Journal of Property Tax Assessment & Administration
Abstract
This article evaluates the new automated valuation model (AVM), geographically weighted regression (GWR), which incorporates a geospatial factor, the parcel’s x-y coordinates obtained from the geographic information system (GIS), in the model specification. It also introduces and evaluates a variant of GWR called geographic-attribute weighted regression (GAWR), which incorporates the parcel’s similarity to surrounding parcels as well its location. The study used an existing research dataset (Moore 2006) to compare value estimates obtained through incorporation of parcel x-y coordinates in the AVM model specification with estimates produced by commonly used AVMs that do not include a geospatial factor. The findings were extended and validated by applying the new methodology to datasets from the City of Norfolk and Fairfax County, Virginia. The study was conducted using a rigorous experimental design with statistical hypothesis testing, frequently missing in papers reporting comparison of results produced from various AVMs.
First Page
5
Last Page
28
Keywords
Mass appraisal techniques, Geographic base file systems, Regression analysis
Notes
The paper on which this article is based was presented September 1, 2010, at the International Association of Assessing Officers’ 76th Annual International Conference on Assessment Administration in Orlando, Florida. It expands upon the paper presented in Little Rock, Arkansas, on March 9, 2010, at the 2010 GIS/CAMA Technologies Conference sponsored by IAAO and the Urban and Regional Information Systems Association (URISA).
Recommended Citation
Moore, J. W., & Myers, J. (2010). Using geographic-attribute weighted regression for CAMA modeling. Journal of Property Tax Assessment & Administration, 7(3), 5-28. Retrieved from https://researchexchange.iaao.org/jptaa/vol7/iss3/1
