First launched by the U.S. Department of Housing and Urban Development (HUD) and Department of Transportation (DOT) in November 2013, the Location Affordability Index (LAI) provides ubiquitous, standardized household housing and transportation cost estimates for all 50 states and the District of Columbia. Because what is affordable is different for everyone, users can choose among eight household profiles—which vary by household income, size, and number of commuters—and see the impact of the built environment on affordability in a given location while holding household demographics constant.

Version 3 updates the constituent data sets with 2012-2016 American Community Survey data and makes several methodological tweaks, most notably moving to modeling at the Census tract level rather at the block group. As with Version 2, the inputs to the simultaneous equation model (SEM) include six endogenous variables—housing costs, car ownership, and transit usage for both owners and renters—and 18 exogenous variables, with vehicle miles traveled still modeled separately due to data limitations.To learn more about the Location Affordability Index (v.3) visit: https://www.hudexchange.info/programs/location-affordability-index/, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Date of Coverage: 2012-2016 Data Dictionary: DD_Location Affordability Indev v.3.0LAI Version 3 Data and MethodologyLAI Version 3 Technical Documentation

HUD administers Federal aid to local Housing Agencies (HAs) that manage housing for low-income residents at rents they can afford. Likewise, HUD furnishes technical and professional assistance in planning, developing, and managing the buildings that comprise low-income housing developments. This dataset provides the location and resident characteristics of public housing development buildings. Location data for HUD-related properties and facilities are derived from HUD's enterprise geocoding service. While not all addresses are able to be geocoded and mapped to 100% accuracy, we are continuously working to improve address data quality and enhance coverage. Please consider this issue when using any datasets provided by HUD. When using this data, take note of the field titled “LVL2KX” which indicates the overall accuracy of the geocoded address using the following return codes: ‘R’ - Interpolated rooftop (high degree of accuracy, symbolized as green) ‘4’ - ZIP+4 centroid (high degree of accuracy, symbolized as green) ‘B’ - Block group centroid (medium degree of accuracy, symbolized as yellow) ‘T’ - Census tract centroid (low degree of accuracy, symbolized as red) ‘2’ - ZIP+2 centroid (low degree of accuracy, symbolized as red) ‘Z’ - ZIP5 centroid (low degree of accuracy, symbolized as red) ‘5’ - ZIP5 centroid (same as above, low degree of accuracy, symbolized as red) Null - Could not be geocoded (does not appear on the map) For the purposes of displaying the location of an address on a map only use addresses and their associated lat/long coordinates where the LVL2KX field is coded ‘R’ or ‘4’. These codes ensure that the address is displayed on the correct street segment and in the correct census block. The remaining LVL2KX codes provide a cascading indication of the most granular level geography for which an address can be confirmed. For example, if an address cannot be accurately interpolated to a rooftop (‘R’), or ZIP+4 centroid (‘4’), then the address will be mapped to the centroid of the next nearest confirmed geography: block group, tract, and so on. When performing any point-in polygon analysis it is important to note that points mapped to the centroids of larger geographies will be less likely to map accurately to the smaller geographies of the same area. For instance, a point coded as ‘5’ in the correct ZIP Code will be less likely to map to the correct block group or census tract for that address. In an effort to protect Personally Identifiable Information (PII), the characteristics for each building are suppressed with a -4 value when the “Number_Reported” is equal to, or less than 10. To learn more about Public Housing visit: https://www.hud.gov/program_offices/public_indian_housing/programs/ph/ Development FAQs - IMS/PIC | HUD.gov / U.S. Department of Housing and Urban Development (HUD), for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Data Dictionary: DD_Public Housing Buildings Date Updated: Q4 2024

The Community Development Block Grant (CDBG) program requires that each CDBG funded activity must either principally benefit low- and moderate-income persons, aid in the prevention or elimination of slums or blight, or meet a community development need having a particular urgency because existing conditions pose a serious and immediate threat to the health or welfare of the community and other financial resources are not available to meet that need. With respect to activities that principally benefit low- and moderate-income persons, at least 51 percent of the activity's beneficiaries must be low and moderate income. For CDBG, a person is considered to be of low income only if he or she is a member of a household whose income would qualify as "very low income" under the Section 8 Housing Assistance Payments program. Generally, these Section 8 limits are based on 50% of area median. Similarly, CDBG moderate income relies on Section 8 "lower income" limits, which are generally tied to 80% of area median. These data are from the 2011-2015 American Community Survey (ACS). To learn more about the Low to Moderate Income Populations visit: https://www.hudexchange.info/programs/acs-low-mod-summary-data/, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Data Dictionary: DD_Low to Moderate Income Populations by Block GroupDate of Coverage: ACS 2020-2016

The TIGER/Line Files are shapefiles and related database files (.dbf) that are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line File is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The primary legal divisions of most States are termed counties. In Louisiana, these divisions are known as parishes. In Alaska, which has no counties, the equivalent entities are the organized boroughs, city and boroughs, and municipalities, and for the unorganized area, census areas. The latter are delineated cooperatively for statistical purposes by the State of Alaska and the Census Bureau. In four States (Maryland, Missouri, Nevada, and Virginia), there are one or more incorporated places that are independent of any county organization and thus constitute primary divisions of their States. These incorporated places are known as independent cities and are treated as equivalent entities for purposes of data presentation. The District of Columbia and Guam have no primary divisions, and each area is considered an equivalent entity for purposes of data presentation. The Census Bureau treats the following entities as equivalents of counties for purposes of data presentation: Municipios in Puerto Rico, Districts and Islands in American Samoa, Municipalities in the Commonwealth of the Northern Mariana Islands, and Islands in the U.S. Virgin Islands. The entire area of the United States, Puerto Rico, and the Island Areas is covered by counties or equivalent entities. The 2010 Census boundaries for counties and equivalent entities are as of January 1, 2010, primarily as reported through the Census Bureau's Boundary and Annexation Survey (BAS).

Denotes the service areas, and pertinent information associated with HUD's Regional Field Offices.HUD is organized into 10 Regions where each Region is managed by a Regional Administrator, who also oversees the Regional Office. Each Field Office within a Region is managed by a Field Office Director, who reports to the Regional Administrator. There is at least one HUD Field Office in every State and a total of 10 Regional Offices. Staff who answer the main office telephone will be able to respond to or direct your calls to the appropriate person.To learn more about the HUD Field Office Locations visit: https://www.huduser.gov/portal/regions/Regional.html, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Data Dictionary: DD_HUD Field Office JurisdictionsDate of Coverage: Current

Section 1400Z–1(b)(1)(A) of the Code allowed the Chief Executive Officer (CEO) of each State to nominate a limited number of population census tracts to be designated as Zones for purposes of §§ 1400Z–1 and 1400Z–2. Revenue Procedure 2018–16, 2018–9 I.R.B. 383, provided guidance to State CEOs on the eligibility criteria and procedure for making these nominations. Section 1400Z–1(b)(1)(B) of the Code provides that after the Secretary receives notice of the nominations, the Secretary may certify the nominations and designate the nominated tracts as Zones.

Section 1400Z–2 of the Code allows the temporary deferral of inclusion in gross income for certain realized gains to the extent that corresponding amounts are timely invested in a qualified opportunity fund. Investments in a qualified opportunity fund may also be eligible for additional tax benefits. To learn more about Qualified Opportunity Zones visit: https://www.cdfifund.gov/opportunity-zones, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Date of Coverage: 12/2019Data Dictionary: DD Opportunity Zone Eligible Census Tracts

Difficult Development Areas (DDA) for the Low Income Housing Tax Credit program are designated by U.S. Department of Housing and Urban Development (HUD) and defined in statute as areas with high construction, land, and utility costs relative to its Area Median Gross Income (AMGI). DDAs in metropolitan areas are designated along Census ZIP Code Tabulation Area (ZCTA) boundaries. DDAs in non-metropolitan areas are designated along county boundaries. DDAs may not contain more than 20% of the aggregate population of metropolitan and non-metropolitan areas, which are designated separately. To learn more about Difficult Development Areas (DDA) visit: https://www.huduser.gov/portal/datasets/qct.html, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Data Dictionary: DD_Difficult Development Areas Date of Coverage: 2024-2025Last Updated: 01-2025

First launched by the U.S. Department of Housing and Urban Development (HUD) and Department of Transportation (DOT) in November 2013, the Location Affordability Index (LAI) provides ubiquitous, standardized household housing and transportation cost estimates at the Census block-group level for the majority of the populated area of the United States. Because what is affordable is different for everyone, users can choose among eight household profiles—which vary by household income, size, and number of commuters—and see the impact of the built environment on affordability in a given neighborhood location while holding household demographics constant.

In Version 1, these estimates were originally generated with data from several federal sources and vehicle miles traveled (VMT) data from Illinois EPA using separate OLS regression models for household housing costs, VMT, car ownership, and transit usage. Version 2, in addition to updating all the constituent data sources, represents a significant a methodological and technical advance from Version 1, modelling auto ownership, housing costs, and transit usage for both homeowners and renters are concurrently using simultaneous equation modeling (SEM) to capture the interrelationship of these factors. The inputs to the SEM include these six endogenous variables and 18 exogenous variables, with VMT still modeled separately due to data limitations.

To learn more about the Location Affordability Index (v.2.0) visit: https://www.hudexchange.info/programs/location-affordability-index/. Data Dictionary: DD_Location Affordability Indev v.2.0 Date of Coverage: 2008-2012

The U.S. Department of Housing and Urban Development (HUD) periodically receives "custom tabulations" of Census data from the U.S. Census Bureau that are largely not available through standard Census products. These datasets, known as "CHAS" (Comprehensive Housing Affordability Strategy) data, demonstrate the extent of housing problems and housing needs, particularly for low income households. The primary purpose of CHAS data is to demonstrate the number of households in need of housing assistance. This is estimated by the number of households that have certain housing problems and have income low enough to qualify for HUD’s programs (primarily 30, 50, and 80 percent of median income). CHAS data provides counts of the numbers of households that fit these HUD-specified characteristics in a variety of geographic areas. In addition to estimating low-income housing needs, CHAS data contributes to a more comprehensive market analysis by documenting issues like lead paint risks, "affordability mismatch," and the interaction of affordability with variables like age of homes, number of bedrooms, and type of building.This dataset is a special tabulation of the 2016-2020 American Community Survey (ACS) and reflects conditions over that time period. The dataset uses custom HUD Area Median Family Income (HAMFI) figures calculated by HUD PDR staff based on 2016-2020 ACS income data. CHAS datasets are used by Federal, State, and Local governments to plan how to spend, and distribute HUD program funds. To learn more about the Comprehensive Housing Affordability Strategy (CHAS), visit: https://www.huduser.gov/portal/datasets/cp.html, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. To learn more about the American Community Survey (ACS), and associated datasets visit: https://www.census.gov/programs-surveys/acs Data Dictionary: DD_ACS 5-Year CHAS Estimate Data by County Date of Coverage: 2016-2020

2016-2020 ACS 5-Year estimates of demographic variables (see below) compiled at the State level.The American Community Survey (ACS) 5 Year 2016-2020 demographic information is a subset of information available for download from the U.S. Census. Tables used in the development of this dataset include: B01001 - Sex By Age; B03002 - Hispanic Or Latino Origin By Race; B11001 - Household Type (Including Living Alone); B11005 - Households By Presence Of People Under 18 Years By Household Type; B11006 - Households By Presence Of People 60 Years And Over By Household Type; B16005 - Nativity By Language Spoken At Home By Ability To Speak English For The Population 5 Years And Over; B25010 - Average Household Size Of Occupied Housing Units By Tenure, and; B15001 - Sex by Educational Attainment for the Population 18 Years and Over; To learn more about the American Community Survey (ACS), and associated datasets visit: https://www.census.gov/programs-surveys/acs Data Dictionary: DD_ACS 5-Year Demographic Estimate Data by StateDate of Coverage: 2016-2020

The Fair Housing Assistance Program (FHAP) is a formula grant program established under the Fair Housing Act which allows for state and local agencies to assist in the enforcement of the Act. If such agencies are determined by FHEO to enforce state or local fair housing laws that are substantially equivalent to the federal Fair Housing Act, they can receive financial and operational assistance from FHEO to enforce such laws. There are periodic changes to the roster of FHAP agencies as new agencies are admitted to the program, or existing agencies withdraw or are determined to no longer have laws and procedures which are substantially equivalent to the federal statue. FHAP agencies receive funding from HUD to support their efforts. Payments are made once a year after the end of the period of performance for that agency. To learn more about the Fair Housing Assistance Program (FHAP) visit: https://www.hud.gov/program_offices/fair_housing_equal_opp/partners/FHAP

Data Dictionary: DD_Fair Housing Assistance Program (FHAP) Date of Coverage: FY2023

A FEMA housing inspection for renters is used to assess personal property loss and for owners to assess damage to their home as well as personal property. This inspection is done to determine eligibility for FEMA Individual Assistance. For both rental and owner inspections, if the property has flood damage the inspector measures the height of the flooding. They indicate the highest floor of the flooding (for example, Basement, 1st floor, 2nd floor, etc…) and the extent of the flooding in that room. In addition, for the units without flooding, HUD has estimated minor/major/severe damage based on the damage inspection estimates for real property (owner) and personal property (renter).In an effort to maintain the confidentiality of residents this file only presents data on block groups with ten or more damaged housing units. The suppression of block groups with fewer than ten damaged housing units results in an exclusion of approximately 6% of the total flooded units. These data reflect Hurricane Sandy damage in the states of New York, New Jersey, Connecticut, and Rhode Island. These data are incomplete, as each day there are additional registrants and inspections. This should be a viewed as a preliminary snapshot to assist with planning.To learn more about HUD's long-term recovery efforts for victims of Hurricane Sandy visit: https://www.hud.gov/sandyrebuilding, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov.

The Housing Opportunities for Persons with AIDS (HOPWA) program funds are distributed to states and cities by formula allocations and made available as part of the area's Consolidated Plan. Persons living with HIV/AIDS and their families may require housing that provides emergency, transitional, or long-term affordable solutions. In addition, some projects are selected in national competitions to serve as service delivery models or operate in non-formula areas. Grantees partner with nonprofit organizations and housing agencies to provide housing and support to beneficiaries.

To learn more about the HOPWA program visit: https://portal.hud.gov/hudportal/HUD?src=/program_offices/comm_planning/aidshousing, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Data Dictionary: DD_HOPWA Grantee Areas

Date of Coverage: FY 2024 Data Updated: Annually

The United States Department of Agriculture's (USDA), Rural Development (RD) Agency operates a broad range of programs that were formally administered by the Farmers Home Administration to support affordable housing and community development in rural areas. RD helps rural communities and individuals by providing loans and grants for housing and community facilities. RD provides funding for single family homes, apartments for low-income persons or the elderly, housing for farm laborers, childcare centers, fire and police stations, hospitals, libraries, nursing homes and schools. To learn more, visit: https://www.rd.usda.gov/about-rd/agencies/rural-housing-service, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Data Dictionary: DD_Rural_Housing_AssetsDate of Coverage: 2018

The FHA insured Multifamily Housing portfolio consists primarily of rental housing properties with five or more dwelling units such as apartments or town houses, but can also be nursing homes, hospitals, elderly housing, mobile home parks, retirement service centers, and occasionally vacant land. Please note that this dataset overlaps the Multifamily Properties Assisted layer. The Multifamily property locations represent the approximate location of the property. Location data for HUD-related properties and facilities are derived from HUD's enterprise geocoding service. While not all addresses are able to be geocoded and mapped to 100% accuracy, we are continuously working to improve address data quality and enhance coverage. Please consider this issue when using any datasets provided by HUD. When using this data, take note of the field titled “LVL2KX” which indicates the overall accuracy of the geocoded address using the following return codes: ‘R’ - Interpolated rooftop (high degree of accuracy, symbolized as green) ‘4’ - ZIP+4 centroid (high degree of accuracy, symbolized as green) ‘B’ - Block group centroid (medium degree of accuracy, symbolized as yellow) ‘T’ - Census tract centroid (low degree of accuracy, symbolized as red) ‘2’ - ZIP+2 centroid (low degree of accuracy, symbolized as red) ‘Z’ - ZIP5 centroid (low degree of accuracy, symbolized as red) ‘5’ - ZIP5 centroid (same as above, low degree of accuracy, symbolized as red) Null - Could not be geocoded (does not appear on the map) For the purposes of displaying the location of an address on a map only use addresses and their associated lat/long coordinates where the LVL2KX field is coded ‘R’ or ‘4’. These codes ensure that the address is displayed on the correct street segment and in the correct census block. The remaining LVL2KX codes provide a cascading indication of the most granular level geography for which an address can be confirmed. For example, if an address cannot be accurately interpolated to a rooftop (‘R’), or ZIP+4 centroid (‘4’), then the address will be mapped to the centroid of the next nearest confirmed geography: block group, tract, and so on. When performing any point-in polygon analysis it is important to note that points mapped to the centroids of larger geographies will be less likely to map accurately to the smaller geographies of the same area. For instance, a point coded as ‘5’ in the correct ZIP Code will be less likely to map to the correct block group or census tract for that address. In an effort to protect Personally Identifiable Information (PII), the characteristics for each building are suppressed with a -4 value when the “Number_Reported” is equal to, or less than 10. To learn more about HUD Insured Multifamily Properties visit: https://www.hud.gov/program_offices/housing/mfh Data Dictionary: DD_HUD Insured Multifamilly Properties Date of Coverage: 12/2023

The American Community Survey (ACS) 5 Year 2016-2020 housing estimate data is a subset of information derived from the following census tables:B25002 - Occupancy Status;B25009 - Tenure By Household Size;B25021 - Median Number Of Rooms By Tenure;B25024 - Units In Structure;B25032 - Tenure by Units In Structure;B25036 - Tenure By Year Structure Built;B25037 - Median Year Structure Built By Tenure;B25041 – Bedrooms;B25042 - Tenure By Bedrooms;B25056 - Contract Rent;B25058 - Median Contract Rent;B25068 - Bedrooms By Gross Rent;B25077 - Median Value;B25097 - Mortgage Status By Median Value (Dollars), and;B25123 - Tenure By Selected Physical And Financial Conditions.To learn more about the American Community Survey (ACS), and associated datasets visit: https://www.census.gov/programs-surveys/acs, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov.

Data Dictionary: DD_ACS 5-Year Housing Estimate Data by County Date of Coverage: 2016-2020

Public Housing was established to provide decent and safe rental housing for eligible low-income families, the elderly, and persons with disabilities. Public housing comes in all sizes and types, from scattered single family houses to high-rise apartments for elderly families. There are approximately 1.2 million households living in public housing units, managed by over 3,300 housing agencies (HAs). HUD administers Federal aid to local housing agencies (HAs) that manage the housing for low-income residents at rents they can afford. HUD furnishes technical and professional assistance in planning, developing and managing these developments. Location data for HUD-related properties and facilities are derived from HUD's enterprise geocoding service. While not all addresses are able to be geocoded and mapped to 100% accuracy, we are continuously working to improve address data quality and enhance coverage. Please consider this issue when using any datasets provided by HUD. When using this data, take note of the field titled “LVL2KX” which indicates the overall accuracy of the geocoded address using the following return codes: ‘R’ - Interpolated rooftop (high degree of accuracy, symbolized as green) ‘4’ - ZIP+4 centroid (high degree of accuracy, symbolized as green) ‘B’ - Block group centroid (medium degree of accuracy, symbolized as yellow) ‘T’ - Census tract centroid (low degree of accuracy, symbolized as red) ‘2’ - ZIP+2 centroid (low degree of accuracy, symbolized as red) ‘Z’ - ZIP5 centroid (low degree of accuracy, symbolized as red) ‘5’ - ZIP5 centroid (same as above, low degree of accuracy, symbolized as red) Null - Could not be geocoded (does not appear on the map) For the purposes of displaying the location of an address on a map only use addresses and their associated lat/long coordinates where the LVL2KX field is coded ‘R’ or ‘4’. These codes ensure that the address is displayed on the correct street segment and in the correct census block. The remaining LVL2KX codes provide a cascading indication of the most granular level geography for which an address can be confirmed. For example, if an address cannot be accurately interpolated to a rooftop (‘R’), or ZIP+4 centroid (‘4’), then the address will be mapped to the centroid of the next nearest confirmed geography: block group, tract, and so on. When performing any point-in polygon analysis it is important to note that points mapped to the centroids of larger geographies will be less likely to map accurately to the smaller geographies of the same area. For instance, a point coded as ‘5’ in the correct ZIP Code will be less likely to map to the correct block group or census tract for that address. To learn more about Public Housing visit: https://www.hud.gov/program_offices/public_indian_housing/programs/ph/, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Data Dictionary: DD_Public Housing Authorities Date Updated: 12/2024 Q3 2024

Processing for much of the Single Family FHA mortgages is centralized into one of four Homeownership Centers (HOC) located in Atlanta, Philadelphia, Denver, and Santa Ana; each supporting specific geographic region. Although most questions are handled by the FHA Resource Center (not the HOC) for immediate acknowledgement and tracking, certain case specific issues will subsequently be referred to the appropriate center.

To learn more about Homeownership Centers (HOC) visit: https://www.hud.gov/program_offices/housing/sfh/sfhhocs

Date of Coverage: Current Data Updated: As Needed

The data provided here denotes the authors’ revised service areas for a subset of 377 Public Housing Authorities (PHAs) for which HUD previously estimated service areas. Using HUD administrative data on the location of Housing Choice Voucher holders, HUD’s estimated service areas were revised to better capture voucher activity. Specifically, the authors developed two different tests and correction procedures. The first assesses if the estimated service area omits a sizable share of voucher holder locations (so is “too small”), and if so, adjusts to include census designated places or counties containing at least 5 percent of a PHA’s voucher holders. The second test checks whether the estimated service boundary includes areas the PHA does not appear to serve and that are clearly served by another PHA (so is “too large”), in this case adjusting by removing those areas. 148 of the 377 PHA estimated service areas were found to be too small, too large, or both, and so have revised service areas that differ from HUD’s estimated service areas. The detailed methodology is provided below. Additionally, a spreadsheet is supplied that identifies geographies that were added to and dropped from HUD’s estimated services to create the revised service areas for affected PHAs.

This is an experimental dataset that is designed to aid researchers in studying the HCV program. The methodology and the service areas themselves have not been validated by HUD’s Office of Public and Indian Housing (PIH) or the Public Housing Agencies. For additional discussion of the approach, see Tauber et al. (2024); please contact the authors with any questions or comments.Data Dictionary: DD_Extensions to Estimated Housing Authority Service Areas MethodologyMethodology: Extensions to Estimated Housing Authority Service Areas Methodology

Reference:Tauber, Kristen, Ingrid Gould Ellen, and Katherine O’Regan. 2024. “Whom Do We Serve? Refining Public Housing Agency Service Areas.” Cityscape 26(1) (2024): 395-400.

These data were developed by the Office of Environment and Energy (OEE) to help users identify tribes that may have an interest in the location of federally funded projects and provides tribal contact information to assist users with initiating Section 106 consultation under the National Historic Preservation Act (54 U.S.C. § 300101 et seq.).For questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov.