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

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arxiv 2008.02629 v1 pith:VY7UPQBC submitted 2020-06-20 q-fin.GN

Monica Azqueta-Gavaldon , Gonzalo Azqueta-Gavaldon , Inigo Azqueta-Gavaldon , Andres Azqueta-Gavaldon This is my paper · ORCID
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keywords estaterealunitsdatainvestmentrentalgatherlearning
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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.

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