A Comparison of Regression and Artificial Intelligence Methods in a Mass Appraisal Context

Jozef M. Zurada · Alan S. Levitan · Jian Guan

175 citations0/0 files storedJournal of Real Estate Research

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Housing Market and Economics Forecasting Techniques and Applications Urban Planning and Valuation

Abstract

This paper describes a comparative study where several regression and artificial intelligence (AI)-based methods are used to assess properties in Louisville, Kentucky. Four regressionbased methods [traditional multiple regression analysis (MRA), and three non-traditional regression-based methods, Support Vector Machines using sequential minimal optimization regression (SVM-SMO), additive regression, and M5P trees], and three AI-based methods [neural networks (NNs), radial basis function neural network (RBFNN), and memory-based reasoning (MBR)] are applied and compared under various simulation scenarios. The results indicate that non-traditional regressionbased methods perform better in all simulation scenarios, especially with homogeneous data sets. AI-based methods perform well with less homogeneous data sets under some simulation scenarios.

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References

1998Spatial Autocorrelation: A Primer 2001Predicting Housing Value: A Comparison of Multiple Regression Analysis and Artificial Neural Networks 1995An Exploration of Neural Networks and Its Application to Real Estate Valuation 2004House Price Prediction: Hedonic Price Model vs. Artificial Neural Network 2004Spatial Statistics and Real Estate 2009Neural Network Hedonic Pricing Models in Mass Real Estate Appraisal 2010Predicting House Prices with Spatial Dependence: A Comparison of Alternative Methods 2003Modeling Spatial Variation in Housing Prices: A Variable Interaction Approach

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2018Building real estate valuation models with comparative approach through case-based reasoning 2019Mass Appraisal Models of Real Estate in the 21st Century: A Systematic Literature Review 2020A mass appraisal assessment study using machine learning based on multiple regression and random forest 2018Improving property valuation accuracy: a comparison of hedonic pricing model and artificial neural network 2017FORECASTING SPATIAL DYNAMICS OF THE HOUSING MARKET USING SUPPORT VECTOR MACHINE

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