This data collection contains a stratified 1-percent sample of households, with separate records for each household, each "sample line" respondent, and each person in the household. These records were encoded from microfilm copies of original handwritten enumeration schedules from the 1950 Census of Population. Geographic identification of the location of the sampled households includes Census regions and divisions, states (except Alaska and Hawaii), Standard Metropolitan Areas (SMAs), and State Economic Areas (SEAs). The data collection was constructed from and consists of 20 independently-drawn subsamples stored in 20 discrete physical files. The 1950 Census had both a complete-count and a sample component. Individuals selected for the sample component were asked a set of additional questions. Only households with a sample line person were included in the 1950 Public Use Microdata Sample. The collection also contains records of group quarters members who were also on the Census sample line. Each household record contains variables describing the location and composition of the household. The sample line records contain variables describing demographic characteristics such as nativity, marital status, number of children, veteran status, education, income, and occupation. The person records contain demographic variables such as nativity, marital status, family membership, and occupation. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR at https://doi.org/10.3886/ICPSR08251.v1. We highly recommend using the ICPSR version as they may make this dataset available in multiple data formats in the future.

Older housing can impact the quality of the occupant's health in a number of ways, including lead exposure, housing quality, and factors that may exacerbate respiratory conditions, like asthma. Data from the U.S. Census Bureau contains Census Tract estimates of housing age, and Allegheny County assessment data provides parcel-level information on the year residential properties were built.

Support for Health Equity datasets and tools provided by Amazon Web Services (AWS) through their Health Equity Initiative.

How many households are in the U.S.?

In 2023, there were 131.43 million households in the United States. This is a significant increase from 1960, when there were 52.8 million households in the U.S.

What counts as a household?

According to the U.S. Census Bureau, a household is considered to be all persons living within one housing unit. This includes apartments, houses, or single rooms, and consists of both related and unrelated people living together. For example, two roommates who share a living space but are not related would be considered a household in the eyes of the Census. It should be noted that group living quarters, such as college dorms, are not counted as households in the Census.

Household changes

While the population of the United States has been increasing, the average size of households in the U.S. has decreased since 1960. In 1960, there was an average of 3.33 people per household, but in 2023, this figure had decreased to 2.51 people per household. Additionally, two person households make up the majority of American households, followed closely by single-person households.

This study was conducted under the auspices of the Center for Studies in Demography and Ecology at the University of Washington. It is a nationally representative sample of the population of the United States in 1900, drawn from the manuscript returns of individuals enumerated in the 1900 United States Census. Household variables include region, state and county of household, size of household, and type and ownership of dwelling. Individual variables for each household member include relationship to head of household, race, sex, age, marital status, number of children, and birthplace. Immigration variables include parents' birthplace, year of immigration and number of years in the United States. Occupation variables include occupation, coded by both the 1900 and 1950 systems, and number of months unemployed. Education variables include number of months in school, whether respondents could read or write a language, and whether they spoke English. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR at https://doi.org/10.3886/ICPSR07825.v1. We highly recommend using the ICPSR version as they may make this dataset available in multiple data formats in the future.

description: This polygon shapefile provides county or county-equivalent boundaries for the conterminous United States and was created specifically for use with the data tables published as Selected Items from the Census of Agriculture for the Conterminous United States, 1950-2012 (LaMotte, 2015). This data layer is a modified version of Historic Counties for the 2000 Census of Population and Housing produced by the National Historical Geographic Information System (NHGIS) project, which is identical to the U.S. Census Bureau TIGER/Line Census 2000 file, with the exception of added shorelines. Excluded from the CAO_STCOFIPS boundary layer are Broomfield County, Colorado, Menominee County, Wisconsin, and the independent cities of Virginia with the exception of the 3 county-equivalent cities of Chesapeake City, Suffolk, and Virginia Beach. The census of agriculture was not taken in the District of Columbia for 1959, but available data indicate few if any farms in that area, the polygon was left in place to preserve the areas of the surrounding counties. Baltimore City, Maryland was combined with Baltimore County and the St. Louis City, Missouri, was combined with St. Louis County. La Paz County, Arizona was combined with Yuma County, Arizona and Cibola County, New Mexico was combined with Valencia County, New Mexico. Minor county border changes were at a level of precision beyond the scope of the data collection. A major objective of the census data tabulation is to maintain a reasonable degree of comparability of agricultural data from census to census. The tabular data collection is from 14 different censuses where definitions and data collection techniques may change over time and while the data are mostly comparable, a degree of caution should be exercised when using the data in analysis procedures. While the data are at a county-level resolution, a regional approach is more appropriate than a county-by-county analysis. The main purpose of this layer is to provide a base to generate a county raster for the allocation of agricultural census values to specific (agricultural) pixels. Vector format is provided so the raster pixel size can be user designated. References cited: LaMotte, A.E., 2015, Selected items from the Census of Agriculture at the county level for the conterminous United States, 1950-2012: U.S. Geological Survey data release, http://dx.doi.org/10.5066/F7H13016. National Historical Geographic Information System, Minnesota Population Center, 2004, Historic counties for the 2000 census of population and housing: Minneapolis, MN, University of Minnesota, accessed 03/18/2013 at http://nhgis.org; abstract: This polygon shapefile provides county or county-equivalent boundaries for the conterminous United States and was created specifically for use with the data tables published as Selected Items from the Census of Agriculture for the Conterminous United States, 1950-2012 (LaMotte, 2015). This data layer is a modified version of Historic Counties for the 2000 Census of Population and Housing produced by the National Historical Geographic Information System (NHGIS) project, which is identical to the U.S. Census Bureau TIGER/Line Census 2000 file, with the exception of added shorelines. Excluded from the CAO_STCOFIPS boundary layer are Broomfield County, Colorado, Menominee County, Wisconsin, and the independent cities of Virginia with the exception of the 3 county-equivalent cities of Chesapeake City, Suffolk, and Virginia Beach. The census of agriculture was not taken in the District of Columbia for 1959, but available data indicate few if any farms in that area, the polygon was left in place to preserve the areas of the surrounding counties. Baltimore City, Maryland was combined with Baltimore County and the St. Louis City, Missouri, was combined with St. Louis County. La Paz County, Arizona was combined with Yuma County, Arizona and Cibola County, New Mexico was combined with Valencia County, New Mexico. Minor county border changes were at a level of precision beyond the scope of the data collection. A major objective of the census data tabulation is to maintain a reasonable degree of comparability of agricultural data from census to census. The tabular data collection is from 14 different censuses where definitions and data collection techniques may change over time and while the data are mostly comparable, a degree of caution should be exercised when using the data in analysis procedures. While the data are at a county-level resolution, a regional approach is more appropriate than a county-by-county analysis. The main purpose of this layer is to provide a base to generate a county raster for the allocation of agricultural census values to specific (agricultural) pixels. Vector format is provided so the raster pixel size can be user designated. References cited: LaMotte, A.E., 2015, Selected items from the Census of Agriculture at the county level for the conterminous United States, 1950-2012: U.S. Geological Survey data release, http://dx.doi.org/10.5066/F7H13016. National Historical Geographic Information System, Minnesota Population Center, 2004, Historic counties for the 2000 census of population and housing: Minneapolis, MN, University of Minnesota, accessed 03/18/2013 at http://nhgis.org

Household is an occupied housing unit. Householder is a person in whose name the housing unit is rented or owned. This person must be at least 15 years old. Family household is a household in which there is at least 1 person present who is related to the householder by birth, marriage or adoption. Family is used to refer to a family household. In general, family consists of those related to each other by birth, marriage or adoption.

This data uses the householder's person weight to describe characteristics of people living in households. As a result, estimates of the number of households do not match estimates of households from the Housing Vacancy Survey (HVS). The HVS is weighted to housing units, rather than the population, in order to more accurately estimate the number of occupied and vacant housing units. For more information about the source and accuracy statement of the Annual Social and Economic Supplement (ASEC) of the Current Population Survey (CPS) see the technical documentation accessible at: http://www.census.gov/programs-surveys/cps/technical-documentation/complete.html

Graph and download economic data for Homeownership Rate in the United States (RHORUSQ156N) from Q1 1965 to Q1 2025 about homeownership, housing, rate, and USA.

This study of trends in California from 1940 to 1980 fills in some of the information voids for this period. It is based on data from, the U.S. Decennial Census micro data for 1940 and 1950, better known as the Public Use Microdata Samples or "PUMS" data. Variables, variable names and variable order have been normalized for ease of use and analysis.

A computerized data set of demographic, economic and social data for 227 countries of the world. Information presented includes population, health, nutrition, mortality, fertility, family planning and contraceptive use, literacy, housing, and economic activity data. Tabular data are broken down by such variables as age, sex, and urban/rural residence. Data are organized as a series of statistical tables identified by country and table number. Each record consists of the data values associated with a single row of a given table. There are 105 tables with data for 208 countries. The second file is a note file, containing text of notes associated with various tables. These notes provide information such as definitions of categories (i.e. urban/rural) and how various values were calculated. The IDB was created in the U.S. Census Bureau''s International Programs Center (IPC) to help IPC staff meet the needs of organizations that sponsor IPC research. The IDB provides quick access to specialized information, with emphasis on demographic measures, for individual countries or groups of countries. The IDB combines data from country sources (typically censuses and surveys) with IPC estimates and projections to provide information dating back as far as 1950 and as far ahead as 2050. Because the IDB is maintained as a research tool for IPC sponsor requirements, the amount of information available may vary by country. As funding and research activity permit, the IPC updates and expands the data base content. Types of data include: * Population by age and sex * Vital rates, infant mortality, and life tables * Fertility and child survivorship * Migration * Marital status * Family planning Data characteristics: * Temporal: Selected years, 1950present, projected demographic data to 2050. * Spatial: 227 countries and areas. * Resolution: National population, selected data by urban/rural * residence, selected data by age and sex. Sources of data include: * U.S. Census Bureau * International projects (e.g., the Demographic and Health Survey) * United Nations agencies Links: * ICPSR: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/08490

The available data show the development of total residential buildings, including mixed-use buildings (e.g. non-residential buildings, which also include dwellings or housing opportunities), emergency accommodation and dwellings. In addition, the number of households per dwelling and the population per dwelling are reported for 83 years. These data show the evolution of the quality of housing and the proportion of particularly precarious dwellings over time. The compilation is based on the comprehensive population and building censuses carried out since 1871. Due to the extensive territorial changes over the 83-year period covered, which thus also cover a period prior to the existence of the federal state North Rhine-Westphalia, the comments are of particular importance. Due to the considerable scope of the study description the comments are additionally offered via a downloadable PDF file. The data on residential buildings are part of an extremely comprehensive data compilation of the primary researcher Harald Klaudat. This data compilation is divided into several sub-studies. While the study ZA8682 focuses on population and therefore presents the distribution of the population according to age, sex, and marital status as well as the number of births and deaths, the study ZA8683 presents the development of religious affiliation of the population in North Rhine-Westphalia over 120 years.This study with the number ZA8706 is dedicated to the sub-area of residential buildings. While the data of the studies ZA8682 and ZA8683 are under the online-database Histat topic ´Population´, this part of the study was imported under the topic ´Building´ in histat. The data refer to the following administrative districts with their urban districts, independent towns, and rural districts:01. Regierungsbezirk (= county) Aachen02. Regierungsbezirk (= county) Arnsberg03. Regierungsbezirk (= county) Düsseldorf04. Regierungsbezirk (= county) Cologne 05. Regierungsbezirk (= county) Minden resp. Detmold06. Regierungsbezirk (= county) Münster07. Gesamtgebiet NRW (whole territory or North Rhine-Westphalia in general) The following topics are covered in the data tables for each administrative district: - Area of the respective county or district- Number of normal residential buildings- Number of non-residential buildings containing apartments.- Number of temporary dwellings and accommodation occupied.- Total number of dwellings (= sum of ´normal residential buildings´ + ´non-residential buildings with dwellings´ + ´inhabited emergency dwellings and accommodation´) per square km- Inhabitants per square km- Inhabitants per dwelling- Households per dwelling This data is available for the following occupational and census data:- 1.12.1885 (territory of 1885)- 1.12.1900 (territory of 1900)- 1.12.1910 (territory of 1910/12)- 13.9.1950 (territory of 1950)- 6.6.1961 (territory of 1961)- 25.10.1968 (territory of 1970)- 31.12.1968 for inhabitants per sqkm and per dwelling and for households per dwelling (territory of 1.1.1970) Datatables in HISTAT, Topic ´Bauen´ 1 Reg-Bez. Aachen: Wohngebäude 1885-1968 2a Reg-Bez. Arnsberg, Stadtkreise: Wohngebäude 1885-1968 2b Reg-Bez. Arnsberg, Landkreise: Wohngebäude 1885-1968 3a Reg-Bez. Düsseldorf, Stadtkriese: Wohngebäude 1885-1968 3b Reg-Bez. Düsseldorf, Landkreise: Wohngebäude 1885-1968 4 Reg-Bez. Koeln: Wohngebäude 1885-1968 5 Reg-Bez. Minden bzw. Detmold/ Land Lippe bis 1947: Wohngebäude 1885-1968 6 Reg-Bez. Münster: Wohngebäude 1885-1968 7 Gesamtgebiet bzw. Nordrhein-Westfalen (NRW): Wohngebäude 1885-1968

The 2001 Residential Finance Survey (RFS) was sponsored by the Department of Housing and Urban Development (HUD) and conducted by the Census Bureau. The RFS is a follow-on survey to the 2000 decennial census designed to collect, process, and produce information about the financing of all nonfarm, residential properties. Previous RF surveys have been integral parts of the decennial censuses since 1950. Primary users of RFS data in addition to HUD include the Bureau of Economic Analysis, Fannie Mae and Freddie Mac, and the Congress. Data are collected, tabulated, and presented for properties, the standard unit of reference for financial transactions related to housing. In the RFS, a property is defined as all the buildings and land covered by a single first mortgage. The sample for the RFS is stratified by property size, with large properties overrepresented in the sample. Very large properties are selected with certainty to control their effect on the reliability of the estimates. The RFS is the only standardized single source of detailed information on property, mortgage, and financial characteristics for multiunit properties. Both property owners and mortgage lenders are interviewed, resulting in more accurate information on property and mortgage characteristics. As part of the decennial census, the RFS is mandatory. This is important in collecting information from mortgage lenders.

Graph and download economic data for Average Sales Price of Houses Sold for the United States (ASPUS) from Q1 1963 to Q1 2025 about sales, housing, and USA.

PI-provided abstract: The Census Bureau took the Residential Finance Survey (RFS) as part of the decennial census from 1950-2000. The RFS is the only survey designed to collect and produce data about the financing of nonfarm, privately-owned residential properties. The RFS is a unique survey for several reasons: It collects, tabulates, and presents data for properties, the standard unit of reference for financial transactions related to housing. In most other demographic surveys, the unit of reference is the person, household, or housing unit. It is the only source of information on property, mortgage, and financial characteristics for multi-unit rental properties. Information on multi-family loans and properties is particularly difficult to obtain, but is important to understand if progress is to be made in the development of standards for underwriting multi-family mortgages. It conducts interviews of property owners and mortgage lenders, resulting in more accurate information on property and mortgage characteristics. The RFS is the only survey which is able to provide a comprehensive view of mortgage finance in the USA, by providing information not only about the loan itself from the lender, but also information about the property owner's demographic characteristics. As part of the decennial census, it is mandatory. This is important in collecting information from mortgage lenders. The RFS is exempt from statutes prohibiting release of financial records by financial institutions. It is able to subdivide the industry into relevant components. Different parts of the industry have excellent information on their own loans and clients, but not that of the industry as a whole. Information on lending by individual investors or small groups of investors such as pension funds is collected only by the RFS.