In 2023, the rate of homeownership among White people living in the United States was 74.3 percent. Comparatively, 45.7 percent of Black people owned a home in the same year.

The homeownership among White people in the United States was **** percent, the highest out of all ethnicities, in 2023. American Dream Part of the “American Dream” is the idea of owning a home. It is seen as a status symbol and an indicator of wealth. People take a lot of pride in owning a home, and hope to do so at the earliest age possible. It is the idea of having a white picket fence with a nuclear family, a dog, and a car or two which is seen as the stereotypical “end goal”. However, in the aftermath of the 2008 recession, the rate of homeownership in the United States fell steadily until 2016. The recession hindered people’s chances of owning a home, due to less credit being available and their own fears about being stuck with a home in negative equity if another recession were to occur. As a result, the homeownership rate in the United States has barely increased in the past few years. Factors affecting homeownership Homeownership varies based on different factors. Married-couple families have the highest homeownership rates among different family statuses. Unsurprisingly, households with high incomes have the highest homeownership rates.

Graph and download economic data for Homeownership Rates by Race and Ethnicity: Black Alone in the United States (BOAAAHORUSQ156N) from Q1 1994 to Q2 2025 about homeownership, African-American, rate, and USA.

Home ownership persists as the primary way that families build wealth. Housing researchers and advocates often discuss the racial home ownership gap, particularly for Black and Hispanic households (Urban Institute, Pew Hispanic Center). Historical policies such as redlining, steering, and municipal underbounding have effects that stay with us today.This map shows the overall home ownership rate and the home ownership rate by race/ethnicity of householder in a chart in the pop-up. Map is multi-scale showing data for state, county, and tract.This map uses these hosted feature layers containing the most recent American Community Survey data. These layers are part of the ArcGIS Living Atlas, and are updated every year when the American Community Survey releases new estimates, so values in the map always reflect the newest data available.

Graph and download economic data for Homeownership Rates by Race and Ethnicity: Non-Hispanic White Alone in the United States (NHWAHORUSQ156N) from Q1 1994 to Q2 2025 about homeownership, white, non-hispanic, rate, and USA.

70% of White British households owned their own homes – the highest percentage out of all ethnic groups.

The homeownership rate in the United States amounted to nearly ** percent in the third quarter of 2024. While there are many factors that affect people’s decision to buy a house, the recent decrease can be attributed to the higher mortgage interest rates, which make taking out a mortgage less affordable for potential buyers, especially considering the surge in house prices in recent years. Which factors affect homeownership? Age and ethnicity have a strong correlation with homeownership. Baby boomers, for example, are twice as likely to own their home than Millennials. Also, the homeownership rate among white Americans is substantially higher than among any other ethnicity. How does the U.S. homeownership rate compare with other countries? Having a home is an integral part of the “American Dream”. Compared with selected European countries, the U.S. ranks alongside the United Kingdom, Cyprus, and Ireland. Many countries in Europe, however, exceed ** percent homeownership rate.

The average homeownership rate in the United States remained mostly unchanged in 2023. Homeownership improved the most among the Asian population, increasing by 1.3 percentage points, while among American Indian or Alaskan Native, it declined by 1.8 percentage points. Overall, the share of white homeowners was higher than any other race.

The American Samoa Summary File contains data on population and housing subjects compiled from questions on the 2010 American Samoa Census questionnaire. Population subjects include age, sex, children ever born, citizenship status, foreign-born status, disability status, educational attainment, ethnic origin or race, family type, grandparents as caregivers, group quarters population, health insurance coverage status, household type and relationship, employment status and subsistence activity, work experience, class of worker, industry, occupation, place of work, journey to work, travel time to work, language spoken at home and frequency of language usage, marital status, nativity, year of entry, place of birth, parents' place of birth, earnings, income, remittances sent abroad, poverty status, residence in 2009, reason for moving, school enrollment, vocational training, military dependents and veteran status. Housing subjects include air conditioning, battery-operated radio ownership, computer ownership, gross rent, internet service, kitchen facilities, cooking facilities, mortgage status, number of rooms, number of bedrooms, occupancy status, occupants per room, plumbing facilities, condominium fee, selected monthly owner costs, sewage disposal, water supply, source of water, telephone service available, tenure, type of building materials, units in structure, vacancy status, value of home, vehicles available, year householder moved into unit and year structure built. The data are organized in 405 tables, one variable per table cell, which are presented at up to 19 levels of observation, including American Samoa as a whole, districts (including two separate atolls), counties, villages, census tracts, block groups, blocks and 5-digit ZIP Code Tabulation Areas. Fifty tables are presented at the block level and higher, 250 at the block group level and higher and 105 at the census tract level and higher. Additionally, the tables are iterated for the urban and rural geographic components of districts/atolls and 21 geographic components of American Samoa as a whole: 15 urban components (total urban, urbanized areas, urban clusters, and urbanized areas and urban clusters of various population sizes) and 6 rural components (total rural, rural areas outside places, rural areas inside places and inside places of various population sizes). Due to problems in the initial version, the Census Bureau ultimately issued the tables as three data products. The first or main release comprises 32 data files with all the tables except PBG7 (Nativity by Citizen Status by Year of Entry), PBG9 (Year of Entry for the Foreign-born Population) and ten tables on selected monthly owner costs, the tables HBG72, HBG73, HBG74, HBG75, HBG76, HBG77, HBG78, HCT17, HCT18, and HCT19. The second, called the American Samoa Year of Entry Summary File, consists of two data files with the tables PBG7 and PBG9. The third is a document file with the ten tables on selected monthly owner costs. This data collection comprises a codebook and three ZIP archives. The first archive contains the 32 data files in the main release, the second the two Year of Entry data files and the third contains the document file with the ten selected monthly owner costs tables and additional technical documentation.

Knowing who your consumers are is essential for businesses, marketers, and researchers. This detailed demographic file offers an in-depth look at American consumers, packed with insights about personal details, household information, financial status, and lifestyle choices. Let's take a closer look at the data:

Personal Identifiers and Basic Demographics At the heart of this dataset are the key details that make up a consumer profile:

Unique IDs (PID, HHID) for individuals and households Full names (First, Middle, Last) and suffixes Gender and age Date of birth Complete location details (address, city, state, ZIP) These identifiers are critical for accurate marketing and form the base for deeper analysis.

Geospatial Intelligence This file goes beyond just listing addresses by including rich geospatial data like:

Latitude and longitude Census tract and block details Codes for Metropolitan Statistical Areas (MSA) and Core-Based Statistical Areas (CBSA) County size codes Geocoding accuracy This allows for precise geographic segmentation and localized marketing.

Housing and Property Data The dataset covers a lot of ground when it comes to housing, providing valuable insights for real estate professionals, lenders, and home service providers:

Homeownership status Dwelling type (single-family, multi-family, etc.) Property values (market, assessed, and appraised) Year built and square footage Room count, amenities like fireplaces or pools, and building quality This data is crucial for targeting homeowners with products and services like refinancing or home improvement offers.

Wealth and Financial Data For a deeper dive into consumer wealth, the file includes:

Estimated household income Wealth scores Credit card usage Mortgage info (loan amounts, rates, terms) Home equity estimates and investment property ownership These indicators are invaluable for financial services, luxury brands, and fundraising organizations looking to reach affluent individuals.

Lifestyle and Interests One of the most useful features of the dataset is its extensive lifestyle segmentation:

Hobbies and interests (e.g., gardening, travel, sports) Book preferences, magazine subscriptions Outdoor activities (camping, fishing, hunting) Pet ownership, tech usage, political views, and religious affiliations This data is perfect for crafting personalized marketing campaigns and developing products that align with specific consumer preferences.

Consumer Behavior and Purchase Habits The file also sheds light on how consumers behave and shop:

Online and catalog shopping preferences Gift-giving tendencies, presence of children, vehicle ownership Media consumption (TV, radio, internet) Retailers and e-commerce businesses will find this behavioral data especially useful for tailoring their outreach.

Demographic Clusters and Segmentation Pre-built segments like:

Household, neighborhood, family, and digital clusters Generational and lifestage groups make it easier to quickly target specific demographics, streamlining the process for market analysis and campaign planning.

Ethnicity and Language Preferences In today's multicultural market, knowing your audience's cultural background is key. The file includes:

Ethnicity codes and language preferences Flags for Hispanic/Spanish-speaking households This helps ensure culturally relevant and sensitive communication.

Education and Occupation Data The dataset also tracks education and career info:

Education level and occupation codes Home-based business indicators This data is essential for B2B marketers, recruitment agencies, and education-focused campaigns.

Digital and Social Media Habits With everyone online, digital behavior insights are a must:

Internet, TV, radio, and magazine usage Social media platform engagement (Facebook, Instagram, LinkedIn) Streaming subscriptions (Netflix, Hulu) This data helps marketers, app developers, and social media managers connect with their audience in the digital space.

Political and Charitable Tendencies For political campaigns or non-profits, this dataset offers:

Political affiliations and outlook Charitable donation history Volunteer activities These insights are perfect for cause-related marketing and targeted political outreach.

Neighborhood Characteristics By incorporating census data, the file provides a bigger picture of the consumer's environment:

Population density, racial composition, and age distribution Housing occupancy and ownership rates This offers important context for understanding the demographic landscape.

Predictive Consumer Indexes The dataset includes forward-looking indicators in categories like:

Fashion, automotive, and beauty products Health, home decor, pet products, sports, and travel These predictive insights help businesses anticipate consumer trends and needs.

Contact Information Finally, the file includes ke...

This collection contains individual-level and 1-percent national sample data from the 1960 Census of Population and Housing conducted by the Census Bureau. It consists of a representative sample of the records from the 1960 sample questionnaires. The data are stored in 30 separate files, containing in total over two million records, organized by state. Some files contain the sampled records of several states while other files contain all or part of the sample for a single state. There are two types of records stored in the data files: one for households and one for persons. Each household record is followed by a variable number of person records, one for each of the household members. Data items in this collection include the individual responses to the basic social, demographic, and economic questions asked of the population in the 1960 Census of Population and Housing. Data are provided on household characteristics and features such as the number of persons in household, number of rooms and bedrooms, and the availability of hot and cold piped water, flush toilet, bathtub or shower, sewage disposal, and plumbing facilities. Additional information is provided on tenure, gross rent, year the housing structure was built, and value and location of the structure, as well as the presence of air conditioners, radio, telephone, and television in the house, and ownership of an automobile. Other demographic variables provide information on age, sex, marital status, race, place of birth, nationality, education, occupation, employment status, income, and veteran status. The data files were obtained by ICPSR from the Center for Social Analysis, Columbia University.

This paper examines the association between the Great Recession and real assets among families with young children. Real assets such as homes and cars are key indicators of economic well-being that may be especially valuable to low-income families. Using longitudinal data from the Fragile Families and Child Wellbeing Study (N = 4,898), we investigate the association between the city unemployment rate and home and car ownership and how the relationship varies by family structure (married, cohabiting, and single parents) and by race/ethnicity (White, Black, and Hispanic mothers). Using mother fixed-effects models, we find that a one percentage point increase in the unemployment rate is associated with a -0.5 percentage point decline in the probability of home ownership and a -0.7 percentage point decline in the probability of car ownership. We also find that the recession was associated with lower levels of home ownership for cohabiting families and for Hispanic families, as well as lower car ownership among single mothers and among Black mothers, whereas no change was observed among married families or White households. Considering that homes and cars are the most important assets among middle and low-income households in the U.S., these results suggest that the rise in the unemployment rate during the Great Recession may have increased household asset inequality across family structures and race/ethnicities, limiting economic mobility, and exacerbating the cycle of poverty.

US Census American Community Survey (ACS) 2020, 5-year estimates of the key housing characteristics of Census Tracts geographic level in Orange County, California. The data contains 406 fields for the variable groups H01: Housing occupancy (universe: total housing units, table X25, 3 fields); H02: Units in structure (universe: total housing units, table X25, 11 fields); H03: Population in occupied housing units by tenure by units in structure (universe: total population in occupied housing units, table X25, 13 fields); H04: Year structure built (universe: total housing units, table X25, 15 fields); H05: Rooms (universe: total housing units, table X25, 18 fields); H06: Bedrooms (universe: total housing units, table X25, 21 fields); H07: Housing tenure by race of householder (universe: occupied housing units, table X25, 51 fields); H08: Total population in occupied housing units by tenure (universe: total population in occupied housing units, table X25, 3 fields); H09: Vacancy status (universe: vacant housing units, table X25, 8 fields); H10: Occupied housing units by race of householder (universe: occupied housing units, table X25, 8 fields); H11: Year householder moved into unit (universe: occupied housing units, table X25, 18 fields); H12: Vehicles available (universe: occupied housing units, table X25, 18 fields); H13: Housing heating fuel (universe: occupied housing units, table X25, 10 fields); H14: Selected housing characteristics (universe: occupied housing units, table X25, 9 fields); H15: Occupants per room (universe: occupied housing units, table X25, 13 fields); H16: Housing value (universe: owner-occupied units, table X25, 32 fields); H17: Price asked for vacant for sale only, and sold not occupied housing units (universe: vacant for sale only, and sold not occupied housing units, table X25, 28 fields); H18: Mortgage status (universe: owner-occupied units, table X25, 10 fields); H19: Selected monthly owner costs, SMOC (universe: owner-occupied housing units with or without a mortgage, table X25, 45 fields); H20: Selected monthly owner costs as a percentage of household income, SMOCAPI (universe: owner-occupied housing units with or without a mortgage, table X25, 26 fields); H21: Contract rent distribution and rent asked distribution in dollars (universe: renter-occupied housing units paying cash rent and vacant, for rent, and rented not occupied housing units, table X25, 7 fields); H22: Gross rent (universe: occupied units paying rent, table X25, 28 fields), and; X23: Gross rent as percentage of household income (universe: occupied units paying rent, table X25, 11 fields). The US Census geodemographic data are based on the 2020 TigerLines across multiple geographies. The spatial geographies were merged with ACS data tables. See full documentation at the OCACS project GitHub page (https://github.com/ktalexan/OCACS-Geodemographics).

The United States Virgin Islands Summary File contains data on population and housing subjects compiled from questions on the 2010 United States Virgin Islands Census questionnaire. Population subjects include age, sex, children ever born, citizenship status, foreign-born status, disability status, educational attainment, race, Hispanic or Latino origin, family type, grandparents as caregivers, group quarters population, health insurance coverage status, household type and relationship, employment status, work experience, class of worker, industry, occupation, place of work, journey to work, travel time to work, language spoken at home and ability to speak English, marital status, nativity, year of entry, place of birth, parents' place of birth, earnings, income, poverty status, residence in 2009, school enrollment, vocational training and veteran status. Housing subjects include acreage, agricultural sales, business on property, computer ownership, internet service, kitchen facilities, cooking fuel, mortgage status, number of rooms, number of bedrooms, occupancy status, occupants per room, plumbing facilities, purchase of water from water vendor, gross rent, condominium status and fee, mobile home costs, selected monthly owner costs, sewage disposal, source of water, telephone service available, tenure, units in structure, vacancy status, value of home, vehicles available, year householder moved into unit and year structure built. The data are organized in 548 tables, one variable per table cell, which are presented at up to 21 levels of observation, including the United States Virgin Islands as a whole, islands, census subdistricts, places (census designated places and towns), estates, census tracts, block groups, blocks and 5-digit ZIP Code Tabulation Areas. Altogether, 110 tables are presented at the block level and higher, 288 at the block group level and higher and 150 at the census tract level and higher. Additionally, the tables are iterated for the urban and rural geographic components of islands and 21 geographic components of the United States Virgin Islands as a whole: 15 urban components (total urban, urbanized areas, urban clusters, and urbanized areas and urban clusters of various population sizes) and 6 rural components (total rural, rural areas outside places, rural areas inside places and inside places of various population sizes). Due to problems in the initial version, the Census Bureau ultimately issued the Summary File as two data products. The first or main release comprises 50 data files with all the tables except 11 tables on selected monthly owner costs, the tables HBG66, HBG67, HBG68, HBG69, HBG70, HBG71, HBG72, HBG73, HCT19, HCT20 and HCT21. The second, supplemental release consists of a document file with the 11 tables on selected monthly owner costs. ICPSR provides each product as a separate ZIP archive. The archive with the supplemental release also includes additional technical documentation prepared by the Bureau.

This statistic shows the share of timeshare owners in the United States in 2015, by ethnicity. Of the total timeshare owners surveyed, ** percent were White and ** percent were Hispanic.

This data was compiled by the Mapping Prejudice Project and shows the location of racial covenants recorded in Hennepin County between 1910 and 1955. Racial covenants were legal clauses embedded in property records that restricted ownership and occupancy of land parcels based on race. These covenants dramatically reshaped the demographic landscape of Hennepin County in the first half of the twentieth century. In 1948, the United States Supreme Court ruled racial covenants to be legally unenforceable in the Shelly v. Kraemer decision. Racial covenants continued to be inserted into property records, however, prompting the Minnesota state legislature to outlaw the recording of new racial covenants in 1953. The same legislative body made covenants illegal in 1962. The practice was formally ended nationally with the passage of the Fair Housing Act in 1968.

This data collection is part of the American Housing Metropolitan Survey (AHS-MS, or "metro") which is conducted in odd-numbered years. It cycles through a set of 21 metropolitan areas, surveying each one about once every six years. The metro survey, like the national survey, is longitudinal. This particular survey provides information on the characteristics of a Seattle metropolitan sample of housing units, including apartments, single-family homes, mobile homes, and vacant housing units in 2009. The data are presented in eight separate parts: Part 1, Home Improvement Record, Part 2, Journey to Work Record, Part 3, Mortgages Recorded, Part 4, Housing Unit Record (Main Record), Recodes (One Record per Housing Unit), and Weights, Part 5, Manager and Owner of Rental Units Record, Part 6, Person Record, Part 7, High Burden Unit Record, and Part 8, Recent Mover Groups Record. Part 1 data include questions about upgrades and remodeling, cost of alterations and repairs, as well as the household member who performed the alteration/repair. Part 2 data include journey to work or commuting information, such as method of transportation to work, length of trip, and miles traveled to work. Additional information collected covers number of hours worked at home, number of days worked at home, average time respondent leaves for work in the morning or evening, whether respondent drives to work alone or with others, and a few other questions pertaining to self-employment and work schedule. Part 3 data include mortgage information, such as type of mortgage obtained by respondent, amount and term of mortgages, as well as years needed to pay them off. Other items asked include monthly payment amount, reason mortgage was taken out, and who provided the mortgage. Part 4 data include household-level information, including demographic information, such as age, sex, race, marital status, income, and relationship to householder. The following topics are also included: data recodes, unit characteristics, and weighting information. Part 5 data include information pertaining to owners of rental properties and whether the owner/resident manager lives on-site. Part 6 data include individual person level information, in which respondents were queried on basic demographic information (i.e. age, sex, race, marital status, income, and relationship to householder), as well as if they worked at all last week, month and year moved into residence, and their ability to perform everyday tasks and whether they have difficulty hearing, seeing, and concentrating or remembering things. Part 7 data include verification of income to cost when the ratio of income to cost is outside of certain tolerances. Respondents were asked whether they receive help or assistance with grocery bills, clothing and transportation expenses, child care payments, medical and utility bills, as well as with rent payments. Part 8 data include recent mover information, such as how many people were living in last unit before move, whether last residence was a condo or a co-op, as well as whether this residence was outside of the United States.

Home ownership persists as the primary way that families build wealth. Housing researchers and advocates often discuss the racial home ownership gap, particularly for Black and Hispanic households (Urban Institute, Pew Hispanic Center). Historical policies such as redlining, steering, and municipal underbounding have effects that stay with us today.This map shows the overall home ownership rate and the home ownership rate by race/ethnicity of householder in a chart in the pop-up. Map is multi-scale showing data for state, county, and tract.This map uses these hosted feature layers containing the most recent American Community Survey data. These layers are part of the ArcGIS Living Atlas, and are updated every year when the American Community Survey releases new estimates, so values in the map always reflect the newest data available.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau's Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2019 American Community Survey 1-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Data for the households, families, occupied housing units, owner-occupied housing units, and renter-occupied housing units lines refer to the specified race, Hispanic or Latino, American Indian or Alaska Native, or ancestry of the householder shown in the table. Data in the "Total population" column are shown regardless of the race, Hispanic or Latino, American Indian or Alaska Native, or ancestry of the person..The Census Bureau introduced a new set of disability questions in the 2008 ACS questionnaire. Accordingly, comparisons of disability data from 2008 or later with data from prior years are not recommended. For more information on these questions and their evaluation in the 2006 ACS Content Test, see the Evaluation Report Covering Disability..Industry titles and their 4-digit codes are based on the 2017 North American Industry Classification System. The Industry categories adhere to the guidelines issued in Clarification Memorandum No. 2, "NAICS Alternate Aggregation Structure for Use By U.S. Statistical Agencies," issued by the Office of Management and Budget..Occupation titles and their 4-digit codes are based on the 2018 Standard Occupational Classification..Logical coverage edits applying a rules-based assignment of Medicaid, Medicare and military health coverage were added as of 2009 -- please see https://www.census.gov/library/working-papers/2010/demo/coverage_edits_final.html for more details. Select geographies of 2008 data comparable to the 2009 and later tables are available at https://www.census.gov/data/tables/time-series/acs/1-year-re-run-health-insurance.html. The health insurance coverage category names were modified in 2010. See https://www.census.gov/topics/health/health-insurance/about/glossary.html#par_textimage_18 for a list of the insurance type definitions..Data about computer and Internet use were collected by asking respondents to select "Yes" or "No" to each type of computer and each type of Internet subscription. Therefore, respondents were able to select more than one type of computer and more than one type of Internet subscription..The category "with a broadband Internet subscription" refers to those who said "Yes" to at least one of the following types of Internet subscriptions: Broadband such as cable, fiber optic, or DSL; a cellular data plan; satellite; a fixed wireless subscription; or other non-dial up subscription types..An Internet "subscription" refers to a type of service that someone pays for to access the Internet such as a cellular data plan, broadband such as cable, fiber optic or DSL, or other type of service. This will normally refer to a service that someone is billed for directly for Internet alone or sometimes as part of a bundle.."With a computer" includes those who said "Yes" to at least one of the following types of computers: Desktop or laptop; smartphone; tablet or other portable wireless computer; or some other type of computer..Caution should be used when comparing data for computer and Internet use before and after 2016. Changes in 2016 to the questions involving the wording as well as the response options resulted in changed response patterns in the data. Most noticeable are increases in overall computer ownership or use, the total of Internet subscriptions, satellite subscriptions, and cellular data plans for a smartphone or other mobile device. For more detailed information about these changes, see the 2016 American Community S...

This data collection is part of the American Housing Metropolitan Survey (AHS-MS, or "metro") which is conducted in odd-numbered years. It cycles through a set of 21 metropolitan areas, surveying each one about once every six years. The metro survey, like the national survey, is longitudinal. This particular survey provides information on the characteristics of a New Orleans metropolitan sample of housing units, including apartments, single-family homes, mobile homes, and vacant housing units in 2009. The data are presented in eight separate parts: Part 1, Home Improvement Record, Part 2, Journey to Work Record, Part 3, Mortgages Recorded, Part 4, Housing Unit Record (Main Record), Recodes (One Record per Housing Unit), and Weights, Part 5, Manager and Owner of Rental Units Record, Part 6, Person Record, Part 7, High Burden Unit Record, and Part 8, Recent Mover Groups Record. Part 1 data include questions about upgrades and remodeling, cost of alterations and repairs, as well as the household member who performed the alteration/repair. Part 2 data include journey to work or commuting information, such as method of transportation to work, length of trip, and miles traveled to work. Additional information collected covers number of hours worked at home, number of days worked at home, average time respondent leaves for work in the morning or evening, whether respondent drives to work alone or with others, and a few other questions pertaining to self-employment and work schedule. Part 3 data include mortgage information, such as type of mortgage obtained by respondent, amount and term of mortgages, as well as years needed to pay them off. Other items asked include monthly payment amount, reason mortgage was taken out, and who provided the mortgage. Part 4 data include household-level information, including demographic information, such as age, sex, race, marital status, income, and relationship to householder. The following topics are also included: data recodes, unit characteristics, and weighting information. Part 5 data include information pertaining to owners of rental properties and whether the owner/resident manager lives on-site. Part 6 data include individual person level information, in which respondents were queried on basic demographic information (i.e. age, sex, race, marital status, income, and relationship to householder), as well as if they worked at all last week, month and year moved into residence, and their ability to perform everyday tasks and whether they have difficulty hearing, seeing, and concentrating or remembering things. Part 7 data include verification of income to cost when the ratio of income to cost is outside of certain tolerances. Respondents were asked whether they receive help or assistance with grocery bills, clothing and transportation expenses, child care payments, medical and utility bills, as well as with rent payments. Part 8 data include recent mover information, such as how many people were living in last unit before move, whether last residence was a condo or a co-op, as well as whether this residence was outside of the United States.