GIS/ValTech 2027

Machine learning can do more than predict values. It can help us understand why properties behave similarly, identify meaningful comparables and adjustments, and reveal how markets organize themselves across space. This session moves from opening the machine learning black box to drawing data-driven neighborhood boundaries, showing how explainable AI, information theory, and even techniques borrowed from gerrymandering can turn complex algorithms into practical market insight.

Gerrymandering for Explainability: Automated Neighborhoods Using Gerrymandering Algorithms and Information Theory
Keywords: Artificial Intelligence & Machine Learning, GIS & Spatial Analysis, Intermediate
John Kossa, Data Scientist, Lee County Property Appraiser’s Office - TX

Explainable Machine Learning for Mass Appraisal: From Prediction to Market Insight
Keywords: Artificial Intelligence & Machine Learning, Mass Appraisal & Valuation Modeling, Advanced
Joe Wehrli, Enterprise Architecture Manager, Tyler Technologies - OH