Accounting for locational, temporal, and physical similarity of residential sales in mass appraisal modeling: the development and application of geographically, temporally, and characteristically weighted regression
Geographically weighted regression (GWR) has been recognized in the assessment community as a viable automated valuation model (AVM) to help overcome, at least in part, modeling hurdles associated with location, such as spatial heterogeneity and spatial autocorrelation of error terms. Although previous researchers have adjusted the GWR weights matrix to also weight by time of sale or by structural similarity of properties in AVMs, the research described in this paper is the first that has done so by all three dimensions (i.e., location, structural similarity, and time of sale) simultaneously. Using 24 years of single-family residential sales in Fairfax, Virginia, we created a new locally weighted regression (LWR) AVM called geographically, temporally, and characteristically weighted regression (GTCWR) and compared it with GWR-based models with fewer weighting dimensions.
Valuation - mathematical models, Valuation - data processing, Automated valuation model (AVM)
This paper is an abridged version of a dissertation prepared in fulfillment of a doctorate by coauthor Paul Bidanset at Ulster University. The paper was originally presented at the 21st GIS/CAMA Technologies Conference, March 6-9, 2017, in Chattanooga, Tennessee.
Bidanset, P. E., McCord, M., Lombard, J. R., Davis, P., & McCluskey, W. (2017). Accounting for locational, temporal, and physical similarity of residential sales in mass appraisal modeling: the development and application of geographically, temporally, and characteristically weighted regression. Journal of Property Tax Assessment & Administration, 14(2), 5-13. Retrieved from https://researchexchange.iaao.org/jptaa/vol14/iss2/1