In 2024, there were approximately **** million housing cost burdened households among renters in the United States. A household is considered to be moderately burdened when the housing costs exceed 30 percent of the family income. Severely burdened households, on the other hand, spend more than 50 percent of their income on rent.

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.

In 2021, there were approximately ** million housing cost burdened households in the United States. A household is considered to be moderately cost burdened when the housing costs exceed ** percent of the family income. Severely burdened households, on the other hand, spend over ** percent of their income on housing.

Cost-burdened Households by County reports the number and percent of households that spend at least 30 percent of annual household income on housing costs, by householder status.

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 2024, 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 2024 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, 2024 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (3 December 2025).; 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).

Information about number of households by annual income level and housing costs as a percentage of total income. Housing costs include mortgage/rent and utilities. Data sourced by US Census American Community Survey (ACS) 5-Year Estimates. See ACS Table B25106 "Tenure by Housing Costs as a Percentage of Household Income in the Past 12 Months"; Universe: Occupied housing units. This dataset's API field names match the US Census Variable / API names (see also metadata spreadsheet in attachments section).

This data layer depicts, by census tracts, gross rent as a percentage of household income in the past 12 months for the San Francisco Bay Region. The source data, from the United States Census Bureau, has been reprocessed by the Metropolitan Transportation Commission.

To produce this feature set, the Metropolitan Transportation Commission downloaded American Community Survey (ACS) table B25070 to create a feature set representing rent as a percentage of household income by the following categories: ● Rent less than 30% of household income ● Rent is 30.0% to 49.9% of household income ● Rent is greater than or equal to 50% of household income

The resulting attribute table had all margin of error fields deleted, percentage fields added, county code field added, jurisdiction name added, and the source field names were changed.

The source table used to develop this feature service is from the United States Census Bureau, 2015-2019 American Community Survey 5-Year Estimates and can be downloaded from https://data.census.gov/cedsci/table?q=B25070%3A%20GROSS%20RENT%20AS%20A%20PERCENTAGE%20OF%20HOUSEHOLD%20INCOME%20IN%20THE%20PAST%2012%20MONTHS&g=0400000US06%241500000&tid=ACSDT5Y2019.B25070

In 2021, there were approximately **** million housing cost burdened renter households in the United States, with close to ** million being severely burdened. About *** million households with an annual income below ****** U.S. dollars were severely burdened. A household is considered to be moderately cost burdened when the housing costs exceed ** percent of the family income. Severely burdened households, on the other hand, spend over ** percent of their income on rent.

This data is included as part of the Environmental Health Disparities Version 3.0 map. To see this map, visit our webpage. For more technical information on this map and the model used, visit our technical report (link). Background The U.S. Department of Housing and Urban Development calls a household “cost-burdened” if they spend over 30 percent of their income on housing. Rising rent, lack of housing, and stagnant wages have led to a crisis in housing affordability. Without a financial safety net, emergency expenses or job loss can cause people to lose their homes. Renters and households with less financial opportunity are the most impacted by the housing affordability crisis. Redlining, laws, and practices systematically excluded people of color from home ownership. This has resulted in the housing crisis disproportionately impacting historically minoritized communities. Housing cost burden is related to many of the socioeconomic conditions that affect health and well-being. People trying to find affordable housing may be forced to live in areas with more pollution. As a result, people experiencing housing cost burden are at higher risk of exposure to air pollution and loss of life. People experiencing housing cost burden may have to choose between paying for housing or other necessities. They may also delay medical care and services due to financial insecurity. This can lead to long-term health impacts. Chronic stress from worrying about the ability to pay for housing can also worsen physical and mental health. Evidence Housing cost burdens influence health in many ways. These include financial stress and the unaffordability of basic necessities such as healthy food or health care services [1, 2]. There is a strong link between housing burden and health disparities such as hypertension [3], mental health status [4], and cancer [5]. Increasing income inequality affects how burdened communities are by housing costs [6]. In Washington, 43 percent of all households, and 65 percent of households that are renting, are housing burdened. Data source American Community Survey 5-year estimates, DP04 - Selected Housing Characteristics Methods The U.S. Census Bureau’s American Community Survey (ACS) asks respondents detailed questions on social and economic topics. This measure was developed using census tract-level housing data from the ACS’s 2018-2022 5-year estimates. This measure represents the percent of households that report spending over 30 percent of their gross income on housing in the past 12 months. Total housing costs include rent or mortgage, utilities, taxes, insurance, and other housing fees such as condo fees. For more information on how ACS data is collected and processed, refer to ACS General Data Users Handbooks. Data Source Variables Used Calculations Performed* ACS 5-year average, DP04 - Selected Housing Characteristics B25140_001, B25140_003, B25140_004, B25140_007, B25140_008, B25140_011, B25140_012 # Households spending >30% of income on housing: sum(B25140_003, B25140_004, B25140_007, B25140_008, B25140_011, B25140_012) # Housing units: B25140_001 % Households spending >30% of income on housing: sum (B25140_003, B25140_004, B25140_007, B25140_008, B25140_011, B25140_012) / B25140_001 * For margin of error (MOE) calculations, refer to U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data Appendix 1. For MOEs in which we derived either the numerator or denominator of a proportion from multiple ACS variables, see "Calculating MOEs for Aggregated Count Data." For MOEs derived from proportions, see "Calculating MOEs for Derived Proportions." The data table shows the estimate for this variable minus the MOE (lower ME) and the estimate plus the MOE (upper ME). Caveats The measurement of income used for this measure does not reflect noncash benefits such as food stamps, healthcare, and housing assistance. Additionally, studies have shown that non-wage income is typically underreported on the ACS. The margin of error shows how much uncertainty there is about whether the survey data accurately represents the full population. The confidence interval is the estimate plus or minus the margin of error. There is a 90 percent probability that the true population value is within the confidence interval, after accounting for sampling variability. All survey data have some margin of error due to sampling variability. Results from smaller populations are less reliable because of their smaller sample sizes, leading to a larger margin of error. Counts for American Indian, Alaska Native, Native Hawaiian, and Pacific Islander populations are known to be less reliable. The survey design attempts to address these issues through increased sampling rates in smaller populations and on Tribal lands. The data may also have non-sampling errors, which aren’t shown in the tables. These can happen if there are problems with the survey questions, if there are issues with processing or weighting the data, or if certain groups of people don’t respond [7]. Individuals with a distrust for government, more concerns about privacy, and who are very busy are less likely to respond to the survey. This measure is aggregated across the census tract and does not represent each individual community within the tract. These data should always be supplemented with local data and equitable engagement for more accurate insights. ACS bundles data in one-year, three-year, or five-year groups to get more reliable results. To have census tract data on all 39 counties in Washington, we use the ACS five-year grouping. Sources Harkness, J., & Newman, S. (2005). Housing affordability and children’s well-being: Evidence from the national survey of America’s families. Housing Policy Debate, 16(2), 223-55. Meltzer, R., & Schwartz, A. (2015). Housing affordability and health: evidence from New York City. Housing Policy Debate, 26(1), 1-25. Pollack, C., Griffin, B., & Lynch, J. (2010). Housing affordability and health among homeowners and renters. American Journal of Preventive Medicine, 39(6), 515-21. Baker, E., Lester, L., Mason, K. et al. (2020). Mental health and prolonged exposure to unaffordable housing: a longitudinal analysis. Social Psychiatry and Psychiatric Epidemiology, 55, 715–721. Thompson, C., Nianogo, R., Leonard, T. (2024). Unaffordable housing and cancer: novel insights into a complex question. JNCI Cancer Spectrum. 8(3). 1-3. Dunn, J. (2000). Housing and Health Inequalities: Review and Prospects for Research. Housing Studies, 15(3), 341-66. Pickering, K. (2022, December 9). Nonresponse in census surveys [PDF]. Federal Economic Statistics Advisory Committee. U.S. Bureau of Economic Analysis. https://apps.bea.gov/fesac/meetings/2022-12-09/Pickering-FESACNonresponse-in-Census-Surveys-12092022.pdf CitationWashington Tracking Network, Washington State Department of Health. Web. "Unaffordable Housing (>30% of Income)". Data obtained from the American Community Survey, 2019-2023, DP04 - Selected Housing Characteristics Data. Published September 2025.

Tenure by Housing Costs as a Percentage of Household Income in the Past 12 Months County and State values are from the American Community Survey (ACS) 1 Year Survey

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.

This statistic presents the number of housing cost burdened households among household owners in the United States in 2017, by household income. A household is considered to be housing cost burdened when the housing costs exceed ** percent of the family income. In 2017, there were approximately ***** million cost burdened households among owners in the United States with household income of over ****** U.S. dollars.

This map shows the relationship between the median age housing units were built and percent of cost-burdened renters in an area. The pop-up is configured to show:Median year housing units builtPercent of cost-burdened renter householdsThe data in this map contains the most recent American Community Survey (ACS) data from the U.S. Census Bureau. The Living Atlas layer in this map updates annually when the Census releases their new figures. To learn more, visit this FAQ, or visit the ACS website.

Discover where Gen Z renters are most cost-burdened and explore housing affordability trends in their largest U.S. metros.

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).

In 2019, there were **** million cost-burdened renter households in the United States. A household is considered to be housing cost-burdened when the housing costs exceed ** percent of the family income. California had ************* cost-burdened renter households, which accounted for **** percent of all renter households in the state.

Retirement Notice: This item is in mature support as of February 2026 and will retire in December 2027. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version.This layer shows housing costs as a percentage of household income. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Income is based on earnings in past 12 months of survey. This layer is symbolized to show the percent of renter households that spend 30.0% or more of their household income on gross rent (contract rent plus tenant-paid utilities). To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top left. Vintage: 2019-2023ACS Table(s): B25070, B25091Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.gov The United States Census Bureau's American Community Survey (ACS):About the Survey Geography & ACS Technical Documentation News & Updates This ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data. Data Note from the Census: Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables. Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases. Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page. Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution. The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate. The data for this geographic area cannot be displayed because the number of sample cases is too small.

This indicator is defined as the median of the distribution of the share of total housing costs (net of housing allowances) in the total disposable household income (net of housing allowances) presented by age group.

Euro Area - Median of the housing cost burden distribution: Males was 13.40% in December of 2024, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for Euro Area - Median of the housing cost burden distribution: Males - last updated from the EUROSTAT on February of 2026. Historically, Euro Area - Median of the housing cost burden distribution: Males reached a record high of 16.00% in December of 2013 and a record low of 12.70% in December of 2020.

The percent of low- and middle-income households (annual household income $75K or less) in Pennsylvania that pay over 30% of household income to rent or mortgage payments.