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Full-Text Articles in Urban Studies

Evaluating Spatial Model Accuracy In Mass Real Estate Appraisal: A Comparison Of Geographically Weighted Regression And The Spatial Lag Model, Paul E. Bidanset, John R. Lombard Jan 2014

Evaluating Spatial Model Accuracy In Mass Real Estate Appraisal: A Comparison Of Geographically Weighted Regression And The Spatial Lag Model, Paul E. Bidanset, John R. Lombard

School of Public Service Faculty Publications

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 ...


The Effect Of Kernel And Bandwidth Specification In Geographically Weighted Regression Models On The Accuracy And Uniformity Of Mass Real Estate Appraisal, Paul E. Bidanset, John R. Lombard Jan 2014

The Effect Of Kernel And Bandwidth Specification In Geographically Weighted Regression Models On The Accuracy And Uniformity Of Mass Real Estate Appraisal, Paul E. Bidanset, John R. Lombard

School of Public Service Faculty Publications

The article presents a study which examines the performance of kernel and bandwidth specification in geographically weighted regression (GWR) models in mass real estate appraisal. The kernels employed in the study are the bi-square kernel and the Gaussian kernel. Data from the sales of single-family homes in Norfolk, Virginia from 2010 to 2012 are highlighted.