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City committee hears machine-learning appraisal analysis that flags ~20% valuation outliers
Summary
A vendor presentation showed a machine-learning model trained on two years of sales and tax data that flags property valuations with roughly B110B1 20% variance for review; committee members pressed for more parcel attributes, longer time series and assessor coordination.
A presentation to the Finance Executive Committee on Nov. 12 outlined a machine-learning program designed to detect anomalies between assessed values and likely market prices.
Speakers from the vendor team said the model was trained on two years of property tax, sales and fair-market-value data and uses a gradient-boosting decision-tree approach to identify statistical outliers. "We began with historical data, including 2 years of property tax sales and fair market value data that we were provided," the presenter (Speaker 4) said, adding that…
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