SELECTED HOUSING CHARACTERISTICS GROSS RENT AS PERCENTAGE OF INCOME - DP04 Universe - Occupied units paying rent Survey-Program - American Community Survey 5-year estimates Years - 2020, 2021, 2022 Gross rent as a percentage of household income is a computed ratio of monthly gross rent to monthly household income (total household income divided by 12). The ratio is computed separately for each unit and is rounded to the nearest tenth. Units for which no rent is paid and units occupied by households that reported no income or a net loss comprise the category “Not computed."

Graph and download economic data for Consumer Price Index for All Urban Consumers: Rent of Primary Residence in U.S. City Average (CUUR0000SEHA) from Dec 1914 to May 2025 about primary, rent, urban, consumer, CPI, inflation, price index, indexes, price, and USA.

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.

Household Income By Gross Rent As A Percentage Of Household Income Report based on US Census and American Community Survey Data.

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).

The American Community Survey (ACS) is a nationwide survey conducted by the U.S. Census Bureau that is designed to provide communities a fresh look at how they are changing. It is a critical element in the Census Bureau's reengineered decennial census program, incorporating the detailed socioeconomic and housing questions that were previously asked on the decennial census long form into the ACS questionnaire. The ACS now collects and produces this detailed population and housing information every year instead of every ten years. Data are collected on an on-going basis throughout the year and are released each year for large geographic areas, those with 65,000 persons or more. However, sample sizes are not large enough for annual releases that cover smaller areas, those with less than 65,000 persons. Data that are suitable for areas with 20,000 to 65,000 persons are accumulated over three years and termed a three-year period estimate, the first of which was for the 2005-2007 period. Data that are suitable for areas with less than 20,000 persons are accumulated over five years and termed a five-year period estimate, the first of which was for the 2005-2009 period. The data in this series of RGIS Clearinghouse tables are for all New Mexico counties and are based on the 2005-2009 ACS Five-Year Period Estimates collected between January 2005 and December 2009. These data tables are a summary of all major housing topics published through the ACS, providing information about the condition of housing, and illuminating various financial characteristics of the housing stock. Major topics include housing occupancy, year structure built, rooms and bedrooms, housing tenure (owners and renters), year householder moved into unit, vehicles available, type of house heating fuel, units without complete plumbing and kitchen facilities or without telephone service, occupants per room, home value, mortgage status, monthly owner costs, owner costs as a percentage of household income, gross rent, and gross rent as a percentage of household income. Percentages are shown along with numeric estimates for most data items. Because the data are based on a sample the Census Bureau also provides information about the magnitude of sampling error. Consequently, the estimated margin of error (MOE) is shown next to each data item. Each housing topic is covered in a separate file in both Excel and CSV formats. These files, along with file-specific descriptions (in Word and text formats) are available in a single zip file.

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 right. Current Vintage: 2019-2023ACS Table(s): B25070, B25091 Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis 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 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.

Los Angeles Index of Displacement PressureThe Los Angeles Index of Displacement Pressure combines measures that past research efforts and our own original research have shown correlate with future change and displacement pressure. Created in 2015/2016, the index primarily uses data from 2012-2015.These seven measures are applied at the Census Tract level for tracts where >=40% of households earn less than the City's median income. The measures are grouped into two classes: change factors and displacement pressure factors.Change factor measures are those that suggest future revitalization is likely due to investment, projected housing price gains, and proximity to recently changed areas. On the other hand, displacement pressure factors capture areas with a high concentration of existing residents who may have difficulty absorbing massive rent increases that often accompany revitalization. The Los Angeles Index of Displacement Pressure captures the intersection between these two classes.Change Measures Transportation InvestmentMeasure 1: Distance to current rail stations (within a 1/2 mile radius. Tracts beyond 1/2 mile receive no score for this measure). Source: LA MetroMeasure 2: Distance to rail stations under construction/recently opened in 2016 (within a 1/2 mile radius. Tracts beyond 1/2 mile receive no score for this measure)Source: LA Metro Proximity to Rapidly Changing NeighborhoodsMeasure 3: Distance to the closest "top tier" changing neighborhood, as defined by the Los Angeles Index of Neighborhood Change (within a 1 mile radius. Tracts beyond 1 mile receive no score for this measure)Source: The Los Angeles Index of Neighborhood Change Housing MarketMeasure 4: Change in housing price projections from 2015 to 2020 Source: ESRI Community Analyst Displacement Pressure FactorsMeasure 5: Percent of households that rentSource: American Community Survey, Five-Year Estimate, 2014Measure 6: Percent of households that are extremely rent burdened (pay >=50% of household income on rent)Source: American Community Survey, Five-Year Estimate, 2014Measure 7: The number of affordable properties and housing units that are due to expire by 2023.Source: The Los Angeles Housing Element, 2012Date updated: April 7, 2018Refresh rate: Never - Historical data

The American Community Survey 5-year Data Profile (DP04) of Selected Housing Characteristics was downloaded from the U.S. Census Bureau for state, county, place, reservation, house district, senate district and tract geographies in the state of Montana.Selected housing characteristics in this data set include: HOUSING OCCUPANCY, UNITS IN STRUCTURE, YEAR STRUCTURE BUILT, ROOMS, BEDROOMS, HOUSING TENURE, YEAR HOUSEHOLDER MOVED INTO UNIT, VEHICLES AVAILABLE, HOUSE HEATING FUEL, SELECTED CHARACTERISTICS, OCCUPANTS PER ROOM, VALUE, MORTGAGE STATUS, SELECTED MONTHLY OWNER COSTS (SMOC), SELECTED MONTHLY OWNER COSTS AS A PERCENTAGE OF HOUSEHOLD INCOME (SMOCAPI), GROSS RENT, GROSS RENT AS A PERCENTAGE OF HOUSEHOLD INCOME (GRAPI). Source: U.S. Census Bureau, 2017-2021 American Community Survey 5-Year Estimates. Downloaded December 2022.Please refer to the American Community Survey section of the U.S. Census Bureau website for detailed information about this data set.

The American Community Survey 5-year Data Profile (DP04) of Selected Housing Characteristics was downloaded from the U.S. Census Bureau for state, county, place, reservation, house district, senate district and tract geographies in the state of Montana.Selected housing characteristics in this data set include: HOUSING OCCUPANCY, UNITS IN STRUCTURE, YEAR STRUCTURE BUILT, ROOMS, BEDROOMS, HOUSING TENURE, YEAR HOUSEHOLDER MOVED INTO UNIT, VEHICLES AVAILABLE, HOUSE HEATING FUEL, SELECTED CHARACTERISTICS, OCCUPANTS PER ROOM, VALUE, MORTGAGE STATUS, SELECTED MONTHLY OWNER COSTS (SMOC), SELECTED MONTHLY OWNER COSTS AS A PERCENTAGE OF HOUSEHOLD INCOME (SMOCAPI), GROSS RENT, GROSS RENT AS A PERCENTAGE OF HOUSEHOLD INCOME (GRAPI). Source: U.S. Census Bureau, 2015-2019 American Community Survey 5-Year Estimates. Downloaded December 2020.Please refer to the American Community Survey section of the U.S. Census Bureau website for detailed information about this data set.