Cityscape: A Journal of Policy Development and Research
Geographically weighted regression (GWR) has been shown to greatly increase the performance of ordinary least squares-based appraisal models, specifically regarding industry standard measurements of equity, namely the price-related differential and the coefficient of dispersion (COD; Borst and McCluskey, 2008; Lockwood and Rossini, 2011; McCluskey et al., 2013; Moore, 2009; Moore and Myers, 2010). Additional spatial regression models, such as spatial lag models (SLMs), have shown to improve multiple regression real estate models that suffer from spatial heterogeneity (Wilhelmsson, 2002). This research is performed using arms-length residential sales from 2010 to 2012 in Norfolk, Virginia, and compares the performance of GWR and SLM by extrapolating each model's performance to aggregate and subaggregate levels. Findings indicate that GWR achieves a lower COD than SLM.
Original Publication Citation
Bidanset, P. E., & Lombard, J. R. (2014). Evaluating spatial model accuracy in mass real estate appraisal: A comparison of geographically weighted regression and the spatial lag model. Cityscape: A Journal of Policy Development and Research, 16(3), 169-182.
Bidanset, Paul E. and Lombard, John R., "Evaluating Spatial Model Accuracy in Mass Real Estate Appraisal: A Comparison of Geographically Weighted Regression and the Spatial Lag Model" (2014). School of Public Service Faculty Publications. 26.