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Testing The Generalization Of Automated Real Estate Property Evaluation Models

Author

Listed:
  • Eric Tzimas

    (Hobsido, Athens)

  • Manolis Kritikos

    (Athens University of Economics and Business)

Abstract

The goal of this paper is to analyze the implementation of an automation valuation model in real estate and provide insight regarding its behavior when faced with real world data. An automated valuation model was implemented using two different datasets from Ames Iowa and Athens Greece. The models implemented were a KNeighborsRegressor, a GradientBoostingRegressor, a DecisionTreeRegressor, a Random Forest Regressor, a Stacked Regressor, and a Neural Network. The best scoring model for both datasets was the Random Forest Regressor. Two different methods were used for the evaluation of the above models. These methods include testing using twenty percent of the starting dataset and testing using a custom dataset created by authorized property appraisers. In both techniques, the models scored similarly, with only a three percent difference in accuracy, showcasing the rigidity and robustness of the valuation model when faced with external and quality assured data.

Suggested Citation

  • Eric Tzimas & Manolis Kritikos, 2022. "Testing The Generalization Of Automated Real Estate Property Evaluation Models," Journal of Information Systems & Operations Management, Romanian-American University, vol. 16(2), pages 273-282, December.
    • Handle: RePEc:rau:jisomg:v:16:y:2022:i:2:p:273-282
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    References listed on IDEAS

    as
    1. Mahdieh Yazdani, 2021. "Machine Learning, Deep Learning, and Hedonic Methods for Real Estate Price Prediction," Papers 2110.07151, arXiv.org.
    2. K.C. Lam & C.Y. Yu & C.K. Lam, 2009. "Support vector machine and entropy based decision support system for property valuation," Journal of Property Research, Taylor & Francis Journals, vol. 26(3), pages 213-233, August.
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