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Developing a real estate yield investment deviceusing granular data and machine learning

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  • Monica Azqueta-Gavaldon
  • Gonzalo Azqueta-Gavaldon
  • Inigo Azqueta-Gavaldon
  • Andres Azqueta-Gavaldon

Abstract

This project aims at creating an investment device to help investors determine which real estate units have a higher return to investment in Madrid. To do so, we gather data from Idealista.com, a real estate web-page with millions of real estate units across Spain, Italy and Portugal. In this preliminary version, we present the road map on how we gather the data; descriptive statistics of the 8,121 real estate units gathered (rental and sale); build a return index based on the difference in prices of rental and sale units(per neighbourhood and size) and introduce machine learning algorithms for rental real estate price prediction.

Suggested Citation

  • Monica Azqueta-Gavaldon & Gonzalo Azqueta-Gavaldon & Inigo Azqueta-Gavaldon & Andres Azqueta-Gavaldon, 2020. "Developing a real estate yield investment deviceusing granular data and machine learning," Papers 2008.02629, arXiv.org.
    • Handle: RePEc:arx:papers:2008.02629
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    References listed on IDEAS

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    1. Christian Hott & Pierre Monnin, 2008. "Fundamental Real Estate Prices: An Empirical Estimation with International Data," The Journal of Real Estate Finance and Economics, Springer, vol. 36(4), pages 427-450, May.
    2. Waldo L. Born & Stephen A. Pyhrr, 1994. "Real Estate Valuation: The Effect of Market and Property Cycles," Journal of Real Estate Research, American Real Estate Society, vol. 9(4), pages 455-486.
    3. David López-Rodríguez & María de los Llanos Matea, 2019. "Recent developments in the rental housing market in Spain," Economic Bulletin, Banco de España, issue SEP.
    4. Limsombunchai, Visit, 2004. "House Price Prediction: Hedonic Price Model vs. Artificial Neural Network," 2004 Conference, June 25-26, 2004, Blenheim, New Zealand 97781, New Zealand Agricultural and Resource Economics Society.
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