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.

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 Aug 2025 about primary, rent, urban, consumer, CPI, inflation, price index, indexes, price, and USA.

Price to Rent Ratio in the United States increased to 134.20 in the fourth quarter of 2024 from 133.60 in the third quarter of 2024. This dataset includes a chart with historical data for the United States Price to Rent Ratio.

Comprehensive demographic dataset for Manhattan, New York City, NY, US including population statistics, household income, housing units, education levels, employment data, and transportation with year-over-year changes.

There is more to housing affordability than the rent or mortgage you pay. Transportation costs are the second-biggest budget item for most families, but it can be difficult for people to fully factor transportation costs into decisions about where to live and work. The Location Affordability Index (LAI) is a user-friendly source of standardized data at the neighborhood (census tract) level on combined housing and transportation costs to help consumers, policymakers, and developers make more informed decisions about where to live, work, and invest. Compare eight household profiles (see table below) —which vary by household income, size, and number of commuters—and see the impact of the built environment on affordability in a given location while holding household demographics constant.*$11,880 for a single person household in 2016 according to US Dept. of Health and Human Services: https://aspe.hhs.gov/computations-2016-poverty-guidelinesThis layer is symbolized by the percentage of housing and transportation costs as a percentage of income for the Median-Income Family profile, but the costs as a percentage of income for all household profiles are listed in the pop-up:Variables used in HUD's calculations include 24 measures such as people per household, average number of rooms per housing unit, monthly housing costs (mortgage/rent as well as utility and maintenance expenses), average number of cars per household, median commute distance, vehicle miles traveled per year, percent of trips taken on transit, street connectivity and walkability (measured by block density), and many more.To learn more about the Location Affordability Index (v.3) visit: https://www.hudexchange.info/programs/location-affordability-index/. There you will find some background and an FAQ page, which includes the question:Date of Coverage: 2012-2016 Date Released: March 2019Date Downloaded from HUD Open Data: 12/18/21Further Documentation:LAI Version 3 Data and MethodologyLAI Version 3 Technical Documentation

Abstract

The purpose of the SEPHER data set is to allow for testing, assessing and generating new analysis and metrics that can address inequalities and climate injustice. The data set was created by Tedesco, M., C. Hultquist, S. E. Char, C. Constantinides, T. Galjanic, and A. D. Sinha.

Methodology

SEPHER draws upon four major source datasets: CDC Social Vulnerability Index, FEMA National Risk Index, Home Mortgage Disclosure Act, and Evictions datasets. The data from these source datasets have been merged, cleaned, and standardized and all of the variables documented in the data dictionary.

CDC Social Vulnerability Index

CDC Social Vulnerability Index (SVI) dataset is a dataset prepared for the Centers for Disease Control and Prevention for the purpose of assessing the degree of social vulnerability of American communities to natural hazards and anthropogenic events. It contains data on 15 social factors taken or derived from Census reports as well as rankings of each tract based on these individual factors, groups of factors corresponding to four related themes (Socioeconomic, Household Composition & Disability, Minority Status & Language, and Housing Type & Transportation) and overall. The data is available for the years 2000, 2010, 2014, 2016, and 2018.

FEMA National Risk Index

The National Risk Index (NRI) dataset compiled by the Federal Emergency Management Agency (FEMA) consists of historic natural disaster data from across the United States at a tract-level. The dataset includes information about 18 natural disasters including earthquakes, tsunamis, wildfires, volcanic activity and many others. Each disaster is detailed out in terms of its frequency, historic impact, potential exposure, expected annual loss and associated risk. The dataset also includes some summary variables for each tract including the total expected loss in terms of building loss, human loss and agricultural loss, the population of the tract, and the area covered by the tract. It finally includes a few more features to characterize the population such as social vulnerability rating and community resilience.

Home Mortgage Disclosure Act

The Home Mortgage Disclosure Act (HMDA) dataset contains loan-level data for home mortgages including information on applications, denials, approvals, and institution purchases. It is managed and expanded annually by the Consumer Financial Protection Bureau based on the data collected from financial institutions. The dataset is used by public officials to make decisions and policies, uncover lending patterns and discrimination among mortgage applicants, and investigate if lenders are serving the housing needs of the communities. It covers the period from 2007 to 2017.

Evictions

The Evictions dataset is compiled and managed by the Eviction Lab at Princeton University and consists of court records related to eviction cases in the United States between 2000 and 2016. Its purpose is to estimate the prevalence of court-ordered evictions and compare eviction rates among states, counties, cities, and neighborhoods. Besides information on eviction filings and judgments, the dataset includes socioeconomic and real estate data for each tract including race/ethnic origin, household income, poverty rate, property value, median gross rent, rent burden, and others.

We used individual-level death data to estimate county-level life expectancy at 25 (e25) for Whites, Black, AIAN and Asian in the contiguous US for 2000-2005. Race-sex-stratified models were used to examine the associations among e25, rurality and specific race proportion, adjusted for socioeconomic variables. Individual death data from the National Center for Health Statistics were aggregated as death counts into five-year age groups by county and race-sex groups for the contiguous US for years 2000-2005 (National Center for Health Statistics 2000-2005). We used bridged-race population estimates to calculate five-year mortality rates. The bridged population data mapped 31 race categories, as specified in the 1997 Office of Management and Budget standards for the collection of data on race and ethnicity, to the four race categories specified under the 1977 standards (the same as race categories in mortality registration) (Ingram et al. 2003). The urban-rural gradient was represented by the 2003 Rural Urban Continuum Codes (RUCC), which distinguished metropolitan counties by population size, and nonmetropolitan counties by degree of urbanization and adjacency to a metro area (United States Department of Agriculture 2016). We obtained county-level sociodemographic data for 2000-2005 from the US Census Bureau. These included median household income, percent of population attaining greater than high school education (high school%), and percent of county occupied rental units (rent%). We obtained county violent crime from Uniform Crime Reports and used it to calculate mean number of violent crimes per capita (Federal Bureau of Investigation 2010). This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: Request to author. Format: Data are stored as csv files. This dataset is associated with the following publication: Jian, Y., L. Neas, L. Messer, C. Gray, J. Jagai, K. Rappazzo, and D. Lobdell. Divergent trends in life expectancy across the rural-urban gradient among races in the contiguous United States. International Journal of Public Health. Springer Basel AG, Basel, SWITZERLAND, 64(9): 1367-1374, (2019).

These are the data for the Demographic Profiles displayed on austintexas.gov/demographics. These profiles were published in 2025, but display data from 2023 and 2024. Most data are from the 2023 American Community Survey (the most recent available at the time of publication), but some data have other sources. All data come from the American Community Survey estimates except for: Total Population - City of Austin Planning Department (2023) (City and Council Districts only) Population Low-Moderate Income - Dept. of Housing and Urban Development LMISD Summary Data (5 year 2016-2020) Occupied Housing Units - City of Austin Planning Department (2023) (City and Council Districts only) Median Home Closing Price - Austin Board of Realtors (2024) Average Monthly Rent - ApartmentTrends.com by Austin Investor Interests (Q4 2024) Income Restricted Units - City of Austin Affordable Housing Inventory (March 2025) Housing Units - City of Austin Planning Department (2023)(City only) Population Density - Esri Updated Demographics (2024) (County, MSA, Council Districts) Daytime Population Density - Esri Updated Demographics (2024) (County, MSA, Council Districts) Population Density - Calculation derived from 2023 Population Estimates, City of Austin Demographics & Data Division (City only) Daytime Population Density - 2023 Population Estimates, City of Austin Demographics & Data Division (City only) Selected Land Use Percentages - City of Austin Land Use Inventory (2024) Transit Stops - Capital Metro (January 2025) City, County, and MSA data are 1-Year ACS estimates. Council Districts are 5-year ACS estimates. Some datapoints may not be available for all geographies. More information and links to these alternate sources, when available, can be found at austintexas.gov/demographics. These profiles are updated annually. City of Austin Open Data Terms of Use – https://data.austintexas.gov/stories/s/ranj-cccq

Comprehensive demographic dataset for Lakewood, CO, US including population statistics, household income, housing units, education levels, employment data, and transportation with year-over-year changes.

Comprehensive demographic dataset for Cache County, UT, US including population statistics, household income, housing units, education levels, employment data, and transportation with year-over-year changes.