Out of a total of *** million housing units in New York City in 2021, approximately ******* homes had housing costs between ** and ** percent of the household budget. New York City is notoriously known for its shortage of affordable housing: Overall, for a large percentage of New York City residents, housing costs exceeded ** percent.

This map shows households that spend 30 percent or more of their income on housing, a threshold widely used by many affordable housing advocates and official government sources including Housing and Urban Development. Census asks about income and housing costs to understand whether housing is affordable in local communities. When housing is not sufficient or not affordable, income data helps communities: Enroll eligible households in programs designed to assist them.Qualify for grants from the Community Development Block Grant (CDBG), HOME Investment Partnership Program, Emergency Solutions Grants (ESG), Housing Opportunities for Persons with AIDS (HOPWA), and other programs.When rental housing is not affordable, the Department of Housing and Urban Development (HUD) uses rent data to determine the amount of tenant subsidies in housing assistance programs.Map opens in Atlanta. Use the bookmarks or search bar to view other cities. Data is symbolized to show the relationship between burdensome housing costs for owner households with a mortgage and renter households:This map uses these hosted feature layers containing the most recent American Community Survey data. These layers are part of the ArcGIS Living Atlas, and are updated every year when the American Community Survey releases new estimates, so values in the map always reflect the newest data available.

In 2023, public housing residents in Alaska, Arkansas, and the U.S. Virgin Islands had the highest household incomes compared to their respective local median incomes in the United States. In these areas, the average public housing household incomes constituted at least 32 percent of the local median income. In contrast, states like Maryland, Ohio, Washington, Guam, and the District of Columbia exhibited the lowest proportions, where households housed in social housing earned less than 20 percent of the local median income.

The house price-to-income ratio in the United States has reached concerning levels, with the index hitting ***** in the second quarter of 2025. This indicates that house prices have outpaced income growth by *****percent since 2015, highlighting a growing affordability crisis in the housing market. The widening gap between home prices and wages is putting homeownership out of reach for many Americans, particularly as real wages have remained stagnant. Rising home prices and stagnant wages While average annual real wages in the United States have increased slightly since 2014, home prices have soared. The median sales price of existing single-family homes reached a record-high in 2024, representing a substantial increase over the past five years. This disparity between wage growth and home price appreciation has led to a significant decrease in housing affordability across the country. Affordability challenges in the U.S. housing market The U.S. Housing Affordability Index, which measures whether a family earning the median income can afford a median-priced home, plummeted in 2024, marking the second-worst year for homebuyers since records began. This decline in affordability is reflected in homebuyer sentiment, with homebuyer sentiment plummeting.

This dataset contains information about the percent of income households spend on housingin cities in San Mateo County. This data is for owner occupied housing with or without a mortgage. This data was extracted from the United States Census Bureau's American Community Survey 2014 5 year estimates.

These Socioeconomic Indicators are from the American Community Survey, 2014 5-year estimates. They are at Zip Code level for Oakland, Macomb and Wayne Counties.

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.

The Housing Affordability Index value in the United States plummeted in 2022, surpassing the historical record of ***** index points in 2006. In 2024, the housing affordability index measured **** index points, making it the second-worst year for homebuyers since the start of the observation period. What does the Housing Affordability Index mean? The Housing Affordability Index uses data provided by the National Association of Realtors (NAR). It measures whether a family earning the national median income can afford the monthly mortgage payments on a median-priced existing single-family home. An index value of 100 means that a family has exactly enough income to qualify for a mortgage on a home. The higher the index value, the more affordable a house is to a family. Key factors that drive the real estate market Income, house prices, and mortgage rates are some of the most important factors influencing homebuyer sentiment. When incomes increase, consumer power also increases. The median household income in the United States declined in 2022, affecting affordability. Additionally, mortgage interest rates have soared, adding to the financial burden of homebuyers. The sales price of existing single-family homes in the U.S. has increased year-on-year since 2011 and reached ******* U.S. dollars in 2023.

Graph and download economic data for Other Financial Information: Estimated Market Value of Owned Home by Deciles of Income Before Taxes: Fifth 10 Percent (41st to 50th Percentile) (CXU800721LB1506M) from 2014 to 2023 about owned, market value, information, percentile, estimate, tax, financial, income, housing, and USA.

Graph and download economic data for Expenditures: Housing by Deciles of Income Before Taxes: Lowest 10 Percent (1st to 10th Percentile) (CXUHOUSINGLB1502M) from 2014 to 2023 about percentile, tax, expenditures, income, housing, and USA.

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.

Egypt Average Household Income: Percentage Distribution: Urban: Estimated Rental Value of Dwelling data was reported at 10.200 % in 2015. This records a decrease from the previous number of 10.600 % for 2013. Egypt Average Household Income: Percentage Distribution: Urban: Estimated Rental Value of Dwelling data is updated yearly, averaging 10.200 % from Jun 2005 (Median) to 2015, with 5 observations. The data reached an all-time high of 10.800 % in 2011 and a record low of 6.600 % in 2005. Egypt Average Household Income: Percentage Distribution: Urban: Estimated Rental Value of Dwelling data remains active status in CEIC and is reported by Central Agency for Public Mobilization and Statistics. The data is categorized under Global Database’s Egypt – Table EG.H012: Average Household Income.

Key Table Information.Table Title.Housing Costs as a Percentage of Household Income in the Past 12 Months (Some Other Race Alone Householder).Table ID.ACSDT1Y2024.B25140F.Survey/Program.American Community Survey.Year.2024.Dataset.ACS 1-Year Estimates Detailed Tables.Source.U.S. Census Bureau, 2024 American Community Survey, 1-Year Estimates.Dataset Universe.The dataset universe of the American Community Survey (ACS) is the U.S. resident population and housing. For more information about ACS residence rules, see the ACS Design and Methodology Report. Note that each table describes the specific universe of interest for that set of estimates..Methodology.Unit(s) of Observation.American Community Survey (ACS) data are collected from individuals living in housing units and group quarters, and about housing units whether occupied or vacant. For more information about ACS sampling and data collection, see the ACS Design and Methodology Report..Geography Coverage.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year.Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Sampling.The ACS consists of two separate samples: housing unit addresses and group quarters facilities. Independent housing unit address samples are selected for each county or county-equivalent in the U.S. and Puerto Rico, with sampling rates depending on a measure of size for the area. For more information on sampling in the ACS, see the Accuracy of the Data document..Confidentiality.The Census Bureau has modified or suppressed some estimates in ACS data products to protect respondents' confidentiality. Title 13 United States Code, Section 9, prohibits the Census Bureau from publishing results in which an individual's data can be identified. For more information on confidentiality protection in the ACS, see the Accuracy of the Data document..Technical Documentation/Methodology.Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section.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 roughly 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 ACS Technical Documentation). The effect of nonsampling error is not represented in these tables.Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..Weights.ACS estimates are obtained from a raking ratio estimation procedure that results in the assignment of two sets of weights: a weight to each sample person record and a weight to each sample housing unit record. Estimates of person characteristics are based on the person weight. Estimates of family, household, and housing unit characteristics are based on the housing unit weight. For any given geographic area, a characteristic total is estimated by summing the weights assigned to the persons, households, families or housing units possessing the characteristic in the geographic area. For more information on weighting and estimation in the ACS, see the Accuracy of the Data document.Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the ...

The European Union Statistics on Income and Living Conditions (EU-SILC) collects timely and comparable multidimensional microdata on income, poverty, social exclusion and living conditions.

The EU-SILC collection is a key instrument for providing information required by the European Semester (_[1]) and the European Pillar of Social Rights, and the main source of data for microsimulation purposes and flash estimates of income distribution and poverty rates.

AROPE remains crucial to monitor European social policies, especially to monitor the EU 2030 target on poverty and social exclusion. For more information, please consult EU social indicators.

The EU-SILC instrument provides two types of data:

• Cross-sectional data pertaining to a given time or a certain time period with variables on income, poverty, social exclusion and other living conditions.
• Longitudinal data pertaining to individual-level changes over time, observed periodically over four‐or more year rotation scheme (Annex III (2) of 2019/1700).

EU-SILC collects:

• annual variables,
• three-yearly modules,
• six-yearly modules,
• ad-hoc new policy needs modules,
• optional variables.

The variables collected are grouped by topic and detailed topic and transmitted to Eurostat in four main files (D-File, H-File, R-File and P-file).

The domain ‘Income and Living Conditions’ covers the following topics: persons at risk of poverty or social exclusion, income inequality, income distribution and monetary poverty, living conditions, material deprivation, and EU-SILC ad-hoc modules, which are structured into collections of indicators on specific topics.

In 2023, in addition to annual data, in EU-SILC were collected: the three yearly module on labour market and housing, the six yearly module on intergenerational transmission of advantages and disadvantages, housing difficulties, and the ad hoc subject on households energy efficiency.

Starting from 2021 onwards, the EU quality reports use the structure of the Single Integrated Metadata Structure (SIMS).

_{([1]) The European Semester is the European Union’s framework for the coordination and surveillance of economic and social policies.}

Housing Costs As Percent Of Income in San Mateo County, from ACS- DP04 2012

ACS, 2012, 2010-2012, 3 year estimates, DP04

Income limits used to determine the income eligibility of applicants for assistance under three programs authorized by the National Housing Act. These programs are the Section 221(d)(3) Below Market Interest Rate (BMIR) rental program, the Section 235 program, and the Section 236 program. These income limits are listed by dollar amount and family size, and they are effective on the date issued. Due to the Housing and Economic Recovery Act of 2008 (Public Law 110-289), Income Limits used to determine qualification levels as well as set maximum rental rates for projects funded with tax credits authorized under section 42 of the Internal Revenue Code (the Code) and projects financed with tax exempt housing bonds issued to provide qualified residential rental development under section 142 of the Code (hereafter referred to as Multifamily Tax Subsidy Projects (MTSPs)) are now calculated and presented separately from the Section 8 income limits.

Percentage of Households: One Family: Couple: Source of Income: Zero or Negative Income data was reported at 0.700 % in 2020. This records an increase from the previous number of 0.500 % for 2018. Percentage of Households: One Family: Couple: Source of Income: Zero or Negative Income data is updated yearly, averaging 0.450 % from Jun 2003 (Median) to 2020, with 10 observations. The data reached an all-time high of 0.800 % in 2003 and a record low of 0.400 % in 2016. Percentage of Households: One Family: Couple: Source of Income: Zero or Negative Income data remains active status in CEIC and is reported by Australian Bureau of Statistics. The data is categorized under Global Database’s Australia – Table AU.H040: Survey of Income and Housing: Percentage of Households: by Source of Income.

The rise in housing prices impacts households differently depending on their income. In Greece, households with incomes below the median spent over ** percent of their disposable income on housing, while those with incomes above the median spent around ** percent.

Tenure by Housing Costs as a Percentage of Household Income in the Past 12 Months County and State values are from the American Community Survey (ACS) 1 Year Survey

Tenure by Housing Costs as a Percentage of Household Income in the Past 12 Months County and State values are from the ACS 1 Year Survey (B25106_001E,B25106_006E,B25106_010E,B25106_014E,B25106_018E,B25106_022E,B25106_028E,B25106_032E,B25106_036E,B25106_040E,B25106_044E)