3-8 in Math and English/Language Arts. Mississippi Mississippi Assessment Program End-Of-Course: In 2015-2016, students in Mississippi took The Mississippi Assessment Program (MAP). MAP is designed to measure student achievement on the Mississippi College-and Career-Readiness Standards (MS CCRS) for English Language Arts and Mathematics and to provide valid and reliable results to guide instruction through data driven instruction. The MAP EOC will assess students in grades 3-8 in Algebra I and English II. Mississippi Assessment Program: In 2015-2016, students in Mississippi took The Mississippi Assessment Program (MAP). MAP is designed to measure student achievement on the Mississippi College-and Career-Readiness Standards (MS CCRS) for English Language Arts and Mathematics and to provide valid and reliable results to guide instruction through data driven instruction. The MAP will assess students in grades 3-8 in English Language Arts and Mathematics. Mississippi Curriculum Test, 2nd Edition: In 2012-2013 Mississippi used the Mississippi Curriculum Test, 2nd Edition (MCT2) to test students in grades 3 through 8 in language arts and math. The MCT is a standards-based test, which means it measures how well students are mastering specific skills defined for each grade by the state of Mississippi. The goal is for all students to score at or above proficient on the test. Mississippi Science Test: In 2012-2013 Mississippi used the Mississippi Science Test (MST) to test students in grades 5 and 8 in science. The MST is a standards-based test, which means it measures how well students are mastering specific skills defined for each grade by the state of Mississippi. The goal is for all students to score at or above proficient on the test. The Partnership for Assessment of Readiness for College and Careers (PARCC): The PARCC assessment replaced Mississippi Curriculum Test – 2nd edition (MCT2) for English Language arts and Mathematics for grades 3 through 8 and the Subject Area Testing Program – 2nd edition (SATP2) for Algebra I and English II. The test results provide valuable information for parents and communities on whether students are learning and making progress in school. The PARCC test results are the first for Mississippi’s students to measure higher learning goals. According to PARCC, students scoring Level 4 or 5 are meeting or exceeding expectations. Mississippi Subject Area Testing Program (SATP): In 2012-2013 Mississippi used the Subject Assessment Testing Program (SATP) to test students in English II, writing, algebra I, biology I and U.S. history at the completion of each course. Students must pass all parts of the SATP in order to graduate from high school. The SATP is a standards-based test, which means it measures how well students are mastering specific skills defined for each grade by the state of Mississippi. The goal is for all students to pass the test. Montana Smarter Balanced Assessment: In 2016-2017, students in Montana took the SBAC assessment, which measures grades 3-8 in ELA and Math. Criterion-Referenced Test (CRT): In 2013-2014 Montana used the Criterion-Referenced Test (CRT) in Science only. The Science CRT is aligned to Montana Content Standards. Only students in grades 4, 8, and 10 will take the Science CRT. The CRT is a standards-based test, which means it measures specific skills defined for each grade by the state of Montana. The goal is for all students to score at or above the proficient level. Reading and Math will be assessed using Smarter Balanced. North Carolina North Carolina End-of-Grade Tests: In 2016-2017 North Carolina used End-of-Grade (EOG) tests to assess students in grades 3 through 8 in reading and math, and grades 5 and 8 in science. The EOG is a standards-based test, which means it measures how well students are mastering specific skills defined for each grade by the state of North Carolina. Students must pass the grade 8 EOG test in order to graduate from high school. The goal is for all students to score at or above the proficient level on the tests. North Carolina End-of-Course Tests: In 2016-2017 North Carolina used End-of-Course (EOC) tests to assess high school students in Mathematics, English II, and Biology. The EOC tests are standards-based, which means they measure how well students are mastering specific skills defined for each grade by the state of North Carolina. The goal is for all students to score at or above the proficient level on the tests. North Dakota North Dakota State Assessments (NDSA): In 2016-17, North Dakota used the North Dakota State Assessment (NDSA) to test students in grades 3 through 8 and 11 in reading and math, and in science in grades 4, 8 and 11. Results represent students enrolled in the school for the entire academic year. The NDSA is a standards-based test, which means it measures how well students are mastering the specific skills defined for each grade by the state of North Dakota. The goal is for all students to score at or above the proficient level. Nebraska Nebraska Student-Centered Assessment System: In 2017-18 Nebraska used the Nebraska Student-Centered Assessment System (NSCAS) assessment to test students in grades 3 through 8 and 11 in english language arts and math, and grades 5, 8, and 11 in science. The NSCAS is a statewide assessment system that embodies Nebraska’s holistic view of students and helps them prepare for success in postsecondary education, career, and civic life. Nebraska State Accountability (NeSA): In 2016-2017 Nebraska used the Nebraska Student-Centered Assessment System (NSCAS) assessment to test students in grades 3 through 8 and 11 in english language arts. Nebraska also used the Nebraska State Accountability (NeSA) to test students in grades 3 through 8 and 11 in math, and in grades 5, 8 and 11 in science. These assessments are standards-based tests, which means it measures how well students are mastering specific skills defined for each grade by the state of Nebraska. The goal is for all students to score at or above proficient on the test. New Hampshire NH Statewide Assessment System: In 2017-2018, the state of New Hampshire administered the NH Statewide Assessment System (NH SAS) to students in grades 3-8 for English Language Arts and mathematics, and in grades 5, 8, and 11 in science. The NH SAS is a standards-based, computer adaptive test aligned to the NH Academic Standards. Smarter Balanced Assessment: In 2016-2017, students in New Hampshire took the Smarter Balanced Assessment. Educators from Smarter Balanced states worked together to develop high-quality assessments that provide more accurate and meaningful information about what students are learning. The Smarter Balanced assessments replace existing tests in English and mathematics for grades 3-8 and high school. Administered online, these new assessments adapt to each student’s ability, giving teachers and parents better information to help students succeed. New Jersey New Jersey Assessment of Skills and Knowledge: In 2016-2017 New Jersey used the New Jersey Assessment of Skills and Knowledge (NJ ASK) to test students in grades 4 and 8 in science. The NJ ASK is a standards-based test, which means it measures how well students are mastering specific skills defined for each grade by the state of New Jersey. The goal is for all students to score at or above the proficient level. The Partnership for Assessment of Readiness for College and Careers (PARCC): Statewide assessments have been used for decades in New Jersey and are designed to measure student progress toward achieving our academic standards. PARCC is a multi-state consortium that allows states, including New Jersey, to pool resources and expertise to develop a meaningful, comparable high-quality assessment – one that can be used to guide our efforts to continually improve our educational system by supporting teaching and learning, identifying struggling schools, informing teacher development, and providing parents with feedback on their own child’s strengths and challenges. In 2016-17, the PARCC exam was administered to students. New Jersey Biology Competency Test (NJBCT): In 2016-2017 New Jersey used the New Jersey Biology Competency Test (NJBCT) to assess students in Biology. The New Jersey Biology Competency Test (NJBCT) is standards-based, which means it measures how well students are mastering specific skills defined by the state of New Jersey. The goal is for all students to score at or above the proficient level on the test. New Jersey High School Proficiency Assessment: In 2013-2014 New Jersey used the High School Proficiency Assessment (HSPA) to test students in grade 11 in language arts literacy and math. The HSPA is a standards-based test, which means it measures how well students are mastering specific skills defined for each grade by the state of New Jersey. Students are required to pass the HSPA in order to graduate. The goal is for all students to score at or above the proficient level. New Mexico The Partnership for Assessment of Readiness for College and Careers (PARCC): In 2016-2017, New Mexico used the PARCC assessment to test students in grades 3-12 in Math and grades 3-11 in Reading. New Mexico Standards Based Assessment (SBA): In 2016-2017, New Mexico used the New Mexico Standards-Based Assessment (NMSBA) to test students in grades 4, 7 and 11 in Science. Nevada Smarter Balanced Assessment: In 2016-2017, NV tested students in grades 3-8 using SBAC standards for math and reading subjects. High School Proficiency Examination (HSPE): In 2013-2014 Nevada used the High School Proficiency Examination (HSPE) to assess high school students in reading, writing, math and science. The HSPE is a high school graduation requirement. The HSPE is a standards-based test, which means it measures specific skills defined for each grade by the state of Nevada. The goal is for all students to score at or above the state standard. Criterion Referenced Tests (CRT): In 2013-2014 Nevada used the Criterion Referenced Test (CRT) to test students in grades 3 through 8 in reading and math, and in grades 5 and 8 in science. The CRT is a standards-based test, which means it measures specific skills defined for each grade by the state of Nevada. The goal is for all students to score at or above the state standard. New York NY State Common Core Regents Exam: In 2016-2017, high school students in New York took the Regents Common Core in English, Algebra 1, Algebra II, and Geometry courses. This exam is aligned with Common Core standards. New York State Assessments: Beginning in the 2012-2013 school year, the New York State Department of Instruction implemented new assessments designed to be aligned with the Common Core State Standards. The new standards for proficiency in these subjects are higher than in previous years and the percent of students earning a proficient score is expected to be lower as a result of this change. In 2016-2017 New York used the New York State Assessments to test students in grades 3 through 8 in English language arts and math, and in grades 4 and 8 in Science. At present 2015-2016 results are available only for English language arts and math. The tests are standards-based, which means they measure how well students are mastering specific skills defined for each grade by the state of New York. The goal is for 90% of students to meet or exceed grade-level standards on the tests. New York State Regents Exams: In 2016-2017, high school students had the option of taking course examinations in the old Regents assessment, which is being replaced by the Regents Common Core. Ohio Ohio State Test: In 2016-2017, students took state tests in math, English language arts, science and social studies to measure how well they are meeting the expectations of their grade levels. The tests match the content and skills that are taught in the classroom every day and measure real-world skills like critical thinking, problem solving and writing. Ohio Achievement Assessments (OAA): In 2014-2015 Ohio used the Ohio Achievement Assessment (OAA) to test students in grade 3 in reading. The OAA is a standards-based test, which means it measures specific skills defined for each grade by the state of Ohio. The goal is for all students to score at or above proficient on the test. Ohio Graduation Tests (OGT): In 2015-2016 Ohio used the Ohio Graduation Test (OGT) to test students in grade 11 in reading, writing, math, science and social studies. The OGT is a high school graduation requirement for public schools and chartered private schools. The OGT is a standards-based test, which means it measures how well students are mastering specific skills defined by the state of Ohio. The goal is for all students to score at or above proficient on the test. Ohio Next Generation Assessment: In 2014-2015, Ohio implemented the Next Generation Assessment for Science and Social Studies in grades 3-8. It replaced the Ohio Achievement Assessment (OAA). The Partnership for Assessment of Readiness for College and Careers (PARCC): The new Ohio’s State Tests in English language arts, mathematics, science and social studies were administered for the first time during the 2014-2015 academic year. The math and English language arts tests were produced by the Partnership for Assessment of Readiness for College and Careers (PARCC), of which Ohio was a member until July 1, 2015. Future tests in these subjects will be Ohio-specific tests developed by Ohio educators with the American Institutes for Research (AIR). The science and social studies tests will continue to be Ohio-specific tests developed by Ohio educators in cooperation with AIR. Oklahoma Oklahoma Core Curriculum Tests (OCCT): In 2015-2016, Oklahoma used the Oklahoma Core Curriculum Tests (OCCT) to test students in grades 3 through 8 in reading and math, and grades 5 and 8 in science and social studies. The OCCT is a standards-based test, which means it measures specific skills defined for each grade by the state of Oklahoma. The goal is for all students to score at or above the satisfactory level on the test. Oklahoma Core Curriculum Tests (OCCT) End-of-Instruction: In 2015-2016 Oklahoma used the Oklahoma Core Curriculum Tests End-of-Instruction (OCCT EOI) exams to test students in high school in several subjects. The OCCT EOI is a high school graduation requirement. The OCCT EOI exams are standards-based tests, which means they measure specific skills defined for each subject by the state of Oklahoma. The goal is for all students to score at or above the satisfactory level on the test. Oklahoma School Testing Program: In 2016-17, the Oklahoma State Department of Education administered assessments through the Oklahoma School Testing Program (OSTP) to provide evidence of student proficiency of grade-level standards to inform progress toward career- and college-readiness (CCR) and support student and school accountability. State assessment scores provide a reliable measure that can be compared across schools and districts by serving as a point-in-time snapshot of what students know and can do relative to the Oklahoma Academic Standards. The OSTP was administered to students in English Language Arts and Mathematics in grades 3-8, and grade 10. The test was also administered to students in Science in grades 5, 8, and 10, and in US History in grade 10. Oregon Smarter Balanced Assessment: Oregon now has K-12 learning standards aligned with the expectations of colleges and employers. These standards are designed to provide students with the knowledge and skills they need at each step along their educational journey so they can graduate high school prepared for future success. These tests move beyond the rote memorization and fill in the bubble format of past multiple choice tests. Students were asked to write, reason, think critically, and solve multi-step problems that better reflect classroom learning and the real world. Students who receive a 3 or 4 on the test (on a 4-point scale) are considered on track to graduate high school college- and career-ready. Those who receive a 1 or a 2 will receive additional support to help them reach this new higher standard. Oregon Assessment of Knowledge and Skills (OAKS): In 2016-2017 Oregon used the Oregon Assessment of Knowledge and Skills (OAKS) to test students in grades 5, 8 and 11 in science. The OAKS is a standards-based test, which means it measures how well students are mastering specific skills defined for each grade by the state of Oregon. The goal is for all students to score at or above the state standard. Pennsylvania Pennsylvania System of School Assessment: In 2015-2016, Pennsylvania used the Pennsylvania System of State Assessments (PSSA) to test students in grades 3 through 8 math and english language arts, and in grades 4 and 8 in science. The PSSA is a standards-based test, which means it measures how well students are mastering specific skills defined for each grade by the state of Pennsylvania. The goal is for all students to score at or above proficient on the test. The Pennsylvania Keystone Exams: In 2015-2016, Pennsylvania used the Keystone Exams to assess high school students in Algebra I, English, and Biology. The Keystone Exams are standards-based, which means they measure how well students are mastering specific skills defined for each grade by the state of Pennsylvania. The goal is for all students to score at or above the proficient level on the tests. Rhode Island The Partnership for Assessment of Readiness for College and Careers (PARCC): The 2016-17 results of the Partnership for Assessment of Readiness for College and Careers (PARCC) assessments provide a first look at whether students are meeting the expectations of the new learning standards in literacy and mathematics. The PARCC exam is administered in grades 3 through 9 for English Language Arts, grades 3 through 8 for Math, and to students who took Algebra I, Algebra II and Geometry. These standards are designed to prepare students for success in their next grade level, in postsecondary learning, and in career opportunities. New England Common Assessment Program (NECAP): In 2016-17 Rhode Island used the New England Common Assessment Program (NECAP) to test students in grades 4, 8 and 11 in science. The NECAP is a standards-based test, which means it measures specific skills defined for each grade by the state of Rhode Island. The goal is for all students to score at or above the proficient level. South Carolina South Carolina Palmetto Assessment of State Standards (PASS): In 2017-2018, students took The South Carolina Palmetto Assessment of State Standards (SCPASS). SCPASS is a statewide assessment administered to students in science in the 4th, 6th and 8th grade and in social studies in the 5th and 7th grade. All students in these grade levels are required to take the SCPASS except those who qualify for the South Carolina Alternate Assessment (SC-Alt). SCPASS includes tests in two subjects: science and social studies. End-of-Course Examination Program (EOCEP) Tests: In 2017-2018 South Carolina used the End-of-Course Examination Program (EOCEP) to test middle and high school students in Algebra I, Biology I, English I, and US History & the Constitution. The EOCEP provides tests for high school core courses and for courses taken in middle school for high school credit. The EOCEP is a standards-based test program, which means it measures how well students are mastering specific skills defined for each grade by the state of South Carolina. The goal is for all students to score a D or above. College and Career Ready Assessments (READY): In 2017-2018, students took the South Carolina College- and Career-Ready Assessments (SC READY). The SC Ready is a statewide assessment that includes tests in English Language Arts (ELA) and mathematics administered to students in grades 3-8. All students in grades 3-8 are required to participate in the SC READY, except those who qualify for the South Carolina National Center and State Collaborative (SC-NCSC) alternate assessment. The initial administration of the SC READY was in spring 2016, and the SC READY test results will be used for state and federal accountability purposes. SC ACT Aspire: ACT Aspire English language arts (writing, English, reading) and mathematics tests were administered statewide to students in grades 3-8 in Spring 2015. South Dakota Smarter Balanced Assessment: In 2015-2016, students in South Dakota took the SBAC assessment in ELA and Math for grades 3-8 and 11. Dakota State Test of Educational Progress (Dakota STEP): In 2015-2016 South Dakota used the Dakota State Test of Educational Progress (Dakota STEP) to test students in grades 5, 8, and 11 in Science. The Dakota STEP is standards-based, which means it is aligned to South Dakota’s educational standards and measures specific skills defined for each grade by the state. The standards-based Dakota STEP results are displayed on GreatSchools profiles. The goal is for all students to score at or above the proficient level. Tennessee Tennessee Comprehensive Assessment Program (TCAP): In 2016-2017 Tennessee used the Tennessee Comprehensive Assessment Program (TCAP) Achievement Test to test students in grades 3 through 8 in reading/language arts, math, science and social studies. The TCAP is a standards-based test that measures specific skills defined for each grade by the state of Tennessee. The goal is for all students to score at or above the proficient level. Gateway/End-of-Course (EOC): In 2016-2017 Tennessee used the Gateway/End-of-Course (EOC) exams to test high school students in language arts, math, science, and social studies upon completion of relevant courses. Students must pass the algebra I, English II, and biology I tests, called the Gateway exams, in order to graduate. The Gateway/EOC exams are standards-based tests that measure how well students are mastering specific skills defined by the state of Tennessee. The goal is for all students to score at or above the proficient level. Texas State of Texas Assessments of Academic Readiness (STAAR): In 2016-2017, the State of Texas Assessments of Academic Readiness (STAAR) was used to test students in reading and math in grades 3 through 8; in writing in grades 4 and 7; in science in grades 5 and 8; in social studies in grade 8; and end-of-course assessments for English I and II, Algebra I and II, biology and US History. STAAR is a standards-based test, which means it measures how well students are mastering specific skills defined for each grade by the state of Texas. The goal is for all students to score at or above the state standard. Utah Student Assessment of Growth and Excellence (SAGE): In 2017-2018, students in UT took the SAGE assessment.SAGE is a system of assessments designed to measure student success and growth over the years. SAGE tests are based on the Utah Core Standards, a set of academic standards that raise our expectations for students and teachers. Virginia Virginia Standards of Learning (SOL) End-of-Course: In 2016-2017 Virginia used the Standards of Learning (SOL) End-of-Course tests to assess students in reading, writing, math, science and history/social science subjects at the end of each course, regardless of the student’s grade level. The SOL End-of-Course tests are standards-based, which means they measure how well students are mastering specific skills defined for each grade by the state of Virginia. High school students must pass at least six SOL End-of-Course tests to graduate. The goal is for all students to pass the tests. Virginia Standards of Learning: In 2016-2017 Virginia used the Standards of Learning (SOL) tests to assess students in reading and math in grades 3 through 8, writing in grades 5 and 8, and science in grades 3, 5 and 8. The SOL tests are standards-based, which means they measure how well students are mastering specific skills defined for each grade by the state of Virginia. The goal is for all students to pass the tests. Vermont Smarter Balanced Assessment: In 2016-2017, students in Vermont took The Smarter Balanced assessment, which replaced Vermont’s previous assessment, the NECAP, in 2016. The new assessment of English Language Arts and Mathematics asks students to demonstrate and apply their knowledge and skills in areas such as critical thinking, analytical writing, and problem solving. The Smarter Balanced assessment is aligned with the Common Core State Standards, uses state of the art computer adaptive testing and accessibility technologies, and provides a continuum of summative, interim and formative tools that can be used for a variety of educational purposes. New England Common Assessment Program (NECAP): In 2016-2017, students in Vermont took the science assessment, which is part of the New England Common Assessment Program (NECAP). It is designed to measure students’ scientific literacy and inquiry. The NECAP science assessment, which combines scores from multiple choice and short answer questions with results from an inquiry task that requires students to analyze and interpret findings from an actual science experiment. Washington Washington Measurements of Student Progress (MSP): In 2016-2017, Washington used the Measurements of Student Progress (MSP) to test students science in grades 5 and 8. The MSP is a standards-based test, which means it measures how well students are mastering specific skills defined for each grade by the state of Washington. The goal is for all students to score at or above the state standard. Washington End-of-Course (EOC) Exams: In 2016-2017, the EOC currently tests Biology and are standards-based, which means they measure how well students are mastering specific skills defined for each grade by the state of Washington. The goal is for all students to score at or above the state standard. Smarter Balanced Assessment: In 2016-2017, WA tested students in English and Math with the Smarter Balanced Assessment. Smarter Balanced tests align to the new K-12 learning standards in English language arts and math (Common Core), which are more difficult than previous standards. Washington High School Proficiency Exam (HSPE): In 2013-2014 Washington used the High School Proficiency Exam (HSPE) to test students in reading and writing in grade 10. Math skills are tested by the End-of-Course (EOC) exams. The HSPE is a standards-based test, which means it measures how well students are mastering specific skills defined for each grade by the state of Washington. The goal is for all students to score at or above the state standard. Wisconsin Badger Exam: The Badger Assessment is a statewide Wisconsin Student Assessment System (WSAS) standardized exam. The exam was given to students in grades 3 through 8 and measured student achievement in two subject areas: English language arts (ELA) and mathematics. Forward: In 2017-18 Wisconsin administered the Wisconsin Forward Exam. The Exam is designed to gauge how well students are doing in relation to the Wisconsin Academic Standards. These standards outline what students should know and be able to do in order to be college and career ready. The Forward Exam is administered online in the spring of each school year at grades 3-8 in English Language Arts (ELA) and mathematics, in grades 4 and 8 in Science, and in grades 4, 8, and 10 in Social Studies. Wisconsin Student Assessment System (WSAS): In 2014-2015 Wisconsin used the Wisconsin Student Assessment System (WSAS), which includes the WKCE and WAA, to test students in grades 4, 8 and 10 science and social studies. The WSAS is a standards-based test, which means it measures how well students are mastering specific skills defined for each grade by the state of Wisconsin. The goal is for all students to score at or above the proficient level. In private schools, only voucher program participants are tested. West Virginia West Virginia General Summative Assessment: In 2018-2019, students in West Virginia took the General Summative Assessment. The West Virginia General Summative Assessment was administered to students in Grades 3-8 in reading and mathematics, and grades 5 and 8 in science. Wyoming Wyoming Test of Proficiency and Progress: In 2018-19, Wyoming administered the Wyoming Test of Proficiency and Progress (WY-TOPP).The WY-TOPP is a system of online assessments that are given to students in grades 3-10 in English language arts and mathematics, and given to students in grades 4, 8, and 10 in science. The goal is for all students to score at or above the proficient level. Proficiency Assessments for Wyoming Students (PAWS): In the 2016-2017 school year, Wyoming administered the Proficiency Assessments for Wyoming Students (PAWS). Students in grades 3 through 8, and 11 were tested in reading and math. Students in grades 4, 8, and 11 also took the science portion of the PAWS test. PAWS tests are standards-based, which means they measure how well students are mastering specific skills defined for each grade by the state of Wyoming. The goal is for all students to score at or above the proficient level.

GreatSchools district-level data is not available for download from PolicyMap.

< Back to list of datasets

GreatSchools School Points

Topics:

Public and Private Primary and Secondary Schools, selected test scores by school, school ratings

Source:

GreatSchools

Years Available:

varied, 2012 to 2018

Geographies:

point

Public Edition or Subscriber-only:

subscriber-only

Download Available:

no

For more information:

http://www.greatschools.net

Last updated on PolicyMap:

October 2019

Description:

GreatSchools is a national, independent nonprofit organization providing elementary, middle and high school information for public, private, and charter schools nationwide. PolicyMap licenses GreatSchools school directory, school ratings, and test score information for incorporation in PolicyMap. Schools whose coordinates fall outside the county in which they’re listed are not displayed on the map. The GreatSchools Overall School Rating is a measure of overall school performance by state. The GreatSchools rating system is based on a score ranging from 1 to 10, with 10 having the highest performance. GreatSchools Summary Ratings are based on test scores, student or academic progress, college readiness, equity, advanced courses, and discipline and attendance disparities. In states where not all information is available, a rating based on test scores is given. GreatSchools ratings are designed to be a starting point to help parents compare schools, and should not be the only factor used in school selection. More information about GreatSchools Ratings can be found here. School ratings should not be compared across states, as they are relative to the state in which the school operates. For information about tests administered in each state, please see the Data Directory entry for GreatSchools School District Performance.

GreatSchools School Points data is downloadable only for individual Census Tracts and Block Groups.

< Back to list of datasets

Harvard/UC Berkeley Equality of Opportunity Project

Topics:

Economic Mobility

Source:

Harvard University and University of California at Berkeley

Years Available:

2013

Geographies:

Commuting zones

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

http://www.equality-of-opportunity.org/

Description:

These data come from research put out by Harvard University and the University of California at Berkeley as part of The Equality of Opportunity Project. Through this project, the researchers set out to examine geographical differences in economic mobility rates throughout the country and to look at the impact of tax expenditures on intergenerational mobility. As part of this study, the researchers released data on the probability that a child growing up with parents with an annual household income in a certain income quintile will have an annual household in a certain quintile as an adult. On PolicyMap, we used this data to display the percent chance that children from low- and middle-income families will achieve certain income ranges as adults. These data are mapped to Commuting Zones (CZs), which PolicyMap created using geographic crosswalks provided by the source. Based on Census data, CZs are geographical aggregations of counties based on commuting patterns that are similar to metro areas but also cover rural areas. Children are assigned to the CZ based on their location at age 16 (no matter where they live today), and the location is thus interpreted as where the child grew up.

For a full report on the researchers’ findings, see http://obs.rc.fas.harvard.edu/chetty/tax_expenditure_soi_whitepaper.pdf. For more information about the Equality of Opportunity Project or the Commuting Zone geography, visit http://www.equality-of-opportunity.org.

< Back to list of datasets

Head Start

Details:

Locations of Head Start centers

Topics:

Head Start centers

Source:

Head Start Locator-Office of the Administration for Children and Families Early Childhood Learning and Knowledge Center

Years Available:

2021

Geographies:

Point

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

http://eclkc.ohs.acf.hhs.gov/hslc/data/center-data

Last updated on PolicyMap:

April 2021

Description:

PolicyMap downloads the geocoded Head Start locations using the Head Start locator at the website listed above. Head Start locations are classified as Early Head Start, Head Start, Migrant or Seasonal Head Start, or American Indian and Alaskan Native Head Start. Individual centers receive funding from a grantee authority and are located in defined federal regions.

< Back to list of datasets

Healthcare Cost and Utilization Project (HCUP)

Details:

Opioid hospitalization and emergency department visits as counts, rates, and percent changes

Topics:

Opioid hospitalizations and emergency department visits

Source:

Healthcare Cost and Utilization Project

Years Available:

2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017

Geographies:

state

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

https://www.hcup-us.ahrq.gov/faststats/OpioidUseServlet

Last updated on PolicyMap:

November 2019

Description:

The Healthcare Cost and Utilization Project (HCUP) is a Federal-State-Industry partnership, sponsored by the Agency for Healthcare Research and Quality (AHRQ), that brings together the data collection efforts of State data organizations, hospital associates, private data organizations, and the Federal government to create a national resource of encounter-level healthcare data. Due to limited geographic coverage for 2018 this data is not displayed on PolicyMap, but will be added once a larger data sample has been collected. HCUP opioid-related hospital use includes both hospital inpatient stays, recorded as hospital discharges, and emergency department (ED) visits. Each discharge or visit is recorded as a separate event, regardless of how many times an individual patient may visit a hospital in a year. Inpatient stays are when patients are admitted to and treated in community hospitals. Community hospitals are defined as short-term, non-Federal, general, and other hospitals, excluding hospital units of other institutions (e.g., prisons). Included among community hospitals are obstetrics and gynecology, otolaryngology, orthopedic, cancer, pediatric, public, and academic medical hospitals. Excluded are community hospitals that are also long-term care facilities such as rehabilitation, psychiatric, and alcoholism and chemical dependency hospitals. ED visits are defined as treat-and-release hospital encounters (i.e., they do not result in a hospital admission to the same hospital). The number of inpatient stays and ED visits are adjusted to account for missing data. Inpatient stays and ED visits for opioid-related hospital use include both opioid-related disorders and opioid poisoning and other adverse effects. Specifically, hospital discharges and ED visits related to opioid use were identified by any diagnosis in the following ranges of ICD-10-CM and ICD-9-CM codes: F11 series (except F11.21); T40 series (0X1, 0X4, 0X5, 1X1, 1X4, 2X1, 2X4, 2X5, 3X1, 3X4, 3X5, 4X1, 4X4, 4X5, 601, 604, 605, 691, 694, 695); 304.00-304.02; 304.70-304.72; 305.50-305.52; 965.00-965.02; 965.09; 970.1; E850.0-E850.2; E935.0-E935.2; E940.1. Inpatient stays and ED associated with multiple opioid diagnosis codes are only recorded once. For more information on these codes and how they changed from ICD-9-CM to ICD-10-CM codes, please visit https://www.hcup-us.ahrq.gov/datainnovations/ICD-10CaseStudyonOpioid-RelatedIPStays042417.pdf Age refers to the age of the patient at admission. Discharges or visits missing age are excluded from results reported by age. Income is based on the median household income of the patient’s ZIP Code of residence. Quartiles are defined so that the total U.S. population is evenly distributed across four groups. The value ranges for the national income quartiles vary by year. Income quartile is missing if the patient is homeless or foreign. Patient location is based on the National Center for Health Statistics scheme to study the relationship between urbanization and health. For this dataset, there are five categories: large central metropolitan (counties in metropolitan statistical areas (MSAs) of 1 million or more population that contain the entire population of the largest principal city of the MSA, have their entire population contained in the largest principal city of the MSA, or contain at least 250,000 inhabitants of any principal city of the MSA); large fringe metropolitan (suburbs) (counties in MSAs of 1 million or more population that did not qualify as large central metropolitan counties); medium metropolitan (counties in MSAs of populations of 250,000 to 999,999); small metropolitan (counties in MSAs of population less than 250,000); and rural (comprised of counties in micropolitan statistical areas and nonmetropolitan counties that did not qualify as micropolitan). Information about insurance type is a hospital’s response to the question, “Who is expected to pay the hospital for a given service?”, which may be different than the actual means of payment. Expected primary payers include: Medicare, Medicaid, private insurance, and the uninsured. Discharges and ED visits with other, missing, or invalid expected primary payer are not reported in Fast Stats reporting by payer. These excluded records typically represent approximately 3 to 6 percent of all discharges or visits. Discharges with the expected primary payer of self-pay, charity, and no charge are classified as uninsured. For HCUP Partner organizations that identify State and local programs serving low-income, uninsured populations (e.g., Indian Health Services, county indigent, migrant health programs, Ryan White Act, Hill-Burton Free Care), discharges for these payers also are classified as uninsured. About one-third of the HCUP Partner organizations include this level of detail in their coding of expected payer. The rate of inpatient stays and rate of ED visits are calculated per 100,000 people (U.S. residents). Population-based rates are presented for trends of opioid-related inpatient stays and ED visits reported overall and by age, community-level income, and patient location. HCUP uses population and demographic data from Claritas, a vendor that compiles U.S. Census Bureau data. Rates are not calculated for expected payer (insurance type) because there is no current source of national population insurance estimates that align with HCUP’s definition of expected primary payer.

For more information on the data and methodology visit https://www.hcup-us.ahrq.gov/faststats/OpioidUseServlet.

< Back to list of datasets

Health Resources and Services Administration (HRSA)

Details:

Locations of US Dept of Health and Human Services Health Resources and Services Administration Nursing Facilities; Locations of hospitals and critical access hospitals; Medically Underserved Areas; Counts and rates of health resources, Heath Professional Shortage Areas

Topics:

nursing facilities, hospitals, critical access hospitals, hospital beds, emergency room visits, rates of doctors and dentists, Federally Qualified Health Centers (FQHCs), FQHCs and Look-alikes, health center grantee performance, Medically Underserved Areas

Source:

HRSA

Years Available:

2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021

Geographies:

points, zip code, county, tract

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

https://data.hrsa.gov/hdw/tools/dataportal.aspx http://bphc.hrsa.gov https://data.hrsa.gov/topics/health-workforce/ahrf https://data.hrsa.gov/

Last updated on PolicyMap:

January 2022

Description:

PolicyMap downloads Nursing Facility, Hospital, Critical Access Hospital, and Federally Qualified Health Center (FQHC) points from the HRSA Geospatial Database. These geocoded locations from the HRSA Geospatial Data Warehouse are from a “Provider of Service” extract from the Online Survey and Certification Reporting System database maintained by Centers for Medicare and Medicaid Services. They are included in the HRSA Warehouse because they are the most readily-obtainable data on various classes of health care facility such as hospitals, hospices, rural health clinics, etc. The Nursing Facility locations provided by HRSA are those facilities participating in Medicare and Medicaid for individuals requiring nursing care and assistance with daily life activities. The Hospitals are those facilities participating in Medicare and Medicaid Services for individuals requiring temporary or long-term medical treatment. The Critical Access Hospitals are those institutions participating in Medicare and Medicaid and meeting the following requirements: being located in rural areas and being located more than 35 miles from any other Hospital or Critical Access Hospital, having no more than 25 inpatient beds and maintaining an average length of stay of 96 hours per patient for acute inpatient care, and providing 24 hour emergency care services. “Federally Qualified Health Centers (FQHCs)” (often referred to as “Community Health Centers”) receive funding under the Health Center Cluster federal grant program to provide care for underserved populations. The types of providers eligible include Community Health Centers, Migrant Health Centers, Health Care for the Homeless Programs, Public Housing Primary Care Programs, and care providers for some tribal organizations. On PolicyMap there is also a dataset called “Community Health Centers and Look-Alikes”, which PolicyMap downloads from the Health Resources and Services Administration (HRSA) website. This includes those receiving grants and community health centers that are eligible but not currently receiving grant funding. Although they are not receiving grants, these providers – or “look-alikes” – are eligible for some benefits including enhanced reimbursement from Medicare and Medicaid. Mapping both FQHCs and “look-alikes” might provide a fuller picture of the health-care safety net in a community. PolicyMap joins individual health center performance data to the Community Health Centers and Look-Alikes point dataset. HRSA tracks this performance data via the Uniform Data System (UDS), which is a reporting requirement for grantees of the following HRSA primary care programs, as defined in the Public Health Service Act: Community Health Center, Migrant Health Center, Health Care for the Homeless, Public Housing Primary Care. Because the UDS data is self-reported, some performance data did not match up with the Community Health Centers and Look-Alikes dataset, resulting in a 95.8% match. For information about health center performance data, see: https://bphc.hrsa.gov/datareporting/index.html. Medically Underserved Areas (MUAs) are census tracts designated by the Health Resources and Services Administration as having too few primary care providers, high infant mortality, high poverty, and/or high elderly population. See: http://muafind.hrsa.gov/. Medically Underserved Populations (MUP) are areas where a specific population group is underserved, including groups with economic, cultural, or linguistic barriers to primary medical care. If a population group does not meet the criteria for an MUP, but exceptional conditions exist which are a barrier to health services, they can be designated with a recommendation from the state’s Governor. Due to what HRSA terms a “source data error”, some areas have multiple designations. In these instances, if any designation is MUA, MUA is shown on the map. If MUP and Governor are designated for a single area, MUP is shown. If multiple IMU scores are provided, the lowest score is shown on the map. Although MUA and MUP data is shown on PolicyMap at the tract level, it is provided by HRSA at the tract, county, and minor civil division (MCD). County and MCD level data is shown at the tract level. In cases where a tract was only partially covered by an applicable MCD, it was labeled as not being an MUA or MUP. The data layers on PolicyMap related to health care professions, health facilities, and hospital utilization are all from HRSA’s Area Health Resource File (AHRF). This data is compiled by HRSA from multiple original data sources, which means that different indicators are available for different years. PolicyMap calculated all the rates in this dataset using the Census’s population estimates for the appropriate year. For more information about the AHRF, see: https://data.hrsa.gov/data/about. The data source for FQHC locations has changed. PolicyMap will be removing this dataset in 2020, unless a comparable data source is found. PolicyMap will continue to post updates to the Community Health Centers and Look-alikes dataset.

Health Professional Shortage Areas (HPSAs) are defined by the Health Resources and Services Administration (HRSA) as areas that need more health providers in primary care, dental health, or mental health. All HPSAs are defined on the basis of three basic criteria: the ratio of population to health providers, percent of population below the federal poverty level, and travel time to the nearest source of care outside the HPSA area. Dental HPSAs also consider an area’s water flouridation status. Mental HPSAs also consider substance and alcohol abuse prevalence, and percentage of the population over the age of 65 or under the age of 18. Primary Care HPSAs also consider infant mortality rate and low birth weight rate. HPSAs may be designated as “geographic” or “population” shortage areas. A geographic HPSA is an area where all residents may experience a shortage of providers. A population HPSA is an area where a specific group of people may experience a shortage of providers. If a census tract is designated as a HPSA more than once, the status and scores for the most recently updated HPSA are shown. HRSA updates HPSA status frequently, so there may be a lag between the data shown on PolicyMap and the latest designation from HRSA. Go to https://data.hrsa.gov/tools/shortage-area to check whether a site of interest lies within a HPSA.

< Back to list of datasets

HUD Affirmatively Furthering Fair Housing (AFFH)

Details:

Low poverty index, labor market engagement index, school proficiency index, low transportation cost index, transit trips index, jobs proximity index, environmental health index

Topics:

Poverty, transportation costs, transit, job proximity, school proficiency, environmental health, opportunity

Source:

Dept. Housing and Urban Development (HUD)

Years Available:

2015

Geographies:

Block group, tract

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

https://www.hudexchange.info/resource/4848/affh-data-documentation/

Description:

As part of its Affirmatively Furthering Fair Housing (AFFH) initiative, HUD released a series of opportunity indices. To assist PolicyMap users engaged in AFFH efforts, PolicyMap has loaded the following opportunity indices to PolicyMap: low poverty index, labor market engagement index, school proficiency index, low transportation cost index, transit trips index, jobs proximity index and environmental health index. For more information about the component data and methodology that HUD used in creating these indices, please consult their AFFH data documentation here.

< Back to list of datasets

HUD and USPS

Details:

USPS business and residential vacancy, count and percent of vacant business and residential units that have been vacant less than 12 months, more than 12 months, percent change in vacancy and no-stat addresses by quarter and by year

Topics:

vacancy

Source:

Dept. Housing and Urban Development US Postal Service Vacancy

Years Available:

2008Q1, 2008Q2, 2008Q3, 2008Q4, 2009Q1, 2009Q2, 2009Q3, 2009Q4, 2010Q1, 2010Q2, 2010Q3

Geographies:

tract, county

Public Edition or Subscriber-only:

API and Widget only

Download Available:

no

For more information:

http://www.huduser.org/DATASETS/usps

Description:

The Department of Housing and Urban Development (HUD) receives quarterly aggregate data from the United States Postal Service (USPS) on addresses identified by the USPS as having been “vacant” or “No-Stat” in the previous quarter. These data represent the universe of all addresses in the United States and are updated every three months. No-Stat addresses include Rural Route addresses vacant for 90 days or longer, addresses for businesses or homes under construction and not yet occupied, and addresses in urban areas identified by a carrier as not likely to be active for some time. PolicyMap did not calculate percents of vacant and No-Stat addresses for those areas with less than five addresses. These areas are identified in PolicyMap as having Insufficient Data. As of June 30, 2008, HUD and the USPS offer data divided into three categories: business, residential and other. For purposes of posting meaningful data, PolicyMap chose not to map “other” vacant or no-stat counts or percents. However, the total vacant, total percent vacant, the total No-Stat and total percent No-Stat include the sum of all three categories: business, residential and other.

A few notes of caution with respect to percent change variables: In March 2010 the US Postal Service implemented new procedures to improve the accuracy of its vacancy indicators. This led to a large increase nationally, with much more drastic fluctuations in some local areas. Comparisons across time periods spanning the first and second quarters of 2010 may be problematic. For 2007 and 2008 the USPS geocoding methodology and some of the USPS business practices produced anomalies, which may result in spikes in the total address count in a tract that can not necessarily be attributed as growth since the previous year. Also, zip code splitting, may result in similar spikes or drops in total addresses that can not necessarily be attributed to growth or decline.

< Back to list of datasets

HUD: Annual Homeless Assessment Report (AHAR)

Details:

Homeless population counts, and percent change in homeless counts, sheltered and unsheltered population counts

Topics:

Homelessness

Source:

U.S. Dept. of Housing and Urban Development, Office of Community Planning and Development, Annual Homeless Assessment Report to Congress

Years Available:

2007-2020

Geographies:

State, Continuum of Care

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

https://www.hudexchange.info/hdx/guides/ahar/

Last updated on PolicyMap:

March 2022

Description:

The U.S. Department of Housing and Urban Development (HUD) submits annual reports to Congress about homelessness in the United States. The reports include point-in-time counts of homeless persons on a single night in January based on local community counts. These counts are submitted annually to HUD by local Continuums of Care (CoC) as part of the competitive funding process.

< Back to list of datasets

HUD: Picture of Subsidized Households

Detail:

Counts and percents of residents and households receiving housing subsidies, Section 8 voucher recipients, subsidized household rent and income, income as percent of area median family income, extremely low income recipients, Subsidized households by type, race, and ethnicity, locations of HUD’s subsidized housing sites, percent occupied, household size, household income, rent contributions, federal spending, disabled residents, senior-headed households

Topics:

housing subsidies, Section 8 rental assistance, public housing, multifamily

Source:

US Department of Housing and Urban Development’s A Picture of Subsidized Households

Years Available:

2009-2021

Geographies:

tract, county, place, state, points

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

https://www.huduser.gov/portal/datasets/assthsg.html

Last updated on PolicyMap:

May 2022

Description:

The Department of Housing and Urban Development (HUD) conducts a periodic survey of all households living in HUD-subsidized housing. HUD compiles this information into a series of reports called A Picture of Subsidized Households where household data are aggregated by program at various geographies including tract, county, place, and state. The programs in this report include public housing, Housing Choice Vouchers, Mod Rehab, Project Based Section 8, RentSup/RAP, Section 236/BMIR, and Section 202 and 811 Supportive Housing programs. Point-level data on HUD’s multifamily and public housing sites are available on PolicyMap. PolicyMap downloaded and geocoded data on multifamily and public housing sites from three different HUD resources. Wherever possible, PolicyMap linked the data using the property ID. The three datasets included are the Multifamily Assistance and Section 8 Contracts, A Picture of Subsidized Households, and the REAC assessment scores report. All points are geocoded by HUD; where coordinates were not available, the point was not included on the map. 99.99% of public housing properties and 96% of multifamily properties contain coordinates and appear on the map. A Picture of Subsidized Households data is provided by HUD at the contract level, not property level. Multifamily properties may contain multiple contracts. For PolicyMap, contracts are aggregated together to create single values for each property. All contracts available in the Picture data are included, regardless of the contracts listed in the Multifamily Assistance database. Only properties listed in the HUD Multifamily database are included.

State, county, place, and tract level data from Picture of Subsidized Households are aggregated for all HUD subsidy programs, and are also available for the Housing Choice Voucher recipients and for public housing residents. Data at the tract level is only available for HCV and public housing data. Percent calculations against the general population were made by PolicyMap by using Census 2010 data as denominators for 2012, and the latest American Community Survey data for 2013 and later.

< Back to list of datasets

HUD Community Renewal Initiative Designations

Detail:

Locations of designated Empowerment Zones, Enterprise Communities, and Renewal Communities nationwide. Attributes include community name and geography type at designation (2000 U.S. Census Tracts, 1990 U.S. Census Tracts, or 1990 U.S. Census Tracts, designated by the USDA)

Topics:

RC/EZ/EC Designations

Source:

US Department of Housing and Urban Development (HUD) Community Renewal Initiative

Years Available:

1994-2011

Geographies:

Polygon

Public Edition or Subscriber-only:

Subscriber Only

Download Available:

No

For more information:

https://www.hud.gov/program_offices/comm_planning/economicdevelopment/programs/rc/tour

Description:

The HUD Initiative for Renewal Communities, Empowerment Zones, and Enterprise Communities (RC/EZ/EC) provided federal grants, tax incentives, and partnerships with government, for-profit, and non-profit entities to promote job creation and economic development in economically distressed communities. After the original authorization by the Omnibus Reconciliation Act of 1993, which designated 9 empowerment zones and 95 enterprise communities, there were three rounds of community applications, culminating in extension of the benefits provided through the Community Renewal Tax Relief Act of 2000. Ultimately, 19 Enterprise, 67 Renewal, and 44 Empowerment communities were designated or extended between 1994 and 2011.

< Back to list of datasets

HUD Community Development Block Grant Activity Locations

Detail:

Locations of CDBG Activities nationwide, including activity type, funding amount, completion date, grantee name, and type

Topics:

CDBG activity locations

Source:

US Department of Housing and Urban Development (HUD)

Years Available:

As of 2018

Geographies:

point

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

http://hub.arcgis.com/datasets/HUD::community-development-block-grant-activity

Last updated on PolicyMap:

February 2019

Description:

The Community Development Block Grant (CDBG) program, which was enacted by Congress in 1974, is intended to fund local government activities benefiting low and moderate income people such as ensuring access to affordable housing, creating jobs through retention and expansion of local businesses, and providing basic services to the most vulnerable residents. CDBG funds are allocated on a formula basis and grantees need to submit a Consolidated Plan showing that the activities funded will benefit in majority low and moderate income people. This dataset includes the primary locations of CDBG activities that have occurred since 1996. Below are the CDBG activity categorizations used by HUD: Asset Acquisition – activity related to acquisition, including disposition, clearance and demolition, and clean-up of contaminated Sites/brownfields. Economic Development – activity related to economic development, including commercial or industrial rehab, commercial or industrial land acquisition, commercial or industrial construction, commercial or industrial infrastructure development, direct assistance to businesses, and micro-enterprise assistance. Housing – activity related to housing, including multifamily rehab, housing services, code enforcement, operation and repair of foreclosed property and public housing modernization. Public Improvements – activity related to public improvements, including senior centers, youth centers, parks, street improvements, water/sewer improvements, child care centers, fire stations, health centers, non-residential historic preservation, etc. Public Services – activity related to public services, including senior services, legal services, youth services, employment training, health services, homebuyer counseling, food banks, etc. Other – activity related to urban renewal completion, non-profit organization capacity building, and assistance to institutions of higher education.

To learn more about the CDBG program, please see the following HUD website: https://www.hud.gov/program_offices/comm_planning/communitydevelopment/programs.

< Back to list of datasets

HUD Community Development Block Grant Eligibility Criteria

Detail:

Block group eligibility status for Community Development Block Grant (CDBG) program, Medium Income Persons, Low and Moderate Income Persons, Low Income Persons

Topics:

CDBG eligible block groups

Source:

US Department of Housing and Urban Development (HUD)

Years Available:

2019

Geographies:

Block group, Census Tract, Place, County

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

https://www.hudexchange.info/manage-a-program/acs-low-mod-summary-data/

Last updated on PolicyMap:

July 2019

Description:

The Community Development Block Grant (CDBG) program, which was enacted by Congress in 1974, is intended to fund local government activities benefiting low and moderate income people such as ensuring access to affordable housing, creating jobs through retention and expansion of local businesses, and providing basic services to the most vulnerable residents. CDBG funds are allocated on a formula basis and grantees need to submit a Consolidated Plan showing that the activities funded will benefit in majority low and moderate income people. In order to be eligible for CDBG funds on an area basis, at least 51% of the activity’s beneficiaries must be of low and moderate income. The U.S. Department of Housing and Urban Development (HUD) defines as low and moderate income all individuals living in a household with income below 80% of the area median family income. Through FY 2013, HUD published a summary of the number of people who are low and moderate income at the split block group level. During this time, HUD based its figures on the decennial Census. In FY 2014, HUD started basing its figures on the Census’ American Community Survey (2006-2010). Because ACS has a smaller sample and is less precise than the decennial Census, for FY 2014, HUD published a summary of the number of people who are low and moderate income at the block group, but not the split block group, level. For FY 2019, HUD based its figures on the 2011-2015 ACS. In addition, HUD publishes every year a list of “exception grantees,” which are areas with smaller overall concentrations of low and moderate income people. In these areas, the block groups in the highest quartile in terms of concentration of persons of low and moderate income are deemed eligible. The list of exception grantees can be found at: https://www.hudexchange.info/manage-a-program/acs-low-mod-summary-data-exception-grantees/. Finally, HUD publishes every year a list of block groups in “uncapped areas.” These are metropolitan areas that are allowed to use different income limits – as specified by HUD – in their Consolidated Plans. To find out if you are an “uncapped” area and what your special income limits are please visit the HUD website at: https://www.hudexchange.info/manage-a-program/acs-low-mod-summary-data-uncapped-grantees/.

PolicyMap displays the number and percentage of persons with medium income (household income below 120% AMI), low and moderate income (household income below 80% AMI), and low income (household income below 50% AMI) at the block group, census tract, place, and county geographies.

< Back to list of datasets

HUD Fair Market Rents

Detail:

Fair Market Rent, as established by HUD, for rental units by bedroom size

Topics:

rental rates, fair market rent, small area fair market rent

Source:

US Department of Housing and Urban Development Fair Market Rents

Years Available:

2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022

Geographies:

county subdivision, zip code

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

https://www.huduser.gov/portal/datasets/fmr.html

Last updated on PolicyMap:

March 2022

Description:

Fair Market Rent (FMR) is established by the Department of Housing and Urban Development (HUD) for each fiscal year. FMR is used primarily to determine payment standard amounts for Federal housing assistance programs. FMR is a gross rent estimate and includes the shelter rent, plus the cost of all tenant-paid utilities, except telephones, cable or satellite television service, and internet service. The levels at which FMR is set is expressed as a percentile point within the rent distribution of standard-quality rental housing units. The most recent FMR reflects the estimated 40th and 50th percentile rent levels, meaning that 40% or 50% of rental units can be rented at or below this threshold. FY 2022 FMRs are based on using 5-year, 2015-2019 data collected by the American Community Survey (ACS). See https://www.huduser.gov/portal/datasets/fmr.html#2022_documents for details.

Small Area FMR is a demonstration project in many metropolitan areas throughout the country that relies on American Community Survey (ACS) five-year estimates data. The Small Area FMR data are released at the ZIP code level. They are created at ZIP Code Tabulation Areas (ZCTAs), which are areas that approximate ZIP code boundaries, though the two are distinctly different. Actual zip codes that are not included in the ZCTA database are added to the Small Area FMR database. To show all these areas, the data is displayed on a ZIP code map, which does not match the ZCTA map. Due to this difference in geographies, users should be cautious interpreting the map.

< Back to list of datasets

HUD Federal Block Grant Allocations

Detail:

Funds allocated by HUD to Federal Block Grant programs

Topics:

Community Development Block Grants, HOME Investment Partnerships, Housing Opportunities for Persons with AIDS (HOPWA), Emergency Shelter Grants

Source:

Office of Community Planning and Development, US Department of Housing and Urban Development

Years Available:

FY2009, FY2010, FY2011, FY2012, FY2013, FY2014, FY2015, FY2016, FY2017, FY2018

Geographies:

HUD Formula Grantee Boundaries (2018)

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

http://portal.hud.gov/hudportal/HUD?src=/program_offices/comm_planning/about/budget

Last updated on PolicyMap:

February 2019

Description:

The US Department of Housing and Urban Development (HUD) releases annual funding allocations for the programs administered by the Office of Community Planning and Development (CPD). These programs include: Community Development Block Grants (CDBG); HOME Investment Partnerships (HOME); Housing Opportunities for Persons with AIDS (HOPWA); and Emergency Shelter Grants (ESG).

These datasets are mapped according to CPD’s custom boundaries for Formula Allocations. Data are mapped to the most recent boundaries available. See: https://hudgis-hud.opendata.arcgis.com/

< Back to list of datasets

HUD Housing Choice Voucher Housing Opportunity Index

Detail:

Housing Opportunity Index Score

Topics:

Housing Choice Voucher, HCV, Public Housing Authorities, PHAs, poverty, rental units

Source:

Office of Policy Development and Research, US Department of Housing and Urban Development

Years Available:

2011

Geographies:

Census tracts, block groups

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

http://www.huduser.org/portal/publications/pubasst/housing_choice_voucher

Description:

The Housing Choice Voucher Marketing Opportunity Index is an index for every Census tract and block group to identify the area’s potential opportunity for Housing Choice Voucher holders seeking housing. It is a measure of neighborhoods’ high quality housing and neighborhood conditions. A higher number indicates a higher potential opportunity for HCV holders seeking housing. It can be used by Public Housing Authorities to help voucher holders find neighborhoods that have low poverty rates, available rental units at or below Fair Market Rent limits, a high level of employment and educational opportunities, and a low density of households who receive housing assistance. The calculation through which the index is calculated is available in a PDF document at the link provided. The calculation through which the index is calculated is available in a PDF document downloadable from the page at the link provided above.

< Back to list of datasets

HUD Income Limits

Details:

Area Median Income for all families, and by family size at 30%, 50%, 60%, 80% and 120% of AMI, owner affordability, renter affordability

Topics:

Area Median Incomes, affordability and cost burdens

Source:

US Department of Housing and Urban Development Income Limits

Years Available:

2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022

Geographies:

county subdivision

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

http://www.huduser.org/portal/datasets/il.html

Last updated on PolicyMap:

May 2022

Description:

The Department of Housing and Urban Development (HUD) established Area Median Incomes (AMI) for households of various sizes, which are used to determine eligibility for HUD’s assisted housing programs, including Public Housing, Section 8 Housing Assistance Payments program, Section 202 housing for the elderly, and Section 811 housing for persons with disabilities.

Many non-federal and non-housing programs also use HUD’s income guidelines, often specifying a percentage of the median income that a household’s income must fall below in order to qualify. PolicyMap includes AMI at a variety of percentages for a variety of household sizes. The 30%, 50% (Very Low Income), and 80% (Low Income) of median income by family size as well as the overall area median income are provided by HUD. PolicyMap calculated 60% of Area Median Income by multiplying the 50% threshold by 1.2 and calculated 120% of AMI by multiplying the 50% threshold by 2.4, per instructions in the LIHTC legislation, on HUD’s website, and in communications between PolicyMap and the HUD User electronic help desk resource. The income thresholds as they are calculated in PolicyMap may not be appropriate for your needs if your programs or requirements specify a different method for determining income thresholds. In particular, the Housing and Economic Recovery Act of 2008 (HERA) specifies different Income Limits for qualification levels and rental rates under section 42 of the Internal Revenue Code and projects financed with tax-exempt housing bonds under section 142 of the Code. Projects in service in 2007 or 2008 should rely on the Multifamily Tax Subsidy Income Limits (MTSP). See: http://www.huduser.org/portal/datasets/mtsp.html.

< Back to list of datasets

HUD Location Affordability Index

Details:

Housing and transportation costs as a percentage of household income

Topics:

Cost of living, housing, transportation, income

Source:

US Department of Housing and Urban Development Location Affordability Portal

Years Available:

2019

Geographies:

tracts

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

https://www.hudexchange.info/programs/location-affordability-index/

Last updated on PolicyMap:

May 2019

Description:

The Location Affordability Index is a calculation designed by the Department of Housing and Urban Development (HUD) in order to estimate the costs of housing and transportation across the country. Data is provided on the percentage of household income spent on housing, transportation, and the combination of the two. Separate values are also offered for owner and renter households. Annual cost spent on transportation and annual total public transit trips are also included. HUD’s methodology was significantly updated in Version 3 of the Location Affordability Index Model (LAIM), which was released in March of 2019. The biggest difference between Version 2 and 3 of the LAIM was the reduction in geographies available – Version 3 only modelled results for Census Tracts. HUD uses different calculations for eight different types of households, with different sizes, incomes, and number of commuters. They are defined as follows:
HOUSEHOLD TYPE SIZE OF HH INCOME # COMMUTERS
Median-Income Family 4 Median Income for Region 2
Very Low-Income Individual 1 National Poverty Line 1
Working Individual 1 50% of Median Income for Region 1
Single Professional 1 135% of Median Income for Region 1
Retired Couple 2 80% of Median Income for Region 0
Single-Parent Family 3 50% of Median Income for Region 1
Moderate Income Family 3 80% of Median Income for Region 1
Dual-Professional Family 4 150% of Median Income for Region 2
The LAIM Version 3 was produced from U.S. Census American Community Survey (ACS) 2012-2016 survey data, U.S. Census TIGER files, and the U.S. Census Longitudinal Employment-Household Dynamics (LEHD) Origin-Destination Employment Statistics (LODES) and OnTheMap 7 data, which was built on 2014 Census data. Since this study was released by HUD in 2019, that is the data year given on PolicyMap.

A very thorough explanation of the calculations made by HUD are available here: https://www.hudexchange.info/programs/location-affordability-index/.

< Back to list of datasets

HUD Low-Income Housing Tax Credit (LIHTC) Database

Details:

Locations of LIHTC funded projects nationwide. Project details including address, number of units and low income units, year the credit was allocated and the project placed in service, type of credit, type of construction, and other sources of financing used

Topics:

Affordable housing, property acquisition, rehabilitation and construction

Source:

US Department of Housing and Urban Development Low-Income Housing Tax Credit (LIHTC) Database

Years Available:

1997-2020

Geographies:

points

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

http://lihtc.huduser.org/

Last updated on PolicyMap:

July 2022

Description:

PolicyMap downloaded the properties listed in HUD’s LIHTC Database in July 2022. HUD provided valid geocoding for 95% of projects. The LIHTC program was created by the Tax Reform Act of 1986, and gives state and local LIHTC allocating agencies authority to issue tax credits for acquisition, rehabilitation or new construction of low income rental housing.

< Back to list of datasets

HUD’s Multifamily Assistance and Section 8 Contracts Database

Details:

Locations of HUD’s subsidized housing sites, number of residents, size of households, contract information, assisted units, expiration information

Topics:

HUD multifamily sites

Source:

US Department of Housing and Urban Development’s Multifamily Assistance and Section 8 Contracts Database

Years Available:

2021

Geographies:

points

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

https://www.hud.gov/program_offices/housing/mfh/exp/mfhdiscl

Last updated on PolicyMap:

May 2022

Description:

The Multifamily Assistance and Section 8 Contracts Database was created to provide HUD partners/clients with a way of measuring the potential impact of expiring project-based subsidy contracts in their communities. PolicyMap linked the HUD Multifamily Assistance Properties to the HUD Multifamily Assistance and Section 8 Contracts to show the details for up to four contracts per property. The most recent four contracts are shown; if a property has more than four contracts, the number of additional older contracts is displayed. PolicyMap downloaded and geocoded data on HUD’s multifamily sites from three different resources at HUD and, wherever possible, PolicyMap linked the data using the property ID. The three datasets included are the Multifamily Assistance and Section 8 Contracts, A Picture of Subsidized Households and the REAC assessment scores report. PolicyMap was able to locate 96% of multifamily properties on a map.

This data is displayed with HUD’s A Picture of Subsidized Households, which shows data about the subsidized households at these properties. Picture data is provided by HUD at the contract level, not property level. For PolicyMap, contracts are aggregated together to create single values for each property. All contracts available in the Picture data are included, regardless of the contracts listed in the Multifamily Assistance database. Only properties listed in the HUD Multifamily database are included.

< Back to list of datasets

HUD Promise Zone Designees

Details:

Areas designated as Promise Zones and lead applicants

Topics:

Promise Zones

Source:

US Department of Housing and Urban Development (HUD)

Years Available:

2016

Geographies:

Polygon

Public Edition or Subscriber-only:

Public Edition

Download Available:

no

For more information:

https://www.hudexchange.info/promise-zones/

Description:

In areas designated as Promise Zones, the federal government will partner to help local agencies access resources. Under the Promise Zones program, federal partners will collaborate with local leaders on economic development, education, and crime-related projects. Federal incentives available to areas designated as Promise Zones are listed at https://www.hudexchange.info/promise-zones/federal-partner-funding-and-technical-assistance-opportunities/. The twelve federal partners in the Promise Zones program include the U.S. Department of Agriculture, U.S. Department of Commerce, Corporation for National and Community Service, U.S. Department of Education, U.S. Department of Health and Human Services, U.S. Department of Housing and Urban Development, U.S. Department of Justice, U.S. Department of Labor, National Endowment for the Arts, Small Business Administration, U.S. Department of Transportation, and U.S. Department of the Treasury. In total, 20 urban, rural, and tribal communities will be designated as Promise Zones by the end of 2016.

< Back to list of datasets

HUD Racially and Ethnically-Concentrated Areas of Poverty

Details:

Racially and ethnically-concentrated areas of poverty (R/ECAP)

Topics:

Housing, federal guidelines, race, poverty

Source:

US Department of Housing and Urban Development

Years Available:

1990, 2000, 2010

Geographies:

Census Tract

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

https://www.hudexchange.info/programs/affh/resources/

Last updated on PolicyMap:

September 2019

Description:

This dataset, provided by the US Department of Housing and Urban Development (HUD), identifies census tracts that meet or exceed HUD’s established thresholds for racially and ethnically-concentrated areas of poverty (R/ECAPs). These tracts have a non-white population that is greater than or equal to 50% and meet either of the following poverty criteria: the poverty rate of a tract is 1) higher than 40% or 2) more than three times the average poverty rate of tracts in the metropolitan area. The racial/ethnic threshold is lowered to 20% for tracts that are located outside of metropolitan/micropolitan areas. HUD used component data from the decennial census (2010) and the American Community Survey (2009-2013) to determine which geographies met these criteria in 2010, and component data from Brown Longitudinal Tract Database, based on decennial census data from 2000 and 1990, to determine R/ECAP geographies for 1990 and 2000. HUD provides data from all years at the 2010 census tract boundaries. The data posted on PolicyMap was downloaded from https://hudgis-hud.opendata.arcgis.com/datasets/racially-or-ethnically-concentrated-areas-of-poverty-r-ecaps in September 2019.

< Back to list of datasets

HUD Qualified Census Tracts and Difficult Development Areas

Details:

Qualified Census Tracts and Difficult Development Areas, as established by HUD

Topics:

Qualified Census Tract, Area Median Gross Income, Low Income Housing Tax Credit

Source:

US Department of Housing and Urban Development Qualified Census Tracts and Difficult Development Areas

Years Available:

2009, 2010, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019

Geographies:

Census Tract, County, ZCTA, County Subdivision

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

http://qct.huduser.gov/index

Description:

Qualified Census Tracts: PolicyMap downloads data on Low-Income Housing Tax Credit (LIHTC) Qualified Census Tracts (QCT) from tables at HUD’s website. A Qualified Census Tract is any census tract (or equivalent geographic area defined by the Bureau of the Census) in which at least 50 percent of households have an income less than 60 percent of the Area Median Gross Income (AMGI). There is a limit on the number of Qualified Census Tracts in any Metropolitan Statistical Area (MSA) or Primary Metropolitan Statistical Area (PMSA) that may be designated to receive an increase in eligible basis: all of the designated census tracts within a given MSA/PMSA may not together contain more than 20 percent of the total population of the MSA/PMSA. For purposes of HUD designations of Qualified Census Tracts, all non-metropolitan areas in a state are treated as if they constituted a single metropolitan area. Difficult Development Areas: PolicyMap downloads data on Difficult Development Areas (DDA) from tables at HUD’s website. A Difficult Development Area is any area designated by the Secretary of HUD as an area that has high construction, land, and utility costs relative to the Area Median Gross Income (AMGI). All designated Difficult Development Areas in Metropolitan Statistical Areas (MSA) or Primary Metropolitan Statistical Areas (PMSA) may not contain more than 20 percent of the aggregate population of all MSAs/PMSAs, and all designated areas not in metropolitan areas may not contain more than 20 percent of the aggregate population of all non-metropolitan counties.

Beginning in 2016, DDAs are comprised of Counties and Zip Code Tabulation Areas (ZCTAs). Some ZCTAs sit in multiple counties and county subdivisions, and only certain portions of the ZCTA are DDAs. Therefore, the data is shown on the map as a cross between ZCTAs and county subdivisions, so every area on the map is shown with the correct status.

< Back to list of datasets

HUD’s Real Estate Assessment Center (REAC) Physical Inspection Scores

Details:

REAC physical inspection scores, inspection dates, physical condition category

Topics:

HUD multifamily and public housing sites, REAC scores

Source:

US Department of Housing and Urban Development’s Real Estate Assessment Center (REAC)

Years Available:

2021

Geographies:

points

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

http://www.huduser.org/portal/datasets/pis.html

Last updated on PolicyMap:

May 2022

Description:

HUD’s Real Estate Assessment Center (REAC) conducts physical property inspections of roughly 20,000 properties owned, insured or subsidized by HUD per year. The purpose of these inspections is to make sure that assisted families are provided with decent, safe and sanitary housing in good repair. Inspected properties receive an overall score from 0 to 100 based on five criteria: site, building exterior, building systems, common areas and units. Multifamily inspections note whether a site has health and safety violations or smoke detector issues. The REAC score received by the property sets its “standard” and dictates how often HUD will return to evaluate the property:
  • A score of 90 points or higher is designated a Standard 1 and required to undergo a physical inspection once every three years.
  • A score between 80 and 89 is designated a Standard 2 and required to undergo a physical inspection once every two years.
  • A score of less than 80 points is designated a Standard 3 and required to undergo an annual physical inspection.
Standard 3 performing properties with scores below 60 are referred to HUD Departmental Enforcement Center.

PolicyMap downloads geocoded data on HUD’s multifamily and public housing sites from three different resources at HUD and wherever possible PolicyMap linked the data using the property ID. The three HUD housing datasets included are the Multifamily Assistance and Section 8 Contracts, A Picture of Subsidized Households and the REAC assessment scores report. All points are geocoded by HUD; where coordinates were not available, the point was not included on the map.

< Back to list of datasets

HUD Renewal Communities, Empowerment Zones, and Enterprise Communities

Details:

Renewal Communities, Empowerment Zones, and Enterprise Communities, as established by HUD

Topics:

Renewal Communities, Empowerment Zones, Enterprise Communities

Source:

HUD Community Planning & Development

Years Available:

2009

Geographies:

Census Tract

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

http://www.hud.gov/offices/cpd/economicdevelopment/programs/rc/index.cfm and http://www.hud.gov/offices/cpd/economicdevelopment/programs/rc/tour/census.xls

Description:

Renewal Communities(RC), Empowerment Zones(EZ) and Enterprise Communities(EC) are part of a federally funded community renewal initiative to revitalize distressed urban and rural areas. Businesses located within these three designations are eligible for specific benefits. The Renewal Community tax incentives are worth approximately $5.6 billion to eligible businesses of all sizes in Renewal Communities. These incentives encourage businesses to open, expand, and to hire local residents. The incentives include employment credits, a 0% tax on capital gains, accelerated depreciation through Commercial Revitalization Deductions, and other incentives. See http://www.hud.gov/utilities/intercept.cfm?/offices/cpd/economicdevelopment/library/taxincentivesrc.pdf for complete details. The Empowerment Zone tax incentives are worth approximately $5.3 billion to small and large businesses in Empowerment Zones. These incentives encourage businesses to open and expand and to hire local residents. Empowerment Zone incentives include employment credits, low-interest loans through EZ facility bonds, reduced taxation on capital gains, and other incentives. See http://www.hud.gov/utilities/intercept.cfm?/offices/cpd/economicdevelopment/library/taxincentivesez.pdf for complete details. HUD does not provide a detailed description of Enterprise Communities.

Incentive zones that were authorized before 2000 were specified in terms of 1990 Census Tracts. In PolicyMap it is only possible to display shading for 2000 Census Tracts. If 75% or more of the area of a 2000 Census Tract was deemed an Empowerment Zone, Renewal Community, or Enterprise Community in 1990 (according to the overlap of the 1990 boundary file), then that Census Tract is designated to be of that Zone or Community in PolicyMap.

< Back to list of datasets

Institute of Museum and Library Services, Museum Universe Data File

Details:

Museum locations

Topics:

museums

Source:

Institute of Museum and Library Services

Years Available:

2018

Geographies:

point

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

https://www.imls.gov/research-evaluation/data-collection/museum-data-files

Last updated on PolicyMap:

August 2019

Description:

The Museum Universe Data file contains the locations and basic information of known museums and related organizations. Museums include aquariums, arboretums, botanical gardens, art museums, children’s museums, general museums, historic houses and sites, history museums, nature centers, natural history and anthropology museums, planetariums, science and technology centers, specialized museums, and zoological parks. IMLS compiled this information from federal tax records, private foundations, and third-party commercial vendors. Points were geocoded by IMLS.

< Back to list of datasets

Institute of Museum and Library Services, Public Libraries Survey

Details:

Public library outlet locations

Topics:

Public libraries

Source:

Institute of Museum and Library Services (IMLS), Office of Planning, Research and Evaluation and U.S. Census Bureau

Years Available:

2017

Geographies:

point

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

https://www.imls.gov/research-evaluation/data-collection/public-libraries-survey/explore-pls-data/pls-data

Last updated on PolicyMap:

October 2019

Description:

The Public Libraries Survey (PLS) is conducted annually by the IMLS. The data file includes all public libraries and outlets identified by state library agencies in the 50 States, the District of Columbia, and the outlying areas of Guam, the Northern Mariana Islands, Puerto Rico and the U.S. Virgin Islands.

Library outlet locations include central libraries, branches, bookmobiles, and books-by-mail locations. Points were geocoded by IMLS based on addresses provided by the survey respondent (library administrator), and in some cases were matched to the center point of the postal zip code or zip code division. IMLS was able to locate 99.9% of library outlets on a map.

< Back to list of datasets

International Business Innovation Association

Details:

Locations of business incubators

Topics:

Business incubators

Source:

International Business Innovation Association

Years Available:

2019

Geographies:

point

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

https://www.inbia.org/

Description:

The International Business Innovation Association is a trade group serving over 2,100 business incubators and related organizations worldwide. Business incubators are programs that provide support services and resources for entrepreneurial companies during their “start-up” phase. InBIA provided PolicyMap with a list of business incubators in the United States.

< Back to list of datasets

IRS Statistics of Income – Individual Income Tax Statistics

Details:

number of tax returns, exemptions and dependents, adjusted gross income, salaries and wages, tax liability, balance due, refunds, itemized deductions, total deductions, charitable contributions, capital gains, dividends, child tax credit, child and dependent care expenses, earned income tax credit (EITC), home mortgage interest deduction, real estate taxes, IRA deduction, pensions, social security benefits, state and local taxes, unemployment compensation, alternative minimum tax (AMT), residential energy credit, total income, self-employment health insurance deduction, IRA payments, student loan interest deduction, tuition and fees deduction, retirement savings contribution credit, self-employment tax, Additional Medicare tax, net investment income tax, education credits, health care individual responsibility payment, premium tax credit, returns prepared by a volunteer, returns prepared by Volunteer Income Tax Assistance (VITA), returns prepared by Tax Counseling For The Elderly, returns prepared by a volunteer with earned income credit (EITC), computer prepared returns, returns filed electronically, refunds issued with direct deposit.

Topics:

tax returns

Source:

IRS Statistics of Income Division, Individual Income Tax Statistics

Years Available:

2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019

Geographies:

zip code, county, state

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

https://www.irs.gov/statistics/soi-tax-stats-individual-income-tax-statistics-zip-code-data-soi

Last updated on PolicyMap:

April 2022

Description:

The Internal Revenue Service’s Statistics of Income (IRS SOI) provides detailed information on tax returns including income and liability data, and detailed tax deduction and credit information. Information comes from individual Form 1040 tax returns. Each year’s data includes returns filed in the 12-month period after the tax year (so, data for 2012 includes all returns filed from January 1, 2013 to December 31, 2013). Note that a small number of returns filed during this period represent a year previous to the most recent tax year, and are still included. Not all individuals are required to submit a tax return. Returns with negative adjusted gross income are excluded from the data. Returns representing a specified (but unreleased) percent of the value of an amount are excluded (for example, if one return was responsible for 80% of the residential energy credit dollar amount in a zip code, it may be excluded). The ZIP code shown on a tax return may not reflect the taxpayer’s actual residence. Returns with ZIP codes not matching the state are excluded. The ZIP code reflects only the ZIP code written on the tax return; for this data, no attempt is made by the IRS to verify the return’s address. ZIP codes on PolicyMap reflect the most recent ZIP code map available; an address may have changed ZIP codes in the intervening time, but the ZIP code data is not changed. State-level data are an aggregation of county-level data in each state. They are not equal to state-level data provided by the IRS. Beginning in 2012, all counts of returns are rounded to the nearest 10. Any category with fewer than 20 returns per ZIP code are excluded from the data by the IRS. Any average or percent with a denominator of less than 10 is suppressed by PolicyMap.

Numbers of returns and aggregate amounts are made available by the IRS. Percents and averages are calculated by PolicyMap. Percents use total returns as a denominator, and averages use returns of that type as a denominator (for example, average charitable contributions is total charitable contributions divided by the number of returns claiming any charitable contributions). Generally, returns represent households, not individuals.

< Back to list of datasets

IRS Statistics of Income – Migration Data

Details:

count and percent of tax filers that are in-migrants, out-migrants, net migrants; aggregate and average adjusted gross income from migration.

Topics:

in-migration and out-migration flows; aggregate and average adjusted gross income of migrants

Source:

IRS Statistics of Income Division, County-to-County Migration Data Files

Years Available:

2003-04, 2004-05, 2005-06, 2006-07, 2007-08, 2008-09, 2009-2010, 2010-2011, 2011-2012, 2012-2013, 2013-2014, 2014-2015

Geographies:

county

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

http://www.irs.gov/uac/SOI-Tax-Stats-Migration-Data

Description:

The Internal Revenue Service’s Statistics of Income (IRS SOI) division produces annual estimates of migration flows using domestic and foreign tax returns. Returns are matched to returns from the previous year using the primary tax filer’s social security number. If the address associated with the return is located in a different county than the previous year, the return is identified as a migrant. In-migrants are those who filed tax returns in different counties, states, or abroad in the previous year but filed locally in the current year; out-migrants filed taxes locally in the previous year but filed in different counties, states, or abroad for the current year. Individual taxpayers cannot be identified, either directly or indirectly, from these tabulations. The data released by the IRS for these calculations have undergone suppression procedures to ensure no inappropriate disclosure of information. There are two limitations to the completeness of that data that should be considered when using IRS migration data. First, up to the 2009-2010 time period, the data only capture returns processed by late September of the year following the tax year, which covers roughly 95-98 percent of all returns. Second, those who are not required to file tax returns are not counted by the IRS, so lower income people and senior citizens are likely underrepresented. Beginning with 2010-2011, the IRS began using full-year tax return instead of partial-year. Overall, this increases the number of returns by 5 percent, and high-income returns by approximately 25 percent. Caution should be exercised in making comparisons across this time period. More information is available here: https://www.irs.gov/pub/irs-soi/soi-a-inmig-id1509.pdf.

Data from the IRS is given for the year of the tax filing; people generally file their returns the year following the tax year (tax year 2013 returns are filed in 2014). PolicyMap displays the tax year, which is the year the migration occurred, not the filing year.

< Back to list of datasets

Johns Hopkins Bloomberg School of Public Health – COVID-19 Excess Mortality Risk Index

Details:

Mortality risk of COVID-19 in the US

Topics:

US COVID-19 risk predictions, general adult population, Medicare population age 65 and older

Source:

Johns Hopkins University

Years Available:

2020

Geographies:

place, county, state, nation

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

http://covid19risktools.com/

Description:

Researchers at the Johns Hopkins Bloomberg School of Public Health and the University of Maryland created a statistical model of how likely an adult (age 18+) is to die of COVID-19 as compared to the national average, based on demographic information and underlying health conditions. The researchers developed the model based on two sources of information: (1) multivariate-adjusted estimate of risk associated with gender, social deprivation index and 12 pre-existing conditions from the recently published UK-based OpenSAFELY study, and (2) death rates associated with different age and racial/ethnic groups in the US published by the Centers for Disease Control and Prevention (CDC) after performing external covariate adjustment accounting for the correlation of these factors with other risk factors in the model. The researchers applied their statistical model to publicly available demographic and health data to create (1) an Index of Excess Risk (IER) which describes how likely the average adult in a city (Census place) or state is to die from COVID-19 compared to the national average, (2) estimated proportion and size of the general adult population in a city or state that exceed different risk thresholds, and (3) estimated proportion of the projected deaths among general adult population in a city or state that exceed different risk thresholds. Validation analyses were conducted based on the projected risks and data on recent deaths in the US to show that the model is well calibrated for the US population. They also applied the model to CMS data for the Medicare population age 65 and older to arrive at county- and state-level estimates.

The researchers also published a calculator that assesses individual risk of death from COVID-19 here: http://covid19risktools.com/.

< Back to list of datasets

Kaiser Health News, Centers for Medicare and Medicaid Services

Details:

Intensive care unit beds

Topics:

Hospital capacity, COVID-19

Source:

Kaiser Health News

Years Available:

2019

Geographies:

county

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

https://khn.org/news/as-coronavirus-spreads-widely-millions-of-older-americans-live-in-counties-with-no-icu-beds/

Last updated on PolicyMap:

March 2020

Intensive care units (ICU) are stocked with the specialized equipment needed to save the lives of people hospitalized with the most severe COVID-19 symptoms. Kaiser Health News estimated the number of available ICU beds in each county using data from the Healthcare Provider Cost Reporting Information System database, published by Centers for Medicare and Medicaid Services (CMS). For this analysis, Kaiser Health News included intensive care unit beds, surgical intensive care unit beds, coronary care unit beds, and burn intensive care unit beds reported by CMS. These calculations do not include intensive care unit beds in Veterans Affairs hospitals.

< Back to list of datasets

Mapbox and OpenStreetMap Contributors: Satellite Imagery

Details:

Base maps, satellite imagery

Topics:

Base maps

Source:

Mapbox and OpenStreetMap Contributors

Years Available:

current

Geographies:

NA

Public Edition or Subscriber-only:

Subscriber-only

Download Available:

no

For more information:

https://www.mapbox.com/maps/satellite

Last updated on PolicyMap:

Rolling

Mapbox Satellite hosts satellite imagery for use in web-based mapping applications. Mapbox’s satellite image base maps also contain overlying road, place name, and other geographic data sourced from OpenStreetMap. Mapbox Satellite images are sourced from several different satellite programs or commercial aggregators of satellite data, including MODIS, Landsat 5, Landsat 7, and Maxar Vivid. Mapbox continuously updates their satellite data, and these updates are incorporated into PolicyMap as they are released by Mapbox.

< Back to list of datasets

MapTiler and OpenStreetMap Contributors: Base Maps

Details:

Base maps, standard, light, terrain

Topics:

Base maps

Source:

MapTiler and OpenStreetMap Contributors

Years Available:

current

Geographies:

NA

Public Edition or Subscriber-only:

Public Edition

Download Available:

no

For more information:

https://www.maptiler.com/copyright/

Last updated on PolicyMap:

Rolling

MapTiler styles and hosts base maps for web-based mapping applications. Their base maps provide complete coverage of the United States at all available zoom levels. MapTiler sources their map data from OpenStreetMap. They pull in data updates from OpenStreetMap regularly, roughly on a weekly basis. These updates are incorporated into PolicyMap as they are released by MapTiler.

< Back to list of datasets

National Center for Charitable Statistics (NCCS) at the Urban Institute

Details:

nonprofit locations

Topics:

nonprofits, tax-exempt entities, public charities, private foundations

Source:

Urban Institute NCCS Core PC File

Years Available:

2011, 2012, 2013, 2014, 2015

Geographies:

point

Public Edition or Subscriber-only:

subscriber-only

Download Available:

no

For more information:

https://nccs-data.urban.org/index.php

Description:

The Urban Institute’s National Center for Charitable Statistics (NCCS) is the national clearinghouse of data on the nonprofit sector in the United States. The NCCS Core 2015 PC File combines descriptive information from the IRS Business Master File (BMF) and financial variables from the IRS Return Transaction Files (RTF). The BMF is a cumulative file containing descriptive information on all active tax-exempt organizations. Data contained on the BMF are mostly derived from IRS Forms 1023 and 1024. The RTF are a source of all financial data for all organizations that file IRS Forms 990, Form 990-EZ, or Form 990-PF. Organizations not required to file Form 990, including religious organizations and those with less than $25,000 in gross receipts, are generally excluded from the file. NCCS also excludes a small number of other organizations, such as foreign organizations or those that are generally considered part of government. To create the Core file, NCCS first verifies and corrects, if needed, the financial data in the RTF using the Statistics of Income-coded return, and it manually reviews organizations’ 990s on GuideStar when necessary. Next, NCCS matches records from the BMF to records in the RTF. Finally, NCCS enhances the data by adding the following fields available in PolicyMap: classification for each organization using the National Taxonomy of Exempt Entities (labeled “NTEE Major Group” in PolicyMap), and total revenue (labeled as such in PolicyMap). Other NCCS variables in PolicyMap include the following: EIN, or Employer Identification Number; Fiscal Year, or fiscal year defined by organization during which filing occurred; and Ruling Date, or year of IRS ruling or determination letter recognizing an organization’s exempt status. Reason for 501(c)(3) Status reflects an organization’s type at the time it obtained recognition of its exempt status from the IRS. Public Charity or Private Foundation indicates whether an organization is (1) a public charity, which is a 501(c)(3) organization that receives significant public support or falls into another category that entitles them to automatic public charity status, or (2) a private foundation, which is an organization created to distribute money to public charities or individuals, required to distribute at least five percent of their assets each year. The number of employees is from the “full 990” version of the Core file. For more information about other variables available on PolicyMap, please see https://nccs-data.urban.org/NCCS-data-guide.pdf The Urban Institute geocoded every nonprofit in the Core 2015 PC File available on PolicyMap. PolicyMap geocoded addresses that were not matched in the original file, resulting in a 99% overall match rate.

Urban Institute nonprofits data is downloadable only for individual Census Tracts and Block Groups.

< Back to list of datasets

National Center for Education Statistics

Details:

Count and percent of students who receive free and reduced price school lunches, English Language Learners, student/teacher ratio, graduation rate, Individualized Education Program students (special education)

Topics:

free and reduced price school lunches, education

Source:

Common Core of Data, National Center for Educational Statistics, provided by the US Department of Education

Years Available:

2000 – 2017

Geographies:

school district

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

http://nces.ed.gov/ccd/

Last updated on PolicyMap:

May 2019

Description:

The Common Core of Data (CCD) is a program of the U.S. Department of Education’s National Center for Education Statistics that collects selected data about all public schools, public school districts and state education agencies in the United States every year. Data are supplied by state education agency officials. School district data was downloaded from the Elementary/Secondary Information System (ElSi), available at http://nces.ed.gov/ccd/elsi/. Percentages calculated by PolicyMap (such as percent of students who are English Language Learners) were suppressed where the numerator was greater than the denominator (such as when a school district reported more English Language Learners than total students).

< Back to list of datasets

National Center for Education Statistics, Common Core of Data

Details:

School-level data on public school information, grades offered, total students, full-time-equivalent teachers, student/teacher ratio, free and reduced-price lunch eligible students, Title I eligibility, magnet school, charter school, shared-time school, enrollment by grade, enrollment by race, and data related to the Office of Minority and Women Inclusion

Topics:

Education

Source:

Common Core of Data, National Center for Educational Statistics, provided by the U.S. Department of Education

Years Available:

2019-2020 school year

Geographies:

point

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

http://nces.ed.gov/ccd/

Last updated on PolicyMap:

August 2021

Description:

The Common Core of Data (CCD) is a program of the U.S. Department of Education’s National Center for Education Statistics that collects selected data about all public schools, public school districts and state education agencies in the United States every year. Data are supplied by state education agency officials. Some schools may have very low student counts. According to the NCES “A student may attend more than one school, but each student is counted only once, in the school where he/she spends most of the school day—the “home school” or “school of record.” For example, a student may attend a regular high school for most of the day and a career/technical (CTE) high school part time. That student is typically counted in the membership of the regular high school, not the CTE high school.” Some schools may have no student counts. This is because they contract with other schools or agencies to provide services for some students. Those students are not reported for the receiving school in order to avoid duplication. However, where all services are provided by a contracting school, no student counts are reported for the sending school. Certain indicators provided in this data on PolicyMap do not come from the NCES. Student/teacher ratio was calculated by PolicyMap by dividing the total number of students by the number of full-time-equivalent classroom teachers. The percentages of students of a given race were calculated by PolicyMap. The links to the GreatSchools school pages were made using a table from GreatSchools. Three indicators are included for consideration in matters related to the Office of Minority and Women Inclusion (OMWI). These indicators label a school as being all-female, majority-minority, or in an inner city. All-female schools were calculated by summing the number of female students in each race category (the NCES does not provide total numbers of students by sex). If that number is equal to the number of students in the school, it is label as all female. This simply means that all the students in the school in the given school year were female; it does not mean that the school is by policy an all-female school. Majority-minority was calculated by dividing the number of white students by the number of total students. If the percent of students who are white is less than 50%, the school is labeled majority minority. The inner city label was not calculated using NCES data. A spatial calculation was made using ACS data, using methodology similar to that developed by the Initiative for a Competitive City (see http://www.icic.org/research-and-analysis/research-definitions). Using this methodology, a census tract in a metropolitan statistical area is considered to be part of an inner city if its poverty rate is 20% or higher, or it meets at least two of the three following criteria:
  • Poverty rate 1.5 times or more than that of the MSA
  • Median household income .5 or less than that of the MSA
  • Unemployment rate (using ACS) of 1.5 times or more than that of the MSA

Schools without coordinates are excluded from the data.

< Back to list of datasets

National Center for Education Statistics, Integrated Postsecondary Education Data System

Details:

community college locations, 4-year college and university locations

Topics:

Postsecondary education, enrollment, graduation rates, degrees awarded

Source:

National Center for Education Statistics, Integrated Postsecondary Education Data System

Years Available:

2010 – 2017

Geographies:

point, state

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

http://nces.ed.gov/ipeds/datacenter/

Last updated on PolicyMap:

May 2019

Description:

The Integrated Postsecondary Education Data System (IPEDS) provides data on colleges, universities, and technical and vocational postsecondary institutions in the United States.

The point-level community college locations data on PolicyMap consist of all the institutions in the IPEDS’ universe classified as 2-but-less-than-4-year or less than 2-year. The 4-year colleges and universities data consist of all institutions in the IPEDS’ universe classified as four or more years. Most of the data in these datasets is for the 2016-2017 academic year (with enrollment data applying to Fall of 2016). Graduation rates, however, apply to August of 2016. Data regarding institutions’ financial performance reflect Government Accounting Standards Board (GASB) reporting. PolicyMap downloaded these data from the IPEDS data center in August 2018. The latitude and longitude of the points were provided by the source.

< Back to list of datasets

National Center for Education Statistics, Private School Universe Survey

Details:

Private school locations

Topics:

Private school, education, religious orientation, level of school, size of school, length of school year, length of school day, enrollment, single sexed or coeducational, number of teachers employed, student/teacher ratio, free or reduced-price school lunches, Title I participation

Source:

Private School Universe Survey (PSS), National Center for Educational Statistics, provided by the US Department of Education

Years Available:

2016-2017

Geographies:

point

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

http://nces.ed.gov/surveys/pss/index.asp

Last updated on PolicyMap:

September 2019

Description:

The Private School Universe Survey (PSS) is a program of the U.S. Department of Education’s National Center for Education Statistics that collects selected data about all private schools every two years. Data are supplied through a survey that is compiled by administrative personnel in private schools. The NCES definition of a private school is “A private school is not supported primarily by public funds, provides classroom instruction for one or more of grades K-12 or comparable ungraded levels, and has one or more teachers. Organizations or institutions that provide support for home schooling without offering classroom instruction for students are not included.”

< Back to list of datasets

National Conservation Easement Database

Details:

Conservation Easements

Topics:

Open Space, Protected Areas

Source:

National Conservation Easement Database, U.S Endowment for Forestry and Communities

Years Available:

As of 2019

Geographies:

polygon

Public Edition or Subscriber-only:

Widget, API-only

Download Available:

no

For more information:

http://www.conservationeasement.us

Last updated on PolicyMap:

June 2019

Description:

The National Conservation Easement Database (NCED) is a national database of conservation easements. It is compiled of records from land trusts and public agencies throughout the United States. Partner organization The Trust for Public Land collects public easement data, and Ducks Unlimited collects private easement data. Only publicly available information is included. In some instances, the land trust requests concealing the exact location of an easement; in such cases, they are excluded from the map.

According to the NCED, the data is not complete. The NCED provides estimates of completeness by state, which can be accessed here: https://www.conservationeasement.us/completeness/.

< Back to list of datasets

National Credit Union Administration

Details:

Credit union branch locations

Topics:

Credit unions

Source:

National Credit Union Administration

Years Available:

2021

Geographies:

point

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

https://www.ncua.gov/analysis/Pages/call-report-data/quarterly-data.aspx

Last updated on PolicyMap:

March 2022

Description:

The National Credit Union Administration (NCUA) provides data for all U.S. credit unions as well as the addresses of credit union branches. These data are from the 5300 Call Report, submitted quarterly by credit unions to the NCUA, and downloaded in March 2022 from https://www.ncua.gov/analysis/Pages/call-report-data/quarterly-data.aspx. All information in this data, aside from office type and contact information, applies to the credit union and not the individual branch. Financial counseling/education, online banking, and percent loans delinquent were calculated by PolicyMap. PolicyMap geocoded all branch location points, and was able to locate 96% of the given addresses on a map.

< Back to list of datasets

National Oceanic and Atmospheric Association (NOAA)/National Weather Service

Details:

Average UV Index

Topics:

UV Index

Source:

NOAA and National Weather Service

Years Available:

2008-2016

Geographies:

place

Public Edition or Subscriber-only:

API only

Download Available:

no

For more information:

http://www.cpc.ncep.noaa.gov/products/stratosphere/uv_index/uv_annual.shtml/

Description:

The National Oceanic and Atmospheric Association (NOAA) and the National Weather Service provide Annual Time Series UV Index data through their Climate Prediction Center. The average UV Index is an average of every UV Index issued within the year by the NOAA/National Weather Service for selected cities. When issuing the UV Index, the NOAA uses the World Health Organization’s Exposure Categories of 0-2 as being low, 3-5 as moderate, 6-7 as high, 8-10 as very high, and 11 or more as extreme.

< Back to list of datasets

National Oceanic and Atmospheric Association (NOAA) Weather Conditions

Details:

Monthly maximum/minimum temperatures, heat and wind chill indices, annual inches of snow, annual inches of rainfall, annual sunny days

Topics:

Maximum/minimum temperatures, comfort index, weather

Source:

NOAA

Years Available:

As of 1990

Geographies:

county, place, zip code, metro area, state

Public Edition or Subscriber-only:

API only

Download Available:

no

For more information:

http://www.ncdc.noaa.gov/cgi-bin/climaps/climaps.pl

Description:

The National Oceanic and Atmospheric Association (NOAA) provides data on temperatures for each day of each month. The average maximum and minimum temperatures for each month are calculated by PolicyMap by taking the mean of the range provided by the NOAA and calculating a weighted average based on geographic area. The indices commonly known as the “Comfort Index” include a Heat Index (HI) and a Wind Chill Temperature, both of which are calculated in degrees Fahrenheit. The HI is a measure of how hot it feels in July at the middle of the day when relative humidity is added to the temperature. The HI is derived from a calculation provided by the National Weather Service. Temperature and relative humidity data are provided by the NOAA. Wind chill is a measure of how cold it feels at the middle of the day when wind speed is added to the temperature. Wind chill is derived from a calculation provided by the National Weather Service. Temperature and wind speed data are provided by the NOAA. Wind chill temperature cannot be calculated for areas with temperatures of 50 degrees Fahrenheit or higher or for wind speeds less than 3 miles per hour. The NOAA provides data on precipitation and sun for each day of each month. The average annual rainfall days, the average annual snowfall days and the average annual sunny days are estimated by PolicyMap by taking the mean of the range provided for each type of day provided by the NOAA and calculating a weighted average based on geographic area.

Each of these indicators is available for the continental United States and for selected parts of Alaska and Hawaii. They are not available for Puerto Rico.

< Back to list of datasets

National Park Service Federal Historic Preservation Tax Incentives

Topics:

Historic Tax Credit Projects

Source:

Technical Preservation Services, National Park Service

Years Available:

2001 – 2021

Geographies:

point

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

http://www.nps.gov/tps/index.htm

Last updated on PolicyMap:

July 2022

Description:

Technical Preservation Services, a division of the National Park Service, provided PolicyMap with a list of all approved Federal Historic Preservation Tax Incentives Program Part 3 applications from fiscal year 2001 through fiscal year 2021. Part 3 application approvals represent completed and certified projects eligible for the 20% federal tax credit for the rehabilitation of a historic building. The tax credit applies to qualifying costs associated with capital improvements when undertaking a substantial rehabilitation consistent with the historic character of a certified historic building. Project Costs in this data represent the estimated rehabilitation costs, or qualified rehabilitation expenditures (QREs) submitted to the National Park Service on the Part 3 application. The indicated date is for certification of the project by the National Park Service, and is not necessarily when the construction project was completed. The data represent tax credit project approvals since October 1, 2001, through September 30th of the most recently available fiscal year. Street addresses are those submitted to the National Park Service on the Part 3 applications; PolicyMap was able to locate 96% of the addresses on a map. Data are updated annually.

The Federal Historic Preservation Tax Incentives Program is administered by the National Park Service, State Historic Preservation Offices, and the Internal Revenue Service. For more information on Federal Historic Tax Credits visit the National Park Service: http://www.nps.gov/tps/index.htm. For additional information visit the National Trust Community Investment Corporation: https://ntcic.com/invest/htc/

< Back to list of datasets

New Localism Advisors: Social Needs Index

Details:

Qualified Opportunity Zone eligibility composite Social Needs Index

Topics:

Qualified Opportunity Zones, Social Needs Index

Source:

New Localism Advisors

Years Available:

2012-2016

Geographies:

census tract

Public Edition or Subscriber-only:

premium subscriber only

Download Available:

no

For more information:

https://www.thenewlocalism.com/wp-content/uploads/2018/03/Guiding-Principles-for-Opportunity-Zones_TheNewLocalism_March92018.pdf

PolicyMap Exclusive:

yes

Description:

Social need rankings were calculated for Qualified Opportunity Zone eligible Low Income Community (LIC) census tracts by New Localism Advisors using 2012-2016 Census American Community Survey data. Tracts with higher rankings (closer to 10) are considered higher need. The social need index takes into consideration the following variables: population below poverty, households with rent or mortgages greater than 30% of income, population not in the labor force, population with less than a high school degree, and Gini index of concentrated income. Factor analysis was applied to the variables to create a weighted composite index of social need. Rankings are relative to all other eligible LIC tracts in the state.

This index was developed by New Localism Advisors to determine priority investment locations in Qualified Opportunity Zones. To learn more please visit https://www.thenewlocalism.com/wp-content/uploads/2018/03/Guiding-Principles-for-Opportunity-Zones_TheNewLocalism_March92018.pdf.

< Back to list of datasets

New York Times: COVID-19 Cases and Deaths

Details:

Cases and deaths from COVID-19

Topics:

COVID-19, coronavirus

Source:

New York Times

Years Available:

2020, 2021

Geographies:

County, state

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

https://github.com/nytimes/covid-19-data/blob/master/README.md

Last updated on PolicyMap:

see data description on the maps page

Description:

A team at the New York Times has created a national database of COVID-19 cases and deaths based on local reporting and has made the database available to the public. This includes all “confirmed” cases where a person has tested positive for COVID-19. Since many who were exposed to COVID-19 have not been tested, reported numbers may underrepresent the prevalence of the virus. This data is reported at the state and county levels. The New York Times has modified the county level data in several important ways. The five counties that comprise New York City have been grouped into a single entity called “New York City.” Kansas City, Missouri is reported separately from the counties that it intersects. Some cruise ships or aircraft carriers with infected passengers or crew have been counted according to the county where the vessel has docked or where the affected people were treated. See https://github.com/nytimes/covid-19-data/blob/master/README.md for specific information on geographic exceptions.

PolicyMap calculated the percent change in cases and deaths and rates per 100,000 people. Rates were calculated using the 2014-2018 population estimates published by the U.S. Census Bureau. The populations of Platte, Clay, Jackson, and Cass counties in Missouri excluding the portion of the population that lives within Kansas City were estimated using block group data. This dataset is updated daily by the New York Times, and downloaded and published frequently by PolicyMap.

< Back to list of datasets

OpenFlights.org and PolicyMap

Details:

areas likely to fall within the path of flights as they take off and land at nearby airports

Topics:

flight paths

Source:

Open Flights

Years Available:

2012

Geographies:

Zip code, neighborhood

Public Edition or Subscriber-only:

API only

Download Available:

no

For more information:

http://www.openflights.org/data

Description:

OpenFlights.org collects information on airport locations and airline routes. PolicyMap downloaded this information, and used spatial analysis to simulate flights in order to estimate which neighborhoods and zip codes likely fall into the path of airplanes taking off and landing at nearby airports. This data is used to indicate the possible presence of noise pollution from airplanes flying over neighborhoods.

< Back to list of datasets

Precisely ZIP Code Boundaries

Details:

Zip code names and boundaries

Topics:

ZIP Codes

Source:

Precisely

Years Available:

October 2018

Geographies:

5-digit postal ZIP Codes, national

Public Edition or Subscriber-only:

Public Edition

Download Available:

no

For more information:

https://dataguide.precisely.com/world-boundaries-7LP-44WA.html?utm_medium=Redirect-PB&utm_source=Direct-Traffic

Last updated on PolicyMap:

November 2018

Description:

ZIP Code boundary files on PolicyMap are licensed from Precisely (formerly Pitney Bowes). Precisely builds its ZIP codes boundary files from individual addresses to align boundaries with streets; it is the source recommended for business by the US Postal Service.

< Back to list of datasets

PolicyMap Severe COVID-19 Health Risk Index

Details:

Risk of severe COVID-19 symptoms

Topics:

Public health, COVID-19, coronavirus

Source:

PolicyMap

Years Available:

2020

Geographies:

County, tract, ZCTA

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

https://www.nytimes.com/interactive/2020/05/18/us/coronavirus-underlying-conditions.html?action=click&module=Spotlight&pgtype=Homepage

Last updated on PolicyMap:

May 2020

Data Download:

Severe COVID-19 Health Risk Index

PolicyMap Exclusive:

yes

Description:

PolicyMap worked with journalists at the New York Times to create this index assessing a county’s relative risk of its population developing severe COVID-19 symptoms. The index represents the relative risk for a high proportion of residents in each county to develop serious health complications from COVID-19 because of underlying health conditions identified by the CDC as contributing to a person’s risk of developing severe symptoms from the virus. These conditions include COPD, heart disease, high blood pressure, diabetes, and obesity. Estimates of COPD, heart disease, high blood pressure, and diabetes and obesity prevalence at the tract and ZCTA level are from PolicyMap’s Health Outcome Estimates. Estimates of diabetes and obesity prevalence at the county level are from the CDC’s U.S. Diabetes Surveillance System. The raw score represents a sum of the estimated number of people ever diagnosed with each health condition. The normalized index represents a sum of the share of the adult population ever diagnosed with each health condition. Normalized scores can be used to compare risk between areas with different populations. The raw and normalized indices should not be interpreted as a true representation of the number or percentage of people affected by the five conditions, since these shares are not mutually exlusive; those diagnosed with two or more conditions count two or more times.

Normalized scores were then converted to percentiles and z scores for easier interpretation. Percentiles rank counties from the lowest score to the highest on a scale of 0 to 100, where a score of 50 represents the median value. A county’s z score shows how many standard deviations above or below the average a county’s risk level falls. A score of 0.6, for example, would mean that the county has a higher risk than average, but is still within one standard deviation of the average and is therefore not unusually high. Risk categories from very low to very high are assigned based on z scores.

< Back to list of datasets

PolicyMap & American Community Survey (ACS): Low and Moderate Incomes (LowMod)

Details:

Local median income as a share of area median income, for households and families

Topics:

Low Mod, local median income as a share of area median income

Source:

PolicyMap calculation of ACS data

Years Available:

2016-2020

Geographies:

census tracts, block groups

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

http://www.census.gov/acs/www/

Last updated on PolicyMap:

July 2022

Description:

PolicyMap calculated local median income as a share of area median income using American Community Survey (ACS) estimates of median household income and median family income. For all tracts and block groups located within Census-defined metropolitan areas, this calculation is local median income as a share of metro-area median income. For tracts and block groups outside of Census-defined metro areas, this is local median income as a share of the non-metro state median income. PolicyMap has chosen specific breaks in the data, following on commonly used and understood guidelines: ≤30% of area median income >30% and ≤50% of area median income >50% and ≤80% of area median income >80% and ≤120% of area median income >120% of area median income However, PolicyMap subscribers can edit the breaks in the ranges if some other set of breaks is preferred.

Please be aware that the thresholds and data sources used in this calculation can vary, and federal agencies may require specific calculations for some program applications. The Community Development Block Grant (CDBG) program defines low and moderate income tracts based on what percent of the population is low or moderate income, rather than by comparing median local values to the surrounding metro area (See HUD Community Development Block Grant Eligibility Criteria above). The Community Reinvestment Act (CRA) specifies what years of income data to include in the calculation – 2000 data for local median income and 2004 data for area median income. (See Community Reinvestment Act Eligibility Criteria above). Both CDBG and CRA low and moderate income calculations can be found on PolicyMap under the Federal Guidelines tab.

< Back to list of datasets

PolicyMap & American Community Survey: Racial Homeownership Gap

Details:

homeownership, race/ethnicity

Topics:

Homeowners, race, ethnicity, diversity

Source:

U.S. Census American Community Survey (ACS), PolicyMap

Years Available:

2006-2010, 2011-2015, 2016-2020

Geographies:

Block group, tract, ZCTA, county subdivision, county, congressional district, metro division, metro area, state

Public Edition or Subscriber-only:

premium

Download Available:

yes

For more information:

http://www.census.gov/acs

Last updated on PolicyMap:

June 2022

Description:

PolicyMap calculated the racial homeownership gap data layers using the Census Bureau’s American Community Survey 2005-2009, 2010-2014, 2015-2019 estimates. For both percentage gap and ratio, PolicyMap used a total of eight homeownership and renter indicators by race/ethnicity categories provided by the US Census Bureau. These included the ethnic category Hispanic or Latino and the following seven racial categories: Non-Hispanic White, Black or African American, American Indian or Alaska Native, Asian, Native Hawaiian or Pacific Islander, some other race, and two or more races. The racial homeownership gap represents the percentage gap between Non-Hispanic White homeowners to a given race or ethnicity. PolicyMap calculated the gap using percent of owner households by race subtracted from percent of Non-Hispanic White owner households. The ratio compares the percent of owner households of a given race to percent of Non-Hispanic White owner households. PolicyMap calculated the ratios using percent of owner households of a given race to percent of Non-Hispanic White owner households. Differences between ratio and percentage gap lie in the usage of the indicators. The ratio is for comparing geographies around the country to each other over periods of time. Values closer to one in the ratio indicate a more equitable or equal to Non-Hispanic White. Percentage gaps show the difference between a given race to Non-Hispanic White but doesn’t quantify equitability to Non-Hispanic White. The same calculations were used for the renter’s gap. The US Census identifies the householder as the person in whose name the home is owned, being bought or rented. If there is no such person, present any household member 15 years and older can serve as the householder for the purpose of the Census.

Geographies with percent calculations are suppressed in cases where the denominator of the calculation was less than 10 households.

< Back to list of datasets

PolicyMap and Centers for Disease Control and Prevention (CDC) Behavioral Risk Factor Surveillance System, Health Outcome Estimates and Risk Factor Estimates

Details:

Chronic health conditions (arthritis, asthma, COPD, depression, diabetes, high blood pressure, hypertension, stroke, heart disease, heart disease and heart attacks), weight (overweight, obesity), self-assessed health status (poor, good, very good/excellent), self-assessed mental and physical health, fruit and vegetable consumption, alcohol consumption, tobacco use, primary doctor, routine checkup within last year, HIV test, flu vaccine

Topics:

Health-risk behavior, diabetes and related risk factors, physical health, access to health care, perceived health, chronic health conditions

Source:

2017 and 2018 CDC Behavioral Risk Factor Surveillance System; 2010 Decennial Census; 2017 and 2018 Census ACS PUMS 5-year estimates; 2009 Metropolitan and Micropolitan Census

Years Available:

2017, 2018

Geographies:

census tract, ZCTA, county, state, nation

Public Edition or Subscriber-only:

premium subscriber-only

Download Available:

yes

For more information:

http://www.cdc.gov/brfss/annual_data/annual_data.htm

Last updated on PolicyMap:

November 2020

PolicyMap Exclusive:

yes

Description:

PolicyMap’s Health Outcome Estimates and Risk Factor Estimates were created using data from the CDC’s Behavioral Risk Factor Surveillance System (BRFSS), the U.S. Census Bureau’s 2010 Decennial Census, 2017 and 2018 ACS PUMS 5-year estimates, and 2009 Metropolitan and Micropolitan Census delineations. The census tract estimates were calculated using a multilevel model with post-stratification based on demographic and geographic characteristics. Predicted responses were then post-stratified using Census population estimates for sex, age, racial/ethnic groups, education attainment, and metro area status. PolicyMap developed this model based on the methods used by the CDC and Robert Wood Johnson Foundation to create the 500 Cities Project data. State and national estimates are calculated directly from the BRFSS survey response data using CDC’s recommended weighting methodology. County estimates were caclulated by aggregating census tract estimates, and may not add up to state totals. Data was suppressed for geographies with adult populations less than 10. Diabetes and Related Risk Factors: BRFSS includes questions on diabetes and related risk factors, including body mass index (BMI) classification, physical activity, and fruit and vegetable consumption. Respondents were considered to have diabetes if they responded “yes” to the question “Has a doctor ever told you that you have diabetes?” Women who only had diabetes during pregnancy were not included. Both type 1 and type 2 diabetes are included. Respondents were considered obese if their BMI (as determined by height and weight) was 30 or greater. Respondents were overweight if their BMI was 25 or greater. Respondents were asked how many times per day, week, or month they consumed fruit and vegetables, including 100 percent pure fruit juices, green leafy or lettuce salads, potatoes (not including fried potatoes), and other fruit and vegetables. Respondents reporting less than one serving of fruit or vegetable per day were recorded as having a low consumption, and those reporting five or more servings per day as having a high consumption. Chronic Conditions: The BRFSS survey asks respondents their health status for common chronic conditions. Respondents were asked if a doctor, nurse, or other health professional has ever told them they had the condition. Chronic health conditions selected for PolicyMap include arthritis, asthma, Chronic Obstructive Pulmonary Disease (COPD), depression, high cholesterol, hypertension (high blood pressure), heart disease (coronary heart disease and angina), heart disease and heart attacks, and stroke. COPD, emphysema, and chronic bronchitis are grouped together due to high comorbidity among these conditions. To calculate the estimated asthma rate, survey responses are limited to adults who have been told they currently have asthma; respondents indicating they have formerly received an asthma diagnosis are not included. Access to Health Care: Survey respondents were asked to report on their access to, and utilization of, health care resources. Respondents were asked to report if there was a person they considered to be their personal doctor or health care provider. To calculate the personal doctor indicator, responses of “Yes, only one” and “More than one” were included. Regular health screenings are recommended by CDC to diagnose and prevent health conditions. Respondents were asked to indicate the length of time since their last routine physical exam or checkup (not an exam for a specific injury, illness, or condition); responses of 12 months or fewer were used to calculate the annual checkup indicator. Respondents were asked if they had received the flu vaccine in the past 12 months, including a shot in the arm or intradermal injection; and nasal spray, mist, or drop. Since 2010, CDC has recommended that all adults be vaccinated for the flu, particularly those at risk of serious complications. To assess the HIV test rate, the BRFSS survey has respondents state if they have ever been tested for HIV. Respondents were asked not to count tests that were part of a blood donation. Self-Assessed Health: To determine self-assessed (perceived) health, respondents were asked to rate their overall health as poor, fair, good, very good, or excellent. Self-assessed health status can be a reliable estimate of population health and well-being. Respondents were also asked how many days in the last 30 days they experienced poor mental or physical health. Responses of 14 or more poor mental or physical health days were reported. Health-Risk Behaviors: Health-risk behaviors are those that contribute to negative health outcomes, such as illness or injury. BRFSS includes questions relating to use of tobacco and alcohol products. Risk behaviors can be underreported due to social desirability bias in response to survey questions. To calculate current smoker status, respondents were asked to indicate if they now smoke cigarettes “every day,” “some days,” or “not at all.” Current smokers are considered those who smoke at least some days. Respondents were also asked if they had smoked at least 100 cigarettes (equivalent to five packs) in their lifetime. Current or former smoking status is associated with negative health outcomes. Consumption of alcoholic beverages was assessed by asking respondents how many drinks they consumed during the past 30 days. A drink is equivalent to a 12-ounce beer, 5-ounce glass of wine, or single shot of liquor. The definitions of heavy drinking and binge drinking differ for males and females: heavy drinking for men is more than two drinks per day, and one or more drinks per day for women; binge drinking refers to 5 or more drinks per occasion for men, and four or more drinks per occasion for women.

< Back to list of datasets

PolicyMap & the Federal Reserve Bank of Philadelphia

Details:

Housing Quality

Topics:

estimated housing repair costs

Source:

PolicyMap, the Federal Reserve Bank of Philadelphia, Census, Gordian

Years Available:

2018

Geographies:

metro area

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

policymap.com/issues/housing-quality

Last updated on PolicyMap:

September 2019

Description:

PolicyMap and the Federal Reserve Bank of Philadelphia developed estimates of housing repair costs for each occupied housing unit surveyed in the 2017 American Housing Survey, then aggregated them to the MSA level. The researchers developed a set of repair scenarios based on responses to questions about housing problems and structural characteristics and worked with construction experts at Gordian to arrive at estimated repair costs using their RSMeans database. This dataset was used to inform a national housing quality analysis. Estimated repair needs for the largest 15 Metropolitan Statistical Areas were calculated using this dataset and scaled based on local differences in construction costs. Cost estimates are provided in 2018 dollars. See policymap.com/issues/housing-quality for more information on this dataset.

< Back to list of datasets

PolicyMap & GreatSchools

Details:

Proximity to high performing public schools

Topics:

School performance, school ratings

Source:

PolicyMap calculation of GreatSchools data

Years Available:

2018

Geographies:

census tracts

Public Edition or Subscriber-only:

Subscriber-only

Download Available:

no

For more information:

http://www.greatschools.net

Last updated on PolicyMap:

October 2019

Description:

PolicyMap calculated the shortest distance within 50 miles to a public school with a GreatSchools Overall School Rating of 9 or 10 for each Census Tract in the same state. This representation of access to high performing public schools is limited by the fact that GreatSchools does not assign a rating to every public school in the nation. GreatSchools school ratings should not be compared across states; as such, a public school with a 9 or a 10 in one state may not be comparable to a public school with a 9 or a 10 in another state. This analysis does not take into account political boundaries or catchment areas within states that may make a public school inaccessible.

< Back to list of datasets

PolicyMap, HUD, and Census: Home Ownership and Rental Affordability Estimates

Details:

Number and Percent of Owner-occupied or Renter-occupied housing units affordable for families at 30%, 50%, 60%, 80% and 120% of AMI

Topics:

Area Median Incomes, affordability, housing, home ownership

Source:

PolicyMap, Census, HUD

Years Available:

2014-2018

Geographies:

Block group, Census tract, County, State

Public Edition or Subscriber-only:

Subscriber-only

Download Available:

no

For more information:

http://www.huduser.org/portal/datasets/il.html

Last updated on PolicyMap:

May 2020

PolicyMap Exclusive:

yes

Description:

PolicyMap created indicators on Home Ownership and Rental Affordability using data on incomes, housing values, and rental costs published by HUD and the Census Bureau. Rental Affordability calculations assume that a family can afford to spend 30% or less of their income on rent, which corresponds to the threshold for housing cost burden as defined by the Census Bureau. This translates to 1/36th of the family’s annual income each month. For example, a family of four with an income of $30,000 could afford to rent a two-bedroom apartment for $750 or less per month. Due to a limitation in ACS 5-year gross monthly rent estimates, gross monthly rent is only calculated for rental units that were less than $1,500 per month, regardless of rental unit size. Since affordability of units over this limit cannot be estimated rental units with monthly rents greater than $1,500 were not included in these rental affordability calculations. This likely leads to undercounting of affordability in areas with higher median incomes and housing costs, as well as for families that typically require larger housing units. Home Ownership Affordability estimates assume that a family can afford to purchase a home valued at three times their annual salary. This means that a family of four with an income of $30,000 could afford to purchase a home valued at less than $90,000. These estimates can be used to compare the relative affordability of the housing stock in different areas, or to determine whether the existing housing stock in an area is sufficient to meet the needs of low- or middle-income residents.

The 2014-2018 calculations rely on HUD’s FY2020 Area Median Income (AMI) data. The 30%, 50% (Very Low Income), and 80% (Low Income) of median income by family size as well as the overall area median income are provided by HUD. PolicyMap calculated 60% of Area Median Income by multiplying the 50% threshold by 1.2 and calculated 120% of AMI by multiplying the 50% threshold by 2.4, per instructions in the Low Income Housing Tax Credit (LIHTC) legislation, on HUD’s website, and in communications between PolicyMap and the HUD User electronic help desk resource. Counts of owner- and renter-occupied housing units by value or rental price were obtained from the Census Bureau’s 2014-2018 American Community Survey.

< Back to list of datasets

PolicyMap, HUD, and NPS: Multiple Tax Credit Project Locations

Details:

Low income housing, tax credit programs

Topics:

low income housing

Source:

PolicyMap analysis of HUD and NPS data

Years Available:

2001-present

Geographies:

Points

Public Edition or Subscriber-only:

Premium Subscribers Only

Download Available:

yes

For more information:

https://www.nps.gov/tps/tax-incentives.htm https://www.huduser.gov/portal/datasets/lihtc.html

Last updated on PolicyMap:

January 2022

PolicyMap Exclusive:

yes

Description:

PolicyMap created the Multiple Tax Credit Project Locations dataset to gauge the degree to which low income housing developers rely on both the Historic Tax Credit Program and the Low Income Housing Tax Credit Program in assembling the funding needed to build or refurbish low income housing. This novel dataset combines Historic Tax Credit Program locations published annually by the National Park Service with Low Income Housing Tax Credit Program locations published annually by HUD, and identifies locations included in both programs.

Locations in one dataset are matched with those in the other if they share the same physical address and/or project name, and if the projects are within three years of one another. Before matching, the physical addresses are standardized using a proprietary geocoder licensed by PolicyMap. The project names are matched using a “fuzzy” matching method that determines how similar two text values are to one another. Project names that were highly similar were considered to match. Because of the methods used to match projects between the two datasets, there may be some “false positives” and “false negatives” in the dataset.

< Back to list of datasets

PolicyMap & National Center for Education Statistics (NCES)

Details:

Average total cost of college, tuition and fees at a 2-year public institution, average total cost of college, tuition and fees at a 4-year public institution

Topics:

Cost of college, tuition

Source:

PolicyMap, NCES

Years Available:

2018

Geographies:

State

Public Edition or Subscriber-only:

Subscriber-only

Download Available:

no

For more information:

http://nces.ed.gov/ipeds/datacenter/

Last updated on PolicyMap:

May 2019

PolicyMap Exclusive:

yes

Description:

PolicyMap created estimates of the average cost of public college in the United States using NCES’s Integrated Postesecondary Education Data System (IPEDS) data on 2-year and 4-year public institutions. For each institution category, the sum of the cost for in-state tuition, in-state fees, books and supplies, on-campus room and board, and other on-campus expenses was multiplied by the full-time undergraduate enrollment, using the total full-time enrollment within the state to calculate the state average. Institutions with either no full-time undergraduate enrollment, or no information for both in-state tuition and in-state fees were excluded from the calculations. Cost of public college data is based on a custom download of preliminary data from http://nces.ed.gov/ipeds/datacenter/InstitutionByName.aspx. It applies to the 2017-2018 school year and was downloaded in May, 2019.

< Back to list of datasets

PolicyMap, Quantitative Innovations (QI), AHRQ, and Census: Medical Spending Estimates

Details:

Estimated aggregated total spending, estimated aggregated out of pocket spending, estimated average out of pocket spending per person

Topics:

Economy, healthcare, spending

Source:

PolicyMap, Quantitative Innovations, U.S. Department of Health and Human Services Agency for Healthcare Research and Quality, and U.S. Census American Community Survey

Years Available:

2019

Geographies:

Census tract, ZCTA, Census place, city, county, CBSA, state

Public Edition or Subscriber-only:

Premium subscriber-only

Download Available:

yes

For more information:

https://meps.ahrq.gov/mepsweb/

Last updated on PolicyMap:

December 2021

PolicyMap Exclusive:

yes

Description:

PolicyMap and Quantitative Innovations (QI) developed Medical Spending data by combining information on payments for medical care from the Department of Health and Human Services’ Agency for Healthcare Research and Quality (AHRQ) Medical Expenditure Panel Survey (2019) with demographic and health data from the U.S. Census American Community Survey (2015-2019). Medical Spending data provides estimates of two main kinds of medical payments aggregated to various geographies, 1) the total amount paid to the healthcare provider by the patient or their family and through health insurance, and 2) the amount paid out of pocket by the patient or their family. For out of pocket payments, PolicyMap also estimated average payments per person spent on medical care. Medical Spending data expense categories include all medical costs, prescription medications, medical office visits, dental care, and eyeglasses and contact lenses. Spending on office visits includes visits to physicians, physician’s assistants, nurses, chiropractors, midwives, optometrists, therapists, social workers, and other healthcare providers. Dental care costs include visits to dentists, dental hygienists, orthodontists, and other dental care providers. Quantitative Innovations is a data strategy and applied analytics advisory firm that helps clients turn their data into strategic insights and actions. The Medical Expenditure Panel Survey, conducted by the Agency for Healthcare Research and Quality, is an annual survey administered to both households and healthcare providers. Household surveys cover health conditions of household members, healthcare spending, access to healthcare, and more.

The American Community Survey (ACS), conducted by the Census Bureau, replaced the long form from the Decennial Census for 2010. The ACS provides estimates for many demographic, social, economic and housing characteristics for a moving five-year window.

< Back to list of datasets

PolicyMap, Quantitative Innovations (QI), BLS, and Census: Consumer Spending Habits

Details:

Estimated average spending per household, estimated aggregate household expenditures, and estimated percent of household expenditures spent by expense category

Topics:

economy, spending

Source:

PolicyMap, Quantitative Innovations, Bureau of Labor Statistics Consumer Expenditure Survey, and U.S. Census American Community Survey

Years Available:

2020

Geographies:

Census tract, ZCTA, Census place, city, congressional district, county, CBSA, state

Public Edition or Subscriber-only:

Premium subscriber-only

Download Available:

no

For more information:

https://www.bls.gov/cex/

Last updated on PolicyMap:

June 2022

PolicyMap Exclusive:

yes

Description:

PolicyMap and Quantitative Innovations (QI) developed Consumer Spending Habits data by combining information on household spending behavior from the Bureau of Labor Statistics Consumer Expenditure Survey (2019-2020) with demographic and economic data from the U.S. Census American Community Survey (2016-2020). Consumer Spending Habits data provides small area estimates of household expenditures for several major categories of household expenses. “Households” are defined here as “consumer units,” individuals living together who share major expenses. This differs from the Census definition of household, which may contain multiple consumer units if household members do not share all major expenses. PolicyMap and QI estimated total household expenditures, average household spending, and average percent of household income spent, by expense category. Available expense categories are total expenditures, food, food at home, food away from home, alcohol, education, and housing. Quantitative Innovations is a data strategy and applied analytics advisory firm that helps clients turn their data into strategic insights and actions. The Consumer Expenditure Survey, conducted by the Bureau of Labor Statistics (BLS), provides information on major household expenses such as housing and food. People who are surveyed report the amount of money that they or anyone else in their “consumer unit” spent on items within certain categories over a specified amount of time. The BLS publishes Consumer Expenditure Survey microdata and summary tables once or twice a year, respectively.

The American Community Survey (ACS), conducted by the Census Bureau, replaced the long form from the Decennial Census for 2010. The ACS provides estimates for many demographic, social, economic and housing characteristics for a moving five-year window.

< Back to list of datasets

PolicyMap & Urban Mapping

Details:

Proximity to public transit rail stops

Topics:

Public transit, mass transit

Source:

PolicyMap calculation of Urban Mapping Inc. data

Years Available:

2009

Geographies:

census tracts

Public Edition or Subscriber-only:

Subscriber-only

Download Available:

no

Description:

PolicyMap calculated the shortest distance to a public transit rail stop for the centroid of each Census Tract in the nation. Also calculated was the sum of public transit rail stops within various distances of the centroid of the Census Tract. This representation of access to public transit is limited by the geographic coverage of Urban Mapping Inc. data, outlined in the entry for Urban Mapping below in the Data Directory. This analysis does not take into account physical barriers (eg, rivers, highways) that may make a transit stop inaccessible, nor a transit line’s frequency or destination.

< Back to list of datasets

Reinvestment Fund Study of Childcare Access (Philadelphia)

Details:

Reinvestment Fund Study of Childcare Access

Topics:

Childcare access

Source:

Reinvestment Fund

Years Available:

2013

Geographies:

block groups, points

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

Description:

Reinvestment Fund’s study of childcare access in Philadelphia is an analysis estimating the supply of and demand for childcare services in the Philadelphia region.

Data Sources:

Unfortunately, there is no single data source that permits the adequate modeling of the supply of childcare. Nor is there a single source of data that indicates the demand for childcare services. Supply figures are often, at best, an estimate. And where demand data does exist, it is difficult to directly know which children are in childcare and which are not; which are in childcare near where their parents live or near where they work. Because of this, Reinvestment Fund statistically estimated both the supply of and demand for childcare by combining data from several different datasets to best approximate both sides of the supply/demand equation. The following data were used to approximate supply. The amount of information contained in each database varies considerably; they are listed in order below from the dataset with the most information to the least information:
  • OCDEL database (June 2013 – updates quarterly) – Includes all 1,888 state licensed childcare centers in Philadelphia. The database includes information on the location, capacity, Keystone STARS rating (i.e., quality), and whether the center has certain types of programs (Head Start, Pre-K, or school age).
  • PA DED License and Enrollment data for Pre-K (June 2013 – updates annually) – Enrollment data from 118 Pre-K programs in Philadelphia. It includes enrollment information only.
  • National Establishment Time Series (NETS) (2011 – updates annually) – Includes 859 establishments listed under the Standard Industrial Code (SIC) “8351-Child Daycare Services” in the historical listing of all business establishments in Philadelphia. These 859 establishments are not in the OCDEL data.
  • InfoUSA (circa 2012, ongoing updates) – Contains only location information for 200 centers not in any other database.
  • Head Start (circa 2013 – updates annually) – Locations of 102 Head Start centers without enrollment information.
For demand, Reinvestment Fund relied primarily on various releases of data from the US Census. All databases were acquired or aggregated to the Census block group level:
  • Census 2010 (updates every 10 years) – Counts of children ages 5 years and under.
  • American Community Survey (2007-2011 5-yr sample – updates annually) – Information on the destination of workers with children under the age of 5.
  • Longitudinal Employer Household Dynamics (LEHD) (2011 – updates annually) – Detailed information on the origin and destination of workers.

Methodology:

When developing a measure of the supply of childcare, it is necessary to ensure an unduplicated count of centers and a reasonable estimation of the capacity of those centers. To get an unduplicated count of childcare providers, Reinvestment Fund geocoded the locations of childcare centers in each of the datasets listed above and identified providers in the same location; duplicates were eliminated so as not to double-count. The OCDEL data was the baseline; all of its 1,888 records were included. There were 859 records in the NETS data that were not also in OCDEL. There were only 200 records in the InfoUSA dataset that did not also exist in either the OCDEL or NETS databases. Finally, there were 102 Head Start programs that did not appear in any other databases. The Department of Education Pre-K enrollment file included 118 centers that did not appear in any other database. Only two of the datasets acquired (OCDEL and the Department of Education Pre-K enrollment file) included capacity or enrollment information for childcare programs. It was therefore necessary to estimate the capacity of programs contained exclusively in other databases. While the NETS database did not include capacity information, it did provide information on the number of employees and total annual revenues of childcare centers. There were 457 records that appeared in both the OCDEL and NETS databases and therefore had a full set of information (capacity, number of employees, total revenues, etc.) that could be used in an analysis to develop an algorithm that estimates the capacity of a center based on the information contained in the NETS database. After looking at the number of employees, the total revenues, and even the characteristics of the area where the childcare center is located, the best predictor of the capacity of a childcare center in NETS was the number of employees. Each employee in a childcare center in the NETS data equaled roughly 5 available seats in capacity. The InfoUSA database contained only information on the location of childcare centers. Upon further investigation of these sites through the internet and phone calls, Reinvestment Fund determined that the 200 centers exclusively in this database were generally small, single employee operations. Therefore, Reinvestment Fund estimated a capacity of 5 for these centers. One of Reinvestment Fund’s primary goals with this project was to determine what demand looked like if you assume that parents wanted childcare close to their home and what it looked like if they sought childcare near their place of work. While the Census can be used to determine where children live, understanding where the parents of those children work is a bit more complex. The LEHD data has information on the origin and destination of every worker in Philadelphia. However, LEHD does not tell us how many of those workers have children who need care, or whether they would prefer bringing their children with them on their journey rather than using childcare near their homes.

The ACS 5-year sample individual level file has detailed information about the composition of the household, but less specific data on where people work. Using the ACS, Reinvestment Fund was able to determine that 18% of workers who work in Philadelphia but live outside the city have children under 5; that compares to 12% of the workers who live and work in the city. However, just because these workers have children, doesn’t mean they need childcare or that they would bring their children close to their place of work for care. Several national studies can give some insight. A report from the US Census using the Survey of Income Program Participation (SIPP) showed that 42% of households with a working mother use childcare within their own home, meaning that 58% seek care outside of their home. A report on the childcare arrangements of working parents in Cook County, Illinois found that 31% of parents with children in care have arrangements located on their way to work. However, one quarter have arrangements that take them further away from work. These studies will ultimately be used to adjust estimates of demand for childcare.

< Back to list of datasets

Reinvestment Fund 2018 Limited Supermarket Access (LSA) Analysis

Details:

Reinvestment Fund Limited Supermarket Access (LSA) Analysis

Topics:

Food access, food security

Source:

Reinvestment Fund

Years Available:

2010, 2011, 2012, 2013, 2014, 2015, 2016

Geographies:

Block groups and Limited Supermarket Access (LSA) clusters of block groups

Public Edition or Subscriber-only:

Public Edition

Download Available:

no

For more information:

https://www.reinvestment.com/research-publications/2018-update-analysis-of-limited-supermarket-access

Description:

Reinvestment Fund’s Limited Supermarket Access (LSA) analysis is a tool to identify areas across the 48 contiguous United States and the District of Columbia that have both inadequate and inequitable access to healthy food and sufficient market demand for new or expanded food retail operations. Click here to read an overview of the analysis. Using the LSA data on PolicyMap, a diverse range of clients—including policymakers, government agencies, lending institutions, community organizations, and researchers—can investigate LSA Areas within their geographies of interest and craft strategies based on the conditions specific to those communities.
Identifying LSA Areas
The LSA analysis measures access to healthy food by identifying areas that are well-served by supermarkets and those that have relatively limited access to supermarkets. Reinvestment Fund uses supermarkets (grocery stores with at least $2 million in annual sales) as a proxy for healthy food access because a review of the relevant research shows that supermarkets, compared to smaller stores (e.g., corner stores), most consistently offer the greatest variety of healthy foods at the lowest prices. Supermarket location data comes from the 2017 Nielsen TDLinx database. Supermarkets include the following store types from that database: supermarkets, supercenters, limited assortment stores, and natural food stores. Superettes and dollar stores are excluded because they are less likely to offer a wide range of healthy foods. Wholesale clubs are excluded because they require a paid membership. Military commissaries are excluded because they are not open to the public. Other data sources include the 2010 Decennial Census and the American Community Survey for population, residential land area, households, household incomes, and car ownership rates and the 2016 Bureau of Labor Statistics Consumer Expenditure Survey for spending on food for consumption at home. Access means different things in different places. In densely-populated urban areas, supermarkets tend to be located more closely together, and a neighborhood may have relatively limited access if its nearest store is a mile or two away. In rural areas, many if not most communities are miles from the nearest supermarket. To account for this variation, Reinvestment Fund assigns every census block group in the nation to one of seven classes based on that block group’s population density and, for densely populated block groups, car ownership. Within each class, Reinvestment Fund calculates the typical distance traveled to the nearest supermarket by residents of well-served block groups, i.e., block groups with a median household income at or above 120 percent of area median income (AMI). This reference distance is used under the assumption that in a functioning free market, there will generally exist an adequate complement of amenities like supermarkets in areas where incomes are above average. The seven population density and car ownership classes, their minimum and maximum population densities, their minimum and maximum percent of households without motor vehicles, and their reference distances are presented in Table 1 below.
Table 1. Block Group Population Density and Car Ownership Classes
Class Population Density Min. Population Density Max. Pct Households with no car Min. Pct Households with no car Max. Reference Distance (miles)
Density 1 0.0 10.4 N/A N/A 12.89
Density 2 10.4 64.2 N/A N/A 7.64
Density 3 64.2 296.9 N/A N/A 4.66
Density 4 296.9 1236.1 N/A N/A 2.59
Density 5 1236.1 3741.8 N/A N/A 1.57
Density 6, High Car 3741.8 161342.7 0.0% 12.6% 1.04
Density 6, Low Car 3741.8 813265.2 12.6% 96% 0.34
Each block group is then assigned a Low Access Score, which represents the percentage by which that block group’s distance to the nearest supermarket would have to be reduced to equal the typical distance for well-served block groups in that class. Block groups with access scores greater than or equal to 45 are considered limited-access. In those limited-access areas, residents must travel almost twice as far to a supermarket as residents in well-served block groups with similar population density and car ownership. Finally, contiguous limited-access block groups with a collective population of at least 5,000 people are combined to form LSA Areas—areas with both limited access to supermarkets and potentially enough market demand to support new or expanded supermarket operations.
Additional Metrics
In addition to identifying LSA Areas, Reinvestment Fund estimates retail food demand, supply, and leakage to determine the magnitude of each LSA area’s access problem and its potential remedy. Using household income ranges and their respective percentages of income spent on food to be prepared at home, Reinvestment Fund estimates the demand for retail food. Reinvestment Fund also estimates the supply of retail food based on estimated sales of food items from all stores—both supermarkets and non-supermarkets—within each block group’s reference distance. Leakage is the result of subtracting supply from demand. Block groups and LSA Areas where demand exceeds supply “leak” their locally unmet demand to other places; areas where supply exceeds demand receive “leaked” demand from less well-served areas.
Details for Block Group-Level Indicators
Indicator Description
LSA Status Whether a block group is part of an LSA Area or not. LSA Areas are composed of contiguous block groups with a Low Access Score of 45 or greater and a combined population of 5,000 people.Subsequent to the base year of the analysis (2010), a new LSA Area forms only when its population estimate is statistically significantly 5,000 or greater. A pre-existing LSA Area may have an estimated population of less than 5,000 people if (1) its block groups were part of an LSA Area in the previous year and (2) its population estimate is not statistically significantly less than 5,000. Groups of block groups that do not satisfy both conditions (1) and (2) are not designated LSA Areas.
Low Access Score The percent by which a block group’s distance to the nearest supermarket must be reduced to equal the reference distance for that block group’s population density and car ownership class. Low Access Scores indicate the degree to which residents are underserved by supermarkets. Residents of a block group with a higher Low Access Score must travel longer distances to access a supermarket than residents of block groups with lower Low Access Scores. Low Access Scores on PolicyMap range from 0 to 100; block groups with a Low Access Score of 0 have a distance to the nearest supermarket that is less than or equal their population density and car ownership class’s reference distance.
Retail Food Leakage Estimated annual demand for retail food (i.e., groceries) that cannot be met locally, at either supermarkets or other stores, and so “leaks” to other areas. “Locally” is defined as within a block group’s population density and car ownership class’s reference distance. Like LSA Status and Low Access Scores, leakage is a measure of need; block groups with greater amounts of leakage are less well-served by food retailers, both supermarkets and other grocery stores. Estimates on PolicyMap are rounded to the nearest $1,000.
Retail Food Leakage Percent Estimated percent of annual demand that cannot be met locally, at either supermarkets or other stores, and so “leaks” to other areas. “Locally” is defined as within a block group’s population density and car ownership class’s reference distance. Like LSA Status and Low Access Scores, leakage is a measure of need; block groups with greater amounts of leakage are less well-served by food retailers, both supermarkets and other grocery stores.
Retail Food Supply Estimated annual supply of retail food (i.e., groceries) in dollars. Estimates are derived from weekly all-commodity volume (ACV) estimates from the 2017 Nielson TDLinx database and store type-specific multipliers reflecting the typical share of each store type’s ACV that comes from retail food. All store types are included, not just those defined as supermarkets. Estimates on PolicyMap are rounded to the nearest $1,000.
Retail Food Demand Estimated annual demand for retail food (i.e., groceries) in dollars. Estimates are derived from counts or estimates of households (from the 2010 Decennial Census for 2010 and from the American Community Survey thereafter), estimates of household income (from the American Community Survey for all years), and estimated percents of household income spent on food to be prepared at home (from the 2016 Bureau of Labor Statistics Consumer Expenditure Survey). Estimates on PolicyMap are rounded to the nearest $1,000.
Population Density and Car Ownership Class The class to which a block group is assigned based on its population density and its percent of households with no motor vehicle. Population density is calculated as people per square mile of populated land area. Water area and land area with no population is excluded. Population data is from the 2010 Decennial Census (for year 2010) or the American Community Survey 5-Year Estimates (for years 2011 through 2016). Land area is from the U.S. Census Bureau’s 2010 TIGER/Line Shapefiles. Percent of households with no motor vehicle for all years 2010 through 2016 is from the American Community Survey 5-Year Estimates. Subsequent to the base year of the analysis (2010), a block group initially assigned to a class different than that block group’s class in the previous year must meet two conditions to remain in that class. That is, the block group must have a population density estimate that is (1) statistically significantly different than that block group’s population density estimate in the previous year and (2) statistically significantly within the bounds of that block group’s new class. Block groups that do not satisfy both conditions (1) and (2) remain in the class to which they were assigned in the previous year. In all years of the analysis, a block group’s car ownership estimate must be statistically significantly within the car ownership bounds of the Density 6, Low Car class for that block group to be assigned to that class. Block groups with car ownership estimates insignificantly within that class’s bounds are assigned to the Density 6, High Car class.
Population Population according to the 2010 Decennial Census (for year 2010) or the American Community Survey 5-Year Estimates (for years 2011 through 2016). LSA Areas are defined in part by having a population of at least 5,000 people. Subsequent to the base year of the analysis (2010), a new LSA Area forms only when its population estimate is statistically significantly 5,000 or greater. A pre-existing LSA Area may have an estimated population of less than 5,000 people if (1) its block groups were part of an LSA Area in the previous year and (2) its population estimate is not statistically significantly less than 5,000. Groups of block groups that do not satisfy both conditions (1) and (2) are not designated LSA Areas.
Households Households according to the 2010 Decennial Census (for year 2010) or the American Community Survey 5-Year Estimates (for years 2011 through 2016).
Details for LSA Area Indicators
Indicator Description
Low Access Score The population-weighted average percent by which an LSA Area’s distance to the nearest supermarket must be reduced to equal the reference distances for that LSA Area’s block groups’ population density and car ownership classes. Low Access Scores indicate the degree to which residents are underserved by supermarkets. Residents of an LSA Area with a higher Low Access Score must travel longer distances to access a supermarket than residents of an LSA Area with a lower Low Access Score. LSA Areas are defined in part by having a Low Access Score of at least 45.
Retail Food Leakage Estimated annual demand for retail food (i.e., groceries) that cannot be met locally, at either supermarkets or other stores, and so “leaks” to other areas. “Locally” is defined as within an LSA Area’s block groups’ population density and car ownership classes’ reference distances. Like Low Access Scores, leakage is a measure of need; LSA Areas with greater amounts of leakage are less well-served by food retailers, both supermarkets and other stores. Estimates on PolicyMap are rounded to the nearest $1,000.
Retail Food Leakage Percent Estimated percent of annual demand that cannot be met locally, at either supermarkets or other stores, and so “leaks” to other areas. “Locally” is defined as within an LSA Area’s block groups’ population density and car ownership classes’ reference distances. Like Low Access Scores, leakage is a measure of need; LSA Areas with greater amounts of leakage are less well-served by food retailers, both supermarkets and other stores.
Retail Food Supply Estimated annual supply of retail food (i.e., groceries) in dollars. Estimates are derived from weekly all-commodity volume (ACV) estimates from the 2017 Nielson TDLinx database and store type-specific multipliers reflecting the typical share of each store type’s ACV that comes from retail food. All stores types are included, not just those defined as supermarkets. Estimates on PolicyMap are rounded to the nearest $1,000.
Retail Food Demand Estimated annual demand for retail food (i.e., groceries) in dollars. Estimates are derived from counts or estimates of households (from the 2010 Decennial Census for 2010 and from the American Community Survey thereafter), estimates of household income (from the American Community Survey for all years), and estimated percents of household income spent on food to be prepared at home (from the 2016 Bureau of Labor Statistics Consumer Expenditure Survey). Estimates on PolicyMap are rounded to the nearest $1,000.
Population Population according to the 2010 Decennial Census (for year 2010) or the American Community Survey 5-Year Estimates (for years 2011 through 2016). LSA Areas are defined in part by having a population of at least 5,000 people. Subsequent to the base year of the analysis (2010), a new LSA Area forms only when its population estimate is statistically significantly 5,000 or greater. A pre-existing LSA Area may have an estimated population of less than 5,000 people if (1) its block groups were part of an LSA Area in the previous year and (2) its population estimate is not statistically significantly less than 5,000. Groups of block groups that do not satisfy both conditions (1) and (2) are not designated LSA Areas.
Households Households according to the 2010 Decennial Census (for year 2010) or the American Community Survey 5-Year Estimates (for years 2011 through 2016).

< Back to list of datasets

Reinvestment Fund 2019 Rural Food Access Investment Area (RFAIA) Analysis

Details:

Reinvestment Fund Rural Food Access Investment Area (RFAIA) Analysis

Topics:

Food access, food security

Source:

Reinvestment Fund

Years Available:

2016

Geographies:

Block groups and Rural Food Access Investment Area (RFAIA) clusters of block groups

Public Edition or Subscriber-only:

Public Edition

Download Available:

no

For more information:

https://www.reinvestment.com/research-publications/2018-update-analysis-of-limited-supermarket-access

Last updated on PolicyMap:

April 2020

Description:

Reinvestment Fund’s Rural Food Access Investment Area (RFAIA) analysis is a tool to identify rural areas across the 48 contiguous United States that have both inadequate and inequitable access to healthy food and sufficient market demand for new or expanded food retail operations. Click here to read an overview of the analysis. Using the RFAIA data on PolicyMap, a diverse range of clients—including policymakers, government agencies, lending institutions, community organizations, and researchers—can investigate RFAIA Areas within their geographies of interest and craft strategies based on the conditions specific to those communities.
Identifying RFAIA Areas
The RFAIA analysis measures access to healthy food by identifying areas that are well-served by supermarkets and those that have relatively limited access to supermarkets. Reinvestment Fund uses supermarkets (grocery stores with at least $2 million in annual sales) as a proxy for healthy food access because a review of the relevant research shows that supermarkets, compared to smaller stores (e.g., corner stores), most consistently offer the greatest variety of healthy foods at the lowest prices. Supermarket location data comes from the 2017 Nielsen TDLinx database. Supermarkets include the following store types from that database: supermarkets, supercenters, limited assortment stores, and natural food stores. Superettes and dollar stores are excluded because they are less likely to offer a wide range of healthy foods. Wholesale clubs are excluded because they require a paid membership. Military commissaries are excluded because they are not open to the public. Other data sources include the 2010 Decennial Census and the American Community Survey for population, residential land area, households, household incomes, and car ownership rates and the 2016 Bureau of Labor Statistics Consumer Expenditure Survey for spending on food for consumption at home. Access means different things in different places. In densely populated urban areas, supermarkets tend to be located more closely together, and a neighborhood may have relatively limited access if its nearest store is a mile or two away. In rural areas, many if not most communities are miles from the nearest supermarket. To account for this variation, Reinvestment Fund assigns every census block group in the nation to one of seven classes based on that block group’s population density and, for densely populated block groups, car ownership. Within each class, Reinvestment Fund calculates the typical distance traveled to the nearest supermarket by residents of well-served block groups, i.e., block groups with a median household income at or above 120 percent of area median income (AMI). This reference distance is used under the assumption that in a functioning free market, there will generally exist an adequate complement of amenities like supermarkets in areas where incomes are above average. The seven population density and car ownership classes, their minimum and maximum population densities, their minimum and maximum percent of households without motor vehicles, and their reference distances are presented in Table 1 below.
Table 1. Block Group Population Density and Car Ownership Classes
Class Population Density Min. Population Density Max. Pct Households with no car Min. Pct Households with no car Max. Reference Distance (miles)
Density 1 0.0 10.4 N/A N/A 12.89
Density 2 10.4 64.2 N/A N/A 7.64
Density 3 64.2 296.9 N/A N/A 4.66
Density 4 296.9 1236.1 N/A N/A 2.59
Density 5 1236.1 3741.8 N/A N/A 1.57
Density 6, High Car 3741.8 161342.7 0.0% 12.6% 1.04
Density 6, Low Car 3741.8 813265.2 12.6% 96% 0.34
Each block group is then assigned a Rural Low Access Score, which represents the percentage by which that block group’s distance to the nearest supermarket would have to be reduced to equal the typical distance for well-served block groups in that class. In the LSA analysis, communities with Low Access Scores at or above 0.45 are designated as having limited access to supermarkets and are eligible to be identified as LSA Areas, while communities with Limited Access Scores below 0.45 are considered well-served. The 0.45 represents the percent reduction in a resident’s travel to a supermarket to have access that is equal to what a resident would experience in a typical middle/upper income area. For the RFAIA analysis this threshold is lowered to 0.35 to create a more inclusive threshold, which permits opportunities to identify areas where strategic investments can create new, or preserve existing, food retail options. Block groups with Rural Low Access Scores of 0.35 or greater are considered underserved and eligible for inclusion in IAs. Block groups with Rural Low Access Scores below 0.35 are considered relatively well-served; these block groups are ineligible for inclusion in RFAIAs. Finally, contiguous limited-access block groups with a collective population of at least 2,000 people are combined to form RFAIA Areas—areas with both limited access to supermarkets and potentially enough market demand to support new or expanded supermarket operations.
Defining Rural Block Groups
The unit of analysis for the RFAIA analysis is the 2010 U.S. Census Bureau block group. Block groups with at least half of their area in one or more U.S. Census designated places with an estimated population of more than 50,000 people and/or in one or more Census urbanized areas of such places are classified as “urban.” These block groups are not included in the analysis. All other block groups, including those in small cities and towns with no more than 50,000 residents, are considered “rural.”
Details for Block Group-Level Indicators
Indicator Description
RFAIA Status Whether a block group is part of an RFAIA Area or not. RFAIA Areas are composed of contiguous block groups with a Low Access Score of 35 or greater and a combined population of 2,000 people.
Division A Census Division is a group of states and the District of Columbia established by the Census Bureau. The current nine divisions are intended to represent relatively homogeneous areas that are subdivisions of the four census geographic regions (https://www2.census.gov/geo/pdfs/maps-data/maps/reference/us_regdiv.pdf).
Rural Status Block groups with at least half of their area in one or more U.S. Census designated places with an estimated population of more than 50,000 people and/or in one or more Census urbanized areas of such places are classified as “urban.” All other block groups, including those in small cities and towns with no more than 50,000 residents, are considered “rural.”
Rural Low Access Score The population-weighted average percent by which an investment area’s distance to the nearest supermarket must be reduced to equal the reference distance for that investment area’s population density and car ownership class. Rural Low Access Scores indicate the degree to which residents are underserved by supermarkets. Residents of an investment area with a higher Rural Low Access Score must travel longer distances to access a supermarket than residents of block groups with lower Rural Low Access Scores. Rural Low Access Scores on PolicyMap range from 0 to 100; investment areas with a Rural Low Access Score of 0 have a distance to the nearest supermarket that is less than or equal their population density and car ownership class’s reference distance.
Population Density and Car Ownership Class The class to which a block group is assigned based on its population density and its percent of households with no motor vehicle. Population density is calculated as people per square mile of populated land area. Water area and land area with no population is excluded. Population data is from the American Community Survey 5-Year Estimates (2012-2016). Land area is from the U.S. Census Bureau’s 2010 TIGER/Line Shapefiles. Percent of households with no motor vehicle is from the American Community Survey 5-Year Estimates (2012-2016). The car ownership class assignments are identical to those used in the Limited Supermarket Analysis, which has a base year of 2010. A block group assigned to a class different than that block group’s class in the previous year must meet two conditions to remain in that class. That is, the block group must have a population density estimate that is (1) statistically significantly different than that block group’s population density estimate in the previous year and (2) statistically significantly within the bounds of that block group’s new class. Block groups that do not satisfy both conditions (1) and (2) remain in the class to which they were assigned in the previous year. In all years of the analysis, a block group’s car ownership estimate must be statistically significantly within the car ownership bounds of the Density 6, Low Car class for that block group to be assigned to that class. Block groups with car ownership estimates insignificantly within that class’s bounds are assigned to the Density 6, High Car class.
Population Population according to the American Community Survey 5-Year Estimates (2012- 2016). RFAIA Areas are defined in part by having a population of at least 2,000 people.
Households Households according the American Community Survey 5-Year Estimates (2012-2016).
Population in Households Population in Households according to the American Community Survey 5-Year Estimates (2012-2016). RFAIA Areas are defined in part by having a population of at least 2,000 people.
Total Area in Square Miles Land area was calculated using the U.S. Census Bureau’s 2010 TIGER/Line Shapefiles.
Details for RFAIA Area Indicators
Indicator Description
RFAIA Status Whether a block group is part of an RFAIA Area or not. RFAIA Areas are composed of contiguous block groups with a Low Access Score of 35 or greater and a combined population of 2,000 people American Community Survey (2012-2016).
Division A Census Division is a group of states and the District of Columbia established by the Census Bureau. The current nine divisions are intended to represent relatively homogeneous areas that are subdivisions of the four census geographic regions (https://www2.census.gov/geo/pdfs/maps-data/maps/reference/us_regdiv.pdf).
Rural Status Block groups with at least half of their area in one or more U.S. Census designated places with an estimated population of more than 50,000 people and/or in one or more Census urbanized areas of such places are classified as “urban.” These block groups are not included in the analysis. All other block groups, including those in small cities and towns with no more than 50,000 residents, are considered “rural.”
Rural Low Access Score The population-weighted average percent by which an investment area’s distance to the nearest supermarket must be reduced to equal the reference distance for that investment area’s population density and car ownership class. Low Access Scores indicate the degree to which residents are underserved by supermarkets. Residents of an investment area with a higher Low Access Score must travel longer distances to access a supermarket than residents of block groups with lower Low Access Scores. Low Access Scores on PolicyMap range from 0 to 100; investment areas with a Low Access Score of 0 have a distance to the nearest supermarket that is less than or equal their population density and car ownership class’s reference distance.
Population Population according to the American Community Survey 5-Year Estimates (2012- 2016). RFAIA Areas are defined in part by having a population of at least 2,000 people.
Households Households according the American Community Survey 5-Year Estimates (2012- 2016).
Population in Households Population in Households according to the American Community Survey 5-Year Estimates. RFAIA Areas are defined in part by having a population of at least 2,000 people.
Population Density Population density is calculated as people in households per square mile of populated land area. Water area and land area with no population is excluded. Population data is from the American Community Survey Estimates (2012- 2016). Land area is from the U.S. Census Bureau’s 2010 TIGER/Line Shapefiles. In order to be considered in a Rural Food Access Investment Area, the cluster population density must be above their Division’s density threshold.
Total Area in Square Miles Total area was calculated using the U.S. Census Bureau’s 2010 TIGER/Line Shapefiles.

< Back to list of datasets

Reinvestment Fund Market Value Analyses (MVAs)

Detail:

Reinvestment Fund real estate market evaluation and valuation for Philadelphia, Baltimore, Washington DC, Milwaukee, Jacksonville, Kansas City, Houston, Dallas and areas of the state of New Jersey

Topics:

market value analyses, real estate

Source:

Reinvestment Fund

Years Available:

various

Geographies:

blockgroups in selected markets

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

https://www.reinvestment.com/initiatives/market-value-analysis/

Description:

Reinvestment Fund’s Market Value Analyses (MVAs) are typologies of local real estate markets, designed to help governments and private investors target investment and prioritize action in ways that can leverage investment and revitalize neighborhoods. To develop this analysis, Reinvestment Fund uses a statistical technique known as cluster analysis that helps to uncover patterns in data. Cluster analysis does this by forming groups of areas that are similar along a set of selected values that describe those areas. While the groups are formed to be as uniform as possible within, the groups are also as dissimilar as possible from one another. Using this technique, the MVA is able to reduce vast amounts of data on hundreds of thousands of properties and hundreds of areas down to a manageable, meaningful typology of market types that can inform area-appropriate programs and decisions regarding the allocation of resources. Reinvestment Fund uses many indicators in its analyses including various combinations of the following: average home sale price, percent change in average home sale price over time, percent owner occupancy, percent vacancy, percent vacant lots, percent of rental units that are Section 8, percent commercial, percent of properties with foreclosure, percent prime home purchase loans, number of new construction permits, number of Sheriff sales as a percent of owner occupied units, number of public housing units, percent of properties deemed dangerous, percent of structures demolished, percent of high risk or very high risk credit scores for predatory lending, and percent of housing units built before 1950.

Working with the MVA client, Reinvestment Fund forms geographic study areas for the cluster analysis. Although many of these study areas are displayed using similar color schemes, they can not be compared. Please consult the description relevant to the study area for a full description of each MVA.

FLORIDA

Jacksonville, FL (2018)

Reinvestment Fund’s Market Value Analysis (MVA) describes the characteristics of the block groups within a study area. In 2018, Reinvestment Fund updated the Market Value Analysis in Jacksonville. The MVA indicators in Duval County are noted below and represent the dimensions upon which block groups are analyzed:

  • Median Sales Price: Duval County Property Appraiser’s file of all recorded sales between 1/1/2016 through 12/31/2017 for residential sales of $1,000 or more.
  • Coefficient of Variation: The coefficient of variation, derived from the Duval County Property Appraiser’s file of sales, represents the variability of sale prices within the block group. (High numbers represent places with wide variations in sale prices.)
  • Foreclosure as a Percent of Sales: Foreclosure filings (Duval County Clerk of Court) were added to a Duval County file of bank-owned, registered vacant properties in 2018. This figure is then divided by the number of sales in 2016-2017 (from County Assessor’s file).
  • Percent Water Shut-off: JEA file of properties where water service has been shut off divided by the total number of residential properties. This is an indicator of vacancy.
  • Percent >$5K Rehab: Duval County records of all building permits issued between 1/1/2016 through 12/31/2017 for new construction and substantial rehabilitation (estimated value greater than $5,000) of properties divided by the total number of residential parcels.
  • Percent Homestead Exempt: Duval County Parcel File, total number of residential properties claiming a Homestead Exemption divided by the number of residential parcels.
  • Percent Publicly Subsidized Rental: Represents Duval County and City of Jacksonville Housing Authority owned developments, and HUD-assisted rental housing developments including Housing Choice Vouchers (aggregated to the Census Block Group by the Shimberg Center for Housing Studies, University of Florida), divided by the number of renter-occupied housing units (ACS 2016).
  • Percent Residential Area: Duval County Property Appraiser’s File. This figure represents residential land divided by all developable land area.
The tables below show each component’s average for each MVA category. Reinvestment Fund cluster analysis revealed nine market types, characterized as follows:
  • Market Type A: Highest home prices, lowest number of foreclosure filings relative to sales volume (foreclosure rate), high owner occupancy rate, highest level of permit activity, lowest rate of water shut-offs.
  • Market Type B: High home prices, second lowest foreclosure rate relative to sales volume but about twice the rate as Market A, highest percent owner occupied, lowest coefficient of variance of sales price.High home prices, second lowest foreclosure rate relative to sales volume but about twice the rate as Market A, highest percent owner occupied, lowest coefficient of variance of sales price.
  • Market Type C: Relatively high home prices, lowest percentage of residential land area, second highest rate of permit activity.
  • Market Type D: Home prices close to the citywide average, low sales price variance, foreclosures as a percentage of sales a bit higher than the citywide average.
  • Market Type E: Second lowest percentage of properties with homestead exemptions, second highest percentage of publicly subsidized rentals, foreclosures as a percent of sales higher than the citywide average.
  • Market Type F: Home prices slightly more than half the citywide average, about average foreclosures as a percent of sales, fewer Homestead Exemptions than average, and utility shut-offs slightly below the citywide average.
  • Market Type G: Third lowest homeownership rate, higher than average percent of publicly subsidized rentals, home prices roughly a third of the citywide average, high number of foreclosures as a percent of sales, percent water shut-offs that are higher than the citywide average.
  • Market Type H: Second lowest home sale prices, highest coefficient of variance of sales, lowest owner occupancy rate, highest percent of publicly subsidized rental, highest percent water shut-offs.
  • Market Type I: Lowest home sale prices, second lowest owner occupancy rate, second highest coefficient of variance of sales, second highest percent water shut-offs.
  • Nonresidential Mask: All nonresidential parcels>50,000sf in the Duval County Property Appraiser’s file were merged to create a non-residential mask for the county.
  • Insufficient Data: All block groups that had fewer than 5 sales in 2016-2017; these block groups tend to be either entirely rental housing or non-residential uses.
Jacksonville, FL (2015)

Reinvestment Fund’s Market Value Analysis (MVA) describes the characteristics of the block groups within a study area. The MVA indicators in Jacksonville are noted below and represent the dimensions upon which block groups are analyzed:

  • Median and Average Sales Price: Duval County Property Appraiser’s file of all recorded sales between 1/1/2013 through 12/31/2014 for residential sales of $1,000 or more. Only the Median Sale Price was used in the MVA model.
  • Coefficient of Variation: The coefficient of variation, derived from the Duval County Property Appraiser’s file of sales, represents the variability of sale prices within the block group. (High numbers represent places with wide variations in sale prices.)
  • Foreclosure as a Percent of Sales: Florida is a non-judicial foreclosure state; foreclosures had to be estimated by using Duval County Property Appraiser’s Office sales data to note properties that were sold to banks, along with a Duval County file of bank-owned, registered vacant properties 2013 through 2014. This figure, a rough estimate of foreclosure filings, is then divided by the number of sales in 2013-2014 (from City Assessor’s file).
  • Percent Water Shut-off: Duval County Water Department file of properties where water service has been shut off divided by the total number of residential properties. This is an indicator of vacancy.
  • Percent >$5K Rehab: Duval County records of all building permits issued between 1/1/2013 through 12/31/2014 for new construction and substantial rehabilitation (estimated value greater than $5,000) of properties divided by the total number of residential housing units (ACS 2013).
  • Percent Homestead Exempt: Duval County Parcel File, total number of residential properties claiming a Homestead Exemption divided by the number of residential Housing Units according to ACS 2013. This represents the percent of all occupied housing units that are occupied by owners.
  • Percent Publicly Subsidized Rental: Represents Duval County and City of Jacksonville Housing Authority owned developments, and HUD-assisted rental housing developments including Housing Choice Vouchers (aggregated to the Census Block Group by the Shimberg Center for Housing Studies, University of Florida), divided by the number of renter-occupied housing units from the City Parcel File.
  • Percent Residential Area: Duval County Property Appraiser’s File. This figure represents residential land divided by all developable land area.
The tables below show each component’s average for each MVA category. Reinvestment Fund cluster analysis revealed nine market types, characterized as follows:
  • Market Type A: Highest home prices, lowest number of foreclosure filings relative to sales volume (foreclosure rate), highest owner occupancy rate, highest level of permit activity, lowest rate of water shut-offs, second highest percentage of sales that are condominiums.
  • Market Type B: High home prices, second lowest foreclosure rate relative to sales volume but over twice the rate as Market A, second highest percent owner occupied, second lowest coefficient of variance of sales price, very low percentage of condominium sales.
  • Market Type C: Relatively high home prices, notably higher rates of utility shut offs compared to A and B markets, lowest percentage of residential land area, third highest percentage of condo sales.
  • Market Type D: Home prices close to the citywide average, lowest sales price variance, foreclosures as a percentage of sales a bit higher than the citywide average.
  • Market Type E: Largely a condominium market, highest percentage of condo sales by a large margin, lowest percentage of properties with homestead exemptions, percentage of publicly subsidized rental approximately twice the rate as D and F1 markets, foreclosures as a percent of sales matches the citywide average.
  • Market Type F1: home prices slightly less than half the citywide average, higher foreclosures as a percent of sales than the citywide average, Homestead Exemptions near the citywide average, utility shut-offs at twice the rate of markets A-E.
  • Market Type F2: Second lowest homeownership rate, highest percent of publicly subsidized rental by nearly three times the rate as the next highest market (H), home prices roughly half the citywide average, high number of foreclosures as a percent of sales, highest percentage of sales that are multi-unit, percent water shut-offs that are close to the citywide average but twice the rate of markets A-E.
  • Market Type G: Second lowest home sale prices, sales prices roughly half that of the F markets, second highest coefficient of variance of sales, third highest percent of publicly subsidized rental, percent water shut-offs that are higher than the citywide average, second lowest percentage of condominium sales.
  • Market Type H: Lowest home sale prices, sales prices roughly half that of the G markets, highest vacancy rate, second lowest owner occupancy rate, highest coefficient of variance of sales, highest percent water shut-offs, water shut offs are over three times the rate of the next highest market (H), no condo sales recorded
  • Nonresidential Mask: All nonresidential parcels>50,000sf in the Duval County Property Appraiser’s file were merged to create a non-residential mask for the county.
  • Insufficient Data: All block groups that had fewer than 5 sales in 2013-2014; these block groups tend to be either entirely rental housing or non-residential uses.

MARYLAND

Baltimore, MD (2017)

Reinvestment Fund’s Market Value Analysis (MVA) describes the characteristics of the block groups within a study area. In 2017, Reinvestment Fund updated the Market Value Analysis in Baltimore. The MVA indicators are noted below and represent the dimensions upon which block groups are analyzed:

  • Median Sales Price: Median sales price of sales transactions that occurred between 2015q3 and 2017Q2. Median is calculated with and without condominium sales; the higher of the two values is used. Data from City of Baltimore.
  • Sales Price Variation: The coefficient of variation, derived from the City of Baltimore’s file of sales, represents the variability of sale prices within the block group. (High numbers represent places with wide variations in sale prices.)
  • Vacant Buildings and Vacant Lots: Percent of Residential Land Area that is either vacant Residential Land or occupied by Residential Properties that have been cited as vacant at any point in time from City records where the citation has not been “Closed” or “Abated”. Data from City of Baltimore.
  • Foreclosure as a Percent of Sales: Mortgage foreclosure filings between 2015q3 and 2017q2, as a share of residential sales for the same period. Data from the City of Baltimore.
  • Building Permits: Properties with building permits totaling $10,000 or more between 2015q3 and 2017q2. As a share of total residential parcels. Data from City of Baltimore.
  • Residential Density: Residential housing units per residential land acre. This includes vacant and unoccupied residential properties. Data from City of Baltimore.
  • Owner Occupancy: Percent of occupied housing units reported as owner occupied. Data from 2012-2016 Census American Community Survey (ACS).
  • Subsidized Housing: Number of rental units with subsidies as a share of all housing units (HUD MF, LIHTC, HCV, HABC, Elderly, 202). Data from City of Baltimore and HUD.
The tables below show each component’s average for each MVA category. Reinvestment Fund cluster analysis revealed ten market types, characterized as follows:
  • Market Type A: The typical home sales price in “A” markets is approximately 5 times the City median of $75,000. These markets have the second highest average percentage of properties (5.0%) that have over $10,000 in building permits during 2015q3-2017q2. Households in these “A” markets tend to be moderately owner occupied. “A” markets also have the lowest level of foreclosure filings as a percent of sales (7.7% of sales) in the city. On average, there are few publicly subsidized rental housing options in these markets (2.9% of all rental units). “A” markets are the least dense housing market with an average only 8.2 housing units per residential acre.
  • Market Type B: At nearly $223,970, the “B” markets’ typical home sales price is just under two times the city median. Permitting activity (4.8%) is less than that in “A” markets, and is much higher than the city average. Differing from “A” markets, these “B” markets are slightly less owner occupied (55.9%). Of the rental households, an average of 2.4% per block group are receiving public subsidy. “B” markets typically have higher vacancy (1.0%) than “A” markets but far are below the city average (6.8%).
  • Market Type C: Baltimore city’s “C” markets have home sale prices ($191,953) above the city average. Permitting activity (5.2%) is the highest in the city. “C” markets are minimally owner occupied (21.2%); of the large number of rental properties, a majority (57.7%) are publicly subsidized. With an average vacancy rate of 5.8%, “C” markets rank in the middle of the market types.
  • Market Type D: The typical home sale prices in “D” markets ($102,989) is closest to the city average. Permitting activity in “D” markets (3.5%) is also just over the city average. “D” markets have the highest average homeownership rate (78.1%) and the third lowest density (10.0 units per acre) of all markets. “D” markets have the third lowest average vacancy in the city (1.4%), and comprise over 8,207 rental households, with an average of 3.7% receiving some form of subsidy.
  • Market Type E: “E” markets block groups typical home sales price is approximately $89,397, roughly 20% below the city average. The market is moderately (32.2%) owner occupied, third lowest in the city. Permitting activity in “E” markets (3.6%) is slightly above the city average. The vacancy rate in “E” markets (3.8%) is the fourth lowest in the city.
  • Market Type F: The typical home sales price in “F” markets is approximately $52,015, less than half of the city average, and foreclosure filings represent 30.3% of all sales. Permitting activity in “F” markets (2.6%) is the fifth lowest rate in the city. These markets are nearly evenly split between owners (55.8%) and renters. Of the renter households, an average of 11.9% per block group are receiving public subsidy; the fourth lowest level in the city.
  • Market Type G: At $34,827, typical home sales prices in these “G” markets are almost two-thirds below the city average. In a typical block group, nearly 24.5% of all sales are by banks. An average of 20.1% of households own their home, the lowest average of all markets. Of the renter occupied households, on average 77.8% of them are subsidized, the highest average of all markets. At 9.1%, “G” markets have the third highest vacancy rate of all market types. Permitting activity in “G” markets (2.5%) is below the city average.
  • Market Type H: The typical home sales price in “H” markets is $31,332, just below two-thirds the city average. On average, foreclosure filings represent 25.6% of all sales in “H” block groups. Permitting activity in “H” markets (1.9%) is the third lowest of all market types. “H” markets typically have 51.4% homeowners and 48.6% renters; on average 13.0% of renter households receive public rental subsidy, the fifth lowest percentage among market types. The average vacancy rates in “H” markets (7.0%) are close to the average of the city.
  • Market Type I: The typical home sales price in these “I” markets is $16,508, approximately 30% of the Richmond city average. Permitting activity in “I” markets is the second lowest in the city at 1.1%. “I” markets typically have 42.5% homeowners and of the 57.5% that are renters, 18.3% of those households are receiving some form of subsidy.
  • Market Type J: “J” markets have the highest level of vacancy; approximately 1/5th of all residential land is either vacant buildings or vacant land. The typical home sales price in “J” markets is approximately $9,249, less than twelve times the city average, and foreclosure filings represent 15.8% of all sales. Permitting activity in “J” markets (0.7%) is the lowest rate in the city. This market has one-third of the population represented as owners (33.4%). Of the renter households, an average of 21.6% per block group are receiving public subsidy; the third highest level in the city.
Baltimore, MD (2014)

Reinvestment Fund’s Market Value Analysis (MVA) describes the characteristics of the block groups within a study area. In 2014, Reinvestment Fund updated the Market Value Analysis in Baltimore. Indicators are noted below and represent the dimensions upon which block groups are analyzed:

  • Median Sales Price: City of Baltimore parcel dataset file of recorded sales between 2012 and 2014Q2 for residential sales of $1,000 or more. The median residential home sales price between, values under $1,000 were filtered out as non-arm’s length transactions. Residential sales were identified by usegroup in (‘R’,’M’,’U’) which is the “residential”, “apartment”, or “condo” designation, respectively.
  • Coefficient of Variation: The coefficient of variation, derived from the City of Baltimore parcel dataset file of recorded sales, represents the variability of sale prices within the block group. (High numbers represent places with wide variations in sale prices.)
  • Foreclosure as a Percent of Residential Parcels: Maryland is a non-judicial foreclosure state. Foreclosures from the City of Baltimore dataset, 2012-2014Q2, were divided by the count of residential parcels (from the City of Baltimore parcel dataset).
  • Vacant Housing Parcels as a Percent of Residential Parcels:The count of vacant housing parcels, provided from the City of Baltimore as of September 2014, were divided by the count of residential parcels (from the City of Baltimore parcel dataset).
  • Percent of Housing Units that are Owner Occupied: The count of housing units that are owner occupied, 2014, divided by the count of all occupied housing units (from the City of Baltimore parcel dataset).
  • Building permits as a Percent of Residential Parcels: The count of parcels whose sum of building permits exceeds $10,000, 2012-2014Q2, divided by the count of total residential parcels (from the City of Baltimore parcel dataset). Permits during the time period are tagged to each parcel and then aggregated. Parcels which have a combined permit value greater than $10,000 are then included in this count.
  • Commercial/Industrial land area as a Percent of Total Land Area: Commercial and industrial land area is calculated from the City of Baltimore parcel dataset, 2014.
  • Vacant Residential Lots as a Percent of Residential Parcels: Count of residential parcels that are vacant land, November 2014, divided by the count of residential parcels (from the City of Baltimore parcel dataset). Areas with steep slope and with edges along the city boundary were removed.
  • Percent Publicly Subsidized Rental: Count of Section 8 housing vouchers (from the City of Baltimore Housing office), divided by the count of renter occupied housing units (from the City of Baltimore parcel dataset).
  • Housing Units per Square Mile: The count of housing units in 2014 (from the City of Baltimore parcel dataset) divided by the land area in square miles.
The tables below show each component’s average for each MVA category. The City of Baltimore uses Reinvestment Fund’s Market Value Analysis to create a Housing Market Typology, used by the Department of Housing, Housing Code Enforcement Division, and other stakeholders to strategically allocate public resources in alignment with neighborhood housing market conditions. The Housing Market Typology typically includes five categories, which correspond to eight distinct market types identified by Reinvestment Fund:
  • Regional Choice – Type A: Block groups designated Regional Choice represent competitive housing markets with higher home prices and lower numbers of foreclosure filings relative to residential parcels (foreclosure rate). They have among the highest owner occupancy rate, highest level of permit activity, and lowest rate of vacant housing units.
  • Middle Market Choice – Types B and C:
  • Market Type C: : Block groups in the Middle Market Choice category have home prices above the city average. Type B has the second lowest foreclosure rate relative to sales volume, but twice the rate as Market A, as well as the second lowest coefficient of variance of sales price, second highest level of permit activity, highest density of housing units per square mile. Type C has lower rates of vacant housing units, higher rates of foreclosures (twice that of Market B), the highest owner occupancy rates, and lowest rates of vacant residential lots.
  • Middle Market – Type D: Block groups in the Middle Market category have home prices close to the citywide average. These markets are defined by higher rates of foreclosures, below average rates of vacant housing and vacant lots, and permit activity close to the citywide average.
  • Middle Market Stress – Types E and F: Block groups in Middle Market Stressed category have home prices less than half the citywide average. Type E is characterized by a percentage of publicly subsidized rental approximately twice the rate as D markets, the highest rate of foreclosures on average in the city, and lower levels of permit activity. Type F has higher foreclosure rates than the citywide average, below average vacant housing units. Type F has the second highest rates of vacant residential lots and the highest percent commercial/industrial land.
  • Distressed – Types G and H Block groups in the Distressed Market category have experienced significant deterioration of the housing stock. Home prices in these markets are well below citywide average. Type G has an average number of foreclosures as a percent of parcels, the second highest level of vacant housing units, and the second lowest homeownership rate. Type H has the lowest rates of owner occupancy, highest rates of vacant housing and vacant lots, and lowest rates of permitting.
  • Not Classified: All block groups that had fewer than 5 sales in 2012-2014Q2; these block groups tend to be either entirely rental housing or non-residential uses.
Baltimore, MD (2011)
In 2011, Reinvestment Fund updated the Baltimore Market Value Analysis for the City of Baltimore. The City of Baltimore used Reinvestment Fund’s Market Value Analysis to create a Housing Market Typology, used by the Department of Housing, Housing Code Enforcement Division, and other stakeholders to strategically allocate public resources in alignment with neighborhood housing market conditions. The Housing Market Typology includes five categories, which correspond to eight distinct market types identified by Reinvestment Fund: Regional Choice (A and B), Middle Market Choice (C), Middle Market (D), Middle Market Stressed (E), and Distressed (F, G, and H).
  • Regional Choice – Types A and B: Block groups designated Regional Choice represent competitive housing markets with high owner-occupancy rates and property values in comparison to all other market types. Foreclosure, vacancy and abandonment rates are low. Substantial market interventions are not necessary in the Regional Choice category. Basic municipal services such as street maintenance are essential to maintaining these markets.
  • Middle Market Choice – Type C: Block groups in the Middle Market Choice category have housing prices above the city’s average with strong ownership rates, and low vacancies. However, these areas show slightly increased foreclosure rates. Modest incentives and strong neighborhood marketing should be used to keep these communities healthy, with the potential for growth.
  • Middle Market – Type D: Block groups in the Middle Market category have median sale values of $91,000 (above the City’s average of $65,000) as well as high homeownership rates. These markets experienced higher foreclosure rates when compared to more competitive markets, with slight population loss. Neighborhood stabilization and aggressive marketing of vacant houses should be considered in this category. Diligent housing code enforcement is also essential to maintain the existing housing stock.
  • Middle Market Stressed – Type E: Block groups in the Middle Market Stressed category have slightly lower home sale values than the City’s average, and have not shown significant sale price appreciation. Vacancies and foreclosure rates are high, and the rate of population loss has increased in this market type, according to the 2010 Census data. Based on these market conditions, intervention strategies should support homeowners who may be facing economic hardships due to adverse changes in the national economy.
  • Distressed – Types F, G, and H: Block groups in the Distressed Market category have experienced significant deterioration of the housing stock. This market category contains the highest vacancy rates and the lowest homeownership rates, compared to the other market types. Block groups in this category have also experienced the most substantial population losses in the City during the past decade. Comprehensive housing market inventions should be targeted in this market category, including site assembly, tax increment financing, and concentrated demolitions to create potential for greater public safety and new green amenities.
Baltimore, MD (2008)
In 2008 Reinvestment Fund updated the Baltimore Market Value Analysis with the Baltimore City Planning Department and Baltimore Housing.

Reinvestment Fund cluster analysis revealed nine market types, characterized as follows:

  • Competitive: Neighborhoods in this category, like Federal Hill, Canton, and Homeland, have robust housing markets with high owner-occupancy rates and high property values. Foreclosure, vacancy, and abandonment rates are all very low. Most direct interventions are not necessary in the Competitive market. Basic municipal services such as street maintenance are essential to maintaining these markets. While densities do vary, single family detached homes predominate and these areas typically don’t have a mix of housing types.
  • Emerging: neighborhoods in the “Emerging” category, such as Abell, Hampden and Mt. Vernon, have robust housing markets but with homeownership rates slightly below the citywide average; this category appeals to property owners interested in tapping into a strong rental market. Median sales price is above $244,000. Additional incentives for development and investment in the Emerging market would recognize its potential for growth. There is more variety in housing types and more commercial areas than in the competitive cluster.
  • Stable: This cluster includes neighborhoods such as Reservoir Hill, Lauraville and Violetville. Median sale price is around $160,000 and the rate of foreclosure is just below the City average of 5%. In Stable markets, the City should consider stabilizing and marketing any vacant houses. Traditional housing code enforcement is also essential to maintain the existing housing stock. Homeownership is still significant at 55%.
  • Transitional: Neighborhoods in the “Transitional” category, such as Allendale, Belair Edison and Kenilworth Park, are found typically at the inner edge of the stable neighborhoods. These neighborhoods have moderate real estate values with median sales prices between $80,000-$100,000, with higher median sales in areas with commercial land uses. Foreclosure rates are slightly higher than average, but occupancy rates are still higher than average. This cluster also has the highest rate of rental subsidy. The city should support homeowners who may be facing economic hardships due to the national economy.
  • Distressed: These neighborhoods, which include Middle East, North Penn and Westport, have nearly four times the level of vacant homes and vacant lots as found in other categories. Sale prices typically range from $36,000-$40,000. Distressed markets tend to rely on comprehensive housing market interventions, such as site assembly and tax increment financing. One of the six criteria for identifying the Growth Promotion Areas includes neighborhoods located in distressed markets. Demolitions in the Distressed markets should be clustered to create potential for greater public safety and well as marketability. The housing type here is predominantly rowhouse.
Baltimore, MD (2005)
In 2005 Reinvestment Fund developed a Market Value Analysis for the City of Baltimore Planning Department.

Reinvestment Fund cluster analysis revealed seven market types, characterized as follows:

  • Competitive: high owner occupancy, high property values, and low abandonment.
  • Emerging: fairly high homeownership rates, relatively low foreclosure rate, variety in housing type and greater number of commercial properties.
  • Stable: slightly above average foreclosure rate, high homeownership rate, relatively new housing stock.
  • Transitional: moderate average sales price, high homeownership rate, and very high foreclosure rate.
  • Distressed: very high vacancy rate, very high percentage of vacant lots, low homeownership rate and lowest average sales price.

MISSOURI

Kansas City, MO

Reinvestment Fund’s Market Value Analysis (MVA) describes the characteristics of the block groups within a study area. The MVA indicators in Kansas City are noted below and represent the dimensions upon which block groups are analyzed:

  • Median Home Values: The median value of all residential home sales occurring between 2014 and 2016q2, excluding homes purchased by a Land Bank authority or purchased for values below $1,000 or above $3,000,000. Data source for this indicator is RealQuest.
  • Variance of Sales Price: The coefficient of variance of residential home sales occurring between 2014 and 2016q2. Excludes homes purchased by a Land Bank authority or purchased for values below $1,000 or above $3,000,000. Calculation for this indicator is (Average Value ÷ Standard Deviation). Data source for this indicator is RealQuest.
  • Share of Homes with Permits $1k+ or New Construction: The share of residential properties with non-demolition permits valued more than $1,000 issued between 2014 and 2015. Data source for this indicator is the City of Kansas City, MO.
  • Distressed Sales as a Share of Sales: Share of residential property sales between 2014 and 2016q2 where the seller or owner was a bank or the sale type was flagged as a non-standard transaction (e.g. “Sheriffs Sale”, “Tax Deed”, “Foreclosure Deed”). Data source for this indicator is RealQuest.
  • Share of Homes with Maintenance Violations: The share of residential properties that were issued a maintenance-related violation between 2014 and 2015. The data source for this indicator is the City of Kansas City, MO.
  • Vacant Properties as a Share of Residential Properties: The share of residential properties that were owned by a bank, cited on the city’s dangerous buildings list, cited for vacancy, listed in the city’s vacancy property registry, or requested a permit for demolition between 2014 and 2015. The data source for this indicator is the City of Kansas City, MO.
  • Density of Housing Units: Number of households per acre of land. Equals count of owner and renter occupied households divided by acres of residential land. The data source for this indicator is the City of Kansas City, MO.
  • Percent Owner Occupied Households: Percent of households that reported owning their home. Data source for this indicator is the U.S. Census Bureau’s American Community Survey 2010-2014.
  • Share of Rentals in Single Family Homes: Share of households renting their home in a building with one to four units. Data source for this indicator is the U.S. Census Bureau’s American Community Survey 2010-2014.
  • Share of Households with Subsidy (Excluding Senior Housing): Number of subsidized units that were not exclusively for seniors as a share of all households. Data source for this indicator is HUD’s Picture of Subsidized Housing.
The tables below show each component’s average for each MVA category. Reinvestment Fund’s cluster analysis revealed nine market types (A, B, C, D, E, F, G, H, I). These markets are described below.
  • Market Type A: Highest value homes in the city, lower density than market type B, lowest rate of subsidized households along with market type B, lowest share of homes with maintenance violations and distressed sales.
  • Market Type B: High value homes, more multifamily buildings than A markets, low percentage of subsidized households and vacant homes, more investment activity (i.e. building permits) than market type A.
  • Market Type C: Moderate value homes, highest household density of all markets in the city, largely renter occupied households, higher share of subsidized households and maintenance violations than market type D but also more investment activity (i.e. building permits).
  • Market Type D: Moderate value homes but slightly lower than C markets, largely owner occupied households, low share of subsidized households, homes with maintenance violations, vacant homes and distressed sales but also a small percentage of homes with building permits indicating lower level of investment than market type C.
  • Market Type E: Moderate value homes, but median sales price almost half that of C and D markets. Lowest household density in the city, largely owner occupied homes. Shares of homes with maintenance violations and distressed sales higher than market types C/D.
  • Market Type F: Moderate value homes, but median sales price almost half that of C and D markets. More subsidized rental households and distressed sales than E markets.
  • Market Type G: Low value homes, owner occupied versus renter occupied households pretty evenly split, high share of subsidized households, homes with violations and distressed sales. Share of vacant homes more than double that of market type F.
  • Market Type H: Low value homes, median sale price less than half that of market type G. Distressed sales account for almost half of all home sales, high vacancy rate and high percentage of violations.
  • Market Type I: Lowest value homes in the city, highest vacancy rate in the city, distressed sales account for more than half of all homes sales.
  • Non Residential: Non residential areas of the city where there were fewer than 100 housing units in a given block group.
  • Insufficient Data: All block groups that had fewer than 5 residential sales; these block groups tend to be either entirely rental housing or non-residential uses.

NEW JERSEY

Atlantic Highlands, NJ
In 2007 Reinvestment Fund developed a Market Value Analysis of the Atlantic Highlands for the New Jersey Department of Community Affairs.

Reinvestment Fund cluster analysis revealed eight market types, characterized as follows:

  • Dark Purple: highest average sales price, fairly high percent commercial, highest percent owner occupied.
  • Light Purple: relatively high percent owner occupied and relatively low percent foreclosure.
  • Dark Blue: fairly high average sales price, fairly low percent of rental that is Section 8.
  • Light Blue: highest residential parcel change rate, relatively high percent owner occupied, highest percent of rental that is Section 8.
  • Light Yellow: fairly high average sales price, very low percent owner occupied.
  • Dark Yellow: very high residential parcel change rate, fairly low percent of rental that is Section 8.
  • Light Orange: fairly low average sales price, fairly high percent foreclosure.
  • Dark Orange: very low percent owner occupied, very high percent foreclosure.
Camden, NJ

In 2000 Reinvestment Fund developed a Market Value Analysis of Camden for the New Jersey Department of Community Affairs. Reinvestment Fund cluster analysis revealed six market types, as follows:

  • High Value: highest average sales price at $116,864, very low vacancy rate, majority owner-occupied, and the lowest number of Section 8 certificates.
  • Strong Value: high average sales price, high rate of homeownership, low number of Section 8 certificates, lowest number of demolition permits per capita, and lowest vacancy rate at 0.3%.
  • Steady: highest rate of homeownership at 79%, highest number of alteration and addition permits per capita, lowest number of older homes, and average number of vacancies.
  • Transitional: fairly low average residential sales price, above average owner-occupied.
  • Distressed Public Market: highest number of Section 8 certificates and low average home sales price.
  • Reclamation: highest number of older homes, lowest average sales price at $18,063, highest vacancy rate at 16.9%, lowest home ownership rate at 44.5%, and highest number of those with high or very high risk credit.
Meadowlands, NJ
In 2007 Reinvestment Fund developed a Market Value Analysis of the Meadowlands for the New Jersey Department of Community Affairs.

Reinvestment Fund cluster analysis revealed five market types, characterized as follows:

  • Purple: highest owner occupancy, lowest percent commercial, higher average sale price, highest percent of residential permits.
  • Dark Blue: high owner occupancy, low percent commercial, slightly higher average sale price, foreclosures evident.
  • Light Blue: average sales price, 52% owner occupied, evident vacant parcels.
  • Light Yellow: low owner occupancy, highest percent commercial, average sales price, foreclosure activity.
  • Dark Yellow: lowest owner occupancy, high percent commercial, lowest average sales price, lowest percent of residential permits.
Newark, NJ
In 2007 Reinvestment Fund developed a Market Value Analysis of Newark for the New Jersey Department of Community Affairs. Reinvestment Fund cluster analysis revealed eight market types, as follows:
  • Dark Purple: no subsidized rental units and highest mean sales price.
  • Medium Purple: lowest percent owner occupied at 16%, highest percent commercial land, and lowest percent sheriff sales.
  • Light Purple: very low percent subsidized rental and relatively high mean residential sales price.
  • Light Yellow: highest percent subsidized rental at 68%, highest percent of vacant parcels, and highest rate of new residential construction.
  • Dark Yellow: low percent subsidized rental and high percent sheriff sales.
  • Light Orange: very high percent subsidized rental, low mean residential sales price and very high percent vacant.
  • Medium Orange: high percent owner occupied, lowest percent commercial land at 2%, no subsidized rental units, and high rate of sales price variation.
  • Dark Orange: highest percent owner occupied, lowest mean residential sales price, and highest percent sheriff sales at 18%.
The Oranges, NJ
In 2007 Reinvestment Fund developed a Market Value Analysis of the Oranges for the New Jersey Department of Community Affairs.

Reinvestment Fund cluster analysis revealed eight market types, characterized as follows:

  • Dark Purple: highest owner occupancy, no subsidized rental housing, highest average sales price, lowest foreclosure rate, lowest percent commercial, highest rate of new residential permits.
  • Light Purple: high owner occupancy, low percent commercial, no subsidized rental housing, low foreclosures.
  • Dark Blue: high owner occupancy, relatively high home prices, relatively low foreclosure rate.
  • Light Blue: average owner occupancy, low subsidized housing, average residential prices, relatively low foreclosure rate.
  • Dark Yellow: Low owner occupancy, low average sales price, high foreclosure rate.
  • Light Yellow: average owner occupancy, very high percent Section 8.
  • Dark Orange: lowest owner occupancy, lowest average sales price, high foreclosure rate, high rate of subsidized housing high rate of vacancy.
  • Light Orange: Highest rate of subsidized housing, highest rate of foreclosure, highest rate of vacancy.
Riverline, NJ
In 2007 Reinvestment Fund developed a Market Value Analysis of the Riverline (along the light rail line extending from Trenton to Camden) for the New Jersey Department of Community Affairs.

Reinvestment Fund cluster analysis revealed five market types, characterized as follows:

  • Purple: highest owner occupancy, lowest percent commercial, no Section 8 housing, highest average sales price, lowest foreclosure rate, greatest residential change.
  • Blue: relatively low percent commercial mix, very low Section 8 rental housing, relatively strong average residential sales price, very low foreclosure rate and very low residential change.
  • Dark Yellow: low average sales price, relatively high foreclosure rate, some commercial.
  • Light Yellow: average percent commercial, average foreclosure rate, average sale prices.
  • Orange: very low percent owner occupied, comparatively high percent commercial, very low average sales price, and very high foreclosure rate.
Vineland, NJ
In 2007 Reinvestment Fund developed a Market Value Analysis of the Vineland area (including Millville and Bridgeton) for the New Jersey Department of Community Affairs.

Reinvestment Fund cluster analysis revealed six market types, characterized as follows:

  • Purple: highest average sales price, high owner occupancy, and low presence of subsidized housing, lower vacancy.
  • Blue: highest owner occupancy, slightly higher than average sale prices, lowest percent subsidized housing, lowest percent of foreclosures.
  • Light Blue: below average sale prices, very high percentage of subsidized rental units, low rate of new residential construction.
  • Yellow: below average sale prices, low owner occupancy, high level of commercial, high percent Section 8 rentals.
  • Light Orange: lowest owner occupancy, highest percent commercial, highest percent of Section 8 rentals, and very high rate of new residential construction.
  • Dark Orange: lowest average sales price at $33,930, lowest percent commercial, highest percent of foreclosures, highest percent of subsidized rental units.
Washington Township, NJ
In 2007 Reinvestment Fund developed a Market Value Analysis of the Washington Township area for the New Jersey Department of Community Affairs.

Reinvestment Fund cluster analysis revealed seven market types, characterized as follows:

  • Purple: highest owner occupancy, very low percent Section 8 rental, highest residential sales price, lowest foreclosure rate, highest rate of new residential construction.
  • Dark Blue: very low owner occupancy, highest percent commercial, lowest percent Section 8 rental, and relatively high sale prices.
  • Medium Blue: very high owner occupancy, lowest percent of subsidized rental, low foreclosure rate.
  • Light Blue: average percent commercial, average foreclosure rate, lower than average sale prices.
  • Light Yellow: low owner occupancy, high percent commercial, lower than average sale prices, very low rate of new residential construction.
  • Yellow: highest percent of Section 8 rentals, very low mean residential sales price, and relatively high percent commercial.
  • Orange: lowest owner occupancy, lowest average residential sales price, highest percent of foreclosures, lowest rate of new residential construction.

PENNSYLVANIA

Allegheny County, PA (2016)

Reinvestment Fund’s Market Value Analysis (MVA) describes the characteristics of the block groups within a study area. The MVA indicators in the 2016 MVA for Allegheny County are noted below and represent the dimensions upon which block groups are analyzed:

  • Median Sale Price: Median residential real estate sale price for sales of $1,000 or more from 2013 through 2015. Median sales price has been calculated both with and without condominiums; the model uses the higher of the two for all block groups. The data source is the Allegheny County Office of Property Assessments (courtesy of the Western Pennsylvania Regional Data Center).
  • Coefficient of Variation of Sales Price: The coefficient of variation describes the variability of sale prices within the block group. The coefficient of variation is calculated by dividing the standard deviation of sale prices by the mean. The data source is the Allegheny County Office of Property Assessments (courtesy of the Western Pennsylvania Regional Data Center).
  • Percent Mortgage Foreclosure: Foreclosure filings from 2013 through 2015 as a percentage of owner occupied households. The data source is the Allegheny County Department of Court Records.
  • Residential Vacancy: Residential vacancy is the percentage of residential addresses where mail has not been collected for at least 90 days. The residential vacancy indicator was calculated as a four quarter average from the second quarter of 2015 through the first quarter of 2016. The data source is Valassis Lists.
  • Percent of Parcels Built 2008 or Later: New construction activity was measured by calculating the percent of residential parcels with a building constructed in 2008 or later as a percentage of all residential parcels. The data source is the Allegheny County Office of Property Assessments’ assessment file (downloaded from the Western Pennsylvania Regional Data Center in January, 2016).
  • Percent of Parcels in “Poor” or Worse Condition: The Allegheny County Office of Property Assessments rates the condition of buildings on each parcel in Allegheny County from “Excellent” to “Unsound”. Concentrations of blight were measured by calculating the number of residential parcels in “Poor”, “Very Poor”, or “Unsound” condition divided by all residential parcels. The data source is the Allegheny County Office of Property Assessments (downloaded from the Western Pennsylvania Regional Data Center in January, 2016).
  • Percent Owner Occupied: The percent of owner occupied households. The data source is the 2010 – 2014 American Community Survey.
  • Percent Subsidized Rental Units: Subsidized rental units are measured as the sum of units in public housing developments and multi-family assistance properties and the sum of housing choice vouchers divided by the number of rental units. The data source is the Allegheny County Housing Authority.

The table below shows each component’s average for each MVA category.

Reinvestment Fund’s cluster analysis revealed nine market types, characterized as follows:

  • Robust “A”: Highest home values, largest level of new construction, highest owner occupancy levels, and little housing distress (such as residential vacancy and foreclosure).
  • Robust “B”: Elevated home values, substantial amounts of new construction, high levels of owner occupancy, and little housing distress.
  • Steady “C”: Above average home values, about average levels of new construction, high levels of owner occupancy, and little housing distress.
  • Steady “D”: Slightly below average home values, half the countywide average amount of new construction, more renters than owners, and about average levels of foreclosure and residential vacancy.
  • Steady “E”: Slightly lower than average home values, half the countywide average amount of new construction, high levels of owner occupancy, low levels of residential vacancy, about average levels of foreclosure.
  • Transitional “F”: Home values about half the countywide average, little new construction, more owners than renters, and about average levels of foreclosure and residential vacancy.
  • Transitional “G”: Below average home values, little new construction, slightly more owners than renters, and about twice the countywide average levels of foreclosure and residential vacancy.
  • Distressed “H”: Home values well below the countywide average, little new construction, more renters than owners, elevated levels of residential vacancy, and the highest levels of foreclosure in the County.
  • Distressed “I”: Lowest home values in Allegheny County, little new construction, about an even share of owners and renters, the highest levels of residential vacancy, and elevated levels of foreclosure.
Bethlehem, PA (2017)

Reinvestment Fund’s Market Value Analysis is a unique tool for characterizing markets as it creates an internally referenced index of a municipality’s residential real estate market. In 2017, the first Market Value Analysis was developed for Bethlehem, PA. Below are the indicators used in the 2017 Bethlehem MVA:

  • Median Sales Price: Median sales price of sales transactions that occurred between 2015 and 2017Q2. Data sources for this indicator were Lehigh and Northampton Counties.
  • Variance of Sales Price: The coefficient of variance of sales price for sales transactions that occurred between 2015 and 2017Q2. Data sources for this indicator were Lehigh and Northampton Counties.
  • Two-to-Four Family Sales: The share of sales transactions that were for two-to-four family properties, sold between 2015 and 2017Q2. Data sources for this indicator were Lehigh and Northampton Counties.
  • Condo Sales: The share of sales transactions that were condos, sold between 2015 and 2017Q2. Data sources for this indicator were Lehigh and Northampton Counties.
  • Owner Occupancy: The share of households that reported owning their home. Data source for this indicator was American Community Survey, 2011-2015.
  • Subsidized Housing: The share of rental units with subsidies. Data sources for this indicator were City of Bethlehem, US Department of Housing and Urban Development, and American Community Survey, 2011-2015.
  • Housing Density: Residential housing units per residential land area. Data sources for this indicator were Lehigh and Northampton Counties and American Community Survey, 2011-2015.
  • Investor Purchases: The share of sales transactions that were sold to investors, sold between 2015 and 2017Q2. Data source for this indicator was Lehigh and Northampton Counties.
  • Multiple Permits: The share of residential parcels with at least two permits between 2015 and 2017 (July). Data source for this indicator was the City of Bethlehem.
  • New Construction Permits: The share of residential parcels with new construction building permits between 2011 and 2017 (July). Data source for this indicator was City of Bethlehem.
  • Distressed Properties: The share of residential parcels that were registered in Pro Champs between 2015 and 2017 (Oct.), registered in Pro Champs prior to 2015 but the status remains open, or received an Act 91 Notice between 2015 and 2017Q2. Data source for this indicator was the City of Bethlehem, Pennsylvania Housing Finance Agency, and Lehigh and Northampton Counties.
  • Multiple Violations: The share of residential parcels with a violation that had at least five violation citations between 2015 and 2017 (July). Data source for this indicator was the City of Bethlehem.
  • Blight: The share of residential parcels that experienced a water shutoff and/or were identified in the Blight Survey. Data source for this indicator was the City of Bethlehem.
The table below shows each component’s average for each MVA category. The 2017 Bethlehem Market Value Analysis identified seven distinct market types, from A to G. Below are brief descriptions of each market type: Strong Markets
  • A Markets are the strongest markets in Bethlehem and are largely characterized by high sales prices, low levels of distress, and low owner occupancy rates.
  • B Markets are also strong markets with high sales prices, low levels of distress, and highest home ownership rates in Bethlehem.
Middle Markets
  • C Markets were the only block groups with substantial new construction activity and had the highest levels of condominium sales.
  • D Markets generally represent “middle” markets with a median sale price ($143,933), slightly below the citywide average. “D” markets also have a roughly even split between owner and renter households, average levels of distress, and average numbers of properties with multiple violations.
  • E Markets also represent a portion of the “middle” market, although these block groups have a slightly lower median sales price and greater signs of both distress and investor purchases than “D” markets.
Stressed Markets
  • F Markets have the lowest homeownership rates in the city and are also home to the greatest concentrations of subsidized rental housing. They also had the highest share of investor purchases and mixed signs of stress.
  • G Markets are the most stressed in Bethlehem. The median home sales price ($69,047) is the lowest in the city and investors make up nearly half of all residential sales. Over a quarter of properties have multiple violations and elevated levels of blight and distressed.
Philadelphia, PA (2018)

Reinvestment Fund’s Market Value Analysis (MVA) describes the characteristics of the block groups within a study area. The MVA indicators in Philadelphia are noted below and represent the dimensions upon which block groups are analyzed:

  • Median Sales Prices, Condo-Adjusted, 2016 – 2018Q2: Median residential sales prices, excluding non-arms-length transactions (sales prices under $1,000), adjusted for sales of condos*, 2016 to 2018Q2 (Philadelphia Office of Property Assessment)
  • Variance for Sales, 2016 – 2018Q2: Coefficient of variance for residential sales prices, 2016 to 2018Q2 (Philadelphia Office of Property Assessment)
  • Permits, 2016 – 2018Q2: Count of properties with permits for major renovations 2016 to 2018Q2 as share of all residential parcels (Philadelphia Department of License and Inspections)
  • New Construction, 2013– 2018Q2: Count of residential properties with permits for new construction 2013 to 2018Q2 as a share of all residential parcels (Philadelphia Department of License and Inspections)
  • Vacant Homes and Residential Land, 2018: Count of residential parcels with vacant homes or vacant land as a share of all residential parcels (Philadelphia Office of License and Inspections)
  • Foreclosure Filings, 2016 – 2018Q2: Count of residential parcels with foreclosure filing issued between 2016 and 2018Q2 as a share of residential sales (Philadelphia Prothonotary’s Office)
  • Housing Density, 2018: Count of housing units per acre (Philadelphia Office of Property Assessment)
  • Owner-Occupied Households, 2016: Share of households that owned their home (American Community Survey, 5-year Estimates, 2011-2016)
  • Subsidized Rental Housing Units, 2018: Count of subsidized rental housing (including Project Based Section 8, LIHTC, and Vouchers) as a share of all renter-occupied households (Philadelphia Housing Authority, HUD)
  • Condominium Presence, 2018: Share of housing units that are located in condominiums (Philadelphia Office of Property Assessment)
Table showing cutoff values for market types. *Median sales prices were adjusted to account for the sale of condos within each block group. Generally, condo sales reduce median home value estimates. For each block group, the median sales price used in the model is the higher of the median price of all arms-length transactions or the median price of all arms-length transactions excluding condos. **Variable used in final model.

The table below shows each component’s average for each MVA category.

Table showing demographics for market types.
  • Market Type A: Highest home sales prices and share of units that are condos. Lowest foreclosure, vacancy and subsidized rental housing rates. High levels of renovation and new construction. Predominantly renters.
  • Market Type B: High sale prices and condo presence, largest incidence of private investor activity and new construction. Notable presence of subsidized units compared to other areas with high home prices.
  • Market Type C: Highest homeownership rate, moderate foreclosure activity, low vacancy and subsidy rates.
  • Market Type D: Sales price close to city median, highest share of renters, high permitting activity but low rates of new construction. Higher than average rates of vacancy and subsidy.
  • Market Type E: Median sales prices below city average. Lowest share of permits and little new construction. High homeownership, high foreclosure.
  • Market Type F: Low permitting and construction. Highest foreclosure rate. Elevated subsidy and vacancy.
  • Market Type G: Highest subsidy usage for renters. Evenly split renters and owners. High foreclosure and vacancy rates.
  • Market Type H: Low sale prices, highest rates of vacancy and foreclosure in the city. Roughly one in five renters rely on subsidy.
  • Market Type I: Lowest sale prices and highest sale variance, highest vacancy rate. Moderate homeownership. New construction essentially nonexistent.
  • Nonresidential Mask: All nonresidential parcels>50,000sf in the Philadelphia Property Appraiser’s file were merged to create a non-residential mask for the county.
  • Not Rated: All block groups that had fewer than 5 sales in 2016-2018; these block groups tend to be either entirely rental housing or non-residential uses.
Philadelphia, PA (2015)

Reinvestment Fund’s Market Value Analysis (MVA) describes the characteristics of the block groups within a study area. The MVA indicators in the 2015 MVA for Philadelphia are noted below and represent the dimensions upon which block groups are analyzed:

  • Median Sale Price: Philadelphia Office of Property Assessment’s (OPA) file of all recorded deeds between 1/1/2013 through 6/30/2015 for residential sales of $1,000 or more. Median sales price has been calculated both with and without condominiums; the model uses the higher of the two for all blockgroups.
  • Coefficient of Variation: The coefficient of variation, derived from the OPA file, represents the variability of sale prices within the block group. (High numbers represent places with wide variations in sale prices.) The coefficient of variation (CV) is defined as the ratio of the standard deviation to the mean. CV = std dev / mean
  • Percent Owner-Occupied: U.S. Census, American Community Survey data (2009-2013) representing the percent of all occupied housing units that are occupied by owners.
  • Percent Vacant: Philadelphia Department of License and Inspections’ inventory of vacant housing in Philadelphia, 2010-2014, divided by the total number of housing units.
  • Percent New Construction: Philadelphia Office of Property Assessment records of all new residential properties built 2008-2015 as a percent of all residential parcels.
  • Foreclosure as a Percent of Sales: This figure represents all foreclosure filings 2013-2015Q2 (Philadelphia Prothonotary’s Office) divided by the number of sales in 2013-2015Q2 (OPA).
  • Percent Public/Assisted Housing: Represents Philadelphia Public Housing Authority owned developments and HUD-assisted rental housing developments (permanent housing units, not housing choice vouchers) divided by the number of renter-occupied housing units (ACS 2009-2013).
  • Housing Density: Housing units (ACS 2009-2013) per acre of residential land area (OPA)
  • Percent Permits: Philadelphia Department of License and Inspections’ inventory of residential properties with building permits 2013-2015 as a percent of residential properties (OPA)
  • Percent Condominium: Percent of all single family residential parcels that are classified as condominiums (OPA)

The table below shows each component’s average for each MVA category.

  • Regional Choice (A1): Highest home prices, highest percent of condominiums, highest density, low number of foreclosure filings relative to sales volume, second lowest owner occupancy rate, highest level of permit activity.
  • Regional Choice (A2): High home prices, second lowest foreclosure rate relative to sales volume, relatively low percent owner occupied compared to the citywide average.
  • Steady (B): Relatively high home prices, lowest sales price variance, low level of new construction, highest home ownership rate, lowest density, lowest level of subsidized housing.
  • Steady (C): Home prices above citywide median, third highest level of condominiums, substantially higher foreclosures as a percent of sales than A-C categories, lowest level of homeownership.
  • Transitional (D): Home prices equal to or slightly above citywide median, second lowest level of vacancies, second highest homeownership rate, second lowest density, lowest amount of condominiums, negligible amount of new construction, second lowest variance in sale prices.
  • Transitional (E): Sales prices below city median, second highest rate of foreclosures as a percent of sales, negligible amount of new construction, homeownership rate above the city average (53% per the ACS 2009-2013).
  • Stressed (F): Third highest homeownership rate compared to other categories, home prices below the citywide average, highest number of foreclosures as a percent of sales.
  • Stressed (G): Homeownership at or slightly below citywide average, home prices well below the citywide median, third highest number of foreclosures as a percent of sales, third highest percent of public/assisted housing.
  • Distressed (H): Very low home values, second highest sales price variance, equal to or slightly below the citywide average homeownership rate, second highest level of vacancies, second highest level of subsidized housing.
  • Distressed (I): Lowest home sale prices, highest vacancy rate, third lowest level of homeownership, lowest level of permit activity, highest level of publicly assisted rental housing.
Philadelphia, PA (2011)

In 2011, Reinvestment Fund developed a Market Value Analysis for the City of Philadelphia.

  • Median and Mean Sale Price: Philadelphia Board of Revision of Taxes’ (BRT) file of all recorded deeds between 1/1/2010 through 12/31/2011 for residential sales of $1,000 or more. Only the Median Sale Price was used in the MVA model.
  • Coefficient of Variation: The coefficient of variation, derived from the BRT file, represents the variability of sale prices within the block group. (High numbers represent places with wide variations in sale prices.)
  • Percent Owner-Occupied: U.S. Census data (2010) representing the percent of all occupied housing units that are occupied by owners.
  • Percent Vacant: Philadelphia Department of License and Inspections’ inventory of vacant land and housing in Philadelphia, 2009-2012, divided by the total number of housing units.
  • Percent New Construction: Philadelphia Department of Licenses and Inspections’ records of all permits issued between 1/1/2010 through 12/31/2011 for new construction and substantial rehabilitation of properties.
  • Percent Commercial: Philadelphia Department of City Planning’s Land Use File. This figure represents commercial land – not including parking lots – divided by all developed land.
  • Foreclosure as a Percent of Sales: Philadelphia Prothonotary’s Office’s file of foreclosure filings 2010-Q1 2011. This figure represents all foreclosure filings 2010-Q1 2011 divided by the number of sales in 2010-2011 (from BRT).
  • Percent Public/Assisted Housing: Represents Philadelphia Public Housing Authority owned developments and HUD-assisted rental housing developments (permanent housing units, not housing choice vouchers) divided by the 2010 Census number of renter-occupied housing units.

The table below shows each component’s average for each MVA category.

  • Regional Choice (A): Highest home prices, low number of foreclosure filings relative to sales volume, lowest owner occupancy rate, highest level of new construction activity.
  • Regional Choice (B): High home prices, lowest foreclosure rate relative to sales volume, relatively low percent owner occupied compared to the citywide average, highest percent commercial mix.
  • High Value (C): Relatively high home prices, high level of new construction, relatively low ownership rate compared to the citywide average.
  • Steady 1 (D): Relatively high home prices compared to the citywide average, , fairly active level of new construction, substantially higher foreclosures as a percent of sales than Regional Choice and High Value categories.
  • Steady 2 (E): Second lowest level of vacancies, second highest homeownership rate, lower level of new construction compared to previous categories, lowest coefficient of variance in sale prices.
  • Transitional (F): Highest homeownership rate, higher foreclosures as a percent of sales than previous categories, second lowest coefficient of variance in sales prices.
  • Transitional (G): High homeownership rate compared to other categories, home prices below the citywide average, high number of foreclosures as a percent of sales, second highest percent of public/assisted housing.
  • Distressed (H): Highest percent of foreclosures as a percent of sales, relatively low home prices, high homeownership rate, elevated vacancies.
  • Distressed (I): Lowest home sale prices, highest vacancy rate, below average owner occupancy rate, highest level of publicly assisted rental housing.
Philadelphia, PA (2008)
In 2008, Reinvestment Fund developed a Market Value Analysis for the City of Philadelphia.

Reinvestment Fund cluster analysis revealed eight market types, characterized as follows:

  • Regional Choice A: highest home prices, lowest number of foreclosure filings, high percent owner occupied.
  • Regional Choice B: low foreclosure, low percent owner occupied, relatively high percent commercial mix.
  • High Value C: high number of residential properties with tax abatements, relatively high home prices, high residential density.
  • Steady 1D: relatively high homeownership, home prices relatively high and stable, few vacancies.
  • Steady 2D: few vacancies, relatively high homeownership, high number of residential properties with tax abatements.
  • Transitional E: relatively high and steady home prices and population shifts.
  • Transitional F: high number of foreclosures, population shifts, relatively high density.
  • Distressed G: high number of foreclosures, relatively low home prices, population shifts, elevated vacancies.
  • Distressed: lower home prices, high vacancy rate, predominantly homeowners, much publicly assisted housing.
Philadelphia, PA (2001)
In 2001, Reinvestment Fund developed a Market Value Analysis for the City of Philadelphia.

Reinvestment Fund cluster analysis revealed eight market types, characterized as follows:

  • Regional Choice: highest home prices, mix of uses, older homes in excellent condition.
  • High Value: high home prices, price appreciation, population stability and some growth, less commercial activity, high rate of homeownership.
  • Steady: predominantly homeowners, home prices relatively high and stable, homes in good condition, few vacancies.
  • Transitional (Up): relatively high and steady home prices and population shifts.
  • Transitional (Steady): steady home prices, no robust appreciation, population shifts.
  • Transitional (Down): population shifts, worn housing, dangerous properties, elevated vacancies.
  • Distressed: lower home prices, physical decay, older homes, elevated vacancies, predominantly homeowners, much publicly assisted housing, substantial population loss.
  • Reclamation: population loss, low property values, physical deterioration, hyper-abandonment, dangerous buildings.
Reading, PA
In 2011 Reinvestment Fund developed a Market Value Analysis of Reading, Pennsylvania for Reading, Berks County, Pennsylvania.

Reinvestment Fund cluster analysis revealed eight market types, characterized as follows:

  • Dark Purple: highest median sales price, highest sales price variation, some commercial presence, moderate foreclosure rate, highest rate of new construction.
  • Light Purple: high owner occupancy, low amount of commercial, high residential sales price, lowest foreclosure rate, lower rate of new residential construction.
  • Dark Blue: moderate owner occupancy, high percent commercial, moderate foreclosure rate, and relatively high sale prices.
  • Light Blue: low percent commercial, average foreclosure rate, relatively high sale prices.
  • Light Yellow: moderate owner occupancy, high percent commercial, moderate sale prices, very low rate of new residential construction.
  • Yellow: relatively high percent vacancy, fairly low median sales price, and relatively low percent commercial.
  • Orange: no new construction activity, lowest owner occupancy, low residential sales price, high percent of foreclosures, highest home sale price variation.
  • Red: lowest median sales price, highest vacancy rate, moderate foreclosure rate, lower owner occupancy.

TEXAS

Dallas, TX (2017)
Reinvestment Fund’s Market Value Analysis (MVA) describes the characteristics of the block groups within a study area. The MVA indicators in Dallas are noted below and represent the dimensions upon which block groups are analyzed:
  • Median Sales Price: Median residential real estate sales price for sales of $1,000 or more from 2015 (Q3&4) through 2017 (Q1&2). Reinvestment Fund imputed a price to rent equivalence for block groups with less than 5 sales and high levels of rental occupancy based on ACS median gross rent and Zillow price to rent index. The data source for sales price is InfoUSA.
  • Coefficient of Variation of Sales Price: The coefficient of variation describes the variability of sale prices within the block group. The coefficient of variation is calculated by dividing the standard deviation of sale prices by the mean. The data source is InfoUSA.
  • Percent Mortgage Foreclosure: Foreclosure listings from 2015 (Q3&4) through 2017 (Q1&2) as a percentage of owner occupied households. The data source is the Foreclosure Listings Service.
  • Residential Vacancy: Residential vacancy is the percentage of residential addresses where mail has not been collected for at least 90 days. The residential vacancy indicator was calculated as an average of the first through fourth quarters of 2016. The data source is Valassis Lists.
  • Percent of New Construction Units: New construction activity was measured by calculating the percent of permitted new consturction units from 2015 (Q3&4) through 2017 (Q1&2) as a percentage of all housing units. The data source is the City of Dallas Department of Planning & Urban Design.
  • Percent of Rehab Permits: Parcels with a total permit value of $1,000 or more from 2015 (Q3&4) through 2017 (Q1&2) as a percentage of all housing units. The data source is the City of Dallas Department of Planning & Urban Design.
  • Percent of Parcels with a Code Violation Lien: Percent of residential parcels with a code violation lien from 2015 (Q3&4) through 2017 (Q1&2). The data source is the City of Dallas Department of Code Compliance.
  • Owner occupied units as a % of occupied units: 2015 American Community Survey data (from the Census Bureau) representing the percent of all occupied housing units that are occupied by owners.
  • Percent Subsidized Rental Units: Subsidized rental units are measured as the sum of units in public housing developments, multi-family assistance properties, LIHTC developments, housing choice vouchers, and locally subsidized units divided by the number of rental units. The data source is the City of Dallas Department of Planning & Urban Design.
The table below shows each component’s average for each MVA category. Population and Housing Distribution by Market Type Reinvestment Fund’s cluster analysis revealed nine market types, characterized as follows:
  • Regional Choice “A”: Highest home values, largest level of new construction, high owner occupancy levels, and little housing distress (such as residential vacancy and foreclosure).
  • Regional Choice “B”: Elevated home values, highest amounts of rehab. permits, highest levels of owner occupancy, and little housing distress.
  • Regional Choice “C”: Elevated home values, above average levels of new construction, high levels of renter occupancy, and little housing distress.
  • Steady “D”: Double average home values, high levels of rehab. permits, more owners than renters, and low levels of foreclosure and residential vacancy.
  • Steady “E”: About average home values, highest household density, highest levels of renter occupancy, some residential vacancy and foreclosure.
  • Steady “F”: Home values slightly below the citywide average, little new construction, more owners than renters, and about average levels of foreclosure and residential vacancy.
  • Transitional “G”: Below average home values, little new construction, more renters than owners, and highest levels of subsidized rentals.
  • Transitional “H”: Home values well below the citywide average, little new construction, more owners than renters, elevated levels of residential vacancy and foreclosure.
  • Distressed “I”: Lowest home values in Dallas, slightly below average levels of new construction, about an even share of owners and renters, the highest levels of residential code violation liens, vacancy, and foreclosure.
Houston, TX (2016)
Reinvestment Fund’s Market Value Analysis (MVA) describes the characteristics of the block groups within a study area. The MVA indicators in Houston are noted below and represent the dimensions upon which block groups are analyzed:
  • Median sales price: The middle value of residential sales, where half the sales are above the calculated median value and half are below. Median sales price is calculated from InfoUSA’s nationwide database file of all recorded sales between 1/1/2014 through 12/31/2015. Sales were filtered for residential sales greater than $1,000 and less than $4 million.
  • Variability of sales prices: The coefficient of variation, derived from the InfoUSA database of sales, represents the variability of sale prices within the block group. (High numbers represent places with wide variations in sale prices.)
  • Foreclosure filings as a percent of sales: Harris County’s foreclosure filings starting in April 2014 through August 2016, derived from Constables’ Foreclosure Auction Daily Court Review publication. This figure represents all foreclosure filings from April 2014 through August 2016 divided by the number of sales in 2014-2015 (from InfoUSA database).
  • Vacant properties as a % of housing units: Harris County properties with water service shut off (as of August 2016) + City of Houston Dept. of Neighborhoods dangerous buildings (as of Oct 2016) + Harris County demolished residences (2014-2016, courtesy of the Kinder Institute), divided by the total number of residential housing units (ACS 2015). Parcels which had both a demolished residence and a new construction permit were removed.
  • Building permits as a % of housing units: City of Houston records of all building permits (single family and multifamily construction) issued between 1/1/2014 through 12/31/2015, divided by the total number of residential housing units (ACS 2015).
  • Owner occupied units as a % of occupied units: 2015 American Community Survey data (from the Census Bureau) representing the percent of all occupied housing units that are occupied by owners.
  • Subsidized rental stock as a % of rental units: The sum of units owned and/or managed by the Houston Housing Authority, rental assisted housing units from the City of Houston and Houston Housing Authority, and Low-Income Housing Tax Credit (LIHTC) units, divided by the number of occupied rental units (ACS 2015).
  • Commercial or industrial area as % of all land area: From the Houston-Galveston Area Council land use parcel file, this figure represents the land area categorized as non-residential divided by the sum of all land use types, 2016.
  • Housing violations as a % of housing units: From the City of Houston building citations and violations file, this figure represents citations from 1/1/2014 through 6/31/2016 divided by the number of housing units (ACS 2015).
The tables below show each component’s average for each MVA category. Population and Housing Distribution by Market Type Reinvestment Fund’s cluster analysis revealed nine market types, characterized as follows:
  • Market Type A: Highest home prices, lowest number of foreclosure filings relative to sales volume (foreclosure rate), high number of permits relative to housing units, percent owner occupied well above the citywide average.
  • Market Type B: High home prices, second lowest foreclosure rate relative to sales volume, percent owner occupied below the citywide average.
  • Market Type C: Relatively high home prices, highest percent owner occupied, foreclosure rate as a percentage of sales substantially below the citywide average.
  • Market Type D: Home prices are comparable to the citywide average, foreclosures as a percentage of sales are comparable to other middle markets, percent owner occupied below the citywide average.
  • Market Type E: Home prices are below the citywide average, third highest homeownership rate, foreclosures as a percent of sales below the citywide average.
  • Market Type F: Homeownership rate well below the citywide average, elevated foreclosures as a percent of sales, home prices significantly below the citywide average.
  • Market Type G: Fourth highest homeownership rate, home prices substantially below the citywide average, third highest average number of foreclosures as a percent of sales, comparable vacancy rate relative to citywide average.
  • Market Type H: Second lowest home sale prices, highest coefficient of variance of sales, highest percent of publicly subsidized rental, highest percent for vacancy as share of housing units, and highest foreclosures as a percent of sales.
  • Market Type I: Lowest home sale prices, lowest owner occupancy rate, second highest coefficient of variance of sales, second highest foreclosures as a percent of sales.
Houston, TX (2013)
Reinvestment Fund’s Market Value Analysis (MVA) describes the characteristics of the block groups within a study area. The MVA indicators in Houston are noted below and represent the dimensions upon which block groups are analyzed:
  • Median sales price: The middle value of residential sales, where half the sales are above the calculated median value and half are below. Median sales price is calculated from InfoUSA’s nationwide database file of all recorded sales between 1/1/2010 through 12/31/2011. Sales were filtered for residential sales greater than $1,000 and less than $2 million.
  • Variability of sales prices: The coefficient of variation, derived from the InfoUSA database of sales, represents the variability of sale prices within the block group. (High numbers represent places with wide variations in sale prices.)
  • Foreclosure filings as a percent of sales: Harris County’s foreclosure filings 2010 through 2011, derived from Constables’ Foreclosure Auction Daily Court Review publication. This figure represents all foreclosure filings in 2010 and 2011 divided by the number of sales in 2010-2011 (from InfoUSA database).
  • Residential water shutoffs as a % of housing units: Harris County’s report of properties where water service has been shut off as of August 2012 – divided by the total number of residential housing units. This is an indicator of vacancy.
  • Building permits as a % of housing units: City of Houston records of all building permits (demolition, single family construction, and multifamily construction) issued between 1/1/2010 through 12/31/2012, divided by the total number of residential housing units.
  • Owner occupied units as a % of occupied units: 2010 US Census data representing the percent of all occupied housing units that are occupied by owners.
  • Public housing subsidies of rental stock as a % of rental units: The sum of units owned and/or managed by the Houston Housing Authority and rental assisted housing units from the City of Houston and Harris County in 2012, divided by the number of rental units.
  • Commercial or industrial area as % of all land area: From the Harris County Appraisal District File, this figure represents the land area categorized as non-residential divided by the sum of all land use types.
  • Housing violations as a % of housing units: From the Harris County building violations file, this figure represents all violations from 1/1/2010 through 12/31/2012 divided by the number of housing units.
The tables below show each component’s average for each MVA category. Reinvestment Fund’s cluster analysis revealed nine market types, characterized as follows:
  • Market Type A: Highest home prices, lowest number of foreclosure filings relative to sales volume (foreclosure rate), highest number of permits relative to housing units, percent owner occupied well above the citywide average.
  • Market Type B: High home prices, second lowest foreclosure rate relative to sales volume, percent owner occupied substantially below the citywide average.
  • Market Type C: Relatively high home prices, highest percent owner occupied, foreclosure rate as a percentage of sales substantially below the citywide average.
  • Market Type D: Home prices are comparable to the citywide average, foreclosures as a percentage of sales are elevated relative to other middle markets, percent owner occupied substantially below the citywide average.
  • Market Type E: Home prices that are comparable to the citywide average, second highest homeownership rate, foreclosures as a percent of sales below than the citywide average.
  • Market Type F: Homeownership rate above the citywide average, elevated foreclosures as a percent of sales, highest number of violations as a percent of housing units, home prices below the citywide average.
  • Market Type G: third lowest homeownership rate, home prices below the citywide average, above average number of foreclosures as a percent of sales, percent water shut-offs that are similar to the citywide average.
  • Market Type H: Second lowest home sale prices, highest coefficient of variance of sales, second highest percent of publicly subsidized rental, highest percent water shut-offs.
  • Market Type I: Lowest home sale prices, lowest owner occupancy rate, second highest coefficient of variance of sales, second highest percent water shut-offs, highest percent of publicly subsidized rental.

VIRGINIA

Richmond, VA (2017)
Reinvestment Fund’s Market Value Analysis (MVA) describes the characteristics of the block groups within a study area. In 2017, Reinvestment Fund developed a Market Value Analysis for the City of Richmond, VA. Reinvestment Fund’s cluster analysis revealed nine market types, characterized as follows: Market Type A
  • 32 of the region’s 461 block groups have been characterized as “A” markets.
  • 8.8% (68,848) of the region’s 2011-2015 population and 7.9% (23,926) of its households.
  • Typical home sales price in “A” markets is approximately $501,292, nearly 2.5 times the regional median.
  • Highest average percentage of properties (5.9%) that have been built in the last 10 years
  • Elevated rates of permitting activity (11.6%) relative to the regional average.
  • Owner occupancy rates (90.1%) well above the regional average.
  • “A” markets have the lowest level of bank sales (2.6% of sales) in the region.
  • There are few publicly subsidized rental housing options in these markets (0.4% of all rental units).
  • “A” markets are the least dense housing market with an average only 1.9 housing units per residential acre.
Market Type B
  • High 23 of the region’s 461 block groups have been characterized as “B” markets.
  • Highest density of all of the markets at an average of 17.2 housing units per residential acre
  • 5.3% (41,700) of the region’s 2011-2015 population and 6.7% (20,252) of its households.
  • At nearly $426,000, the “B” markets’ typical home sales price is just over two times the regional median.
  • Permitting activity (5.0%) is slightly below the regional average.
  • “B” markets are predominantly renter occupied, with 33% of all households being owner occupied.
Market Type C
  • 82 of the region’s 461 block groups have been characterized as “C” markets.
  • More suburban in form, with an average of 3.2 housing units per acre
  • 19.9% (155,458) of the region’s 2011-2015 population and 19.5% (58,660) of its households.
  • Home sale prices ($274,479) above the regional average.
  • Permitting activity (7.2%) is second highest in the region.
  • Predominantly owner occupied (83%); of the limited number of rental properties, few (3%) are publicly subsidized.
  • The average rate of bank sales, 6%, is double that of “A” and “B” markets.
Market Type D
  • 53 of the region’s 461 block groups have been characterized as “D” markets.
  • 11.9% (92,974) of the region’s residents and 13.2% (39,877) of its households.
  • The typical home sale prices in “D” markets ($195,175) is just under the regional average.
  • Permitting activity in “D” markets (5.7%) is just under the regional average.
  • Lowest average homeownership rate (29%)
  • Second highest density (9.8 units per acre) of all of the markets.
  • Comprise over 28,700 rental households, with an average of 7% receiving some form of subsidy.
Market Type E
  • 103 of the region’s 461 block groups were characterized as “E” markets.
  • These block groups were home to 22.8% (178,048) of the region’s 2011-2015 population and 21.6% of its households.
  • The typical home sales price in “E” markets is approximately $182,686, roughly 10% below the regional average.
  • The market is largely (80%) owner occupied, third highest in the region.
  • Bank sales are roughly equal to the regional average.
  • Percent of presidential properties built since 2008 (2.6%) is slightly below the regional average.
  • Permitting activity in “E” markets (5.5%) is slightly below the regional average.
Market Type F
  • 30 of the region’s 461 block groups were characterized as “F” markets.
  • 6.8% (53,482) of the region’s 2011-2015 population and 7% (20,978) of its households.
  • The typical home sales price in “F” markets is approximately $140,358, just over two-thirds the regional average
  • On average, 21% of all sales are bank sales.
  • Permitting activity in “F” markets (10.6%) is the third highest rate in the city.
  • These markets are nearly evenly split between owners (48%) and renters.
  • Of the renter households, an average of 77% per block group are receiving public subsidy; the second highest level in the region.
Market Type G
  • 62 of the region’s 461 block groups were characterized as “G” markets.
  • 11.6% (90,655) of the region’s 2011-2015 population and 11.8% (35,626) of its households.
  • At $117,611, typical home sales prices in these “G” markets are just above half the regional average.
  • In a typical block group, nearly 30% of all sales are by banks.
  • An average of 59% of households own their home, the fourth highest average of all markets.
  • Of the renter occupied households, on average 6.5% of them are subsidized.
  • The third highest vacancy rate (3%) of all market types.
  • Permitting activity in “G” markets (4.9%) is below the regional average.
Market Type H
  • 31 of the region’s 461 block groups were characterized as “H” markets.
  • 4.1% (32,453) of the region’s 2011-2015 population and 3.9% (11,640) of its housing units.
  • The typical home sales price in “H” markets is $63,465, just below one-third the regional average.
  • On average, bank sales account for 32.8% of all sales in “H” block groups.
  • Permitting activity in “H” markets (3.7%) is the second lowest of all market types.
  • “H” markets typically have 41% homeowners and 59% renters.
  • On average 12% of renter households receive public rental subsidy, the third highest percentage among market types.
  • Average vacancy rates in “H” markets (8.5%) are the highest in the region and over 2.5 times as high as the next highest market.
Market Type I
  • 18 of the region’s 461 block groups were characterized as “I” markets.
  • These block groups are the third most densely built (7.2 units per residential acre)
  • 3.3% (26,112) of the region’s 2011-2015 population and 3.1% (9,401) of its households.
  • The typical home sales price in these “I” markets is $53,597, approximately 25% of the Richmond regional average.
  • Permitting activity in “I” markets is the lowest in the region at 2.0%.
  • “I” markets typically have 30% homeowners and of the 70% that are renters.
  • On average, 89% of the renter households are receiving some form of subsidy.
The tables below show each component’s average for each MVA category.

WASHINGTON, DC

In 2006 Reinvestment Fund developed a Market Value Analysis for Washington, DC.

Reinvestment Fund cluster analysis revealed eight market types, characterized as follows:

  • Dark purple: highest median sales price, lowest percent vacant and highest percent prime loans.
  • Light purple: high percentage owner occupied and relatively high median sales price.
  • Dark blue: highest percent owner occupied, lowest percent commercial, relatively low percent prime loans, highest percent of Section 8 housing at 19%.
  • Medium blue: higher than average sale prices, and average rate of vacancy.
  • Light blue: low percent owner occupied, highest percent commercial, average sale prices.
  • Dark orange: very low percent owner occupied, highest percent vacant, below average median sales price.
  • Light orange: lowest percent owner occupied, below average sale prices, high rate of vacancy
  • Yellow: above average owner occupancy, lowest median sales price, lowest percent prime loans, high rate of vacancy.

WISCONSIN

Milwaukee, WI

Reinvestment Fund’s Market Value Analysis (MVA) describes the characteristics of the block groups within a study area. The MVA indicators in Milwaukee are noted below and represent the dimensions upon which block groups are analyzed:

  • Median and Average Sales Price: Office of the City Assessor file of all recorded sales between 1/1/2011 through 12/31/2012 for residential sales of $1,000 or more. Only the Median Sale Price was used in the MVA model.
  • Coefficient of Variation: The coefficient of variation, derived from the City Assessor’s file of sales, represents the variability of sale prices within the block group. (High numbers represent places with wide variations in sale prices.)
  • Foreclosure as a Percent of Sales: Milwaukee Office of City Development’s file of foreclosure filings 2011 through 2012. This figure represents all foreclosure filings in 2011 and 2012 divided by the number of sales in 2011-2012 (from City Assessor’s file).
  • Percent Duplex/Multi-Fam Sales: Milwaukee City Master File representing all multi-unit properties sold divided by the total number of sales 2011-2012 (from City Assessor’s file).
  • Percent Water Shut-off: Milwaukee City Water Department file of properties where water service has been shut off divided by the total number of residential properties. This is an indicator of vacancy.
  • Percent New Construction/>$10K Rehab: Milwaukee Department of Neighborhood Services records of all building permits issued between 1/1/2010 through 12/31/2012 for new construction and substantial rehabilitation (estimated value greater than $10,000) of properties divided by the total number of residential properties.
  • Percent Owner-Occupied: Milwaukee City Master File representing the percent of all occupied housing units that are occupied by owners.
  • Percent Publicly Subsidized Rental: Represents Milwaukee Public Housing Authority owned developments, and HUD-assisted rental housing developments including Housing Choice Vouchers from both the City of Milwaukee and Milwaukee County, divided by the number of renter-occupied housing units from the City Master File.
  • Percent Non-Residential Area: Milwaukee City Master File. This figure represents non-residential land – not including parking lots – divided by all developed land.

The tables below show each component’s average for each MVA category.

Reinvestment Fund cluster analysis revealed nine market types, characterized as follows:

  • Market Type A: Highest home prices, lowest number of foreclosure filings relative to sales volume (foreclosure rate), second lowest owner occupancy rate, second highest percentage of sales that are duplex or multi-family.
  • Market Type B: High home prices, second lowest foreclosure rate relative to sales volume, highest percent owner occupied, lowest coefficient of variance of sales price.
  • Market Type C: Relatively high home prices, highest percentage of non-residential land, foreclosure rate as a percentage of sales substantially below the citywide average.
  • Market Type D: Relatively high home prices compared to the citywide average, foreclosures as a percentage of sales below the citywide average, percent of sales that are multi-unit are above the citywide average.
  • Market Type E: Home prices that are substantially below the citywide average, second highest homeownership rate, highest percentage of publicly subsidized rental, foreclosures as a percent of sales higher than the citywide average.
  • Market Type F: Second highest percentage of non-residential area, higher foreclosures as a percent of sales than the citywide average, higher percent of sales that are multi-unit than the citywide average.
  • Market Type G: Second lowest homeownership rate, home prices below the citywide average, high number of foreclosures as a percent of sales, highest percentage of sales that are multi-unit, percent water shut-offs that are substantially higher than the citywide average.
  • Market Type H: Second lowest home sale prices, percentage of sales that are multi-unit below the citywide average, second highest coefficient of variance of sales, second highest percent of publicly subsidized rental, percent water shut-offs that are substantially higher than the citywide average.
  • Market Type I: Lowest home sale prices, highest vacancy rate, lowest owner occupancy rate, highest coefficient of variance of sales, highest percent water shut-offs.

Also, it is worth noting that the Milwaukee MVA widget also includes additional data layers that are not on the PolicyMap maps page and were not included in the MVA analysis, and thus are not listed above. These include the Total Number of Establishments, Total Number of Employees, and Total Number of Sales, which are all from the National Establishment Time-Series (NETS). PolicyMap received these layers, as well as residential sales indicators, from Reinvestment Fund’s Policy Solutions department.

< Back to list of datasets

Small Business Administration: Paycheck Protection Program (PPP) Loans, 7(a) Loans, and 504 Loans

Details:

Borrower information, loan information, originating lender information

Topics:

small business loans, economic development, COVID-19

Source:

Small Business Administration (SBA)

Years Available:

2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021

Geographies:

point

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

https://data.sba.gov/dataset/ppp-foiahttps://data.sba.gov/dataset/7-a-504-foia

Last updated on PolicyMap:

August 2021

Description:

Paycheck Protection Program The Paycheck Protection Program (PPP), administered by the Small Business Administration (SBA), was a forgivable loan program for small businesses signed into law in response to the threat to small businesses and their employees posed by the COVID-19 economic downturn. The PPP Loan program enabled businesses meeting certain criteria to apply for loans to be used for payroll and other expenses. Provided businesses met all requirements, the loans would then be forgiven. Information on businesses that received PPP loans greater than $150,000 dollars are represented in the PPP Loan Recipient dataset on PolicyMap and are broken down by reported industry based on 2 digit NAICS codes. Information about the businesses, including business name, business owner details, and industry (both 2 and 6 digit NAICS codes) are published here “as is” from the original SBA data. PolicyMap supplemented the data from the SBA by providing business sector and business descriptions based on reported 2 and 6 digit NAICS codes pulled from the Census Bureau’s published list of NAICS codes. PolicyMap also flagged businesses as “minority-owned” if the reported owner race was a race other than white and if the reported owner ethnicity was an ethnicity other than non-Hispanic. Businesses without reported owner race/ethnicity were not flagged as minority-owned. 7(a) Loans The 7(a) loan program, administered by the Small Business Administration (SBA), is the most common loan program. The loan can be used for: short and long-term working capital, refinancing current business debt, purchasing furniture, fixtures, supplies, and real estate. There is a maximum $5 million loan amount for borrowers. Information on additional uses can be found on the SBA site. Information on the businesses that received 7(a) loans up to $5,000,000 dollars are represented on PolicyMap. Users can filter by various criteria like industry, loan amount, and business type under the toggle. Information about the businesses, including business name, business owner details, and industry (both 2 and 6-digit NAICS codes) are published here “as is” from the original SBA data. PolicyMap supplemented the data from the SBA by providing business sector and business descriptions based on reported 2 and 6 digit NAICS codes pulled from the Census Bureau’s published list of NAICS codes. 504 Loans The 504-loan program, administered by the Small Business Administration (SBA), provides long-term, fixed rate financing of up to $5,000,000 for major fixed assets that promote business growth and job creation. The loans are available through Certified Development Companies (CDCs), community-based partners who regulate nonprofits and promote economic development in their communities, are regulated and certified by SBA. Eligibility is based on SBA criteria. Information on the businesses that received 504 loans up to $5,000,000 dollars are represented on PolicyMap. Users can filter by various criteria like industry, loan amount, and business type under the toggle. Information about the businesses, including business name, business owner details, and industry (both 2 and 6-digit NAICS codes) are published here “as is” from the original SBA data.

PolicyMap supplemented the data from the SBA by providing business sector and business descriptions based on reported 2 and 6 digit NAICS codes pulled from the Census Bureau’s published list of NAICS codes.

< Back to list of datasets

Social Security Administration: Supplemental Security Income

Details:

count, percent and percent change of population with disabilities, by age and selected disability type

Topics:

public assistance, public health, people with disabilities, youth, blind

Source:

SSI Recipients by State and County

Years Available:

2003-2018

Geographies:

county, state

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

https://www.ssa.gov/policy/docs/statcomps/ssi_sc/2018/index.html

Last updated on PolicyMap:

October 2019

Description:

The SSI program is a cash assistance program for low-income aged, blind, or disabled people. States have the option of supplementing their residents’ SSI payments and may choose to have the additional payments administered by the federal government. When a state chooses federal administration, the Social Security Administration maintains the payment records and issues the federal payment and the state supplement in one check. SSI data in PolicyMap are for federal and federally administered state payments only. State-administered supplementary payments are not included. The data come from the Supplemental Security Record, the principal administrative data file for the SSI program. To avoid disclosure of the reason for individuals’ eligibility, data on eligibility categories are suppressed for counties with fewer than 15 recipients or where all recipients are in the same category. Therefore, county counts may not sum to reported state numbers. The amount of payments is not shown for counties with fewer than four recipients. These suppressed payment data are included in the state and national totals.

To calculate the percentages in a given area, Census Population Estimates for counties and states were used. Information can be found at http://www.census.gov/popest/.

< Back to list of datasets

Substance Abuse and Mental Health Services Administration (SAMHSA)

Details:

Locations of mental health treatment facilities, locations of drug and alcohol treatment facilities, locations of buprenorphine physicians

Topics:

Behavioral health, mental health treatment, drug and alcohol treatment

Source:

Substance Abuse and Mental Health Services Administration (SAMHSA)

Years Available:

2019

Geographies:

points

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

http://www.samhsa.gov/data/

Last updated on PolicyMap:

August 2021

Description:

PolicyMap downloaded the geocoded mental health and drug and alcohol treatment facilities from the Substance Abuse and Mental Health Services Administration’s (SAMHSA) Behavioral Health Treatment Services Locator, a product of SAMHSA’s Center for Behavioral Health Statistics and Quality (CBHSQ). The Locator is compiled from responses to CBHSQ’s annual surveys of treatment facilities (the National Survey of Substance Abuse Treatment Services and the National Mental Health Services Survey). For more information about which centers are eligible for inclusion in the Behavioral Health Treatment Services Locator, please see SAMHSA’s website: https://findtreatment.samhsa.gov/locator/about.

PolicyMap also downloaded and geocoded the addresses of physicians certified to prescribe buprenorphine to combat opioid dependency from the SAMHSA Buprenorphine Treatment Physician Locator. Eligibility and certification requirements for prescribing buprenorphine are set by the Drug Addiction Treatment Act of 2000 (DATA 2000). SAMHSA works with physicians to assist in obtaining waivers to meet the requirements of DATA 2000 and also tracks the number of “DATA – Certified Physicians” throughout the country. For more information, please visit: http://www.samhsa.gov/programs-campaigns/medication-assisted-treatment/physician-program-data.

< Back to list of datasets

The Institute for College Access & Success (TICAS), The Project on Student Debt

Details:

average loan amount for bachelor’s degrees, percent of graduates with student loans, average debt and percent with debt for public, private and all graduates

Topics:

student loans, student debt

Source:

CollegeInSight, from the Institute for College Access and Success (TICAS)

Years Available:

2001, 2004 – 2017

Geographies:

state

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

http://projectonstudentdebt.org/

Last updated on PolicyMap:

April 2019

Description:

The Project on Student Debt is an initiative of the Institute for College Access and Success (TICAS), a nonprofit organization dedicated to making college available and affordable to people from all backgrounds. To create this database, TICAS licensed Common Data Set (CDS) survey data from Peterson’s Undergraduate Financial Aid and Undergraduate Databases. CDS is a shared survey used by publishers of college guides. The data provided here have several limitations that may impact state averages. While colleges are asked to report cumulative debt, they may not be aware of all private loans that are carried by students. In addition, debt already accumulated from incoming transfer students is not included. Finally, very few for-profit colleges are included in the survey responses where borrowing levels are on average higher.

For detailed information about individual schools see the TICAS website: http://projectonstudentdebt.org/state_by_state-data.php.

< Back to list of datasets

Nielsen TDLinx Grocery Retail Locations

Details:

Grocery Retail Locations

Topics:

Grocery Retail Locations

Source:

Nielsen TDLinx

Years Available:

2010, 2011, 2012, 2013, 2014, 2015, 2016

Geographies:

point

Public Edition or Subscriber-only:

Subscriber-only

Download Available:

no

For more information:

https://www.nielsen.com/us/en/

Description:

The Nielsen TDLinx database provides universal coverage and unique codes for every store in retail trade channels and for every outlet in on-premise trade channels. Available on PolicyMap are grocery stores (i.e., stores in the TDLinx grocery trade channel, as well as dollar stores from the mass merchandiser channel), including their name, address, Reinvestment Fund-defined service level (i.e., full-service or limited-service), and store type. The points displayed on PolicyMap for a given year represent the stores that were open at any point during that year, as calculated by Reinvestment Fund using TDLinx data. Reinvestment Fund classifies conventional supermarkets, limited-assortment supermarkets, natural/gourmet supermarkets, supercenters, and warehouse grocery stores with at least 5,000 square feet selling space as full service. Reinvestment Fund manually validated stores of these types with less than 5,000 square feet selling space to confirm that they offer a full range of fresh and healthy foods; stores that do offer a full range of fresh and healthy foods, and stores for which it was not possible to make a determination, are classified as limited-service. Superettes and dollar stores are classified as limited-service because a review of relevant research shows that they generally offer a narrower variety of fresh and healthy foods at generally higher prices than do supermarkets. Conventional club stores are classified as limited-service because they require paid memberships. Military commissaries are classified as limited-service because they are not open to the public. Store types presented on PolicyMap are derived from Nielsen TDLinx sub-channels. Nielsen TDLinx uses official industry-standard definitions for each trade channel and sub-channel when available and rigorous developed definitions when not. Industry endorsement and support of these definitions has come from Trade Associations like The Food Marketing Institute (FMI), Grocery Manufacturers of America (GMA), and National Association of Convenience Stores (NACS), as well as Trade Publications like Convenience Store News and Progressive Grocer. Nielsen store type definitions and example stores are provided below.
Conventional Supermarket
A conventional supermarket is a traditional full-line, self-service grocery store with annual sales volume of $2 million or more. This definition applies to individual stores regardless of total company size or sales, and therefore includes both chain and independent locations. Examples: Kroger, Food Lion, IGA, Cub Foods
Limited Assortment Supermarket
A limited assortment supermarket has a limited selection of items in a reduced number of categories. These stores typically offer everyday low pricing. Principal differentiation from a conventional supermarket is often in the reduced size and completeness of produce and non-food categories such as Health and Beauty Care (HBC), cleaning supplies, paper products and general merchandise products. A limited assortment supermarket has few, if any, full service departments, and less product variety and customer service than a conventional supermarket. Limited Assortment stores will occasionally have an annual sales volume of less than $2MM. Examples: Aldi Food Store, Save A Lot
Natural/Gourmet Supermarket
A natural or gourmet foods supermarket is a self-service grocery store primarily offering natural, organic, or gourmet foods. These stores will either focus product offerings around healthy living with fresh produce and natural products, or around gourmet food preparations with upscale oils, spices, cheeses, meats and produce. Natural/gourmet foods supermarkets typically have expanded fresh foods departments and/or prepared food selections. These supermarkets also typically have a limited, if any, health and beauty care and general merchandise selection. A Natural/Gourmet supermarket does not have over 50% of product offerings in one category, as is the case with traditional butcher shops, delis, produce stands, or nutritional supplement stores. Natural/Gourmet stores will occasionally have an annual sales volume of less than $2MM. Examples: Trader Joes, Whole Foods, Dean & DeLuca. Note: Ethnic supermarkets do not qualify as Natural/Gourmet Foods supermarkets.
Supercenter
A supercenter is a retail unit with a full-line supermarket and a full-line discount merchandiser under one roof. It may have separate or combined checkouts. Examples: Wal-Mart Supercenter, Meijer Supermarket
Warehouse Grocery
A warehouse store is a grocery store with limited service that eliminates frills and concentrates on price appeal. Items are displayed for sale in their original shipping carton rather than placed individually on shelves. This type of store also sells bulk food and large size items. In addition, these stores frequently act as suppliers to smaller food stores and often derive as much revenue from wholesale activities as they do from retail. Warehouse stores will occasionally have an annual sales volume of less than $2MM. Examples: Cash & Carry, Smart & Final
Conventional Club Stores
Conventional club stores include membership club stores distributing packaged and bulk foods and general merchandise. They are characterized by high volume on a restricted line of popular merchandise in a no frills environment. The average club stocks approximately 4,000 SKUs, 40 percent of which are grocery items. Examples: BJ’s, Sam’s Club
Military Commissary
A commissary is a grocery store operated by the U.S. Defense Commissary Agency within the confines of a military installation. A commissary can fit within any of the grocery formats. Military Commissaries will occasionally have an annual sales volume of less than $2MM. Examples: Fort Hood DECA Commissary, Fort Riley DECA Commissary
Superette
A superette is a conventional grocery store with an annual sales volume between $1 million and $2 million. Typically superettes are independent, but many are affiliated with groups like IGA, Inc. Examples: IGA, Maynards, La Michoacana, Select Markets
Dollar Store

A dollar store is one that focuses on fast turnover of inexpensive consumables including health and beauty care, cleaning supplies, dry foods and candy, as well as toys, housewares, home decor, party goods, pet foods, stationery and school supplies. Most merchandise is priced below $10 with no merchandise over $20. The most common price point is $1.00. The typical size of a Dollar Store is between 3,000 and 30,000 selling square feet. Only major chains are represented in this channel, as there are many independently owned and operated Dollar Stores. Examples: Family Dollar, Dollar General

< Back to list of datasets

Thunderforest, OpenStreetMap contributors: Transportation Layer

Details:

Public Transit, Transportation

Topics:

Base map layers, transportation

Source:

Thunderforest, OpenStreetMap contributors

Years Available:

rolling

Geographies:

Point, Line

Public Edition or Subscriber-only:

Subscriber-only

Download Available:

no

For more information:

https://www.thunderforest.com/maps/transport/

Last updated on PolicyMap:

rolling

Description:

The transportation layer on PolicyMap comes from Thunderforest, a project founded by Andy Allen and operated by Gravitystorm Limited. The layer shows bus and rail lines, stations, and stops throughout the United States at various geographies. Additionally, the layer includes freight and passenger rail. Passenger rail lines include local transit authorities, regional lines, and Amtrak. Stations for local and regional lines appear when the user zooms into the map. Amtrak stations appear when the user zooms out of the map. Additionally, novelty rail lines appear on the map that does not connect passengers to destinations. In current rail projects, some display future stations even if they are not currently operational, while others do not appear on the layer. Freight lines like CSX, Norfolk Southern, etc., appear on the map, but do not include station stops. Also, freight lines connected to companies or rail yards appear. Some local passenger rail lines include bus service connections for the last mile at terminating stations. Users need to confirm whether a rail line continues or a shuttle service begins as the layer elements do not differ.

Bus routes and stops include local transit authorities, local shuttles, long-range buses, and regional bus services. The layer does not differentiate between services or authorities; it only consists of the route number for each line. Some bus routes are private services and do not reflect public transportation. Only the start and end stations are available for long-range buses; intermediate stops do not appear on the layer.

< Back to list of datasets

Trust for Public Land: ParkServe

Details:

Park Designation, Park Owner Type, Level of Access, Park Access Demographics within a 10-minute walk, park priority areas

Topics:

health, physical activity, open space

Source:

Trust for Public Land

Years Available:

2020

Geographies:

Polygon

Public Edition or Subscriber-only:

Public Edition

Download Available:

no

For more information:

https://www.tpl.org/parkserve

Last updated on PolicyMap:

November 2021

Description:

Trust for Public Land (TPL) is a national non-profit that conserves land for social use. The TPL ParkServe dataset measures and analyzes access to parks in 14,000 urban areas across the United States. TPL established 10-minute walk service areas based on a nationwide walkable road network as part of an analysis of current access to parks in cities, towns, and communities. This analysis was created to ensure that everyone lives within easy walking distance of a well-maintained park. The ParkServe data shows the total population and other population demographics that are served within a10-minute walk by each park. The data also shows the designation type, access type, the owner level and size of each parks. TPL has also published the results of their Park Priority Areas analysis. Park priority areas are areas of neighborhoods, defined here as Census block groups, that are not within a 10 minute walk of a park. These areas are ranked on the following criteria: people per acre, health, heat, respiratory hazard, density of low income households, and density of people of color. These criteria are standardized, weighted, and combined into a single score, which is then translated into a ranking for each city, where 1 is the lowest, and 3 is the highest need.

The SNAP Retailer Locator at the USDA website contains a list of all retailers that accept SNAP payments (sometimes known as food stamps).

The health criterion includes prevalence of poor mental health, and low physical activity, sourced from the CDC’s PLACES dataset, and ranked from 1 to 5 for each city. Respiratory hazard data is sourced from the EPA’s EJScreen dataset. Population of color and low income households data is sourced from ESRI’s 2020 Census Block Group Demographic Forecast.

< Back to list of datasets

United States Department of Agriculture (USDA), Food and Nutrition Service

Details:

SNAP Retail Locations

Topics:

health, food access, Food Stamps

Source:

U.S. Department of Agriculture, Food and Nutrition Service

Years Available:

2021

Geographies:

Point

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

http://www.fns.usda.gov/snap/retailerlocator

Last updated on PolicyMap:

September 2021

Description:

The SNAP Retailer Locator at the USDA website contains a list of all retailers that accept SNAP payments (sometimes known as food stamps).

< Back to list of datasets

United States Department of Agriculture, Economic Research Service (ERS/USDA) Food Desert Locator

Details:

People with low access to supermarkets or large grocery stores, low-income low access, children low access, seniors low access, housing units with no vehicles, population, urban or rural tracts

Topics:

health, food access, supermarkets

Source:

U.S. Department of Agriculture, Economic Research Service

Years Available:

2006

Geographies:

Census Tracts

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

http://www.ers.usda.gov/data/foodDesert/index.htm

Description:

The Food Desert Locator is a project of the Economic Research Service, the economic information and research division of the U.S. Department of Agriculture. The Locator contains data about food access determined by the Treasury Department, Health and Human Services, and the Agriculture Department (USDA). A committee comprised of these three departments, along with staff from the Economic Research Service (ERS/USDA) determined a definition of food deserts used within the data and for determining eligibility for HFFI funds. It is an update of the 2006 USDA Food Desert data.

Low access is defined in this study as (a) in urban tracts, the percentage of people that live more than one mile from a supermarket or large grocery store or (b) in rural tracts, the percentage of people that live more than 10 miles from a supermarket or large grocery store. These data were published by the Economic Research Service (ERS/USDA) as a part of a 2009 report to U.S. Congress. In the 2009 report, the ERS used 1-kilometer square grids as the base of the analysis as a method for measuring distance from the nearest source of healthy foods. For the 2011 release of the data online, the ERS converted the grid data to the census tract level data. Other data sources used in this report include a list of Stores Authorized to Receive SNAP benefits, as well as data from Trade Dimensions TDLinx, both from the year 2006. Data is only shown for Census tracts identified as having low access. Census tracts not identified as having low access appear as grey in the map.

< Back to list of datasets

United States Department of Agriculture (USDA) Agricultural Marketing Service

Details:

farmers’ markets

Topics:

local foods

Source:

U.S. Department of Agriculture, Agricultural Marketing Service

Years Available:

2021

Geographies:

County, State, points

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

https://www.ams.usda.gov/local-food-directories/farmersmarkets

Last updated on PolicyMap:

August 2021

Description:

Farmers’ market points are taken from USDA’s Agricultural Marketing Service (AMS) through the Farmers’ Market Directory. The Directory is a reporting system where local farmers’ market managers list the locations of their markets and basic details. The data may not include all farmers’ markets. AMS reports geographic coordinates as reported through the Directory. PolicyMap geocoded market addresses whose geographic coordinates fell outside the reported county, and was able to display 99% of the provided locations on a map.

The Farmers’ Market Directory is updated on a rolling basis. AMS data is updated on PolicyMap annually.

< Back to list of datasets

United States Department of Labor, Wage and Hour Division

Details:

State minimum wage

Topics:

Minimum wage

Source:

Wage and Hour Division, United States Department of Labor

Years Available:

2022

Geographies:

states

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

http://www.dol.gov/whd/minwage/america.htm

Last updated on PolicyMap:

March 2022

Description:

The United States Department of Labor compiles the minimum wage laws in each state. The minimum wages in this data apply to nonsupervisory nonfarm private sector employment. Though there is a federally mandated minimum wage currently set at $7.25 per hour, some states have legislated their own minimum wage. Some states have a statutory minimum wage below the federal minimum wage; in these cases, they are superseded by the federal minimum wage, and the federal minimum wage is shown on PolicyMap. Where the state minimum wage is higher than the federal minimum wage, the state minimum wage applies and is shown. Some local municipalities and counties have minimum wages higher than the wage set by the state; these are not included in this data.

A number of states have minimum wage levels linked to the consumer price index. They generally increase annually around January 1st. Some states’ changes occur in July. This data represents the minimum wage on January 1, 2022.

< Back to list of datasets

United States Department of Agriculture, Economic Research Service (ERS/USDA) Food Access Research Atlas

Details:

Low access to supermarkets, supercenters, and grocery stores; low income low access, urban/rural classification

Topics:

health, food access, supermarkets, food deserts

Source:

U.S. Department of Agriculture, Economic Research Service

Years Available:

2010, 2015 ,2019

Geographies:

Census Tracts

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

http://www.ers.usda.gov/data-products/food-access-research-atlas/download-the-data.aspx

Last updated on PolicyMap:

August 2021

Description:

The Food Access Research Atlas is a project of the Economic Research Service, the economic information and research division of the U.S. Department of Agriculture. The Atlas contains data about food access and can be used for determining eligibility for HFFI funds. Low access is defined as being far from a supermarket, supercenter, or large grocery store. A census tract has low access status if a certain number of share of individuals in the tract live far from a supermarket. There are various measures for distance from a supermarket that this data uses. The original Food Desert Locator (which this replaces) defined low access as living 1 mile away from a supermarket in urban areas, and 10 miles away in rural areas. This study adds measures for 0.5 miles in urban areas, and 20 miles in rural areas. Using these distance measurements, a census tract is defined as low access if there are at least 500 people or 33 percent of the population within the tract with low access. To assemble the data, the country is divided into 0.5-km grids, and data on population are aerially allocated to the grids. Distance to the nearest supermarket is measured for each grid cell by calculating the distance between the geographic center of the .5-km grid and the center of the grid with the nearest supermarket. The numbers are then aggregated to the census-tract level. Low-income tracts are defined as where the tract’s poverty rate is greater than 20 percent, the tract’s median family income (MFI) is less than or equal to 80 percent of the statewide MFI, or the tract is in a metropolitan area and has an MFI less than or equal to 80 percent of the metropolitan area’s MFI.

For additional information, including the source data used for the study, see the study’s documentation here: https://www.ers.usda.gov/data-products/food-access-research-atlas/documentation/.

< Back to list of datasets

United States Department of Agriculture (USDA) Food Environment Atlas

Details:

Low Income children obesity rates, SNAP and WIC programs, farms with direct sales to consumers, direct-sale farm revenue

Topics:

health, federal nutrition programs, local foods

Source:

U.S. Department of Agriculture, Economic Research Service

Years Available:

Various (2006, 2007, 2008, 2009, 2010, 2011, 2012)

Geographies:

County

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

http://www.ers.usda.gov/FoodAtlas/

Description:

The Food Environment Atlas is a project of the Economic Research Service, the economic information and research division of the U.S. Department of Agriculture. The Atlas assembles data about food choices, health and well-being, and community characteristics. Data are available at various geographies including county, state and region. Health related indicators, including low-income preschool obesity rates, come from the Centers for Disease Control and Prevention (CDC). The low income preschool figures were derived by a CDC analysis of the Pediatric Nutrition Surveillance System, see http://www.cdc.gov/mmwr/preview/mmwrhtml/mm6231a4.htm?s_cid=mm6231a4_w. Data on the Supplemental Nutrition Assistance Program (SNAP), formerly known as the Food Stamp Program, come from the Food and Nutrition Service of USDA’s SNAP Benefits Redemption Division. Data on the Women, Infants, and Children (WIC) program come from the Food and Nutrition Service of USDA’s Program Analysis and Monitoring Branch, Supplemental Food Programs Division. SNAP benefits data are calculated by the Bureau of Economic Analysis at the U.S. Department of Commerce. Low-income participants in the SNAP program come from Small Area Income and Poverty Estimates, U.S. Census Bureau. Population data used to determine rates is from the U.S. Census Bureau. Data on farms with direct sales to consumer are from the 2007 Agricultural Census, see: http://www.agcensus.usda.gov/Publications/2007/index.asp.

PolicyMap will remove the WIC and SNAP data published in the Food Environment Atlas in 2021 if the data is not updated by the source.

< Back to list of datasets

United States Department of Agriculture (USDA) Rural Development, Multifamily Housing

Details:

Locations of USDA Rural Development multifamily properties, direct loans and guaranteed loans

Topics:

USDA Rural Development multifamily properties

Source:

USDA Rural Development, Rural Housing Service

Years Available:

2019

Geographies:

points

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

http://www.sc.egov.usda.gov/data/data_files.html

Last updated on PolicyMap:

September 2019

Description:

Direct Loans
The Rural Housing Service of USDA Rural Development provided locations of active multifamily properties, project information, and household characteristics (including race, disability, and income). Rural multifamily projects include Section 514 and 515 labor housing (including off-farm and on-farm), USDA Rural Housing Service Multifamily Preservation and Revitalization Demonstration Program (MPR), and Low Income Housing Tax Credits (LIHTC). Housing subsidy information includes USDA RD Section 521, HUD Section 8, HUD Housing Choice Voucher, and other subsidies. This data includes properties that have been transferred to a new borrower: consolidations, sales, and transfers of old projects are included in the “property transfer” calculation. PolicyMap calculated the number of properties managed per management company; the top management companies are included as a filter in the data. The data also includes estimated property exit year which identifies the year in which the property may exit the USDA portfolio and transition to market rate. Property exit data is current as of June 2017. Points were geocoded by USDA.
Guaranteed Loans

Active properties included in the USDA Section 538 multifamily guaranteed loan program were also released by USDA Rural Development. Attributes available for this data include loan, property and community characteristics. Loan characteristics for each property include obligation fiscal year, lender and borrower information, loan amounts, total development cost, and a federal LIHTC tax credit indicator. Property information includes number of units, units by bedroom size and average contract rent.

< Back to list of datasets

United States Department of Agriculture (USDA) Rural Development, Single Family Housing

Details:

Summary of USDA Rural Development single family housing direct loans and guaranteed loans

Topics:

USDA Rural Development single family direct loans and guaranteed loans

Source:

USDA Rural Development, Rural Housing Service

Years Available:

2016

Geographies:

County, Congressional District

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

http://www.sc.egov.usda.gov/data/data_files.html

Description:

Direct Loans
The Rural Housing Service of USDA Rural Development provided aggregated data on borrower and loan characteristics for single family properties included in the Section 502 single family housing direct loan program. Data is aggregated by county and congressional district. Borrower characteristics include income, racial and ethnic classifications, as well as household characteristics such as number of dependents. Loan characteristics include average loan amounts for all active loans as well as loans within specified fiscal year ranges. Data in PolicyMap was aggregated to the county and congressional district level by USDA.
Guaranteed Loans
The Rural Housing Service of USDA Rural Development provided aggregated data on borrower, loan and property characteristics for single family properties included in the Section 502 single family housing guaranteed loan program. Data is aggregated by county and congressional district. Borrower characteristics include income, racial and ethnic classifications, first-time homebuyer status as well as household characteristics such as number of dependents. Property characteristics include information on the housing development type and the individual structure type and square footage. Loan characteristics include average loan amount, appraised value and loan-to-value ratio.

Data in PolicyMap was aggregated to the county and congressional district level by USDA.

< Back to list of datasets

United States Department of Agriculture (USDA) Rural Development, Single Family and Multifamily Ineligible Areas

Details:

USDA Rural Development single family and multifamily ineligible areas

Topics:

USDA Rural Development areas of program ineligibility

Source:

USDA Rural Development, Rural Housing Service

Years Available:

as of 2019

Geographies:

Polygons

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

http://www.sc.egov.usda.gov/data/data_files.html

Last updated on PolicyMap:

March 2019

Description:

Areas determined as ineligible by USDA Rural Development for single family and multifamily property loans and grants. Data available for download at http://www.sc.egov.usda.gov/data/data_files.html.

< Back to list of datasets

United States Department of Agriculture (USDA) Rural Business Service Ineligible Areas

Details:

USDA Rural Business Service ineligible areas

Topics:

USDA Rural Business Service areas of program ineligibility

Source:

USDA Rural Development, Rural Business Service

Years Available:

as of 2019

Geographies:

Polygons

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

http://www.sc.egov.usda.gov/data/data_files.html

Last updated on PolicyMap:

March 2019

Description:

Areas determined as ineligible by USDA Rural Development for rural business loans, grants, and training. Data available for download at http://www.sc.egov.usda.gov/data/data_files.html.

< Back to list of datasets

United States Department of Agriculture (USDA), Soil Survey Geographic (SSURGO) Database

Details:

Prime Farmland

Topics:

Sensitive lands, environment

Source:

Natural Resources Conservation Service, U.S. Department of Agriculture

Years Available:

2020

Geographies:

Polygon

Public Edition or Subscriber-only:

Subscriber and API only

Download Available:

no

For more information:

http://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/survey/?cid=nrcs142p2_053627

Last updated on PolicyMap:

May 2020

Description:

The Soil Survey Geographic (SSURGO) Database contains data on prime farmland throughout the country based on soil quality. Included in this data are areas classified as “All areas are prime farmland” and “Prime Farmland if improved”.

< Back to list of datasets

United States Department of Agriculture (USDA), Watershed Boundary Dataset

Details:

Watersheds

Topics:

Watersheds, subwatersheds, basins, environment

Source:

Coordinated effort between the United States Department of Agriculture-Natural Resources Conservation Service (USDA-NRCS), the United States Geological Survey (USGS), and the Environmental Protection Agency (EPA)

Years Available:

2020

Geographies:

Polygon

Public Edition or Subscriber-only:

Public Edition

Download Available:

no

For more information:

https://www.nrcs.usda.gov/wps/portal/nrcs/main/national/water/watersheds/dataset/

Last updated on PolicyMap:

May 2021

Description:

The Watershed Boundary Dataset contains boundaries of “hydrologic units” throughout the country. According to the USDA, “Hydrologic unit boundaries define the aerial extent of surface water drainage to a point.” The hydrologic units are nested in a hierarchical system, decreasing in size from basins (HUC 6), to subbasins (HUC 8), to watersheds (HUC10), to subwatersheds (HUC 12). The boundaries can assist in environmental, land use, and water use planning.

< Back to list of datasets

United States Elections Project

Details:

Turnout rate among voting eligible population

Topics:

Voter turnout

Source:

United States Election Project

Years Available:

2000, 2002, 2004, 2006, 2008, 2010, 2012, 2014, 2016, 2018, 2020

Geographies:

states

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

http://www.electproject.org/

Last updated on PolicyMap:

May 2021

Description:

The United States Elections Project uses multiple sources to calculate a voter turnout rate that takes into account the voting eligible population (VEP), and not just the voting age population (VAP). The turnout rate was calculated by dividing the number of votes for the highest office in the election (often president, governor, or congress) by the voting age population minus non-citizens and those in prison, on probation, or on parole, for states where such people are ineligible to vote. Some states permanently disenfranchise felons and people who have been judged mentally incompetent; these exclusions are not included in the data. Votes from people living overseas are counted in the vote total, but such people are not counted in the denominator, except at the national level. More information can be found at http://www.electproject.org/home/voter-turnout/faq.

< Back to list of datasets

University of Richmond, University of Maryland, Virginia Tech, and Johns Hopkins University – Mapping Inequality: Redlining in New Deal America

Details:

Risk boundaries

Topics:

Residential lending, neighborhood risk

Source:

University of Richmond, University of Maryland, Virginia Tech, and Johns Hopkins University

Years Available:

1935-1940

Geographies:

Polygon

Public Edition or Subscriber-only:

Public Edition

Download Available:

no

For more information:

https://dsl.richmond.edu/panorama/redlining/#loc=3/45.40/-114.79&opacity=0.8&text=intro

Description:

Mapping Inequality: Redlining in New Deal America, published by the Digital Scholarship Lab at University of Richmond in collaboration with University of Maryland, Virginia Tech, and Johns Hopkins University, includes a collection of digital maps on area security and descriptions for major urban centers developed by the Home Owners’ Loan Corporation (HOLC) from 1935 to 1940. The maps include color coded areas based on grades assigned to them by HOLC officers. Grades were assigned based on input from mortgage lenders, developers, and real estate appraisers, and were used as a measure of credit worthiness and risk on neighborhood and metropolitan levels. Area grades range from A to D, with A denoting ‘Excellent’, B denoting ‘Still Desirable’, C denoting ‘Definitely Declining’, and D denoting ’Hazardous’. According to the source, project researchers made the digital maps publicly available “in the hope that the public will be able to understand the effects of federal housing policy and local implementation in their own communities.”

On PolicyMap, this dataset is available for a total of 148 urban regions across the U.S., in 28 states. The Home Owners’ Loan Corporation (HOLC) originally compiled this information for nearly 250 cities. After downloading the shapefiles from the Mapping Inequality website, PolicyMap merged and dissolved the polygons by grade level for each urban region to ease navigation of the dataset. The data is made available by the source through a CC-NC-SA license and thus is available for general PolicyMap users but is not available for licensing through our API. Licensing information can be found here.

< Back to list of datasets

University of South Carolina, Hazards and Vulnerability Research Institute (HVRI), Baseline Resilience Indicators for Communities (BRIC)

Details:

BRIC index

Topics:

disaster risk, resilience

Source:

University of South Carolina Hazards and Vulnerability Research Institute

Years Available:

2010, 2015

Geographies:

County

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

http://artsandsciences.sc.edu/geog/hvri/bric

Last updated on PolicyMap:

June 2019

Description:

The Baseline Indicators for Communities (BRIC) index models community resilience to natural hazards. BRIC was developed by the Hazards and Vulnerability Research Institute at the University of South Carolina. HVRI provides training, research, outreach, and leadership pertaining to the field of hazard vulnerability and resilience. BRIC considers six categories of disaster resilience: social, economic, infrastructural, community capital, institutional, and environmental. The US National Academy of Science defines disaster resilience as the ability to prepare and plan for, absorb, recover from, and more successfully adapt to adverse events. Such events may include floods or hurricanes in the context of BRIC. Overall resilience scores are calculated at the county level by summing category scores. BRIC can be used to compare resilience across counties, monitor county resilience over time, and identify specific attributes of resilient communities. The primary data source for BRIC is the Census American Community Survey. The variables used for each category of disaster resilience are listed below, followed by a list of the data sources: Category Variables: Social Resilience: absolute difference between percent population over 25 with college education and percent population over 25 with less than high school education (inverted), percent population below 65 years of age, percent households with at least one vehicle, percent households with telephone service available, percent population proficient in English, percent population without sensory, physical, or mental disability, percent population under age 65 with health insurance, psychosocial support facilities per 10,000 persons, food security rate, physicians per 10,000 persons. Economic Resilience: percent owner-occupied housing units, labor force employed, gini coefficient (inverted), percent employees not in farming, fishing, forestry, extractive industry, or tourism, absolute difference between male and female median income (inverted), ratio of large to small businesses, large retail stores per 10,000 persons, percent labor force employed by federal government. Infrastructural Resilience: percent housing units not mobile homes, percent vacant housing units that are for rent, number hospital beds per 10,000 persons, major road egress points per 10,000 persons, percent housing units built prior to 1970 or after 2000, number hotels/motels per 10,000 persons, number public schools per 10,000 persons, rail miles per square mile, percent population with access to broadband internet service. Community Capital Resilience: percent population not foreign-born persons who came to US within previous 5 years, percent population born in state of current residence, percent voting age population participating in recent election, number affiliated with a religious organization per 10,000 persons, number civic organizations per 10,000 persons, number Red Cross volunteers per 10,000 persons, number Red Cross training workshop participants per 10,000 persons. Institutional Resilience: ten year average per capita spending for mitigation projects, percent housing units covered by National Flood Insurance Program, distance from count seat to state capital (inverted), distance from county seat to nearest county seat within a Metropolitan Statistical Area (inverted), number governments and special districts per 10,000 persons (inverted), number Presidential Disaster Declarations divided by number of loss-causing hazard events for ten year period, percent population in communities covered by Citizen Corps programs, population change over previous five-year period (inverted), percent population within 10 miles of nuclear power plant, crop insurance policies per square mile. Environmental Resilience: farms marketing products through Community Supported Agriculture per 10,000 persons, percent land in wetlands, megawatt hours per energy consumer (inverted), average percent perviousness, Water Supply Stress Index (inverted). Data Sources: US Census Bureau USA Counties Database County and City Data Book County Business Patterns Decennial Census Small Area Health Insurance Estimates Current Population Estimate American Community Survey Three-Year Estimates American Community Survey Five-Year Estimates US Federal Emergency Management Agency Hazard Mitigation Grant Program Presidential Disaster Declarations Database Citizen Corps Councils National Flood Insurance Program US Geological Survey National Land Cover Dataset National Atlas US Bureau of Labor Statistics Quarterly Census of Employment and Wages US Department of Agriculture Census of Agriculture US Department of Education National Center for Education Statistics US Energy Information Administration Electricity Consumption US Federal Communications Commission Broadband Internet Access US Forest Service Water Supply Stress Index US Nuclear Regulatory Commission Nuclear Power Plants Database US Oak Ridge National Library Railroad Network University of South Carolina Hazards and Vulnerability Research Institute Spatial Hazard Events and Losses Database for the US (SHELDUS) Association of Religion Data Archives Religious Congregations and Membership Study Environmental Working Group Farm Subsidies Feeding America Map the Meal Gap The Guardian US 2012 Presidential Election American Red Cross Volunteers and Preparedness Training Dun and Bradstreet Million Dollar Database (2010 BRIC) Robert Wood Johnson Foundation & University of Wisconsin

County Health Rankings and Roadmaps (2015 BRIC)

< Back to list of datasets

University of Wisconsin: Area Deprivation Index

Details:

Income, education, employment, housing quality

Topics:

Social determinants of health

Source:

University of Wisconsin

Years Available:

2019

Geographies:

block group

Public Edition or Subscriber-only:

Public Edition

Download Available:

no

For more information:

https://www.neighborhoodatlas.medicine.wisc.edu/

Last updated on PolicyMap:

October 2021

Description:

Researchers at the University of Wisconsin created the Area Deprivation Index (ADI) based on a methodology originally developed by the Health Resources and Services Administration. The Area Deprivation Index ranks Census block groups on the basis of socioeconomic disadvantage in the areas of income, education, employment, and housing quality. Underlying indicators are from the American Community Survey 5-year estimates, 2015-2019. Higher Area Deprivation Index scores have been shown to correlate with worse health outcomes in measures such as life expectancy. Two types of scores and ranks are reported—national and state. National scores are reported as percentiles, where 100 is the highest disadvantage, and 1 is the lowest. State scores are reported as deciles, where 10 is the highest and 1 is the lowest. For ease of interpretation, PolicyMap grouped ADI scores into quintiles, or 5 categories of the same size, and labeled them from Very Low to Very High. Block groups may be suppressed because of insufficient data from the underlying source data. Kind AJH, Buckingham W. Making Neighborhood Disadvantage Metrics Accessible: The Neighborhood Atlas. New England Journal of Medicine, 2018. 378: 2456-2458. DOI: 10.1056/NEJMp1802313. PMCID: PMC6051533.

University of Wisconsin School of Medicine and Public Health. 2019. Area Deprivation Index v.3. Downloaded from https://www.neighborhoodatlas.medicine.wisc.edu/ September 24, 2021.

< Back to list of datasets

USAspending.gov

Details:

Contract, grant and loan transactions made by DOE, DOT and HUD

Topics:

Federal government spending

Source:

USAspending.gov

Years Available:

2016, 2017, 2018

Geographies:

Zip code, place, county, state

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

http://www.usaspending.gov

Last updated on PolicyMap:

June 2019

Description:

USAspending.gov is a publicly accessible website providing information on all financial assistance being administered by federal agencies. The site was mandated by the Federal Funding Accountability and Transparency Act of 2006 with the intention of informing the American public on how taxpayer money is being spent.

PolicyMap aggregated FY2016, FY2017 and FY2018 contract, grant and loan transaction data by the place of performance to the zip code, county, and state geographies for three federal agencies: the Department of Housing and Urban Development, the Department of Education, and the Department of Transportation. In the case of the Department of Education grants and loans, these are aggregated to the place rather than the county geography. The aggregations include prime awards of both negative and positive amounts. According to the source, negative dollar amounts may occur because the agency reduced or withdrew a portion of the original award amount, there is a negative subsidy on a loan and the funds are being returned to the Treasury, or if duplicate corrections reports have been submitted by the agency. Since the data is provided by the source at the transaction level, a contract, grant or loan can be represented more than once in the count. According to the source, in many cases, multiple transactions are clustered to the center of a zip code. Due to some contract, grant and loan transactions being statewide or just not having a zip code, place or county assigned to them, zip code, place and county aggregations do not always add up to the state aggregations. Thus, the state-level aggregations can be expected to be the most complete. Areas for which no contract, grant or loan transactions were reported appear on the map as having Insufficient Data. For more detail on what is included in the USASpending.gov data, please see the USASpending.gov website.

< Back to list of datasets

U.S. Bureau of Economic Analysis

Details:

Total SNAP benefits, percent change in total SNAP benefits

Topics:

health, federal nutrition programs

Source:

U.S. Bureau of Economic Analysis

Years Available:

2000-2011

Geographies:

County, state

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

http://www.bea.gov/iTable/index_regional.cfm

Description:

The Bureau of Economic Analysis provides data on the geographic distribution of economic activity within the country.

Supplemental Nutrition Assistance Program (SNAP) benefits are issued to qualifying low-income individuals to supplement their ability to purchase food. Eligibility is determined by state authorities’ interpretations of Federal regulations. The U.S. Department of Agriculture (USDA) pays the cost of the assistance.

< Back to list of datasets

U.S. Department of Transportation, Bridge Conditions

Details:

Locations of bridges in poor condition, count and percent of bridges in poor condition

Topics:

transportation, public infrastructure, deteriorating infrastructure

Source:

U.S. Department of Transportation

Years Available:

2020

Geographies:

Point, county subdivision, county, congressional district, metro area, state

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

http://www.fhwa.dot.gov/bridge/deficient.cfm

Last updated on PolicyMap:

May 2021

Description:

Bridge conditions of Good, Fair and Poor are defined in accordance with the Pavement and Bridge Condition Performance Measure final rule published in January 2017. Bridge condition is determined by the lowest rating for deck condition, superstructure condition, substructure condition or culvert condition. If the lowest rating is greater than or equal to 7, the bridge is classified as Good; if less than or equal to 4, the bridge is classified as Poor; if equal to 5 or 6, the bridge is classified as Fair. The source of the data is the National Bridge Inventory (NBI), a database of more than 600,000 bridges located on public roads. This data was compiled in 2020 by the Federal Highway Administration (FHWA) and collected from individual state departments of transportation. Bridges are assigned geographic coordinates in the source data. Bridge condition information does not reflect recently implemented improvements.

Count and percent indicators were determined based on a spatial join performed by PolicyMap that involved matching geocoded bridge locations to standard Census geographic boundaries. Bridges without geographic coordinates assigned were not included in the spatial join.

< Back to list of datasets

U.S. Fish and Wildlife Service, Critical Habitat

Details:

Critical habitat of endangered, threatened, and recovering species

Topics:

Sensitive lands, environment, endangered species, threatened species

Source:

U.S. Fish and Wildlife Service, U.S. Department of the Interior

Years Available:

As of 2019

Geographies:

Block group

Public Edition or Subscriber-only:

Widget, API only

Download Available:

no

For more information:

http://ecos.fws.gov/crithab/

Last updated on PolicyMap:

May 2020

Description:

These are areas of habitat believed to be essential to endangered and threatened species’ conservation. Federal agencies are required to consult with the U.S. Fish and Wildlife Service to make sure actions they carry out, fund, or authorize do not destroy or negatively affect critical habitat.

The source data was provided in polygon format. For display here, block groups are shaded as being critical habitats if they touch any critical habitat polygon. If a block group touches more than one critical habitat of different severity (eg. “Endangered” and “Threatened”), the most severe status is displayed, where endangered is more severe than threatened, and threatened is more severe than recovery. Only habitat with “Final” status are included.

< Back to list of datasets

US Fish and Wildlife Service, National Wetlands Inventory

Details:

Wetlands

Topics:

Sensitive lands, environment

Source:

U.S. Fish and Wildlife Service, U.S. Department of the Interior

Years Available:

2011

Geographies:

Polygon

Public Edition or Subscriber-only:

API only

Download Available:

no

For more information:

http://www.fws.gov/wetlands/

Description:

The National Wetlands Inventory provides information on the extant and status of wetlands across the country.

< Back to list of datasets

U.S. Election Atlas

Details:

Nationwide counts and percentages for elections for president, Senate, and House of Representatives, as well as turnout rate

Topics:

Elections, politics

Source:

Dave Leip’s Atlas of U.S. Presidential Elections

Years Available:

2004, 2006, 2008, 2010, 2012, 2014, 2016, 2018, 2020

Geographies:

County, state, congressional districts (for congressional races)

Public Edition or Subscriber-only:

Subscriber-only

Download Available:

no

For more information:

http://uselectionatlas.org/

Last updated on PolicyMap:

April 2021

Description:

Dave Leip’s Atlas of U.S. Presidential Elections provides information on elections for president, senate, and house of representatives. It also provides information on turnout to these elections. Included are the general elections of 2004, 2006, 2008, 2010, 2012, 2014, 2016, 2018, and 2020. Midterm elections are not included. County-level data for Alaska is not included because Alaska does not report its election results by county. Turnout data is only available for presidential elections, as midterm elections in different states do not have a standard top-of-the-ballot race by which turnout is calculated. “Margin of victory” maps provide a handy guide to see who won a given geography, and by how much. Values are calculated by subtracting the number of votes for the runner-up candidate from the number of votes for the winning candidate, and dividing that number by the total number of votes cast. Ranges, and not specific numbers, are available for each area. “Change in percent” calculations were calculated by subtracting the 2004 presidential candidate’s vote percentage from the 2008 candidate’s vote percentage. For example, if John Kerry won 45% of a county in 2004, and Barack Obama won 55% of that county in 2008, the change in percent would be 10%. Note that for House of Representative elections, this is calculated every two years. When elections are uncontested (ie., a candidate from one party runs, and no other candidates from another party run), we label the non-contesting parties as having received zero percent of the vote, even though they did not appear on the ballot. In some congressional districts where a candidate ran unopposed, no vote total was tallied by election officials; we label candidates in these districts as having “Insufficient data” for vote counts, and the winner as having 100 percent of the vote for the percent and change-in-percent calculations.

The Congressional district boundaries changed in 2012 due to redistricting. Some states gained districts and some states lost districts, and within each state, the shapes of most districts changed. For this reason, change-in-percent calculations are not possible from the 2010 Congressional election to the 2012 election. Additionally, some boundaries in Florida, North Carolina, and Virginia changed in 2016 due to redistricting; percent changes are not shown in 2016 for these districts. Results from the North Carolina 9th Congressional District are suppressed.

< Back to list of datasets

US Geological Survey (USGS) National Elevation Dataset (NED)

Details:

Average elevation in meters as of 2013.

Topics:

Elevation, altitude

Source:

United States Geological Survey, United State Department of the Interior

Years Available:

2015

Geographies:

County, place, zipcode, CBSA, state

Public Edition or Subscriber-only:

API only

Download Available:

no

For more information:

http://ned.usgs.gov/

Description:

The United States Geological Survey’s National Elevation Dataset (NED) is the USGS’s primary source for elevation data. PolicyMap calculates the average elevation as a weighted average of all available elevation counts in a given area. Because the NED does not provide full coverage of elevation data for the entire United States, some areas may be represented by an average of the available data. The USGS updates its database as improved source data becomes available.

< Back to list of datasets

US Geological Survey (USGS), National Hydrography Dataset (NHD)

Details:

Water bodies

Topics:

Sensitive lands, environment

Source:

United States Geological Survey, United State Department of the Interior

Years Available:

2011

Geographies:

Polygon

Public Edition or Subscriber-only:

API only

Download Available:

no

For more information:

http://nhd.usgs.gov/

Description:

The National Hydrography Dataset (NHD) contains surface water information for the United States. It contains features such as lakes, ponds, streams, rivers, canals, dams, and streamgages.

< Back to list of datasets

U.S. Small Business Administration, Small Business Development Centers

Details:

Small Business Development Centers

Topics:

Small business

Source:

United States Small Business Administration

Years Available:

2017

Geographies:

Point

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

http://www.sba.gov/tools/local-assistance/sbdc

Description:

Small Business Development Centers (SBDCs) help small business and entrepreneurs with free business consulting and low-cost training services including business plan development, manufacturing assistance, financial packaging and lending assistance, exporting and importing support, disaster recovery assistance, procurement and contracting aid, market research help, 8(a) program support, and healthcare guidance. SBDCs are hosted by universities and state economic development agencies, and funded through a partnership with SBA.

< Back to list of datasets

Urban Mapping

Details:

Public Transit Rail Lines

Topics:

Public transit, mass transit

Source:

Urban Mapping Inc.

Years Available:

2009

Geographies:

Lines and points

Public Edition or Subscriber-only:

Subscriber-only

Download Available:

no

Description:

Urban Mapping Inc. provided PolicyMap with public transit rail lines for 53 transit systems in the US. Data is available for systems in the following areas:

System Location
Altamont Commuter Express Stockton-San Jose, CA
Bay Area Rapid Transit San Francisco Bay Area
Caltrain San Francisco Bay Area
Capital Metropolitan Transportation Authority Austin, TX
Central Puget Sound Regional Transit Authority Greater Seattle, WA
Chicago Transit Authority “L” Trains Greater Chicago, IL
Dallas Area Rapid Transit Greater Dallas, TX
Denver Regional Transportation District Light Rail Greater Denver, CO
Detroit People Mover Detroit, MI
Greater Cleveland Regional Transit Authority Rapid Transit Greater Cleveland, OH
Hudson-Bergen Light Rail Hudson County, NJ
Jacksonville Transit Authority Skyway Jacksonville, FL
Las Vegas Monorail Las Vegas Strip
Long Island Rail Road Greater New York, NY
Los Angeles Metropolitan Transportation Authority Greater Los Angeles, CA
Maryland Area Regional Commuter Trains Baltimore-Washington Area
Maryland Transit Administration Light Rail Greater Baltimore, MD
Maryland Transit Administration Metro Subway Greater Baltimore, MD
Massachusetts Bay Transportation Authority Commuter Rail Greater Boston, MA
Massachusetts Bay Transportation Authority Subway Greater Boston, MA
Memphis Area Transit Authority Trolley Memphis, TN
Metro-North Commuter Railroad Company Greater New York, NY
Metropolitan Atlanta Rapid Transit Authority Greater Atlanta, GA
Metropolitan Transit Authority of Harris County Light Rail Houston, TX
Miami-Dade Transit Greater Miami, FL
Minneapolis-Saint Paul Metro Transit Light Rail Minneapolis, MN
Newark Light Rail Newark, NJ
New Orleans Regional Transit Authority Streetcars New Orleans, LA
New York Transit Authority Subway New York, NY
Niagara Frontier Transportation Authority Light Rail Buffalo, NY
NJ Transit Commuter Rail New Jersey
Northeast Illinois Regional Commuter Railroad Greater Chicago, IL
Northern Indiana Commuter Transportation District Greater Chicago, IL
Port Authority of Allegheny County Light Rail Greater Pittsburgh, PA
Port Authority of New York and New Jersey Airtrain New York JFK and Newark Liberty Airports
Port Authority Trans-Hudson Greater New York, NY
Port Authority Transit Corporation Speedline Greater Philadelphia, PA
River LINE Trenton-Camden, NJ
Sacramento Regional Transit District Light Rail Greater Sacramento, CA
San Diego Metropolitan Transit System Trolley Greater San Diego, CA
San Diego North County Transit District Greater San Diego, CA
San Francisco Municipal Railway San Francisco, CA
Shore Line East New London-New Haven, CT
Southeastern Pennsylvania Transportation Authority Rapid Transit Greater Philadelphia, PA
Southeastern Pennsylvania Transportation Authority Regional Rail Greater Philadelphia, PA
Southern California Regional Rail Authority Greater Los Angeles, CA
South Florida Regional Transportation Authority Miami-West Palm Beach, FL
St. Louis MetroLink Greater St. Louis, MO
Utah Transit Authority Transit Express Greater Salt Lake City, UT
Virginia Railway Express Greater Washington, DC
Washington Metropolitan Area Transit Authority Metrorail Greater Washington, DC

< Back to list of datasets

Valassis Lists

Detail:

business and residential postal vacancy, count and percent of business and residential units that are vacant, count and percent of business and residential units that are no stat, percent change in vacancy and no-stat addresses by quarter and by year

Topics:

vacancy

Source:

Valassis Lists

Years Available:

2014Q3, 2014Q4, 2015Q1, 2015Q2, 2015Q3, 2015Q4, 2016Q1, 2016Q2, 2016Q3, 2016Q4, 2017Q1, 2017Q2, 2017Q3, 2017Q4, 2018Q1, 2018Q2, 2018Q3, 2018Q4, 2019Q1, 2019Q2, 2019Q3, 2019Q4, 2020Q1, 2020Q2, 2020Q3, 2020Q4, 2021Q1, 2021Q2

Geographies:

block group, tract, place, zip, county, CBSA (metro area), state

Public Edition or Subscriber-only:

Subscriber-only

Download Available:

no

For more information:

http://www.valassislists.com/all_inclusive.php

Last updated on PolicyMap:

October 2021

Description:

Valassis Lists, one of the nation’s largest direct mail marketing company, compiles a resident and business list with over 99% coverage of all addresses. PolicyMap’s vacancy data from Valassis Lists contains combined data from two distinct data products: vacancy and no-stat counts for business and residential addresses from the United States Postal Service (USPS), and the USPS Computerized Delivery Sequence (CDS). These data reflect a point-in-time snapshot of vacancy at the end of each quarter. The data do not include a measure of how long an individual address has been vacant or no-stat. Downloading Valassis Lists data from PolicyMap in any format is prohibited. Vacant addresses are those where mail has not been collected for at least 90 days (and typically for no more than 9 months). No-stat addresses include inactive addresses that are under construction, demolished, blighted, or are otherwise unable to receive postal mail. Rural route addresses that are vacant for more than 90 days are also classified as no-stat. For vacancy and no stat counts, PolicyMap aggregated address-level data, provided by Valassis Lists, to the block group level and higher geographies. PO Boxes addresses and addresses classified by USPS as deleted were not included in the aggregations. For counts of the total number of residential and business postal address, PolicyMap used block group level counts from the CDS database, as provided by Valassis Lists. PO Boxes addresses and addresses classified by USPS as deleted were not included. Because of discrepancies between data sources, some block groups may show vacant or no-stat addresses in excess of the total number of addresses from CDS. PolicyMap aggregated the block group values provided by the source to larger geographies. In some cases, Valassis Lists could not assign vacancy and no-stat values to a valid block group. In some cases, the address could not be accurately geo-located by the source, resulting in an incomplete census identifier. For zip+4 or zip code matches, an exact block group could not always be determined if the block group code assigned to the address is located in a different county than the zip code. For 2021 Q2 data, about 1.5% of vacant residential addresses, 1.3% of business vacancies, and 20.7% of no-stat addresses in the original data could not be matched due to these geocoding issues. Rural areas show more geocoding anomalies than urban areas. The values from these unmatched block group records are included in county, CBSA and state records, depending on availability. Block groups without valid data are disregarded in aggregate values (tract, zip code, and Census place). Block group, tract, place, and zip values for no-stat and vacancy should be used with caution, as these numbers may be low or inexact. Percent change from the previous quarter and previous year are calculated for all indicators where possible.

Valassis Lists data is not available for download from PolicyMap.

< Back to list of datasets

Washington State Department of Health, Vox Media, & PolicyMap

Detail:

Lead Exposure Risk Index

Topics:

lead exposure risk

Source:

Washington State Department of Health, Vox Media, PolicyMap

Years Available:

2015-2019

Geographies:

tract

Public Edition or Subscriber-only:

Public Edition

Download Available:

yes

For more information:

https://github.com/voxmedia/data-projects/tree/master/vox-lead-exposure-risk,https://assets.documentcloud.org/documents/2644455/Expert-Panel-Childhood-Lead-Screening-Guidelines.pdf

Last updated on PolicyMap:

April 2021

PolicyMap Exclusive:

yes

Description:

Lead based paint was banned in 1978, but the risk of lead exposure persists. The Washington State Department of Health (WSDOH) developed an index for lead exposure risk that considers age of housing and poverty as primary risk factors. Vox Media worked with WSDOH to apply the lead exposure risk index nationally. PolicyMap applied the Vox Media methodology to the 2015-2019 American Community Survey (ACS) data on poverty rates and age of housing stock. Lead exposure risk due to age of housing is estimated using the number of houses of in a given age range multiplied by the lead exposure risk for houses of that age range. Lead exposure risk due to poverty is estimated using the number of people with incomes less than 125% of the federal poverty level divided by the number of income-earning people in a given census tract; the higher the proportion of income-earners in or near poverty, the higher the estimated lead risk. A multivariate statistical analysis on the relationship between age of housing, poverty, and blood lead levels (BLL) in young children published by the CDC in 2013 — combined with data on lead-based paint prevalence in US housing units by age from a 2002 study — were used to weight the housing age and poverty variables.

PolicyMap suppressed data for census tracts with more than fifty percent of the population living in group quarters.

< Back to list of datasets

Selected Premium Subscriber Datasets

 

PA Department of Community & Economic Development (PA DCED)

Details:

Community & Economic Development data

Topics:

Community Economic Development, DCED

Source:

PA Department of Community & Economic Development (DCED)

Years Available:

2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012

Geographies:

County

Public Edition or Subscriber-only:

Public Edition

Download Available:

no

For more information:

http://www.dced.state.pa.us/investmenttracker/ http://www.newpa.com/

Description:

The Pennsylvania Department of Community and Economic Development (PA DCED) has worked with PolicyMap to spatially display the information available on their Investment Tracker website. The website and the data PolicyMap obtained from PA DCED as of May 9, 2012 contain information on projects that are either underway or have been completed across the state of Pennsylvania, as well as the number of jobs created and the amount of money invested in these projects. There are a number of circumstances in which double counting occurs in the data. If a project crosses either county or Congressional District boundaries, both counties or Districts take credit for the entire investment amount and the number jobs created by the project. Projects indicated as “statewide” also include the entire investment amount and the number of jobs created by the project. For example, if a given project is indicated in the Investment Tracker data as being a part of Montgomery County, Philadelphia County, and Statewide, the two counties and the state are each allocated the entire amount and the total number of jobs. Also, if a project is funded by more than one program, all programs listed claim the entire number of jobs created by the project. For example, if the Ashley Furniture Industries project, which is funded by the OppGrant program and the JCTC program, creates 720 jobs, both the OppGrant program and the JCTC program will each claim they created 720 jobs. Projects that occur at the county level are projects that took place specifically within that county. For example, if a project is listed as taking place in Bucks County, the project will count towards the county total. Likewise, if a project is listed as taking place in Philadelphia, Montgomery, and Bucks County, the project will be counted towards the county totals for all three counties. Statewide or Multi-County projects are those that occur at the state level. These are projects specifically listed as “statewide,” “state projects,” or projects that occur in “all” counties. These projects occur on a state level and are not counted towards the county totals in addition to the statewide totals. As such, statewide totals are typically smaller than the individual county totals. Statewide totals only take into account projects that take place on a statewide level, and do not represent a cumulative sum of the total investments made across all counties.

The PA DCED also has information regarding programs and projects that occur on a regional level. Regional projects include the following: Governor’s Center for Local Government Services Regions, Pennsylvania Department of Community & Economic Development Regions, Governor’s Action Team (GAT) Regions, Partnerships for Regional Economic Performance (PREP) Regions, Local Development Districts, and Pennsylvania Regional Export Network Regions.

< Back to list of datasets