Automated Valuation Models Can Help Identify Home Appraisal Bias in Minority Communities

Automated Valuation Models, or AVMs, can be the solution needed to identify potential racial bias in home appraisals, according to a study released today by Veros Real Estate Solutions (VEROS^®), an industry leader in enterprise risk management and collateral valuation services.

In the past several years, discussions around potential bias or discrimination in housing markets have been reignited with a focus on the property valuation process – beginning specifically with appraisals and extending to questions about potential algorithmic bias in AVMs. Several studies have been conducted, often coming to contradictory conclusions. Through an in-depth analysis of 50 Chicago ZIP codes, Veros sought to determine whether there is any evidence of bias in its AVM in minority communities. Specifically, the study assessed whether the percentage of undervalued properties was related to the ZIP code’s racial composition. Chicago was selected because its ethnically diverse population spans the full spectrum of racial composition.

The results revealed no evidence of racial basis in the VeroVALUE^SM AVM.

“We found that the proportion of properties that are undervalued by 15% or more is not correlated with the proportion of Black, Hispanic, Asian, or White populations,” said Eric Fox, Veros Real Estate Solutions Chief Economist. “This is crucial because it addresses concerns about undervaluations of minority-owned properties.”

Veros’ study is especially significant as the gap in minority homeownership has been pulled into the spotlight. One year ago, in June of 2021, President Biden announced the creation of a task force on Property Appraisal and Valuation Equity, known as PAVE, to focus on eliminating racial bias in home appraisals.

Since then, discussions have emerged on whether AVMs could be part of this solution.

Veros’ study suggests that professional-grade AVMs can, in fact, be leveraged to provide an independent valuation that could be used to help flag any potential instances of bias.

“The appraisal industry can use an effective, professional-grade AVM like VeroVALUE to address this problem,” Fox added. “An objective, cost-effective solution would be to use such an AVM to check if appraised values are in agreement and if so, would be deemed low risk for bias. Those appraisals in significant disagreement with a professional-grade AVM could be appropriately flagged or escalated for a more detailed review.”

Veros stressed the need for more analysis across all property valuation tools and services to determine the absence of any human or algorithmic bias.

“We also plan to continue the analysis of the VeroVALUE AVM by conducting studies in other cities across the country to obtain a more granular idea of its performance across regions,” added Veros Research Economist Reena Agrawal.

About Veros Real Estate Solutions

A mortgage technology innovator since 2001, Veros is a proven leader in enterprise risk management and collateral valuation services. The firm combines the power of predictive technology, data analytics, and industry expertise to deliver advanced automated solutions that control risk and increase profits throughout the mortgage industry, from loan origination to servicing and securitization. Veros’ services include automated valuation, fraud, and risk detection; and portfolio analysis, forecasting, and next-generation collateral risk management platforms. Veros is the primary architect and technology provider of the GSEs’ Uniform Collateral Data Portal® (UCDP®). Veros also works closely with the FHA to support its Electronic Appraisal Delivery (EAD) portal. The company is also making the home buying process more efficient for our nation’s Veterans through its appraisal management work with the Department of Veterans Affairs. For more information, visit www.veros.com or call 866-458-3767.

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Veros’ study suggests that professional-grade AVMs can, in fact, be leveraged to provide an independent valuation that could be used to help flag any potential instances of bias.