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

TL;DR

This paper develops a machine learning-based investment tool for Madrid real estate, utilizing granular data from Idealista.com to predict rental prices and assess investment returns.

Contribution

It introduces a data collection pipeline, descriptive analysis, a return index, and machine learning models for rental price prediction in real estate.

Findings

Descriptive statistics of 8,121 real estate units

A return index based on neighborhood and size

Implementation of machine learning algorithms for price prediction

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.