In 2023, the real median household income for householders aged 15 to 24 was at 54,930 U.S. dollars. The highest median household income was found amongst those aged between 45 and 54. Household median income for the United States since 1990 can be accessed here.

In 2023, just over 50 percent of Americans had an annual household income that was less than 75,000 U.S. dollars. The median household income was 80,610 U.S. dollars in 2023. Income and wealth in the United States After the economic recession in 2009, income inequality in the U.S. is more prominent across many metropolitan areas. The Northeast region is regarded as one of the wealthiest in the country. Maryland, New Jersey, and Massachusetts were among the states with the highest median household income in 2020. In terms of income by race and ethnicity, the average income of Asian households was 94,903 U.S. dollars in 2020, while the median income for Black households was around half of that figure. What is the U.S. poverty threshold? The U.S. Census Bureau annually updates its list of poverty levels. Preliminary estimates show that the average poverty threshold for a family of four people was 26,500 U.S. dollars in 2021, which is around 100 U.S. dollars less than the previous year. There were an estimated 37.9 million people in poverty across the United States in 2021, which was around 11.6 percent of the population. Approximately 19.5 percent of those in poverty were Black, while 8.2 percent were white.

Graph and download economic data for Share of Net Worth Held by the Top 0.1% (99.9th to 100th Wealth Percentiles) (WFRBSTP1300) from Q3 1989 to Q2 2025 about shares, net worth, wealth, percentile, Net, and USA.

In 2023, about 26.9 percent of Asian private households in the U.S. had an annual income of 200,000 U.S. dollars and more. Comparatively, around 13.9 percent of Black households had an annual income under 15,000 U.S. dollars.

Graph and download economic data for Share of Net Worth Held by the Top 1% (99th to 100th Wealth Percentiles) (WFRBST01134) from Q3 1989 to Q1 2025 about net worth, wealth, percentile, Net, and USA.

Graph and download economic data for Median Personal Income in the United States (MEPAINUSA646N) from 1974 to 2024 about personal income, personal, median, income, and USA.

This statistic shows the median household income in the United States from 1990 to 2023 in 2023 U.S. dollars. The median household income was 80,610 U.S. dollars in 2023, an increase from the previous year. Household incomeThe median household income depicts the income of households, including the income of the householder and all other individuals aged 15 years or over living in the household. Income includes wages and salaries, unemployment insurance, disability payments, child support payments received, regular rental receipts, as well as any personal business, investment, or other kinds of income received routinely. The median household income in the United States varies from state to state. In 2020, the median household income was 86,725 U.S. dollars in Massachusetts, while the median household income in Mississippi was approximately 44,966 U.S. dollars at that time. Household income is also used to determine the poverty line in the United States. In 2021, about 11.6 percent of the U.S. population was living in poverty. The child poverty rate, which represents people under the age of 18 living in poverty, has been growing steadily over the first decade since the turn of the century, from 16.2 percent of the children living below the poverty line in year 2000 to 22 percent in 2010. In 2021, it had lowered to 15.3 percent. The state with the widest gap between the rich and the poor was New York, with a Gini coefficient score of 0.51 in 2019. The Gini coefficient is calculated by looking at average income rates. A score of zero would reflect perfect income equality and a score of one indicates a society where one person would have all the money and all other people have nothing.

This statistic shows the average annual total money earnings of individuals in the United States in 2023, by age group. In 2023, the average worker in the United States aged 45 to 54 earned an average of ****** U.S. dollars per year. That made ** to 54-year-olds the highest earning age group, on average, in 2023.

The Distributional Financial Accounts (DFAs) provide a quarterly measure of the distribution of U.S. household wealth since 1989, based on a comprehensive integration of disaggregated household-level wealth data with official aggregate wealth measures. The data set contains the level and share of each balance sheet item on the Financial Accounts' household wealth table (Table B.101.h), for various sub-populations in the United States. In our core data set, aggregate household wealth is allocated to each of four percentile groups of wealth: the top 1 percent, the next 9 percent (i.e., 90th to 99th percentile), the next 40 percent (50th to 90th percentile), and the bottom half (below the 50th percentile). Additionally, the data set contains the level and share of aggregate household wealth by income, age, generation, education, and race. The quarterly frequency makes the data useful for studying the business cycle dynamics of wealth concentration--which are typically difficult to observe in lower-frequency data because peaks and troughs often fall between times of measurement. These data will be updated about 10 or 11 weeks after the end of each quarter, making them a timely measure of the distribution of wealth.

In the first quarter of 2025, almost two-thirds percent of the total wealth in the United States was owned by the top 10 percent of earners. In comparison, the lowest 50 percent of earners only owned 2.5 percent of the total wealth. Income inequality in the U.S. Despite the idea that the United States is a country where hard work and pulling yourself up by your bootstraps will inevitably lead to success, this is often not the case. In 2023, 7.4 percent of U.S. households had an annual income under 15,000 U.S. dollars. With such a small percentage of people in the United States owning such a vast majority of the country’s wealth, the gap between the rich and poor in America remains stark. The top one percent The United States was the country with the most billionaires in the world in 2025. Elon Musk, with a net worth of 342 billion U.S. dollars, was among the richest people in the United States in 2025. Over the past 50 years, the CEO-to-worker compensation ratio has exploded, causing the gap between rich and poor to grow, with some economists theorizing that this gap is the largest it has been since right before the Great Depression.

Graph and download economic data for Minimum Wealth Cutoff for the Top 0.1% (99.9th to 100th Wealth Percentiles) (WFRBLTP1311) from Q3 1989 to Q3 2022 about wealth, percentile, and USA.

The poly shapefile shows income levels needed to support a family of four living in low-cost areas withing the states. It also shows percent income above the average poverty levels (Fed poverty level $20,650)for the U.S.

Projections of the Population (against the 1990 Census), By Age 65+ year old at individual State level: 1995 to 2025. Data provided by Census although I added calculations for percent change. (Numbers in thousands. Resident population. Series A projections. For more details, see Population Paper Listings #47, "Population Projections for States, by Age, Sex, Race, and Hispanic Origin: 1995 to 2025.")

Beta Notice: This item is currently in beta and is intended for early access, testing, and feedback. It is not recommended for production use, as functionality and content are subject to change without notice.This map shows median age in the US by country, state, county, tract, and congressional district for 2023. ArcGIS Online account required for use.The pop-up is configured to show median age, median age by sex, child age (under 18) population, senior age (over 65) population, the age dependency ratio, and population by 5 year age increments. Blending is used at the Tract level to highlight areas of human settlement. Congressional district is turned off by default and can be enabled in the Layers pane. Esri 2023 Age Dependency Ratio is the estimated ratio of the child population (Age 0-17) and senior population (Age 65+) to the working-age population (Age 18-64) in the geographic area. This ratio is then multiplied by 100. Higher ratios denote that a greater burden is carried by working-age people. Lower ratios mean more people are working who can support the dependent population. Read more. See Updated Demographics for more information on Esri Demographic variables.Esri Updated Demographics represent the suite of annually updated U.S. demographic data that provides current-year and five-year forecasts for more than two thousand demographic and socioeconomic characteristics, a subset of which is included in this layer. Included are a host of tables covering key characteristics of the population, households, housing, age, race, income, and much more. Esri's Updated Demographics data consists of point estimates, representing July 1 of the current and forecast years. Get started with U.S. Updated DemographicsHow to use and interpret U.S. Updated DemographicsEsri Updated Demographics DocumentationMethodologyEssential Esri Demographics vocabulary This ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. This layer requires an ArcGIS Online subscription and does not consume credits. Please cite Esri when using this data. For information about purchasing additional Esri's Updated Demographics data, contact datasales@esri.com. Fields available:GEOIDNameState NameState Abbreviation2023 Total Population (Esri)2023 Household Population (Esri)2023 Group Quarters Population (Esri)2023 Population Density (Pop per Square Mile) (Esri)2023 Total Households (Esri)2023 Average Household Size (Esri)2023 Total Housing Units (Esri)2023 Owner Occupied Housing Units (Esri)2023 Renter Occupied Housing Units (Esri)2023 Vacant Housing Units (Esri)2020-2023 Population: Compound Annual Growth Rate (Esri)2020-2023 Households: Compound Annual Growth Rate (Esri)2023 Housing Affordability Index (Esri)2023 Percent of Income for Mortgage (Esri)2023 Wealth Index (Esri)2023 Socioeconomic Status Index (Esri)2023 Generation Alpha Population (Born 2017 or Later) (Esri)2023 Generation Z Population (Born 1999 to 2016) (Esri)2023 Millennial Population (Born 1981 to 1998) (Esri)2023 Generation X Population (Born 1965 to 1980) (Esri)2023 Baby Boomer Population (Born 1946 to 1964) (Esri)2023 Silent & Greatest Generations Population (Born 1945/Earlier) (Esri)2023 Population by Generation Base (Esri)2023 Child Population (Age <18) (Esri)2023 Working-Age Population (Age 18-64) (Esri)2023 Senior Population (Age 65+) (Esri)2023 Child Dependency Ratio (Esri)2023 Age Dependency Ratio (Esri)2023 Senior Dependency Ratio (Esri)2023 Total Population Age 0-4 (Esri)2023 Total Population Age 5-9 (Esri)2023 Total Population Age 10-14 (Esri)2023 Total Population Age 15-19 (Esri)2023 Total Population Age 20-24 (Esri)2023 Total Population Age 25-29 (Esri)2023 Total Population Age 30-34 (Esri)2023 Total Population Age 35-39 (Esri)2023 Total Population Age 40-44 (Esri)2023 Total Population Age 45-49 (Esri)2023 Total Population Age 50-54 (Esri)2023 Total Population Age 55-59 (Esri)2023 Total Population Age 60-64 (Esri)2023 Total Population Age 65-69 (Esri)2023 Total Population Age 70-74 (Esri)2023 Total Population Age 75-79 (Esri)2023 Total Population Age 80-84 (Esri)2023 Total Population Age 85+ (Esri)2023 Median Age (Esri)2023 Male Population (Esri)2023 Median Male Age (Esri)2023 Female Population (Esri)2023 Median Female Age (Esri)2023 Total Population by Five-Year Age Base (Esri)2023 Total Daytime Population (Esri)2023 Daytime Population: Workers (Esri)2023 Daytime Population: Residents (Esri)2023 Daytime Population Density (Pop per Square Mile) (Esri)2023 Civilian Population Age 16+ in Labor Force (Esri)2023 Employed Civilian Population Age 16+ (Esri)2023 Unemployed Population Age 16+ (Esri)2023 Unemployment Rate (Esri)2023 Civilian Population 16-24 in Labor Force (Esri)2023 Employed Civilian Population Age 16-24 (Esri)2023 Unemployed Population Age 16-24 (Esri)2023 Unemployment Rate: Population Age 16-24 (Esri)2023 Civilian Population 25-54 in Labor Force (Esri)2023 Employed Civilian Population Age 25-54 (Esri)2023 Unemployed Population Age 25-54 (Esri)2023 Unemployment Rate: Population Age 25-54 (Esri)2023 Civilian Population 55-64 in Labor Force (Esri)2023 Employed Civilian Population Age 55-64 (Esri)2023 Unemployed Population Age 55-64 (Esri)2023 Unemployment Rate: Population Age 55-64 (Esri)2023 Civilian Population 65+ in Labor Force (Esri)2023 Employed Civilian Population Age 65+ (Esri)2023 Unemployed Population Age 65+ (Esri)2023 Unemployment Rate: Population Age 65+ (Esri)2023 Child Economic Dependency Ratio (Esri)2023 Working-Age Economic Dependency Ratio (Esri)2023 Senior Economic Dependency Ratio (Esri)2023 Economic Dependency Ratio (Esri)2023 Hispanic Population (Esri)2023 White Non-Hispanic Population (Esri)2023 Black/African American Non-Hispanic Population (Esri)2023 American Indian/Alaska Native Non-Hispanic Population (Esri)2023 Asian Non-Hispanic Population (Esri)2023 Pacific Islander Non-Hispanic Population (Esri)2023 Other Race Non-Hispanic Population (Esri)2023 Multiple Races Non-Hispanic Population (Esri)2023 Diversity Index (Esri)2023 Population by Race Base (Esri)2023 Population Age 25+: Less than 9th Grade (Esri)2023 Population Age 25+: 9-12th Grade/No Diploma (Esri)2023 Population Age 25+: High School Diploma (Esri)2023 Population Age 25+: GED/Alternative Credential (Esri)2023 Population Age 25+: Some College/No Degree (Esri)2023 Population Age 25+: Associate's Degree (Esri)2023 Population Age 25+: Bachelor's Degree (Esri)2023 Population Age 25+: Graduate/Professional Degree (Esri)2023 Educational Attainment Base (Pop 25+)(Esri)2023 Household Income less than $15,000 (Esri)2023 Household Income $15,000-$24,999 (Esri)2023 Household Income $25,000-$34,999 (Esri)2023 Household Income $35,000-$49,999 (Esri)2023 Household Income $50,000-$74,999 (Esri)2023 Household Income $75,000-$99,999 (Esri)2023 Household Income $100,000-$149,999 (Esri)2023 Household Income $150,000-$199,999 (Esri)2023 Household Income $200,000 or greater (Esri)2023 Median Household Income (Esri)2023 Average Household Income (Esri)2023 Per Capita Income (Esri)2023 Households by Income Base (Esri)2023 Gini Index (Esri)2023 P90-P10 Ratio of Income Inequality (Esri)2023 P90-P50 Ratio of Income Inequality (Esri)2023 P50-P10 Ratio of Income Inequality (Esri)2023 80-20 Share Ratio of Income Inequality (Esri)2023 90-40 Share Ratio of Income Inequality (Esri)2023 Households in Low Income Tier (Esri)2023 Households in Middle Income Tier (Esri)2023 Households in Upper Income Tier (Esri)2023 Disposable Income less than $15,000 (Esri)2023 Disposable Income $15,000-$24,999 (Esri)2023 Disposable Income $25,000-$34,999 (Esri)2023 Disposable Income $35,000-$49,999 (Esri)2023 Disposable Income $50,000-$74,999 (Esri)2023 Disposable Income $75,000-$99,999 (Esri)2023 Disposable Income $100,000-$149,999 (Esri)2023 Disposable Income $150,000-$199,999 (Esri)2023 Disposable Income $200,000 or greater (Esri)2023 Median Disposable Income (Esri)2023 Home Value less than $50,000 (Esri)2023 Home Value $50,000-$99,999 (Esri)2023 Home Value $100,000-$149,999 (Esri)2023 Home Value $150,000-$199,999 (Esri)2023 Home Value $200,000-$249,999 (Esri)2023 Home Value $250,000-$299,999 (Esri)2023 Home Value $300,000-$399,999 (Esri)2023 Home Value $400,000-$499,999 (Esri)2023 Home Value $500,000-$749,999 (Esri)2023 Home Value $750,000-$999,999 (Esri)2023 Home Value $1,000,000-$1,499,999 (Esri)2023 Home Value $1,500,000-$1,999,999 (Esri)2023 Home Value $2,000,000 or greater (Esri)2023 Median Home Value (Esri)2023 Average Home Value (Esri)2028 Total Population (Esri)2028 Household Population (Esri)2028 Population Density (Pop per Square Mile) (Esri)2028 Total Households (Esri)2028 Average Household Size (Esri)2023-2028 Population: Compound Annual Growth Rate (Esri)2023-2028 Households: Compound Annual Growth Rate (Esri)2023-2028 Per Capita Income: Compound Annual Growth Rate (Esri)2023-2028 Median Household Income: Compound Annual Growth Rate (Esri)2028 Diversity Index (Esri)2028 Median Household Income (Esri)2028 Average Household Income (Esri)2028 Per Capita Income (Esri)

U.S. citizens with a professional degree had the highest median household income in 2023, at 172,100 U.S. dollars. In comparison, those with less than a 9th grade education made significantly less money, at 35,690 U.S. dollars. Household income The median household income in the United States has fluctuated since 1990, but rose to around 70,000 U.S. dollars in 2021. Maryland had the highest median household income in the United States in 2021. Maryland’s high levels of wealth is due to several reasons, and includes the state's proximity to the nation's capital. Household income and ethnicity The median income of white non-Hispanic households in the United States had been on the rise since 1990, but declining since 2019. While income has also been on the rise, the median income of Hispanic households was much lower than those of white, non-Hispanic private households. However, the median income of Black households is even lower than Hispanic households. Income inequality is a problem without an easy solution in the United States, especially since ethnicity is a contributing factor. Systemic racism contributes to the non-White population suffering from income inequality, which causes the opportunity for growth to stagnate.

This layer shows median household income by race and by age of householder. 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. Median income and income source is based on income in past 12 months of survey. This layer is symbolized to show median household income. 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): B19013B, B19013C, B19013D, B19013E, B19013F, B19013G, B19013H, B19013I, B19049, B19053Data 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 layer shows median household income by race and by age of householder. This is shown by tract, county, and state 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. Median income and income source is based on income in past 12 months of survey. This layer is symbolized to show median household income. 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): B19013B, B19013C, B19013D, B19013E, B19013F, B19013G, B19013H, B19013I, B19049, B19053Data 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.

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 population for the nation, states, counties, cities, and towns and estimates of housing units and the group quarters population for states and counties..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..Source: U.S. Census Bureau, 2023 American Community Survey 1-Year Estimates.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..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..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..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..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

This layer shows housing costs as a percentage of household income by age. 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 predominant housing type for householders where the householder is age 65+ and spending at least 30% of their income on housing. 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): B25072, B25093 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.