Accuracy

AVM testing is broken and has been for some time, which means that we don’t really know how much we can or should rely on AVMs for accurate valuations. AVMetrics is proud to unveil our game-changing Predictive Testing Methodology (PTM™), designed specifically to circumvent the problem that is invalidating all current testing.

In this study, we have performed a comparative side-by-side analysis of both our Legacy and our new PTM™ testing systems. We used an outcomes analysis to see how the models were anchoring their valuation estimates after a listing price was published. We obtained value estimates for the same properties for use in both testing methodologies to ensure that the sampled properties were identical. This gave us a measure of the difference in the model(s) testing outcomes between the two methodologies. We believe that this new testing method establishes a new “normal” for the prediction accuracy of models uninfluenced by listing price testing outcomes.

Wierks' conclusions are right on target with our beliefs that improving AVM accuracy, precision and confidence scoring are making them more useful to industry, and that appropriate testing is a prerequisite to their widespread adoption.

Using appraisals seems like a great way to get more benchmarks for testing AVMs, and since AVMs and appriasals are trying to do the same thing: predict sales prices, it seems intuitively appealing. However, there are some fundamental problems with using appriased values to test AVMs. Appraised values are subjective and inconsistent, and they introduce bias and compound errors. This article explains why arms' length transactions are the only appropriate way to test AVMs.