Affinities and Complementarities of Methods and Information Sets in the Estimation of Prices in Real Estate Markets

Abstract

In this article, we evaluate the predictive power of multiple machine learning methods using different sets of information, such as location, amenities, socioeconomic characteristics, and available infrastructure nearby, in both residential and commercial real estate markets. This analysis allows us to understand what type of information is the most relevant for each market, which methods are best suited for certain explanatory variables, and the degree of complementarity among different covariates. Our results indicate that the combination of multiple data sources consistently leads to better forecasting and that flexible machine learning models outperform linear regression or spatial methods by taking advantage of the complex interactions between explanatory variables of different sources. From a substantive point of view, we found that residential sale markets have a higher prediction error compared with their rent counterparts, with house sales being the market with the largest estimation error. In terms of the explanatory power of different information sets in different markets, we observe that socioeconomic and location variables have the highest impact on the prediction for sale markets and that, in relative terms, amenities and proximity to places of interest are more important for rental than sale residential markets.