HOUSINGNEWS REPORT
MY TAKE
AVMs could also deliver estimates that are based on a reconciliation of multiple data sources. An example of this is the ATTOM Data Solutions (parent company of RealtyTrac) “attomized” data. This is a data warehouse that stores the reconciled property-level data from several sources. This is important for several reasons. First, when using a single data source, there may be inherent biases in the raw data and how those data are collected. By using a reconciled database, the opportunity to reconcile differences at the property level is presented. Say that 123 Main Street in Cartersville, Georgia is listed in one data source (e.g. tax assessor data) as having three bedrooms and two bathrooms. Further assume that a second data source (e.g. a current MLS listing) reports that the same property has four bedrooms and two bathrooms. An important difference here is the contributory value of the fourth bedroom if the MLS listing data is used instead of the tax assessor data; that contributory value could mean the difference in AVM estimate of $15,000 to $20,000 for this property, all else held constant. Recent research by Andy Krause
being different than the best source for number of bathrooms). This is important because this process finally fulfills the true promise of multi-sourcing property data to estimate AVM values — not just for the sake of creating redundancies (which does have some value) but also in creating a new “super set” of synthesized data that is 1) not available from any one source on its own and 2) not available from multiple sources utilized in a binary fashion (i.e. either one source or the other for all properties in a state or county). Conclusion In my opinion, the time has come for AVM vendors to start adding more value to the outputs that they provide customers. Standard outputs, such as the AVM point estimate and a measure of confidence in the estimate (often conveyed using the FSD), are just that — standard. Value-added outputs of interest to customers may include reason codes, statistically-derived confidence intervals around the AVM point estimate, the number of comparable sales transactions used to value a given subject property, and explanations of the underlying data source used to generate the AVM estimates. [1] An in-depth discussion of the various flavors of residential automated valuation models or AVMs (e.g. distressed, contemporaneous, lender-grade, marketing-grade, etc.), the difference between AVMs and computer assisted mass appraisal (CAMA) systems, and various ways to estimate “value” [e.g. broker price opinions (BPOs), appraiser-assisted AVMs, form appraisals] are important related topics that are beyond the scope of this particular article.
and me (2016) in the Journal of Real Estate Practice and Education describes the importance of documenting the reconciliation of between-source data variation to ensure the “best” valuation possible and replicability by other professionals in our industry.
So, the question that confronts us is which data source should you use?
In the “ATTOMized” data, the reconciled data for 123 Main Street used to determine the AVM estimate is from the source that has been deemed most up-to-date, accurate, and reliable for that given jurisdiction and that given property based on myriad factors, including timeliness of delivery from the source, percent of fields consistently populated, and previous performance in producing accurate AVM values. The best source will often be different from one jurisdiction to another, even within the same state, county, city, or ZIP code. The best source may even differ for different fields on the same property (e.g. the best source for number of bedrooms may end up
ATTOM Data Solutions • P13
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