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).

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.

Displacement risk indicator showing how many households within the specified groups are facing severely housing cost burden (contributing more than 50% of monthly income toward housing costs).

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.

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.

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.

Analysis of ‘Rent Burden Greater than 50%’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/98bb7b6a-079b-4659-af21-27e5e167c432 on 27 January 2022.

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

Displacement risk indicator showing how many households within the specified groups are facing severely housing cost burden (contributing more than 50% of monthly income toward housing costs).

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

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

Analysis of ‘Rent Burden Greater than 30%’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/b00a909f-0194-420c-8595-65f4b2e539b2 on 27 January 2022.

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

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) .

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

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

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.

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.

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).

This U.S. Census Bureau American Community Survey (ACS) five-year estimates data set includes information about rent cost burden levels, calculated as gross rent as a percentage of household income in the past 12 months, in a number of geographic areas ranging from statewide to census tract. The data set includes median gross rent data from 2009-2016.

In 2019, almost half of all renters in the United States were considered to be either moderately or severely cost-burdened, but there was variation among U.S. states. For instance, ** percent of renters in Florida were cost-burdened, whereas **** percent of North Carolina renters were considered cost-burdened. A household is considered to be housing cost burdened when the housing costs exceed ** percent of the family income.

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.

Households by burden of housing costs (mortgage or rent, heating, electricity, etc.) by size of the household. National.

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Data from the U.S. Department of Housing and Urban Development Office of Policy Development and Research (HUD PD&R) and American Community Survey provided by the Urban Institute. This metric reports the share of low-income households at three income levels, low-income (below 80 percent of area median income, or AMI), very low-income (below 50 percent of AMI), and extremely low-income (below 30 percent of AMI), that spend more than half (>50%) of their household income on rent.