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

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  • Jozef Zurada
  • Alan Levitan
  • Jian Guan

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.

Suggested Citation

  • Jozef Zurada & Alan Levitan & Jian Guan, 2011. "A Comparison of Regression and Artificial Intelligence Methods in a Mass Appraisal Context," Journal of Real Estate Research, Taylor & Francis Journals, vol. 33(3), pages 349-388, January.
    • Handle: RePEc:taf:rjerxx:v:33:y:2011:i:3:p:349-388
    DOI: 10.1080/10835547.2011.12091311
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