Can a machine understand real estate pricing? – Evaluating machine learning approaches with big data

Marcelo Cajias

ERES from European Real Estate Society (ERES)

Abstract: In the era of internet and digitalization real estate prices of dwellings are predominantly collected live by multiple listing services and merged with supporting data such as spatio-temporal geo-information. Despite the computational requirements for analyzing such large datasets, the methods for analyzing big data have evolved substantially and go much far beyond the traditional regression. In this context, the usage of machine learning technologies for analyzing prices in the real estate industry is not commonplace. This paper applies machine learnings algorithms on a data set of more than 3 Mio. observations in the German residential market to explore the predicting accuracy of methods such as the random forests regressions, XGboost and the stacked regression among others. The results show a significant reduction in the forecasting variance and confirm that artificial intelligence understands real estate prices much deeper.

Keywords: Big Data in real estate; German housing; Machine learning Algorithms; Random forest; XGBoost (search for similar items in EconPapers)
JEL-codes: R3 (search for similar items in EconPapers)
Date: 2019-01-01
New Economics Papers: this item is included in nep-big, nep-cmp, nep-for, nep-pay and nep-ure
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Persistent link: https://EconPapers.repec.org/RePEc:arz:wpaper:eres2019_232

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