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I know where you will invest in the next year – Forecasting real estate investments with machine learning methods

Author

Listed:
  • Marcelo Cajias
  • Jonas Willwersch
  • Felix Lorenz

Abstract

Real estate transactions can be seen as a spatial point pattern over space and time. That means, that real estate transactions occur in places where at a certain point of time certain characteristics are given that lead to an investment decision. While the decision-making process by investors is impossible to capture, this paper applies new methods for capturing the conditions under which real estate transactions are made over space and time. In other words, we explain and forecast real estate transactions with machine learning methods including both real estate transactions, geographical information and most importantly microeconomic data.

Suggested Citation

  • Marcelo Cajias & Jonas Willwersch & Felix Lorenz, 2019. "I know where you will invest in the next year – Forecasting real estate investments with machine learning methods," ERES eres2019_171, European Real Estate Society (ERES).
    • Handle: RePEc:arz:wpaper:eres2019_171
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    File URL: https://eres.architexturez.net/doc/oai-eres-id-eres2019-171
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    More about this item

    Keywords

    Machine Learning; Point pattern analysis; Real estate transactions; Spatial-temporal analysis; Surveillance analysis;
    All these keywords.

    JEL classification:

    • R3 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location

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