Racial disparities in access to credit and homeownership have emerged as a major hurdle for minority communities in their quest to build equity and accumulate wealth. The housing sector has been a prime focus of the debate on the wealth gap, with discussions centering on the alleged racial bias in appraisals and extending to all valuation solutions and approaches. As industry stakeholders examine the implications of racial discrimination in the housing market, many have begun to question whether such biases can be traced back to the historical practice of redlining.
Although the Fair Housing Act of 1968 made redlining illegal, there is a contention that historically biased data may influence property valuation solutions. Such data could be embedded in algorithms used to determine property values.
Given the enormity of racial disparities, an ongoing effort exists to address the problem. This has led to a renewed focus on the need for greater transparency and accountability in the appraisal process and testing other valuation methods for biases.
The origin of redlining can be found in the policies developed by the Home Owners Loan Corporation (HOLC) created in 1933 by the government to reduce home foreclosures during the Great Depression. The HOLC was a temporary program designed to purchase underwater mortgages and refinance them at easier terms for borrowers.
To develop a viable housing program, the HOLC created color-coded maps that evaluated urban neighborhoods for mortgage risk. The evaluation process factored in the target property’s attributes and the characteristics and racial composition of the neighborhood where it was situated. The color-coded system designated redlined areas as “high risk” and ineligible for federal mortgage insurance.
Notably, a considerable proportion of the neighborhoods that received red ratings were occupied by Black residents. As a result, these neighborhoods were denied access to the financial resources necessary for home ownership and improvements.
Some researchers have concluded that the long-term consequences of discriminatory policies such as redlining include lower household incomes, a greater likelihood of living in prevalent poverty neighborhoods, and lower credit scores.
Additionally, research has shown that housing segregation continues to impact the financial well-being of minority households significantly. Historical discriminatory practices prevented Black families from purchasing homes in white suburban areas and forced them to remain in economically disadvantaged neighborhoods, often as renters. Furthermore, limited credit opportunities have hindered neighborhood and community development, exacerbating the economic disadvantage faced by minority households.
One modern-day outcome of historical redlining is that homes located within historically redlined neighborhoods typically have lower living areas and lot sizes than homes in non-redlined areas, leading to lower median house prices than larger homes. This has negatively impacted minority households, which are disproportionately located in these areas and have faced significant barriers to home ownership and wealth accumulation.
Policymakers and industry leaders must continue to prioritize fair housing practices and work towards dismantling the systemic barriers that have kept so many minority households from achieving financial stability and security.
One way the industry can work towards dismantling these barriers is by using Automated Valuation Models (AVMs). AVMs that have been thoroughly assessed for accuracy and bias can be used to check whether appraisals or other sources of property valuation may be at risk for incorrect valuation based on a property’s location in a historically redlined area.
AVMs offer a distinct benefit because they do not rely on any data related to historical redlining maps or demographic information concerning the parties involved in real estate transactions. Also, AVMs are low-cost and easy to use, making it possible to run an analysis quickly and efficiently against each appraised property. Appraised values that agree with a professional-grade AVM with a high confidence score would generally be deemed low risk for bias. However, appraisals in significant disagreement with a professional-grade AVM could be flagged for a more detailed review.
In an environment where housing finance stakeholders consider accuracy and fairness across the entire valuation spectrum, the AVM is a proven and invaluable tool for quality control regardless of the valuation product or service.
Reena Agrawal is a Research Economist at Veros Real Estate Solutions, a leader in enterprise risk management, disaster data solutions, and collateral valuation services. Reena is a subject matter expert for all affairs related to real estate economic research and valuation solution.