Wenfei Xu
Cornell University, NY·Social Science
Federal mortgage programs have helped millions of Americans buy homes. Yet it is hard to tell who actually received these loans and how they changed neighborhoods, because most past studies relied on agency reports rather than records of actual loans.
- Fields
Social Science
Public Health & Epidemiology
- Topics
housing policy
mortgages
Federal Housing Administration
- Grants
- 1 known grant, through May 2027
What they work on
- Build a public, geocoded dataset of roughly 280,000 FHA-insured and VA-guaranteed mortgages from land records in 20 U.S. counties spanning 40 years.
- Describe the spatial and demographic patterns of who received government-insured mortgages, using individual borrower data and address-level property information with econometric methods.
- Test hypotheses about how FHA and VA loans shaped homeownership, wealth and neighborhood composition, comparing population groups and urban versus rural counties.
More about the research
This collaborative project builds a new public dataset to answer those questions for mortgages insured by the Federal Housing Administration (FHA) and guaranteed by the Veterans Administration (VA).
Why it matters
A detailed public record of federally backed mortgages could let researchers, policymakers and community groups judge how well FHA and VA programs have served U.S. households. The findings could inform discussions of wealth building, neighborhood outcomes, intergenerational mobility and demographic gaps, and may help improve how mortgage markets work.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Winnie Van Dijk
Yale University, CT·Social Science
Build a comprehensive database of federal neighborhood revitalization project boundaries and timing by digitizing federal, state and municipal archives, project reports and historical newspapers, using large language models to summarize and classify the documents.
Elizabeth Roberto
Rice University, TX·Social Science·Early-career
Measure how road network connectivity relates to residential spatial variation and proximity to local public services across U.S. cities, using spatial and network analysis of road networks and demographic data.
Constantine Kontokosta
New York University, NY·Social Science
Identify representativeness bias in smartphone mobility data, meaning which groups are over- or undercounted, and develop methods to correct it.
The school
Cornell University: 37 labs on PI Pages, $1.6B a year on research, 537 research doctorates a year.
Reach out
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