Graph and download economic data for Homeownership Rate in the United States (RHORUSQ156N) from Q1 1965 to Q2 2025 about homeownership, housing, rate, and USA.

About 36 percent of homeowners in England were aged 65 and above, which contrasts sharply with younger age groups, particularly those under 35. Young adults between 25 and 35, made up 15 percent of homeowners and had a dramatically lower homeownership rate. The disparity highlights the growing challenges faced by younger generations in entering the property market, a trend that has significant implications for wealth distribution and social mobility. Barriers to homeownership for young adults The path to homeownership has become increasingly difficult for young adults in the UK. A 2023 survey revealed that mortgage affordability was the greatest obstacle to property purchase. This represents a 39 percent increase from 2021, reflecting the impact of rising house prices and mortgage rates. Despite these challenges, one in three young adults still aspire to get on the property ladder as soon as possible, though many have put their plans on hold. The need for additional financial support from family, friends, and lenders has become more prevalent, with one in five young adults acknowledging this necessity. Regional disparities and housing supply The housing market in England faces regional challenges, with North West England and the West Midlands experiencing the largest mismatch between housing supply and demand in 2023. This imbalance is evident in the discrepancy between new homes added to the housing stock and the number of new households formed. London, despite showing signs of housing shortage, has seen the largest difference between homes built and households formed. The construction of new homes has been volatile, with a significant drop in 2020, a rebound in 2021 and a gradual decline until 2024.

Graph and download economic data for Consumer Unit Characteristics: Percent Homeowner by Age: from Age 25 to 34 (CXUHOMEOWNLB0403M) from 1990 to 2023 about consumer unit, age, homeownership, 25 years +, percent, and USA.

The homeownership rate was the highest among Americans in their early 70s and the lowest among people in their early 20s in 2024. In that year, approximately **** percent of individuals aged 70 to 74 resided in a residence they owned, compared to approximately ** percent among individuals under the age of 25. On average, **** percent of Americans lived in an owner-occupied home. The homeownership rate was the highest in 2004 but has since declined.

Our US Home Ownership Data is gathered and aggregated via surveys, digital services, and public data sources. We use powerful profiling algorithms to collect and ingest only fresh and reliable data points.

Our comprehensive data enrichment solution includes various data sets that can help you address gaps in your customer data, gain a deeper understanding of your customers, and power superior client experiences. 1. Geography - City, State, ZIP, County, CBSA, Census Tract, etc. 2. Demographics - Gender, Age Group, Marital Status, Language etc. 3. Financial - Income Range, Credit Rating Range, Credit Type, Net worth Range, etc 4. Persona - Consumer type, Communication preferences, Family type, etc 5. Interests - Content, Brands, Shopping, Hobbies, Lifestyle etc. 6. Household - Number of Children, Number of Adults, IP Address, etc. 7. Behaviours - Brand Affinity, App Usage, Web Browsing etc. 8. Firmographics - Industry, Company, Occupation, Revenue, etc 9. Retail Purchase - Store, Category, Brand, SKU, Quantity, Price etc. 10. Auto - Car Make, Model, Type, Year, etc. 11. Housing - Home type, Home value, Renter/Owner, Year Built etc.

Consumer Graph Schema & Reach: Our data reach represents the total number of counts available within various categories and comprises attributes such as country location, MAU, DAU & Monthly Location Pings:

Data Export Methodology: Since we collect data dynamically, we provide the most updated data and insights via a best-suited method on a suitable interval (daily/weekly/monthly).

Consumer Graph Use Cases: 360-Degree Customer View: Get a comprehensive image of customers by the means of internal and external data aggregation. Data Enrichment: Leverage Online to offline consumer profiles to build holistic audience segments to improve campaign targeting using user data enrichment Fraud Detection: Use multiple digital (web and mobile) identities to verify real users and detect anomalies or fraudulent activity. Advertising & Marketing: Understand audience demographics, interests, lifestyle, hobbies, and behaviors to build targeted marketing campaigns.

The distribution of all owner-occupier households in England in 2024 varied per age group, as well as the type of home financing. The older the age group, the larger the share of owner-occupier homeowners who purchased their home outright. A share of 2.1 percent of own outright homeowners were between the ages of 25 to 34, whereas a share of 62.1 percent of own outright homeowners were aged 65 and over. Although this is the case, the largest share of homeowners who purchased their house with a mortgage was in the age range of 35 to 44 years old.

This map's colors indicate which age group is has the most people in each area. Esri aggregated the Census data into 10-year age groups. Age groups include Under 10, 10 to 19, 20 to 29 and so forth. Data is from the U.S. Census Bureau’s 2020 Census Demographic and Housing Characteristics. The map's layers contain total population counts by sex, age, and race groups for Nation, State, County and Census Tract in the United States and Puerto Rico.About the dataYou can use this map as is and you can also modify it to use other attributes included in its layers. This map's layers contain total population counts by sex, age, and race groups data from the 2020 Census Demographic and Housing Characteristics. This is shown by Nation, State, County, Census Tract, Block Group boundaries. Each geography layer contains a common set of Census counts based on available attributes from the U.S. Census Bureau. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis.Vintage of boundaries and attributes: 2020 Demographic and Housing Characteristics Table(s): P1, H1, H3, P2, P3, P5, P12, P13, P17, PCT12 (Not all lines of these DHC tables are available in this feature layer.)Data downloaded from: U.S. Census Bureau’s data.census.gov siteDate the Data was Downloaded: May 25, 2023Geography Levels included: Nation, State, County, Census Tract, Block GroupNational Figures: included in Nation layer The United States Census Bureau Demographic and Housing Characteristics: 2020 Census Results 2020 Census Data Quality Geography & 2020 Census Technical Documentation Data Table Guide: includes the final list of tables, lowest level of geography by table and table shells for the Demographic Profile and Demographic and Housing Characteristics.News & Updates This map is ready to be used in ArcGIS Pro, ArcGIS Online and its configurable apps, Story Maps, dashboards, Notebooks, Python, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the U.S. Census Bureau when using this data. Data Processing Notes: These 2020 Census boundaries come from the US Census TIGER geodatabases. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For Census tracts and block groups, 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 and block group 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 unchanged and available as attributes within the data table (units are square meters).  The layer contains all US states, Washington D.C., and Puerto Rico. Census tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99). Block groups that fall within the same criteria (Block Group denoted as 0 with no area land) have also been removed.Percentages and derived counts, are calculated values (that can be identified by the "_calc_" stub in the field name). Field alias names were created based on the Table Shells file available from the Data Table Guide for the Demographic Profile and Demographic and Housing Characteristics. Not all lines of all tables listed above are included in this layer. Duplicative counts were dropped. For example, P0030001 was dropped, as it is duplicative of P0010001.To protect the privacy and confidentiality of respondents, their data has been protected using differential privacy techniques by the U.S. Census Bureau.Web map design by Maddie Haynes, Esri Professional Services

The dataset contains the figures for external migration since 2014 from the small-scale housing market observation in Düsseldorf. The data are given in separate files for moves and advances in the respective years. On the basis of newly defined residential quarters, the small-scale housing market observation enables a detailed spatial analysis of the status quo on the housing market. It includes data since 2014 and is updated annually. Each residential district is assigned to a residential district type. These types are derived from the construction typology and the construction age as well as the different land use. The residential quarters thus characterise the different, small-scale sub-markets in Düsseldorf. In your own datasets you will find geo-information on the residential district boundaries and key tables for the residential quarters. The interactive offer of the data can be found on the website of the Office for Statistics and Elections at: https://www.duesseldorf.de/statistik-und-wahlen/statistik-und-stadtforschung/analysen/wohnungsmarktbeobachtung.html Here you will also find explanatory terms and the data collection for housing market observation in the PDF and XLS file formats. The tables are divided into the following age categories: 0 to under 6 years, 6 to under 10 years, 10 to under 18 years, 18 to under 25 years, 25 to under 30 years, 30 to under 50 years, 50 to under 65 years, 65 years and over The files with the excavations from the residential quarters by age group contain the following column information: Residential area: Housing ID Excavations 0 to under 6 years: Number of advances between 0 and under 6 years old Trains 6 to under 10 years: Number of advances of 6 to under 10-year-olds Excavations 10 to under 18 years: Number of advances of 10 to under 18 year olds Trains 18 to under 25 years: Number of trainees aged 18 to less than 25 years old Excavations 25 to under 30 years: Number of advances of 25 to under 30 year olds Excavations 30 to under 50 years: Number of advances between 30 and under 50 years old Excavations 50 to under 65 years: Number of advances of 50 to less than 65-year-olds 65 years of age and over: Number of group 65-year-olds and older persons The files with the moves from the residential quarters by age group contain the following column information: Residential area: Housing ID Allowances 0 to under 6 years: Number of moves between 0 and under 6 year olds Allowances 6 to less than 10 years: Number of trainees aged 6 to less than 10 years old Allowances 10 to under 18 years: Number of trainees aged 10 to less than 18 years old Trainees 18 to under 25 years: Number of trainees aged 18 to under 25 Allowances 25 to under 30 years: Number of trainees aged 25 to under 30 Trainees 30 to under 50 years: Number of trainees aged 30 to under 50 Allowances 50 to under 65 years: Number of trainees aged 50 to under 65 Trainees 65 years and over: Number of group 65-year-olds and older moves The files with total numbers of moves and moves from the residential quarters contain the following column information: Residential area: Housing ID Year: Number of year of survey Note: The allocation of the districts to the residential quarters results from the district numbering. The first two places give the district and the district. The subsequent digits indicate the consecutive numbering of the residential quarters.

Home Ownership Rate in the United States decreased to 65.10 percent in the first quarter of 2025 from 65.70 percent in the fourth quarter of 2024. This dataset provides the latest reported value for - United States Home Ownership Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

People under the age of ** comprised the largest share of renters in the U.S. in 2023. Almost half of the population that lives in a rental apartment fell in this age group, while the eldest generation of 65-year-olds and older accounted for ** percent. This disparity can be explained by the vast differences in homeownership rates between these age groups.

Approximately ** percent of Americans aged 26 to 34 who bought a home were first-home buyers, whereas ** percent of home buyers between 35 and 44 bought their first home in that year. Gen Z and Millennial first-time buyers It is no surprise that many Gen Z (18 to 24 years old) and Millennial (25 to 43 years old) home buyers are mostly first-time home buyers. These home buyers are in the early stages of their careers, or still studying in some cases, and often struggling to repay student debt, so they need to save for many years before they afford a down payment. When do they sell? These generations tend to stay in their first homes for several years, which means that the majority of home sellers are older than them. The share of income needed to afford a trade-up home is significantly lower than the money needed for a starter home. A trade-up home is a larger and more expensive home, which homeowners often buy after living in their starter home, or their first home, for several years. This progression generally happens when homeowners have climbed the career ladder and increased their incomes.

This indicator is defined as the median of the distribution of the share of total housing costs (net of housing allowances) in the total disposable household income (net of housing allowances) presented by age group.

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

The American Community Survey (ACS) helps local officials, community leaders, and businesses understand the changes taking place in their communities. It is the premier source for detailed population and housing information about our nation.Part of the American Community Survey (ACS) looks to define demographic and housing estimates. We use the 5-Year Estimates to have a greater level of precision to our data, according to the Distinguishing features of ACS 1-year, 1-year supplemental, 3-year, and 5-year estimates table.We query attributes of the S0101 (Age and Sex) Group of questions for years available.This dataset has been narrowed down to Cary township using following the geographies codes supported for the ACS dataset:state: 37county: 183county subdivision: 90536

The data set contains population figures by age group from the small-scale housing market observation in Düsseldorf since 2014. On the basis of newly defined residential quarters, the small-scale housing market observation enables a detailed, spatial analysis of the status quo on the housing market. It includes data since 2014 and is updated annually. Each residential quarter is assigned to a residential district type. These types derive from the building typology and the construction age as well as the different land uses. The residential quarters thus characterise the different, small-scale sub-markets in Düsseldorf. In your own data sets you will find geoinformation about the residential quarter boundaries and key tables for the residential quarters. The interactive offer on the data can be found on the page of the Office of Statistics and Elections at: https://www.duesseldorf.de/statistik-und-wahlen/statistik-und-stadtforschung/analysen/wohnungsmarktbeobachtung.html Here you will also find explanations of terms and the data collection for housing market observation in the file formats PDF and XLS. The files contain the following column information: Residential quarters: Number of the quarters 0 to less than 6 years: Number of population under 6 years of age in residential quarters 6 to less than 10 years: Number of people in the age group from 6 to under 10 in the residential quarter 10 to less than 18 years: Number of people in the age group from 10 to below 18in residential quarters 18 to less than 25 years: Number of people in the age group from 18 to under 25 in the residential quarter 25 to less than 30 years: Number of people in the age group from 25 to under 30 in the residential quarter 30 to less than 50 years: Number of people in the age group from 30 to under 50 in the residential quarter 50 to less than 65 years: Number of people in the age group from 50 to under 65 in the residential area 65 years and older: Number of population in the age group of 65 years or more The allocation of the districts to the residential quarters is determined by the quarter numbering. The first two places are the district and the district. The subsequent digits indicate the sequential numbering of the residential quarters.

SSTF 1 contains sample data weighted to represent the total population. In addition, the file contains 100-percent counts and unweighted sample counts for total persons and total housing units in the 1990 Census. Population variables include citizenship, ability to speak English, age, number of children ever born, class of worker, disability status, earnings in 1989, educational attainment, employment status, household size, industry, labor force status, language spoken at home, occupation, poverty status in 1989, school enrollment, and year of entry into the United States. Housing variables include gross rent, housing units, kitchen facilities, mortgage status, plumbing facilities, tenure, units in structure, and year householder moved into unit. The data are also crosstabulated and presented in a variety of tables. Crosstabulations include citizenship and year of entry by all other variables, age (groups) by sex by school enrollment or college enrollment or educational attainment and employment status, age by poverty status by sex, relationship by family type by subfamily type, and employment status by hours worked last week and year last worked. The dataset includes both "A" and "B" records. "A" records have three population (PA) and three housing (HA) tables. The "B" records present more detail in 66 population (PB) and 10 housing (HB) tables, and are divided into 22 segments of 8,142 characters each.

Number and proportion of population in core housing need, by selected economic family characteristics of persons, sex, age group and selected demographic characteristics, Canada, provinces and territories.

This data collection contains summary statistics on population and housing subjects derived from the responses to the 2010 Census questionnaire. Population items include sex, age, average household size, household type, and relationship to householder such as nonrelative or child. Housing items include tenure (whether a housing unit is owner-occupied or renter-occupied), age of householder, and household size for occupied housing units. Selected aggregates and medians also are provided. The summary statistics are presented in 71 tables, which are tabulated for multiple levels of observation (called "summary levels" in the Census Bureau's nomenclature), including, but not limited to, regions, divisions, states, metropolitan/micropolitan areas, counties, county subdivisions, places, ZIP Code Tabulation Areas (ZCTAs), school districts, census tracts, American Indian and Alaska Native areas, tribal subdivisions, and Hawaiian home lands. There are 10 population tables shown down to the county level and 47 population tables and 14 housing tables shown down to the census tract level. Every table cell is represented by a separate variable in the data. Each table is iterated for up to 330 population groups, which are called "characteristic iterations" in the Census Bureau's nomenclature: the total population, 74 race categories, 114 American Indian and Alaska Native categories, 47 Asian categories, 43 Native Hawaiian and Other Pacific Islander categories, and 51 Hispanic/not Hispanic groups. Moreover, the tables for some large summary areas (e.g., regions, divisions, and states) are iterated for portions of geographic areas ("geographic components" in the Census Bureau's nomenclature) such as metropolitan/micropolitan statistical areas and the principal cities of metropolitan statistical areas. The collection has a separate set of files for every state, the District of Columbia, Puerto Rico, and the National File. Each file set has 11 data files per characteristic iteration, a data file with geographic variables called the "geographic header file," and a documentation file called the "packing list" with information about the files in the file set. Altogether, the 53 file sets have 110,416 data files and 53 packing list files. Each file set is compressed in a separate ZIP archive (Datasets 1-56, 72, and 99). Another ZIP archive (Dataset 100) contains a Microsoft Access database shell and additional documentation files besides the codebook. The National File (Dataset 99) constitutes the National Update for Summary File 2. The National Update added summary levels for the United States as a whole, regions, divisions, and geographic areas that cross state lines such as Core Based Statistical Areas.

In April 2019, ** percent of Gen X renters said they planned to continue renting in the United States and the only ** percent said they planned to purchase a home. In February 2018, ** percent planned to buy a home in the near future, so the trend towards homeownership has fallen slightly in this age group.