In the wake of the refi boom, nonprime and alternative-A lending have become popular strategies to sustain and increase volume. That means more risk of fraud or inadequately collateralized loans next year.
Most top lenders have adopted automated valuation models. But when foreclosures occur, valuation is only a reference point; stakeholders in a loan or loan pool mostly care about what their loss is likely to be.
Lenders should understand whether and how collateral risk assessment is built into the automated valuation models they use and should install suitable tools to spot collateral risk and identity fraud. Filtering out risky loans before underwriting can also save a bundle in administrative costs.
The best providers of AVMs (including my company) keep their recipes secret. But market velocity and volatility, foreclosure activity, and historical trends are all vital elements in assessing collateral.
Velocity is a capricious variable. For example, it can skew automated valuation "hits" simply because automated underwriting has accelerated origination volume and caused a temporary surge.
Radically fluctuating housing prices in a given market can also lead to wide swings in the reliability of AVM numbers. Foreclosure activity or too few data points can also cloud and dilute results.
Other market conditions can also increase the risk. For example, where "flipping" has inflated property values, an AVM estimate whose input includes flip values will probably be inflated. AVM providers should therefore screen out flips. And an experienced AVM provider should be able to tell you how historical trends can be used within an AVM array to validate results.
Certain automated valuation models perform better than others in particular geographic areas. Lenders should compare them. One reason for the differences may be whether the AVM provider has weighted initial data points by region to improve accuracy. Failure to do so increases the potential for inflated value that can contribute to the opportunity for fraud.
The combination of collateral risk assessment, identity fraud detection, and prudent use of AVMs is essential to mitigating lenders' risk.
