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.

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 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 ...

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.

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.

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.

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 housing units 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

This map service displays data derived from the 2008-2012 American Community Survey (ACS). Values derived from the ACS and used for this map service include: Total Population, Population Density (per square mile), Percent Minority, Percent Below Poverty Level, Percent Age (less than 5, less than 18, and greater than 64), Percent Housing Units Built Before 1950, Percent (population) 25 years and over (with less than a High School Degree and with a High School Degree), Percent Linguistically Isolated Households, Population of American Indians and Alaskan Natives, Population of American Indians and Alaskan Natives Below Poverty Level, and Percent Low Income Population (Less Than 2X Poverty Level). The map service was created for inclusion in US EPA mapping applications.

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

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 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.

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.

Abstract

The Farm Structure Survey (FSS) is a survey of national interest, which is carried out both as a sample survey and as a census, in order to collect objective quantitative information relating to the structure of the farming sector. Every ten years an exhaustive survey (Basic FSS or Agricultural Census) is carried out. The first Agricultural Census was conducted in 1950, after the Second World War. Since 1950 five censuses of agriculture and livestock farming have been held, in 1961,1971,1981,1991 and 1999/2000. From 1961 to 1991 censuses were conducted simultaneously with the General Population and Housing Census. The Agricultural Census of 1991 was the last census carried out at the same time with the General Censuses for Population, Households etc. The Agricultural Censuses of 1999/2000 and 2009/2010 were carried out before the General Population Censuses of 2001 and 2011, respectively.

Objective: The purpose of FSS is to determine the basic structural features of the agricultural and livestock holdings, which encapsulate the agricultural picture of Greece at the specific time. The developments of the agricultural holdings’ structure constitute the main element for the National and Community policy drawing up in the Agricultural Sector. Therefore, the collection of objective and reliable data is absolutely necessary in order to draw up time series tables concerning the holdings’ characteristics.

Geographic coverage

National coverage

Analysis unit

Households

Universe

The statistical unit is the agricultural holding, defined as a single unit, both technically and economically, which has a single management and which undertakes agricultural activities listed in Annex I to the European Parliament and Council Regulation (EC) No. 1166/2008 within the economic territory of the EU, as either its primary or its secondary activity. In addition, Communal Departments (local administrative units - LAU 2) were included in the AC to provide data regarding the area of common permanent grassland (using a specific questionnaire).

Kind of data

Census/enumeration data [cen]

Sampling procedure

a. Frame The basic farm register (BFR) used for the AC was based on the register from the 1999 census, updated using the FSS surveys of 2003, 2005 and 2007 and the annual agricultural surveys. The BFR was also updated using two registers of the Ministry of Rural Development and Food (on New Farmers and Organic Farming).The total number of the sampling frame accounts to 843.007 holdings (816.357 holdings from the basic Register of ELSTAT and 26.650 holdings from the Registers of the Greek Ministry of Rural Development and Food) for the agricultural census and 59.967 for the SAMP survey. ELSTAT made use of the registers of the Ministry of Rural Development and Food only concerning the New Farmers and Organic Farming and it compared those registers with the register of ELSTAT. Afterwards, the data of the registers were compared and crosschecked on the basis of the identification data of holder. However, there were some cases where the registers of the Ministry were not complete and some of the data were missing, such as the date of birth, the tax registration number, etc. Cases that could not be matched with ELSTAT’s farm register were kept separately in two temporary file-registers. During the conduct of the agricultural-livestock census, these temporary file-registers were made available to the interviewers together with the basic register.

b. Complete and/or sample enumeration methods The AC 2010 was carried out based on a complete enumeration basis.

Mode of data collection

Face-to-face [f2f]

Research instrument

Two questionnaires were used in the AC, to collect: (i) individual census data from the agricultural holdings and (ii) data on common land from Communal Departments.

One questionnaire was used for the SAPM. The AC and SAPM questionnaires covered all 16 core items recommended in the WCA 2010.

0001 Identification and location of agricultural holding 0002+ Legal status of agricultural holder 0003 Sex of agricultural holder 0004 Age of agricultural holder 0005 Household size 0006 Main purpose of production of the holding 0007 Area of holding according to land use types 0008 Total area of holding 0009 Land tenure types on the holding 0010 Presence of irrigation on the holding 0011 Types of temporary crops on the holding 0012 Types of permanent crops on the holding and whether in compact plantation 0013 Number of animals on the holding for each livestock type 0014 Presence of aquaculture on the holding 0015+ Presence of forest and other wooded land on the holding 0016 Other economic production activities of the holding's enterprise

Cleaning operations

a. DATA PROCESSING AND ARCHIVING The data entry was done almost exclusively using OCR and only in some special cases, where it was not possible to scan the questionnaires, were the data entered manually into the database. The hot deck approach was used for data imputation. The auxiliary variables, used to define the imputation classes for holdings were municipality/commune, type of farming, and economic size. The data processing was carried out in the period from November 2010 to March 2012.

b. CENSUS DATA QUALITY Follow-up interviews were conducted in cases where missing or incorrect data were detected. In most cases, these were done by telephone. The census data were validated against the data from previous FSSs, as well as from other agricultural surveys, and some administrative data sources.

Data appraisal

The final results of the AC, at national level, were available in November-December 2012 in the form of detailed tables. A publication presenting the results of the AC was prepared in September 2013, in electronic and printed formats. The publication is available on the ELSTAT website (only in Greek).

Der Datensatz enthält für die gegebenen Länder jeweils zwei Zeitreihen für die Wohneigentumsquote. Die erste Zeitreihe besteht aus den Rohdatenpunkten. Die Wohneigentumsquote ist in den meisten Ländern nur zu bestimmen Volks- oder Wohnungszählungszeitpunkten erhoben worden. Deswegen liegen für die Rohdaten Messungen nur zu einzelnen Zeitpunkten vor. Die Rohdaten aller Länder können aus dem Menü ‚Beschreibung‘ (blauer Button) unter dem letzten Punkt ‚Materialien zur Studie‘ / ‚Download weiterer Texte zu dieser Studie im PDF Format (Forschungsberichte, Publikationen, Materialien zur Studie)‘ (orangener Button mit PDF-Symbol) als Excel-Datei heruntergeladen werden. Die zweite Zeitreihe geht von der gleichen Datengrundlage aus und fügt eine lineare Interpolation hinzu, damit die Variable in Panelanalysen verwendet werden kann. Die lineare Interpolation kann man damit rechtfertigen, dass die Wohneigentumsquote eine sich nur langsam verändernde Größe ist. Ferner zeigen die jüngeren jährlichen Daten aus Umfragen, dass die Reihe keine großen Sprünge macht. Die interpolierten Zeitreihen befinden sich im Datenteil der Studie (orangener Button mit der Aufschrift ‚146 Zeitreihen (1900-2015) 1 Tabelle). Hier kann die Tabelle entweder komplett downgeloadet werden, oder es können Ländergruppen nach Kontinent oder einzelne Länder ausgewählt werden. Zur Definition der Wohneigentumsquote, der Ländervergleichbarkeit und länderspezifischen Besonderheiten sollten folgende methodische Punkte berücksichtigt werden: Erstens gibt es die auf die Wohnungseinheiten basierende Definition der Wohneigentumsquote, die alle selbstgenutzten Wohn-Einheiten zählt und sie durch alle Gebäude-Einheiten teilt. Diese Definition gilt für die Daten, die auf den Wohnungszählungen der Länder basieren, und der Autor S. Kohl bezieht sich auf diese Definition für die frühesten Zeiträume der Wohneigentums-Quoten. Zweitens hängt die auf Gebäude- bzw. Wohn-Einheiten basierende Definition davon ab, was als Gebäude-Einheit zählt und was zum Wohnungsbestand gehört. Die häufigsten internationalen Vergleiche basieren auf UN (UN 1974, Doling 1997: 35: 154) oder EU-Daten, die lediglich die jeweiligen nationalen statistischen Definitionen wiederholen, die sich erheblich unterscheiden (Behring, Helbrecht und Goldrian 2002). Obwohl die Definitionen der Wohneinheit zwischen den OECD-Ländern sehr ähnlich sind (vgl. Donnison und Ungerson 1982: 42), ist die Einbeziehung von z.B. Anhängern, Saison- und Wohnmobilen in den USA eine Ausnahme (US-Census 2013), die rund 7% des Wohnungsbestandes ausmachen und zu einer deutlich überdurchschnittlichenWohneigentumsquote führen. Diese Einheiten würden, wenn sie statistisch signifikant wären, in Deutschland wahrscheinlich nicht als Wohneinheiten gelten. Der Wohnungsbestand kann sich unterscheiden je nach dem, ob Unterkünfte wie Ferienhütten, Zweitwohnsitze, Wohnwagen, Schiffe, saisonale Wohneinheiten, leerstehende oder zeitweise unbewohnte Einheiten als Wohneinheiten behandelt werden. Die deutsche Definition des Wohnungsbestandes gehört zu den konservativeren im Vergleich zu denjenigen anderer nationaler Statistikämter (Destatis 1989: 7, SE / CZR 2004). Die einheitsbasierte Definition wird durch Kriegszerstörungen verzerrt, wie in Deutschland in den 1950er Jahren, als die offizielle Wohneigentumsquote auf Einheitsbasis mit 39,1% angegeben wurde. Die Zerstörung von überwiegend städtischem Wohnungsbau durch Luftschutzbauten hatte den gesamten Wohnungsbestand reduziert. Der Autor stützt sich deshalb im Falle von Deutschland auf die realistischere Hausbesitzquote von 26,7% im Jahr 1950 stützen (Glatzer 1980: 246). Zweitens gibt es haushaltsbasierte Definitionen der Wohneigentumsquote, die alle Eigentümer-Haushalte (Wohnungs-Eigentümer und Haus-Eigentümer) in das Verhältnis setzt zur Gesamtzahl der Haushalte. Diese Definition, die auf repräsentativen Umfragedaten basiert, ersetzte die auf Wohneinheiten basierenden Daten ab den 1980er Jahren. Der Autor bezieht sich auf diese Definition für die neueren Daten seiner Wohneigentumsquoten. Umfragen berücksichtigen tendenziell Wohnungs- und Hauseigentümer aus den mittleren Klassen stärker als andere Bevölkerungsgruppen. Dies scheint vor allem bei den Eurostat-Umfragen zu gelten, die deutlich höhere Zahlen liefern als nationale Erhebungen, weil das Verhältnis von befragten Eigentümerhaushalten zu allen Befragten höher ist als wohneinheitenbasierte Berechnungen. Dadurch kommt es zu einer Verzerrung bzw. zu höheren Eigentums-Quoten. Aus diesem Grund hat sich der Autor, soweit möglich, auf Quellen außerhalb von Eurostat gestützt, um den Vergleich mit Nicht-EU-Ländern nicht zu verzerren. Eine dritte Definition ist bevölkerungsbezogen und setzt die in Eigenheimen lebende Bevölkerung in das Verhältnis zur Bevölkerung insgesamt (Braun 2004). Diese Definition führt aufgrund der statistischen Prävalenz von Familien in den Eigentümerhaushalten zu höheren Wohneigentumsquoten als die erstgenannte. Dies ist wichtig, wenn man beispielsweise nach Sozialisationseffekten von Wohneigentum sucht, spielt aber in den Vergleichsdaten dieser Studie keine Rolle. Weiterhin existiert viertens eine objektbasierte Definition, die sich auf die Anzahl der Haushalte, die Immobilien besitzen, konzentriert. Die Wohneigentumsquote nach dieser Definition kann höher als die wohneinheitenbasierte Definition sein, weil Mieter mit Immobilienbesitz hier auch als Eigentümer zählen. Diese Definition findet in der Studie allerdings keine Anwendung. Eine fünfte Definition umfasst alle Wohnimmobilien, die in Privatbesitz sind (Privateigentum), im Gegensatz zu denen, die dem Staat oder den Unternehmen gehören (Jenkis 2010). Diese Definition ist wichtig im Kontext der kommunistischen Länder, aber auch in den westlichen Ländern, wo Genossenschaften oder Unternehmen einen großen Anteil am gesamten Immobilienbesitz hatten. Der Autor bezieht sich auf diese Zahl als Proxy für die Eigennutzung im Fall einiger kommunistischer Länder, in denen das verbleibende Privateigentum stark mit dem Besitz eines Einfamilienhausbesitzers korreliert.“ (Sebastian Kohl) Die Datentabellen zu dieser Studie kann in Online-Datenbank Histat unter dem Thema ‚Bauen‘ downgeloadet werden. Der Download für die Rohdaten wird über die Studienbeschreibung unter ‚Materialien zur Studie‘ angeboten. Die interpolierten Zeitreihen befinden sich im Datenteil der Studie (orangener Button mit der Aufschrift ‚146 Zeitreihen (1900-2015) 1 Tabelle). Anmerkungen:„Methodological note about home ownership statistics: There are five different measures that one can distinguish. First, there is the unit-based definition which counts all owner-occupied units and divides them by all units. This definition prevails for the data based on the countries’ housing censuses and I rely on it for the earliest periods. First, it depends on what counts as “owning” in critical cases where the bundle of rights of owner-occupiers is restricted (they cannot freely sell the underlying land or unit, for instance) or entirely unregulated. I followed the existing definition – which counts many owner-occupiers in the Global South in spite of unclear property rights. I decided to count “cooperative ownership” in the Scandinavian countries as “owner occupation”. For even though the bundle of rights was restricted in the early days, cooperative owners had to put money down for housing, which is essentially different from renting. Second, the unit-based definition depends on what counts as a unit and on what belongs to the housing stock. Most common international comparisons are based on UN (UN 1974, Doling 1997: 35: 154) or EU collected data that merely repeat the respective national statistical definitions which differ quite considerably (Behring, Helbrecht, and Goldrian 2002). Though OECD countries adopt quite similar definition of housing unit (cf. Donnison and Ungerson 1982: 42) the US’ inclusion of trailers, seasonal and mobile homes is an exception (US-Census 2013), constituting around 7% of the housing stock with significantly above-average homeownership rate. These units, were they statistically significant, would probably not count as housing units in Germany, for instance. The housing stock can differ as to whether one includes recreational housing units such as tourist cabins, secondary residences, trailers, ships, seasonal housing units, vacant or temporarily unoccupied units. An intra-European comparison of what various national statistical institutes count in the housing stock of the homeownership rate reveals the German definition to be among the most conservative (Destatis 1989: 7, SE/CZR 2004), i.e. were other countries to adopt the German definition, their homeownership rate would be even higher. This observation holds also for the US-German comparison: as the US Census definition of homeownership rate includes seasonal and other mobile units, it tends to be lower than it would be according to the German definition. The unit-based definition is distorted by war-time destructions such as in Germany in the 1950s, when the official unit-based homeownership rate is given as 39,1%. Air-raid destructions of predominantly urban tenement housing had reduced the overall housing stock and two million people still lived in barracks with many others doubling up, 35,6% of households subleasing and the secretary of housing estimating a housing deficit of 4,8 million units, mostly rental (Schulz 1994: 32-35). I will therefore rely on the more realistic household-based homeownership rate of 26,7% in 1950 (Glatzer 1980: 246). Second, there is household-based definitions which counts all owner-occupying households divided by the overall number of households. This definition, based on representative survey data, began to replace the unit-based data from the 1980s onwards and I rely

These data, intended for use in conjunction with JUVENILE DELINQUENCY AND ADULT CRIME, 1948-1977 [RACINE, WISCONSIN]: THREE BIRTH COHORTS (ICPSR 8163), are organized into two different types: Block data and Home data. Part 1, Block Data, contains the characteristics of each block in Racine for the years 1950, 1960, and 1970 as selected from the United States Census of Housing for each of these years. The data are presented for whole blocks for each year and for blocks agglomerated into equal spaces so that comparison may be made between the 1950, 1960, and 1970 data. In addition, land use and target density (gas stations, grocery and liquor stores, restaurants, and taverns) measures are included. The data were obtained from land use maps and city directories. These block data have been aggregated into census tracts, police grid areas, natural areas, and neighborhoods for the purpose of describing the spatial units of each in comparable fashion for 1950, 1960, and 1970. The information contained within the Block Data file is intended to be used to merge ecological data with any of the files described in the ICPSR 8163 codebook. The Home datasets (Parts 2-6) contain selected variables from the Block Data file merged with the Cohort Police Contact data or the Cohort Interview data from ICPSR 8163. The Home datasets represent the merged files used by the principal investigators for their analysis and are included here only as examples of how the files from ICPSR 8163 may be merged with the Block data.

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