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

Author

Listed:
  • Jozef Zurada

    (University of Louisville)

  • Alan S. Levitan

    (versity of Louisville)

  • Jian Guan

    (University of Louisville)

Abstract

The limitations of traditional linear multiple regression analysis (MRA) for assessing value of real estate property have been recognized for some time. Artificial intelligence (AI) based methods, such as neural networks (NNs), have been studied in an attempt to address these limitations, with mixed results, weakened further by limited sample sizes. This paper describes a comparative study where several regression and AI-based methods are applied to the assessment of real estate properties in Louisville, Kentucky, U.S.A. Four regression-based methods (traditional 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 (NNs, radial basis function neural network (RBFNN), and memory-based reasoning (MBR)) have been applied and compared under various simulation scenarios. The results, obtained using a very large data sample, indicate that non-traditional regression-based 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 S. Levitan & Jian Guan, 2011. "A Comparison of Regression and Artificial Intelligence Methods in a Mass Appraisal Context," Journal of Real Estate Research, American Real Estate Society, vol. 33(3), pages 349-388.
    • Handle: RePEc:jre:issued:v:33:n:3:2011:p:349-388
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    Citations

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    Cited by:

    1. João A. Bastos & Jeanne Paquette, 2024. "On the uncertainty of real estate price predictions," Working Papers REM 2024/0314, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.
    2. Juergen Deppner & Marcelo Cajias, 2024. "Accounting for Spatial Autocorrelation in Algorithm-Driven Hedonic Models: A Spatial Cross-Validation Approach," The Journal of Real Estate Finance and Economics, Springer, vol. 68(2), pages 235-273, February.
    3. Takahiro Yoshida & Daisuke Murakami & Hajime Seya, 2024. "Spatial Prediction of Apartment Rent using Regression-Based and Machine Learning-Based Approaches with a Large Dataset," The Journal of Real Estate Finance and Economics, Springer, vol. 69(1), pages 1-28, July.
    4. Horvath, Sabine & Soot, Matthias & Zaddach, Sebastian & Neuner, Hans & Weitkamp, Alexandra, 2021. "Deriving adequate sample sizes for ANN-based modelling of real estate valuation tasks by complexity analysis," Land Use Policy, Elsevier, vol. 107(C).
    5. Sebastian Gnat & Mariusz Doszyn, 2020. "Parametric and Non-parametric Methods in Mass Appraisal on Poorly Developed Real Estate Markets," European Research Studies Journal, European Research Studies Journal, vol. 0(4), pages 1230-1245.
    6. William Cheung & Lewen Guo & Yuichiro Kawaguchi, 2021. "Automated valuation model for residential rental markets: evidence from Japan," Journal of Spatial Econometrics, Springer, vol. 2(1), pages 1-34, December.
    7. Sisman, S. & Aydinoglu, A.C., 2022. "Improving performance of mass real estate valuation through application of the dataset optimization and Spatially Constrained Multivariate Clustering Analysis," Land Use Policy, Elsevier, vol. 119(C).
    8. Kokot Sebastian & Gnat Sebastian, 2019. "Simulative Verification of the Possibility of using Multiple Regression Models for Real Estate Appraisal," Real Estate Management and Valuation, Sciendo, vol. 27(3), pages 109-123, September.
    9. Tien Foo Sing & Jesse Jingye Yang & Shi Ming Yu, 2022. "Boosted Tree Ensembles for Artificial Intelligence Based Automated Valuation Models (AI-AVM)," The Journal of Real Estate Finance and Economics, Springer, vol. 65(4), pages 649-674, November.
    10. Susanna Levantesi & Gabriella Piscopo, 2020. "The Importance of Economic Variables on London Real Estate Market: A Random Forest Approach," Risks, MDPI, vol. 8(4), pages 1-17, October.
    11. Doan, Quang Cuong, 2023. "Determining the optimal land valuation model: A case study of Hanoi, Vietnam," Land Use Policy, Elsevier, vol. 127(C).
    12. Doszyń Mariusz, 2020. "Econometric Support of a Mass Valuation Process," Folia Oeconomica Stetinensia, Sciendo, vol. 20(1), pages 81-94, June.
    13. Unel, Fatma Bunyan & Yalpir, Sukran, 2023. "Sustainable tax system design for use of mass real estate appraisal in land management," Land Use Policy, Elsevier, vol. 131(C).
    14. Daikun Wang & Victor Jing Li, 2019. "Mass Appraisal Models of Real Estate in the 21st Century: A Systematic Literature Review," Sustainability, MDPI, vol. 11(24), pages 1-14, December.
    15. My-Linh Thi Nguyen, 2020. "The Hedonic Pricing Model Applied to the Housing Market," International Journal of Economics & Business Administration (IJEBA), International Journal of Economics & Business Administration (IJEBA), vol. 0(3), pages 416-428.
    16. Yilmazer, Seckin & Kocaman, Sultan, 2020. "A mass appraisal assessment study using machine learning based on multiple regression and random forest," Land Use Policy, Elsevier, vol. 99(C).
    17. Joyce de Souza Zanirato Maia & Ana Paula Arantes Bueno & Joao Ricardo Sato, 2023. "Applications of Artificial Intelligence Models in Educational Analytics and Decision Making: A Systematic Review," World, MDPI, vol. 4(2), pages 1-26, May.
    18. Sebastian Gnat, 2021. "Property Mass Valuation on Small Markets," Land, MDPI, vol. 10(4), pages 1-14, April.
    19. Gnat Sebastian, 2020. "Impact of the Regularization of Regression Models on the Results of the Mass Valuation of Real Estate," Folia Oeconomica Stetinensia, Sciendo, vol. 20(1), pages 163-176, June.

    More about this item

    JEL classification:

    • L85 - Industrial Organization - - Industry Studies: Services - - - Real Estate Services

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