Racial Justice x Technology Policy

Data Collection: the Backbone of Racial Bias in Mortgage Lending Algorithms

This study will use mixed methods to examine the relationship between housing and housing insecurity, race, gender, and technology from three viewpoints: 1. The individual level, examining those who apply for a home mortgage and are denied; 2. The sub-national level in the United States, capturing county and zipcode trends in housing across the United States; and 3. Qualitatively focusing on a broader exploration of the mortgage lending process including the variables and information used in banking and lending algorithms.  

The first analysis will answer the following question: What are the main reasons that individuals are denied a home mortgage and does this this vary by race, gender, and geography?  The second analysis will use machine learning and cluster analysis to examine the variation in the housing market (type of housing, value of homes) by social determinant of health clusters.  The qualitative questions will enhance the quantitative analysis and ask the following questions: How are banking and lending algorithms trained and regulated? How do these algorithms overlap with race, gender and loan outcomes?

Check Out the Team's RJxTP Awards Powerpoint

Read the Team's Research Proposal

Research Team

Diana Bowser, ScD

Lisa Thorn, Graduate Research Assistant

Lisa Kim Thorn, MPP'23