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

Susanna Levantesi and Gabriella Piscopo
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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

Risks, 2020, vol. 8, issue 4, 1-17

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.

Keywords: house price prediction; real estate; machine learning; random forest (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:gam:jrisks:v:8:y:2020:i:4:p:112-:d:432614

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