Appraisals, Automated Valuation Models, and Mortgage Default

28 Pages Posted: 7 Apr 2006

See all articles by Austin Kelly

Austin Kelly

Federal Housing Finance Agency (FHFA)

Date Written: April 2006


Previous research has suggested the possibility that professional appraisals or econometric estimates of collateral value may be indicative of credit risk. This paper examines the issue by estimating the probability of a mortgage default (defined both as 90 day delinquency and as a claim on mortgage insurance) as a function of the difference between sales price of a home and the estimated value of the home at the time of the purchase, produced by both an appraisal and by an Automated Valuation Model (AVM). Logistic regression is used to estimate the quarterly hazard of a serious delinquency, or claim, as a function of a host of standard control variables, and the percent difference between the sales price and the appraisal and/or AVM estimate. The data consist of a nationally representative random sample of about 5,000 FHA insured single family mortgages endorsed in Fiscal Years 2000, 2001, and 2002, observed through January 31, 2006, and a sample of about 1,000 FHA loans from the Atlanta MSA in the same time period. The records are augmented with the results from an AVM. The difference between the sale price and the appraisal or AVM estimate is found to significantly increase the probability of delinquency, and increase the probability of foreclosure, significantly so in the national sample. Also, transactions that are valued with higher precision have lower default propensities. Additionally, the differences are found to increase loss given default in the small subset of loans that had completed the property disposition process.

Keywords: mortgage, default, appraisal, automated valuation model, AVM

JEL Classification: G21, R21

Suggested Citation

Kelly, Austin J., Appraisals, Automated Valuation Models, and Mortgage Default (April 2006). Available at SSRN: or

Austin J. Kelly (Contact Author)

Federal Housing Finance Agency (FHFA) ( email )

1700 G St. NW
Washington, DC 20552
United States
202 414-1336 (Phone)

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