Should I Contact Him or Not? – Quantifying the Demand for Real Estate with Interpretable Machine Learning Methods

Marcelo Cajias and Joseph-Alexander Zeitler

ERES from European Real Estate Society (ERES)

Abstract: In the light of the rise of the World Wide Web, there is an intense debate about the potential impact of online user-generated data on classical economics. This paper is one of the first to analyze housing demand on that account by employing a large internet search dataset from a housing market platform. Focusing on the German rental housing market, we employ the variable ‘contacts per listing’ as a measure of demand intensity. Apart from traditional economic methods, we apply state-of-the-art artificial intelligence, the XGBoost, to quantify the factors that lead an apartment to be demanded. As using machine learning algorithms cannot solve the causal relationship between the independent and dependent variable, we make use of eXplainable AI (XAI) techniques to further show economic meanings and inferences of our results. Those suggest that both hedonic, socioeconomic and spatial aspects influence search intensity. We further find differences in temporal dynamics and geographical variations. Additionally, we compare our results to alternative parametric models and find evidence of the superiority of our nonparametric model. Overall, our findings entail some potentially very important implications for both researchers and practitioners.

Keywords: eXtreme Gradient Boosting; Machine Learning; online usergenerated search data; Residential Real Estate (search for similar items in EconPapers)
JEL-codes: R3 (search for similar items in EconPapers)
Date: 2021-01-01
New Economics Papers: this item is included in nep-big, nep-cmp, nep-isf and nep-ure
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Persistent link: https://EconPapers.repec.org/RePEc:arz:wpaper:eres2021_70

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