Brief How Automated Valuation Models Can Disproportionately Affect Majority-Black Neighborhoods
Michael Neal, Sarah Strochak, Linna Zhu, Caitlin Young
Display Date
File
File
Download Report
(218.86 KB)

Automated valuation models (AVMs), which allow for contact-free assessment of a home’s market value, hold great promise for reducing the costs and increasing the accuracy of home valuations. They have also been critical to supporting the real estate market during the COVID-19 pandemic. But AVMs in majority-Black neighborhoods produce larger errors, relative to the underlying sales price, than AVMs in majority-white neighborhoods, potentially contributing to the wide housing wealth gap between Black and white homeowners. By refining the current AVM, this valuation disparity could be eliminated, and the benefits of homeownership could be more equitably available to all homeowners.

Research Areas Wealth and financial well-being Race and equity Housing finance Housing
Tags Federal housing programs and policies Asset and debts Racial and ethnic disparities Housing and the economy Agency securitization Homeownership Financial products and services Wealth inequality Mobility Inequality and mobility Opportunity and ownership Racial homeownership gap
Policy Centers Housing Finance Policy Center