Details: |
non-business and business bankruptcy filings |
Topics: |
bankruptcy filings |
Source: |
American Bankruptcy Institute Bankruptcy Filing Statistics-Annual Business and Non-Business Filings |
Years Available: |
2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009 |
Geographies: |
state |
Free or Subscriber-only: |
free |
For more information: |
http://www.abiworld.org/Content/NavigationMenu/ NewsRoom/BankruptcyStatistics/Bankruptcy_Filings_1.htm |
The American Bankruptcy Institute provides information on consumer and business bankruptcy filings each quarter. Data are from the Administrative Office of the US Courts.
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 |
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/ |
Boxwood Means, Inc., the real estate research firm and Value Added Reseller of residential and commercial data from the nation's largest vendor of real estate information, 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 the recent home sales for 2006 and 2007 (annual), quarterly figures for all quarters of 2007 and the first three quarters of 2008, and percent changes in median sale prices from 2001 to 2006, 2003 to 2006, and 2005 to 2006. 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. 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 in italics 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*, Butler County*, Chilton County*, Colbert County*, Dale County*, Dallas County*, Etowah County*, Henry County*, Houston County*, Jefferson County*, Lauderdale County*, Lawrence County*, Lee County*, Macon County*, Madison County*, Marengo County*, Marion County*, Marshall County*, Mobile County, Montgomery County, Randolph County*, Russell County*, St. Clair County, Shelby County, Sumter County*, Talladega 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*, 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, 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, Huerfano 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, San Juan 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*, Bryan County*, Bulloch County*, Burke County*, Butts County, Calhoun County*, Camden County*, Carroll County, Catoosa County, Charlton County*, Chatham County, Chattahoochee County, Chattooga County*, Cherokee County, Clarke County, Clay County*, Clayton 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*, Dougherty County, Douglas County, Echols County*, Effingham County, Elbert County*, Emanuel 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*, Long County*, Lowndes County, Lumpkin County, McDuffie County, Macon County*, Madison County, Marion County*, Miller County*, Mitchell County*, Montgomery County*, Morgan County*, Murray County*, Muscogee County, Newton County, Oconee County, Oglethorpe County, Paulding County, Pickens County, Pierce County*, Pike County*, Polk County, Pulaski County*, Putnam County, Quitman County*, Rabun County, Randolph County*, Richmond County, Rockdale County, Screven County*, Seminole County*, Spalding County, Stephens County, Stewart County*, Sumter County*, Talbot County*, Tattnall County*, Taylor County*, Telfair County*, Thomas County*, Tift County*, Toombs County*, Towns County, Troup County*, Turner County*, Twiggs County*, Union County, Upson County*, Walker County, Walton County, Ware County*, Warren County*, Wayne County*, Webster County*, Wheeler County*, White County, Whitfield County, Wilcox County*, Wilkinson County*, Worth County*
Hawaii: Hawaii County, Honolulu County, Kauai County, Maui County
Idaho: Ada County, Bannock County, Benewah County*, Bingham County, Blaine County*, Bonner County, Bonneville County, Boundary County, Butte County*, Camas County*, Canyon County, Clark County*, Custer County*, Franklin County*, Fremont County*, Jefferson County*, Jerome County*, Kootenai County, Latah County*, Madison County*, Nez Perce County, Payette County*, Power County*, Shoshone County, Teton County*, Twin Falls County*, Valley County, Washington County*
Illinois: Bond County*, Boone County*, Carroll County*, Cass County*, Champaign County, Christian County*, Clark County*, Clay County*, Clinton County, Coles County, Cook County, DeKalb County, Douglas County*, DuPage County, Edgar County*, Franklin County*, Gallatin County*, Grundy County, Hamilton County*, Hardin County*, Henderson County*, Iroquois 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, Madison County, Marshall County*, Mason County*, Mercer County*, Monroe County, Montgomery County*, Morgan County*, Ogle County, Peoria County, Randolph County, Richland County*, Rock Island County, St. Clair County, Saline County*, Sangamon County, Scott 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*, Boone County*, Cass County*, Clark County*, Daviess County*, Dearborn County*, DeKalb County*, Delaware County*, Dubois County*, Elkhart County, Floyd County*, Grant County*, Hamilton County, Hancock County*, Harrison County*, Hendricks County*, Henry County, Howard County*, Jasper County, Johnson County*, Kosciusko County*, Lake County, LaPorte County*, Madison County, Marion County, Marshall County*, Miami County*, Monroe County, Montgomery County*, Morgan County*, Newton County*, Noble County*, Porter County, Posey County*, Putnam County*, Randolph County, St. Joseph County, Shelby County*, Spencer County*, Tippecanoe County, Vanderburgh County*, Vigo County, Wabash County*, Warrick County*, Wayne County*, Wells County*, Whitley County*
Iowa: Adair County, Adams County*, Allamakee County, Appanoose County, Audubon County*, Benton County*, Black Hawk County*, Boone County, Bremer 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*, 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, Humboldt County, Ida County*, Jackson County*, Jasper County, Jefferson County, Johnson County*, Jones County, Keokuk County, Lee County, Linn County, Louisa County, Lucas County*, Lyon County*, Madison County, Mahaska County*, Marion County, Monroe County*, Montgomery County, Muscatine County*, OBrien County*, Palo Alto County*, Plymouth County, Polk County, Pottawattamie County, Poweshiek County, Ringgold County*, Sac County*, Scott County, Shelby County, Story County, Tama County*, Union 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*, Ballard County, Bath County*, Bell County*, Boone County, Bourbon County, Boyd County*, Boyle County*, Bracken County*, Breckinridge County*, Bullitt County*, Butler County*, Caldwell County*, Calloway County, Campbell County, Carter County*, Casey County*, Christian County, Clark County*, Clay County, Clinton County*, Cumberland County*, Daviess County*, Fayette County*, Floyd County*, Fulton County*, Gallatin County*, Grant County*, Graves County*, Greenup County*, Hancock County*, Hardin County*, Harrison County, Hart County*, Henderson County*, Hickman County*, Hopkins County*, Jefferson County, Johnson County*, Kenton County, Lee County*, Lewis County*, Lyon County*, McLean County*, Marshall County*, Martin County*, Mason County*, Monroe County*, Nelson County*, Nicholas 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*, Beauregard Parish*, Bossier Parish, Caddo Parish*, Calcasieu Parish*, Cameron Parish*, Catahoula Parish*, Claiborne Parish*, Concordia Parish*, De Soto Parish*, East Baton Rouge Parish, Grant Parish*, Iberia Parish*, Jefferson Parish, Lafayette Parish*, Lafourche 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. John the Baptist Parish, St. Martin Parish*, St. Mary Parish*, St. Tammany Parish, Tangipahoa Parish, Terrebonne Parish*, Union Parish*, Washington Parish*, West Baton Rouge Parish*
Maine: Androscoggin County*, Franklin County*, Hancock County*, Kennebec County*, Penobscot 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*, 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, Macomb County, Manistee County*, Mason County*, Mecosta County*, Midland County, Missaukee County*, Monroe County, Montcalm County*, Muskegon County, Newaygo County*, Oakland County, Oceana County*, Ogemaw County*, Ontonagon County*, Osceola 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*, Blue Earth County, Brown County*, Carlton County*, Carver County, Chippewa County*, Chisago County, Clay County, Cook County*, Cottonwood County*, Dakota County, Dodge County*, Faribault County, Fillmore County*, Freeborn County*, Goodhue County, Hennepin County, Hubbard County, Isanti County, Kandiyohi County, Koochiching County, Lake County, Le Sueur County, Lincoln County, Lyon County*, Martin County, Nicollet County, Nobles County*, Norman County*, Olmsted County, Otter Tail County*, Pine County*, Pope County, Ramsey County, Redwood County*, Rice County, St. Louis County, Scott County, Sherburne County, Stearns County, Wadena County*, Washington County, Watonwan County*, Wilkin County*, Wright County
Mississippi: Attala County*, Bolivar County*, Calhoun County*, Clarke County*, DeSoto County, Greene County*, Grenada County*, Issaquena County*, Jefferson Davis County*, Lincoln County, Madison County*, Perry 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: Bates County*, Boone County*, Buchanan County*, Butler County, Cape Girardeau County*, Cass County, Christian County*, Clay County, Cooper County*, Dallas County*, Dunklin County*, Franklin County, Greene County, Howell County, Jackson County, Jasper County, Jefferson County, Lafayette 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: Cascade County, Dawson County, Deer Lodge County, Fergus County, Flathead County, Gallatin County, Granite County, Judith Basin County, Lake County, Lewis and Clark County, Lincoln County, McCone County*, Madison County, Mineral County, Missoula County, Ravalli County, Yellowstone County
Nebraska: Adams County*, Antelope County*, Banner County*, Blaine County*, Boone County*, Box Butte County*, Buffalo County*, Butler County*, Cass County, Cherry County*, Colfax County, Cuming County, Custer County*, Dakota County, Deuel County*, Dodge County*, Douglas County, Dundy County*, Franklin County*, Gage County*, Garfield County*, Gosper County*, Greeley County*, Hall County*, Hamilton County*, Harlan County*, Hayes County*, Hitchcock County*, Hooker County*, Howard County*, Johnson County*, Kearney County*, Keith County*, Keya Paha County*, Knox County*, Lancaster County, Lincoln County*, Logan County*, Loup County*, McPherson County*, Madison County*, Nemaha County*, Otoe County*, Pawnee County*, Perkins County*, Pierce County*, Polk County*, Red Willow County, Richardson County*, Rock County*, Sarpy County, Saunders County, Scotts Bluff County, Seward County*, Sherman County*, Thayer County*, Thomas 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*, Curry County*, Dona Ana County, Eddy County, Lincoln County, Los Alamos County*, Otero County, Sandoval County, Santa Fe 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, 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: Barnes County, Benson County*, Burleigh County, Cass County, Cavalier County*, Dunn County*, Foster County*, Grand Forks County, Griggs County*, McHenry County*, McKenzie County*, McLean County*, Morton County, Nelson County, Pembina County, Pierce County*, Ramsey County, Ransom County*, Richland County, Rolette County*, Sargent County, Stark County, Steele County*, Stutsman 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, 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, 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, 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, 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*, Deschutes County, Douglas County, Hood River County*, Jackson County, Josephine County, Klamath County, Lane County, Lincoln County, Linn County, Malheur County*, Marion County, Multnomah County, Polk County, Tillamook County, Umatilla County, Union County*, Wasco County*, Washington County, Yamhill County
Pennsylvania: Adams County*, Allegheny County, Beaver County, Berks County, Blair County*, Bucks County, Butler County, Carbon County*, Centre County, Chester County, Clarion County*, Clearfield County*, Columbia County*, Cumberland County, Dauphin County, Delaware County, Erie County, Fayette County*, Fulton County*, Greene County*, Huntingdon County*, Indiana County, Juniata County*, Lackawanna County*, Lancaster County, Lawrence County, Lehigh County, Luzerne County, Lycoming County*, Mifflin County*, Monroe County, Montgomery County, Montour County, Northampton County, Perry County*, Philadelphia County*, Pike County*, Potter County*, Somerset County*, Tioga 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: Aiken 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: Day County*, Fall River County*, Faulk County*, Harding County*, Hutchinson County*, Lincoln County*, Minnehaha County*, Moody County*, Potter County*, Roberts County*, Shannon County*, Spink County*, Union 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: Angelina County, Aransas County, Archer County*, Atascosa County, Bandera County, Bastrop County, Bell County*, Bexar County, Blanco County, Bosque County*, Brazoria County, Brazos County, Brown County*, Burnet County, Calhoun County*, Cameron County, Chambers County, Cherokee County*, Collin County, Comal County, Cooke County, Culberson County*, Dallas County, Denton County, Eastland County*, Ector County*, Edwards County*, Ellis County, El Paso County, Erath County*, Fannin County*, Fort Bend County, Freestone County*, Galveston County, Gillespie County, Gonzales 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: Box Elder County, Davis County, Iron 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: Albemarle County*, Alleghany County*, Amherst County*, Arlington County, Augusta County*, Bath County*, Bland County*, Botetourt County*, Buchanan County*, Buckingham County*, Campbell County*, Caroline County, Carroll County*, Chesterfield County, Clarke County, Culpeper County, Dinwiddie County*, Fairfax County, Fauquier County, Frederick County, Goochland County*, Halifax County*, Hanover County, Henrico County, Highland County*, James City County, King George County, King William County*, Loudoun County, Louisa County, Madison County*, Mecklenburg County*, Middlesex County*, Montgomery County*, New Kent County*, Orange County, Page County*, Prince Edward County*, Prince William 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*, Wythe County*, York County, Alexandria City, Bedford City*, Charlottesville City*, Chesapeake 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, 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, 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*, Brooke County*, Cabell County*, Calhoun County*, Fayette County*, Gilmer County*, Grant County*, Hampshire County*, Hardy County*, Harrison 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: Laramie County, Lincoln County*, Natrona County, Teton County*
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.
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, 2001, 2002, 2003, 2004, 2005, 2006, 2007 |
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 |
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. This dataset is similar to the IRS Statistics of Income data but differs in some of the topics it covers as well as in the level of detail given about EITC returns as a group. 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.
For more details on the dataset, see their user guide at http://www.brookings.edu/metro/EITC/EITC-Data.aspx
Details: |
Brownfield site locations |
Topics: |
Brownfields |
Source: |
Brownfields Sites Reports, US EPA |
Years Available: |
2009 |
Geographies: |
points |
Free or Subscriber-only: |
free |
For more information: |
http://epa.gov/brownfields/ |
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 May of 2009.
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 |
Geographies: |
county, Metropolitan Division, CBSA, state, place |
Free or Subscriber-only: |
free |
For more information: |
http://www.bls.gov/lau/lauov.htm |
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 values presented in PolicyMap are annual averages for the years listed.
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.
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, total population, population density, population by race and ethnicity, age, sex, people with disabilities, 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 US Census, Summary File 3; Claritas, Inc. |
Years Available: |
2000, 2009, 2014 |
Geographies: |
blockgroup, Census tract, zip code (2009 and 2014 only), county subdivision (2000 only), Census place (city), county, state, CBSA (metro area), nation |
Free or Subscriber-only: |
Free (2000); Subscriber Only (2009 and 2014) |
For more information: |
http://www.census.gov/Press-Release/www/2002/sumfile3.html http://www.claritas.com/claritas/Default.jsp?ci=3&si=1&pn=demographics |
Demographic data for 2000 is from the US Bureau of the Census' Summary File 3 (SF3). This dataset is derived from the longer version 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.
Estimates and projections of data for 2009 and 2014 are from Claritas, Inc., a consumer data and demographics firm that produces annual small-area estimates that update many of the data from the decennial census. Claritas also produces projections, taking into account a variety of factors, to estimate the likely characteristics and counts of households and people five years into the future. Claritas projections are estimates made on the basis of certain broad assumptions about how populations change.
PolicyMap is licensed to provide 2009 and 2014 Claritas data to our subscribers. TRF also used Claritas estimates and projections to perform a variety of calculations in PolicyMap, which are also available only to our subscribers.
For both Census and Claritas data, TRF calculates and provides percentages through PolicyMap. TRF does not, however, calculate percentages in cases where the denominator of the calculation is less than ten. For example, if an area has a population of 9 people and 8 of those people are living in poverty, this area will appear grayed out in PolicyMap for the percent of people living in poverty. TRF does this because the calculation would otherwise show an 89% poverty rate and would likely skew the interpretation of the map.
Claritas estimates and projections are based on many independent datasets from which they estimate how and where the population will change. These datasets take into account inflation, so income and value figures from Claritas refer to the dollars of the year that the data pertains to. For example, home value in 2009 is given in 2009 dollars; home value for 2014 is given in what 2014 dollars are estimated to be; etc.
| 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 Pattern Data |
Years Available: |
2003, 2004, 2005, 2006, 2007 |
Geographies: |
zip code, county, state, CBSA |
Free or Subscriber-only: |
free |
For more information: |
http://www.census.gov/epcd/cbp/view/cbpview.html |
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.
The number of jobs per NAICS category are reported at the county and state level. At the zip code level, in order to preserve anonymity, CBP does not disclose a specific number of employees but rather reports the number of establishments with employees in various ranges. Where CPB provides the number of employees as falling within a range, TRF represents these areas as having Insufficient Data on the map.
| 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, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010 |
Geographies: |
county, state, CBSA, national |
free |
free |
For more information: |
http://www.census.gov/const/www/permitsindex.html |
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, CBSA 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/const/www/C40/variance.html. 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.
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 |
Geographies: |
county, state |
Free or Subscriber-only: |
free |
For more information: |
http://www.census.gov/hhes/www/sahie/ |
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. (A question on health insurance coverage has recently been added to the American Community Survey, but widespread reporting on that question is not yet available.).
Details: |
number of families receiving food stamps |
Topics: |
food stamps |
Source: |
US Census Small Area Income and Poverty Estimates |
Years Available: |
2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007 |
Geographies: |
county, state |
Free or Subscriber-only: |
free |
For more information: |
http://www.census.gov/hhes/www/saipe/ |
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 and counties. This dataset mainly serves administrators of federal programs who need current statistics on the demonstrated need of places.
Details: |
count of births, count and rate of infant deaths |
Topics: |
infant birth and mortality |
Source: |
CDC National Center for Health Statistics, National Vital Statistics System |
Years Available: |
2000, 2001, 2002, 2003, 2004, 2005, 2006 |
Geographies: |
county, state |
Free or Subscriber-only: |
free |
For more information: |
http://www.cdc.gov/nchs/nvss/about_nvss.htm |
The Center for Disease Control (CDC) dataset provides the number of births, 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.
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 |
Geographies: |
state, CBSA, Metropolitan Division |
Free or Subscriber-only: |
free |
For more information: |
http://apps.nccd.cdc.gov/brfss/ |
The Center 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. The CDC only reports these numbers for states. States for which data is not available in a given year are represented as having Insufficient Data on the map.
Details: |
number and percent of mothers by age and trimester in which prenatal care was received |
Topics: |
prenatal care |
Source: |
CDC National Center for Health Statistics, Office of Analysis and Epidemiology, Division of Vital Statistics |
Years Available: |
2000, 2001, 2002, 2003, 2004, 2005, 2006 |
Geographies: |
county, state |
Free or Subscriber-only: |
free |
For more information: |
http://www.cdc.gov/nchs/datawh/vitalstats/VitalStatsbirths.htm |
The Center for Disease Control (CDC) dataset provides 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. Additional prenatal care categories give these numbers and rates for mothers under age 20.
The data also 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.
The CDC only reports data on prenatal care for counties with populations of 100,000 or more. The CDC only reports data on births to young mothers (under 20 years, under 18 years, and between 18 and 19 years, inclusive) for counties with populations of 250,000 or more.
Detail: |
Superfund site locations |
Topics: |
Superfund |
Source: |
CERCLIS Sites Reports, US EPA Office of Solid Waste and Emergency Response |
Years Available: |
2007 |
Geographies: |
points |
Free or Subscriber-only: |
free |
For more information |
http://cfpub.epa.gov/supercpad/cursites/srchsites.cfm |
TRF received the EPA's National Priorities List from the EPA for use in PolicyMap. The EPA regularly updates this list. The points in PolicyMap are as of March 2007.
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 2009 |
Geographies: |
Census Tract |
Free or Subscriber-only: |
free |
For more information: |
http://www.cdfifund.gov/what_we_do/census.asp |
The Community Development Financial Institution (CDFI) Fund, a division of the US Department of the Treasury, administers the New Market 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. For more on these programs users should consult the CDFI Fund website directly: www.cdfifund.gov.
These designations are current as of January 2009 but may be changed at any time by the CDFI Fund. For this reason, users should verify eligibility directly with the CDFI Fund. Information in PolicyMap does not include Native America Areas.
Details: |
New Market Tax Credit Program Eligibility |
Topics: |
NMTC Program Eligibility, Severe Distress, Very Low-Income, Poverty, Unemployment, HUBZone, Medically Underserved Area, Renewal Area, High Migration Rural County, ARC/DRA Area, AMI |
Source: |
Community Development Financial Institutions Fund, US Department of the Treasury and The Reinvestment Fund |
Years Available: |
As of 2010 |
Geographies: |
Census Tract |
Free or Subscriber-only: |
free |
For more information: |
http://www.cdfifund.gov/docs/nmtc/2010/2010-NMTC-Application-Final-4-19-2010.pdf |
The Community Development Financial Institutions (CDFI) Fund, a division of the US Department of the Treasury, administers the New Market 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 Market 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 2000 or (2) Poverty Rate of 20% or greater in 2000. 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 2000; having a poverty rate at or above 30% in 2000*; or having an unemployment rate of at least 1.5 times the national unemployment rate in 2000. Census tracts meeting the NMTC Primary Criteria for Severely Distressed Status are comparatively less in need as those Census tracts meeting the NMTC Secondary Criteria for Severely Distressed Status. PolicyMap provides a map of those Census tracts that are considered Severely Distressed because they satisfy the Primary Criteria, which is located in "Primary Criteria: Severely Distressed". PolicyMap also includes a map of those Census tracts that meet all three of the Primary Criteria, indicating great need in "Primary Criteria: Meet all 3 Primary Criteria". (Although the CDFI Fund does not consider meeting all three criteria as a standard, PolicyMap shows the Census tracts that meet all three requirements as an indicator of particularly Severely Distressed areas in need of investment.) 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 CDFI Fund Hot Zone, a Medically Underserved Area (MUA), a Federal Empowerment Zone, Enterprise Community or Renewal Community, a High Migration Rural County, 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), or a FEMA Distaster Area. 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. PolicyMap provides a map of those Census tracts that are considered Severely Distressed because they satisfy the Secondary Criteria, which is located in "Secondary Criteria: Severely Distressed". Also included in this submenu are the data for each of the factors that constitute the Secondary Criteria for NMTC Severely Distressed.
The data used to create these the calculations for the CDFI Fund NMTC Eligibility maps include numerous sources, listed below. The currency of the data used in these calculations is also included:
| Median Family Income | Census SF3 2000 |
| Area Median Income | HUD Income Limits 2000 |
| Poverty Rate | Census SF3 2000 |
| Unemployment Rate | Census SF3 2000 |
| SBA HUBZones | Small Business Administration HUBZones |
| CDFI Hot Zones | CDFI Fund Census Download List |
| Medically Underserved Areas | US Department of Health and Human Services Health Resources and Services Administration Shortage Areas |
| Federal Empowerment Zones | HUD Renewal Communities, Empowerment Zones, and Enterprise Communities |
| High Migration Rural Counties | CDFI Fund Download List |
| Brownfield locations | EPA Brownfields |
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.
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 |
Geographies: |
county |
Free or Subscriber-only: |
free |
For more information: |
http://www.ffiec.gov/CRA/ |
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.
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: |
2010 |
Geographies: |
Census Tract |
Free or Subscriber-only: |
free |
For more information: |
http://www.ffiec.gov/CRA/ |
The Community Reinvestment Act (CRA), which was enacted by Congress in 1977, is intended to encourage depository institutions to help meet the credit needs of the communities in which they operate, including low- and moderate-income neighborhoods, consistent with safe and sound banking operations. CRA requires that each insured depository institution's record in helping meet the credit needs of its entire community be evaluated periodically. These examinations are conducted by federal agencies: the Board of Governors of the Federal Reserve System (FRB), the Federal Deposit Insurance Corporation (FDIC), the Office of the Comptroller of the Currency (OCC), and the Office of Thrift Supervision (OTS). That record is taken into account in considering an institution's application for deposit facilities, including mergers and acquisitions.
In order to gauge CRA performance, the evaluation looks for bank activity in low- and moderate-income neighborhoods, nonmetropolitan distressed and underserved areas, and federally designated disaster areas. These areas are identified by calculating tract income level. This is the Median Family Income (MFI) of each tract divided by Area Median Family Income (AMFI). For 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 is 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.
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 |
Geographies: |
state |
Free or Subscriber-only: |
free |
For more information: |
http://www.dhs.gov/ximgtn/statistics/ |
The Department of Homeland Security's Yearbook of Immigration Statistics is an annual publication on documented foreign nationals in the United States. PolicyMap contains data by state on the number of people granted Legal Permanent Resident (LPR) status, by region of their birth, and by selected countries. The selected countries of birth included in PolicyMap were determined by the number of people that country sent between 2004 and 2008. If the volume of immigrants receiving green cards in any year was more than 15,000 people, the country was included.
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, 2006, 2007, 2008 |
Geographies: |
selected counties and places |
Free or Subscriber-only: |
free |
For more information: |
http://www.fbi.gov/ucr/ucr.htm |
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.
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. FBI UCR data should not be compared across places or counties, and should not be compared from one year to another. 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.
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 was reported to the FBI for selected places and counties by local law enforcement agencies.
Details: |
Rates of adherence by denomination, Counts of denominations |
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 |
Geographies: |
county, state |
Free or Subscriber-only: |
free |
For more information: |
http://www.thearda.com/mapsReports/rcms_notes.asp |
2000 data were collected by the Association of Statisticians of American Religious Bodies (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 data are based on counts of members of denominations. While quite comprehensive, this 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.
The data reported on Jews and Muslims are estimates rather than counts. For more information see: http://www.thearda.com/mapsReports/rcms_notes.asp.
Topics: |
Public, Public Charter and Private School District performance, selected test scores by district |
Source: |
GreatSchools |
Years Available: |
varied, 2004 to 2009 |
Geographies: |
school district |
Free or Subscriber-only: |
subscriber-only |
For more information: |
http://www.greatschools.net |
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. TRF identified a threshold for each state to determine which data would show a descriptive map of school performance. Therefore, some data are not represented in PolicyMap where TRF found data was unavailable in most school districts.
PolicyMap displays data for the following standardized tests:
Alaska
Alaska Standards Based Assessment (SBA): In 2007-2008 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.
Alabama
Alabama High School Graduation Exam (AHSGE): In 2007-2008 Alabama used the Alabama High School Graduation Exam (AHSGE) to test high school students in reading, math, language, science and social studies. The AHSGE is a standards-based test, which means it measures specific skills defined for each grade by the state of Alabama. High school students must pass the AHSGE in order to graduate. The goal is for all students to pass the test.
Alabama Reading and Mathematics Test (ARMT): In 2007-2008 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.
Arkansas
Benchmark Exams (BE): In 2007-2008 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 2007-2008 Arkansas used the End of Course Exam to test high school students in algebra I and geometry. The results for End of Course Exams administered in spring 2007 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 2007-2008 Arizona's Instrument to Measure Standards (AIMS) was used to test students in reading, writing and mathematics in grades 3 through 8 and 10. AIMS is a standards-based test, which means it measures how well students have mastered Arizona's learning standards. The goal is for all students to meet or exceed state standards on the test.
California
California Standards Test (CST): In 2007-2008 California used the California Standards Tests (CSTs) to test students in English language arts and math in grades 2 through 11; science in grades 5, 8 and 10; and history-social science in grades 8, 10 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
Colorado Student Assessment Program (CSAP): In 2007-2008 Colorado used the Colorado Student Assessment Program (CSAP) to test students' skills in reading, writing and mathematics in grades 3 through 10 and in science in grades 5, 8 and 10. The CSAP 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 2007-2008 Connecticut used the Connecticut Mastery Test (CMT) to test students' skills in reading, writing and math in grades 3 through 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 2007-2008 Connecticut used the Connecticut Academic Performance Test (CAPT) to test students' skills in reading, writing, science and mathematics 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.
Delaware
Delaware Student Testing Program (DSTP): In 2007-2008 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.
District of Columbia
District of Columbia Comprehensive Assessment System (DC-CAS): In 2007-2008 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. The results displayed on GreatSchools profiles are for all grades combined for each subject. 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.
Florida
Florida Comprehensive Assessment Test (FCAT): In 2007-2008 Florida used the Florida Comprehensive Assessment Test (FCAT) to test students in grades 3 through 10 in reading and math and in grades 5, 8 and 11 in science. The FCAT Writing+ test replaced the former FCAT writing test given in grades 4, 8 and 10. The FCAT 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 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 2007-2008 Georgia administered the Criterion-Referenced Competency Tests (CRCT) in reading, English language arts and math in grades 1 through 8 and in science and social studies 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 2007-2008 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.
Hawaii
Hawaii State Assessment (HSA): In 2007-2008 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, which means it 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.
Idaho
Idaho Standards Achievement Test (ISAT): In 2007-2008 Idaho used the Idaho Standards Achievement Test (ISAT) to test students in grades 2 through 10 in reading, math and language usage. The scores from the spring administration for grades 3 through 8 and 10 are displayed on GreatSchools profiles. 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 ISAT is a high school graduation requirement. The goal is for all students to score at or above the proficient level.
Illinois
Illinois Standards Achievement Test (ISAT): In 2007-2008 Illinois used the Illinois Standards Achievement Test (ISAT) to test students in grades 3 through 8 in reading and math and students in grades 4 and 7 in science. The reading and math results are displayed on GreatSchools profiles. 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.
Indiana
Indiana Statewide Testing for Educational Progress-Plus (ISTEP+): In 2008-2009 Indiana used the Indiana Statewide Testing for Educational Progress-Plus (ISTEP+) assessment to test students in grades 3 through 10 in English/language arts and math and in grades 5 and 7 in science. The ISTEP+ is a standards-based test, which means it measures specific skills defined for each grade by the state of Indiana. High school students are required to pass the grade 10 ISTEP+ to receive a high school diploma. The goal is for all students to score at the passing level on the test.
Iowa
Iowa Test of Educational Development (ITED): In 2007-2008 Iowa used the Iowa Test of Educational Development (ITED) to test students in grade 11 in reading and math. The scores reflect the performance of students enrolled for the full academic year. The ITED is a norm-referenced test, which means it measures how well students in Iowa score in comparison to their peers nationwide. Students who score at the 40th percentile are considered proficient. The goal is for all students to score at or above the proficient level.
Iowa Test of Basic Skills (ITBS): In 2007-2008 Iowa used the Iowa Test of Basic Skills (ITBS) to test students in grades 3 through 8 in reading and math. The scores reflect the performance of students enrolled for the full academic year. The ITBS is a norm-referenced test, which means it measures how well students in Iowa score in comparison to their peers nationwide. Students who score at the 40th percentile are considered proficient. The goal is for all students to score at or above the proficient level.
Kansas
Kansas State Assessments (KSA): In 2007-2008 Kansas used the Kansas State Assessments (KSA) to test students in grades 3 though 8 and 11 in reading and math and in grades 5, 8 and 11 in writing. 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 2007-2008 Kentucky used the Kentucky Core Content Tests (KCCT) to assess students in grades 3 through 8 and 10 through 12 in several subjects. 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 results reflect the performance of students enrolled for at least 100 days before testing. 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. KCCT academic indices range between 0 and 140, with 100 as the statewide goal for all students.
Louisiana
Louisiana Educational Assessment Program for the 21st Century (LEAP 21): In 2007-2008 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.
Graduate Exit Examination for the 21st Century (GEE 21): In 2007-2008 Louisiana used the Graduate Exit Examination for the 21st Century (GEE 21) to test grade 10 students in math and English language arts and grade 11 students in science and social studies. The GEE 21 is a high school graduation requirement. The GEE 21 is a standards-based test, which means it measures specific skills defined for each grade by the state of Louisiana.
Maine
Maine Educational Assessment (MEA): In 2007-2008 Maine used the Maine Educational Assessment (MEA) to test students in grades 3 through 8 in reading and math, in grades 5 and 8 in writing and in grades 4 and 8 in science. The MEA 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 2007-2008 Maine used the Maine High School Assessment (MHSA) to test students in grade 11 in critical reading, math, writing and science. The results reported show how well students are mastering state standards, specific skills defined by the state of Maine. The goal is for all students to score at or above the state standard.
Maryland
Maryland School Assessment (MSA): In 2007-2008 Maryland used the Maryland State Assessment (MSA) to test students in grades 3 through 8 in reading and math. 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 2007-2008 Maryland used the Maryland High School Assessments (HSA) to test students in English 2, algebra, biology and government upon completion of each course. The HSA 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 Maryland. Beginning with the class of 2009, students are required to pass the tests in order to graduate. Students graduating before 2009 must take the HSA, but are not required to earn a particular passing score. The goal is for all students to pass the tests.
Massachusetts
Massachusetts Comprehensive Assessment System (MCAS): In 2007-2008 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 and 8 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.
Michigan
Michigan Educational Assessment Program (MEAP): In 2007-2008 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 means that 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.
Michigan Merit Examination (MME): In 2007-2008 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 2007-2008 Minnesota used the Minnesota Comprehensive Assessment-II (MCA-II) to test students in grades 3 through 8 and 10 in reading and in grades 3 through 8 and 11 in math. 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 meet or exceed standards.
Mississippi
Mississippi Curriculum Test (MCT): In 2007-2008 Mississippi used the Mississippi Curriculum Test (MCT) to test students in grades 2 through 8 in reading, 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 2007-2008 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.
Missouri
Missouri Assessment Program (MAP): In 2007-2008 Missouri used the Missouri Assessment Program (MAP) to test students in grades 3 through 8 and 10 in math and in grades 3 through 8 and 11 in communication arts. 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.
Montana
Criterion-Referenced Test (CRT): In 2006-2007 Montana used the Criterion-Referenced Test (CRT) to assess students in grades 3 though 8 and 10 in reading and math. 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.
Nebraska
School-based, Teacher-led, Assessment and Reporting System (STARS): In 2007-2008 Nebraska used the School-based Teacher-led Assessment and Reporting System (STARS) to test students in grades 3 through 8 and 11 in reading, math and writing. The results for grades 4, 8 and 11 are displayed on GreatSchools profiles. The STARS 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.
Nevada
Criterion-Referenced Test (CRT): In 2007-2008 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 2007-2008 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 Hampshire
New England Common Assessment Program (NECAP): In 2007-2008 New Hampshire 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 specific skills defined for each grade by the state of New Hampshire. The goal is for all students to score at or above the proficient level.
New Jersey
New Jersey Assessment of Skills and Knowledge (NJ ASK): In 2007-2008 New Jersey used the New Jersey Assessment of Skills and Knowledge (NJ ASK) to test students in grades 3 through 7 in language arts literacy and math and in grade 4 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.
Grade Eight Proficiency Assessment (GEPA): In 2006-2007 New Jersey used the New Grade Eight Proficiency Assessment (GEPA) to test students in grade 8 in language arts literacy, math and science. The GEPA 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 2007-2008 New Jersey used the High School Proficiency Assessment (HSPA) to test students in grade 11 in language arts literacy, math and science. The HSPA is a standards-based test, which means it measures how well students are mastering specific skills defined for each grade by the state of New Jersey. Students are required to pass the HSPA in order to graduate. The goal is for all students to score at or above the proficient level.
New Mexico
New Mexico Standards-Based Assessment (NMSBA): In 2007-2008 New Mexico used the New Mexico Standards-Based Assessment (NMSBA) to test students in grades 3 through 9 in reading, math and science. 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 2007-2008 New Mexico used the New Mexico High School Standards Assessment (NMHSSA) to test students in grade 11 in reading and math. 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.
New York
New York State Assessments (NYSA): In 2006-2007 New York used the New York State Assessments to test students in grades 3 through 8 in English language arts and math, in grades 4 and 8 in science and in grades 5 and 8 in social studies. The 2006-2007 results for English language arts are displayed on GreatSchools profiles. Math results will be added after their release by the New York State Education Department. The tests are standards-based, which means they measure how well students are mastering specific skills defined for each grade by the state of New York. The goal is for 90% of students to meet or exceed grade-level standards on the tests.
New York State Regents Examinations (RE): In 2006-2007 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 and physics 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.
North Carolina
End of Class Tests (EOC): In 2006-2007 North Carolina used End-of-Course (EOC) tests to assess high school students in algebra I, algebra II, English I, biology, civics and economics, United States history and geometry. 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 2006-2007 North Carolina used End-of-Grade (EOG) tests to assess students in grades 3 through 8 in reading and math. The EOG tests are standards-based, which means they measure how well students are mastering specific skills defined for each grade by the state of North Carolina. The goal is for all students to score at or above the proficient level on the tests.
North Dakota
North Dakota State Assessment (NDSA): In 2006-2007 North Dakota used the North Dakota State Assessment (NDSA) to test students in grades 3 through 8 and 11 in reading/language arts and math. 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.
Ohio
Ohio Achievement Test (OAT): In 2007-2008 Ohio used the Ohio Achievement Test to test students in grades 3 through 8 in reading and math, in grades 4 and 7 in writing and in grades 5 and 8 in science and social studies. The Ohio Achievement Test 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 2007-2008 Ohio used the Ohio Graduation Test (OGT) to test students in grade 10 in reading, writing, math, science and social studies. Beginning with the class of 2007, 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 2007-2008 Oklahoma used the Oklahoma Core Curriculum Tests (OCCT) to test students in grades 3 through 8 in several subjects. The results for reading and math 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 2007-2008 Oklahoma used the Oklahoma Core Curriculum Tests End-of-Instruction (OCCT EOI) exams to test students in high school in several subjects. The results for reading and math are displayed on GreatSchools profiles. 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 2007-2008 Oregon used the Oregon Assessment of Knowledge and Skills (OAKS) to test students in grades 3 through 8 and 10 in reading and math and in grades 4, 7 and 10 in writing. 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 2007-2008 Pennsylvania used the Pennsylvania System of State Assessments (PSSA) to test students in grades 3 through 8 and 11 in math and reading and in grades 5, 8 and 11 in writing. The scores reflect the performance of students enrolled for the full academic year. 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 2007-2008 Rhode Island 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 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 2006-2007 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 the state standard.
Palmetto Achievement Challenge Tests (PACT): In 2006-2007 South Carolina used the Palmetto Achievement Challenge Tests (PACT) to test students in grades 3 through 8 in English/language arts, math, social studies and science. The PACT 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.
Tennessee
Tennessee Comprehensive Assessment Program (TCAP): In 2007-2008 Tennessee used the Tennessee Comprehensive Assessment Program (TCAP) Achievement Test to test students in grades 3 through 8 in reading/language arts, math, science and social studies. The results for reading/language arts and math are displayed on GreatSchools profiles. The TCAP is a standards-based test, which means it 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 2007-2008 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. The results for algebra I and English 2 are displayed on GreatSchool profiles. The scores reflect the performance of students enrolled for the full academic year. Students must pass the algebra I, English 2 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 2007-2008, 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 2007-2008 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.
Vermont
New England Common Assessment Program (NECAP): In 2007-2008 Vermont used the New England Common Assessment Program (NECAP) to test students in grades 3 through 8 in reading and math and grades 5 and 8 in writing. The results displayed on GreatSchools profiles are for all grades combined for each subject. 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.
Virginia
Standards of Learning (SOL): In 2007-2008 Virginia used the Standards of Learning (SOL) tests to assess students in reading and math in grades 3 through 8, in writing in grades 5 and 8, in science in grades in 3, 5 and 8 and in history/social science in grades 3 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 2007-2008 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.
Washington
Washington Assessment of Student Learning (WASL): In 2007-2008 Washington used the Washington Assessment of Student Learning (WASL) to test students in reading and math in grades 3 through 8 and 10, in writing in grades 4, 7 and 10 and in science in grades 5, 8 and 10. The WASL 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. Beginning with the class of 2008, the grade 10 WASL will be a high school graduation requirement. The goal is for all students to score at or above the state standard.
Wisconsin
Wisconsin Knowledge and Concepts Examinations (WKCE): In 2007-2008 Wisconsin used the Wisconsin Knowledge and Concepts Examination - Criterion-Referenced Tests (WKCE-CRT) 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 WKCE-CRT 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 (WESTEST): In 2007-2008 West Virginia used the West Virginia Educational Standards Test (WESTEST) to test students in grades 3 through 8 and 10 in reading, math and science, and in grades 3 through 8 in social studies. The WESTEST 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 2007-2008 Wyoming administered the Proficiency Assessments for Wyoming Students (PAWS) in reading, writing and math to students in grades 3 through 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.
Topics: |
Public and Private Primary and Secondary Schools, selected test scores by school, school ratings |
Source: |
GreatSchools |
Years Available: |
varied, 2004 to 2009 |
Geographies: |
point |
Free or Subscriber-only: |
school directory information is free; scores and ratings are subscriber-only |
For more information: |
http://www.greatschools.net |
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. For information about tests administered in each state, please see the Data Directory entry for GreatSchools School District Performance
TRF has chosen to display the GreatSchools Overall School Rating as 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.
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 |
Source: |
HMDA (Home Mortgage Disclosure Act) |
Years Available: |
2004, 2005, 2006, 2007, 2008 |
Geographies: |
tract, county, place, CBSA, Metropolitan Division, state |
Free or Subscriber-only: |
free |
For more information: |
http://www.ffiec.gov/hmda/ |
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 2008. The 2008 HMDA data reflect the ongoing difficulties in the housing and mortgage markets that began appearing in 2007. Users will find sharp decreases in originations, regardless of race, ethnicity or income. Users will also see an understated decline in higher priced loans (those PolicyMap previously classified as subprime and now classifies as high cost) as a result of widening rate spreads. Additionally, the higher incidence of FHA lending activity in the second half of 2008 will be apparent in the government-insured home loan data. For more information and analysis of the 2008 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/2009/pdf/hmda08draft.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.
TRF classifies loans as high cost if they had a reported rate spread. The rate spread on a loan is the difference between the Annual Percentage Rate (APR) on the loan and the treasury security yields as of the date of the loan's origination. Rate spreads are only reported by financial institutions if the APR is 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 suggests that a loan is of notably higher price than a typical loan, indicating that it can 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. TRF previously denoted high-cost loans as "subprime", but has changed the terminology with the release of the 2008 data to reflect language used by the Federal Reserve Bank. The only difference between categories TRF previously named "subprime" and those we now designate as "high cost" is the name. The "high-cost" designation is not to be confused with "HOEPA". HOEPA loans are a subset of the high-cost loan category.
PolicyMap contains thematic data on Number of loans originated for the purpose of a home purchase that had multiple mortgages in 2004, 2005, 2006, 2007 and 2008. 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. 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.
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 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 on a loan is 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 are only reported by financial institutions if the APR is 3 or more percentage points higher for a first lien loan, or 5 or more percentage points higher for a second lien loan.)
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.
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.
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.
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.
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. For details on RHS-insured lending, see http://www.rurdev.usda.gov/rhs/cf/brief_cp_direct.htm.
Topics: |
All Loans, Purchase Loans, Refinance Loans, Home Improvement Loans, Multifamily Loans, Prime Loans, High-Cost Loans, By Race and Ethnicity Loans, Government-Insured Loans, Conventional Purchase Loans, Manufactured Loans, Loans by Tract Income, Loans by Borrower Income, Loans by Minority Concentration |
Source: |
HMDA (Home Mortgage Disclosure Act) |
Years Available: |
2004, 2005, 2006, 2007, 2008 |
Geographies: |
tract, county, place, CBSA, Metropolitan Division, state |
Free or Subscriber-only: |
free |
For more information: |
http://www.ffiec.gov/hmda/ |
The Federal Reserve Bank of Philadelphia worked with TRF to create custom HMDA calculations for PolicyMap. These calculations of mortgage origination data make different assumptions, in some cases, about the type of loans that might fit a category and therefore may differ from similarly named calculations found elsewhere in PolicyMap. Reading the Details associated with each indicator (which can be viewed by clicking the Details link to the right of the map title) and comparing the descriptions of terms in this entry and in the above HMDA entry should explain any differences between calculations.
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). The Federal Reserve Bank of Philadelphia outlined to TRF a set of desired calculations. The result was the aggregation of all originated loans with a series of filters that describe different subsets of loan with varying characteristics.
PolicyMap contains HMDA data for 2004 through 2008. The 2008 HMDA data reflect the ongoing difficulties in the housing and mortgage markets that began appearing in 2007. Users will find sharp decreases in originations, regardless of race, ethnicity or income. Users will also see an understated decline in higher priced loans (those PolicyMap previously classified as subprime and now classifies as high cost) as a result of widening rate spreads. Additionally, the higher incidence of FHA lending activity in the second half of 2008 will be apparent in the government-insured home loan data. For more information and analysis of the 2008 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/2009/pdf/hmda08draft.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.
TRF classifies loans as high cost if they had a reported rate spread. The rate spread on a loan is the difference between the Annual Percentage Rate (APR) on the loan and the treasury security yields as of the date of the loan's origination. Rate spreads are only reported by financial institutions if the APR is 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 suggests that a loan is of notably higher cost than a typical loan, indicating that it can 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. TRF previously denoted high-cost loans as "subprime", but has changed the terminology with the release of the 2008 data to reflect language used by the Federal Reserve Bank. The only difference between categories TRF previously named "subprime" and those we now designate as "high cost" is the name. The "high-cost" designation is not to be confused with "HOEPA". HOEPA loans are a subset of the high-cost loan category.
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 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 on a loan is 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 are only reported by financial institutions if the APR is 3 or more percentage points higher for a first lien loan, or 5 or more percentage points higher for a second lien loan.)
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.
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.
Details: |
Locations of US Dept of Health and Human Services Health Resources and Services Administration Nursing Facilities; Locations of hospitals and critical access hospitals |
Topics: |
nursing facilities, hospitals, critical access hospitals |
Source: |
HRSA Geospatial Data Warehouse |
Years Available: |
2007 |
Geographies: |
points |
Free or Subscriber-only: |
free |
For more information: |
http://datawarehouse.hrsa.gov/ |
TRF downloaded Nursing Facility, Hospital, and Critical Access Hospital 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.
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 |
Geographies: |
tract, county |
Free or Subscriber-only: |
free |
For more information: |
http://www.huduser.org/DATASETS/usps.html |
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.
One note of caution about the percent change variables for 2007 and 2008: the USPS geocoding methodology and some of the USPS business practices have produced anomalies in the data over time, 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.
Details: |
Homeless population counts, rates, and percent change in homeless counts, sheltered and unsheltered population counts, available bed inventory and rate of bed availability. |
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: |
2006, 2007, 2008, 2009 |
Geographies: |
state |
Free or Subscriber-only: |
free |
For more information: |
http://www.hudhre.info/ |
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.
Detail: |
locations of HUD's public housing projects |
Topics: |
public housing |
Source: |
US Housing and Urban Development's A Picture of Subsidized Households in 2000 |
Years Available: |
2000 |
Geographies: |
points (public housing sites) |
Free or Subscriber-only: |
free |
For more information: |
http://www.huduser.org/picture2000/ |
The Department of Housing and Urban Development (HUD) conducts a periodic survey of households living in HUD-subsidized housing. A Picture of Subsidized Households in 2000 provides characteristics of assisted housing units and residents.
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 |
Topics: |
Housing Subsidies, Section 8 Rental Assistance |
Source: |
US Department of Housing and Urban Development's A Picture of Subsidized Households: 2008 |
Years Available: |
2008 |
Geographies: |
tract, county, place, state |
Free or Subscriber-only: |
free |
For more information: |
http://www.huduser.org/picture2008/index.html |
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 CBSA. 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. Most of the data on PolicyMap from this report is aggregated data on all HUD subsidy programs. However, a subset of the data for the Housing Choice Voucher Program, commonly known as Section 8, is available for some indicators as well.
Percent calculations were attained by dividing counts from HUD by 2000 Census indicators.
Detail: |
Fair Market Rent, as established by HUD, for rental units by bedroom size |
Topics: |
rental rates, fair market rent |
Source: |
US Department of Housing and Urban Development Fair Market Rents |
Years Available: |
2008, 2009, 2010 |
Geographies: |
county subdivision |
Free or Subscriber-only: |
free |
For more information: |
http://www.huduser.org/datasets/fmr.html |
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 estimates and include 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 current definition used is the 40th percentile rent, meaning that 40% of rental units can be rented at or below this threshold.
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 |
Geographies: |
county subdivision |
Free or Subscriber-only: |
free |
For more information: |
http://www.huduser.org/DATASETS/il.html |
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.
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: |
1997-2007 |
Geographies: |
points |
Free or Subscriber-only: |
free |
For more information: |
http://lihtc.huduser.org/ |
TRF downloaded and geocoded the properties listed in HUD's LIHTC Database in September, 2009. TRF was able to locate approximately 97% 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.
Details: |
Locations of HUD's Multifamily properties |
Topics: |
HUD MF |
Source: |
US Department of Housing and Urban Development Multifamily Assistance and Section 8 Contracts Database |
Years Available: |
2007 |
Geographies: |
points |
Free or Subscriber-only: |
free |
For more information: |
http://www.hud.gov/offices/hsg/mfh/exp/mfhdiscl.cfm |
TRF downloaded and geocoded the properties listed in HUD's Multifamily database as of 2/20/2009. TRF was able to locate approximately 99% of these developments on a map. TRF linked the HUD Multifamily Assistance Properties to the HUD Multifamily Assistance and Section 8 Contracts to show up to four contracts per property. The following properties have more than four contracts, although only four contracts are listed in PolicyMap: Mattapan Apts (800008662), Tab II (800008914), Concord Townhouse Cooperative 1-8 (800009868), Greenpointe Regional Housing (800012853), Easter Seals Cerebral Palsy NC Housing, Inc. (800013254), Phoenix Villa Apts (800023265), Subsidized Housing Corporation 4 (800079753), Subsidized Housing Corporation 65 (800079760), Subsidized Housing Corporation 116 (800079830), Subsidized Housing Corporation 28 (800079853), Subsidized Housing Corporation 35 (800079860), Subsidized Housing Corporation 44 (800079875), Community Properties of Ohio Management Portfolio (800218794).
Details: |
Estimated foreclosure risk score, income eligible status, predicted 18 month foreclosure rate, HMDA percent high cost loans, Foreclosure Risk Score, 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 |
Topics: |
foreclosure risk, foreclosure rate, income eligibility, high cost loans |
Source: |
US Department of Housing and Urban Development Neighborhood Stabilization Program Data |
Years Available: |
2008 and 2009, various |
Geographies: |
blockgroups, census tracts |
Free or Subscriber-only: |
free |
For more information: |
http://www.huduser.org/publications/commdevl/nsp_target.html
and http://www.huduser.org/nspgis/nspdatadesc.html |
HUD's new Neighborhood Stabilization Program (www.hud.gov/nsp) provides emergency assistance to state and local governments to acquire and redevelop foreclosed properties that might otherwise become sources of abandonment and blight within their communities. The Neighborhood Stabilization Program (NSP) provides grants to every state and certain local communities to purchase foreclosed or abandoned homes and to rehabilitate, resell, or redevelop these homes in order to stabilize neighborhoods and stem the decline of house values of neighboring homes. The program is authorized under Title III of the Housing and Economic Recovery Act of 2008.
The Housing and Economic Recovery Act of 2008 established three very specific targeting responsibilities for state and local governments implementing the Neighborhood Stabilization Program.
(1) The statute specifies that "all of the funds appropriated or otherwise made available under this section shall be used with respect to individuals and families whose income does not exceed 120 percent of area median income";
(2) It further states that "not less than 25 percent of the funds appropriated or otherwise made available under this section shall be used for the purchase and redevelopment of abandoned or foreclosed homes or residential properties that will be used to house individuals or families whose incomes do not exceed 50 percent of area median income";
(3) Finally, it indicates that grantees should give priority emphasis in targeting the funds that they receive to "those metropolitan areas, metropolitan cities, urban areas, rural areas, low- and moderate-income areas, and other areas with the greatest need, including those--
(A) with the greatest percentage of home foreclosures;
(B) with the highest percentage of homes financed by a subprime Mortgage related loan; and
(C) identified by the State or unit of general local government as likely to face a significant rise in the rate of home foreclosures."
To assist local and state governments at meeting these requirements, TRF has chosen to display blockgroup-level key indicators for the determination of eligibility for NSP funds.
PolicyMap shows whether an area is considered Eligible, Partially Eligible or Not Eligible. Areas that are shown in dark purple on the map as Eligible qualify as areas of low-, moderate, and middle-income (LMMH) benefit, according to HUD. These areas are defined as places where more than 51% of the people in the area had incomes in 2000 less than 120% of Area Median Income (AMI). Areas shown on the map in light purple and labeled as Partially Eligible include both eligible and ineligible areas within a blockgroup; users will need to consult HUD data directly to determine if their site meets income eligibility guidelines. Yellow areas of the map are not eligible. Grey shading in the map indicates that the data released by HUD did not include these areas.
PolicyMap also shows HUD's estimated foreclosure/abandonment risk score for each neighborhood. Scores range from 0 to 10, where 0 indicates that HUD's analysis suggests a very low risk and 10 suggests a very high risk. 10 indicates that an area is in the highest 10 percent of risk nationwide for foreclosure and abandonment based on the combination of HUD's foreclosure risk estimate and vacancy rate. This score does not provide the actual level of foreclosures in each neighborhood, but rather indicates that there is a risk for problems. Grey shading in the map indicates that either the data released by HUD did not include these areas.
PolicyMap shows HUD's predicted 18-month underlying problem foreclosure rate, as well. As is true with the foreclosure/abandonment risk score, this rate does not provide the actual level of foreclosures in an area, but rather predicts what the foreclosure risk might be going forward. This HUD model takes the estimated count of foreclosure starts over 18 months through June 2008 divided by the estimated number of mortgages times 100. Grey shading in the map indicates that either the data released by HUD did not include these areas or that HUD gave these locations more than one rate.
At the Census tract level, PolicyMap shows the Federal Reserve Home Mortgage Disclosure Act (HMDA) data on percent of all loans made between 2004 and 2006 that are high cost. These data represent the percent of conventional loans made between 2004 and 2006 as reported by HMDA where the rate spread is 3 percentage points above the Treasury security of comparable maturity. These data were released by HUD for the NSP. Grey areas of the map indicate that HUD did not provide values for these Census tracts; these areas are shown in the map as having Insufficient Data.
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.
Additional data provided by HUD for the NSP include the HUD USPS Vacancy rates, which are available in the Neighborhood Conditions tab on PolicyMap, and 120% of AMI by family size, available through the NSP Round 2 menu in the State & Local tab. 120% of AMI was calculated by multiplying the 50% income limit by 2.4, per HUD's generic instructions on calculated different income thresholds. For more information on the 120% AMI calculation see the Data Directory entry for HUD Income Limits.
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, GO Zones |
Years Available: |
2009, 2010 |
Geographies: |
Census Tract |
Free or Subscriber-only: |
free |
For more information: |
http://www.huduser.org/DATASETS/qct.html |
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.
GO Zones are areas determined by the President to warrant individual or individual and public assistance from the Federal Government under the Stafford Act by reason of Hurricanes Katrina, Rita, or Wilma. They are treated as DDAs, and the limitation that they do not contain more than 20 percent of the aggregate population of all MSAs/PMSAs is not taken into account.
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 |
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.
Details: |
number and average amount of federal tax returns, average reported charitable contribution, IRA and self-employment pension contributions, Earned Income Tax Credit, reported salaries and wages, capital gains and losses, farm income |
Topics: |
federal income tax returns, number of returns, number of exemptions, AGI, EITC, IRA, pension, charitable contributions |
Source: |
IRS Statistics of Income Division Individual Tax Statistics Zip Code Data |
Years Available: |
2004, 2005, 2006 |
Geographies: |
zip code, state |
Free or Subscriber-only: |
free |
For more information: |
http://www.irs.gov/taxstats/indtaxstats/article/0,,id=96947,00.html |
The Internal Revenue Service's Statistics of Income (IRS SOI) division produces annual summary statistics on selected income and tax items of individual income tax returns filed in a tax year with the IRS. This includes every Form 1040, 1040A, and 1040EZ.
Income tax return filings correspond to neither households nor persons. For example, two married people with one child might choose to file their tax returns together, including the child as a dependent. Or, they might choose to file separately, with the husband claiming the child (with a total of two exemptions on that return) and the wife filing with one exemption for herself. Alternately, there are also cases where this household would file three returns, depending on the income, assets, dividends or capital gains/losses of the child.
In order to protect the privacy of individual filers, data may be suppressed in a few ways and for several reasons. ZIP codes from which fewer than 10 returns were filed were suppressed by IRS SOI. These places are denoted in the legend as Insufficient Data. The data for these ZIP Codes are not included in the state totals.
An additional disclosure protection technique removed any return that represented a specified percentage of the total of any particular cell. For example, if one return represented 75% of the value of a given cell, that return was suppressed from the tabulation. The actual threshold percentage used, however, is not released by the IRS SOI. The returns suppressed in this manner are not included in the state totals. These places are denoted in the legend as Insufficient Data.
TRF does not calculate percentages in cases where the denominator of the calculation is less than ten. For example, if an area has 9 income tax filers and 8 of those filers claimed the EITC, this area will appear grayed out in PolicyMap. TRF does this because the calculation would otherwise show that 89% of filers in an area claimed the EITC and would likely skew the interpretation of the map. These places are denoted in the legend as having Insufficient Data.
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 |
Geographies: |
point |
Free or Subscriber-only: |
subscriber-only |
For more information: |
http://nccs.urban.org/ |
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 2007 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 2007 PC File available on PolicyMap, and they assigned all unmatched addresses to the centerpoint of the listed zip code.
Details: |
count and percent of students who receive free and reduced price school lunches |
Topics: |
free and reduced price school lunches |
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 |
Geographies: |
school district |
Free or Subscriber-only: |
free |
For more information: |
http://nces.ed.gov/ccd/ |
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.
Details: |
Public library outlet locations |
Topics: |
Public libraries |
Source: |
Florida Resources and Environmental Analysis Center (FREAC) of Florida State University (FSU) |
Years Available: |
2004 |
Geographies: |
point |
Free or Subscriber-only: |
free |
For more information: |
http://www.geolib.org/PLGDB.cfm |
Florida Resources and Environmental Analysis Center (FREAC) of Florida State University (FSU) geocoded and improved a public library data file from the National Center for Educational Statistics (NCES) and provided these data to TRF for inclusion in PolicyMap. The data are suitable for use in neighborhood, regional, or state-level planning but should not be used for work requiring high levels of accuracy such as parcel mapping. The outlet file was developed to look at the U.S. Census Bureau demographics down to the block group level. The attribute data were developed in conjunction with Dr. Christie M. Koontz, Director of the GeoLib Program, College of Communications and Information, Florida State University.
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/ |
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.
Details: |
Number of students, percent of students in poverty, number of English language learners and special education students, percent of students proficient in reading and math; federal, state and local per pupil expenditures; school district operating budgets |
Topics: |
School district population, overall student proficiency, school district funding |
Source: |
New America Foundation |
Years Available: |
various (each indicator is only available for select years 2004-2008) |
Geographies: |
school district |
Free or Subscriber-only: |
free |
For more information: |
http://febp.newamerica.net/ |
The New America Foundation's Federal Education Budget Project provides data on federal funding, demographics and student achievement for every school district in the country. Data are accessed from various sources for public research purposes. Total student population, number of English language learners, and special education participation by school district data come from the National Center for Education Statistics' Common Core of Data. The New America Foundation reports the percent of students in poverty by school district based on data from the US Census Bureau Small Area Income and Poverty Estimates (SAIPE). 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. The New America Foundation reports total Federal, State, and Local revenue per pupil and Federal revenue per pupil based on National Center for Education Statistics' Common Core of Data. New America Foundation school district operating budget figures are based on US Census Bureau Public Elementary and Secondary Education data.
Please note that not every indicator is available for every year.
Details: |
high school dropout rates, for school districts in NJ |
Topics: |
Dropout Rates |
Source: |
State of New Jersey Department of Education |
Years Available: |
2006, 2007 |
Geographies: |
school district |
Free or Subscriber-only: |
Free |
For more information: |
http://education.state.nj.us/rc/ |
The State of NJ Department of Education makes available on their website a New Jersey School Report Card. The Report Card contains information ranging from class size to attendance rates, to dropout rates. This dropout rate data is included in PolicyMap as part of The Reinvestment Fund's ongoing work with the Council of New Jersey Grantmakers.
Details: |
building permits by unit type, for counties and municipalities in NJ |
Topics: |
Building permits, housing units |
Source: |
U.S. Bureau of the Census, Manufacturing and Construction Division, prepared by the NJ Department of Labor and Workforce Development |
Years Available: |
2008Q1, 2008Q2, 2008Q3, 2008Q4, 2009Q1 |
Geographies: |
county, county subdivision |
Free or Subscriber-only: |
Free |
For more information: |
http://lwd.dol.state.nj.us/labor/lpa/industry/bp/bp_index.html |
The NJ Department of Labor and Workforce Development makes available a dataset of residential housing units authorized by building permits, from the Census, on a monthly basis for the counties and municipalities of NJ. The building permit data displayed in PolicyMap is summarized by quarter. The municipalities listed in the dataset were able to be assigned to the corresponding county subdivision in most cases. This data is included in PolicyMap as part of The Reinvestment Fund's ongoing work with the New Jersey Housing Mortgage Finance Agency.
Details: |
housing program income limits for NJ affordable housing initiatives |
Topics: |
income limits, COAH |
Source: |
HUD and COAH, prepared by the NJ Housing Mortgage Finance Agency |
Years Available: |
2008 |
Geographies: |
county |
Free or Subscriber-only: |
Free |
For more information: |
http://www.state.nj.us/dca/hmfa/, http://www.state.nj.us/dca/coah/ and http://www.huduser.org/datasets/il/il08/index.html |
The NJ Housing Mortgage Finance Agency provided to PolicyMap income and rent guidelines used in their housing programs. This data is originally from HUD and the NJ Council on Affordable Housing (COAH), but was conveyed to TRF by and intended for the use of NJ HMFA.
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/ |
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.
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/ |
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.
Details: |
Project details vary according to the methodologies of individual states: project category or type, description, cost, completion dates, number of sites, congressional districts, and interstate and state route numbers |
Topics: |
transportation, stimulus spending |
Source: |
State Departments of Transportation via Recovery Accountability and Transparency Board's public-facing website Recovery.gov |
Years Available: |
2009 |
Geographies: |
points and lines |
Free or Subscriber-only: |
Free |
For more information: |
http://www.recovery.gov/ |
The Federal Recovery Accountability and Transparency Board oversees Recovery.gov to help citizens track the spending of funds allocated by the American Recovery and Reinvestment Act (ARRA) of 2009. Project specific detail is only available at the state level, through state departments of transportation. Each state releases a 1511 certification document listing projects approved by the governor. Links to state sites can be found here: http://www.recovery.gov/?q=content/state-local-tribal-and-territorial-resources&mode=map.
As project details are constantly changing, the data loaded on PolicyMap present a static picture from spring/summer 2009. As of this time, many states have not yet released lists of transit projects and most states did not release data on municipal-level projects. State specific data were acquired from the following sources:
| State: | Date Accessed: | For more information: |
|---|---|---|
| Connecticut | June 12, 2009 | Connecticut Department of Transportation, http://www.ct.gov/dot/cwp/view.asp?a=1372&q=436026 |
| Delaware | April 15, 2009 | Delaware Department of Transportation, http://www.deldot.gov/information/projects/recovery/index.shtml |
| Maine | June 11, 2009 | MaineDOT, http://www.maine.gov/mdot/recovery/projectinfo/index.htm |
| Maryland | June 29, 2009 | Maryland Department of Transportation, http://www.mdot.state.md.us/Planning/Economic_Recovery/Index |
| Massachusetts | June 30, 2009 | Massachusetts Executive Office of Transportation, http://youmovemassachusetts.org/stimulus_projectlist.html |
| New Hampshire | June 12, 2009 | New Hampshire Department of Transportation, http://www.nh.gov/dot/recovery/index.htm |
| New Jersey | April 15, 2009 | New Jersey Department of Transportation, http://recovery.nj.gov/recovery/infrastructure/NJDOT%20ARRA%20Project%20List.pdf |
| New York | June 29, 2009 | New York State Department of Transportation, http://www.recovery.ny.gov/maps/arracertifiedprojectsmap.cfm |
| Pennsylvania | April 15, 2009 | PennDOT, http://www.recovery.state.pa.us/portal/server.pt/community/impact/5996/transportation_infrastructure |
| Rhode Island | June 29, 2009 | Rhode Island Department of Transportation, http://www.recovery.ri.gov/programs/pdf/TransportationProjectInformation.pdf |
| Vermont | June 12, 2009 | Vermont Agency of Transportation, http://apps.vtrans.vermont.gov/stimulus/ |
Where projects do not list specific locations, they are represented in PolicyMap as follows: municipal projects at the center of the municipality; countywide projects in the county seat; statewide projects in the state capital.
Because project categories and data gathering methodologies vary extensively, comparisons across states are difficult. Based on the general types of projects undertaken, PolicyMap developed a PolicyMap Project Type category consistent across all states. This is the only field common to each of the states and comparisons across states should be made cautiously.
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 |
Geographies: |
county, state |
Free or Subscriber-only: |
free |
For more information: |
http://www.ssa.gov/policy/docs/statcomps/ssi_sc/2009/index.html |
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/counties/.
Details: |
Grocery Retail Locations |
Topics: |
Grocery Retail Locations |
Source: |
Trade Dimensions |
Years Available: |
2009 |
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 |
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 December 2009.
Grocery Retail Type Definitions:
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
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
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
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
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
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
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
Detail: |
TRF Neighborhood Income Distribution Analysis for the nation |
Topics: |
income, poverty |
Source: |
The Reinvestment Fund (TRF) |
Years Available: |
2007 |
Geographies: |
census tract |
Free or Subscriber-only: |
free |
For more information: |
http://www.trfund.com/planning/index.html |
The Reinvestment Fund's Neighborhood Income Distribution Analysis is based on two indicators: (a) a given census tract's median family income, classified based on regionally-determined income brackets, and (b) a given census tract's income concentration level. For (a), the income classification, TRF used three income bands by region for census tracts: families making less than 35% of median family income in a given region, families making between 35% and 60% of median family income in a given region, and families making more than 60% of median family income in a given region. For (b), the income concentration, TRF developed a score to quantify the census tract level concentration of families across five income bands. We did not include census tracts with fewer than 500 households in the analysis. These tracts are shown as grey areas on the map. Each census tract was cross-classified based on (a) and (b), resulting in nine distinct income distribution concentration types, characterized as follows. Below the income concentration and classification types table and matrix, please see a description of the various regions and their income brackets considered for the income analysis. Beneath these tables, please see our TRF Neighborhood Income Distribution Analysis PowerPoint presentation for a more detailed explanation of the methodology we used, as well as examples of our validation process.



Details: |
TRF Supermarket Study of Low Access Areas |
Topics: |
Food access, food security |
Source: |
The Reinvestment Fund (TRF) |
Years Available: |
2010 |
Geographies: |
Blockgroup and Low Access Area (LAA) clustered blockgroups |
Free or Subscriber-only: |
free: certain indicators have specific subscriber access only |
For more information: |
http://www.trfund.com/planning/index.html |
The Reinvestment Fund's Supermarket Study of Low Access Areas is an analysis of food access for the purpose of: (a) identifying underserved areas showing an urgent need for supermarket development, (b) further stratifying underserved areas as having high/low access to fast food restaurants and superettes (small grocers), and (c) estimating the potential economic impacts of supermarket development on a community by studying leakage.
TRF's methodology was designed to identify low/moderate-income communities whose residents travel longer distances to supermarkets compared to their higher-income counterparts, as defined by cohorts sharing similar values for population density and car ownership. Our approach accounts for the wide-ranging levels of population density and car ownership and their profound influence on how far households are expected to travel to shop for food. Our methodology is based on the assumption 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. This establishes the standard to which all block groups with median household incomes at or below 120% (low/moderate-income or LMI) of their respective area medians are compared. We believe this assumption has merit, given the supermarket industry's intense level of competition for market share in stable, more affluent communities.
Using a GIS Analysis of block group level and point level data including Census 2000 and Trade Dimensions, TRF compared the distance between each LMI block group and its nearest supermarket with the median distance for non-LMI block groups with the same population density and car ownership classification to determine which LMI block groups are underserved by assigning an Access Score. We then identified clusters of block groups as Low Access Areas (LAA) based on whether or not their neighbors were also underserved.
Given that LAAs are made up of blockgroups of varying population sizes, TRF calculated a population weighted Access Score based on the blockgroup's Access Score and the population contribution of each blockgroup to the LAA.
Employing a similar method to that which we used to calculate distance to supermarkets, using InfoUSA data, TRF then identified proximity to limited service stores. Limited service stores are defined as low-priced grocery stores that provide a limited selection of items in a reduced number of categories. Limited service stores have few, if any, service departments, and limited (if any) perishables, as compared to conventional supermarkets.
TRF also assessed the presence of grocery retail leakage and the potential economic impacts of supermarket development on employment opportunities and wages within communities, based upon the extent to which each area exhibits leakage. Leakage occurs when residents of underserved communities travel to adequately- or over-served ("surplus") communities to satisfy their grocery shopping needs. The addition of a supermarket in a leakage community results in a net increase in employment because it does not supplant sales from the community's existing food stores; instead it supplants sales from supermarkets in the surplus communities that formerly served residents in leakage communities.
Using block group level and point level data from Census 2000, Bureau of Labor Statistics' 2008 Consumer Expenditure Survey and Consumer Price Index from 2009, Trade Dimensions 2009 data, and Food Marketing Institute 2009 data, we derived employment and supermarket square footage figures to better understand the magnitude of leakage – knowing how many square feet an underserved area is lacking can help determine which types of store(s) can mitigate the leakage. Trade areas in which demand for food retail significantly exceeds supply is identified as experiencing leakage and therefore is more likely to exhibit a net increase in employment due to the addition of a supermarket.
Low Access Areas represent areas where residents have low access to full-selection grocery stores and thus experience a lack of access to healthy foods. The low access areas do not contain information on the best location for placing a store, however. The Low Access Epicenters identify for each low access area the geographic area where placing a new store will serve the largest population and at the same time addresses the needs of the LAA population most challenged by low access. The Low Access Epicenter is defined as the area where the placement of a new full service store is likely to have the largest impact on addressing low access for the largest number of people.
TRF undertook the following methodology for finding Low Access Epicenters. TRF calculated the population distribution among member block groups for each LAA. This was done by dividing the block groups' population (from Census 2000) by the LAA's total population. This value was then multiplied by the Low Access Score for each block group, producing a population-weighted low access score. These values were then attached to the population-weighted block groups' centroid shapefile. Using this shapefile, a standard distance was calculated for each LAA. The standard distance calculation first locates a mean center among all of the population weighted centroids. The mean center is the centerpoint of all of a LAA's population-weighted block group centroid. A circle with a radius equal to the standard distance is then drawn around the mean center. This circle is what's referred to as the Low Access Epicenter. Low Access Epicenter data is only available to specified subscribers.
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 tract FIPS code (the first 11 characters of the LAA) to enter the state, county and census tract number 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 fresh foods, as compared to higher-income counterparts. Because LAAs are made up of blockgroups of varying population sizes, TRF calculated a population weighted LAA Score based on the blockgroup's Access Score and the population contribution of each blockgroup in the LAA. |
| # 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 | The dollar amount being "leaked" or lost from the LAA. The leakage amount is an indicator of the potential economic impact of supermarket development on employment opportunities and wages within a given community. This indicator is available to specified subscribers. |
| Grocery Retail Leakage Rate | The percentage of total grocery shopping demand for the LAA being "leaked" or lost from the LAA. It is defined as the leakage amount of the LAA divided by the total grocery retail demand within LAA. |
| 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. |
| # 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 low-priced grocery stores that provide a limited selection of items in a reduced number of categories. 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 LAA service radius (defined as the distance within which a supermarket should be located, based on the LAA's higher income counterparts). |
Details: |
TRF Supermarket Study of Low Access Areas for selected metro areas |
Topics: |
Food access, food security |
Source: |
The Reinvestment Fund (TRF) |
Years Available: |
2009 |
Geographies: |
Blockgroup and Low Access Area (LAA) clustered blockgroups |
Free or Subscriber-only: |
free |
For more information: |
http://www.trfund.com/planning/index.html |
The Reinvestment Fund's Supermarket Study of Low Access Areas is an analysis of food access for selected metro areas conducted for the Brookings Institution for the purpose of: (a) identifying underserved areas showing an urgent need for supermarket development, (b) further stratifying underserved areas as having high/low access to fast food restaurants and superettes (small grocers), and (c) estimating the potential economic impacts of supermarket development on a community by studying leakage. The following MSAs were considered as a part of this analysis: Atlanta, Baltimore, Cleveland, Las Vegas, Los Angeles, Louisville, Little Rock, Jackson, MS, Phoenix, and San Francisco/Oakland.
TRF's methodology was designed to identify low/moderate-income communities whose residents travel longer distances to supermarkets compared to their higher-income counterparts, as defined by cohorts sharing similar values for population density and car ownership. Our approach accounts for the wide-ranging levels of population density and car ownership and their profound influence on how far households are expected to travel to shop for food. Our methodology is based on the assumption 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. This establishes the standard to which all block groups with median household incomes at or below 120% (low/moderate-income or LMI) of their respective area medians are compared. We believe this assumption has merit, given the supermarket industry's intense level of competition for market share in stable, more affluent communities.
Using a GIS Analysis of block group level and point level data including Census 2000 and Trade Dimensions, TRF compared the distance between each LMI block group and its nearest supermarket with the median distance for non-LMI block groups with the same population density and car ownership classification to determine which LMI block groups are underserved by assigning an Access Score. We then identified clusters of block groups as Low Access Areas (LAA) based on whether or not their neighbors were also underserved.
Given that LAAs are made up of blockgroups of varying population sizes, TRF calculated a population weighted Access Score based on the blockgroup's Access Score and the population contribution of each blockgroup to the LAA. TRF did not include blockgroups with fewer than 250 households in this analysis.
Employing a similar method to that which we used to calculate distance to supermarkets, using InfoUSA data, TRF then identified proximity to fast food restaurants and superettes. Superettes are defined as small, neighborhood grocers such as bodegas. We then used these proximity measures to better understand the impacts of fast food and superettes on access to healthy food for the LAAs.
TRF also assessed the presence of grocery retail leakage and the potential economic impacts of supermarket development on employment opportunities and wages within communities, based upon the extent to which each area exhibits leakage. Leakage occurs when residents of underserved communities travel to adequately- or over-served ("surplus") communities to satisfy their grocery shopping needs. The addition of a supermarket in a leakage community results in a net increase in employment because it does not supplant sales from the community's existing food stores; instead it supplants sales from supermarkets in the surplus communities that formerly served residents in leakage communities.
Using block group level and point level data from Census 2000, Bureau of Labor Statistics' 2008 Consumer Expenditure Survey and Consumer Price Index from 2009, Trade Dimensions 2009 data, and Food Marketing Institute 2009 data, we derived employment and supermarket square footage figures to better understand the magnitude of leakage – knowing how many square feet an underserved area is lacking can help determine which types of store(s) can mitigate the leakage. Trade areas in which demand for food retail significantly exceeds supply is identified as experiencing leakage and therefore is more likely to exhibit a net increase in employment due to the addition of a supermarket.
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 tract FIPS code (the first 11 characters of the LAA) to enter the state, county and census tract number 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 fresh foods, as compared to higher-income counterparts. LAA Scores were only computed for select metro areas. Because LAAs are made up of blockgroups of varying population sizes, TRF calculated a population weighted LAA Score based on the blockgroup's Access Score and the population contribution of each blockgroup in the LAA. |
| # 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 | The dollar amount being "leaked" or lost from the LAA. The leakage amount is an indicator of the potential economic impact of supermarket development on employment opportunities and wages within a given community. |
| Grocery Retail Leakage Rate | The percentage of total grocery shopping demand for the LAA being "leaked" or lost from the LAA. It is defined as the leakage amount of the LAA divided by the total grocery retail demand within LAA. |
| 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. |
| # 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. |
| # Block Groups in LAA Served by Fast Food Est | The count of block groups within the LAA that are served by fast food. A fast food establishment serves a block group if it falls within the LAA service radius (defined as the distance within which a supermarket should be located, based on the LAA's higher income counterparts). |
| # Block Groups in LAA Served by Superettes | The count of block groups within the LAA that are served by superettes. Superettes are defined as small grocers. A superette serves a block group if it falls within the LAA service radius (defined as the distance within which a supermarket should be located, based on the LAA's higher income counterparts). |
| % of All Block Groups in LAA Served by Fast Food | The degree to which the LAA is served by fast food. It Is defined as the # of Block Groups in LAA Served by Fast Food Est divided by the # of Block Groups in LAA. |
| % of All Block Groups in LAA Served by Superettes | The degree to which the LAA is served by superettes. It Is defined as the # of Block Groups in LAA Served by Superettes divided by the # of Block Groups in LAA. |
Detail: |
TRF real estate market evaluation and valuation for Philadelphia, Baltimore, Washington DC, 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/planning/index.html |
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.
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:

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


TRF cluster analysis revealed eight market types, characterized as follows:
In 2001, TRF developed a Market Value Analysis for the City of Philadelphia.

TRF cluster analysis revealed eight market types, characterized as follows:
In 2006 TRF developed a Market Value Analysis for Washington, DC.

TRF cluster analysis revealed eight market types, characterized as follows:
Details: |
Proximity to high performing public schools |
Topics: |
School performance, school ratings |
Source: |
TRF calculation of GreatSchools data |
Years Available: |
2009 |
Geographies: |
census tracts |
Free or Subscriber-only: |
Subscriber-only |
For more information: |
http://www.greatschools.net |
TRF calculated the shortest distance to a public school with a GreatSchools Overall School Rating of 9 or 10 for the centroid of each Census Tract in the nation. 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. It is also limited by the fact that school ratings should not be compared across states. Therefore, 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 that may make a public school inaccessible.
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 |
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.
Details: |
Adult and low-income preschool obesity rates, adult diabetes rate, low income food stamp recipients, SNAP and WIC programs. |
Topics: |
health, obesity, diabetes, federal nutrition programs |
Source: |
U.S. Department of Agriculture, Economic Research Service |
Years Available: |
Various (2006, 2007, 2008) |
Geographies: |
County or state |
Free or Subscriber-only: |
free |
For more information: |
http://www.ers.usda.gov/FoodAtlas/ |
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 diabetes, adult obesity and low-income preschool obesity rates, come from the Centers for Disease Control and Prevention (CDC). Adult rates are taken from the CDC report "Estimated County-Level Prevalence of Diabetes and Obesity – United States, 2007" accessible online at: http://www.cdc.gov/mmwr/preview/mmwrhtml/mm5845a2.htm. The low income preschool figures were derived by a CDC analysis of the Pediatric Nutrition Surveillance System, see http://www.cdc.gov/pednss/.
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.
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 |
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.
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, 2008 |
Geographies: |
County, state, congressional districts (for congressional races) |
Free or Subscriber-only: |
free |
For more information: |
http://uselectionatlas.org/ |
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 and 2008. Midterm elections are not included. County-level data for Alaska is not included because Alaska does not report its election results by county.
"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 geography.
"Change in percent" calculations were calculated by subtracting the 2004 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 from 2004 to 2008, and does not include the 2006 midterm election.
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 |
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 |
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/ |
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
Detail: |
Investments made by the General Board of Pension and Health Benefits of The United Methodist Church |
Topics: |
Investments |
Source: |
General Board of Pension and Health Benefits of The United Methodist Church |
Years Available: |
Various |
Geographies: |
Points |
Free or Subscriber-only: |
Free |
For more information: |
http://www.gbophb.org |
The General Board of Pension and Health Benefits is a not-for-profit administrative agency of The United Methodist Church, responsible for the general supervision and administration of the retirement, health and welfare benefit plans, programs and funds for more than 74,000 active and retired clergy and lay employees of the Church.
All General Board plans, programs, services and policies are designed to serve and support the financial well-being of participants and their families in accordance with the values and principles of The United Methodist Church.
The General Board manages and invests over $16 billion in assets, is the largest faith-based pension fund in the U.S. and ranks among the top 100 pension plans in the country.
Detail: |
Wilmington Trust Residential Housing Investments |
Topics: |
Workout, REO |
Source: |
Wilmington Trust, Hanley-Wood |
Years Available: |
2009 |
Geographies: |
polygons |
Free or Subscriber-only: |
Subscriber-only: only available to Wilmington Trust |
For more information: |
https://www.wilmingtontrust.com/wtcom/ |
Wilmington Trust Workouts Group contracted with Arcadia Land Company and TRF to develop a comprehensive database and mapping system for their residential housing investments. The following table provides a description of each of the relevant indicators, which are available only to Wilmington Trust and Arcadia Land Company:
All market data is provided by Wilmington Trust Company. Wilmington Trust Company subscribes to Hanley Wood (HW) Market Intelligence and has requested that the HW data that they purchase be incorporated into the database to be uploaded to PolicyMap. No other market data has been collected. This HW data is organized by homebuilder and the GIS data is organized by project. Therefore, some multi-builder projects have data sets for multiple builders. The numbering of the builder is only used to differentiate the data sets and is no indication of builder performance or any other aspect of the builders' relationship to or with the project. HW Market Intelligence data is gathered through builder participation and self reporting. Any questions regarding HW Market Intelligence Data or data collection methodology should be directed to HW Market Intelligence. The current data set is from December, 2009
| Indicator | Description |
|---|---|
| Loan Status | Description of project relative to it's WTC loan status. Possible descriptions include: 1. WTC Performing 2. WTC Workout 3. WTC OREO 4. Not financed by WTC |
| Map Name | The project name that appears on the wall maps and on PolicyMap. This is based on either (i) the name under which the subdivision was approved or (ii) the name under which the project is currently being marketed by the builder / developer. |
| Also Known As | Includes all names referenced to this project from any of the data sources that were provided for mapping. Project name references on WTC loan data are in some cases completely different than the approved subdivision name or even the name under which the project is being marketed. These data sources include: 1. WTC workout loan list 2. WTC performing loan list 3. Hanley Wood Market Intelligence (ending 12/2009) |
| State | The state in which the project is located. |
| County | The county in which the project is located. |
| Town | The post office town that corresponds with the zip code in which the project is located. This data is based on a search of the zip code in Google Maps. |
| Zip Code | The postal zip code in which the project is located. This data is based on the zip code boundaries in Google Earth and PolicyMap. |
| Developer / Owner | For projects that have recorded plans, this data field reflects the name of the Developer, if that name was identified on the record plan. If a developer was not identified on the record plan, the name represents the property owner, as identified on the record plan. For projects that have not received final plan approval, this data reflects the property ownership identified by County Property records. |
| Site Acreage | The acreage of the project site. This data is based on data from record plans, if available, county parcel data, and polygon measurement in Google Earth. |
| School District | The school district in which the site is located. This data is based on data from record plans, if available, as well as a search of the school district boundaries in Google Earth and PolicyMap. |
| Driving Distance to Beach | The distance to the closest of 3 beach destinations, which include (i)Rehoboth Beach, (ii) Bethany Beach, and (iii) Fenwick Island. This data was only measured for projects located in Sussex County using Google Earth online maps and represents driving distance. |
| Approving Jurisdiction | The governing jurisdiction in which the subdivision was approved. This data is based on the record plan and searches of County online mapping systems reflecting municipal boundary lines. |
| Approval Status | Each project has been categorized with the following designations: 1. Approved: Final plan has been approved and recorded 2. Unapproved: No final plan has been recorded and the site has not been sub-divided 3. Preliminary Approval: We were aware of certain WTC project that are Unapproved (as defined above) but had received a Preliminary Approval from the local jurisdiction. We have identified these project with Preliminary Approval status. There may be other projects designated Unapproved that have received a Preliminary Approval. We did not contact planning agencies as part of this process to determine preliminary approval for all unapproved projects. |
| Proposed Units | For projects with an approval status of Preliminary Approval, Proposed Units represents the unit count and product type approved in the preliminary approval. |
| Date Recorded | Every project has been identified with either: 1. No recorded plan 2. The Date that the record plan was recorded with the County. In the case of Planned Unit Developments, phases are recorded incrementally. Therefore, these projects have multiple record plans, but no recorded master plan. The data for these projects reflect only one of the multiple recordings. |
| Improvement Status | An improved lot is one which has infrastructure in place (as identified by completed roadway infrastructure) in order to support a residential structure. An improved lot may or may not have a house built on it. Each project has been categorized with the designations below, based on approval status, online research and field research. Every project that could not be identified as unimproved or fully improved was visited, and the extent of the roadway improvements were marked on a record plan. The marked- up plans were then used to count the number of improved lots and unimproved lots within each project. - Fully improved: All roadway infrastructure, as depicted on the record plan, has been installed. Homes could be built on all sub-divide lots in the project. - Partially Improved: Some portion of the roadway infrastructure, as depicted on the record plan, has been completely installed. Some phase of the approved lots within the subdivision could have homes built on them. - Unimproved: This category includes projects that have no improved lots and no homes could be built on any of the sub-divided lots in the project. This may include either of the following: (i)No construction activity has begun on the site, or (ii) Construction activity has begun, but the project appears to have been abandoned without improving any lots to the level that could support a home. (Note: Lot improvement is not an indication of home sales or home construction. We have not conducted a count of existing homes and we have not conducted any market research to determine the number of homes sold in each subdivision. All data on home sales is being provided by Wilmington Trust Company via subscription to Hanley Wood Market Intelligence) |
| Product Type(s) | The units within each project have been categorized into the five following classifications: 1. Single Family Detached (SFD): An SFD unit is a single residential housing unit that is not attached to any other unit either horizontally or vertically. 2. Twin: A single residential housing unit that is attached to one other additional unit horizontally (side by side sharing a party wall) but not vertically (one unit above another). 3. Townhouse (TH): A single residential housing unit that is attached to two or more additional units horizontally (side by side sharing a party wall) but not vertically (one unit above another). 4. Multi-family (MF): The MF unit is a single building with two or more units attached vertically (one unit above another). 5. Rental: This product classification applies to any product type that is not intended to be sold to individual owner/occupant buyers. In most cases, this is identified on record plans as apartments. "No Final Plan Approved" was entered for all project that have not received final approval and have not been recorded. |
| Ownership Structure | The ownership structure was identified based on research of the record plan, field research and web research of builder websites. The ownership structure identified is based on the intended transfer of ownership rights from the developer to the first owner of the residential unit, and has been defined in the following categories: 1. Fee Simple – The resident/owner of the unit owns the structure as well as the lot or land upon which the structure is built. 2. Condominium – The owner of the unit owns only the internal residential unit but does not own the physical structure within which the unit exists and does not own the land upon which the structure is built. 3. Land Lease – The owner of the unit owns the residential structure but does not own the land upon which the structure is built. In most cases the land is rented from a third party land owner (usually the developer). 4. Rental – The resident of the residential unit leases the unit from the owner and has no ownership in the unit. This is usually a group of units (apartment complex) that may be any of the product types listed, but are all owned and managed by a third party owner (often the developer). |
| Number of Approved "For Sale" Units | Total number of approved units for sale in the project. |
| Lot Breakdown (per record plan) | This note provides the breakdown of unit types indicated by the record plan, which we standardized into the 5 product categories described above. This note also indicates whether the project was recorded as a specific phase. |
| Age Restricted Units | Age restricted (55+) units are based on the age restriction designation on the record plan of the project. Some projects are entirely age restricted, while others are only partially age restricted. The majority of projects have no age restriction. |
| Water Service | Reflects water service and/or provider as indicated on the record plan. When water service was unknown, no data was included. |
| Sewer Service | Reflects sewer service and/or provider as indicated on the record plan. When sewer service was unknown, no data was included. |
| Amenities | Reflects amenities that were identified on the record plans or through internet research. |
| Display Comments | Indicates specific complexities or notes about the project that Arcadia felt were worthy of clarification. Planned Unit Developments (PUD) are commonly noted. These types of projects receive preliminary plan approval for a conceptual plan that provides a maximum density for the project. Each phase of the project is incrementally reviewed for final approval and recordation. Therefore, no master plan is ever recorded, making the ultimate number of units and the product types uncertain. The assumptions made and/or notes regarding these projects were included in this data field. |
| Total Approved Units | Only recorded subdivisions were included in the unit count. Approved Units: Based on record plan review. Improved Units: Based on online research and field survey with record plans. Unimproved Units: Based on online research and field survey with record plans. |
| Total Improved Units | |
| Total Unimproved Units | |
| Approved SFD | |
| Improved SFD | |
| Unimproved SFD | |
| Approved SFA - Townhouses | |
| Improved SFA - Townhouses | |
| Unimproved SFA - Townhouses | |
| Approved SFA - Twins | |
| Improved SFA - Twins | |
| Unimproved SFA - Twins | |
| Approved Multi-family | |
| Improved Multi-family | |
| Unimproved Multi-family | |
| Approved Rental | |
| Improved Rental | |
| Unimproved Rental | |
| Loan 1 - 12 Borrower | Every loan on the WTC "performing loan list" that is associated with the project has been listed and numbered. The same borrower may appear on multiple loans and/or different borrowers may appear on loans associated with the project. |
| Non-Performing Loan 1 - 2 Borrower | The name of the borrower with a loan on the workout list provided by WTC appears in this field. This borrower may also be listed in the "Loan 1-12 Borrower" data field. |
| Homebuilder(s) | Summary of all homebuilders working in the project. |
| Project Sales (Year to Date) | Summary of the year to date sales of all homebuilders working in the project. "Year to Date" from this December, 2009 data set would include all 12 months of 2009. This number is derived from the HW Market Intelligence Data by adding the "Sold (Recent)" data field of all of the builders working in the project. |
| Monthly Project Sales Rate (Year to Date) | Summary of the year to date sales per month of all homebuilders working in the project. "Year to Date" from this December, 2009 data set would include all 12 months of 2009. This number is derived by dividing the "Project Sales (Year to Date)" by 12 (the number of months in the "year to date" for this data set). |
| Total Project Sales (Project to Date) | Summary of the total sales of all homebuilders working in the project throughout the time that they have been at the project. "Project to Date" indicates the entire time that the builder has been selling from this project. This number is derived by adding the "Sold (Project to Date)" data field of all of the builders within the project. |
| Builder 1 Product Type | HW categorizes either Detached or Attached products. |
| Builder 1 Product Type Detail | Indicates the product name/description provided by the builder (example: "townhouse" or "carriage house") |
| Builder 1 Name | The name of the home builder. |
| Builder 1 Min. Lot Size | The minimum lot size. |
| Builder 1 Base Price Range (low) | Low end of the base price range. |
| Builder 1 Base Price Range (high) | High end of the base price range. |
| Builder 1 Base SF range (low) | Low end of the base square foot range of housing product being offered. |
| Builder 1 Base SF range (high) | High end of the base square foot range of housing product being offered. |
| Builder 1 Price / SF (low) | Low end of the price range being offered. |
| Builder 1 Price / SF (high) | Low end of the price range being offered. |
| Builder 1 Sales Open Date | Date when the builder opened this housing product for sale at this project. |
| Builder 1 Units Planned | Number of units planned for the project by the builder. |
| Builder 1 Sold (Recent) | Recent Sales are year to date. For this data set recent sales include all of 2009. |
| Builder 1 Sold (Project to Date) | Project to Date indicates all sales by the builder in that project during the time the builder has been selling at that project. |
| Builder 1 Sales Rate (Recent) | Sales rate indicates sales per month. "Recent" indicates year to date. For this data set recent sales include all of 2009. |
| Builder 1 Sales Rate (Project to Date) | Sales rate indicates sales per month. Project to Date the entire time the builder has been selling at that project. |
| Builder 1 Units Remaining | Units remaining is a number derived from the HW data by subtracting "Sold (Project to Date)" from "Units Planned". |
| Builder 1 Years Supply of Lots | Years supply of lots is a number derived from the HW data by dividing the "Units Remaining" by the "Sales Rate (Recent)" |
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 |
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 monthly basis and released in the second week of the month.
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.
Detail: |
Investments made by members of SAHF |
Topics: |
affordable housing |
Source: |
Stewards of Affordable Housing for the Future (SAHF) |
Years Available: |
various |
Geographies: |
points |
Free or Subscriber-only: |
free |
For more information: |
http://www.sahfnet.org/index.html |
Stewards of Affordable Housing for the Future (SAHF) is a 501(c)(3) network of eight social enterprise nonprofits. SAHF's members provide high quality, affordable rental homes for 80,000 households in 49 states, the District of Columbia, Puerto Rico, and the Virgin Islands.
SAHF's members promote their shared ownership objective, which embraces the notion that stable, affordable rental homes are critically important in people's lives. Through their deal flow, SAHF's members stay on top of policy and marketplace developments nationwide. They come face-to-face almost daily with barriers to preservation of affordable housing for the poor, which enables them to discern patterns. Seeing the patterns and having the expertise, SAHF works with its members to develop policy solutions that work.
SAHF's members include: Mercy Housing, Inc., National Affordable Housing Trust (NAHT), National Church Residences, Inc. (NCR), NHT/Enterprise Preservation Corporation, Preservation of Affordable Housing, Inc. (POAH), Retirment Housing Foundation (RHF), Volunteers of America (VOA), and NHP Foundation (NHP).
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