Predicting housing price in Beijing Via Google and Microsoft AutoML

Abstract

For years, the hedonic regression model has dominated housing price research worldwide. However, the hedonic regression model suffered from the problem of over-simplification and heterogeneity. Machine learning has become a hot method in housing price prediction in recent years. The machine learning method in predicting housing prices is more accurate and precise than the traditional methods. This paper introduced three regression methods in housing price prediction: the traditional hedonic regression model, Google AutoML and Microsoft AutoML. It reviewed the factors that affected housing prices in literature and used the dataset of the housing price in Beijing in Kaggle to study the factors affected the housing price in Beijing. The results showed that Google AutoML had the best performance in predicting housing prices in Beijing. It had the highest R square (0.820) and the least RMSE and MAE. The average housing price in a community was the most important feature that impacted housing price prediction. Number of days open for sale and geographical location ranked the second and the third most important features in predicting the housing price.