HOUSINGNEWS REPORT
MY TAKE
Why do Current AVMs Fall Short? Current AVMs fall short in multiple ways. First, some customers request AVM estimates only, whereas other customers request AVM reports. The return of AVM estimates often includes simply the AVM value for the subject property, and potentially an error statistic (the most common being the Forecast Standard Deviation or FSD), and sometimes a range of possible values for the subject property. Obviously, reports contain much more property detail on the subject property as well as data on the “best” comparable properties, the neighborhood where the subject property is located, and (sometimes) the region where the subject property is located. These outputs returned to customers have been fairly standard for the last 20 years – as far as I can tell, no real innovation in the outputs delivered to customers has occurred. Second, current AVMs fall short in the data used to generate the AVM estimates. From others in the industry, I have learned that some attempt at incorporating different data sources has occurred. In an attempt to mirror contemporary sales price trends, some AVMs use listing data from Multiple Listing Services (MLSs) in generating their estimates while others continue to use only historical comparable sales transactions. Some AVMs use tax assessed value (TAV), which are often updated yearly, in their algorithms. Either way, it seems that the time is right for other “big data” and crowdsourced data
... it seems that the time is right for other “big data” and crowdsourced data to be used in AVMs.”
to be used in AVMs. In the academic literature, it is becoming more common to see Twitter data being used to predict stock prices (e.g., see Bollen, Mao, and Zeng, 2010, “Twitter mood predicts the stock market,” Journal of Computational Science) and Google data to predict movements in house price indices (e.g., see Kulkarni et al. 2009, “Forecasting Housing Prices with Google Econometrics,” George Mason University School of Public Policy Research Paper No. 2009-10). What Else Could AVMs Deliver to Customers? Reason codes or “variances” are common in some industries. For example, one might find on her credit report a
reason for a lower credit score (e.g. too recent opening of an account). In AVMs, reason codes can provide reasons for a particular determination or indications for situations where some variable is “out of tolerance” or outside of a predetermined range of acceptable answers. This would give customers additional insight into the confidence that the AVM provider has in their estimates. One easily computable reason code that would provide additional insight is the number of comparable sales transactions used to produce the valuation estimate for a given subject property. Another is a statistically-derived (bootstrapped) confidence interval around the valuation estimate for each subject property.
ATTOM Data Solutions • P12
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