An Application of XGBoost, LightGBM, CatBoost Algorithms on House Price Appraisal System

This paper compares the predictive power of a conventional hedonic pricing model and three machine learning algorithm (XGBoost, LightGBM, CatBoost) based models by using 620,617 apartment data in Seoul from 2009 to 2019. The results are summarised as follows; First, the predictive power of the machine learning models are significantly high not only in the comparison to the conventional model but also in the absolute accuracy related to its practical usefulness. The mean percentage error of XGBoost, LightGBM, and CatBoost were only, respectively, 3.7%, 3.8%, and 3.6% while those of the hedonic model was around 11%. Second, we found that CatBoost algorithm is slightely more performative to the other two algorithms in terms of overall predictive power and frequency of outlier occurrences. Third, this paper show that an ensemble model of the three algorithms can raise the predictive power further.