Appraisal Accuracy and Automated Valuation Models in Rural Areas
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Appraisal Accuracy and Automated Valuation Models in Rural Areas Alexander N. Bogin 1 & Jessica Shui 1 # This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2019
Abstract Accurate and unbiased property value estimates are essential to credit risk management. Along with loan amount, they determine a mortgage’s loan-to-value ratio, which captures the degree of homeowner equity and is a key determinant of borrower credit risk. For home purchases, lenders generally require an independent appraisal, which, in addition to a home’s sales price, is used to calculate a value for the underlying collateral. A number of empirical studies have shown that property appraisals tend to be biased upwards, and over 90 percent of the time, either confirm or exceed the associated contract price. Our data suggest that appraisal bias is particularly pervasive in rural areas where over 25 percent of rural properties are appraised at more than five percent above contract price. Given this significant upward bias, we examine a host of alternate valuation techniques to more accurately estimate rural property values. Keywords Automated valuation models . Appraisal . Property value . Rural JEL Classification G21 . L85 . R3
Introduction Accurate property value estimates are an essential component of the mortgage underwriting process. Along with the loan amount, they determine a mortgage’s loan-tovalue (LTV) ratio, which captures the degree of homeowner equity and the credit risk of a loan. For home purchases, lenders generally require an independent appraisal, which,
* Jessica Shui [email protected] Alexander N. Bogin [email protected]
1
Federal Housing Finance Agency, Office of Policy Analysis & Research, 400 7th Street SW, Washington, DC 20219, USA
A.N. Bogin, J. Shui
in addition to a home’s sales price, is used to determine a value for the underlying collateral. A number of empirical studies have shown that property appraisals tend to be biased upwards, and over 90% of the time, either confirm or exceed the associated contract price.1 This upward appraisal bias is often particularly pronounced in rural areas where there are fewer comparable sales and more heterogeneity across homes. In fact, our data suggest that more than 25% of rural appraisals exceed the associated contract price by more than 5%. Given the extent and ubiquity of appraisal bias in rural areas, we create a series of alternate automated property value estimates, using a number of machine learning algorithms, to more accurately value the collateral underlying rural purchase-money mortgages. Appraisals are performed by experts with specialized knowledge about local housing markets, but they can face pressures—either apparent or perceived—to arrive at a value estimate at or above the contract price to ensure that a sale goes through. Since the Great Recession, the majority of single-family conforming loans have been sold to or securitized by the government sponsored enterprises (GSEs). For purcha
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