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

Abstract

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.

Investigation of Real Estate Tax Leakage Loss Rates with ANNs

“…However, it is well known that housing prices tend to be spatially dependent, as neighboring houses' physical and environmental characteristics are similar. Therefore, spatial autocorrelation should be considered to increase the accuracy of mass appraisal methods [69]. Some researchers include longitude and latitude values in the dataset or use location-sensitive (kriging, spatial econometric model (SEM), spatially varying coefficient (SVC), etc.)…”

Development of a financial performance measurement model for real estate business: an exploratory study in India

“…The literature on performance measurement of real estate business has advanced with recent studies. McCord et al (2022) uses geospatial approach and machine learning (ML) algorithm to help improve mass appraisal assessment; Renigier-Biłozor et al (2021) use genetics algorithm application in a concept of automated valuation model as an original decision-making support system for individual investment consulting in real estate; and Janowski et al (2021) uses automatic algorithm based on ML and computer vision technology related to LiDAR (big data). However, these studies mainly focus on housing prices and individual decision-making, rather than the financial performance of the real estate business.…”

“…Further, such studies are rare in India (Abu-Jarad et al , 2010; Anand et al , 2005). Additionally, there is very limited research available on how real estate businesses are adapting to advancing theories and methodologies in performance measurement (refer McCord et al (2022), Renigier-Biłozor et al (2021) and Janowski et al (2021).…”

An Overview of Kriging and Cokriging Predictors for Functional Random Fields

“…In the era of expansive datasets, the need for robust estimation techniques has become paramount, leading to the development of methods tailored for reduced rank models to accommodate large spatial data [34]. This extends into practical applications, as showcased by the integration of the spatial eigenvector methodology in regularized regression, enhancing predictive precision in domains such as property valuation [35]. The contemporary relevance of geostatistical tools is further underscored by their application in global challenges, such as the COVID-19 pandemic, where both Bayesian and nonparametric geostatistical models have been employed for data analysis [36].…”