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.

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).

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.

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 Housing Affordability Data System (HADS) is a set of housing unit level datasets that measures the affordability of housing units and the housing cost burdens of households, relative to area median incomes, poverty level incomes, and Fair Market Rents. The purpose of these datasets is to provide housing analysts with consistent measures of affordability and burdens over a long period. The datasets are based on the American Housing Survey (AHS) national files from 1985 through 2005 and the metropolitan files for 2002 and 2004. Users can link records in HADS files to AHS records, allowing access to all of the AHS variables. Housing-level variables include information on the number of rooms in the housing unit, the year the unit was built, whether it was occupied or vacant, whether the unit was rented or owned, whether it was a single family or multiunit structure, the number of units in the building, the current market value of the unit, and measures of relative housing costs. The dataset also includes variables describing the number of people living in the household, household income, and the type of residential area (e.g., urban or suburban).

The Housing Affordability Data System (HADS) is a set of files derived from the 1985 and later national American Housing Survey (AHS) and the 2002 and later Metro AHS. This system categorizes housing units by affordability and households by income, with respect to the Adjusted Median Income, Fair Market Rent (FMR), and poverty income. It also includes housing cost burden for owner and renter households. These files have been the basis for the worst case needs tables since 2001. The data files are available for public use, since they were derived from AHS public use files and the published income limits and FMRs. We are providing these files give the community of housing analysts the opportunity to use a consistent set of affordability measures.This data set appears to not be upated after 2013

Severe housing burden is defined as spending 50% or more of monthly household income on housing. A small number of households without housing cost or income data were excluded from analyses.Given the high cost of housing in Los Angeles County, many residents spend a sizable portion of their incomes on housing every month. Severe housing burden disproportionately affects low-income individuals, renters, and communities of color. Severe housing burden can negatively impact health by forcing individuals and families into low quality or unsafe housing, by causing significant stress, and by limiting the amount of money people have available to spend on other life necessities, such as food or healthcare. It is also an important risk factor for homelessness.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

Housing burden is defined as spending 30% or more of monthly household income on housing. A small number of households without housing cost or income data were excluded from analyses.Given the high cost of housing in Los Angeles County, many residents spend a sizable portion of their incomes on housing every month and are therefore susceptible to significant housing burden. Housing burden disproportionately affects low-income individuals, renters, and communities of color. Housing burden can negatively impact health by forcing individuals and families into low quality or unsafe housing, by causing significant stress, and by limiting the amount of money people have available to spend on other life necessities, such as food or healthcare. It is also an important risk factor for homelessness.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

Analysis of ‘Median of the housing cost burden distribution by degree of urbanisation - EU-SILC survey’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://data.europa.eu/data/datasets/aguyyc15qakxd540moipq on 29 August 2021.

--- Dataset description provided by original source is as follows ---

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 degree of urbanisation.

--- Original source retains full ownership of the source dataset ---

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.

This database automatically captures metadata, the source of which is the GOVERNMENT OF THE REPUBLIC OF SLOVENIA STATISTICAL OFFICE OF THE REPUBLIC OF SLOVENIA and corresponds to the source database entitled "Median burden of housing costs by household income classes, Slovenia, annually".

The actual data is available in PC-Axis format (.px). Among the additional links, you can access the pages of the source portal for insight and selection of data, and there is also the PX-Win program, which can be downloaded for free. Both allow you to select data for display, change the format of the printout and save it in different formats, as well as view and print tables of unlimited size and some basic statistical analyses and graphical representations.

The Housing Affordability Data System (HADS), 2002, is a housing-unit level dataset that measures the affordability of housing units and the housing cost burdens of households, relative to area median incomes, poverty level incomes, and Fair Market Rents. The dataset contains selected variables from the AMERICAN HOUSING SURVEY, 2002: METROPOLITAN MICRODATA (ICPSR 4589), as well as custom, derived variables measuring monthly housing costs, housing cost burdens, assisted housing, and total salary income. Housing-level variables include information on the number of rooms in the housing unit, the year the unit was built, whether it was occupied or vacant, whether the unit was rented or owned, whether it was a single family or multi-unit structure, the number of units in the building, the current market value of the unit, and measures of relative housing costs. The dataset also includes variables describing the number of people living in the household, household income, and the type of residential area (e.g., urban or suburban).

The U.S. Department of Housing and Urban Development (HUD) maintains that tenants are rent burdened if more than 30 percent of household income is used for rent. Data is collected via the US Census ACS 5-year estimates.

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 sex.

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 degree of urbanisation.

This layer shows housing costs as a percentage of household income, by census tracts in the City of Los Angeles. This contains 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.

Housing affordability is a major concern for many Los Angeles County residents. Housing constitutes the single largest monthly expense for most people. Renters are more susceptible than homeowners to high housing costs, especially if they live in a community without rent control or other tenant protection policies. Compared to homeowners, renters are also more likely to experience housing burden or housing instability and have a higher risk for homelessness.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

Priority Equity Communities are census tracts in the SCAG region that have a greater concentration of populations that have been historically marginalized and are susceptible to inequitable outcomes based on several socioeconomic factors. The socioeconomic factors, or priority populations, were selected based on statutorily protected populations and refined with input gathered through outreach processes. The US Census Bureau 2017-2021 American Community Survey 5-Year estimates are used to define each of the thresholds for the priority populations. SCAG’s 2022 High Quality Transit Corridors are used in the Limited Vehicle and Transit Access criteria. This dataset uses 2020 census tracts in the SCAG region. A census tract is considered a Priority Equity Community if there is a concentration above the county average of:• BOTH low-income households and people of color; OR• EITHER low-income households or people of color AND of four or more of the following:• Vulnerable Ages • People with Disabilities• People with Limited English Proficiency• Limited Vehicle and Transit Access • People without a High School Diploma• Single Parent Households• Housing Cost Burdened HouseholdsSCAG prepared the dataset by calculating county-level averages for each criterion and removing census tracts that did not meet the criteria. For more details on the methodology or to request the detailed dataset, please contact environmentaljustice@scag.ca.gov.

Pathways to Removing Obstacles to Housing (PRO Housing) Pathways to Removing Obstacles to Housing, or PRO Housing, is a competitive grant program being administered by HUD. PRO Housing seeks to identify and remove barriers to affordable housing production and preservation.Under the Need rating factor, applicants will be awarded ten (10) points if their application primarily serves a ‘priority geography’. Priority geography means a geography that has an affordable housing need greater than a threshold calculation for one of three measures. The threshold calculation is determined by the need of the 90th-percentile jurisdiction (top 10%) for each factor as computed comparing only jurisdictions with greater than 50,000 population. Threshold calculations are done at the county and place level and applied respectively to county and place applicants. An application can also quality as a priority geography if it serves a geography that scores in the top 5% of its State for the same three measures. The measures are as follows: Affordable housing not keeping pace, measured as (change in population 2019-2009 divided by 2009 population) – (change in number of units affordable and available to households at 80% HUD Area Median Family Income (HAMFI) 2019-2009 divided by units affordable and available at 80% HAMFI 2009).Insufficient affordable housing, measured as number of households at 80% HAMFI divided by number of affordable and available units for households at 80% HAMFI. Widespread housing cost burden or substandard housing, measured as number of households with housing problems at 100% HAMFI divided by number of households at 100% HAMFI. Housing problems is defined as: cost burden of at least 50%, overcrowding, or substandard housing.Applicants may use this web application to search for priority geographies.For more information on Pro Housing, please visit: https://www.hud.gov/program_offices/comm_planning/pro_housing

The Metro Atlanta Housing Strategy is developed by the Research & Analytics Group at the Atlanta Regional Commission. The Atlanta region must offer greater access to quality, affordable housing to maintain our strong economy and high quality of life and empower residents by providing the opportunities they need to succeed. Metro Atlanta has long been an affordable place to live, helping fuel our explosive growth. We need to invest in housing in order to keep this competitive advantage and meet the needs of households across the region.Good housing options should be widely available, in communities large and small, urban and suburban. We all need places to live that won’t break our budgets while offering access to vital resources like healthy food, proximity to job centers, and quality transportation options.The trend lines are clear: housing prices are rising much faster than wages. The supply of housing isn’t keeping up with our fast-growing population, further boosting costs. More than one in three households in our region are “cost burdened” – that is, they spend more than 30% of their income on housing. A strategic, regional approach is needed to increase supply, reduce costs, and preserve affordable units. Our goal: promoting a stronger, healthier housing market that works for everyone.Download the Executive Summary