Identifying Real Estate Opportunities using Machine Learning

Abstract--The real estate market is exposed to many fluctuations in prices, because of existing correlations with many variables, some of which cannot be controlled or might even be unknown. Housing prices can increase rapidly (or in some cases, also drop very fast), yet the numerous listings available online where houses are sold or rented are not likely to be updated that often. In some cases, individuals interested in selling a house (or apartment) might include it in some online listing, and forget about updating the price. In other cases, some individuals might be interested in deliberately setting a price below the market price in order to sell the home faster, for various reasons. In this paper we aim at developing a machine learning application that identifies opportunities in the real estate market in real time, i.e., houses that are listed with a price substantially below the market price. This program can be useful for investors interested in the housing market. The application is formally implemented as a regression problem, that tries to estimate the market price of a house given features retrieved from public online listings. For building this application, we have performed a feature engineering stage in order to discover relevant features that allows attaining a high predictive performance. Several machine learning algorithms have been tested, including regression trees, k-NN and neural networks, identifying advantages and handicaps of each of them. The real estate market is rapidly evolving. A recent report published by MSCI estimates the size of the professionally managed real estate investment market in $8.5 trillion in 2017, increasing a total of $1.1 trillion since the previous year [1]. Of course, the real market size is expected to be much larger when counting assets which are not professionally managed or that are not object of investment. When looked from a macroeconomic perspective, there are many aspects that significantly drive the behavior of this market, such as demographics, interest rates, government regulation and, for short, global economic health. However, looking at the market evolution from a global perspective turns out to be too simplistic. Although the market at a global scale is very tightly correlated, there are many aspects influencing the behavior of markets at a local scale, such as political instability or the emergence of highly demanded "hot spots" that can shift rapidly.