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Machine Learning Algorithms For Forecasting Asset Prices: An Application To The Housing Market

Author

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  • Anton A. Gerunov

    (Faculty of Economics and Business Admin istration, Sofia University "St. Kliment Ohridski")

Abstract

This article investigates the application of advanced machine learning algorithms for forecasting housing prices. To this end we leverage a dataset of 414 observations of housing deals in Taipei and model it with both traditional econometric and novel machine learning algorithms. An exhaustive search among 107 alternative methods is conducted and their forecast accuracy is reported in detail. Using the root mean squared error (RMSE) as a benchmark metric, we find that implementations of the random forest family have superior performance, far surpassing that of more traditional approaches such as the multiple linear regression. The results have utility for both academics and practitioners and can be easily transferred to other forecasting problems in economics and business

Suggested Citation

  • Anton A. Gerunov, 2020. "Machine Learning Algorithms For Forecasting Asset Prices: An Application To The Housing Market," Economics and Management, Faculty of Economics, SOUTH-WEST UNIVERSITY "NEOFIT RILSKI", BLAGOEVGRAD, vol. 17(1), pages 27-42.
    • Handle: RePEc:neo:journl:v:17:y:2020:i:1:p:27-42
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    File URL: http://em.swu.bg/images/SpisanieIkonomikaupload/SpisanieIkonomika2020/_vol.XVII_issue_1_2020-27-42.pdf
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    References listed on IDEAS

    as
    1. Gerunov, Anton, 2016. "Modeling Economic Choice under Radical Uncertainty: Machine Learning Approaches," MPRA Paper 69199, University Library of Munich, Germany.
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    More about this item

    Keywords

    asset prices; real estate; forecasting algorithms; machine learning;
    All these keywords.

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • R31 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Housing Supply and Markets

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