Our Data Directory: PolicyMap data Sources

Administrative Office of the U.S. Courts

Details:

non-business and business bankruptcy filings

Topics:

bankruptcy filings

Source:

Administrative Office of the U.S. Courts on behalf of the Federal Judiciary

Years Available:

2013

Geographies:

county, state

Free or Subscriber-only:

free

For more information:

http://www.uscourts.gov/Statistics/BankruptcyStatistics/2013-bankruptcy-filings.aspx

Description:

The Administrative Office of the U.S. Courts provides information on consumer and business bankruptcy filings. Where the source data showed bankruptcies in one county in multiple districts (for example, El Paso, Texas bankruptcies in Pennsylvania’s Eastern district as well as Texas's Western district), the counts from the county in each district were added together. Where a county was not listed in the source data, we attributed zero bankruptcies.

For the percent of non-business bankruptcies as a percent of population, we used the age 18 and over population from the Census's County Population Estimates.

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Boxwood Means, Inc.

Details:

number of sales, median sale price, aggregate sales amounts, loan-to-value ratios

Topics:

home sales

Source:

Boxwood Means, Inc.

Years Available:

2006, 2007Q1, 2007Q2, 2007Q3, 2007Q4, 2007, 2008Q1, 2008Q2, 2008Q3, 2008Q4, 2008, 2009Q1, 2009Q2, 2009Q3, 2009Q4, 2009, 2010Q1, 2010Q2, 2010Q3, 2010Q4, 2010, 2011Q1, 2011Q2, 2011Q3, 2011Q4, 2011, 2012Q1, 2012Q2, 2012Q3, 2012Q4, 2012, 2013Q1, 2013Q2

Geographies:

blockgroup, tract, county, place, zipcode, state (Various areas of the country are covered. See complete list of counties below.)

Free or Subscriber-only:

Subscriber-only (PolicyMap is prohibited from providing home sale statistics to certain companies. Please see complete list below.)

For more information:

http://www.boxwoodmeans.com/

Description:

Boxwood Means, Inc., the national real estate research firm calculated median home sale price, residential sales volume, loan-to-value ratio and percent change in the median sale price for several time periods. These indicators were provided to TRF at the summary levels of blockgroup, tract, county, Census place, zipcode, and state. Indicators are shown only for areas where there is complete coverage of all contained geographies. TRF has licensed the use of this data from Boxwood Means for use in its PolicyMap application.

PolicyMap includes the counts and median sale prices of recent home sales for 2006 through 2012 (annual), as well as quarterly figures. Percent changes in median sale prices are available for one, three, and five year intervals. Every few months, updated quarterly data will be added to PolicyMap. Due to the elapsed time between property sale transactions, Boxwood's receipt of county sales reports and their transmission of the data to TRF, PolicyMap data will generally lag two quarters behind the current date.

When PolicyMap refers to residential sales, it means the subset of sales that are at-arms-length transactions, over $5,000 in value and only includes properties with a completed structure; so-called "dollar sales," sales of vacant land, development lots, and multifamily residential buildings are excluded. Real Estate Owned (REO) properties are excluded, as are Sheriff Sale transactions. For single family homes damaged by natural disaster, if they are listed as "Single Family Residence" and show an improvement value of $50,000, they are counted as a transaction. If a home's value is completely lost following a natural disaster (equals $0), it is not counted as a transaction. Change calculations are based on the actual sale price at Time 1 and the actual sale price at Time 2. Change calculations are neither adjusted nor controlled for inflation. TRF does not calculate percent change between quarters at the blockgroup level due to insufficient data.

Boxwood Means receives sale records and census identifiers (blockgroup, tract, county) from its vendor. The vendor geocodes home sales using the US Census 2004 Tiger file. Though Boxwood Means' vendor locates most of the records, between 1% and 10% of the records are not assigned a tract or blockgroup designation. Every record in the database includes a physical address (as opposed to a mailing address, Rural Route address, block-lot, or P.O. box), including the county and zipcode+4. Using these geographic markers, through its own software and methods Boxwood Means assigns a census identifier to the remaining records that lack a blockgroup designation.

Boxwood Means matches sale records to state and county addresses with 100% certainty, and to zipcodes with more than 99% certainty. Because of the lesser degree of certainty at smaller levels of geography, and because records are still assigned to a county even though they may not be assigned to a tract, totals for counties may be greater than the sum of the counts in the tracts contained in the counties. Addresses in rural counties and places experiencing rapid development are inherently more difficult to assign a census geography. Census tract and blockgroup matches are generally very strong -- 98% certainty in most counties – but 2-3% of counties have match rates of only 20-30% certainty, mostly due to poor addressing systems and incomplete street data in these places.

Boxwood Means receives their raw data through a source which collects directly from counties and states. Most counties and states who submit data do so on a consistent and quarterly basis. Some entities, though, submit their home sale data only annually, while some areas' coverage may be intermittent. PolicyMap contains home sale data for the following counties. Counties with asterisks have coverage for some, not all, of the time periods in PolicyMap. If you have questions about the coverage for an area that you are interested in, please contact PolicyMap.

Alabama: Autauga County*, Baldwin County, Barbour County*, Bibb County*, Blount County*, Bullock County*, Butler County*, Calhoun County*, Chambers County*, Chilton County*, Clay County*, Cleburne County*, Coffee County*, Colbert County*, Conecuh County*, Covington County*, Cullman County*, Dale County*, Dallas County*, DeKalb County*, Elmore County*, Escambia County*, Etowah County*, Fayette County*, Geneva County*, Henry County*, Houston County*, Jefferson County*, Lamar County*, Lauderdale County*, Lawrence County*, Lee County*, Limestone County*, Lowndes County*, Macon County*, Madison County*, Marengo County*, Marion County*, Marshall County*, Mobile County, Monroe County*, Montgomery County, Morgan County*, Perry County*, Pike County*, Randolph County*, Russell County*, St. Clair County, Shelby County, Sumter County*, Talladega County*, Tallapoosa County*, Tuscaloosa County*, Walker County*, Winston County*

Alaska: Anchorage Municipality, Fairbanks North Star Borough, Haines Borough*, Juneau City and Borough, Kenai Peninsula Borough, Ketchikan Gateway Borough, Kodiak Island Borough, Matanuska-Susitna Borough, Nome Census Area*, Sitka City and Borough*, Wrangell-Petersburg Census Area*

Arizona: Apache County, Cochise County, Coconino County, Gila County, Graham County, Greenlee County*, La Paz County, Maricopa County, Mohave County, Navajo County, Pima County, Pinal County, Santa Cruz County*, Yavapai County, Yuma County

Arkansas: Arkansas County*, Ashley County*, Baxter County*, Benton County, Boone County, Bradley County*, Calhoun County*, Carroll County*, Chicot County*, Clark County*, Clay County*, Cleburne County*, Cleveland County*, Columbia County*, Conway County*, Craighead County, Crawford County, Crittenden County*, Cross County*, Dallas County*, Desha County*, Drew County*, Faulkner County, Franklin County*, Fulton County*, Garland County, Grant County*, Greene County, Hempstead County*, Hot Spring County*, Howard County*, Independence County*, Izard County*, Jackson County*, Jefferson County*, Johnson County*, Lafayette County*, Lawrence County*, Lee County*, Lincoln County*, Little River County*, Logan County*, Lonoke County, Madison County*, Marion County*, Miller County*, Mississippi County*, Monroe County*, Montgomery County*, Nevada County*, Newton County*, Ouachita County*, Perry County*, Phillips County*, Pike County*, Poinsett County*, Polk County*, Pope County*, Prairie County*, Pulaski County, Randolph County*, St. Francis County*, Saline County, Scott County*, Searcy County*, Sebastian County, Sevier County*, Sharp County*, Stone County*, Union County*, Van Buren County*, Washington County, White County, Woodruff County*, Yell County*

California: Alameda County, Alpine County, Amador County, Butte County, Calaveras County, Colusa County, Contra Costa County, Del Norte County, El Dorado County, Fresno County, Glenn County, Humboldt County, Imperial County, Inyo County, Kern County, Kings County, Lake County, Lassen County, Los Angeles County, Madera County, Marin County, Mariposa County, Mendocino County, Merced County, Modoc County, Mono County, Monterey County, Napa County, Nevada County, Orange County, Placer County, Plumas County, Riverside County, Sacramento County, San Benito County, San Bernardino County, San Diego County, San Francisco County, San Joaquin County, San Luis Obispo County, San Mateo County, Santa Barbara County, Santa Clara County, Santa Cruz County, Shasta County, Sierra County, Siskiyou County, Solano County, Sonoma County, Stanislaus County, Sutter County, Tehama County, Trinity County, Tulare County, Tuolumne County, Ventura County, Yolo County, Yuba County

Colorado: Adams County, Alamosa County*, Arapahoe County, Archuleta County, Baca County*, Bent County*, Boulder County, Chaffee County, Cheyenne County*, Clear Creek County, Conejos County*, Costilla County*, Crowley County*, Custer County*, Delta County, Denver County, Dolores County*, Douglas County, Eagle County, Elbert County, El Paso County, Fremont County, Garfield County, Gilpin County, Grand County, Gunnison County, Hinsdale County*, Huerfano County*, Jackson County*, Jefferson County, Kiowa County*, Kit Carson County*, Lake County*, La Plata County, Larimer County, Las Animas County, Lincoln County, Logan County, Mesa County, Mineral County*, Moffat County*, Montezuma County, Montrose County, Morgan County, Otero County, Ouray County, Park County*, Phillips County*, Pitkin County, Prowers County, Pueblo County, Rio Blanco County*, Rio Grande County, Routt County, Saguache County*, San Juan County*, San Miguel County*, Sedgwick County*, Summit County, Teller County, Washington County*, Weld County, Yuma County*

Connecticut: Fairfield County, Hartford County, Litchfield County, Middlesex County, New Haven County, New London County, Tolland County, Windham County

Delaware: Kent County, New Castle County, Sussex County

District of Columbia: District of Columbia

Florida: Alachua County, Baker County, Bay County, Bradford County, Brevard County, Broward County, Calhoun County, Charlotte County, Citrus County, Clay County, Collier County, Columbia County, DeSoto County, Dixie County, Duval County, Escambia County, Flagler County, Franklin County, Gadsden County, Gilchrist County, Glades County, Gulf County, Hamilton County, Hardee County, Hendry County, Hernando County, Highlands County, Hillsborough County, Holmes County, Indian River County, Jackson County, Jefferson County, Lafayette County, Lake County, Lee County, Leon County, Levy County, Liberty County*, Madison County, Manatee County, Marion County, Martin County, Miami-Dade County, Monroe County, Nassau County, Okaloosa County, Okeechobee County, Orange County, Osceola County, Palm Beach County, Pasco County, Pinellas County, Polk County, Putnam County, St. Johns County, St. Lucie County, Santa Rosa County, Sarasota County, Seminole County, Sumter County, Suwannee County*, Taylor County, Union County*, Volusia County, Wakulla County, Walton County, Washington County

Georgia: Appling County*, Atkinson County*, Bacon County*, Baker County*, Baldwin County*, Banks County, Barrow County, Bartow County, Ben Hill County, Berrien County*, Bibb County, Bleckley County*, Brantley County*, Brooks County*, Bryan County*, Bulloch County*, Burke County*, Butts County, Calhoun County*, Camden County*, Candler County*, Carroll County, Catoosa County, Charlton County*, Chatham County, Chattahoochee County, Chattooga County*, Cherokee County, Clarke County, Clay County*, Clayton County, Clinch County*, Cobb County, Coffee County*, Colquitt County*, Columbia County, Cook County*, Coweta County, Crawford County, Crisp County*, Dade County*, Dawson County, Decatur County*, DeKalb County, Dodge County*, Dooly County*, Dougherty County, Douglas County, Early County*, Echols County*, Effingham County, Elbert County*, Emanuel County*, Evans County*, Fannin County, Fayette County, Floyd County, Forsyth County, Franklin County, Fulton County, Gilmer County, Glascock County*, Glynn County*, Gordon County, Grady County*, Greene County, Gwinnett County, Habersham County, Hall County, Hancock County*, Haralson County, Harris County*, Hart County*, Heard County, Henry County, Houston County, Irwin County*, Jackson County, Jasper County, Jeff Davis County*, Jefferson County*, Jenkins County*, Johnson County*, Jones County*, Lamar County*, Lanier County*, Laurens County*, Lee County*, Liberty County*, Lincoln County*, Long County*, Lowndes County, Lumpkin County, McDuffie County, McIntosh County*, Macon County*, Madison County, Marion County*, Meriwether County*, Miller County*, Mitchell County*, Monroe County*, Montgomery County*, Morgan County*, Murray County*, Muscogee County, Newton County, Oconee County, Oglethorpe County, Paulding County, Peach County*, Pickens County, Pierce County*, Pike County*, Polk County, Pulaski County*, Putnam County, Quitman County*, Rabun County, Randolph County*, Richmond County, Rockdale County, Schley County*, Screven County*, Seminole County*, Spalding County, Stephens County, Stewart County*, Sumter County*, Talbot County*, Taliaferro County*, Tattnall County*, Taylor County*, Telfair County*, Terrell County*, Thomas County*, Tift County*, Toombs County*, Towns County, Treutlen County*, Troup County*, Turner County*, Twiggs County*, Union County, Upson County*, Walker County, Walton County, Ware County*, Warren County*, Washington County*, Wayne County*, Webster County*, Wheeler County*, White County, Whitfield County, Wilcox County*, Wilkes County*, Wilkinson County*, Worth County*

Hawaii: Hawaii County, Honolulu County, Kauai County, Maui County

Idaho: Ada County, Adams County*, Bannock County, Bear Lake County*, Benewah County*, Bingham County, Blaine County*, Boise County*, Bonner County, Bonneville County, Boundary County, Butte County*, Camas County*, Canyon County, Caribou County*, Cassia County*, Clark County*, Custer County*, Elmore County*, Franklin County*, Fremont County*, Gem County*, Gooding County*, Idaho County*, Jefferson County*, Jerome County*, Kootenai County, Latah County*, Lemhi County*, Lewis County*, Lincoln County*, Madison County*, Minidoka County*, Nez Perce County, Oneida County*, Owyhee County*, Payette County*, Power County*, Shoshone County, Teton County*, Twin Falls County*, Valley County, Washington County*

Illinois: Adams County*, Bond County*, Boone County*, Bureau County*, Carroll County*, Cass County*, Champaign County, Christian County*, Clark County*, Clay County*, Clinton County, Coles County, Cook County, Crawford County*, Cumberland County*, DeKalb County, Douglas County*, DuPage County, Edgar County*, Fayette County*, Ford County*, Franklin County*, Fulton County*, Gallatin County*, Grundy County*, Hamilton County*, Hardin County*, Henderson County*, Henry County*, Iroquois County*, Jackson County*, Jasper County*, Jefferson County, Jersey County*, Jo Daviess County*, Johnson County*, Kane County, Kankakee County, Kendall County, Knox County*, Lake County, La Salle County, Lawrence County*, Lee County, Livingston County*, Logan County*, McHenry County, McLean County, Macon County, Macoupin County*, Madison County, Marion County*, Marshall County*, Mason County*, Massac County*, Menard County*, Mercer County*, Monroe County, Montgomery County*, Morgan County*, Ogle County, Peoria County*, Perry County*, Piatt County*, Pope County*, Randolph County, Richland County*, Rock Island County, St. Clair County, Saline County*, Sangamon County, Schuyler County*, Scott County*, Shelby County*, Stark County*, Stephenson County*, Tazewell County, Union County*, Vermilion County, Wabash County*, Washington County*, White County*, Whiteside County*, Will County, Williamson County*, Winnebago County, Woodford County*

Indiana: Adams County*, Allen County, Bartholomew County*, Benton County*, Blackford County*, Boone County*, Brown County*, Carroll County*, Cass County*, Clark County*, Clay County*, Clinton County*, Crawford County*, Daviess County*, Dearborn County*, Decatur County*, DeKalb County*, Delaware County*, Dubois County*, Elkhart County, Fayette County*, Floyd County*, Fountain County*, Franklin County*, Fulton County*, Gibson County*, Grant County*, Greene County*, Hamilton County, Hancock County*, Harrison County*, Hendricks County*, Henry County, Howard County*, Huntington County*, Jackson County*, Jasper County, Jay County*, Jefferson County*, Jennings County*, Johnson County*, Knox County*, Kosciusko County*, LaGrange County*, Lake County, LaPorte County*, Lawrence County*, Madison County, Marion County, Marshall County*, Martin County*, Miami County*, Monroe County*, Montgomery County*, Morgan County*, Newton County*, Noble County*, Ohio County*, Orange County*, Owen County*, Parke County*, Perry County*, Pike County*, Porter County, Posey County*, Pulaski County*, Putnam County*, Randolph County, Ripley County*, Rush County*, St. Joseph County, Scott County*, Shelby County*, Spencer County*, Starke County*, Steuben County*, Sullivan County*, Switzerland County*, Tippecanoe County, Tipton County*, Union County*, Vanderburgh County*, Vermillion County*, Vigo County, Wabash County*, Warren County*, Warrick County*, Washington County*, Wayne County*, Wells County*, White County*, Whitley County*

Iowa: Adair County*, Adams County*, Allamakee County*, Appanoose County*, Audubon County*, Benton County*, Black Hawk County*, Boone County, Bremer County, Buchanan County*, Buena Vista County*, Butler County*, Calhoun County, Carroll County*, Cass County*, Cedar County*, Cerro Gordo County, Cherokee County*, Chickasaw County*, Clarke County*, Clay County, Clayton County*, Clinton County*, Crawford County, Dallas County, Davis County*, Decatur County*, Delaware County*, Des Moines County*, Dickinson County*, Dubuque County, Emmet County*, Fayette County*, Floyd County*, Franklin County*, Fremont County*, Greene County*, Grundy County*, Guthrie County*, Hamilton County, Hancock County, Hardin County*, Harrison County*, Henry County*, Howard County*, Humboldt County*, Ida County*, Iowa County*, Jackson County*, Jasper County*, Jefferson County*, Johnson County*, Jones County*, Keokuk County*, Kossuth County*, Lee County, Linn County, Louisa County, Lucas County*, Lyon County*, Madison County*, Mahaska County*, Marion County*, Marshall County*, Mills County*, Mitchell County*, Monona County*, Monroe County*, Montgomery County, Muscatine County*, OBrien County*, Osceola County*, Page County*, Palo Alto County*, Plymouth County*, Pocahontas County*, Polk County, Pottawattamie County, Poweshiek County*, Ringgold County*, Sac County*, Scott County, Shelby County*, Sioux County*, Story County, Tama County*, Taylor County*, Union County*, Van Buren County*, Wapello County*, Warren County, Washington County*, Wayne County*, Webster County*, Winnebago County*, Winneshiek County*, Woodbury County*, Worth County*, Wright County*

Kansas: Allen County*, Anderson County*, Barber County*, Barton County*, Bourbon County*, Brown County*, Butler County, Chase County*, Chautauqua County*, Cherokee County*, Cheyenne County*, Clark County*, Clay County*, Cloud County*, Coffey County*, Comanche County*, Cowley County*, Crawford County*, Decatur County*, Dickinson County*, Doniphan County*, Douglas County, Edwards County*, Elk County*, Ellis County*, Ellsworth County*, Finney County*, Ford County*, Franklin County*, Geary County*, Gove County*, Graham County*, Grant County*, Gray County*, Greeley County*, Greenwood County*, Hamilton County*, Harper County*, Harvey County*, Haskell County*, Hodgeman County*, Jackson County*, Jefferson County*, Jewell County*, Johnson County, Kearny County*, Kingman County*, Kiowa County*, Labette County*, Lane County*, Leavenworth County, Lincoln County*, Linn County*, Logan County*, Lyon County*, McPherson County*, Marion County*, Meade County*, Miami County*, Mitchell County*, Montgomery County*, Morris County*, Morton County*, Nemaha County*, Neosho County*, Ness County*, Norton County*, Osage County*, Osborne County*, Ottawa County*, Pawnee County*, Phillips County*, Pottawatomie County*, Pratt County*, Rawlins County*, Reno County*, Republic County*, Rice County*, Riley County*, Rooks County*, Rush County*, Russell County*, Saline County*, Scott County*, Sedgwick County, Seward County*, Shawnee County, Sheridan County*, Sherman County*, Smith County*, Stafford County*, Stanton County*, Stevens County*, Sumner County*, Thomas County*, Trego County*, Wabaunsee County*, Wallace County*, Washington County*, Wichita County*, Wilson County*, Woodson County*, Wyandotte County

Kentucky: Adair County*, Allen County*, Anderson County*, Ballard County, Barren County*, Bath County*, Bell County*, Boone County, Bourbon County, Boyd County*, Boyle County*, Bracken County*, Breathitt County*, Breckinridge County*, Bullitt County*, Butler County*, Caldwell County*, Calloway County, Campbell County, Carroll County*, Carter County*, Casey County*, Christian County, Clark County*, Clay County, Clinton County*, Crittenden County*, Cumberland County*, Daviess County*, Edmonson County*, Estill County*, Fayette County*, Fleming County*, Floyd County*, Franklin County*, Fulton County*, Gallatin County*, Grant County*, Graves County*, Green County*, Greenup County*, Hancock County*, Hardin County*, Harlan County*, Harrison County, Hart County*, Henderson County*, Henry County*, Hickman County*, Hopkins County*, Jackson County*, Jefferson County, Johnson County*, Kenton County, Larue County*, Laurel County*, Lawrence County*, Lee County*, Letcher County*, Lewis County*, Lincoln County*, Logan County*, Lyon County*, McCracken County*, McCreary County*, McLean County*, Madison County*, Marion County*, Marshall County*, Martin County*, Mason County*, Meade County*, Mercer County*, Metcalfe County*, Monroe County*, Montgomery County*, Morgan County*, Muhlenberg County*, Nelson County*, Nicholas County*, Ohio County*, Oldham County*, Owen County*, Owsley County*, Pendleton County, Perry County*, Pike County*, Powell County*, Pulaski County*, Robertson County*, Rockcastle County*, Rowan County*, Russell County*, Scott County*, Shelby County*, Simpson County*, Spencer County*, Taylor County*, Todd County*, Trigg County, Trimble County*, Union County*, Warren County*, Washington County*, Wayne County*, Webster County, Whitley County*, Wolfe County*, Woodford County*

Louisiana: Acadia Parish*, Allen Parish*, Ascension Parish*, Assumption Parish*, Avoyelles Parish*, Beauregard Parish*, Bienville Parish*, Bossier Parish, Caddo Parish*, Calcasieu Parish*, Caldwell Parish*, Cameron Parish*, Catahoula Parish*, Claiborne Parish*, Concordia Parish*, De Soto Parish*, East Baton Rouge Parish, East Carroll Parish*, East Feliciana Parish*, Evangeline Parish*, Franklin Parish*, Grant Parish*, Iberia Parish*, Iberville Parish*, Jackson Parish*, Jefferson Parish, Lafayette Parish*, Lafourche Parish*, La Salle Parish*, Lincoln Parish*, Livingston Parish*, Madison Parish*, Morehouse Parish*, Natchitoches Parish*, Orleans Parish, Ouachita Parish*, Plaquemines Parish*, Pointe Coupee Parish*, Rapides Parish*, Red River Parish*, Richland Parish*, Sabine Parish*, St. Bernard Parish*, St. Charles Parish*, St. Helena Parish*, St. James Parish*, St. John the Baptist Parish*, St. Landry Parish*, St. Martin Parish*, St. Mary Parish*, St. Tammany Parish, Tangipahoa Parish, Tensas Parish*, Terrebonne Parish*, Union Parish*, Vernon Parish*, Washington Parish*, Webster Parish*, West Baton Rouge Parish*, West Carroll Parish*, West Feliciana Parish*

Maine: Androscoggin County*, Aroostook County*, Cumberland County*, Franklin County*, Hancock County*, Kennebec County*, Knox County*, Lincoln County*, Penobscot County*, Piscataquis County*, Sagadahoc County*, Somerset County*, Waldo County*, Washington County*, York County

Maryland: Allegany County, Anne Arundel County, Baltimore County, Calvert County, Caroline County, Carroll County, Cecil County, Charles County, Dorchester County, Frederick County, Garrett County, Harford County, Howard County, Kent County, Montgomery County, Prince Georges County, Queen Annes County, St. Marys County, Somerset County, Talbot County, Washington County, Wicomico County, Worcester County, Baltimore City

Massachusetts: Barnstable County, Berkshire County, Bristol County, Dukes County, Essex County, Franklin County, Hampden County, Hampshire County, Middlesex County, Nantucket County, Norfolk County, Plymouth County, Suffolk County, Worcester County

Michigan: Alcona County*, Alger County*, Allegan County, Alpena County*, Antrim County*, Arenac County*, Baraga County*, Barry County, Bay County, Benzie County*, Berrien County*, Branch County*, Calhoun County, Cass County*, Cheboygan County*, Clare County*, Clinton County, Crawford County*, Delta County*, Dickinson County*, Eaton County, Emmet County*, Genesee County, Gladwin County*, Grand Traverse County*, Gratiot County*, Hillsdale County*, Houghton County*, Ingham County, Ionia County, Iosco County*, Iron County*, Isabella County*, Jackson County*, Kalamazoo County, Kalkaska County*, Kent County, Keweenaw County*, Lake County*, Lapeer County, Leelanau County*, Lenawee County*, Livingston County, Luce County*, Macomb County, Manistee County*, Marquette County*, Mason County*, Mecosta County*, Menominee County*, Midland County, Missaukee County*, Monroe County, Montcalm County*, Montmorency County*, Muskegon County, Newaygo County*, Oakland County, Oceana County*, Ogemaw County*, Ontonagon County*, Osceola County*, Otsego County*, Ottawa County, Presque Isle County*, Roscommon County*, Saginaw County, St. Clair County, St. Joseph County*, Sanilac County*, Shiawassee County, Tuscola County*, Van Buren County, Washtenaw County, Wayne County, Wexford County*

Minnesota: Aitkin County*, Anoka County, Becker County*, Beltrami County*, Benton County*, Big Stone County*, Blue Earth County, Brown County*, Carlton County*, Carver County, Cass County*, Chippewa County*, Chisago County, Clay County, Clearwater County*, Cook County*, Cottonwood County*, Dakota County, Dodge County*, Douglas County*, Faribault County, Fillmore County*, Freeborn County*, Goodhue County, Grant County*, Hennepin County, Houston County*, Hubbard County, Isanti County, Itasca County*, Jackson County*, Kanabec County*, Kandiyohi County, Koochiching County, Lac qui Parle County*, Lake County, Le Sueur County, Lincoln County, Lyon County*, Martin County, Meeker County*, Mille Lacs County*, Morrison County*, Murray County*, Nicollet County, Nobles County*, Norman County*, Olmsted County*, Otter Tail County*, Pennington County*, Pine County*, Polk County*, Pope County, Ramsey County, Red Lake County*, Redwood County*, Renville County*, Rice County, Rock County*, St. Louis County, Scott County, Sherburne County, Stearns County, Steele County*, Todd County*, Traverse County*, Wabasha County*, Wadena County*, Waseca County*, Washington County, Watonwan County*, Wilkin County*, Winona County*, Wright County, Yellow Medicine County*

Mississippi: Adams County*, Attala County*, Bolivar County*, Calhoun County*, Chickasaw County*, Clarke County*, Copiah County*, Covington County*, DeSoto County, Forrest County*, Greene County*, Grenada County*, Issaquena County*, Jefferson Davis County*, Jones County*, Lafayette County*, Lamar County*, Lee County*, Lincoln County, Lowndes County*, Madison County*, Pearl River County*, Perry County*, Pike County*, Pontotoc County*, Rankin County*, Simpson County*, Sunflower County*, Tallahatchie County*, Tate County*, Tippah County*, Tishomingo County*, Tunica County*, Union County*, Walthall County*, Warren County*, Washington County*, Wayne County*, Webster County*, Wilkinson County*, Winston County*, Yalobusha County*, Yazoo County*

Missouri: Adair County*, Andrew County*, Audrain County*, Bates County*, Boone County*, Buchanan County*, Butler County, Cape Girardeau County*, Cass County, Cedar County*, Christian County*, Clay County, Cooper County*, Crawford County*, Dallas County*, Dent County*, Dunklin County*, Franklin County, Greene County, Grundy County*, Harrison County*, Henry County*, Howell County, Jackson County, Jasper County, Jefferson County, Johnson County*, Lafayette County*, Lincoln County*, Linn County*, McDonald County*, Macon County*, Maries County*, Morgan County*, Newton County*, Ozark County*, Pemiscot County*, Perry County, Pettis County*, Phelps County*, Pike County*, Platte County, Polk County*, Pulaski County*, Putnam County*, Ralls County*, Randolph County*, Ray County*, Reynolds County*, Ripley County*, St. Charles County, St. Clair County*, Ste. Genevieve County*, St. Francois County*, St. Louis County, Saline County*, Schuyler County*, Scotland County*, Scott County*, Shannon County*, Shelby County*, Stoddard County*, Stone County*, Sullivan County*, Taney County*, Texas County*, Vernon County*, Warren County*, Washington County*, Wayne County*, Webster County*, Worth County*, Wright County*, St. Louis City

Montana: Beaverhead County*, Broadwater County*, Carbon County*, Cascade County, Dawson County, Deer Lodge County, Fergus County, Flathead County, Gallatin County, Glacier County*, Granite County*, Jefferson County*, Judith Basin County*, Lake County, Lewis and Clark County, Liberty County*, Lincoln County, McCone County*, Madison County, Meagher County*, Mineral County, Missoula County, Park County*, Petroleum County*, Powder River County*, Ravalli County, Sanders County*, Toole County*, Yellowstone County

Nebraska: Adams County*, Antelope County*, Banner County*, Blaine County*, Boone County*, Box Butte County*, Boyd County*, Brown County*, Buffalo County*, Burt County*, Butler County*, Cass County, Cedar County*, Chase County*, Cherry County*, Cheyenne County*, Clay County*, Colfax County, Cuming County, Custer County*, Dakota County, Dawes County*, Dawson County*, Deuel County*, Dixon County*, Dodge County*, Douglas County, Dundy County*, Fillmore County*, Franklin County*, Frontier County*, Furnas County*, Gage County*, Garden County*, Garfield County*, Gosper County*, Greeley County*, Hall County*, Hamilton County*, Harlan County*, Hayes County*, Hitchcock County*, Holt County*, Hooker County*, Howard County*, Jefferson County*, Johnson County*, Kearney County*, Keith County*, Keya Paha County*, Kimball County*, Knox County*, Lancaster County, Lincoln County*, Logan County*, Loup County*, McPherson County*, Madison County*, Merrick County*, Morrill County*, Nance County*, Nemaha County*, Nuckolls County*, Otoe County*, Pawnee County*, Perkins County*, Phelps County*, Pierce County*, Platte County*, Polk County*, Red Willow County, Richardson County*, Rock County*, Saline County*, Sarpy County, Saunders County, Scotts Bluff County, Seward County*, Sheridan County*, Sherman County*, Sioux County*, Stanton County*, Thayer County*, Thomas County*, Thurston County*, Valley County*, Washington County, Wayne County*, Webster County*, Wheeler County*, York County*

Nevada: Churchill County, Clark County, Douglas County, Elko County, Esmeralda County*, Eureka County*, Humboldt County, Lander County, Lincoln County*, Lyon County, Mineral County*, Nye County, Pershing County*, Storey County*, Washoe County, White Pine County, Carson City

New Hampshire: Belknap County*, Carroll County*, Cheshire County*, Coos County*, Grafton County*, Hillsborough County, Merrimack County*, Rockingham County*, Strafford County*, Sullivan County*

New Jersey: Atlantic County, Bergen County, Burlington County, Camden County, Cape May County, Cumberland County, Essex County, Gloucester County, Hudson County, Hunterdon County, Mercer County, Middlesex County, Monmouth County, Morris County, Ocean County, Passaic County, Salem County*, Somerset County, Sussex County, Union County, Warren County

New Mexico: Bernalillo County, Chaves County, Cibola County*, Colfax County*, Curry County*, Dona Ana County, Eddy County, Guadalupe County*, Lincoln County, Los Alamos County*, Otero County, Quay County*, Rio Arriba County*, Roosevelt County*, Sandoval County, Santa Fe County, Sierra County*, Torrance County*, Valencia County

New York: Albany County, Allegany County, Bronx County, Broome County, Cattaraugus County, Cayuga County, Chautauqua County, Chemung County, Chenango County, Clinton County, Columbia County, Cortland County, Delaware County, Dutchess County, Erie County, Essex County, Franklin County, Fulton County, Genesee County, Greene County, Hamilton County, Herkimer County, Jefferson County, Kings County, Lewis County, Livingston County, Madison County, Monroe County, Montgomery County, Nassau County, New York County, Niagara County, Oneida County, Onondaga County, Ontario County, Orange County, Orleans County, Oswego County, Otsego County, Putnam County, Queens County, Rensselaer County, Richmond County, Rockland County, St. Lawrence County, Saratoga County, Schenectady County, Schoharie County, Schuyler County, Seneca County, Steuben County, Suffolk County, Sullivan County, Tioga County, Tompkins County, Ulster County, Warren County, Washington County, Wayne County, Westchester County, Wyoming County, Yates County

North Carolina: Alamance County, Alexander County, Alleghany County, Anson County, Ashe County*, Avery County, Beaufort County*, Bertie County, Bladen County, Brunswick County, Buncombe County, Burke County*, Cabarrus County, Caldwell County, Camden County, Carteret County, Caswell County*, Catawba County, Chatham County, Cherokee County, Chowan County*, Clay County, Cleveland County, Columbus County, Craven County, Cumberland County, Currituck County, Dare County, Davidson County, Davie County, Duplin County*, Durham County, Edgecombe County*, Forsyth County, Franklin County, Gaston County, Gates County, Graham County*, Granville County, Greene County*, Guilford County, Halifax County, Harnett County, Haywood County, Henderson County, Hertford County, Hoke County, Hyde County*, Iredell County, Jackson County, Johnston County, Jones County, Lee County, Lenoir County*, Lincoln County, McDowell County, Macon County*, Madison County, Martin County, Mecklenburg County, Mitchell County*, Montgomery County, Moore County, Nash County*, New Hanover County, Northampton County, Onslow County*, Orange County, Pamlico County*, Pasquotank County*, Pender County, Perquimans County*, Person County, Pitt County*, Polk County*, Randolph County, Richmond County*, Robeson County, Rockingham County, Rowan County, Rutherford County, Sampson County*, Scotland County, Stanly County, Stokes County, Surry County*, Swain County*, Transylvania County, Tyrrell County*, Union County, Vance County, Wake County, Warren County*, Washington County, Watauga County, Wayne County*, Wilkes County, Wilson County, Yadkin County, Yancey County*

North Dakota: Adams County*, Barnes County, Benson County*, Billings County*, Bottineau County*, Bowman County*, Burke County*, Burleigh County, Cass County, Cavalier County*, Dickey County*, Divide County*, Dunn County*, Emmons County*, Foster County*, Golden Valley County*, Grand Forks County, Grant County*, Griggs County*, Hettinger County*, Kidder County*, LaMoure County*, McHenry County*, McKenzie County*, McLean County*, Mercer County*, Morton County, Mountrail County*, Nelson County*, Oliver County*, Pembina County, Pierce County*, Ramsey County, Ransom County*, Renville County*, Richland County, Rolette County*, Sargent County, Sheridan County*, Slope County*, Stark County, Steele County*, Stutsman County, Towner County*, Traill County*, Walsh County, Ward County, Wells County*, Williams County

Ohio: Adams County*, Allen County, Ashland County, Ashtabula County, Athens County, Auglaize County, Belmont County, Brown County, Butler County, Carroll County, Champaign County, Clark County, Clermont County, Clinton County, Columbiana County, Coshocton County, Crawford County*, Cuyahoga County, Darke County, Defiance County*, Delaware County, Erie County, Fairfield County, Fayette County, Franklin County, Fulton County*, Gallia County*, Geauga County, Greene County, Guernsey County*, Hamilton County, Hancock County, Hardin County*, Harrison County*, Henry County, Highland County, Hocking County*, Holmes County*, Huron County, Jackson County, Jefferson County*, Knox County, Lake County, Lawrence County, Licking County, Logan County, Lorain County, Lucas County, Madison County, Mahoning County, Marion County*, Medina County, Meigs County*, Mercer County, Miami County, Monroe County*, Montgomery County, Morgan County*, Morrow County, Muskingum County, Noble County*, Ottawa County, Paulding County, Perry County*, Pickaway County, Pike County, Portage County, Preble County, Putnam County*, Richland County, Ross County, Sandusky County, Scioto County*, Seneca County*, Shelby County, Stark County, Summit County, Trumbull County, Tuscarawas County, Union County, Van Wert County, Vinton County*, Warren County, Washington County, Wayne County, Williams County*, Wood County, Wyandot County*

Oklahoma: Adair County*, Alfalfa County*, Atoka County*, Beaver County*, Beckham County*, Blaine County*, Bryan County*, Caddo County*, Canadian County, Carter County*, Cherokee County, Choctaw County*, Cimarron County*, Cleveland County, Coal County*, Comanche County*, Cotton County*, Craig County*, Creek County, Custer County*, Delaware County, Dewey County*, Ellis County*, Garfield County*, Garvin County*, Grady County, Grant County*, Greer County*, Harmon County*, Harper County*, Haskell County*, Hughes County*, Jackson County*, Jefferson County*, Johnston County*, Kay County, Kingfisher County, Kiowa County*, Latimer County*, Le Flore County*, Lincoln County*, Logan County, Love County*, McClain County, McCurtain County*, McIntosh County*, Major County*, Marshall County*, Mayes County, Murray County*, Muskogee County*, Noble County*, Nowata County*, Okfuskee County*, Oklahoma County, Okmulgee County*, Osage County, Ottawa County*, Pawnee County*, Payne County, Pittsburg County*, Pontotoc County*, Pottawatomie County, Pushmataha County*, Roger Mills County*, Rogers County, Seminole County*, Sequoyah County*, Stephens County*, Texas County*, Tillman County*, Tulsa County, Wagoner County*, Washington County, Washita County*, Woods County*, Woodward County*

Oregon: Baker County*, Benton County, Clackamas County, Clatsop County, Columbia County, Coos County, Crook County*, Curry County*, Deschutes County, Douglas County, Gilliam County*, Grant County*, Harney County*, Hood River County*, Jackson County, Jefferson County*, Josephine County, Klamath County, Lake County*, Lane County, Lincoln County, Linn County, Malheur County*, Marion County, Morrow County*, Multnomah County, Polk County, Sherman County*, Tillamook County, Umatilla County, Union County*, Wasco County*, Washington County, Wheeler County*, Yamhill County

Pennsylvania: Adams County*, Allegheny County, Armstrong County*, Beaver County, Bedford County*, Berks County, Blair County*, Bradford County*, Bucks County, Butler County, Cambria County*, Cameron County*, Carbon County*, Centre County, Chester County, Clarion County*, Clearfield County*, Clinton County*, Columbia County*, Crawford County*, Cumberland County, Dauphin County, Delaware County, Elk County*, Erie County, Fayette County*, Forest County*, Franklin County*, Fulton County*, Greene County*, Huntingdon County*, Indiana County, Jefferson County*, Juniata County*, Lackawanna County*, Lancaster County, Lawrence County, Lebanon County*, Lehigh County, Luzerne County, Lycoming County*, McKean County*, Mercer County*, Mifflin County*, Monroe County, Montgomery County, Montour County, Northampton County, Northumberland County*, Perry County*, Philadelphia County*, Pike County*, Potter County*, Schuylkill County*, Snyder County*, Somerset County*, Susquehanna County*, Tioga County*, Union County*, Venango County*, Warren County*, Washington County, Wayne County*, Westmoreland County, Wyoming County*, York County*

Rhode Island: Bristol County, Kent County, Newport County, Providence County, Washington County

South Carolina: Abbeville County*, Aiken County, Allendale County*, Anderson County, Bamberg County*, Beaufort County, Berkeley County, Calhoun County*, Charleston County, Cherokee County*, Chester County*, Chesterfield County*, Clarendon County*, Colleton County, Darlington County, Dillon County*, Dorchester County, Edgefield County*, Fairfield County*, Florence County, Georgetown County, Greenville County, Greenwood County*, Hampton County*, Horry County*, Jasper County*, Kershaw County*, Lancaster County, Laurens County*, Lee County*, Lexington County, McCormick County*, Newberry County*, Oconee County, Orangeburg County, Pickens County*, Richland County, Saluda County*, Spartanburg County, Sumter County, Union County*, Williamsburg County*, York County

South Dakota: Beadle County*, Codington County*, Custer County*, Davison County*, Day County*, Douglas County*, Fall River County*, Faulk County*, Harding County*, Hutchinson County*, Lincoln County*, Minnehaha County*, Moody County*, Potter County*, Roberts County*, Sanborn County*, Shannon County*, Spink County*, Union County*, Yankton County*

Tennessee: Anderson County, Bedford County, Benton County, Bledsoe County, Blount County*, Bradley County, Campbell County, Cannon County, Carroll County, Carter County, Cheatham County, Chester County, Claiborne County, Clay County, Cocke County, Coffee County, Crockett County, Cumberland County, Davidson County, Decatur County, DeKalb County*, Dickson County, Dyer County, Fayette County, Fentress County, Franklin County, Gibson County, Giles County, Grainger County, Greene County, Grundy County, Hamblen County, Hamilton County, Hancock County, Hardeman County, Hardin County, Hawkins County, Haywood County, Henderson County, Henry County, Hickman County, Houston County, Humphreys County, Jackson County, Jefferson County, Johnson County, Knox County, Lake County, Lauderdale County, Lawrence County, Lewis County, Lincoln County, Loudon County, McMinn County, McNairy County, Macon County, Madison County, Marion County, Marshall County, Maury County, Meigs County, Monroe County, Montgomery County, Moore County, Morgan County, Obion County*, Overton County, Perry County, Pickett County*, Polk County, Putnam County, Rhea County, Roane County, Robertson County, Rutherford County, Scott County*, Sequatchie County, Sevier County, Shelby County, Smith County, Stewart County, Sullivan County*, Sumner County, Tipton County, Trousdale County, Unicoi County, Union County, Van Buren County, Warren County, Washington County, Wayne County, Weakley County, White County, Williamson County, Wilson County

Texas: Anderson County*, Angelina County, Aransas County, Archer County*, Atascosa County, Bandera County, Bastrop County, Bee County*, Bell County*, Bexar County, Blanco County, Bosque County*, Brazoria County, Brazos County, Brown County*, Burnet County, Calhoun County*, Callahan County*, Cameron County, Carson County*, Cass County*, Castro County*, Chambers County, Cherokee County*, Clay County*, Coleman County*, Collin County, Comal County, Comanche County*, Cooke County, Coryell County*, Crane County*, Crosby County*, Culberson County*, Dallas County, Denton County, DeWitt County*, Donley County*, Duval County*, Eastland County*, Ector County*, Edwards County*, Ellis County, El Paso County, Erath County*, Fannin County*, Fort Bend County, Franklin County*, Freestone County*, Frio County*, Gaines County*, Galveston County, Garza County*, Gillespie County, Gonzales County, Gray County*, Grayson County, Gregg County, Grimes County, Guadalupe County, Hale County, Hall County*, Hamilton County*, Hansford County*, Hardeman County*, Hardin County, Harris County, Harrison County, Hartley County*, Haskell County*, Hays County, Hemphill County*, Henderson County, Hidalgo County*, Hill County, Hockley County*, Hood County, Hopkins County, Houston County*, Howard County*, Hudspeth County*, Hunt County, Hutchinson County*, Irion County*, Jack County*, Jackson County, Jasper County*, Jeff Davis County*, Jefferson County, Jim Hogg County*, Jim Wells County*, Johnson County, Jones County*, Karnes County*, Kaufman County, Kendall County, Kenedy County*, Kent County*, Kerr County*, Kimble County*, King County*, Kinney County*, Kleberg County, Knox County*, Lamar County, Lamb County*, Lampasas County*, La Salle County*, Lavaca County*, Lee County, Leon County, Liberty County, Limestone County*, Lipscomb County*, Live Oak County, Llano County, Loving County*, Lubbock County, Lynn County*, McCulloch County*, McLennan County, McMullen County*, Madison County*, Marion County*, Martin County*, Mason County*, Matagorda County*, Maverick County, Medina County*, Menard County*, Midland County, Milam County*, Mills County*, Mitchell County, Montague County*, Montgomery County, Moore County*, Morris County*, Motley County*, Nacogdoches County*, Navarro County, Newton County*, Nolan County*, Nueces County, Ochiltree County*, Oldham County*, Orange County, Palo Pinto County, Panola County*, Parker County, Parmer County*, Pecos County*, Polk County*, Potter County, Presidio County*, Rains County*, Randall County, Reagan County*, Real County*, Red River County*, Reeves County*, Refugio County*, Roberts County*, Robertson County, Rockwall County, Runnels County*, Rusk County*, Sabine County*, San Augustine County*, San Jacinto County*, San Patricio County, San Saba County*, Schleicher County*, Scurry County*, Shackelford County*, Shelby County*, Sherman County*, Smith County, Somervell County, Starr County*, Stephens County*, Sterling County*, Stonewall County*, Sutton County*, Swisher County*, Tarrant County, Taylor County*, Terrell County*, Terry County*, Throckmorton County*, Titus County*, Tom Green County, Travis County, Trinity County*, Tyler County*, Upshur County*, Upton County*, Uvalde County*, Val Verde County*, Van Zandt County*, Victoria County, Walker County*, Waller County*, Ward County*, Washington County*, Webb County, Wharton County*, Wheeler County*, Wichita County, Wilbarger County*, Willacy County*, Williamson County, Wilson County, Winkler County*, Wise County, Wood County, Yoakum County*, Young County*, Zapata County*, Zavala County*

Utah: Beaver County*, Box Elder County, Cache County*, Carbon County*, Davis County, Iron County*, Millard County*, Salt Lake County, Sevier County*, Summit County, Tooele County, Uintah County*, Utah County, Wasatch County, Washington County, Weber County

Vermont: Addison County*, Bennington County*, Caledonia County*, Chittenden County, Essex County*, Franklin County*, Grand Isle County*, Lamoille County*, Orange County*, Orleans County*, Rutland County, Washington County, Windham County*, Windsor County*

Virginia: Accomack County*, Albemarle County*, Alleghany County*, Amherst County*, Appomattox County*, Arlington County, Augusta County*, Bath County*, Bland County*, Botetourt County*, Brunswick County*, Buchanan County*, Buckingham County*, Campbell County*, Caroline County, Carroll County*, Chesterfield County, Clarke County, Craig County*, Culpeper County, Dickenson County*, Dinwiddie County*, Fairfax County, Fauquier County, Floyd County*, Fluvanna County*, Franklin County*, Frederick County, Gloucester County*, Goochland County*, Grayson County*, Greensville County*, Halifax County*, Hanover County, Henrico County, Highland County*, Isle of Wight County*, James City County, King George County, King William County*, Lancaster County*, Loudoun County, Louisa County, Lunenburg County*, Madison County*, Mathews County*, Mecklenburg County*, Middlesex County*, Montgomery County*, Nelson County*, New Kent County*, Orange County, Page County*, Pittsylvania County*, Powhatan County*, Prince Edward County*, Prince George County*, Prince William County, Pulaski County*, Rappahannock County, Richmond County*, Roanoke County*, Rockingham County, Scott County*, Shenandoah County*, Smyth County, Spotsylvania County, Stafford County, Tazewell County*, Warren County, Washington County*, Westmoreland County*, Wise County*, Wythe County*, York County, Alexandria City, Bedford City*, Bristol City*, Buena Vista City*, Charlottesville City*, Chesapeake City, Clifton Forge City*, Colonial Heights City*, Covington City*, Danville City, Emporia City*, Fairfax City County*, Falls Church City*, Franklin City*, Fredericksburg City, Galax City*, Hampton City, Harrisonburg City*, Hopewell City*, Lexington City*, Lynchburg City*, Manassas City*, Manassas Park City*, Martinsville City*, Newport News City, Norfolk City, Norton City*, Petersburg City*, Poquoson City*, Portsmouth City, Radford City*, Richmond City, Roanoke City*, Salem City*, Staunton City*, Suffolk City, Virginia Beach City, Waynesboro City*, Williamsburg City, Winchester City

Washington: Adams County, Asotin County, Benton County, Chelan County, Clallam County*, Clark County, Columbia County, Cowlitz County, Douglas County, Ferry County*, Franklin County, Grant County, Grays Harbor County, Island County, Jefferson County, King County, Kitsap County, Kittitas County, Lewis County, Mason County, Okanogan County, Pacific County, Pend Oreille County*, Pierce County, San Juan County, Skagit County, Skamania County, Snohomish County, Spokane County, Stevens County*, Thurston County, Wahkiakum County*, Walla Walla County*, Whatcom County, Yakima County

West Virginia: Barbour County*, Berkeley County*, Boone County*, Braxton County*, Brooke County*, Cabell County*, Calhoun County*, Clay County*, Doddridge County*, Fayette County*, Gilmer County*, Grant County*, Hampshire County*, Hardy County*, Harrison County*, Jefferson County*, Kanawha County, Lincoln County*, Logan County*, Marion County*, Marshall County*, Mason County*, Mercer County*, Mineral County*, Mingo County*, Monongalia County*, Monroe County*, Morgan County*, Nicholas County*, Pendleton County*, Pocahontas County*, Preston County*, Putnam County*, Raleigh County*, Roane County*, Summers County*, Taylor County*, Tucker County*, Tyler County*, Wayne County*, Wetzel County*, Wirt County*, Wood County*, Wyoming County*

Wisconsin: Adams County*, Ashland County*, Barron County*, Bayfield County*, Brown County, Buffalo County*, Burnett County, Calumet County, Chippewa County*, Clark County*, Columbia County*, Crawford County*, Dane County, Dodge County, Door County*, Douglas County*, Dunn County*, Eau Claire County*, Florence County*, Fond du Lac County*, Forest County*, Grant County*, Green County*, Green Lake County*, Iowa County*, Iron County*, Jackson County*, Jefferson County, Juneau County*, Kenosha County*, Kewaunee County*, La Crosse County*, Lafayette County*, Langlade County*, Lincoln County*, Manitowoc County, Marathon County, Marinette County*, Marquette County*, Menominee County*, Milwaukee County, Monroe County*, Oconto County*, Oneida County*, Outagamie County, Ozaukee County*, Pepin County*, Pierce County*, Polk County*, Portage County*, Price County*, Racine County, Richland County*, Rock County*, Rusk County*, St. Croix County, Sauk County, Sawyer County*, Shawano County*, Sheboygan County*, Taylor County*, Trempealeau County*, Vernon County*, Vilas County*, Walworth County*, Washburn County*, Washington County*, Waukesha County, Waupaca County, Waushara County*, Winnebago County, Wood County*

Wyoming: Albany County*, Big Horn County*, Carbon County*, Crook County*, Fremont County*, Laramie County, Lincoln County*, Natrona County, Park County*, Sublette County*, Sweetwater County*, Teton County*

PROHIBITED SUBSCRIBERS TO HOME SALE DATA:

Our suppliers may have placed restrictions on the redistribution of home sale information to certain subscribers through PolicyMap. As a result, PolicyMap is prohibited from providing home sale statistics to the companies listed below (unless authorized by our suppliers with prior written approval).

By accessing home sale data in PolicyMap, you acknowledge that you are not an employee of the following mortgage originating institutions: Countrywide Financial Corp., Wells Fargo Home Mortgage, Washington Mutual, Chase Home Finance, Bank of America, CitiMortgage, Inc., GMAC-RFC, GMAC Residential Holdings, IndyMac Bancorp, Inc., Wachovia, American Home Mortgage Investment, Golden West Financial Corp./Wold, SunTrust Mortgage, Inc., National City Mortgage, Aurora Loan Services, Inc. (AA), PHH Mortgage, ABN Amro Mortgage, First Horizon Home Loans, GreenPoint Mortgage Funding, and MortgageIT. You also acknowledge that you are not an employee of one of the following companies: Acxiom, America Online (AOL), C&S Marketing, Choice Point, CoStar Group, Database America, DataQuick, Data Warehouse, Dolan Information Services, Domania, Donnelley, Experian, Equifax, Fidelity National Infomration Services (FNIS), First Data Solutions, FIServ, FNC, Google, Haines, InfoUSA, International Data Management (IDM), iPlace, Lending Tree, Lexis/Nexis, MacDonald-Detweiler, MicroGeneral Corporation, National Information Services, Polk, Seisint, Stewart Title/ Stewart Information, SW Financial, Thompason-West Group, TransUnion, US Search, Veros and Yahoo.

If you are an employee of one of the above listed companies and wish to become a PolicyMap subscriber with access to home sale data, please contact TRF at pmap@policymap.com. We will approach our suppliers on your behalf.

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The Brookings Institution and IRS

Topics:

Returns, New Returns, EITC, Child Tax Credit, Additional Child Tax Credit, Child and Dependent Care Expenses, Education Credits, Student Loan Interest Deduction, Returns with Refund, Returns with Balance Due, Direct Deposit Refund, Refund Anticipation Loan, Refund Anticipation Check, Prepared by Taxpayer, Prepared by Paid Preparer, Prepared by Volunteer Organization, Filed through Free File Alliance, Form 1040, Form 1040A, Form 1040EZ, Returns Filed with ITIN, Returns with Schedules C, E, and/or F, By Adjusted Gross Income

Source:

Metropolitan Policy Program at Brookings

Years Available:

2000-2011

Geographies:

zip code, place, lower state legislative district, upper state legislative district, county, CBSA, congressional district, state

Free or Subscriber-only:

free

For more information:

http://www.brookings.edu/metro/EITC/EITC-Homepage.aspx

Description:

The Metropolitan Policy Program at Brookings has researched and written on the Earned Income Tax Credit since its inception. Brookings receives ZIP Code-level IRS income tax return data for use in its research which they generously shared with TRF for inclusion in PolicyMap. Brookings has developed a method for analyzing and aggregating the ZIP code-level tax return data to different geographic areas including city, county, metropolitan area, state, state legislative district, and congressional district. All data are derived from the Internal Revenue Service's Stakeholder Partnerships, Education, and Communication (IRS-SPEC) Return Information Databases, compiled by the IRS Wage and Investment Research Unit.

Data for 2008 and earlier report amounts from tax returns filed throughout the year. Data for 2009 and later are from tax reports filed from January through June. As people in different income groups are more likely to file later in the year, these two time frames are not comparable, and are therefore shown separately on PolicyMap.

For more details on the dataset, see their user guide at http://www.brookings.edu/metro/EITC/EITC-Data.aspx

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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

Geographies:

county, Metropolitan Division, CBSA, state, place

Free or Subscriber-only:

free

For more information:

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

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.

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.

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Bureau of Labor Statistics Mass Layoff Statistics

Details:

number of mass layoffs

Topics:

layoffs

Source:

Bureau of Labor Statistics Mass Layoff Statistics Program

Years Available:

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

Geographies:

state

Free or Subscriber-only:

Widget, API Only

For more information:

http://www.bls.gov/mls/

Description:

The Bureau of Labor Statistics' Mass Layoff Statistics program produces annual mass layoff data for states, by place of residence. PolicyMap contains state counts of people who have been part of a mass layoff involving fifty or more people during a five week period.

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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 - 2012

Geographies:

county, state, CBSA

Free or Subscriber-only:

free

For more information:

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

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.

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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, people in poverty, families in poverty, population by ethnicity, age, sex, people with disabilities, total population, foreign born population, household characteristics, families, homeowner characteristics, renter characteristics, affordability and cost burdens, unemployment, employment, commute to work, vehicles per household, home heating fuel types

Source:

2000 U.S. Census, Summary File 3; 2010 U.S. Census Summary File 1; 2007-2011 U.S. Census American Community Survey (ACS)

Years Available:

2000, 2010, 2007-2011

Geographies:

blockgroup, Census tract, county subdivision, Census place (city), county, state, CBSA (metro area), Metropolitan Division, nation

Free or Subscriber-only:

Free

For more information:

http://www.census.gov/census2000/sumfile3.html
http://www.census.gov/acs/www/
http://www.census.gov/2010census/

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 2007-2011 is from the U.S. Bureau of the Census' American Community Survey (ACS). This survey has replaced the long form from the Decennial Census for 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. As of December 2010, with the release of the 2005-2009 ACS data, the ACS data now includes small geographic estimates. The ACS data provides demographic, social, economic and housing characteristic estimates on a rolling basis (from 2007-2011), whereas the 2010 Decennial Census provides counts of the population and their basic characteristics (sex, age, race, Hispanic origin, and homeowner status) as a snapshot in time (2010). 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 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 displays the 2007-2011 ACS data and the 2010 SF1 data using the Census' 2010 geographic file boundaries. PolicyMap shows the 2000 SF3 data using the Census' 2000 geographic file boundaries. Because the 2000 and 2010 TIGER file boundaries are not identical, users will likely see differences in boundary areas when toggling from data at the 2000 geographic file boundaries to data at the 2010 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. 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 correspective 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.

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.

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Census Geography Division

Details:

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

Topics:

Boundary files

Source:

US Census Bureau, Geography Division

Years Available:

2000, 2010

Free or Subscriber-only:

free

For more information:

http://www.census.gov/geo/maps-data/data/tiger.html

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, and Congressional District boundary files are all publically available TIGER files. See: http://www.census.gov/geo/maps-data/data/tiger.html.

Every ten years, following the Decennial Census, there are major updates to many of the Census boundaries based on a new population count. Because of the profound changes that were made to underlying geography files in 2010, PolicyMap has chosen to display some data at Census 2000 boundaries and some data at Census 2010 boundaries. In some cases a single dataset will display some years of data at the 2000 vintage and other years at the 2010 vintage. For example, Census 2000 data is shown at the 2000 Census boundaries while 2007-2011 American Community Survey (ACS) data is displayed at the 2010 boundaries.

In cases where a single dataset relies on multiple boundary files, percent changes are calculated using relationship files and other materials provided by the Census. For more, see: http://www.census.gov/geo/maps-data/data/relationship.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.

PolicyMap has loaded the following boundary files with both a 2000 vintage and a 2010 vintage: Block Group, Tract, County and County Subdivision. The State file was unchanged from 2000 to 2010, so only a single state file is loaded in our platform.

Core-based Statistical Area (CBSA) and Metropolitan Division boundary files have been released on a different schedule than the other boundaries as delineated by the Office of Management and Budget (OMB). As a result, PolicyMap has chosen to load only the 2010 vintage of these files. CBSA encompasses both metropolitan and micropolitan areas. For additional information on metro areas and their historical and current delineations please see: http://www.census.gov/population/metro/.

Places - or cities in common parlance - are defined by the Census as either legally incorporated entities or statistical equivalents created by the Census. This boundary file has been updated more regularly on PolicyMap than the other geographies because substantial changes occur on an annual basis. The two vintages of Place boundaries used in PolicyMap are 2009 and 2010. If you search in the Maps and Tables sections of PolicyMap for a city, you will see 2000 and 2010 options appear; this is done only for the sake of consistency and ease of use.

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 and 2010 boundaries by clicking on "Map Boundaries" from the purple bar at the base of the map. You can choose "Select Current" to display the boundaries associated with the dataset currently loaded on the map or choose your desired boundary from a menu of available options by clicking "All."

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 or the 2010 boundary definition. If the boundary has not changed there will only be one item in the dropdown labeled "2000 and 2010 boundary".

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

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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:

2002-2011

Geographies:

blockgroup, Census tract, Census place (city), county, state, CBSA (metro area), ZIP code

Free or Subscriber-only:

Free

For more information:

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

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 Arizona (2002 and 2003), Arkansas (2002), the District of Columbia (2002-2009), Massachusetts (all years), Mississippi (2002 and 2003), New Hampshire (2002), Puerto Rico (all years) and the U.S. Virgin Islands (all years). Data on the resident workforce is available for these locations for all years. However, the employment-based numbers are loaded as insufficient data. 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. Data shown on PolicyMap for 2010 and 2011 contain federal employment. However, additional toggles in the legend allow users to see earlier data without federal employment numbers. In order to calculate percent changes in employment and workforce numbers, PolicyMap subtracted federal employment from 2010 and 2011 numbers. Be aware that change numbers involving any year between 2002 and 2009 will not include federal jobs.

Additional 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. Going forward PolicyMap anticipates that these additional variables will be available with each annual update.

PolicyMap displays all LEHD data at the 2010 boundary geographies. LODES data are released at the 2010 block geography and PolicyMap aggregated up to the other geographies using Census provided relationship tables. All numbers except for ZIP codes will match the data available on OnTheMap. ZIP code data was calculated using a bridge table with a multiplier. LEHD provides data at the ZCTA level; PolicyMap was able to map data at more realistic ZIP code boundaries that are licensed from Maponics, the source recommended for business by the US Postal Service. For more information on ZCTAs see: http://www.census.gov/geo/reference/zctas.html), for more on PolicyMap's zipcode boundaries see: http://www.policymap.com/our-data-directory.html#Maponics ZIP Code Boundaries

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Census County Business Patterns

Details: count and percent of jobs located in a place, by gross and detailed industry classifications

Topics:

industry concentrations

Source:

US Census, Census County Business Patterns

Years Available:

2003 - 2011

Geographies:

zip code, county, state, CBSA

Free or Subscriber-only:

free

For more information:

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

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. 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.

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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 and monthly for 2000-2011

Geographies:

county, state, CBSA, national

free

free

For more information:

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

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.

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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:

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

Geographies:

School districts

free

free

For more information:

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

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 TRF (such as revenue per student) were suppressed for school districts with 0 students in the given year.

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Census' Small Area Health Insurance Estimates

Details:

number of people without health insurance by select age and income categories

Topics:

health insurance, uninsured

Source:

US Census Small Area Health Insurance Estimates

Years Available:

2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013

Geographies:

county, state

Free or Subscriber-only:

free

For more information:

http://www.census.gov/hhes/www/sahie/

Description:

The Census' Small Area Health Insurance Estimates (SAHIE) dataset produces model-based estimates of health insurance coverage for states and counties. This dataset is an estimate based on a model because data on health insurance coverage are not available elsewhere at this time. From 2005 to 2007, the model used data from the Current Population Survey. For 2008 and later, the model uses data from the American Community Survey (ACS). For this reason, the Census does not recommend making comparisons between data from 2005 to 2007 and data from 2008 and later. The data from the ACS is not yet available in the five-year estimates that exist on PolicyMap.

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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:

2000-2012

Geographies:

county, state, school district

Free or Subscriber-only:

free

For more information:

http://www.census.gov/hhes/www/saipe/

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.

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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, National Vital Statistics System

Years Available:

2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012

Geographies:

county, state

Free or Subscriber-only:

free

For more information:

http://www.cdc.gov/nchs/data_access/vitalstats/VitalStats_Births.htm

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.

Information regarding prenatal care comes from two different sources. There are two different birth certificate forms. The most recent version was created in 2003 and is considered the revised version. It made changes to the 1989 version, including changes in how information regarding prenatal care was collected. Changes in how prenatal care information is collected have resulted in a lack of comparability between prenatal care data from the 2003 and 1989 Revisions of the U.S. Standard Certificate of Live Birth. As of 2009, 28 states used the revised form which represents 66% of births to U.S. residents.

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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:

Annual for 2009, 2010, 2011, 2012, 2013; monthly for 2011 - 2013

Geographies:

State

Free or Subscriber-only:

free

For more information:

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

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 monthly and seasonal values, TRF calculated the average of the numerical activity levels for all weeks ending in a given month or season. Flu season is defined as the period beginning in October and ending in March. For geographic spread, TRF assigned a numerical scale where No Activity = 1 and Widespread = 5, in order to calculate the average spread for the month or year. Only states with at least 3 weeks of reported activity per month (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.

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

Details:

count and rate of infant deaths, 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

Topics:

infant mortality, cancer, stroke, heart disease, chronic lower respiratory disease, cause of death, mortality

Source:

CDC National Center for Health Statistics, National Vital Statistics System

Years Available:

Various (2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010)

Geographies:

county, state

Free or Subscriber-only:

free

For more information:

http://wonder.cdc.gov/

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 all age groups, from 2000 through 2010; and deaths from homicide, suicide, motor vehicle traffic, and accidental injury. 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.

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.

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Centers for Disease Control and Prevention (CDC) National Diabetes Surveillance System

Details:

count and percent obese, count and percent diabetic, count and percent physically inactive

Topics:

Obesity, diabetes, physical inactivity

Source:

CDC: National Diabetes Surveillance System

Years Available:

2004, 2005, 2006, 2007, 2008, 2009

Geographies:

County

Free or Subscriber-only:

free

For more information:

http://apps.nccd.cdc.gov/DDT_STRS2/NationalDiabetesPrevalenceEstimates.aspx

Description:

The Centers for Disease Control (CDC) National Diabetes Surveillance System dataset provides county-level data on the percent of those obese, diabetic, and lacking physical activity among adult residents (age 20 and older) of the US. The data was estimated using data from the CDC's Behavioral Risk Factor Surveillance System (BRFSS) and data from the Census's Population Estimates Program. The data is collected through state-based telephone surveys. 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 body mass index (as determined by height and weight) was 30 or greater. Respondents were considered to be physically inactive if they answered "no" to the question, "During the past month, other than your regular job, did you participate in any physical activities or exercises such as running, calisthenics, golf, gardening, or walking for exercise?"

Three years of data are used for individual year's data. For instance, data from 2004, 2005, and 2006 is used for the 2005 estimate. The county-level data are based on indirect model-dependent estimates. Please see the CDC website for more details on the modeling.

Because health data can be dependent on age characteristics, age-adjusted data are available. The age adjusted rate minimizes the effects of different age distributions when comparing data between counties. Rates were adjusted by calculating age specific data for people 20-44, 45-64, and 65 and over. A weighted sum based on the distribution of these groups from the 2000 Census was used to adjust the rates by age. Please see the CDC website for more details on the modeling.

The data is state-specific. Caution should be taken when comparing counties, as the data are meant to be individual point estimates. Only diagnosed diabetes data is available in Puerto Rico.

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Centers for Disease Control and Prevention (CDC) Overweight and Obesity (BMI)

Details:

percent overweight, percent obese, percent neither overweight nor obese

Topics:

overweight and obesity

Source:

CDC Behavioral Risk Factor Surveillance System

Years Available:

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

Geographies:

state, CBSA, Metropolitan Division

Free or Subscriber-only:

free

For more information:

http://apps.nccd.cdc.gov/brfss/

Description:

The Centers for Disease Control (CDC) dataset provides the percent of those overweight, those obese, and those neither overweight nor obese among residents of the US. The CDC defines overweight people as those having a weight classification by Body Mass Index (BMI) between 25.0 and 29.9. It defines obese individuals as having a weight classification by BMI between 30.0 and 99.8. Those people with a weight classification by BMI of less than 24.9 are considered neither overweight nor obese. States for which data is not available in a given year are represented as having Insufficient Data on the map.

Centers for Disease Control and Prevention (CDC) and Rollins School of Public Health at Emory University

Details:

count and rate of persons living with HIV

Topics:

HIV, AIDS, Notifiable Infectious Diseases

Source:

Centers for Disease Control and Prevention (CDC), HIV Incidence and Case Surveillance Branch, Rollins School of Public Health at Emory University (AIDSVu)

Years Available:

2010

Geographies:

county, state

Free or Subscriber-only:

free

For more information:

http://www.AIDSVu.org
http://www.cdc.gov/hiv/statistics/surveillance/

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 at the state level through the CDC's National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention; and through the Rollins School of Public Health at Emory University's AIDSVu site.

Data represent the 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.

Centers for Disease Control and Prevention (CDC) Sexually Transmitted Diseases (STDs)

Details:

count and rate of STD incidence - chlamydia, gonorrhea, syphilis

Topics:

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

Source:

US Department of Health and Human Services (HHS), Centers for Disease Control and Prevention (CDC), National Center for HIV, STD and TB Prevention (NCHSTP), Division of STD/HIV Prevention

Years Available:

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

Geographies:

county, state

Free or Subscriber-only:

Free

For more information:

http://www.cdc.gov/std/

Description:

This Centers for Disease Control and Prevention (CDC) dataset provides the number of new cases of sexual transmitted diseases (STDs) reported each year, and the rate of new STD cases reported for every 100,000 residents, by state and county. Data is available separately for chlamydia, gonorrhea, and syphilis.

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: http://www.cdc.gov/std/stats12/default.htm.

This data provides information about incidence, which explains the rate of occurrence of newly diagnosed cases, as opposed to prevalence (not included in the dataset), which explains the total number of existing cases in a given population over a specific period of time. These two values provide researchers with indicators for risk of contraction (incidence) and the burden of disease (prevalence) within a given population. Prevalence data is not available from the CDC.

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.

Since 2003, all reporting has moved to electronic format. For years prior to 2003, data is only available for states that had already dropped "hard-copy" reporting. In 2005 the STD Surveillance Network (SSuN) was created to improve the capacity and quality of reporting. For more information about interpreting STD data, please visit http://www.cdc.gov/std/stats/default.htm.

Ten-year percent change variables were calculated by PolicyMap. 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.

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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

Topics:

Medicare, Medicaid

Source:

Centers for Medicare and Medicaid Services

Years Available:

2007, 2008, 2009, 2010, 2011

Geographies:

county, state

Free or Subscriber-only:

Free

For more information:

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

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. In 2011, over 31 millon (62%) out of roughly 52 million Medicare beneficiaries nationwide had fee-for-service coverage. 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.

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.

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CDFI (Community Development Financial Institutions) Fund

Details:

Selected federal incentive program designations

Topics:

CDFI Fund Investment Areas, BEA Distressed Communities

Source:

Community Development Financial Institutions Fund, US Department of the Treasury

Years Available:

As of 2013

Geographies:

Census Tract

Free or Subscriber-only:

free

For more information:

http://www.cdfifund.gov/what_we_do/census.asp, http://www.cdfifund.gov/what_we_do/acs/NACA-CDFI-Eligibility.asp, http://www.cdfifund.gov/docs/bea/2013/BEA%20ACS%20Transition%20FAQ%20-%20Final.pdf

Description:

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 2006-2010 American Community Survey (ACS) for 2010 census tracts. For more on these programs users should consult the CDFI Fund website directly: www.cdfifund.gov.

Designations are current as of March 2013 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 America Areas.

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CDFI (Community Development Financial Institutions) Fund and The Reinvestment Fund (TRF)

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, Renewal Area, High Migration Rural County, ARC/DRA Area, AMI, Brownfield, ERS/USDA Food Desert

Source:

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

Years Available:

2013/2014

Geographies:

Census Tract

Free or Subscriber-only:

free

For more information:

http://www.cdfifund.gov/docs/nmtc/2013/Application%20Materials/2013-2014%20NMTC%20Application%20FINAL%20for%20Publication.pdf

Description:

The Community Development Financial Institutions (CDFI) Fund, a division of the US Department of the Treasury, administers the New Markets Tax Credit (NMTC). The Reinvestment Fund (TRF) 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.

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 or (2) Poverty Rate of 20% or greater in the period of 2006-2010. 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").

In order for a Census tract to competitively qualify as a tract for NMTC, it must be in an Eligible Census Tract that is also Severely Distressed. PolicyMap provides a map of those Severely Distressed Census tracts ("NMTC Severely Distressed"). The two options for determining if an area is Severely Distressed are to use either Primary Criteria or Secondary Criteria, which are described, below.

Primary Criteria for NMTC Severely Distressed Meeting the NMTC Severely Distressed Primary 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; having a poverty rate at or above 30% in the period of 2006-2010; having an unemployment rate of at least 1.5 times the national unemployment rate in the period of 2006-2010; or being in a county that is not part of a metropolitan statistical area. PolicyMap provides a map of those Census tracts that are considered Severely Distressed because they satisfy the Primary Criteria. The map is located in "Primary Criteria: Severely Distressed". Also included in this submenu are the data for each of the factors that constitute the Primary Criteria for NMTC Severely Distressed.

*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.

Secondary Criteria for NMTC Severely Distressed Meeting the NMTC Severely Distressed Secondary Criteria is based on whether or not a given Census tract meets basic NMTC Eligibility, plus two of the following factors: 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 2013/2014 Application:

Median Family Income Census ACS 2006-2010
Area Median Income Census ACS 2006-2010
Poverty Rate Census ACS 2006-2010
Unemployment Rate Census ACS 2006-2010
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/econom-devel/project-info/default.aspx)
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, State or Local Economic Zones, or FEMA Disaster Areas.

The data for the 2013/2014 application comes from the 2006-2010 American Community Survey, which is shown at 2010 Census boundaries.

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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:

FY 2003 - FY 2011

Geographies:

ZIP code, census tract, state, points

Free or Subscriber-only:

free

For more information:

http://www.cdfifund.gov/news_events/CDFI-2013-32-CDFI_Fund_Releases_Public_Data_on_New_Markets_Tax_Credit_Investments.asp,
http://www.cdfifund.gov/what_we_do/programs_id.asp?programID=10

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 made between FY 2003 and FY 2011. 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 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. Multiple CDEs may be associated with a single institution or controlling entity, such as a bank or CDFI. More information about CDEs, including the controlling entity, can be found on the CDFI Fund website in the Searchable Award Database.

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. TRF geocoded 257 CDEs with New Markets transactions during the period of FY 2003 through FY 2011, and was able to locate 100% of the addresses on a map.

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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:

2012

Geographies:

County

Free or Subscriber-only:

free

For more information:

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

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 2006-2010 American Community Survey to determine qualifying counties.

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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

Free or Subscriber-only:

free

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.

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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:

2013

Geographies:

County

Free or Subscriber-only:

free

For more information:

http://www.consumerfinance.gov/blog/final-list-of-rural-and-or-underserved-counties-for-use-in-2013/

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).

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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:

2013

Geographies:

Census Tract (2010)

Free or Subscriber-only:

free

For more information:

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

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). Starting in 2012, the CRA changed its formula by calculating AMFI (now designated as "Metropolitan Median Family Income") using Census' 2006-2010 American Community Survey estimates. For AMFI the CRA relies on HUD's 2004 MSA/MD MFI. 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. 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 1990 and 2000 census, or a net migration loss of 5% or more between 1995 and 2000. 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.

To identify tracts that are "designated disaster areas" consult the Federal Emergency Management Agency (FEMA) website: http://wwww.fema.gov. Disaster areas are not mapped in PolicyMap because they are subject to frequent changes.

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Convenient Care Association

Details:

Retail-Based Healthcare

Topics:

Health, Retail-Based Healthcare, Clinics

Source:

Convenient Care Association

Years Available:

2013

Geographies:

Point

Free or Subscriber-only:

Free

For more information:

http://www.ccaclinics.org

Description:

Data obtained from the Convenient Care Association on October 14, 2013. Includes only members of the Convenient Care Association.

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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

Free or Subscriber-only:

Free

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.

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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

Geographies:

State, CBSA

Free or Subscriber-only:

free

For more information:

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

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. The selected countries of birth included in PolicyMap were determined by the number of people that country sent in 2012. If the volume of immigrants receiving green cards in any year was more than 15,000 people, the country was included.

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

Details:

Air quality index

Topics:

air quality

Source:

US EPA

Years Available:

2008, 2009, 2012, 2013

Geographies:

County, CBSA, Place, ZIP, Neighborhood

Free or Subscriber-only:

API only

For more information:

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

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.

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Environmental Protection Agency (EPA), Brownfields Sites Reports

Details:

Brownfield site locations

Topics:

Brownfields

Source:

Cleanups in my Community, US EPA

Years Available:

2013

Geographies:

points

Free or Subscriber-only:

free

For more information:

http://epa.gov/brownfields/

Description:

TRF received the EPA's Brownfields Sites Report List from the EPA for use in PolicyMap. The EPA regularly updates this list. The points in PolicyMap are as of December of 2013. The coordinates used in PolicyMap are provided by the EPA. TRF removed 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.

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Environmental Protection Agency (EPA), CERCLIS Sites Reports

Details:

Superfund site locations

Topics:

Superfund

Source:

CERCLIS Sites Reports, US EPA Office of Solid Waste and Emergency Response

Years Available:

2013

Geographies:

points

Free or Subscriber-only:

free

For more information:

http://www.epa.gov/superfund/sites/phonefax/products.htm

Description:

TRF uses National Priorities List data from the EPA's "List 9 - Active CERCLIS Sites". An algorithm is used to determine the most recent action taken at that site. The geographic coordinates were provided by the EPA. The geographic coordinates are obtained through a shapefile available at the EPA's website at geodata.epa.gov. The points in PolicyMap are as of December 2013.

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.

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Environmental Protection Agency (EPA) 2006 National Land Cover Data (NLCD)

Details:

Forestland

Topics:

Sensitive lands, environment, forestland, land cover

Source:

U.S. Environmental Protection Agency

Years Available:

2006

Geographies:

Polygon

Free or Subscriber-only:

API only

For more information:

http://www.epa.gov/mrlc/nlcd-2006.html

Description:

To construct a layer of forestland data, the following raster fields were selected from the NLCD, and converted to a shapefile:

Deciduous Forest - Areas dominated by trees where 75 percent or more of the tree species shed foliage simultaneously in response to seasonal change.

Evergreen Forest - Areas dominated by trees where 75 percent or more of the tree species maintain their leaves all year. Canopy is never without green foliage.

Mixed Forest - Areas dominated by trees where neither deciduous nor evergreen species represent more than 75 percent of the cover present.

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Environmental Protection Agency (EPA), Landfill Methane Outreach Program

Details:

Landfills that are participants or candidates for the EPA's Landfill Methane Outreach Program

Topics:

Landfills, waste, environment

Source:

Environmental Protection Agency (EPA), Landfill Methane Outreach Program

Years Available:

2012

Geographies:

County

Free or Subscriber-only:

Widget-only

For more information:

http://www.epa.gov/lmop/

Description:

The Environmental Protection Agency (EPA) tracks landfills that are participants or candidates for the EPA's Landfill Methane Outreach Program (LMOP). This program is a voluntary assistance program focused on capturing methane from landfills.

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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:

Fiscal Year 2012

Geographies:

County

Free or Subscriber-only:

API only

For more information:

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

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.

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FBI Uniform Crime Reports

Details:

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

Topics:

Crime Rates

Source:

FBI Uniform Crime Reports

Years Available:

2005-2012

Geographies:

selected counties and places

Free or Subscriber-only:

free

For more information:

http://www.fbi.gov/ucr/ucr.htm

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 geographic identifiers, TRF assigned the data to places and counties using the US Department of Justice's Law Enforcement Agency Identifier Crosswalk. The Crosswalk relates originating agency identifiers to Federal Information Processing Standards Codes (FIPS codes). TRF used 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 TRF 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 TRF.

FBI UCR data should not be compared across places or counties, and should not be compared from one year to another.

TRF divided 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.

Data is shown at 2000 County boundaries.

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FDIC

Details:

FDIC insured bank failures, bank branches

Topics:

Bank failures, bank branches

Source:

Federal Deposit Insurance Corporation

Years Available:

2000 - 2013

Geographies:

Point

Free or Subscriber-only:

free

For more information:

Failures: http://www2.fdic.gov/hsob/hsobRpt.asp
Branches: http://www2.fdic.gov/sod/index.asp

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: http://www2.fdic.gov/hsob/SelectRpt.asp?EntryTyp=30) which is updated on an ongoing basis, and the FDIC Institution Directory (http://www2.fdic.gov/idasp/main.asp), which is updated weekly. PolicyMap is updated on a quarterly basis. 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 bank closing, for all closings since November 26, 2010. For all closings before November 26, 2010, the data are from the FDIC report on June 30, 2010.

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 quarterly.

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Federal Historic Preservation Tax Incentives (National Park Service)

Topics:

Historic Tax Credit Projects

Source:

Technical Preservation Services, National Park Service

Years Available:

2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013

Geographies:

point

Free or Subscriber-only:

free

For more information:

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

Description:

Technical Preservation Services, a division of the National Park Service, provided TRF with a list of all approved Federal Historic Preservation Tax Incentives Program Part 3 applications from fiscal year 2002 through fiscal year 2013. 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 entire cost of historic rehabilitation as estimated by TRF, including, but not limited to the estimated rehabilitation costs 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; TRF was able to locate 100% 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: http://ntcicfunds.com/tax-credit-basics/federal-tax-credit-basics/

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FEMA

Details:

FEMA National Flood Hazard Layer

Topics:

Flood maps

Source:

Federal Emergency Management Agency

Years Available:

2014

Geographies:

Polygon

Free or Subscriber-only:

API only

For more information:

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

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.

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.

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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

Geographies:

census tract, county

Free or Subscriber-only:

free

For more information:

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

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). TRF extracted the database of lending activity from the Peer Small Business Data. TRF aggregated the number of loans by amount of loan and by borrower revenue. TRF 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.

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

Topics:

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

Source:

HMDA (Home Mortgage Disclosure Act)

Years Available:

2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012

Geographies:

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

Free or Subscriber-only:

free

For more information:

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

Description:

The Home Mortgage Disclosure Act (HMDA), which was enacted by Congress in 1975, requires most mortgage lenders located in metropolitan areas to collect data about their housing-related lending activity, report the data annually to the government, and make the data publicly available. The public database of lending activity is called Loan Application Register and Transmittal Sheets (LARS & TS). TRF aggregated originated purchase and refinance loans for owner-occupied, one-to-four family dwellings, in order to construct categories that would be useful to policymakers and descriptive of neighborhoods and markets, such as Prime Refinance Loans, or Purchase Loans to African Americans.

PolicyMap contains HMDA data for 2004 through 2012. The 2012 HMDA data reflect the ongoing difficulties in the housing and mortgage markets that began appearing in 2007. Users will find modest increases in originations in 2012, with a 54% increase in refinancings. Users will also note that all high-cost (those with a reported rate spread) and prime loans for 2012 reflect the reporting rule changes implemented in 2009Q4, discussed below. In order to accurately display the data according to these rule adjustments, PolicyMap divided the 2009 data into 2009Q1-2009Q3 and 2009Q4. The reason for this is that in the fourth quarter of 2009, HMDA changed its rules for reporting rate spreads in an effort to more accurately capture the current high-cost lending activity. Change calculations between previous years and 2009, 2010, 2011 or 2012 should not be made due to the change in HMDA’s definition of high cost. Additionally, the higher incidence of FHA lending activity in the second half of 2008, 2009, 2010 that is apparent in the government-insured home loan data decreased in 2011 and 2012, though it continues to represent around 50% of the market. One additional change in the 2009 data is that, 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.

For more information and analysis of the 2012 HMDA data, see the published article in the Federal Reserve Bulletin, available at http://www.federalreserve.gov/pubs/bulletin/2013/pdf/2012_HMDA.pdf.

The 2007 HMDA data reflect the initial trauma in the housing and mortgage markets. The data show decreases in originations, especially in the loans that PolicyMap classifies as high cost (previously denoted as subprime). While a large part of this effect was due to real changes in lending events, some part of this shift was due to nonreporting by lenders that ceased operations during 2007 and did not file a HMDA report, even though they originated loans during part of 2007. (Loans from institutions that ceased operations due to a merger or acquisition were reported through the acquiring entity.) Although nonreporting affects the completeness of the HMDA data in each year, analysis at the Federal Reserve Bank indicates that nonreporting in 2007 was on a greater scale than in past years, and that the effect of nonreporting amplified the reduction in number of high-cost loans that the data show between 2006 and 2007. For more information and analysis of the 2007 HMDA data, see the published draft of an article that is forthcoming in the Federal Reserve Bulletin, available at http://www.federalreserve.gov/pubs/bulletin/2008/pdf/hmda07draft.pdf.

When performing aggregations and calculations on the HMDA data, medians were not calculated and percents were not computed where the count of loan events of that type or the denominator of the calculation was less than five. These places are identified on the map as having Insufficient Data.

High-Cost Loans and TRF's High-Cost Loan Calculations

TRF classifies loans as high cost if they had a reported rate spread. In 2009, HMDA changed its rules for reporting rate spreads for the fourth quarter of the year in an effort to more accurately capture the current high-cost lending activity. In the first three quarters of 2009 and previous to 2009, the rate spread on a loan was the difference between the Annual Percentage Rate (APR) on the loan and the treasury security yields as of the date of the loan's origination. Rate spreads were only reported by financial institutions if the APR was 3 or more percentage points higher 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. The new rules introduced in the fourth quarter of 2009 indicate that the rate spread on a loan is the difference between the Annual Percentage Rate (APR) on the loan and the estimated average prime offer rate (APOR). With the rule change, rate spreads are only reported by financial institutions if the APR is more than 1.5 percentage points higher 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.

Likewise, all loans without reported rate spreads are considered to be prime, as the APR is within reasonable range of the treasury security yield (or, in the case of 2009Q4, 2010, 2011 and 2012, within reasonable range of the estimated average prime offer rate). TRF 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 Bank. The "high-cost" designation is not to be confused with "HOEPA". HOEPA loans are a subset of the high-cost loan category.

"80-20" or "Piggyback" Loans and TRF's Algorithm for "80-20" or "Piggyback" Loan Estimates

PolicyMap contains thematic data on the number of loans originated for the purpose of a home purchase that had multiple mortgages. 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. The 80-20 or piggyback loan has been used to avoid underwriting standards held by most lenders that require private mortgage insurance (or 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. TRF 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, the loan amounts summed, thus reflecting the total loan for the property transaction. These loans were originated for the purchase of an owner-occupied, one-to-four family dwelling, as reported by HMDA.

"Other" Races

PolicyMap contains data for specific race categories and for grouped race categories, such as those identified as "other" races. "Other" races is defined as American Indian or Alaska Native, Asian, Native Hawaiian or Other Pacific Islander, and individuals who either did not provide information or provided inapplicable information.

Prime Loans and TRF's Prime Loan Calculations

Prime loans are defined as loans with no reported rate spread. TRF assumes for the purpose of its PolicyMap calculations that a loan without a reported rate spread is of a "typical" APR and most likely prime. The rate spread for 2004-2009Q3 on a loan was the difference between the Annual Percentage Rate (APR) on the loan and the treasury security yields as of the date of the loan's origination. For that time period, rate spreads were only reported by financial institutions if the APR was 3 or more percentage points higher for a first lien loan, or 5 or more percentage points higher for a second lien loan, The rate spread for 2009Q4, 2010, 2011 and 2012 is the difference between the Annual Percentage Rate (APR) on the loan and the estimated average prime offer rate (APOR). In that same quarter, rate spreads are reported by financial institutions if the difference between the APR and the APOR is 1.5 or more percentage points for a first lien loan, or 3.5 or more percentage points higher for a second lien loan).

HMDA for Census Places

In its native form, loan applications reported to HMDA do not contain Census Places identifiers. TRF 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 that the largest section of the tract was located.

Government-Insured Loans

The federal government has several entities through which it insures or guarantees consumer home loans. 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.

FHA 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 http://www.hud.gov/buying/loans.cfm.

VA Loans

The Department of Veterans Affairs (VA) is one entity through which the government guarantees consumer loans. The purpose of the VA home loan program is to help veterans finance the purchase of homes with favorable loans terms and interest rates. For more on VA-insured lending, see http://www.homeloans.va.gov/pamphlet.htm.

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.

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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

Free or Subscriber-only:

free

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.

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GreatSchools' School District Performance

Topics:

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

Source:

GreatSchools

Years Available:

varied, 2006 to 2013

Geographies:

school district

Free or Subscriber-only:

subscriber-only

For more information:

http://www.greatschools.org

Description:

GreatSchools is a national, independent nonprofit organization providing elementary, middle and high school information for public, private, and charter schools nationwide. TRF licensed GreatSchools' school district test score information for incorporation in PolicyMap.

PolicyMap displays data for the following standardized tests:

Alaska
Alaska Standards Based Assessment (SBA): In 2010-2011 Alaska used the Standards Based Assessment (SBA) to test students in grades 3 through 10 in reading, math and writing, and 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.
AK High School Graduation Qualifying Exam (HSGQE): In 2010-2011 Alaska used the High School Graduation Qualifying Examination (HSGQE) to test students in grade 10 in reading, writing and math. The HSGQE 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 High School Graduation Exam (AHSGE): In 2011-2012 Alabama used the Alabama High School Graduation Exam (AHSGE) to test high school students in reading, math, language, biology and social studies. High school students must pass the AHSGE in order to graduate. The AHSGE 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 pass the test.
Alabama Reading and Mathematics Test (ARMT): In 2011-2012 Alabama used the Alabama Reading and Mathematics Test (ARMT) to test students in grades 3 through 8 in reading and math. The ARMT 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 the state standard.
Alabama Science Assessment (ASA): In 2011-2012 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.

Arkansas
Benchmark Exams (BE): In 2011-2012 Arkansas used the Benchmark Exam to test students in grades 3 through 8 and 11 in literacy and grades 3 through 8 in math. 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 Exams (EOC): In 2011-2012 Arkansas used the End of Course Exam to test high school students in Algebra I, Geometry and 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.

Arizona
Arizona's Instrument to Measure Standards (AIMS): In 2011-2012 Arizona's Instrument to Measure Standards (AIMS) was used to test students in reading and mathematics in grades 3 through 8 and 10, writing in grades 5, 6, 7, and 10, and in science in grades 4, 8 and 10. AIMS is a standards-based test, which means that it measures how well students have mastered Arizona's learning standards. Students must pass the grade 10 AIMS in order to graduate. The goal is for all students to meet or exceed state standards on the test.

California
California Standards Test (CST): In 2011-2012 California used the California Standards Tests (CSTs) to test students in English language arts in grades 2 through 11; math in grades 2 through 7; science in grades 5, 8 and 10; and history-social science in grades 8 and 11. Middle and high school students also took subject-specific CSTs in math and science, depending on the course in which they were enrolled. 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
Transitional Colorado Assessment Program (TCAP): In 2011-2012 Colorado used the Transitional Colorado Assessment Program (TCAP) to test students' skills in reading, writing and mathematics in grades 3 through 10, and in science in grades 5, 8 and 10. The TCAP is a standards-based test, which means that it measures how well students are mastering specific skills defined for each grade by the state of Colorado. The goal is for all students to score at or above proficient on the test.

Connecticut
Connecticut Mastery Test (CMT): In 2011-2012 Connecticut used the Connecticut Mastery Test (CMT) to test students' skills in reading, writing and math in grades 3 through 8, and in science in grades 5 and 8. The CMT is a standards-based test, which means it measures how well students are mastering specific skills defined for each grade by the state of Connecticut.
Connecticut Academic Performance Test (CAPT): In 2011-2012 Connecticut used the Connecticut Academic Performance Test (CAPT) to test students' skills in reading, writing, science and math in grade 10. The CAPT is a standards-based test, which means it measures how well students are mastering specific skills defined for each grade by the state of Connecticut.

District of Columbia
District of Columbia Comprehensive Assessment System (DC-CAS): In 2011-2012 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. In 2010-2011 students were tested in science in grades 5, 8 and high school. 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 Student Testing Program (DSTP): In 2008-2009 Delaware used the Delaware Student Testing Program (DSTP) to test students in reading and math in grades 2 through 10, in writing in grades 3 through 10, and in science and social studies in grades 4, 6, 8 and 11. Only the scores for reading and math in grades 2 through 10, writing in grades 3 though 10, and science in grades 8 and 11 are displayed on GreatSchools profiles. The DSTP 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.

Florida
Florida Comprehensive Assessment Test (FCAT): In 2011-2012 Florida used the Florida Comprehensive Assessment Test (FCAT) to test students in grades 4, 8 and 10 in writing. The Florida Comprehensive Assessment Test (FCAT) is a standards-based test, which means it measures how well students are mastering specific skills needed to progress through school. The FCAT writing exam is scored on a scale of 1 to 6. The state considers a score of 3 or above as meeting state standards.
Florida Comprehensive Assessment Test (FCAT 2.0) (FCAT2): In 2012-2013 Florida used the Florida Comprehensive Assessment Test (FCAT 2.0) to test students in grades 3 through 10 in reading, 3 through 8 in math, in grades 5 and 8 in science and writing in grades 4, 8 and 10. The FCAT 2.0 is a standards-based test, which means it measures how well students are mastering specific skills defined for each grade by the state of Florida. The FCAT 2.0 has 5 achievement levels, with level 1 being the lowest and level 5 the highest. Florida considers scores of level 3 and higher to be on or above grade level. The goal is for all students to score at or above level 3.
Florida End-of-Course Assessments (EOC) (EOC): In 2012-2013 Florida used the End-of-Course Assessments (EOC) to test students in Algebra 1, Biology 1 and Geometry. The EOC is a standards-based test, which means it measures how well students are mastering specific skills defined for each grade by the state of Florida. The Algebra 1 EOC has 5 achievement levels, with level 1 being the lowest and level 5 the highest. Florida considers scores of level 3 and higher to be on or above grade level. The goal is for all students to score at or above level 3.

Georgia
Criterion-Referenced Competency Tests (CRCT): In 2011-2012 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.
Georgia High School Graduation Test (GHSGT): In 2010-2011 Georgia administered the Georgia High School Graduation Test (GHSGT) in English language arts, math, science and social studies to students in grade 11. The GHSGT 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 all parts of the GHSGT in order to graduate from high school. The goal is for all students to pass the test.
End-of-Course-Tests (EOCT): In 2011-2012 Georgia administered End-of-Course Tests (EOCT) in 9th grade math levels 1 and 2, 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 2011-2012 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.
Middle Grades Writing Assessment (MGWA): In 2011-2012 Georgia administered the Middle Grades Writing Assessment (MGWA) to students in grades 5 and 8. The MGWA 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.

Hawaii
Hawaii State Assessment (HSA): In 2011-2012 Hawaii used the Hawaii State Assessment (HSA) to test students in grades 3 through 8 and 10 in reading and math. 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.

Iowa
Iowa Assessment (NRT): Beginning in 2011-2012 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
Idaho Standards Achievement Test (ISAT): In 2010-2011 Idaho used the Idaho Standards Achievement Test (ISAT) to test students in grades 3 through 8 and 10 in reading, math and language usage, and in grades 5, 7 and 10 in science. The scores from the spring administration are displayed on GreatSchools profiles. The grade 10 ISAT is a high school graduation requirement. The ISAT 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 Idaho. The goal is for all students to score at or above the proficient level.

Illinois
Illinois Standards Achievement Test (ISAT): In 2011-2012 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.
Prairie State Achievement Exam Results (PSAE): In 2011-2012 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 (ISTEP+): In 2011-2012 Indiana used the Indiana Statewide Testing for Educational Progress-Plus (ISTEP+) assessment to test students in grades 3 through 8 in English/language arts and math, grades 4 and 6 in science, and in grades 5 and 7 in social studies. 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 Statewide Testing for Educational Progress-Plus (ISTEP+): In 2011-2012 Indiana used the Indiana Statewide Testing for Educational Progress-Plus (ISTEP+) assessment to test students in grades 3 through 8 in English/language arts and math, grades 4 and 6 in science, and in grades 5 and 7 in social studies. 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.
End-Of-Course (ECA): In 2011-2012 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.
End-Of-Course (ECA): In 2011-2012 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 (KSA): In 2011-2012 Kansas used the Kansas State Assessments (KSA) to test students in grades 3 though 8, 11 and 12 in reading and math; in grades 4, 7, 11 and 12 in science; and in grades 6, 8 and 12 in history-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 Core Content Tests (KCCT): In 2010-2011 Kentucky used the Kentucky Core Content Tests (KCCT) to assess students in grades 3 through 8 and 10 through 12 in reading, social studies, science, writing, and math. The Elementary School results displayed on GreatSchools profiles are for grades 3 through 5 combined for each subject. Middle School results are for grades 6 though 8 combined, and High School results are for grades 10 though 12 combined. The KCCT 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. The goal is for all students to score at or above proficient on the test.

Louisiana
Louisiana Educational Assessment Program for the 21st Century (LEAP 21): In 2011-2012 Louisiana used the Louisiana Educational Assessment Program for the 21st Century (LEAP 21) to assess students in grades 4 and 8 in math, English language arts, science and social studies. The LEAP 21 is a standards-based test, which means it measures specific skills defined for each grade by the state of Louisiana. The goal is for all students to score at or above basic on the test.
Integrated Louisiana Educational Assessment Program (iLEAP): In 2011-2012 Louisiana used the integrated Louisiana Educational Assessment Program (iLEAP) to assess students in grades 3, 5, 6, and 7 in math and English language arts, 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. The goal is for all students to score at or above basic on the test.
End-of-Course Tests (EOC): In 2011-2012 Louisiana used End-of-Course (EOC) tests to test high school students in algebra 1, English 2, biology 1, and geometry. The EOC is a high school graduation requirement. TheEOC is a standards-based test, which means it measures specific skills defined for each grade by the state of Louisiana. The goal is for all students to score at or above basic on the test.

Massachusetts
Massachusetts Comprehensive Assessment System (MCAS): In 2010-2011 Massachusetts used the Massachusetts Comprehensive Assessment System (MCAS) to test students in grades 3 though 8 and 10 in English language arts and math, and in grades 5, 8, and 10 in science. The grade 10 MCAS is a high school graduation requirement. The MCAS 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.
Massachusetts Comprehensive Assessment System Science and Technology/Engineering Tests (MCASSTE): In 2010-2011 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 high school graduation requirement. 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.

Maryland
Maryland School Assessment (MSA): In 2011-2012 Maryland used the Maryland School Assessment (MSA) to test students in grades 3 through 8 in reading and math, and 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.
High School Assessments (HSA): In 2011-2012 Maryland used the Maryland High School Assessments (HSA) to test students in English 2, Algebra, and 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. Beginning with the class of 2009, students are required to pass the tests in order to graduate. The goal is for all students to pass the tests.

Maine
Maine Education Assessment (MEA): In 2011-2012 Maine used the Maine Educational Assessment (MEA) to test students in grades 5 and 8 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.
New England Common Assessment Program (NECAP): In 2011-2012 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.
Maine High School Assessment (MHSA): In 2011-2012 Maine used the Maine High School Assessment (MHSA) to test students in grade 11 in 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 Educational Assessment Program (MEAP): In 2012-2013 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. 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. The new standards for proficiency 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.
Michigan Merit Examination (MME) (MME): In 2011-2012 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 means it 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.

Minnesota
Minnesota Comprehensive Assessment-II (MCA-II): In 2011-2012 Minnesota used the Minnesota Comprehensive Assessment-II (MCA-II) to test students in reading in grades 3 through 8 and 10, and math in grade 11. The MCA-II 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.
Minnesota Comprehensive Assessment-II Graduation Required Assessments for Diploma (MCA-II/GRAD): In 2011-2012 Minnesota used Minnesota Comprehensive Assessment-II Graduation-Required Assessments for Diploma (MCA-II/GRAD) to test students in grade 9 in writing, 10 in reading, and 11 in math. The MCA-II/GRAD is a standards-based test, which means it measures specific skills defined for each grade by the state of Minnesota. Students must pass the MCA-II/GRAD in order to graduate from high school. The goal is for all students to score at or above the state standard.
Minnesota Comprehensive Assessment-III (MCA-III): In 2011-2012 Minnesota used the Minnesota Comprehensive Assessment-III (MCA-III) to test in math in grades 3 through 8, 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 (MAP): In 2011-2012 Missouri used the Missouri Assessment Program (MAP) to test students in grades 3 through 8 in math and communication arts, and in grades 5 and 8 in science. The results for math, communication arts, and science are displayed on GreatSchools profiles. The MAP is a standards-based test, which means it measures how well students are mastering specific skills defined for each grade by the state of Missouri. The goal is for all students to score at or above proficient on the test.
End-of-Course Assessments (EOC): In 2011-2012 Missouri used the End-of-Course (EOC) Assessments to test high school students in Algebra I, Algebra II, Geometry, English I, English II, American History, Government, and Biology. The EOC Assessments are standards-based, which means they measure how well students are mastering specific skills defined by the state of Missouri for each subject. The goal is for all students to score at or above proficient on the test.

Mississippi
Mississippi Curriculum Test (MCT): In 2011-2012 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.
Subject Assessment Testing Program (SATP): In 2011-2012 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.
Mississippi Science Test (MST): In 2011-2012 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.
Mississippi Writing Assessment Program (MWAP): In 2011-2012 Mississippi used the Mississippi Writing Assessment Program (MWAP) to test students in grades 4, 7 and 10 in writing. The MWAP 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 2 on the test.

Montana
Criterion-Referenced Test (CRT): In 2010-2011 Montana used the Criterion-Referenced Test (CRT) to assess students in grades 3 though 8 and 10 in reading and math and grades 4, 8, and 10 in Science. 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.

North Carolina
End of Class Tests (EOC): In 2011-2012 North Carolina used End-of-Course (EOC) tests to assess high school students in Algebra I, English I, 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.
End of Grade Tests (EOG): In 2011-2012 North Carolina used End-of-Grade (EOG) tests to assess students in grades 3 through 8 and 10 in reading and math, and grades 5, 8, and 10 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 Dakota
North Dakota State Assessment (NDSA): In 2007-2008 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. The results for reading and math for all grades combined are displayed on GreatSchools profiles. 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 State Accountability (NeSA): In 2011-2012 Nebraska used the Nebraska State Accountability (NeSA) assessments to test students in grades 3 through 8 and 11 in reading and math; in grades 4, 8 and 11 in writing; and in grades 5, 8 and 11 in science. The NeSA is a standards-based testing program, 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
New England Common Assessment Program (NECAP): In 2011-2012 New Hampshire used the New England Common Assessment Program (NECAP) to test students in grades 3 through 8 and 11 in reading and math, and in grades 5, 8 and 11 in writing. The NECAP is a standards-based test that measures specific skills defined for each grade by the state of New Hampshire. The goal is for all students to score at or above proficiency level 3.

New Jersey
New Jersey Assessment of Skills and Knowledge (NJ ASK): In 2011-2012 New Jersey used the New Jersey Assessment of Skills and Knowledge (NJ ASK) to test students in grades 3 through 8 in language arts literacy and math, and 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.
High School Proficiency Assessment (HSPA): In 2011-2012 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 Jersey Biology Competency Test (NJBCT): In 2011-2012 New Jersey used the New Jersey Biology Competency Test (NJBCT) to assess high school students in Biology. The New Jersey Biology Competency Test (NJBCT) is standards-based, which means it

New Mexico
New Mexico Standards-Based Assessment (NMSBA): In 2011-2012 New Mexico used the New Mexico Standards-Based Assessment (NMSBA) to test students in grades 3 through 8, 10 and 11 in Reading and Math. The NMSBA is a standards-based test, which means it measures specific skills defined for each grade by the state of New Mexico. The goal is for all students to score at or above proficient on the test.
New Mexico High School Standards Assessment (NMHSSA): In 2010-2011 New Mexico used the New Mexico High School Standards Assessment (NMHSSA) to test students in grade 11 in reading, math, science and social studies. The NMHSSA is a standards-based test, which means that it measures how well students are mastering specific skills defined by the state of New Mexico. The goal is for all students to score at or above proficient on the test.

Nevada
Criterion-Referenced Test (CRT): In 2010-2011 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.
High School Proficiency Examination (HSPE): In 2010-2011 Nevada used the High School Proficiency Examination (HSPE) to assess high school students in reading, math and writing. 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.

New York
New York State Assessments (NYSA): In 2011-2012 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. The results for English language arts and math are displayed on GreatSchools profiles and the science results will be added when they are released in the Fall of 2013. 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.
Regents Exams (RE): In 2011-2012 New York used the New York State Regents Examinations to test high school students in English language arts, math, global history and geography, US history and government, living environment, chemistry, earth science, physics and several foreign languages. The results for English language arts, math, living environment, chemistry, earth science, physics, global history and geography, and US history and government are displayed in GreatSchools profiles. Students must take at least five Regents Exams in order to graduate. Scores of 65 and above are passing; scores of 55 and above earn credit toward a local diploma (with the approval of the local board of education). The goal is for all students to pass the tests.

Ohio
Ohio Achievement Test (OAT): In 2010-2011 Ohio used the Ohio Achievement Test (OAT) to test students in grades 3 through 8 in reading and math and in grades 5 and 8 in science. The OAT 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 Test (OGT): In 2010-2011 Ohio used the Ohio Graduation Test (OGT) to test students in grade 10 in reading, writing, math, science and social studies. The OGT is a high school graduation requirement. 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.

Oklahoma
Oklahoma Core Curriculum Tests (OCCT): In 2008-2009 Oklahoma used the Oklahoma Core Curriculum Tests (OCCT) to test students in grades 3 through 8 in reading and math. Students in grade 5 were also tested in writing, science and social studies. Students in grade 7 were tested in geography, and students in grade 8 were tested in writing, science and U.S. history. The results for reading, math, science and social studies are displayed on GreatSchools profiles. 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 End-of-Instruction Exams (OCCT EOI): In 2008-2009 Oklahoma used the Oklahoma Core Curriculum Tests End-of-Instruction (OCCT EOI) exams to test students in high school in algebra I, algebra II, English II, English III, geometry, biology I and U.S. history upon completion of each course. 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.

Oregon
Oregon Assessment of Knowledge and Skills (OAKS): In 2009-2010 Oregon used the Oregon Assessment of Knowledge and Skills (OAKS) to test students in grades 3 through 8 and 10 in reading and math; in grades 4, 7 and 10 in writing; and in grades 5, 8 and 10 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 State Assessments (PSSA): In 2011-2012 Pennsylvania used the Pennsylvania System of State Assessments (PSSA) to test students in grades 3 through 8 and 11 in math and reading, in grades 5, 8 and 11 in writing, and in grades 4, 8 and 11 in science. The results for reading, writing and math are displayed on GreatSchools profiles. 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.

Rhode Island
New England Common Assessment Program (NECAP): In 2011-2012 Rhode Island used the New England Common Assessment Program (NECAP) to test students in grades 3 through 8 and 11 in reading and math; in grades 5, 8 and 11 in writing; and 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
High School Assessment Program (HSAP): In 2011-2012 South Carolina used the High School Assessment Program (HSAP) to test grade 10 students in English/Language Arts and Math. The HSAP is a high school graduation requirement. The HSAP is a standards-based test, 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 at or above proficiency level 2.
Palmetto Assessment of State Standards (PASS): In 2011-2012 South Carolina used the Palmetto Assessment of State Standards (PASS) to test students in grades 3 through 8 in writing, English/Language Arts, Math, Social Studies and Science. The PASS is a standards-based test, 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 at or above the state standard.
End-of-Course Examination Program (EOCEP): In 2011-2012 South Carolina used the End-of-Course Examination Program (EOCEP) to test middle and high school sutdents in Algebra 1, Biology 1, English 1, and US History and Government. The EOCEP provides tests in 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 C or above.

South Dakota
Dakota State Test of Educational Progress (STEP): In 2011-2012 South Dakota used the Dakota State Test of Educational Progress (Dakota STEP) to test students in grades 3 through 8 and 11 in reading and math. 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 2011-2012 Tennessee used the Tennessee Comprehensive Assessment Program (TCAP) Achievement Test to test students in grades 3 through 8 in reading/language arts, math and science. The results for reading/language arts and math are displayed on GreatSchool profiles. The results are for all grades combined for each subject and reflect the performance of students enrolled for the full academic year. 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 (GT/EOC): In 2011-2012 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. This year they introduced two new exams in algebra II and English III. 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
Texas Assessment of Knowledge and Skills (TAKS): In 2010-2011, the Texas Assessment of Knowledge and Skills (TAKS) was used to test students in reading in grades 3 through 9; in writing in grades 4 and 7; in English language arts in grades 10 and 11; in mathematics in grades 3 through 11; in science in grades 5, 8, 10 and 11; and in social studies in grades 8, 10 and 11. TAKS 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 grade 11 Exit Level TAKS is a high school graduation requirement. The goal is for all students to score at or above the state standard.

Utah
Utah Criterion-Referenced Test (CRT): In 2009-2010 Utah used the Utah Criterion-Referenced Test (CRT) to test students in grades 1 through 11 in language arts, in grades 1 through 7 in math, in grades 4 through 9 in science and upon completion of certain courses in grades 8 through 11. The results displayed on GreatSchools profiles are for all grades combined for each subject. The CRT is a standards-based testing program, which means it measures specific skills defined for each grade by the state of Utah. The goal is for all students to score at or above the proficient level.

Virginia
Standards of Learning (SOL): In 2011-2012 Virginia used the Standards of Learning (SOL) tests to assess students in reading, math, and history/social science in grades 3 through 8, in writing in grades 5 and 8, and in science in grades in 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.
Standards of Learning End-of-Course (SOLEOC): In 2011-2012 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. The goal is for all students to pass the tests.

Vermont
New England Common Assessment Program (NECAP): In 2012-2013 Vermont used the New England Common Assessment Program (NECAP) to test students in grades 3 through 8 and 11 in reading and math, grades 5, 8 and 11 in writing and grades 4, 8 and 11 in science. The NECAP reading, math, and writing tests are given in the fall and test students on content taught in the previous year. The science portion of the NECAP is administered in the Spring each year and has its results released the following Fall. The NECAP is a standards-based test, which means it measures specific skills defined for each grade by the state of Vermont. The goal is for all students to score at or above the proficient level on the test.

Washington
Washington Measurements of Student Progress (MSP): In 2011-2012 Washington used the Measurements of Student Progress (MSP) to test students in reading and math in grades 3 through 8, in writing in grades 4 and 7, and in 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.
High School Proficiency Exam (HSPE) (HSPE): In 2011-2012 Washington used End-of-Course (EOC) examinations to assess students in Algebra I, Geometry, Integrated Math I, Integrated Math 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 Washington. The goal is for all students to score at or above the state standard.
End-of-Course (EOC) Exam (EOC): In 2011-2012 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
Wisconsin Student Assessment System (WSAS): In 2011-2012 Wisconsin used the Wisconsin Student Assessment System (WSAS), which includes the WKCE and WAA, to test students in grades 3 through 8 and 10 in math and reading, and in grades 4, 8 and 10 in language arts, 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.

West Virginia
West Virginia Educational Standards Test 2 (WESTEST2): In 2011-2012 West Virginia used the West Virginia Educational Standards Test 2 (WESTEST 2) to test students in grades 3 through 11 in reading, math and social studies, and grades 3 through 9 in science. The WESTEST 2 is a standards-based test, which means it measures specific skills defined for each grade by the state of West Virginia. The goal is for all students to score at or above the proficient level on the test.

Wyoming
Proficiency Assessments for Wyoming Students (PAWS): In 2011-2012 Wyoming administered the Proficiency Assessments for Wyoming Students (PAWS) in reading, writing and math to students in grades 3 through 8 and 11, and in science in grades 4, 8 and 11. 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 score at or above the proficient level.

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GreatSchools School Points

Topics:

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

Source:

GreatSchools

Years Available:

varied, 2004 to 2013

Geographies:

point

Free or Subscriber-only:

school directory information is free; scores and ratings are subscriber-only

For more information:

http://www.greatschools.net

Description:

GreatSchools is a national, independent nonprofit organization providing elementary, middle and high school information for public, private and charter schools nationwide. TRF licensed GreatSchools school directory, school ratings, and test score information for incorporation in PolicyMap. The GreatSchools school directory and subscriber point data, including scores and ratings, is updated with 2013 data.

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 calculates their GreatSchools' Overall School Rating by averaging that school's ratings for all grade/subject combinations. For example, if a state test is given in reading and math in grades 3 through 10, the rating for a school serving grades K-5 would be the average of the ratings for grade 3/math, grade 3/reading, grade 4/math, grade 4/reading, grade 5/math and grade 5/reading. School ratings should not be compared across states, as they are relative to the state in which the school operates. If a given school's rating is high, that means that its test scores are better than the test scores of most other schools in the state.

For information about tests administered in each state, please see the Data Directory entry for GreatSchools School District Performance.

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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

Free or Subscriber-only:

free

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.

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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:

2013

Geographies:

Point

Free or Subscriber-only:

free

For more information:

http://eclkc.ohs.acf.hhs.gov/hslc/HeadStartOffices

Description:

TRF downloaded 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 or Migrant or Seasonal Head Start, and a center can be any combination of the three. Individual centers receive funding from a grantee authority and are located in defined federal regions.

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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

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:

2011, 2012, 2013

Geographies:

points, zip code

Free or Subscriber-only:

free

For more information:

http://datawarehouse.hrsa.gov/
http://bphc.hrsa.gov
http://www.arf.hrsa.gov

Description:

TRF downloaded 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 TRF downloaded and geocoded 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. See: http://datawarehouse.hrsa.gov/data/datadownload/hccdownload.aspx.

In 2013, PolicyMap also began receiving individual health center performance data directly from HRSA and joining it 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. As part of the UDS reporting, health center grantees also report the number of patients they serve in each zip code (with patients being counted in the zip code where they live), which PolicyMap has mapped at the zip code level. For information about the UDS, see: http://bphc.hrsa.gov/healthcenterdatastatistics.

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/. The data on PolicyMap was obtained on December 3rd, 2013. 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.

Data listed under "Health Resources" in the Health tab on PolicyMap are from HRSA's Area Health Resource File (AHRF). The AHRF contains data related to health care professions, health facilities and hospital utilization from a variety of sources. TRF calculated all the rates in this dataset using the Census' population estimates for the appropriate year. For more information about the AHRF, see: http://www.arf.hrsa.gov.

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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

Free or Subscriber-only:

free

For more information:

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

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. TRF 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, TRF 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.

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HUD Annual Homeless Assessment Report (AHAR)

Details:

Homeless population counts, rates, and percent change in homeless counts, sheltered and unsheltered population counts, available bed inventory and rate of bed availability, permanent supportive housing.

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:

2007-2011

Geographies:

state

Free or Subscriber-only:

free

For more information:

http://www.hudhre.info/

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) at part of the competitive funding process.

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HUD's 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:

2009-2012

Geographies:

tract, county, place, state, points

Free or Subscriber-only:

free

For more information:

http://www.huduser.org/portal/datasets/picture/about.html

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 (but are not limited to) public housing, Housing Choice Vouchers, Section 8 project-based housing, New Construction and Substantial Rehabilitation, and Section 202 and 811 Supportive Housing programs.

Point-level data on HUD's multifamily and public housing sites are available on PolicyMap. TRF downloaded and geocoded data on multifamily and public housing sites from three different HUD resources. Wherever possible TRF 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.

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.

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HUD Community Development Block Grant Eligibility Criteria

Detail:

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

Topics:

CDBG eligible block groups

Source:

US Department of Housing and Urban Development (HUD)

Years Available:

2010, 2011, 2012, 2013

Geographies:

Block group, Census Tract, Place, CDBG

Free or Subscriber-only:

free

For more information:

http://www.hud.gov/offices/cpd/systems/census/lowmod/

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. HUD publishes a summary of the number of people who are low and moderate income at the split block group level: http://www.hud.gov/offices/cpd/systems/census/lowmod/.

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: http://www.hud.gov/offices/cpd/systems/census/lowmod/exception.cfm.

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: http://www.hud.gov/offices/cpd/systems/census/lowmod/uncapped.cfm.

Policymap displays the number and percentage of persons with low and moderate income (household income below 80% AMI), low income (household income below 50% AMI) and very low income (household income below 30% AMI) at the block group, census tract, Place and CDBG geographies.

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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

Geographies:

county subdivision, zip code

Free or Subscriber-only:

free

For more information:

http://www.huduser.org/datasets/fmr.html

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 (FY 2014) 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 2014 FMRs are based on using 5-year, 2007-2011 data collected by the American Community Survey (ACS). These data are updated by one-year recent-mover 2011 ACS data using areas where statistically valid one-year ACS data are available.

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.

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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

Geographies:

HUD Formula Grantee Boundaries (2013)

Free or Subscriber-only:

free

For more information:

http://www.hud.gov/offices/cpd/about/budget/index.cfm

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: http://www.hud.gov/offices/cpd/systems/census/al/index.cfm

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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

Free or Subscriber-only:

free

For more information:

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

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.

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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

Geographies:

county subdivision

Free or Subscriber-only:

free

For more information:

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

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. TRF 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 TRF 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.

TRF calculated Owner and Renter Affordability using AMI at a variety of percentages for a variety of household sizes as well as Census data regarding number of housing and rental units. The 2000 Owner Affordability calculations rely on HUD's FY2000 AMI data and housing counts from the decennial Census. The 2011 Renter and Owner Affordability calculations rely on HUD's FY2013 AMI data and housing and rental unit counts from the Census' 2007-2011 American Community Survey. Rental Affordability calculations are based on the assumption that 1/36th of a family's annual income translates to a monthly cost burden of 30% or less. For example, a 4-person family with an income of $30,000 could afford to rent a two bedroom apartment for $750/month or less. Due to the source discrepancy between the 2000 and 2011 Owner Affordability data, cross-year comparisons are not recommended.

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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:

2013

Geographies:

block groups, places, counties, CBSAs

Free or Subscriber-only:

free

For more information:

http://locationaffordability.info/

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.

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
Regional Typical Average Household Size for Region Median Income for Region Average number of Commuters per Household for Region
Regional Moderate Average Household Size for Region 80% of Median Income for Region Average number of Commuters per Household for Region
Dual-Income Family 4 150% of Median Income for Region 2
Low Income 3 50% of Housing and Urban Development Area Median Family Income 1
Single Person Very Low Income 1 National Poverty Line 1
Single Professional 1 200% of Per Capita Income for Region 1
Single Worker 1 Median Per Capita Income for Region 1
Retirees 2 80% of Median Income for Region 0

The Location Affordability Index uses data from a variety of sources. These include the Census's American Community Survey (ACS), the Census's Longitudinal Employer-Household Dynamics (LEHD), Illinois Odometer Readings, and the General Transit Feed Specification (GRFS) data from the Center for Neighborhood Technology (CNT). The ACS data uses 2006-2010 estimates. The LEHD and Illinois Odometer Readings data uses 2008 data. The GRFS data is undated. Since this study was released by HUD in 2013, that is the data year given on PolicyMap.

A very thorough explanation of the calculations made by HUD are available here: http://www.locationaffordability.info/downloads/ModelingCode.pdf.

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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:

1997-2011

Geographies:

points

Free or Subscriber-only:

free

For more information:

http://lihtc.huduser.org/

Description:

TRF downloaded and geocoded the properties listed in HUD's LIHTC Database in December, 2013. TRF was able to locate approximately 99% of these developments on a map. 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.

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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 and public housing sites

Source:

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

Years Available:

2013

Geographies:

points

Free or Subscriber-only:

free

For more information:

http://www.hud.gov/offices/hsg/mfh/exp/mfhdiscl.cfm

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. TRF linked the HUD Multifamily Assistance Properties to the HUD Multifamily Assistance and Section 8 Contracts available as of 9/9/2013 to show the details for up to four contracts per property. The following properties have more than four contracts, although only four contracts are listed in PolicyMap: White River Senior Housing, Inc. (800000618), TAB II (800008914), Bridge Revitalization (800232792), IMPACT HOUSING (800022837), Heritage Manors of Southeast Arkansas (800000634), MATTAPAN APTS (800008662), Greenpointe Regional Housing (800012853), SOUTHLAWN ESTATES (800007413), Dolores-Frances Apartments (800002185), WESTBY HOUSING (800023061), PHOENIX VILLA APTS (800023265), Subsidized Housing Corporation 35 (800079860), Subsidized Housing Corporation 28 (800079853), ELDRIDGE/BARSTOW (800009205), Subsidized Housing Corporation 44 (800079875), Subsidized Housing Corporation 4 (800079753), Subsidized Housing Corporation 116 (800079830), Christopher Homes Inc. (800000544), Subsidized Housing Corporation 65 (800079760).

TRF downloaded and geocoded data on HUD's multifamily sites from three different resources at HUD and wherever possible TRF linked the data using the property ID. The three datasets included are the Multifamily Assistance and Section 8 Contracts, A Picture of Subsidized Households 2008 and the REAC assessment scores report. TRF was able to locate 91% of public housing properties and 93% of multifamily properties on a map.

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HUD Neighborhood Stabilization Program Targeting

Details:

NSP 3 Foreclosure Need Score, tract eligibility, income eligibility, estimated delinquency rate, high-cost loans, estimated foreclosure starts, estimated REOs, properties needed for neighborhood impact, vacancy, no-stat, income limits, NSP 2 Foreclosure Risk Score, NSP 2 Vacancy and Foreclosure Risk Score, foreclosure starts, vacant addresses, mortgages, low-cost and high-leverage mortgages, high-cost and low-leverage mortgages, high-cost and high-leverage mortgages, price change, unemployment rate, NSP 1 Estimated Foreclosure Risk Score, income eligible status, predicted 18 month foreclosure rate, high cost loans

Topics:

foreclosure and delinquency risk, foreclosure rate, income eligibility, high cost loans

Source:

US Department of Housing and Urban Development (HUD) Neighborhood Stabilization Program (NSP) Data

Years Available:

various

Geographies:

block groups, tracts, various

Free or Subscriber-only:

free

For more information:

http://www.hud.gov/offices/cpd/communitydevelopment/programs/neighborhoodspg/
http://www.huduser.org/portal/datasets/NSP.html
http://www.huduser.org/portal/datasets/NSP1.html

Description:

HUD's Neighborhood Stabilization Program (NSP) provides assistance to state and local governments to acquire and redevelop foreclosed and abandoned properties that might otherwise become sources of blight within their communities and induce overall neighborhood decline. The program is currently in its third round, commonly called NSP3, as authorized by the Dodd-Frank Wall Street Reform and Consumer Protection Act (Dodd-Frank Act) of 2010. If you were allocated funding for the NSP3 program and have specific questions about your application for HUD you can contact them directly at http://hudnsphelp.info/.

NSP3 Data:

PolicyMap's NSP3 data is current as of 2010. This is data applicants will need to submit grant applications to HUD. For additional information on this update as well as HUD resources see: http://portal.hud.gov/hudportal/HUD?src=/program_offices/comm_planning/communitydevelopment/programs/neighborhoodspg/nsp3

HUD has provided a mapping tool that allows you to draw boundaries and submit them for HUD to calculate a score and email you back. The underlying data is now available on PolicyMap at the tract and block group geographies, allowing applicants to see the data in real time rather than emailing and waiting for a response. Applicants can check eligibility of geographies and custom target areas by clicking around on the map and assembling supporting numbers and documentation. To access HUD's mapping tool see: http://www.huduser.org/NSP/nsp3.html

Eligibility and Scores:

PolicyMap shows NSP3 eligibility for block groups and tracts throughout the country. To be eligible a neighborhood must either have an NSP3 score of 17 or higher OR have a score greater than or equal to the state's minimum threshold score. The numerical NSP3 scores as well as the state minimum thresholds are available on PolicyMap as well. Applicants can create target areas composed of multiple tracts or block groups. To do so, the eligibility scores of the component tracts or block groups are combined using a weighted average based on the total number of housing units. This housing unit count is also available on PolicyMap to allow applicants to make those calculations.

If you are uncertain about the eligibility of individual properties because they lie on the boundaries of tracts, please contact HUD directly for final clarification. They can answer your question here: http://hudnsphelp.info/.

Supporting Data:

In addition to the eligibility and scoring data, PolicyMap has mapped several other NSP3 indicators to assist local and state groups in preparing their applications including:

-Counts of foreclosure starts and REOs. This is a predictive estimate based on the Estimated Delinquency Rate (described below) rather than a count of local foreclosure starts or completed foreclosures. Estimated foreclosure completions are based on the statewide totals from RealtyTrac between January 2007 and June 2010; estimated foreclosure starts are based on statewide totals from Mortgage Bankers Association between January 2007 and March 2010. In each case HUD allocated the state totals to each local area based on the rate of seriously delinquent loans and the total number of mortgages made according to HMDA data between 2004 and 2007.

-Estimated number of properties needed to have an impact in an identified target area. HUD assumes that at least 20% of REOs in a target area would need to be addressed in order to have a visible impact. The purpose of this figure, according to HUD, is to "encourage grantees to select target areas that are small enough so that their NSP investment has a chance of stabilizing the neighborhood."

-Number of vacant and no-stat properties based on US Postal Service counts. HUD has found that high counts of vacant addresses relative to total addresses is a very good indicator of current or potential serious blight in urban neighborhoods. No-stat addresses are an indicator of vacancy in rural areas as well as heavily distressed properties in some areas. PolicyMap provides the count of these addresses as provided by HUD as well as percent variables, which were calculated by dividing the counts by the total number of addresses. HUD relied on an estimated count of housing units within each block group to assign the vacant and no state counts.

-HUD's Estimated Delinquency Rate. This is HUD's estimated rate, as of June 2010, of mortgages that are 90+ days delinquent or in foreclosure. This value is based on a predictive model built by HUD as follows:

0.523 (intercept)
+0.476 Unemployment Change 3/2005 to 3/2010 (BLS LAUS)
-0.176 Rate of low cost high leverage loans 2004 to 2007 (HMDA)
+0.521 Rate of high cost high leverage loans 2004 to 2007 (HMDA)
+0.090 Rate of high cost low leverage loans 2004 to 2007 (HMDA)
-0.188 Fall in Home Value Since Peak (FHFA Metro and Non‐Metro Area)

-Federal Reserve Home Mortgage Disclosure Act (HMDA) data on percent of all loans made between 2004 and 2007 that are high cost. These data represent the percent of conventional loans made between 2004 and 2007 as reported by HMDA where the rate spread is 3 percentage points above the Treasury security of comparable maturity.

-Income Eligibility: The NSP program requires that funds be used to help individuals and families less than 120% of Area Median Income (AMI). If at least 51% of residents in a target area have incomes at or below 120% of AMI, then the area qualifies for 'area benefit' under NSP. For additional information please see: http://www.hud.gov/offices/cpd/communitydevelopment/programs/neighborhoodspg/5447-N-01NSP3Notice100810.pdf

Activities in areas that do not qualify for area benefit qualify for NSP if the assisted activity "provides or improves permanent residential structures that will be occupied by a household whose income is at or below 120 percent of area median income..." OR "serves a limited clientele whose incomes are at or below 120 percent of area median income."

NSP2 Data Sets:

All data made available through the second round of the NSP program is mapped in PolicyMap at the Census tract level data. More information on these datasets http://www.huduser.org/nspgis/nspdatadesc.html.

NSP1 Data Sets:

To assist local and state governments at meeting these requirements, TRF chose to display block group-level key indicators for the determination of eligibility for NSP funds including: tract eligibility for NSP1, the NSP1 foreclosure risk score, the predicted 18-month foreclosure rate, and the percent of all loans that are high cost.

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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

Geographies:

Census Tract

Free or Subscriber-only:

free

For more information:

http://www.huduser.org/DATASETS/qct.html

Description:

Qualified Census Tracts:
PolicyMap downloaded 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 downloaded data on Low-Income Housing Tax Credit (LIHTC) 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.

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

Details:

Locations of multifamily and public housing sites, 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:

2001 - 2011

Geographies:

points

Free or Subscriber-only:

free

For more information:

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

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. The data currently available include inspections conducted from 2001 through May 2011. Inspected properties receive an overall score from 0 to 100 based on five criteria: site, building exterior, building systems, common areas and units.

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.

TRF downloaded and geocoded data on HUD's multifamily and public housing sites from three different resources at HUD and wherever possible TRF 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 2008 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.

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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

Free or Subscriber-only:

free

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.

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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:

2010

Geographies:

point

Free or Subscriber-only:

free

For more information:

http://www.imls.gov/research/pls_data_files.aspx
http://www.irs.gov/uac/SOI-Tax-Stats-Migration-Data

Description:

The Public Libraries Survey (PLS) is conducted annually by the IMLS for the 2010 fiscal year. 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.

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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:

2004-05, 2005-06, 2006-07, 2007-08, 2008-09, 2009-2010

Geographies:

county

Free or Subscriber-only:

free

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, the data only capture returns processed by late September, 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.

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Maponics ZIP Code Boundaries

Details:

Zip code names and boundaries

Topics:

ZIP Codes

Source:

Maponics

Years Available:

2010Q2

Geographies:

5-digit postal ZIP Codes, national

Free or Subscriber-only:

free

For more information:

http://www.maponics.com/products/gis-data/zip-code-boundaries/overview/

Description:

ZIP Code boundary files on PolicyMap are licensed from Maponics. Maponics 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.

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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:

2005, 2006, 2007, 2008, 2009, 2010

Geographies:

point

Free or Subscriber-only:

subscriber-only

For more information:

http://nccs.urban.org/

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 2010 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.

For more information about other variables available on PolicyMap, please see http://nccsdataweb.urban.org/kbfiles/468/NCCS-data-guide-2006c.pdf

The Urban Institute geocoded every nonprofit in the Core 2010 PC File available on PolicyMap, and they assigned all unmatched addresses to the centerpoint of the listed zip code.

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

Geographies:

school district

Free or Subscriber-only:

free

For more information:

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

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 TRF (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).

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National Credit Union Administration

Details:

Credit union branch locations

Topics:

Credit unions

Source:

National Credit Union Administration

Years Available:

2012

Geographies:

point

Free or Subscriber-only:

free

For more information:

http://www.ncua.gov/DataApps/Pages/default.aspx

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 November 2012 from http://www.ncua.gov/DataApps/QCallRptData/Pages/default.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 TRF. TRF geocoded all branch location points, and was able to locate 99% of the given addresses on a map.

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National Nursing Center Consortium

Details:

Nurse Practitioner-Led Clinics

Topics:

Health, Nurse Practitioners, Clinics

Source:

National Nursing Center Consortium

Years Available:

2013

Geographies:

Point

Free or Subscriber-only:

Free

For more information:

http://www.nncc.us/site/

Description:

Spreadsheet downloaded from National Nursing Center Consortium on May 20, 2013. TRF was able to locate 91% of the points in the spreadsheet.

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National Oceanic and Atmospheric Association (NOAA) Heating/Cooling Degree Days

Details:

Heating degree days, cooling degree days

Topics:

Energy expenditure, heating costs, cooling costs

Source:

NOAA

Years Available:

As of 2008

Geographies:

census tract

Free or Subscriber-only:

Free

For more information:

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

Description:

The National Oceanic and Atmospheric Association (NOAA) provides maps of heating and cooling degree days. Degree days are a unit of measurement used in energy monitoring. Heating and cooling degree days provide rough comparisons between the heating and cooling needs of different regions.

The annual measure of degree days is based on daily values. Each day, the difference between the 65 degrees Fahrenheit and the average outdoor temperature is recorded, when that value is above or below 65 degrees for cooling and heating degree days, respectively. (65 degrees is considered the outdoor temperature at which buildings generally do not need heating or cooling.) These values are summed annually. The maps in PolicyMap represent the average annual degree days for many years.

PolicyMap displays this dataset for the lower 48 states only. Heating and cooling degree days are reported for climate zones that do no correspond to political geographies. For the sake of display in PolicyMap, TRF assigned each census tract the properties of the zone in which it was located, creating a close approximation of the NOAA map.

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

Details:

Average UV Index

Topics:

UV Index

Source:

NOAA and National Weather Service

Years Available:

As of 2008-2012

Geographies:

place

Free or Subscriber-only:

API only

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.

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National Oceanic and Atmospheric Association (NOAA) Weather Conditions

Details:

July and January maximum/minimum temperatures, heat and chill indices, days of snow, days of rainfall, sunny days

Topics:

Max/min temperatures, comfort index, weather

Source:

NOAA

Years Available:

As of 1990

Geographies:

county, place, zip code, metro area, state

Free or Subscriber-only:

API only

For more information:

http://www.ncdc.noaa.gov/oa/ncdc.html/

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 July and January are calculated by TRF by taking the mean of the range provided by the NOAA and calculating a weighted average based on geographic area. TRF chose to include July and January as months representative of peaks within the hottest and coldest months of the seasons.

The indices commonly known as the "Comfort Index" include a Heat Index (HI) and a Wind Chill Temperature, both of which TRF have 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 TRF 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 not available for selected parts of Alaska and Hawaii. They are not available for Puerto Rico.

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New America Foundation

Details:

percent of students proficient in reading and math

Topics:

School district population, overall student proficiency

Source:

New America Foundation

Years Available:

various (each indicator is only available for select years 2004-2011)

Geographies:

school district

Free or Subscriber-only:

free

For more information:

http://febp.newamerica.net/

Description:

The New America Foundation's Federal Education Budget Project provides data on student achievement for every school district in the country. Data are accessed from various sources for public research purposes. Student proficiency data for 4th grade, 8th grade, and high school students are calculated using state-defined proficiency standards of what students should know and be able to do for each grade as required under the No Child Left Behind (NCLB) Act.

Please note that not every indicator is available for every year.

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Non-Employer Statistics (NES)

Details:

count of self-employed artists

Topics:

cultural vitality, arts employment

Source:

US Census Bureau Nonemployer Statistics, as prepared by the Urban Institute

Years Available:

2003, 2004, 2005

Geographies:

county, CBSA, Metropolitan Division, state

Free or Subscriber-only:

Free

For more information:

http://www.census.gov/epcd/nonemployer/

Description:

NES provide annual estimates of self-employed people compiled from IRS tax forms filed by establishments with receipts of at least $1,000 per year. The Arts and Culture Indicators Project (ACIP) of the Urban Institute compiled NES data on independent artists to complement arts employment and wage data from the Occupational Employment Survey (OES). NES summarizes the number of establishments, including self-employed artists, without paid employees that are subject to federal income tax. Most non-employers are self-employed individuals operating small, unincorporated businesses, which may or may not be the owner's principal source of income. NES data are organized by industry and use the North American Industrial Classification System (NAICS). The NES files prepared by ACIP estimate the number and percent of establishments in the industry classified as "independent artists" (NAICS code 71151).

The category of "independent artists" is defined by NAICS as:Independent (i.e., freelance) individuals primarily engaged in performing in artistic productions, in creating artistic and cultural works or productions, or in providing technical expertise necessary for these productions. This industry also includes athletes and other celebrities exclusively engaged in endorsing products and making speeches or public appearances for which they receive a fee.

PolicyMap used this data from the report Cultural Vitality in Communities: Interpretation and Indicators. This report introduces a definition of cultural vitality that includes the range of cultural activity people around the country find significant, then uses this definition understand and source the data necessary to document arts and culture in communities. The information captured by NES is part of an initial set of arts and culture indicators derived from nationally available data.

The data includes part-time and temporary work by artists, as well as work by self-employed artists. The data is a population measurement, and is not subject to sampling error. Limitations include (1) lack of occupational detail: NES groups all types of artists under one category—independent artists—which, unlike OES, cannot be broken up into subcategories of artists; (2) inclusion of non-artists: the independent artist category consists mostly of visual and performing artists, but it also includes several occupations that are not artistic endeavors; (3) exclusion of .off the books. artists: because NES only counts artists who report their earning on their tax forms, not counted are artists not reporting their earnings to the IRS.

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Occupational Employment Statistics (OES)

Details:

count of employed artists by type, average wages

Topics:

cultural vitality, arts employment

Source:

Bureau of Labor Statistics Occupational Employment Statistics, prepared by the Urban Institute

Years Available:

2005, 2006

Geographies:

state, CBSA, Metropolitan Division

Free or Subscriber-only:

Free

For more information:

http://www.bls.gov/OES/

Description:

OES derive from a semiannual mail survey measuring wage rates and occupational employment totals for wage and salary workers in non-farm establishments in the United States. The Arts and Culture Indicators Project (ACIP) of the Urban Institute compiled OES data on employment and wages in 12 detailed arts-related occupations. The OES data use the Office of Management and Budget's Standard Occupational Classification (SOC) system, which includes 801 detailed occupations comprising 23 major occupational groups. ACIP identified 12 occupations relevant to arts and culture, within the major occupation group 27-0000 (Arts, Design, Entertainment, Sports, and Media Occupations). The survey sample of 1.2 million establishments over six panels is drawn from state Unemployment Insurance files. The OES provides cross-industry data files.

PolicyMap used this data from the report Cultural Vitality in Communities: Interpretation and Indicators. This report introduces a definition of cultural vitality that includes the range of cultural activity people around the country find significant, then uses this definition understand and source the data necessary to document arts and culture in communities. The information captured by NES is part of an initial set of arts and culture indicators derived from nationally available data.

OES counts part-time and full-time employees. It may capture some of the part-time artistic work that is performed outside of an artist's "day job". OES has several limitations including (1) exclusion of self-employed workers: because OES exclusively surveys employers, self-employed artists are excluded from OES estimates; (2) missing data: some OES estimates are suppressed because they do not meet the BLS standards for statistical quality or protecting the privacy of individual employers; and (3) sampling error. The true count of artists could vary from the count derived from the sample.

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OpenFlights.org and The Reinvestment Fund

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

Free or Subscriber-only:

API only

For more information:

http://www.openflights.org/data.html

Description:

OpenFlights.org collects information on airport locations and airline routes. TRF 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.

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Public Health Management Corporation Southeastern PA Household Health Survey

Details:

select indicators based on responses to the Southeastern Pennsylvania Household Health Survey.

Topics:

health insurance, dental care, access to health care, cigarettes, children with asthma, depression in older adults, neighborhood conditions

Source:

Public Health Management Corporation

Years Available:

2012

Geographies:

Southeast Pennsylvania zip regions, Philadelphia Health Districts, Philadelphia Planning Analysis Areas, Philadelphia Planning Districts

Free or Subscriber-only:

free

For more information:

http://www.chdbdata.org/
http://www.phmc.org/site/index.php/

Description:

Public Health Management Corporation (PHMC), a Philadelphia-based nonprofit institute for public health research and consulting, provided PolicyMap with data from the 2012 Southeast Pennsylvania Household Health Survey. These data were collected through household phone interviews conducted by PHMC.

Estimates are mapped to Southeast Pennsylvania zip regions, Philadelphia Health Districts, Philadelphia Planning Analysis Areas, and Philadelphia Planning Districts. These areas were created based on crosswalks provided to PolicyMap by PHMC. Southeast Pennsylvania Zip Regions are based on Maponics zip codes; Planning Analysis Areas, Planning Districts, and Health Districts are based on 2010 census tracts within the city of Philadelphia.

Any area with fewer than 30 responses to a survey question are suppressed in the data.

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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, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012

Geographies:

county, state

Free or Subscriber-only:

free

For more information:

http://www.ssa.gov/policy/docs/statcomps/ssi_sc/2011/index.html

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/.

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Trade Dimensions: Grocery Retail Locations

Details:

Grocery Retail Locations

Topics:

Grocery Retail Locations

Source:

Trade Dimensions

Years Available:

2011

Geographies:

point

Free or Subscriber-only:

Subscriber-only

For more information:

http://ca.nielsen.com/content/nielsen/en_ca/product_families/nielsen_trade_dimensions.html

Description:

The Trade Dimensions Retail Site Database provides a store-by-store database for the whole retail industry. Trade Dimensions monitors every store closing, opening, merger, acquisition, and sale in the retail industry. Available on PolicyMap are the name of the grocery store, and the grocery retail type, whose definitions are available below. The points currently on PolicyMap represent those open as of March 2011.

Grocery Retail Type Definitions:

Supermarket - Conventional

A supermarket is a 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. Trade Dimensions utilizes the trade channel definition endorsed by FMI (The Food Marketing Institute) and the leading industry publications. FMI is a nonprofit association of 1,500 food retailers and wholesalers, their subsidiaries and customers. Examples: Kroger, Food Lion, IGA, Cub Foods

Supercenter

A supercenter is a retail unit with a full-line supermarket and a full-line discount merchandiser under one roof. May have separate or combined checkouts. Examples: Wal-Mart Supercenter, Meijer Supermarket

Supermarket - Limited Assortment

A limited assortment supermarket has a limited selection of items in a reduced number of categories. These stores typically offer every day low pricing. Principal differentiation from a conventional supermarket is often in the reduced size and depth of produce and non-food categories such as Health and Beauty Care (HBC), cleaning supplies, paper products and general merchandise. A limited assortment supermarket has few, if any, service departments, and less product variety than a conventional supermarket. Examples: Aldi Food Store, Save-A-Lot

Natural/Gourmet Foods

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, cheese, meat and produce. Natural/gourmet foods supermarkets typically have expanded fresh food departments and/or prepared food selections. These supermarkets also typically have a limited selection, if any, of Health and Beauty Care (HBC) and general merchandise. A natural/gourmet foods supermarket does not have over 50 percent of product offerings in one category, as is the case with traditional butcher shops, delis, produce stands or nutritional supplement stores. Note: Ethnic supermarkets are not considered natural/gourmet foods supermarkets. Examples: Trader Joe's, Whole Foods, Dean & DeLuca

Warehouse Store

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. Examples: Cash & Carry, Smart & Final

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. Examples: Fort Hood DECA Commissary, Fort Riley DECA Commissary

Superette/Small Grocery

A superette is a grocery store with a sales volume ranging from $1 to $2 million annually. Typically superettes are independent, but many are affiliated with groups like IGA, Inc. Small grocery is defined as a grocery store with sales below $1 million annually. Also known as "Mom & Pop" stores. Examples: Country Market, Superior Markets

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TRF Study of Childcare Access (Philadelphia)

Details:

TRF Study of Childcare Access

Topics:

Childcare access

Source:

The Reinvestment Fund (TRF)

Years Available:

2013

Geographies:

block groups, points

Free or Subscriber-only:

free

For more information:

Description:

The 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, TRF 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, TRF 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, TRF 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, TRF determined that the 200 centers exclusively in this database were generally small, single employee operations. Therefore, TRF estimated a capacity of 5 for these centers.

One of TRF'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, TRF 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.

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TRF Study of Limited Supermarket Access (LSA) Areas

Details:

TRF Study of Limited Supermarket Access

Topics:

Food access, food security

Source:

The Reinvestment Fund (TRF)

Years Available:

2011

Geographies:

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

Free or Subscriber-only:

free

For more information:

http://www.trfund.com/understanding-access-to-affordable-and-healthy-foods/

Description:

With support from the Community Development Financial Institutions Fund ("CDFI Fund"), as part of its Financing Healthy Food Options Track, in partnership with Opportunity Finance Network, TRF's Study of Limited Supermarket Access (LSA) is specifically designed to: (1) Establish a valid and reliable method for measuring areas with inadequate access within the continental United States; (2) Locate geographic areas with the strongest need for additional supermarket development and quantify the demand for the area; (3) Allow for LSA areas to be prioritized based on the degree to which they lack access, have grocery demand and experience retail leakage; and (4) Provide a mapping tool to allow a diverse range of clients, including policymakers, government agencies, lending institutions, communities and policy research organizations, to analyze LSA areas within their geographies and craft strategies based upon the conditions in their community. Click here to read an overview of LSAs and how the use PolicyMap to identify and learn more about LSAs.

Identifying LSAs

TRF's methodology is designed to identify areas where residents travel longer distances to reach supermarkets when compared to the average distance traveled by residents of non-low/moderate income areas. Comparative areas are grouped based on similar values for population density and car ownership rates. Our data sources include US Census (2010) for population, households, and residential land area, US Census ACS data (2005-2009) for household income, US Census 2000 for car ownership rates; Bureau of Labor Statistics Consumer Expenditure Survey (2009) for demand for food at home; and Trade Dimensions (2011) for supermarket locations. Supermarkets include the following store types: supermarkets, supercenters, warehouse, limited assortment, military commissary, and natural food stores in the analysis. Superettes are excluded because they are less likely to provide a wide range of fresh groceries.

This methodology's key assumption is that block groups with a median household income greater than 120% of their respective metro area household medians (or non-metro state medians for non-metro areas) are adequately served by supermarkets and thus travel an appropriate distance to access food. This assumption establishes the benchmark to which all block groups are compared. This assumption is based on existing research that indicates an intense level of competition in the supermarket industry in higher-income communities, leading competitors to optimally locate in areas to adequately serve their customers.

Step I. TRF categorizes all block groups in the continental US into classifications using Census data for population density and car ownership rates. This process resulted in 13 categories ranging from: Density 1 (lowest density – highest car ownership) to Density 8 (highest density – lowest car ownership). TRF determined the residential population density by calculating the count of people and dividing it by the square mileage of non-water areas, minus the area of any non-residential census blocks. This residential population density data can be viewed in the TRF Analytics tab of PolicyMap.

Note: Block groups with fewer than 250 people and less than 100 households were excluded because a significant portion of the land area contains non-residential uses (e.g. park land, industrial, commercial, or institutional areas).

Step II. TRF uses Census block groups as the geographic unit of analysis. TRF calculates the distance traveled from the population center of every census block group (block centroid) to the nearest full-service store. For each census block group, a population-weighted distance is established based on road distance traveled for each of the member blocks.

Step III. TRF calculates benchmark distances based on our key assumption noted above. Each benchmark represents the average block group (calculated in Step II) distance of all non-low/moderate income (LMI) block groups and their nearest supermarket, within each category created in Step I. The benchmark distance represents a "comparatively acceptable" distance for households to travel to a supermarket.

Step IV. TRF calculates a low access score for each block group which represents the percent that the block group distance needs to be reduced to reach the reference group distance. These are the block groups' Low Access Scores. TRF compares the distance from each block group's population-weighted centroid to its nearest supermarket to that of its respective benchmark within the same category created in Step I. TRF assigns an Access Score to all block groups having a longer distance than their benchmark distance to a store. All block groups with distances at or less than their reference distance have negative Access Scores. All of these negative values were coded as "0". Therefore an access score of "0" is defined as block groups with distances at or below their reference group difference.

Step V. TRF used spatial and statistical methods to identify which block groups with positive access scores are spatially clustered with neighboring block groups also with high Access Scores. Only those block groups with Access Scores greater than 0 were subjected to this spatial cluster analysis. The block groups with spatial clustering of high low access scores are referred to as LSA areas. They represent areas with the strongest need for additional access to supermarkets.

Step VI. TRF created retail grocery leakage estimates as a way to determine the magnitude of each LSA area's access problem and its potential remedy – leakage represents grocery sales occurring outside of the LSA area boundaries. Using household income categories (Nielsen 2009) and their respective percentages of income spent on "food at home" (Consumer Expenditure Survey, 2011), TRF estimates total retail grocery demand in each LSA area. TRF also calculates the total sales of all food items from all stores (including existing superettes, drug stores, and in some cases supermarkets) within each block's reference distance. Dollars are distributed equally to all households in the reference distance. The total grocery sales is subtracted from demand, resulting in an estimate for retail grocery leakage. Because the access problem is better understood in terms of square feet, TRF converted dollars leaked to square feet using nationwide weighted averages for sales per square foot among full-service grocers.

Details for TRF Supermarket Study of LSA

For a detailed account of the methodology used in this study, please see the descriptive sections above.

Indicator Description
Limited Supermarket Access (LSA) Name The LSA area name includes the county, state, and a number identified with the LSA, due to the fact that many counties have multiple LSA areas.
Population Weighted LSA Access Score An Access Score indicates the degree to which block group's residents are underserved by supermarkets. Block groups with a positive access score must travel longer distances to access a full-service supermarket compared to established benchmark distances. The Access Score value represents the percent in which a block group's distance needs to be reduced in order to have a distance equal to its reference group. Access Scores can range from 0 to 100, with 0 representing areas equal to or less than their benchmark distance and scores greater than 0 representing areas with limited access to full service supermarkets.
# Block Groups in LSA Number of Block Groups in LSA, as of 2011.
Est. Grocery Retail Leakage Amount TRF estimates food retail demand in dollars for an area. Then TRF calculates retail sales captured by existing stores. The difference in demand minus sales represents the amount of "leakage". The leakage estimate is an indicator of store viability for an area. It is available in both dollars and square feet and it can be negative or positive. Calculations are rounded to the nearest $100,000.
Est. Grocery Retail Leakage Rate TRF estimates the grocery retail leakage rate as the percentage of total grocery shopping unmet demand for a given LSA area. The leakage rate is defined as the leakage amount divided by the total grocery retail demand within the LSA member block groups.
Est. Total Grocery Retail Demand TRF estimates total grocery retail demand in dollars for each LSA area for a given year. Grocery retail demand is determined by income as defined by Nielsen and percent of income spent on food prepared at home as defined by the U.S. Bureau of Labor Statistics. It is expressed in both dollars and square feet.
Est. # Grocery Retail Sq Ft Leaked TRF estimates food retail demand dollar amount for an area and then deducts the sales captured by existing stores. The net dollars represent the amount 'leaked' or lost. This calculation is then converted into square feet, based upon a nationwide average relating the number of square feet to the number of sales. The leakage estimate is an indicator of store viability for an area. The leakage square foot calculation is rounded to the nearest 1,000 square feet.
Stores in LSA The number of stores located within the defined LSA areas from the 2011 Trade Dimensions database.
Population Count of population in 2010. Census tracts that have split, merged or otherwise significantly changed from 2000 to 2010 are shown as having 'Insufficient Data.' For Census block group data, TRF created a bridge table between 2010 and 2000 block geographies to accurately display the 2010 data on 2000 blockgroup boundaries.

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TRF Supermarket Study of Low Access Areas

Details:

TRF Supermarket Study of Low Access Areas

Topics:

Food access, food security

Source:

The Reinvestment Fund (TRF)

Years Available:

2010

Geographies:

Block groups and Low Access Area (LAA) clustered block groups

Free or Subscriber-only:

free: certain indicators have specific subscriber access only

For more information:

http://www.trfund.com/understanding-access-to-affordable-and-healthy-foods/

Description:

The Reinvestment Fund's Supermarket Study of Low Access Areas is an analysis estimating food access for the purpose of: (a) identifying areas underserved by full-service supermarkets, (b) further stratifying underserved areas as having significant grocery retail leakage, demand, and lack of access, and (c) providing a tool for a diverse range of clients, including policymakers, government agencies, businesses, foundations, financial institutions, and policy research organizations.

Identifying Low Access Areas (LAA)

TRF's methodology is designed to identify areas where residents travel longer distances to supermarkets compared to the average distance of higher-income areas that share similar values for population density and car ownership rate. Our data sources include US Census (2000) for population living in households, residential land area, and car ownership rate; Bureau of Labor Statistics Consumer Expenditure Survey (2009); and Trade Dimensions (2009) for supermarket locations.

This methodology's key assumption is that block groups with a median household income greater than 120% of their respective metro area household medians (or county medians for non-metro areas) are adequately served by supermarkets and thus travel an appropriate distance. This assumption establishes the benchmark to which all block groups are compared. This assumption is based on existing research that indicates an intense level of competition in the supermarket industry in higher-income communities.

Step I. TRF categorized all block groups in the continental US into categories using Census data for population density and car ownership. This process results in 13 categories ranging from "Density 1 (lowest density) – High Car Ownership" to "Density 7 (highest density) – Low Car Ownership". Note that block groups with fewer than 250 people living in households were excluded because they do not represent the typical community structure, in that a significant portion of their land area contains non-residential uses. TRF determined the residential population density by calculating the count of people and dividing it by the square mileage of the area, minus the area of any non-residential census blocks. This residential population density data can be viewed in the TRF Analytics tab of PolicyMap.

Step II. TRF calculated the benchmark distances to support our key assumption noted above. Each benchmark represents the average distance between the population-weighted centroids of all non low-moderate income (LMI) block groups and their nearest supermarket, within each category created in Step I. The benchmark distance represents a comparatively acceptable distance for households to travel to a supermarket.

Step III. TRF compared the distance between each block group's population-weighted centroid and its nearest supermarket to that of its respective benchmark within the same category created in Step I. All block groups having a longer distance than their benchmark were assigned an Access Score that identifies the extent to which they are underserved.

Step IV. TRF used spatial and statistical methods to identify which underserved block groups are clustered together. These underserved clusters, referred to as low-access areas (LAA), represent areas with the strongest need for additional access to supermarkets.

Step V. TRF created retail grocery leakage estimates as a way to determine the magnitude of each LAA's access problem and its potential remedy – leakage represents grocery purchases made outside of the LAA boundaries. Using household income categories and their respective percentages of income spent on "food at home" (Consumer Expenditure Survey, 2009), TRF estimated total retail grocery demand in each LAA. Total grocery sales occurring within each LAA (from superettes and limited assortment stores) were then subtracted from demand, resulting in estimates for retail grocery leakage. Because the access problem is better understood in terms of square feet, TRF converted dollars leaked to square feet using nationwide weighted averages for sales per square foot.

Details for TRF Supermarket Study of LAA

For a detailed account of the methodology used in this study, please see the descriptive sections above.

Indicator Description
Low Access Area (LAA) Name The FIPS Code of one of the block groups within the LAA. This naming convention was selected so that users can search for LAAs using the census block group FIPS code (the first 12 characters of the LAA) to enter the state, county, census tract, and block group identification code in the Set Map Location search function.
Average LAA Score (population weighted) The degree to which a low/moderate-income community's residents are underserved by supermarkets and must travel longer distances to access full-service supermarkets, as compared to non low-income counterparts. Because LAAs are made up of block groups of varying population sizes, TRF calculated a LAA score that is weighted by population.
# Block Groups in LAA The number of block groups contained in the LAA. A given LAA could consist of one block group, or it could be comprised of multiple adjoining block groups.
Grocery Retail Leakage Amount Leakage represents grocery purchases made outside of an LAA's boundaries, thus the amount being "leaked" or lost from the LAA. It is used to estimate the magnitude of each LAA's access problem and its potential remedy.
Grocery Retail Leakage Rate The percentage of total grocery shopping demand for the LAA being "leaked" or lost to other areas. It is calculated as an LAA's leakage amount divided by its total grocery retail demand.
Total Grocery Retail Demand The dollar amount of grocery demand estimated for the LAA for a given year. Grocery retail demand is determined by income and the percent of income spent on food prepared at home, weighted by number of households. This indicator is available to specified subscribers. Also available as a map layer at the block group level.
# Grocery Retail Sq Ft Leaked The aggregate of the square feet for a given LAA being lost from the LAA to nearby communities' grocery retail establishments. This indicator is available to specified subscribers.
# Limited Service Stores The number of limited service stores in the LAA. Limited service stores are defined as either small independent grocers (superettes) or discount grocery stores offering a limited selection of items in a reduced number of categories (limited assortment). Limited service stores have few, if any, service departments, and limited (if any) perishables, as compared to conventional supermarkets. A Limited Service Store serves a block group if it falls within the benchmark distance.

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TRF Market Value Analyses (MVAs)

Detail:

TRF real estate market evaluation and valuation for Philadelphia, Baltimore, Washington DC, Milwaukee, WI, and areas of the state of New Jersey

Topics:

market value analyses, real estate

Source:

The Reinvestment Fund (TRF)

Years Available:

various

Geographies:

blockgroups in selected markets

Free or Subscriber-only:

free

For more information:

http://www.trfund.com/policy/public-policy/market-value-analysis/

Description:

The 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, TRF 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. TRF 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, TRF 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.

MARYLAND

Baltimore, MD

In 2011, TRF updated the Baltimore Market Value Analysis for the City of Baltimore.

The City of Baltimore used TRF'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 TRF: 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

In 2008 TRF updated the Baltimore Market Value Analysis with the Baltimore City Planning Department and Baltimore Housing.

TRF 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

In 2005 TRF developed a Market Value Analysis for the City of Baltimore Planning Department.

TRF 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.

NEW JERSEY

Atlantic Highlands, NJ

In 2007 TRF developed a Market Value Analysis of the Atlantic Highlands for the New Jersey Department of Community Affairs.

TRF 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 TRF developed a Market Value Analysis of Camden for the New Jersey Department of Community Affairs. TRF 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 TRF developed a Market Value Analysis of the Meadowlands for the New Jersey Department of Community Affairs.

TRF 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 TRF developed a Market Value Analysis of Newark for the New Jersey Department of Community Affairs.

TRF 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 TRF developed a Market Value Analysis of the Oranges for the New Jersey Department of Community Affairs.

TRF 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 TRF 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.

TRF 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 TRF developed a Market Value Analysis of the Vineland area (including Millville and Bridgeton) for the New Jersey Department of Community Affairs.

TRF 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 TRF developed a Market Value Analysis of the Washington Township area for the New Jersey Department of Community Affairs.

TRF 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

Philadelphia, PA

TRF'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 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

In 2008, TRF developed a Market Value Analysis for the City of Philadelphia.

TRF 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

In 2001, TRF developed a Market Value Analysis for the City of Philadelphia.

TRF 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 TRF developed a Market Value Analysis of Reading, Pennsylvania for Reading, Berks County, Pennsylvania.

TRF 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.

WASHINGTON, DC

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

TRF 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

TRF'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.

TRF 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 TRF's Policy Solutions department.

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TRF & American Community Survey (ACS)

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:

TRF calculation of ACS data

Years Available:

2007-2011

Geographies:

census tracts, block groups

Free or Subscriber-only:

free

For more information:

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

Description:

TRF calculated local median income as a share of area median income using 2007-2011 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 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.

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TRF & GreatSchools

Details:

Proximity to high performing public schools

Topics:

School performance, school ratings

Source:

TRF calculation of GreatSchools data

Years Available:

2013

Geographies:

census tracts

Free or Subscriber-only:

Subscriber-only

For more information:

http://www.greatschools.net

Description:

TRF 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.

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TRF & Urban Mapping

Details:

Proximity to public transit rail stops

Topics:

Public transit, mass transit

Source:

TRF calculation of Urban Mapping Inc. data

Years Available:

2009

Geographies:

census tracts

Free or Subscriber-only:

Subscriber-only

For more information:

http://urbanmapping.com/urbanware/mass-transit/coverage.html

Description:

TRF 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.

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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

Free or Subscriber-only:

free

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.

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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:

2013

Geographies:

County, State, points

Free or Subscriber-only:

free

For more information:

http://apps.ams.usda.gov/FarmersMarkets/

Description:

Farmers' market points are taken from USDA's Agricultural Marketing Service (AMS) through the Farmers' Market Directory. The Directory is an 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. TRF geocoded market addresses whose geographic coordinates fell outside the reported county (295 of 8,162 points), 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 will be updated on PolicyMap annually.

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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:

2014

Geographies:

states

Free or Subscriber-only:

free

For more information:

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

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 mandataed 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.

10 states (AZ, CO, FL, MO, MT, NV, OH, OR, VT, and WA) have minimum wage levels linked to the consumer price index. They generally increase annually around January 1st. Nevada's change occurs in July. This data represents the minimum wage on January 1, 2014.

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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

Topics:

health, food access, supermarkets, food deserts

Source:

U.S. Department of Agriculture, Economic Research Service

Years Available:

2010

Geographies:

Census Tracts

Free or Subscriber-only:

free

For more information:

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

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.

Data are from the 2012 report, Access to Affordable and Nutritious Food: Update Estimates of Distances to Supermarkets Using 2010 Data, available here: http://www.ers.usda.gov/publications/err-economic-research-report/err143.aspx. That report includes data a from the 2010 STARS directory of stores authorized to accept Supplemental Nutrition Assistance Program (SNAP) benefits, and the 2010 Trade Dimensions TDLinx directory of stores. Stores were defined as a supermarket or large grocery store if they reported at least $2 million in annual sales and contained all the major food departments found in a traditional supermarket, including fresh produce, fresh meat and poultry, dairy, dry and packaged foods, and frozen foods. Population data comes from the 2010 Census. Rural or urban status is designated by the Census Bureau's urban area definition.

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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)

Geographies:

County

Free or Subscriber-only:

free

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.

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United States Department of Agriculture (USDA) Rural Development

Details:

Locations of USDA Rural Development Multifamily properties

Topics:

USDA MF

Source:

USDA Rural Development Multifamily Housing Database FOIA

Years Available:

2007

Geographies:

points

Free or Subscriber-only:

free

For more information:

http://www.usda.gov/da/foia_guide.htm

Description:

TRF requested and received (via the Freedom of Information Act) the list of properties in the USDA Rural Development's Multifamily database in October 2007. TRF geocoded the properties listed in the USDA's Rural Development Multifamily database as of 11/1/2007. TRF was able to locate approximately 95% of these developments on a map.

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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:

2011

Geographies:

Polygon

Free or Subscriber-only:

API only

For more information:

http://soils.usda.gov/survey/geography/ssurgo/

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", "Farmland of statewide importance", "Farmland of local importance", and "Farmland of unique importance". Not included are conditional areas of prime farmland (e.g. "Prime farmland if drained").

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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

Free or Subscriber-only:

Free

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.

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US Fish and Wildlife Service, Critical Habitat Portal

Details:

Prime Farmland

Topics:

Sensitive lands, environment, endangered species, threatened species

Source:

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

Years Available:

2011

Geographies:

Polygon

Free or Subscriber-only:

API only

For more information:

http://criticalhabitat.fws.gov/crithab/

Description:

The Critical Habitat Portal of the U.S. Fish and Wildlife Service provides information on threatened and endangered species final critical habitat designation across the country. Includes areas relating to threatened, endangered, and species of concern.

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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

Free or Subscriber-only:

API only

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.

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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

Geographies:

County, state, congressional districts (for congressional races)

Free or Subscriber-only:

free

For more information:

http://uselectionatlas.org/

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, and 2012. 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.

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US Geological Survey (USGS) Gap Analysis Program (GAP) Protected Areas of the United States (PAD-US)

Details:

Protected Areas

Topics:

Sensitive lands, environment

Source:

Gap Analysis Program, United States Geological Survey, United State Department of the Interior

Years Available:

2011

Geographies:

Polygon

Free or Subscriber-only:

API only

For more information:

http://gapanalysis.usgs.gov/padus/data/

Description:

The Protected Areas Database of the United States represents public land ownership and conservation lands. The lands are assigned conservation status codes that denote the biodiversity level and natural, recreational, and cultural uses. The following status codes are included in our data:

Private Conservation Land
National Wildlife Refuge
National Forest-National Grassland
Resource Management Area
Habitat or Species Management Area
Recreation Management Area
Protective Management Area - Land, Lake or River
Wilderness Area
State Forest
State Park
Agricultural Protection Land
National Park
Local Conservation Area
Watershed Protection Area
Historic / Cultural Area
Area of Critical Environmental Concern
Wild and Scenic River
National Landscape Conservation System – Wilderness
Marine Protected Area
Research Natural Area
Local Forest
National Landscape Conservation System – Non Wilderness
Research and Educational Land
National Trail
Conservation Program Land
Mitigation Land
National Natural Landmark

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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:

2013

Geographies:

County, place, zipcode, CBSA, state

Free or Subscriber-only:

API only

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.

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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

Free or Subscriber-only:

API only

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.

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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:

2014

Geographies:

Point

Free or Subscriber-only:

free

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.

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Urban Mapping

Details:

Public Transit Rail Lines

Topics:

Public transit, mass transit

Source:

Urban Mapping Inc.

Years Available:

2009

Geographies:

Lines and points

Free or Subscriber-only:

Subscriber-only

For more information:

http://urbanmapping.com/urbanware/mass-transit/coverage.html

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

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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 seasonally 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:

2011Q2, 2011Q3, 2011Q4, 2012Q1, 2012Q2, 2012Q3, 2012Q4, 2013Q1, 2013Q2, 2013Q3, 2013Q4

Geographies:

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

Free or Subscriber-only:

Subscriber-only

For more information:

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

Description:

Valassis Lists, 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. 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. PO Box addresses are not included in the data.

Valassis Lists provided PolicyMap with a file containing counts of all vacant and no-stat residential and business addresses, as well as values for the total number of valid postal addresses from the CDS database, at the block group level. 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 2013 Q4 data, about 4.2% of vacant residential addresses, 1.6% of business vacancies, and 14% of no-stat addresses in the original data could not be matched due to these geocoding issues. Rural areas show significantly 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.

No-stat and CDS address counts are only available beginning in Q3 of 2013. Quarterly percent change indicators for seasonal residential and seasonal business vacancy are available for limited geographies in Q2 of 2013, Q2 of 2012, Q3 of 2011, and Q3 of 2012 due to insufficient data. Percent change from the previous quarter and previous year are calculated for all indicators where possible.

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Selected Premium Subscriber Datasets


CommonBond Communities

Detail:

Investments made by CommonBond Communities

Topics:

affordable housing

Source:

CommonBond Communities

Years Available:

various

Geographies:

points

Free or Subscriber-only:

free

For more information:

http://www.commonbond.org/

Description:

Founded in 1971, CommonBond Communities is the largest nonprofit developer, manager and service provider for affordable homes in the Upper Midwest. The organization preserves, builds and manages apartments and town homes while providing technology-based services and resources for residents. CommonBond Communities manages properties throughout Minnesota, Wisconsin and Iowa. The organizational mission is to build community by creating affordable housing as a steppingstone to success. They live this mission by bringing community members and residents together in community-impacting ways. Their properties provide homes that serve people who often earn no more than 30-60 percent of Area Median Income. Qualified families, seniors and people with special needs live in CommonBond properties and are seen as assets to urban, suburban and rural cities where they reside.

The organization uses a comprehensive approach. CommonBond works with first-rate architects and utilizes quality builders and materials to ensure well-built, affordable housing that lasts. The experienced property management team makes certain that properties are well maintained and their housing is compliant with the complex rules and regulations that govern the industry. The CommonBond staff and volunteers support both residents and resident programs.

CommonBond's point of difference is their nationally recognized Advantage Center resident services. The Advantage Centers are technology-based, on-site resources that enable CommonBond to provide more than housing. The resident services staff often includes social workers, employment specialists or youth workers who provides individual services and programs that specifically address the needs of each resident. The Advantage Centers focuses on preventing homelessness, creating family stability, helping residents secure employment, fostering youth achievement and aiding seniors and people with special needs as they live independently as long as possible.

Learn more at
CommonBond Communities
328 Kellogg Blvd W
St Paul, MN 55102
651-291-1750 (main) | 651-291-1003 (fax)
Web: www.CommonBond.org

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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

Free or Subscriber-only:

Free

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 TRF to spatially display the information available on their Investment Tracker website. The website and the data TRF 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.

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Record Information Services, Inc. (RIS) and Woodstock Institute

Detail:

Chicago Housing Auctions and Foreclosure Filings

Topics:

foreclosure, auction, Chicago

Source:

Record Information Services (Inc.) and Woodstock Institute

Years Available:

Various

Geographies:

Points

Free or Subscriber-only:

subscriber-only

For more information:

http://www.public-record.com/index.asp

Description:

TRF contracted with the Woodstock Institute to download and geocode foreclosure filings and auctions within the City of Chicago available from Record Information Services, Inc. Data does not include commercial foreclosures, and it does not include information on any outcomes other than REO or sold properties. Foreclosure filings were downloaded by input date, while auctions were retrieved by date of sale. Both auctions and foreclosure filings data is updated on a quarterly basis and released in the third week of the quarter.

Record Information Services (RIS) compiles data on foreclosures, real estate, mortgage, and bankruptcy for the State of Illinois. For more information on the raw data available through RIS, they can be reached at 630-557-1000.

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Last Updated: April 4, 2014

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