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

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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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    1. Katsuyuki Tanaka & Takuo Higashide & Takuji Kinkyo & Shigeyuki Hamori, 2019. "Analyzing Industry‐Level Vulnerability By Predicting Financial Bankruptcy," Economic Inquiry, Western Economic Association International, vol. 57(4), pages 2017-2034, October.
    2. Jorge Chica-Olmo, 2007. "Prediction of Housing Location Price by a Multivariate Spatial Method: Cokriging," Journal of Real Estate Research, American Real Estate Society, vol. 29(1), pages 95-114.
    3. Jozef Zurada & Alan S. Levitan & Jian Guan, 2011. "A Comparison of Regression and Artificial Intelligence Methods in a Mass Appraisal Context," Journal of Real Estate Research, American Real Estate Society, vol. 33(3), pages 349-388.
    4. Avi Goldfarb & Shane M. Greenstein & Catherine E. Tucker, 2015. "Economic Analysis of the Digital Economy," NBER Books, National Bureau of Economic Research, Inc, number gree13-1.
    5. Steven C. Bourassa & Eva Cantoni & Martin Hoesli, 2010. "Predicting House Prices with Spatial Dependence: A Comparison of Alternative Methods," Journal of Real Estate Research, American Real Estate Society, vol. 32(2), pages 139-160.
    6. Jian Guan & Jozef Zurada & Alan S. Levitan, 2008. "An Adaptive Neuro-Fuzzy Inference System Based Approach to Real Estate Property Assessment," Journal of Real Estate Research, American Real Estate Society, vol. 30(4), pages 395-422.
    7. Nghiep Nguyen & Al Cripps, 2001. "Predicting Housing Value: A Comparison of Multiple Regression Analysis and Artificial Neural Networks," Journal of Real Estate Research, American Real Estate Society, vol. 22(3), pages 313-336.
    8. Antipov, Evgeny & Pokryshevskaya, Elena, 2010. "Mass appraisal of residential apartments: An application of Random forest for valuation and a CART-based approach for model diagnostics," MPRA Paper 27645, University Library of Munich, Germany.
    9. Emilia Lorenzo & Gabriella Piscopo & Marilena Sibillo & Roberto Tizzano, 2021. "Reverse mortgages through artificial intelligence: new opportunities for the actuaries," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 44(1), pages 23-35, June.
    10. Raden Aswin Rahadi & Sudarso Kaderi Wiryono & Deddy Priatmodjo Koesrindartoto & Indra Budiman Syamwil, 2015. "Factors influencing the price of housing in Indonesia," International Journal of Housing Markets and Analysis, Emerald Group Publishing Limited, vol. 8(2), pages 169-188, June.
    11. Glaeser, Edward L. & Gyourko, Joseph & Morales, Eduardo & Nathanson, Charles G., 2014. "Housing dynamics: An urban approach," Journal of Urban Economics, Elsevier, vol. 81(C), pages 45-56.
    12. Goldfarb, Avi & Greenstein, Shane M. & Tucker, Catherine E. (ed.), 2015. "Economic Analysis of the Digital Economy," National Bureau of Economic Research Books, University of Chicago Press, number 9780226206981.
    13. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    14. Lynn Wu & Erik Brynjolfsson, 2015. "The Future of Prediction: How Google Searches Foreshadow Housing Prices and Sales," NBER Chapters, in: Economic Analysis of the Digital Economy, pages 89-118, National Bureau of Economic Research, Inc.
    15. Bradford Case & John Clapp & Robin Dubin & Mauricio Rodriguez, 2004. "Modeling Spatial and Temporal House Price Patterns: A Comparison of Four Models," The Journal of Real Estate Finance and Economics, Springer, vol. 29(2), pages 167-191, September.
    16. Tanaka, Katsuyuki & Kinkyo, Takuji & Hamori, Shigeyuki, 2016. "Random forests-based early warning system for bank failures," Economics Letters, Elsevier, vol. 148(C), pages 118-121.
    17. Beutel, Johannes & List, Sophia & von Schweinitz, Gregor, 2019. "Does machine learning help us predict banking crises?," Journal of Financial Stability, Elsevier, vol. 45(C).
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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. Yu, Mengting & Principato, Ludovica & Formentini, Marco & Mattia, Giovanni & Cicatiello, Clara & Capoccia, Leonardo & Secondi, Luca, 2024. "Unlocking the potential of surplus food: A blockchain approach to enhance equitable distribution and address food insecurity in Italy," Socio-Economic Planning Sciences, Elsevier, vol. 93(C).
    6. 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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