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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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    1. de Oliveira, Antônio Augusto Ferreira & Reyes-Bueno, Fabián & González, Marco Aurelio Stumpf & da Silva, Everton, 2025. "Comparing traditional and machine learning techniques in apartments mass appraisal in Fortaleza, Brazil," Aestimum, Italian Association of Appraisers and Land Economists, vol. 85.
    2. Cihan Çılgın & Hadi Gökçen, 2025. "A Hybrid Machine Learning Model Architecture with Clustering Analysis and Stacking Ensemble for Real Estate Price Prediction," Computational Economics, Springer;Society for Computational Economics, vol. 66(1), pages 127-178, July.
    3. Tothăzan Helga Flavia, 2022. "The contribution of statistical models in the field of real estate valuation," Timisoara Journal of Economics and Business, Sciendo, vol. 15(1), pages 111-126.
    4. Benzer, Semra & Garabaghi, Farid Hassanbaki & Benzer, Recep & Güni, Hicret Çimen, 2025. "Sustainable environmental education: Some machine learning algorithms in the classification of sustainable environmental attitudes," Evaluation and Program Planning, Elsevier, vol. 112(C).
    5. Mariusz Doszyń, 2024. "Might expert knowledge improve econometric real estate mass appraisal?," The Journal of Real Estate Finance and Economics, Springer, vol. 69(4), pages 719-740, November.
    6. Juergen Deppner & Benedict Ahlefeldt-Dehn & Eli Beracha & Wolfgang Schaefers, 2025. "Boosting the Accuracy of Commercial Real Estate Appraisals: An Interpretable Machine Learning Approach," The Journal of Real Estate Finance and Economics, Springer, vol. 71(2), pages 314-351, August.
    7. Moritz Stang & Bastian Krämer & Cathrine Nagl & Wolfgang Schäfers, 2023. "From human business to machine learning—methods for automating real estate appraisals and their practical implications [Vom Vergleichswertverfahren zum maschinellen Lernen – Methoden zur automatisi," Zeitschrift für Immobilienökonomie (German Journal of Real Estate Research), Springer;Gesellschaft für Immobilienwirtschaftliche Forschung e. V., vol. 9(2), pages 81-108, October.
    8. Jose A. Rodriguez-Serrano, 2025. "Prototype-based learning for real estate valuation: a machine learning model that explains prices," Annals of Operations Research, Springer, vol. 344(1), pages 287-311, January.
    9. Felix Lorenz & Jonas Willwersch & Marcelo Cajias & Franz Fuerst, 2023. "Interpretable machine learning for real estate market analysis," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 51(5), pages 1178-1208, September.
    10. Kim, Woo-sung & Song, Mihyeong & Jeong, Mincheol & Jung, Seung Hwan, 2025. "A supervised learning-based optimization for container pre-loading problem," International Journal of Production Economics, Elsevier, vol. 287(C).

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