Evaluating Spatial Model Accuracy in Mass Real Estate Appraisal: A Comparison of Geographically Weighted Regression and the Spatial Lag Model

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\u27s performance to aggregate and subaggregate levels. Findings indicate that GWR achieves a lower COD than SLM

The Effect of Kernel and Bandwidth Specification in Geographically Weighted Regression Models on the Accuracy and Uniformity of Mass Real Estate Appraisal

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

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

Applying the Geostatistical Eigenvector Spatial Filter Approach into Regularized Regression for Improving Prediction Accuracy for Mass Appraisal

Prediction accuracy for mass appraisal purposes has evolved substantially over the last few decades, facilitated by the evolution in big data, data availability and open source software. Accompanying these advances, newer forms of geo-spatial approaches and machine learning (ML) algorithms have been shown to help improve house price prediction and mass appraisal assessment. Nonetheless, the adoption a of ML within mass appraisal has been protracted and subject to scrutiny by assessment jurisdictions due to their failure to account for spatial autocorrelation and limited practicality in terms of value significant estimates needed for tribunal defense and explainability. Existing research comparing traditional regression approaches has tended to examine unsupervised ML methods such as Random Forest (RF) models which remain more esoteric and less transparent in producing value significant estimates necessary for mass appraisal explainability and defense. Therefore, the purpose of this study is to apply the supervised Regularized regression technique which offers a more transparent alternative, and integrate this with a more nuanced geo-statistical technique, the Eigenvector Spatial Filter (ESF) approach, to more accurately account for spatial autocorrelation and enhance prediction accuracy whilst improving explainability needed for mass appraisal exercises. By undertaking such an approach, the research demonstrates the application of this method can be easily adopted for property tax jurisdictions in a framework which is more interpretable, transparent and useable within mass appraisal given its simple and appealing approach. The findings reveal that the integration of the ESFs improves model explainability, prediction accuracy and spatial residual error compared to baseline classical regression and Elastic-net regularized regression architectures, whilst offering the necessary ‘front-facing’ and flexible structure for in-sample and out-of-sample assessment needed by the assessment community for valuing the unsold housing stock. In terms of policy and practice, the study demonstrates some important considerations for mass appraisal tax assessment and for the improvement of taxation assessment and the alleviation of horizontal and vertical inequity

An exploratory approach for enhancing vertical and horizontal equity tests for ad valorem property tax valuations using geographically weighted regression

Purpose: The purpose of this study is to enhance the estimation of vertical and horizontal inequity within property valuation. Property taxation is a crucial source of finance for local government around the world – based on a presumptive tax base underpinned by estimates of property value, inaccurate real estate valuations used for such ad valorem or value-based property tax calculations potentially lead to a variety of costs, both financial and other, for tax payers and governments alike. More common are increased costs in time, staff and, in some cases, legal fees. Some governments are even bound by acceptability thresholds to promote fairness, equitability and overall government accountability with respect to valuation. Design/methodology/approach: There exist a number of vertical inequity measurements that have undergone academic testing and scrutiny within the property tax industry since the 1970s. While these approaches have proved successful in detecting horizontal and vertical inequity, one recurring disadvantage pertains to measurement error/omitted variable bias, stemming largely from a failure to accurately account for location. A natural progression within property tax research is the application of a more spatially local weighted modelling approach to examine vertical and horizontal inequity. This research, therefore, specifies a geographically weighted regression (GWR) methodology to detect and measure vertical inequity in property valuations. Findings: The findings show the efficacy of using more applied spatial approaches for vertical tax estimation and indeed the limitations of employing conditional mean estimates coupled with delineated boundaries for assessing property tax inequity. The GWR model findings highlight the more fluctuating nature of vertical inequity across the Belfast market for the apartment sector both in a progressive and regressive sense and at different magnitudes. Moreover, the results reveal spatial clustering in the effects and are indicative of systematic inequities related to location inferring that spatial (horizontal) tax inequities are not random. The findings further show increased GWR model predictability overall. Originality/value: This research adds to the existing literature base for evaluating both vertical and horizontal inequity in value-based property taxation at the intra-neighbourhood level. This is accomplished by modifying the Birch–Sunderman approach by transforming the traditional OLS model architecture to a GWR model, thereby allowing coefficient estimates of inequity to vary not only across a jurisdiction, but also at a more local level, while incorporating property characteristic variables. This arguably allows assessors to identify specific geographical areas of concern, saving them money, time and resources on identifying, addressing and correcting for inequity

Nationwide mass appraisal modeling in China: Feasibility analysis for scalability given ad valorem property tax reform

Revenues from property taxes provide substantial operating funds for many jurisdictions. Given the economic significance of these liabilities, the accuracy of assessing them is of great importance to a variety of stakeholders, in particular, property owners and the officials who administer these programs. Assessors are faced with a difficult situation when they have arrived at assessed values only to find that they have unacceptable levels of horizontal or vertical inequity. Even though there may be ways to address this inequity in the short run, there are no approaches to deal with this in the long run. Although this debate has been of interest to both the professional assessment and academic communities, the assessor is still responsible to correct for the issue of inequity if it exists. The question that this project addresses is, How can the assessor deal with the issue of inequity if found at the local level? This is in part accomplished with hedonic or multiple regression analysis

A hedonic approach to determining the sources of inequity

Revenues from property taxes provide substantial operating funds for many jurisdictions. Given the economic significance of these liabilities, the accuracy of assessing them is of great importance to a variety of stakeholders, in particular, property owners and the officials who administer these programs. Assessors are faced with a difficult situation when they have arrived at assessed values only to find that they have unacceptable levels of horizontal or vertical inequity. Even though there may be ways to address this inequity in the short run, there are no approaches to deal with this in the long run. Although this debate has been of interest to both the professional assessment and academic communities, the assessor is still responsible to correct for the issue of inequity if it exists. The question that this project addresses is, How can the assessor deal with the issue of inequity if found at the local level? This is in part accomplished with hedonic or multiple regression analysis