USDA Rural Development Multi-Family Properties, Updated At Last

Here at PolicyMap, there always seems to be buzz about some new dataset or update, and this November we’re buzzing with housing data. Following on the heels of the HUD Multifamily and CDBG Eligibility updates, we’re excited to announce that the USDA Rural Development Multi-Family dataset is up-to-date. The reason that we’re really excited is that this update comes after a fairly lengthy wait – nearly seven years, in fact.

The USDA Rural Development Multi-Family dataset contains the locations of multi-family complexes that receive loans and grants that can be used by very low- to moderate-income families to subsidize mortgages and make structural improvements. The dataset contains the addresses of these properties, as well as the name of management company, total number units (i.e., subsidized or otherwise) by number of bedrooms, and complex type (e.g., elderly and disabled, families).

We first added this data in 2007, as one of the earliest datasets on PolicyMap. We sent a Freedom of Information Act request for an update to this data in July 2012. And then we waited. And waited. Until finally, this summer, a heroic USDA employee found our file, and “fast-tracked” it to its completion. Then came the small matter of actually making the map. Continue reading

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PolicyMap for Public Health!

PolicyMap is excited to be exhibiting at the American Public Health Association conference November 15th -19th! Public health officials and practitioners from around the country have shown great interest in learning how PolicyMap can be a useful tool to streamline their data and mapping needs. Whether tracking health indicators over time, analyzing health risks and preparedness, or uploading patient data to better understand your clientele, PolicyMap can be a great asset for health departments and organizations. PolicyMap is also a simple way to pull much of the data needed for your Community Health Needs Assessment.   Come see us at Booth # 1346 and attend our Presentation: “PolicyMap for CHNA and More!” on Monday November 17th at 1:00 PM in the APHA Exhibitor Theatre.


If your health department is looking for an easy and affordable way to access thousands of indicators, upload your own data, and easily share it all throughout your organization, send us an email today!

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HUD Multifamily Properties: We’ve Made Some Improvements

HUD Multifamily Properties is a monster of a dataset. I mean that in a good way. It combines data on multifamily subsidized properties from three different sources, in order to provide a complete picture of the companies maintaining the sites, the people living in them, and the sites’ physical conditions. It’s a one-stop-shop for HUD multifamily data.

It’s also a monster to process. Though the data comes from three sources, it comes in five databases. Two of the databases don’t report data for properties, but for contracts. In some cases, there are multiple contracts per property. All of the Picture of Subsidized Households data, which shows demographic information about the people that live in the properties, is reported at the contract, not property level. So it’s complicated. Continue reading

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Adding website links to your datasets for the Data Loader

If you’ve used the Data Loader, you know how simple it is to upload a spreadsheet of addresses and have it display as points on a map. The process is easy and we only require a few pieces of data (address, city, state, and/or zip code) to geocode an address, which means users can upload any other data for each address as needed. From dates, names, dollars, and titles, we have given users the option to upload almost any data for an address.

One of the more unique fields when uploading data is the “website” field. This field, once uploaded through the Data Loader, will be an active link in the Info Bubble which will open to that website.
Website Link via Data Loader

To get started, you simply need to add an additional column in your spreadsheet and label it “website“. For each address, you would add a website URL like so;
Spreadsheet Layout

      Please note, URLs must start with the “http://” to be active on PolicyMap. The example text in red above will be displayed as text only and not be an active link to the website.

Continue reading

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FY2014 HUD CDBG Eligibility and CPD Appropriations Data on PolicyMap

As always, we are keeping busy at PolicyMap with lots of data updates! We are very excited to let our users know about updates to several datasets from the Department of Housing and Urban Development (HUD). First of all, we expanded upon the recent Low and Moderate Income Summary data update so that the FY2014 data now displays not only at the block group level, but at the census tract, city and county boundaries as well. The data at these additional geographies were provided directly by HUD.

Secondly, we are pleased to let our users know that we have updated the Community

These are not CDBGs

These are not CDBGs

Development Block Grant (CDBG) eligibility status data layer, which shows whether block groups are eligible for CDBG funding in FY2014. Block groups are deemed eligible if at least 51% of the residents are of low or moderate income (meaning that their income is below 80% of the Area Median Income). Additional block groups are eligible if they are inside the boundary of an “exception grantee” and meet a separate threshold of low and moderate income persons. Some block groups are partially eligible, meaning one or more parts of the block group is not eligible. This is the first year this data is available at the 2010 block group boundaries.
Continue reading

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PolicyMap Webinar: Easy Mapping for Public Health Insights, 11/06

In this session, we will walk through how a user can create assessment areas, collect data for those areas, generate compelling maps, and more!  We will review some of the datasets you might use as well as a number of the unique features on PolicyMap.

Sign up for the webinar, 1-2PM EST, 11/06  

Public health professional use PolicyMap to:

  • Quickly pull data for your CHNA (Community Health Needs Assessments)
  • Analyze the existing public health infrastructure in relation to demand for services
  • Create rich maps and reports in minutes – no GIS training required
  • Download our data, upload yours, and share internally among your epidemiologists and researchers
  • Access the largest place-based data library on the web – one source for all your public health research needs

If you have questions before the webinar, please contact us at!

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Just in time for Halloween: Here comes the Tax Man

Goblins! Witches! Zombies! Werewolves! This time of year we hear a lot about scary things, so here at PolicyMap we thought we’d celebrate with something truly bone-chilling. Yep, you guessed it. Here comes the Tax Man.

Just in time for Halloween, we have added IRS tax data to our platform. While we all fear the reaper as tax returns come due, it turns out our tax data provides some very interesting data and maps. Now on PolicyMap you can find information on tax returns, exemptions, adjusted gross income, and tax liability in communities across the country. This dataset also includes details about tax credits and deductions. Some examples of what you can now map include: student loans, mortgage interest, sales and real estate taxes, child credits, dependent care expenses, unemployment compensation, retirement contributions, and how much people are withdrawing from retirement accounts. We’ll quickly highlight two of these indicators here, and leave it to you to login to PolicyMap to check out the rest under the Incomes & Spending data tab.

Continue reading

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Mapping Community Development Loans with PolicyMap

When PolicyMap was first conceived by The Reinvestment Fund (our parent organization), it was envisioned as a tool to help Community Development Finance Institutions (like TRF) do research for their lending activities. Community Development Finance Institutions (or CDFIs) are financial institutions that make loans in low-income (or otherwise underserved) markets. PolicyMap could help answer questions like: Where are underserved communities? Where can investment create successful development? What kind of investment would be most helpful?

For example, if a neighborhood lacks a supermarket, a CDFI can use PolicyMap to see where existing supermarkets are, and whether the neighborhood will be able to support a supermarket if it gets built. Or if an area lacks affordable housing, a CDFI can use PolicyMap to see if a new housing development will benefit the community.

Since PolicyMap started, our user-base has gone way past CDFIs, but our latest project harkens back to these beginnings, with the OFN Coverage Map.

The Opportunity Finance Network (OFN) is an umbrella organization of CDFIs, and they wanted to answer another set of questions: Where are CDFI investments being made? And where are they not being made? Where should existing CDFIs focus new efforts, and what areas have been successful so far? To answer these questions, they’re using PolicyMap to show where loans have been made by CDFIs across the country. Continue reading

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How do you count a No-Stat address, anyway?

PolicyMap’s postal vacancy data from Valassis Lists has three different measures of vacancy that stem from how the USPS carriers track addresses. The most common type of vacancy is non-seasonal; this is a home or business that’s expected to be occupied year-round. A property can also be seasonally vacant, if it is a vacation home or a business that only operates for part of the year, such as a ski lodge or frozen custard stand. The third category is No-stat. These addresses aren’t actually counted as “vacant,” so what are they, how did they get into the data, and why do they matter?

No-stat is an umbrella category for addresses that the postal service considers unlikely to have mail delivered for one of several reasons. The chart below shows the percent of all addresses considered No-stat in the city of Houston, two ZIP codes within the city (77006, between downtown Houston and the Texas Medical Center, and 77004, in the Third Ward neighborhood), and the county of Colorado TX, just outside the expansive Houston metropolitan area.

Continue reading

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Location Affordability Version 2: Better Than the Original

Brand new datasets are great. When HUD’s Location Affordability data came out last year, we couldn’t wait to add it, because of how simply it illustrated the impact of housing and transportation costs on the budgets of various household types.

What’s better than new datasets? When a new dataset is so useful, its creator decides to make it better. And that just happened with Location Affordability. Soon after the original Location Affordability was released, HUD arranged a conference call of the data’s key users, which we participated in. Based on the feedback from that call, HUD made some significant improvements.

Version 2 of Location Affordability uses new methodology to produce more accurate estimates of housing and transportation costs, so this data which was pretty neat to begin with is now even more powerful. The specifics of the update are pretty wonky, but if you’re reading this blog, you’re probably pretty wonky too, so here are the highlights of the new version, according to HUD:

  • Moving to a Simultaneous Equation Modeling (SEM) approach from Ordinary Least Squares regression: SEM better incorporates and accounts for interaction effects on the model’s dependent variables, resulting in a model that has greater econometric validity
  • Adding variables for housing stock: the model now includes variables for percent of single-family detached housing units and the number of rooms per dwelling unit
  • Adding variables for local commercial amenities
  • Splitting population data by tenure (renter vs. homeowner)

And whereas version 1 covered 94% of the country’s population, version 2 covers 100%.

Also, very importantly, the household types have changed significantly. The affordability data is calculated differently for different households according to number of people, income amount, housing needs, transportation needs, and more.

These are the new household types:

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


This data is free to the public, and is in the Incomes & Spending menu, under “Additional Income & Spending Data”.

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