In 2021, there were approximately 21.6 million housing cost burdened renter households in the United States, with close to 12 million being severely burdened. About six million households with an annual income below 15,000 U.S. dollars were severely burdened. A household is considered to be moderately cost burdened when the housing costs exceed 30 percent of the family income. Severely burdened households, on the other hand, spend over 50 percent of their income on rent.

In 2023, 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.

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).

In 2019, there were 20.3 million cost-burdened renter households in the United States. A household is considered to be housing cost-burdened when the housing costs exceed 30 percent of the family income. California had three million cost-burdened renter households, which accounted for 51.6 percent of all renter households in the state.

In 2019, almost one quarter of all renters in the United States were considered to be severely cost-burdened, but there was variation among U.S. states. For instance, 28.2 percent of renters in Florida were severely cost-burdened, whereas 15.2 percent of North Dakota renters were considered severely cost-burdened. A household is considered to be severely cost-burdened when the rent payments exceed 50 percent of the family income.

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 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.

Which Twin Cities Metro census tracts are cost-burdened? The U.S. Department of Housing and Urban Development states that housing is “affordable” if no more than 30% of a household’s monthly income is needed for rent, mortgage payments and utilities. Households who pay more than 30% of their income on housing costs are considered cost-burdened.This map shows Median Gross Rent as a percentage of Median Household Income for Renters. Click on the census tracts to see the percentage, as well as Monthly Median Household Income for Renters and Median Gross Rent for that area.Source: American Community Survey, 2013 5-year estimates, Tables B25064 (Median Gross Rent), B25119 (Median Household Income by Tenure).Map made by CURA staff, Feb 2015.

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.

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

Displacement risk indicator showing how many households within the specified groups are facing housing cost burden (contributing more than 30% of monthly income toward housing costs).

US Census Bureau American Community Survey 2013-2017 Estimates in the Keys the Valley Region for Population, Households, Tenure, Cost Burden, Poverty, and Age of Housing Stock.The American Community Survey (ACS) is a nationwide survey designed to provide communities with reliable and timely social, economic, housing, and demographic data every year. Because the ACS is based on a sample, rather than all housing units and people, ACS estimates have a degree of uncertainty associated with them, called sampling error. In general, the larger the sample, the smaller thelevel of sampling error. Data associated with a small town will have a greater degree of error than data associated with an entire county. To help users understand the impact of sampling error on data reliability, the Census Bureau provides a “margin of error” for each published ACS estimate. The margin of error, combined with the ACS estimate, give users a range of values within which the actual “real-world” value is likely to fall.Single-year and multiyear estimates from the ACS are all “period” estimates derived from a sample collected over a period of time, as opposed to “point-in-time” estimates such as those from past decennial censuses. For example, the 2000 Census “long form” sampled the resident U.S. population as of April 1, 2000. The estimates here were derived from a sample collected over time from 2013-2017.Data Dictionary - Population, Households, Tenure, Cost Burden, Poverty, Age of Housing Stock· Population: Total Population (B01003)· Households: Total number of households (B25003)· OwnHH: Total number of owner-occupied households (B25003)· RentHH: Total number of renter-occupied households (B25003)· TotalU: Total number of housing units (B25001)· VacantU: Total number of vacant units (B25004)· SeasRecOcU: Total number of Seasonal/Recreational/Occasionally Occupied Units (B25004)· ForSale: Total number of units currently for sale (B25004)· ForRent: Total number of units currently for rent (B25004)· MedianHI: Median Household Income (B25119)· OwnHH3049: Total number of owner-occupied households spending 30-49% of their income on housing costs. (B25095)· POwnHH3049: Percentage of owner-occupied households spending 30-49% of their income on housing costs. (B25095)· OwnHH50: Total number of severely cost-burdened owner-occupied households – those spending 50% or more of their income on housing costs. (B25095)· POwnHH50: Percentage of severely cost-burdened owner-occupied households – those spending 50% or more of their income on housing costs. (B25095)· OwnHH_CB: Total number of owner-occupied, cost-burdened households - those who spend 30% or more of their income on housing costs. (B25095)· POwnHH_CB: Percentage of owner-occupied, cost-burdened households - those who spend 30% or more of their income on housing costs. (B25095)· RenHH3049: Total number of renter-occupied households spending 30-49% of their income on housing costs. (B25070)· PRenHH3049: Percentage of renter-occupied households spending 30-49% of their income on housing costs. (B25070)· RenHH50: Total number of severely cost-burdened renter-occupied households – those spending 50% or more of their income on housing costs. (B25070)· PRenHH50: Percentage of severely cost-burdened renter-occupied households – those spending 50% or more of their income on housing costs. (B25070)· RenHH_CB: Total number of renter-occupied, cost-burdened households - those who spend 30% or more of their income on housing costs. (B25070)· PRenHH_CB: Percentage of renter-occupied, cost-burdened households - those who spend 30% or more of their income on housing costs. (B25070)· Poverty: Population below poverty level. (B17001)· PPoverty: Percentage of population below poverty level. Note poverty status (above or below) is not determined for the entire population. (B17001)· MYearBuilt: Median structure year of construction. (B25035)

This map shows housing costs as a percentage of household income. Severe housing cost burden is described as when over 50% of income in a household is spent on housing costs. For renters it is over 50% of household income going towards gross rent (contract rent plus tenant-paid utilities). Miami, Florida accounts for the having the highest population of renters with severe housing burden costs.The map's topic is shown by tract and county centroids. 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. Current Vintage: 2015-2019ACS Table(s): B25070, B25091Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 10, 2020National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis map 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. 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. 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 clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2010 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. 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.

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).

US Census Bureau American Community Survey 2013-2017 Estimates in the Keys the Valley Region for Population, Households, Tenure, Cost Burden, Poverty, and Age of Housing Stock.

The American Community Survey (ACS) is a nationwide survey designed to provide communities with reliable and timely social, economic, housing, and demographic data every year. Because the ACS is based on a sample, rather than all housing units and people, ACS estimates have a degree of uncertainty associated with them, called sampling error. In general, the larger the sample, the smaller the level of sampling error. Data associated with a small town will have a greater degree of error than data associated with an entire county. To help users understand the impact of sampling error on data reliability, the Census Bureau provides a “margin of error” for each published ACS estimate. The margin of error, combined with the ACS estimate, give users a range of values within which the actual “real-world” value is likely to fall.

Single-year and multiyear estimates from the ACS are all “period” estimates derived from a sample collected over a period of time, as opposed to “point-in-time” estimates such as those from past decennial censuses. For example, the 2000 Census “long form” sampled the resident U.S. population as of April 1, 2000. The estimates here were derived from a sample collected over time from 2013-2017.

Data Dictionary - Population, Households, Tenure, Cost Burden, Poverty, Age of Housing Stock

·
Population: Total Population (B01003)

·
Households: Total number of households (B25003)

·
OwnHH: Total number of owner-occupied households (B25003)

·
RentHH: Total number of renter-occupied households (B25003)

·
TotalU: Total number of housing units (B25001)

·
VacantU: Total number of vacant units (B25004)

·
SeasRecOcU: Total number of Seasonal/Recreational/Occasionally Occupied Units (B25004)

·
ForSale: Total number of units currently for sale (B25004)

·
ForRent: Total number of units currently for rent (B25004)

·
MedianHI: Median Household Income (B25119)

·
OwnHH3049: Total number of owner-occupied households spending 30-49% of their income on housing costs. (B25095)

·
POwnHH3049: Percentage of owner-occupied households spending 30-49% of their income on housing costs. (B25095)

·
OwnHH50: Total number of severely cost-burdened owner-occupied households – those spending 50% or more of their income on housing costs. (B25095)

·
POwnHH50: Percentage of severely cost-burdened owner-occupied households – those spending 50% or more of their income on housing costs. (B25095)

·
OwnHH_CB: Total number of owner-occupied, cost-burdened households - those who spend 30% or more of their income on housing costs. (B25095)

·
POwnHH_CB: Percentage of owner-occupied, cost-burdened households - those who spend 30% or more of their income on housing costs. (B25095)

·
RenHH3049: Total number of renter-occupied households spending 30-49% of their income on housing costs. (B25070)

·
PRenHH3049: Percentage of renter-occupied households spending 30-49% of their income on housing costs. (B25070)

·
RenHH50: Total number of severely cost-burdened renter-occupied households – those spending 50% or more of their income on housing costs. (B25070)

·
PRenHH50: Percentage of severely cost-burdened renter-occupied households – those spending 50% or more of their income on housing costs. (B25070)

·
RenHH_CB: Total number of renter-occupied, cost-burdened households - those who spend 30% or more of their income on housing costs. (B25070)

·
PRenHH_CB: Percentage of renter-occupied, cost-burdened households - those who spend 30% or more of their income on housing costs. (B25070)

·
Poverty: Population below poverty level. (B17001)

·
PPoverty: Percentage of population below poverty level. Note poverty status (above or below) is not determined for the entire population. (B17001)

·
MYearBuilt: Median structure year of construction. (B25035)

This feature service contains data from the American Community Survey: 5-year Estimates Subject Tables for the greater Bozeman, MT area. The attributes come from the Financial Characteristics table (S2503). Processing Notes:Data was downloaded from the U.S. Census Bureau and imported into FME to create an AGOL Feature Service. Each attribute has been given an abbreviated alias name derived from the American Community Survey (ACS) categorical descriptions. The Data Dictionary below includes all given ACS attribute name aliases. For example: Rent_35kto50k_20to29pcnt is equal to the percentage of the population living in a renter-occupied household, with an annual household income of $35,000 to $50,000, spending between 20% to 29% of their income on housing costs in the past 12 months. Data DictionaryACS_EST_YR: American Community Survey 5-Year Estimate Subject Tables data yearGEO_ID: Census Bureau geographic identifierNAME: Specified geographyOwn: Percent of population living in an Owner-occupied householdRent: Percent of population living in a Renter-occupied householdAnnual Household Income20kto35k: Annual household income of $20,000 to $34,99935kto50k: Annual household income of $35,000 to $49,99950kto75k: Annual household income of $50,000 to $74,999Over75k: Annual household income of over $75,000Housing Cost BurdenUnder_20pcnt: Monthly housing costs under 20% of household income in the past 12 months20to29pcnt: Monthly housing costs of 20-29% of household income in the past 12 months30pcntOrMore: Monthly housing costs of over 30% of household income in the past 12 monthsDownload ACS Financial Characteristics data for the greater Bozeman, MT areaAdditional LinksU.S. Census BureauU.S. Census Bureau American Community Survey (ACS)About the American Community Survey

Information is derived from the ACS Housing Costs feature layer, which contains the most current release of data from the American Community Survey (ACS) about housing costs as a percentage of household income.

2020 data points are the average of 2019 and 2021 data points and are included solely to maintain chart continuity. The U.S. Census Bureau did not release 2020 ACS 1-year estimates due to COVID-19. These figures should not be interpreted as an actual estimate for 2020. Some racial and ethnic categories are suppressed to avoid misleading estimates when the relative standard error exceeds 30%.

Data Source: American Community Survey (ACS) 1-Year Estimates

Why This Matters Housing is a basic necessity, and affordable housing is essential for individuals and families to live and thrive in DC.The rising cost of housing threatens residents’ access to safe and stable housing as well as their ability to cover other essential expenses like food, transportation, and childcare.Racial segregation, housing discrimination, and racist urban renewal programs, among other policies and practices, have meant that Black residents and residents of color in the District disproportionately experience the effects of rapidly rising housing costs. The District's Response Leading the nation in policies and investments for low-income rental households. Target of 12,000 new affordable housing units by 2025. Steps taken to preserve and expand affordable housing include the Housing Production Trust Fund, the Affordable Housing Preservation Fund, and the Home Purchasing Assistance Program, among others.

Aggregated tenant characteristics for USDA Rural Development Multifamily Direct Loan programs: Section 515 Rural Rental Housing and Section 514 Farm Labor Housing. Includes property address and aggregated demographic information including female headed-households, elderly aged 62 or older, minors, disability status, race, and ethnicity. Also includes average annual income, average annual income by source of income, cost-burden indicator, zero income indicator, and rental assistance subsidy counts by type of assistance. Can be merged with “USDA Rural Development Multifamily Section 515 Rural Rental Housing and Section 514 Farm Labor Housing Property Characteristics” to link to property characteristics, as well as “USDA Rural Development Multifamily Section 515 Rural Rental Housing and Section 514 Farm Labor Housing Properties Transfers, Consolidations, and Sales” to link with property transaction histories.

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.