Abstract

Machine learning techniques are applied to the analysis of real data on the new housing market of Santiago, Chile. The objective is to compare the predictive performance of the Neural Network, Random Forest and Support Vector Machine approaches with traditional Ordinary Least Squares Regression. The database for our analysis consists of a sample of 16,472 price records for new housing units or residential properties within the area covered. The results of the analysis show that Random Forest performed better than the other models in modeling housing prices. More generally, we conclude that machine learning techniques can provide a useful set of tools for acquiring information on housing markets.