A Study on House Price Prediction Based on Stacking-Sorted-Weighted-Ensemble Model

Abstract

The house price prediction problem is a typical regression problem, and most of the common house price prediction models are single prediction algorithms, which are not ideal in terms of accuracy and stability. For solving this problem, this paper proposes a house price forecasting method based on Stacking-Sorted-Weighted-Ensemble (SSWE) model. Considering the characteristics of different algorithms and giving full play to the advantages of each model, multiple individual forecasting models are fused with the Stacking model. The algorithm validation is performed using the data generated by the system of real estate management department in western Guangdong. The prediction results show that the Stacking model is superior to the single model. Compared with the Stacking regression model, the SSWE model has a 13.6% increase in the root mean square error on the training set but a 0.3% decrease on the test set, indicating that the SSWE model prevents overfitting to a some extent and increases the accuracy and stability of the model.