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The Application of Machine Learning Algorithms for Spatial Analysis: Predicting of Real Estate Prices in Warsaw

Author

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  • Dawid Siwicki

    (Faculty of Economic Sciences, University of Warsaw)

Abstract

The principal aim of this paper is to investigate the potential of machine learning algorithms in context of predicting housing prices. The most important issue in modelling spatial data is to consider spatial heterogeneity that can bias obtained results when is not taken into consideration. The purpose of this research is to compare prediction power of such methods: linear regression, artificial neural network, random forest, extreme gradient boosting and spatial error model. The evaluation was conducted using train, validation, test and k-Fold Cross-Validation methods. We also examined the ability of the above models to identify spatial dependencies, by calculating Moran’s I for residuals obtained on in-sample and out-of-sample data.

Suggested Citation

  • Dawid Siwicki, 2021. "The Application of Machine Learning Algorithms for Spatial Analysis: Predicting of Real Estate Prices in Warsaw," Working Papers 2021-05, Faculty of Economic Sciences, University of Warsaw.
    • Handle: RePEc:war:wpaper:2021-05
    as

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    File URL: https://www.wne.uw.edu.pl/index.php/download_file/6326/
    File Function: First version, 2021
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    References listed on IDEAS

    as
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    3. Jeffrey P. Cohen & Cletus C. Coughlin, 2008. "Spatial Hedonic Models Of Airport Noise, Proximity, And Housing Prices," Journal of Regional Science, Wiley Blackwell, vol. 48(5), pages 859-878, December.
    4. Limsombunchai, Visit, 2004. "House Price Prediction: Hedonic Price Model vs. Artificial Neural Network," 2004 Conference, June 25-26, 2004, Blenheim, New Zealand 97781, New Zealand Agricultural and Resource Economics Society.
    5. Rosen, Sherwin, 1974. "Hedonic Prices and Implicit Markets: Product Differentiation in Pure Competition," Journal of Political Economy, University of Chicago Press, vol. 82(1), pages 34-55, Jan.-Feb..
    6. Danlin Yu & Yehua Dennis Wei & Changshan Wu, 2007. "Modeling Spatial Dimensions of Housing Prices in Milwaukee, WI," Environment and Planning B, , vol. 34(6), pages 1085-1102, December.
    7. Joris Pinkse & Margaret E. Slade, 2010. "The Future Of Spatial Econometrics," Journal of Regional Science, Wiley Blackwell, vol. 50(1), pages 103-117, February.
    8. Can, Ayse, 1992. "Specification and estimation of hedonic housing price models," Regional Science and Urban Economics, Elsevier, vol. 22(3), pages 453-474, September.
    9. Liv Osland, 2010. "An Application of Spatial Econometrics in Relation to Hedonic House Price Modelling," Journal of Real Estate Research, American Real Estate Society, vol. 32(3), pages 289-320.
    10. J. Elhorst, 2010. "Applied Spatial Econometrics: Raising the Bar," Spatial Economic Analysis, Taylor & Francis Journals, vol. 5(1), pages 9-28.
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    Cited by:

    1. Joanna Jaroszewicz & Hubert Horynek, 2024. "Aggregated Housing Price Predictions with No Information About Structural Attributes—Hedonic Models: Linear Regression and a Machine Learning Approach," Land, MDPI, vol. 13(11), pages 1-29, November.

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    More about this item

    Keywords

    spatial analysis; machine learning; housing market; random forest; gradient boosting;
    All these keywords.

    JEL classification:

    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • 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
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • 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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