Author(s)

Bowen Yang ¹, Buyang Cao ¹

Affiliation(s)

¹ Tongji Univeristy, School of Software Engineering, Shanghai, China

Corresponding Author

Bowen Yang

ABSTRACT

Housing price is influenced by multiple factors. The existing housing price forecasting model usually belongs to the so called single predictor model, whose prediction accuracy is not ideal and the over-fitting phenomenon often happens due to the data noise. To resolve these issues, this paper proposes an ensembe lerning-based housing price prediction model incorporating various predictors. To evaluate the effectiveness of the proposed model, extra trees, random forest, GBDT and XGB algorithms are selected for the benchmarks. The dataset used is the California housing price available over the web. The results demonstrate that the proposed method can improve the predicting accuracy and stability compared with other four single prediction models.

KEYWORDS

Housing price, Ensemble Learning, Random forest, Gradient Boosting Decision Tree, XGBoost, Housing Price Prediction Model

CITE THIS PAPER

Bowen, Y. , Buyang, C. , Research on Ensemble Learning-based Housing Price Prediction Model. Big Geospatial Data and Data Science (2018) 1: 1-8.

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