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."

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.

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.

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).

Median Gross Rent As A Percentage Of Household Income (Dollars) Report based on US Census and American Community Survey Data.

The dataset contains current data on low rent and Section 8 units in PHA's administered by HUD. The Section 8 Rental Voucher Program increases affordable housing choices for very low-income households by allowing families to choose privately owned rental housing. Through the Section 8 Rental Voucher Program, the administering housing authority issues a voucher to an income-qualified household, which then finds a unit to rent. If the unit meets the Section 8 quality standards, the PHA then pays the landlord the amount equal to the difference between 30 percent of the tenant's adjusted income (or 10 percent of the gross income or the portion of welfare assistance designated for housing) and the PHA-determined payment standard for the area. The rent must be reasonable compared with similar unassisted units.

Very Important

• Check out the new must-see kernel for this dataset Click Here
• Make Sure to upvote for more datasets and kernel :D

Overview:

Explore the dataset and potentially gain valuable insight into your data science project through interesting features. The dataset was developed for a portfolio optimization graduate project I was working on. The goal was to the monetize risk of company deleveraging by associated with changes in economic data. Applications of the dataset may include. To see the data in action visit my analytics page. Analytics Page & Dashboard and to access all 295,000+ records click here.

• Mortgage-Backed Securities
• Geographic Business Investment
• Real Estate Analysis

For any questions, you may reach us at research_development@goldenoakresearch.com. For immediate assistance, you may reach me on at 585-626-2965. Please Note: the number is my personal number and email is preferred

Statistical Themes:

Note: in total there are 75 fields the following are just themes the fields fall under Home Owner Costs: Sum of utilities, property taxes.

• Second Mortgage: Households with a second mortgage statistics.
• Home Equity Loan: Households with a Home equity Loan statistics.
• Debt: Households with any type of debt statistics.
• Mortgage Costs: Statistics regarding mortgage payments, home equity loans, utilities and property taxes
• Home Owner Costs: Sum of utilities, property taxes statistics
• Gross Rent: Contract rent plus the estimated average monthly cost of utility features
• Gross Rent as Percent of Income Gross rent as the percent of income very interesting
• High school Graduation: High school graduation statistics.
• Population Demographics: Population demographic statistics.
• Age Demographics: Age demographic statistics.
• Household Income: Total income of people residing in the household.
• Family Income: Total income of people related to the householder.

Sources, if you wish to get the data your self :)

2012-2016 ACS 5-Year Documentation was provided by the U.S. Census Reports. Retrieved May 2, 2018, from

• Census estimate, error, and location data
• Census location information

Access All 325,258 Location of Our Most Complete Database Ever:

Providing you the potential to monetize risk and optimize your investment portfolio through quality economic features at unbeatable price. Access all 295,000+ records on an incredibly small scale, see links below for more details:

• Full Dataset: View Full Dataset
• Real Estate Research: View Research

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 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.

Occupancy status, Units, Rooms, Year built, Owner/Renter (Tenure), Mortgage/Rent costs, and more. This service is updated annually with American Community Survey (ACS) 5-year data. Contact: District of Columbia, Office of Planning. Email: planning@dc.gov. Geography: District-wide. Current Vintage: 2019-2023 ACS Table(s): DP04. Data downloaded from: Census Bureau's API for American Community Survey. Date of API call: January 2, 2025. National Figures: data.census.gov. 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. 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 2020 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). Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page. Data processed using R statistical package and ArcGIS Desktop. Margin of Error was not included in this layer but is available from the Census Bureau. Contact the Office of Planning for more information about obtaining Margin of Error values.

Occupancy status, Units, Rooms, Year built, Owner/Renter (Tenure), Mortgage/Rent costs, and more. This service is updated annually with American Community Survey (ACS) 5-year data. Contact: District of Columbia, Office of Planning. Email: planning@dc.gov. Geography: 2022 Wards (State Legislative Districts [Upper Chamber])Current Vintage: 2019-2023 ACS Table(s): DP04. Data downloaded from: Census Bureau's API for American Community Survey. Date of API call: January 2, 2025. National Figures: data.census.gov. 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. 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 2020 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). Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page. Data processed using R statistical package and ArcGIS Desktop. Margin of Error was not included in this layer but is available from the Census Bureau. Contact the Office of Planning for more information about obtaining Margin of Error values.

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, 2016-2020 American Community Survey 5-Year Estimates. Downloaded April 2022.Please refer to the American Community Survey section of the U.S. Census Bureau website for detailed information about this data set.

ACS_17_5YR_DP02_state

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.