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

US Census American Community Survey data table for: Housing subject area. Provides information about: MEDIAN GROSS RENT AS A PERCENTAGE OF HOUSEHOLD INCOME IN THE PAST 12 MONTHS (DOLLARS) for the universe of: Renter-occupied housing units paying cash rent. These data are extrapolated estimates only, based on sampling; they are not actual complete counts. The data is based on 2010 Census Tracts. Table ACS_B25071_MEDIANGROSSRENTPERCENT contains both the Estimate value in the E item for the census topic and an adjacent M item which defines the Margin of Error for the value. The Margin of Error (MOE) is the plus/minus range for the item estimate value, where the range between the Estimate minus the Margin of Error and the Estimate plus the Margin of Error defines the 90% confidence interval of the item value. Many of the Margin of Error values are significant relative to the size of the Estimate value. This table contains 1 item(s) extracted from a larger sequence table. This extracted subset represents that portion of the sequence that is considered high priority. Other portions of this sequence that are not included can be identified in the data dictionary information provided in the Supplemental Information section below. This table information is also provided as a customized layer file: B25071_AREA_MEDIANGROSSRENTPERCENT.lyr where the table information is joined to the 2010 TRACTS_AREA census geography on the GEOID item. Both the table and customized lyr file name do not contain the year descriptor (i.e. 2012-2016) for the current ACS series. This is intentional in order to maintain the same table name in each successive ACS update. The alias of each item's (E)stimate and (M)easure of Error value stores this year date information as beginning YY and ending YY, i.e., 'E1216' and 'M1216' followed by the rest of the alias description. In this way users of the data tables or lyr files that support field aliases can determine which ACS series is being represented by the current table contents.

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.

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.

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

This dataset presents the Rental Affordability Index (RAI) for all dwellings. The data uses a single median income value for all of Australia (enabling comparisons across regions), and spans the quarters Q1 2011 to Q2 2021. The RAI covers all states with available data, the Northern Territory does not form part of this dataset. National Shelter, Bendigo Bank, The Brotherhood of St Laurence, and SGS Economics and Planning have released the RentalAffordability Index (RAI) on a biannual basis since 2015. Since 2019, the RAI has been released annually. It is generally accepted that if housing costs exceed 30% of a low-income household's gross income, the household is experiencing housing stress (30/40 rule). That is, housing is unaffordable and housing costs consume a disproportionately high amount of household income. The RAI uses the 30 per cent of income rule. Rental affordability is calculated using the following equation, where 'qualifying income' refers to the household income required to pay rent where rent is equal to 30% of income: RAI = (Median income ∕ Qualifying Income) x 100 In the RAI, households who are paying 30% of income on rent have a score of 100, indicating that these households are at the critical threshold for housing stress. A score of 100 or less indicates that households would pay more than 30% of income to access a rental dwelling, meaning they are at risk of experiencing housing stress. For more information on the Rental Affordability Index please refer to SGS Economics and Planning. The RAI is a price index for housing rental markets. It is a clear and concise indicator of rental affordability relative to household incomes, applied to geographic areas across Australia. AURIN has spatially enabled the original data using geometries provided by SGS Economics and Planning. Values of 'NA' in the original data have been set to NULL.

The Housing Affordability Data System (HADS) is a set of housing unit level datasets that measures the affordability of housing units and the housing cost burdens of households, relative to area median incomes, poverty level incomes, and Fair Market Rents. The purpose of these datasets is to provide housing analysts with consistent measures of affordability and burdens over a long period. The datasets are based on the American Housing Survey (AHS) national files from 1985 through 2005 and the metropolitan files for 2002 and 2004. Users can link records in HADS files to AHS records, allowing access to all of the AHS variables. Housing-level variables include information on the number of rooms in the housing unit, the year the unit was built, whether it was occupied or vacant, whether the unit was rented or owned, whether it was a single family or multiunit structure, the number of units in the building, the current market value of the unit, and measures of relative housing costs. The dataset also includes variables describing the number of people living in the household, household income, and the type of residential area (e.g., urban or suburban).

This dataset presents the Rental Affordability Index (RAI) for 3 bedroom dwellings. The data uses different income values for each region within the Greater Capital Cities, and spans the quarters Q1 2011 to Q2 2021. The RAI covers all states with available data, the Northern Territory and Western Australia does not form part of this dataset. National Shelter, Bendigo Bank, The Brotherhood of St Laurence, and SGS Economics and Planning have released the RentalAffordability Index (RAI) on a biannual basis since 2015. Since 2019, the RAI has been released annually. It is generally accepted that if housing costs exceed 30% of a low-income household's gross income, the household is experiencing housing stress (30/40 rule). That is, housing is unaffordable and housing costs consume a disproportionately high amount of household income. The RAI uses the 30 per cent of income rule. Rental affordability is calculated using the following equation, where 'qualifying income' refers to the household income required to pay rent where rent is equal to 30% of income: RAI = (Median income ∕ Qualifying Income) x 100 In the RAI, households who are paying 30% of income on rent have a score of 100, indicating that these households are at the critical threshold for housing stress. A score of 100 or less indicates that households would pay more than 30% of income to access a rental dwelling, meaning they are at risk of experiencing housing stress. For more information on the Rental Affordability Index please refer to SGS Economics and Planning. The RAI is a price index for housing rental markets. It is a clear and concise indicator of rental affordability relative to household incomes, applied to geographic areas across Australia. AURIN has spatially enabled the original data using geometries provided by SGS Economics and Planning. Values of 'NA' in the original data have been set to NULL.

As part of the Regional Housing Initiative (RHI), the team conducted a submarket analysis. This analysis identifies 2020 census tracts with similar housing characteristics (density, price, market conditions) and groups them accordingly. This submarket analysis uses a Latent Profile Analysis (LPA) via the mclust package in R to group the region's 1,407 eligible census tracts (tracts with no households or population were removed) into one of eight submarkets. The team reviewed the existing conditions of these submarkets to identify their housing challenges and appropriate policies and strategies for each submarket. Census tables used to gather data from the 2016-2020 American Community Survey 5-Year Estimates. Data Dictionary

Please contact Brian Carney, bcarney@dvrpc.org, for more information.

The Location Affordability Index (LAI) estimates the percentage of a family’s income dedicated to the combined cost of housing and transportation in a given location. Because what is “affordable” is different for everyone, users can choose among a diverse set of family profiles—which vary by household income, size, and number of commuters—and see the affordability landscape for each in a given neighborhood, city, or region. The Location Affordability Index (LAI) estimates three dependent variables of transportation behavior (auto ownership, auto use, and transit use) as functions of 14 independent variables (median income, per capita income, average household size, average commuters per household, residential density, gross density, block density, intersection density, transit connectivity, transit frequency of service, transit access shed, employment access, job diversity, and average commute distance). To hone in on the built environment’s influence on transportation costs, the independent household variables (income, household size, and commuters per household) are set at fixed values to control for any variation they might cause. The LAI also estimates two dependent variables of housing costs (Selected Monthly Owner Costs and Gross Rent) as functions of 16 independent variables: regional median selected monthly owner costs and regional median gross rent in addition to the 14 variables used in the transportation model.

To learn more about the Location Affordability Index (v.1.0) visit: https://www.locationaffordability.info/LAPMethods.pdf, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Data Dictionary: DD_Location Affordability Indev v.1.0. Date of Coverage: 2005-2009 https://www.locationaffordability.info/LAPMethodsV2.pdf

Mexico Ave Qtrly HH Income: Estimate of Housing Rent data was reported at 5,247.000 MXN in 2016. Mexico Ave Qtrly HH Income: Estimate of Housing Rent data is updated yearly, averaging 5,247.000 MXN from Dec 2016 (Median) to 2016, with 1 observations. Mexico Ave Qtrly HH Income: Estimate of Housing Rent data remains active status in CEIC and is reported by National Institute of Statistics and Geography. The data is categorized under Global Database’s Mexico – Table MX.H012: Average Quarterly Household Income.

The Housing Affordability Data System (HADS) is a set of files derived from the 1985 and later national American Housing Survey (AHS) and the 2002 and later Metro AHS. This system categorizes housing units by affordability and households by income, with respect to the Adjusted Median Income, Fair Market Rent (FMR), and poverty income. It also includes housing cost burden for owner and renter households. These files have been the basis for the worst case needs tables since 2001. The data files are available for public use, since they were derived from AHS public use files and the published income limits and FMRs. These dataset give the community of housing analysts the opportunity to use a consistent set of affordability measures. The most recent year HADS is available as a Public Use File (PUF) is 2013. For 2015 and beyond, HADS is only available as an IUF and can no longer be released on a PUF. Those seeking access to more recent data should reach to the listed point of contact.

Mexico Ave Qtrly HH Income: Estimate of Housing Rent (EHR): Aguascalientes data was reported at 4,458.000 MXN in 2016. Mexico Ave Qtrly HH Income: Estimate of Housing Rent (EHR): Aguascalientes data is updated yearly, averaging 4,458.000 MXN from Dec 2016 (Median) to 2016, with 1 observations. Mexico Ave Qtrly HH Income: Estimate of Housing Rent (EHR): Aguascalientes data remains active status in CEIC and is reported by National Institute of Statistics and Geography. The data is categorized under Global Database’s Mexico – Table MX.H018: Average Quarterly Household Income: by State.

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.

This dataset includes one dataset which was custom ordered from Statistics Canada.The table includes information on housing suitability and shelter-cost-to-income ratio by number of bedrooms, housing tenure, status of primary household maintainer, household type, and income quartile ranges for census subdivisions in British Columbia. The dataset is in Beyond 20/20 (.ivt) format. The Beyond 20/20 browser is required in order to open it. This software can be freely downloaded from the Statistics Canada website: https://www.statcan.gc.ca/eng/public/beyond20-20 (Windows only). For information on how to use Beyond 20/20, please see: http://odesi2.scholarsportal.info/documentation/Beyond2020/beyond20-quickstart.pdf https://wiki.ubc.ca/Library:Beyond_20/20_Guide Custom order from Statistics Canada includes the following dimensions and variables: Geography: Non-reserve CSDs in British Columbia - 299 geographies The global non-response rate (GNR) is an important measure of census data quality. It combines total non-response (households) and partial non-response (questions). A lower GNR indicates a lower risk of non-response bias and, as a result, a lower risk of inaccuracy. The counts and estimates for geographic areas with a GNR equal to or greater than 50% are not published in the standard products. The counts and estimates for these areas have a high risk of non-response bias, and in most cases, should not be released. All the geographies requested for this tabulation have been cleared for the release of income data and have a GNR under 50%. Housing Tenure Including Presence of Mortgage (5) 1. Total – Private non-band non-farm off-reserve households with an income greater than zero by housing tenure 2. Households who own 3. With a mortgage1 4. Without a mortgage 5. Households who rent Note: 1) Presence of mortgage - Refers to whether the owner households reported mortgage or loan payments for their dwelling. 2015 Before-tax Household Income Quartile Ranges (5) 1. Total – Private households by quartile ranges1, 2, 3 2. Count of households under or at quartile 1 3. Count of households between quartile 1 and quartile 2 (median) (including at quartile 2) 4. Count of households between quartile 2 (median) and quartile 3 (including at quartile 3) 5. Count of households over quartile 3 Notes: 1) A private household will be assigned to a quartile range depending on its CSD-level location and depending on its tenure (owned and rented). Quartile ranges for owned households in a specific CSD are delimited by the 2015 before-tax income quartiles of owned households with an income greater than zero and residing in non-farm off-reserve dwellings in that CSD. Quartile ranges for rented households in a specific CSD are delimited by the 2015 before-tax income quartiles of rented households with an income greater than zero and residing in non-farm off-reserve dwellings in that CSD. 2) For the income quartiles dollar values (the delimiters) please refer to Table 1. 3) Quartiles 1 to 3 are suppressed if the number of actual records used in the calculation (not rounded or weighted) is less than 16. For cases in which the renters’ quartiles or the owners’ quartiles (figures from Table 1) of a CSD are suppressed the CSD is assigned to a quartile range depending on the provincial renters’ or owners’ quartile figures. Number of Bedrooms (Unit Size) (6) 1. Total – Private households by number of bedrooms1 2. 0 bedrooms (Bachelor/Studio) 3. 1 bedroom 4. 2 bedrooms 5. 3 bedrooms 6. 4 bedrooms Note: 1) Dwellings with 5 bedrooms or more included in the total count only. Housing Suitability (6) 1. Total - Housing suitability 2. Suitable 3. Not suitable 4. One bedroom shortfall 5. Two bedroom shortfall 6. Three or more bedroom shortfall Note: 1) 'Housing suitability' refers to whether a private household is living in suitable accommodations according to the National Occupancy Standard (NOS); that is, whether the dwelling has enough bedrooms for the size and composition of the household. A household is deemed to be living in suitable accommodations if its dwelling has enough bedrooms, as calculated using the NOS. 'Housing suitability' assesses the required number of bedrooms for a household based on the age, sex, and relationships among household members. An alternative variable, 'persons per room,' considers all rooms in a private dwelling and the number of household members. Housing suitability and the National Occupancy Standard (NOS) on which it is based were developed by Canada Mortgage and Housing Corporation (CMHC) through consultations with provincial housing agencies. Shelter-cost-to-income-ratio (4) 1. Total – Private non-band non-farm off-reserve households with an income greater than zero 2. Spending less than 30% of households total income on shelter costs 3. Spending 30% or more of households total income on shelter costs 4. Spending 50% or more of households total income on shelter costs Note: 'Shelter-cost-to-income...

This dataset presents the Rental Affordability Index (RAI) for all dwellings. The data uses different income values for each region within the Greater Capital Cities, and spans the quarters Q1 2011 to Q2 2021. The RAI covers all states with available data, the Northern Territory does not form part of this dataset. National Shelter, Bendigo Bank, The Brotherhood of St Laurence, and SGS Economics and Planning have released the RentalAffordability Index (RAI) on a biannual basis since 2015. Since 2019, the RAI has been released annually. It is generally accepted that if housing costs exceed 30% of a low-income household's gross income, the household is experiencing housing stress (30/40 rule). That is, housing is unaffordable and housing costs consume a disproportionately high amount of household income. The RAI uses the 30 per cent of income rule. Rental affordability is calculated using the following equation, where 'qualifying income' refers to the household income required to pay rent where rent is equal to 30% of income: RAI = (Median Income ∕ Qualifying Income) x 100 In the RAI, households who are paying 30% of income on rent have a score of 100, indicating that these households are at the critical threshold for housing stress. A score of 100 or less indicates that households would pay more than 30% of income to access a rental dwelling, meaning they are at risk of experiencing housing stress. For more information on the Rental Affordability Index please refer to SGS Economics and Planning. The RAI is a price index for housing rental markets. It is a clear and concise indicator of rental affordability relative to household incomes, applied to geographic areas across Australia. AURIN has spatially enabled the original data using geometries provided by SGS Economics and Planning. Values of 'NA' in the original data have been set to NULL.

Table from the American Community Survey (ACS) 5-year series for King County and City of Seattle median values for a variety of topics including age, gross rent, monthly owner costs, family and nonfamily incomes, earnings. Includes the margin of error for the values.Table created for and used in the Neighborhood Profiles application.Vintages: 2010, 2015, 2020, 2023ACS Table(s): B01002, B25064, B25088, B19013, B19113, B19202, B20017Data downloaded from: Census Bureau's Explore Census Data The 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. 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: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 2020 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.

Abstract copyright UK Data Service and data collection copyright owner.The Family Resources Survey (FRS) has been running continuously since 1992 to meet the information needs of the Department for Work and Pensions (DWP). It is almost wholly funded by DWP. The FRS collects information from a large, and representative sample of private households in the United Kingdom (prior to 2002, it covered Great Britain only). The interview year runs from April to March.The focus of the survey is on income, and how much comes from the many possible sources (such as employee earnings, self-employed earnings or profits from businesses, and dividends; individual pensions; state benefits, including Universal Credit and the State Pension; and other sources such as savings and investments). Specific items of expenditure, such as rent or mortgage, Council Tax and water bills, are also covered.Many other topics are covered and the dataset has a very wide range of personal characteristics, at the adult or child, family and then household levels. These include education, caring, childcare and disability. The dataset also captures material deprivation, household food security and (new for 2021/22) household food bank usage. The FRS is a national statistic whose results are published on the gov.uk website. It is also possible to create your own tables from FRS data, using DWP’s Stat Xplore tool. Further information can be found on the gov.uk Family Resources Survey webpage. Safe Room Access FRS data In addition to the standard End User Licence (EUL) version, Safe Room access datasets, containing unrounded data and additional variables, are also available for FRS from 2005/06 onwards - see SN 7196, where the extra contents are listed. The Safe Room version also includes secure access versions of the Households Below Average Income (HBAI) and Pensioners' Incomes (PI) datasets. The Safe Room access data are currently only available to UK HE/FE applicants and for access at the UK Data Archive's Safe Room at the University of Essex, Colchester. Prospective users of the Safe Room access version of the FRS/HBAI/PI will need to fulfil additional requirements beyond those associated with the EUL datasets. Full details of the application requirements are available from Guidance on applying for the Family Resources Survey: Secure Access.FRS, HBAI and PIThe FRS underpins the related Households Below Average Income (HBAI) dataset, which focuses on poverty in the UK, and the related Pensioners' Incomes (PI) dataset. The EUL versions of HBAI and PI are held under SNs 5828 and 8503 respectively. The secure access versions are held within the Safe Room FRS study under SN 7196 (see above). The FRS aims to: support the monitoring of the social security programmesupport the costing and modelling of changes to National Insurance contributions and social security benefitsprovide better information for the forecasting of benefit expenditure From April 2002, the FRS was extended to include Northern Ireland. Latest edition: For the second edition (October 2014) the data were re-grossed following revision of the FRS grossing methodology to take account of the 2011 Census mid-year population estimates. New variable GROSS4 was added to the dataset. In August 2019, at the depositor's request, the Pensioners' Income (PI) dataset (pianon) previously held with the FRS was moved to a separate PI series study, SN 8503.

Dataset contains counts and measures for households from the 2013, 2018, and 2023 Censuses. Data is available by statistical area 2.

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The variables included in this dataset are for households in occupied private dwellings (unless otherwise stated). All data is for level 1 of the classification (unless otherwise stated):

• Count of households in occupied private dwellings
• Access to telecommunication systems (total responses)
• Household crowding index for levels 1 and 2
• Household composition
• Number of usual residents in household
• Average number of usual residents in household
• Number of motor vehicles
• Sector of landlord for households in rented occupied private dwellings
• Tenure of household
• Total household income
• Median ($) total household income
• Weekly rent paid by household for households in rented occupied private dwellings
• Median ($) weekly rent paid by household for households in rented occupied private dwellings.

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Download lookup file from Stats NZ ArcGIS Online or embedded attachment in Stats NZ geographic data service. Download data table (excluding the geometry column for CSV files) using the instructions in the Koordinates help guide.

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Footnotes

Geographical boundaries

Statistical standard for geographic areas 2023 (updated December 2023) has information about geographic boundaries as of 1 January 2023. Address data from 2013 and 2018 Censuses was updated to be consistent with the 2023 areas. Due to the changes in area boundaries and coding methodologies, 2013 and 2018 counts published in 2023 may be slightly different to those published in 2013 or 2018.

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Caution using time series

Time series data should be interpreted with care due to changes in census methodology and differences in response rates between censuses. The 2023 and 2018 Censuses used a combined census methodology (using census responses and administrative data), while the 2013 Census used a full-field enumeration methodology (with no use of administrative data).

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About the 2023 Census dataset

For information on the 2023 dataset see Using a combined census model for the 2023 Census. We combined data from the census forms with administrative data to create the 2023 Census dataset, which meets Stats NZ's quality criteria for population structure information. We added real data about real people to the dataset where we were confident the people who hadn’t completed a census form (which is known as admin enumeration) will be counted. We also used data from the 2018 and 2013 Censuses, administrative data sources, and statistical imputation methods to fill in some missing characteristics of people and dwellings.

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Data quality

The quality of data in the 2023 Census is assessed using the quality rating scale and the quality assurance framework to determine whether data is fit for purpose and suitable for release. Data quality assurance in the 2023 Census has more information.

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Concept descriptions and quality ratings

Data quality ratings for 2023 Census variables has additional details about variables found within totals by topic, for example, definitions and data quality.

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Household crowding

Household crowding is based on the Canadian National Occupancy Standard (CNOS). It calculates the number of bedrooms needed based on the demographic composition of the household. The household crowding index methodology for 2023 Census has been updated to use gender instead of sex. Household crowding should be used with caution for small geographical areas due to high volatility between census years as a result of population change and urban development. There may be additional volatility in areas affected by the cyclone, particularly in Gisborne and Hawke's Bay. Household crowding index – 2023 Census has details on how the methodology has changed, differences from 2018 Census, and more.

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Using data for good

Stats NZ expects that, when working with census data, it is done so with a positive purpose, as outlined in the Māori Data Governance Model (Data Iwi Leaders Group, 2023). This model states that "data should support transformative outcomes and should uplift and strengthen our relationships with each other and with our environments. The avoidance of harm is the minimum expectation for data use. Māori data should also contribute to iwi and hapū tino rangatiratanga”.

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Confidentiality

The 2023 Census confidentiality rules have been applied to 2013, 2018, and 2023 data. These rules protect the confidentiality of individuals, families, households, dwellings, and undertakings in 2023 Census data. Counts are calculated using fixed random rounding to base 3 (FRR3) and suppression of ‘sensitive’ counts less than six, where tables report multiple geographic variables and/or small populations. Individual figures may not always sum to stated totals. Applying confidentiality rules to 2023 Census data and summary of changes since 2018 and 2013 Censuses has more information about 2023 Census confidentiality rules.

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Measures

Measures like averages, medians, and other quantiles are calculated from unrounded counts, with input noise added to or subtracted from each contributing value during measures calculations. Averages and medians based on less than six units (e.g. individuals, dwellings, households, families, or extended families) are suppressed. This suppression threshold changes for other quantiles. Where the cells have been suppressed, a placeholder value has been used.

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Percentages

To calculate percentages, divide the figure for the category of interest by the figure for 'Total stated' where this applies.

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Symbol

-997 Not available

-999 Confidential

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Inconsistencies in definitions

Please note that there may be differences in definitions between census classifications and those used for other data collections.