Home buyer statistics from 100+ million applications showing generational differences in loan amounts and down payments by Homebuyer.com.

Distribution of Late Millennial home buyers by race and ethnicity.

Distribution of Early Baby Boomer home buyers by race and ethnicity.

Distribution of Late Baby Boomer home buyers by race and ethnicity.

Data on resident buyers who are persons that purchased a residential property in a market sale and filed their T1 tax return form: number of and incomes of residential property buyers, sale price, price-to-income ratio by the number of buyers as part of a sale, age groups, first-time home buyer status, buyer characteristics (sex, family type, immigration status, period of immigration, admission category).

Distribution of Gen X home buyers by race and ethnicity.

ZIP codes with the highest number of Gen X home purchases in 2024.

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.

Average household income, debt-to-income ratios, and area median income comparisons for Gen Z home buyers.

According to a survey conducted among over 15,000 respondents in the U.S., between 36 and 55 percent of home buyers who were actively looking to buy a home in the next 12 months were not able to find one at a price they could afford as of the second quarter of 2023. Approximately ** percent of millennials struggled to find a home at an acceptable price, while for Baby Boomers, this percentage was higher at ** percent. According to the source, the main reason for the decline across all generations except for baby boomers was that respondents reported other reasons, such as getting outbid by other buyers or the inability to find a home in the desired neighborhood. In the second quarter of 2023, roughly ** percent of U.S. adults were planning a home purchase in the next year.

The most frequently occurring DTI range for home buyers in each age group.

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.

In 2024, the median income range between of more than 200,000 U.S. dollars was the largest share of homebuyers in the United States that year. Among them, almost ** percent were between 26 and 59 years old. The income range between 100,000 and 124,999 U.S. dollars was the second one with the largest share of homebuyers in the United States that year. Among them, ** percent were between 18 and 34 years old.

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.

Graph and download economic data for Existing Home Sales (EXHOSLUSM495S) from Oct 2024 to Oct 2025 about headline figure, sales, housing, and USA.

Average household income, debt-to-income ratios, and area median income comparisons for Late Millennial home buyers.

This dataset includes home buyers who purchased a home with an Iowa Finance Authority single family mortgage program in the State of Iowa with a loan purchase date between July 1, 2016 and June 30, 2018. The data includes loan Purchase Date, Bond Series, Loan Amount and County.

This table shows the average purchase price that has been paid in the reporting period for existing own homes purchased by a private individual. The average purchase price of existing own homes may differ from the price index of existing own homes. The average purchase price is no indicator for price developments of owner-occupied residential property. The average purchase price reflects the average price of dwellings sold in a particular period. The fact that de dwellings sold differs from one period to another is not taken into account. The following instance explains which problems are entailed by the continually changing of the quality of the dwellings sold. Suppose in February of a particular year mainly big houses with extensive gardens beautifully situated alongside canals are sold, whereas in March many small terraced houses are sold. In that case the average purchase price in February will be higher than in March but this does not mean that house prices are increased. See note 3 for a link to the article 'Why the average purchase price is not an indicator'.

Data available from: 1995

Status of the figures: The figures in this table are immediately definitive. The calculation of these figures is based on the number of notary transactions that are registered every month by the Dutch Land Registry Office (Kadaster). A revision of the figures is exceptional and occurs specifically if an error significantly exceeds the acceptable statistical margins. The average purchasing prices of existing owner-occupied sold homes can be calculated by Kadaster at a later date. These figures are usually the same as the publication on Statline, but in some periods they differ. Kadaster calculates the average purchasing prices based on the most recent data. These may have changed since the first publication. Statistics Netherlands uses figures from the first publication in accordance with the revision policy described above.

Changes as of 17 February 2025: Added average purchase prices of the municipalities for the year 2024.

When will new figures be published? New figures are published approximately one to three months after the period under review.

📝 Dataset Description: This synthetic dataset contains 3,000 residential property listings modeled after real U.S. house sales data (in a Zillow-style format). It is designed for use in real estate analysis, machine learning, data visualization, and web scraping practice.

Each row represents a unique property and includes 16 key features commonly used by real estate agents, investors, and analysts. The data spans multiple U.S. states and cities, with realistic values for price, square footage, bedroom/bathroom count, property type, and more.

✅ Included Fields: Price – Listing price (in USD)

Address, City, State, Zipcode – U.S. formatted property location

Bedrooms, Bathrooms, Area (Sqft) – Core home specs

Lot Size, Year Built, Days on Market

Property Type, MLS ID, Listing Agent, Status

Listing URL – Mock Zillow-style property link

⚙️ Use Cases: Exploratory data analysis (EDA)

Regression/classification model training

Feature engineering and preprocessing

Real estate dashboards and web app mockups

Practice with BeautifulSoup, Pandas, or Power BI

In January 2021, ** percent of respondents in a survey among real estate agents in the United States said that it was easier for home buyers to visualize a property as their future home when the property was staged. Additionally, ** percent of buyer's agents thought that home staging made buyers more willing to walk through a home they saw online.In 2020, 97 percent of home buyers in the U.S. made use of the internet in the process of buying a home.