Delineating market areas used for mass valuation using geographically weighted regression (GWR) and hierarchical cluster analysis (HCA)

Authors

Paul E. Bidanset, Center for Appraisal Research and Technology
Peadar T. Davis, Ulster University
Daniel J. Fasteen, Tyler Technologies
Michael J. McCord, Ulster University
Luc D. Hermans, Netherlands Council for Real Estate Assessment
William J. McCluskey, African Tax Institute

Abstract

This paper aimed to develop a straightforward method for government property tax assessment offices to automate baseline market area delineation using geographically weighted regression (GWR) and hierarchical clustering analysis (HCA). We proposed a methodology of feeding local R2 values from a GWR hedonic model/automated valuation model (AVM), as well as the individual latitude coordinate and longitude coordinate of each sale, into an HCA algorithm. To test the useability of the suggested clusters as market areas in a mass valuation context, a hedonic model that considered each property’s physical features, time of sale, and proposed cluster, was constructed. The coefficient for each cluster dummy variable was statistically significant and the model yielded predictions that were in line with accuracy and uniformity thresholds in the IAAO’s Standard on Ratio Studies, both overall (citywide) and for each market area proposed through clustering. Our findings are particularly relevant to government property tax assessors, who, with market areas that are current and well-defined, can improve office efficiencies and yield more accurate and equitable tax assessments. We hope our research will help more assessment offices realize the benefits of regression-based AVMs and reduce typical lag time associated with implementing regression-based valuation models, particularly when existing assessment neighborhood classifications are too granular and numerous to serve as dummy variables (a very common state of most assessment offices) – or are simply outdated – and location is otherwise unable to be directly represented in the model.

First Page

5

Last Page

26

Keywords

Automated valuation model (AVM), Regression analysis

Recommended Citation

Bidanset, P. E., Davis, P. T., Fasteen, D. J., McCord, M. J., Hermans, L. D., & McCluskey, W. J. (2025). Delineating market areas used for mass valuation using geographically weighted regression (GWR) and hierarchical cluster analysis (HCA). Journal of Property Tax Assessment & Administration, 22(1), 5-26. Retrieved from https://researchexchange.iaao.org/jptaa/vol22/iss1/2