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The Importance of Economic Variables on London Real Estate Market: A Random Forest Approach

Author

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  • Susanna Levantesi

    (Department of Statistics, Sapienza University of Rome, 00161 Rome, Italy)

  • Gabriella Piscopo

    (Department of Economics and Statistical Science, University of Naples Federico II, 80138 Naples, Italy)

Abstract

This paper follows the recent literature on real estate price prediction and proposes to take advantage of machine learning techniques to better explain which variables are more important in describing the real estate market evolution. We apply the random forest algorithm on London real estate data and analyze the local variables that influence the interaction between housing demand, supply and price. The variables choice is based on an urban point of view, where the main force driving the market is the interaction between local factors like population growth, net migration, new buildings and net supply.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jrisks:v:8:y:2020:i:4:p:112-:d:432614
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    References listed on IDEAS

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    2. Cyprian Chwiałkowski & Adam Zydroń, 2021. "Socio-Economic and Spatial Characteristics of Wielkopolski National Park: Application of the Hedonic Pricing Method," Sustainability, MDPI, vol. 13(9), pages 1-17, April.
    3. Mahdieh Yazdani, 2021. "Machine Learning, Deep Learning, and Hedonic Methods for Real Estate Price Prediction," Papers 2110.07151, arXiv.org.
    4. 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).
    5. Cyprian Chwiałkowski & Adam Zydroń & Dariusz Kayzer, 2022. "Assessing the Impact of Selected Attributes on Dwelling Prices Using Ordinary Least Squares Regression and Geographically Weighted Regression: A Case Study in Poznań, Poland," Land, MDPI, vol. 12(1), pages 1-20, December.

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