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Comparison of Machine Learning Algorithms for Mass Appraisal of Real Estate Data

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  • Sevgen Sibel Canaz

    (Department of Real Estate Development and Management, Ankara University, Emniyet, Dögol Cd., 0600 Yenimahalle/Ankara, Turkey)

  • Tanrivermiş Yeşim

    (Department of Real Estate Development and Management, Ankara University, Emniyet, Dögol Cd., 0600 Yenimahalle/Ankara, Turkey)

Abstract

In recent years, machine learning algorithms have been used in the mass appraisal of real estate. In this study, 5 machine learning algorithms are used for residential type real estate. Machine learning algorithms used for mass appraisal in this study are Artificial Neural Networks (ANN), Random Forest (RO), Multiple Regression Analysis (MRA), K-Nearest Neighborhood (k-nn), Support Vector Regression (SVR). To test the study, real estate data collected from the central districts of Ankara, were used. The main purpose of this study is to find out which machine learning algorithm gives the best results for the mass appraisal of real estates and to reveal the most important variables that affect the prices of real estate. According to the results obtained for the city of Ankara, it was observed that the best algorithm for mass appraisal is RF in residential-type real estates, followed by the ANN, k-nn, and linear regression algorithms, respectively. According to the results obtained from the residential real estate, it was concluded that heating and distances to places of importance had the greatest effect on the value.

Suggested Citation

  • Sevgen Sibel Canaz & Tanrivermiş Yeşim, 2024. "Comparison of Machine Learning Algorithms for Mass Appraisal of Real Estate Data," Real Estate Management and Valuation, Sciendo, vol. 32(2), pages 100-111.
    • Handle: RePEc:vrs:remava:v:32:y:2024:i:2:p:100-111:n:1009
    DOI: 10.2478/remav-2024-0019
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    References listed on IDEAS

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    More about this item

    Keywords

    mass appraisal; machine learning algorithms; random forest; artificial neural network; real estate valuation map;
    All these keywords.

    JEL classification:

    • R39 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Other

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