Dan Green
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This article was checked for accuracy as of September 19, 2024. Learn more about our commitments to accuracy and your mortgage education in our editorial guidelines.
Updated: September 19, 2024
Older Millennials are making significant moves in the housing market.
Home buyers aged 35-44, who have experienced both economic growth and downturns, are carefully entering the real estate market.
Our study, “ZIP Code Invasions: Older Millennial Home Buyers Age 35-44,” uses data from the FFEIC and the Home Mortgage Disclosure Act (HMDA) to highlight where older Millennials are purchasing homes.
Older Millennials combine their technological expertise with growing financial resources. Their average loan size in 2022 was $437,904, the highest among all age groups, reflecting their focus on metropolitan areas and affluent suburbs.
For example, 64.47% of home buyers in the 98029 ZIP code of Issaquah, Washington, are aged 35-44. In Pleasanton, California’s 94566, a similar trend is seen. Both areas report household incomes exceeding $130,000.
In New York, Older Millennials are choosing ZIP codes such as 10506 in Bedford and 10583 in Scarsdale, where home values are close to $1.7 million. Other key areas include Troy, Michigan (48084), and South Plainfield, New Jersey (07080), reflecting their impact across the country.
Scroll to the bottom for our complete methodology.
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The data for our study was sourced from the FFEIC website, specifically the Snapshot National Loan Level Dataset for 2018-2023. These files contain HMDA data submitted by U.S. mortgage lenders as of April 30, 2022, with privacy protections applied to applicants and borrowers.
This study focuses on home buyers of 1-4 unit residential properties across the 50 states and Washington, D.C. We excluded purchases of second homes and investment properties, as well as one-time construction loans and open-ended mortgages like home equity lines of credit (HELOC).
Our analysis involved complex database queries, which combined data from the Loan Application Register and U.S. Census Bureau to handle large datasets over 30 GB in size.
We excluded outlier data from applications marked withdrawn and incomplete, as these fell outside typical ranges. Less than 0.5% of the HMDA data was removed for these reasons.
To ensure accuracy, we relied on the strength of the HMDA data and the precision of our queries. The data extraction, filtering, and analysis processes were designed to maximize reliability.
We conducted this research to help first-time home buyers make informed decisions about housing.
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