More than one minimum wage job was required to afford two-bedroom housing in all states in the United States in 2025. At mean wage, Hawaii was the most expensive state, requiring renters to hold about two full-time jobs at a mean wage to afford two-bedroom housing. The fair market rent value of two bedroom housing in Hawaii ranked second most expensive among all states in the United States in 2025.

The Low-Income Housing Tax Credit (LIHTC) is the most important resource for creating affordable housing in the United States today. The LIHTC database, created by HUD and available to the public since 1997, contains information on 48,672 projects and 3.23 million housing units placed in service since 1987. Low-Income Housing Tax Credit Qualified Census Tracts must have 50 percent of households with incomes below 60 percent of the Area Median Gross Income (AMGI) or have a poverty rate of 25 percent or more. Difficult Development Areas (DDA) are areas with high land, construction and utility costs relative to the area median income and are based on Fair Market Rents, income limits, the 2010 census counts, and 5-year American Community Survey (ACS) data.

In October 2015, Mayor Garcetti released Executive Directive 13, Support for Affordable Housing (ED 13). ED 13 is a “Back to Basics” operational directive that helps streamline the development of critical new housing developments that address our housing shortage. This dataset tracks the City's progress towards the goals outlined in the directive: (1) Permitting 100,000 new units from the start of Mayor Garcetti's administration through the end of fiscal year 2021, and (2) Building or preserving 15,000 affordable housing units for low-income households in this same time period.

Listing of state tax credit and subsidies awarded by NYS Homes & Community Renewal’s Office of Finance and Development. Details include award amount, developer name, project location, and accomplishments for completed projects based on project types.

Comprehensive dataset containing 3,765 verified Low income housing program businesses in United States with complete contact information, ratings, reviews, and location data.

A commonly accepted threshold for affordable housing costs at the household level is 30% of a household's income. Accordingly, a household is considered cost burdened if it pays more than 30% of its income on housing. Households paying more than 50% are considered severely cost burdened. These thresholds apply to both homeowners and renters.

The Housing Affordability indicator only measures cost burden among the region's households, and not the supply of affordable housing. The directionality of cost burden trends can be impacted by changes in both income and housing supply. If lower income households are priced out of a county or the region, it would create a downward trend in cost burden, but would not reflect a positive trend for an inclusive housing market.

The housing affordability measure illustrates the relationship between income and housing costs. A household that spends 30% or more of its collective monthly income to cover housing costs is considered to be “housing cost-burden[ed].”[1] Those spending between 30% and 49.9% of their monthly income are categorized as “moderately housing cost-burden[ed],” while those spending more than 50% are categorized as “severely housing cost-burden[ed].”[2]

How much a household spends on housing costs affects the household’s overall financial situation. More money spent on housing leaves less in the household budget for other needs, such as food, clothing, transportation, and medical care, as well as for incidental purchases and saving for the future.

The estimated housing costs as a percentage of household income are categorized by tenure: all households, those that own their housing unit, and those that rent their housing unit.

Throughout the period of analysis, the percentage of housing cost-burdened renter households in Champaign County was higher than the percentage of housing cost-burdened homeowner households in Champaign County. All three categories saw year-to-year fluctuations between 2005 and 2023, and none of the three show a consistent trend. However, all three categories were estimated to have a lower percentage of housing cost-burdened households in 2023 than in 2005.

Data on estimated housing costs as a percentage of monthly income was sourced from the U.S. Census Bureau’s American Community Survey (ACS) 1-Year Estimates, which are released annually.

As with any datasets that are estimates rather than exact counts, it is important to take into account the margins of error (listed in the column beside each figure) when drawing conclusions from the data.

Due to the impact of the COVID-19 pandemic, instead of providing the standard 1-year data products, the Census Bureau released experimental estimates from the 1-year data in 2020. This includes a limited number of data tables for the nation, states, and the District of Columbia. The Census Bureau states that the 2020 ACS 1-year experimental tables use an experimental estimation methodology and should not be compared with other ACS data. For these reasons, and because data is not available for Champaign County, no data for 2020 is included in this Indicator.

For interested data users, the 2020 ACS 1-Year Experimental data release includes a dataset on Housing Tenure.

[1] Schwarz, M. and E. Watson. (2008). Who can afford to live in a home?: A look at data from the 2006 American Community Survey. U.S. Census Bureau.

[2] Ibid.

Sources: U.S. Census Bureau; American Community Survey, 2023 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (17 October 2024).; U.S. Census Bureau; American Community Survey, 2022 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (22 September 2023).; U.S. Census Bureau; American Community Survey, 2021 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (30 September 2022).; U.S. Census Bureau; American Community Survey, 2019 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (10 June 2021).; U.S. Census Bureau; American Community Survey, 2018 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (10 June 2021).;U.S. Census Bureau; American Community Survey, 2017 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2016 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (14 September 2017).; U.S. Census Bureau; American Community Survey, 2015 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (19 September 2016).; U.S. Census Bureau; American Community Survey, 2014 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2013 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2012 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2011 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2010 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2009 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2008 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; 16 March 2016).; U.S. Census Bureau; American Community Survey, 2007 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2006 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2005 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).

This table contains data on the percent of households paying more than 30% (or 50%) of monthly household income towards housing costs for California, its regions, counties, cities/towns, and census tracts. Data is from the U.S. Department of Housing and Urban Development (HUD), Consolidated Planning Comprehensive Housing Affordability Strategy (CHAS) and the U.S. Census Bureau, American Community Survey (ACS). The table is part of a series of indicators in the [Healthy Communities Data and Indicators Project of the Office of Health Equity] Affordable, quality housing is central to health, conferring protection from the environment and supporting family life. Housing costs—typically the largest, single expense in a family's budget—also impact decisions that affect health. As housing consumes larger proportions of household income, families have less income for nutrition, health care, transportation, education, etc. Severe cost burdens may induce poverty—which is associated with developmental and behavioral problems in children and accelerated cognitive and physical decline in adults. Low-income families and minority communities are disproportionately affected by the lack of affordable, quality housing. More information about the data table and a data dictionary can be found in the Attachments.

Comprehensive dataset containing 97 verified Low income housing program businesses in Ohio, United States with complete contact information, ratings, reviews, and location data.

This point feature class represents the locations of Low Income Housing Units within City of Miami. This dataset was obtained from Miami-Dade County Open Data Hub and clipped to contain only locations within the City of Miami. For a countywide layer, please refer to Miami-Dade County Open Data Hub at https://gis-mdc.opendata.arcgis.com/.The Housing & Homeownership dataset displays vital information to support informed homeownership by offering insights into population trends and city planning initiatives. It includes data on housing units—such as subsidized housing, public developments, and program-supported properties—highlighting patterns of housing access and affordability. Data Refresh Frequency: This dataset is refreshed on a weekly basis, regardless of whether any updates have occurred in the source data. Users should note that the data is reprocessed and reloaded each week to ensure availability and consistency, even in the absence of changes.

Public Housing BuildingsThis National Geospatial Data Asset (NGDA) dataset, shared as a Department of Housing and Urban Development feature layer, displays the location of individual buildings within public housing units throughout the U.S. According to HUD's Public Housing Program, "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. HUD administers federal aid to local housing agencies 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."Trenton Housing AuthorityData currency: current federal service (Public Housing Building)NGDAID: 130 (Assisted Housing - Public Housing Buildings - National Geospatial Data Asset (NGDA)OGC API Features Link: Not AvailableFor more information, please visit: Public Housing; PHA Contact InformationSupport documentation: DD Public Housing BuildingsFor feedback please contact: Esri_US_Federal_Data@esri.com NGDA Data SetThis data set is part of the NGDA Real Property Theme Community. Per the Federal Geospatial Data Committee (FGDC), Real Property is defined as "the spatial representation (location) of real property entities, typically consisting of one or more of the following: unimproved land, a building, a structure, site improvements and the underlying land. Complex real property entities (that is "facilities") are used for a broad spectrum of functions or missions. This theme focuses on spatial representation of real property assets only and does not seek to describe special purpose functions of real property such as those found in the Cultural Resources, Transportation, or Utilities themes."For other NGDA Content: Esri Federal Datasets

Financial overview and grant giving statistics of National Low Income Housing Coalition

Housing-Burdened Low-Income Households. Percent of households in a census tract that are both low income (making less than 80% of the HUD Area Median Family Income) and severely burdened by housing costs (paying greater than 50% of their income to housing costs). (5-year estimates, 2013-2017).

The cost and availability of housing is an important determinant of well- being. Households with lower incomes may spend a larger proportion of their income on housing. The inability of households to afford necessary non-housing goods after paying for shelter is known as housing-induced poverty. California has very high housing costs relative to much of the country, making it difficult for many to afford adequate housing. Within California, the cost of living varies significantly and is largely dependent on housing cost, availability, and demand.

Areas where low-income households may be stressed by high housing costs can be identified through the Housing and Urban Development (HUD) Comprehensive Housing Affordability Strategy (CHAS) data. We measure households earning less than 80% of HUD Area Median Family Income by county and paying greater than 50% of their income to housing costs. The indicator takes into account the regional cost of living for both homeowners and renters, and factors in the cost of utilities. CHAS data are calculated from US Census Bureau's American Community Survey (ACS).

The majority of public housing households in the U.S. were of a racial minority in 2023. In about ** percent of the households, the head of the family belonged to a racial minority. That percentage was the lowest in Vermont, at ***** percent, and the highest in Puerto Rico, where a hundred percent of the households were considered a racial minority by the source.

The Department of Housing Preservation and Development (HPD) receives a sub-allocation of 9% Low Income Housing Tax Credits and allocated its credits through one competitive round each calendar year. It is also charged with allocating 4% Low Income Housing Tax Credits to projects receiving tax exempt bonds through New York City Housing Development Corporation. Each entry represents an allocation to a low income housing development project with households at or below 60% of Area Median Income. For the Low Income Housing Tax Credits Awarded by HPD: Project-Level (4% Awards) dataset, please follow this link

The Low-Income Housing Tax Credit (LIHTC) is the primary Federal program for creating affordable housing in the United States. The LIHTC database, created by HUD and available to the public since 1997, contains information on 33,777 projects and almost 2,203,000 housing units placed in service between 1987 and 2010. Created by the Tax Reform Act of 1986, the LIHTC program gives State and local LIHTC-allocating agencies the equivalent of nearly $8 billion in annual budget authority to issue tax credits for the acquisition, rehabilitation, or new construction of rental housing targeted to lower-income households. Although some data about the program have been made available by various sources, HUD's database is the only complete national source of information on the size, unit mix, and location of individual projects. With the continued support of the national LIHTC database, HUD hopes to enable researchers to learn more about the effects of the tax credit program. The LIHTC property locations depicted in this map service represent the general location of the property. The locations of individual buildings associated with each property are not depicted here. The location of the property is derived from the address of the building with the most units.

Financial overview and grant giving statistics of National Low Income Housing Policy Center

A number of studies have explored the relationship between public housing policy, poverty, and crime. This Commentary discusses the results of a recent study, which investigated the effects of closing large public housing developments on crime. To see if the demolitions—and the associated deconcentration of poverty—reduced crime or merely displaced it, researchers examined the case of Chicago. They found that closing large public housing developments and dispersing former residents throughout a wider portion of the city was associated with net reductions in violent crime, at the city level.