In this paper we aim to establish appropriate models to predict housing prices that are affected by multiple factors and we use various machine learning methods to predict house prices. Boosting methods often aim to reduce the bias of a model but can increase its variance. Conversely, bagging methods tend to decrease the variance of a model. Therefore, we attempted to combine these two algorithms to improve the model's capability for predicting house prices. After comparing different methods, we combined the XGBoost and Bagging to construct a novel model for predicting house prices. The experimental results demonstrate that among all the methods, the proposed approach achieves the best prediction performance for housing prices. Furthermore, when compared to using XGBoost alone, our integrated model exhibits improved precision, indicating its efficacy in addressing the challenges associated with housing price prediction.

Xinchao Wu. The Xgboost-Bagging Approach of Predicting House Prices. Academic Journal of Computing & Information Science (2024), Vol. 7, Issue 4: 1-9. https://doi.org/10.25236/AJCIS.2024.070401.

[1] Park, B., and Bae, J. K. (2015). "Using machine learning algorithms for housing price prediction: The case of Fairfax County, Virginia housing data." Expert Systems with Applications, 42(12), 2928-2934.

[2] Bishop, C. M. (2006). Pattern Recognition and Machine Learning (Information Science and Statistics). Springer-Verlag New York, Inc.

[3] Li, H. (2012). "Statistical Learning Methods." Tsinghua University Press.

[4] Varma, A., Sarma, A., Doshi, S., & Nair, R. (2018). "House Price Prediction Using Machine Learning and Neural Networks." In 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT) (pp. 1936-1939). IEEE. 

[5] Breiman, L. (2001) Random Forests. Machine Learning, 45, 5-35.

[6] Breiman, L. (2001). Using iterated bagging to debias regressions. Machine Learning, 45(3), 261-277.

[7] Breiman, Leo. (1996). "Bagging Predictors." Machine Learning.

[8] Friedman, J. H. (2001). Greedy function approximation: a gradient boosting machine. Annals of statistics, 1189-1232.

[9] Ravindra Ravindra, M. P., Meghana, K., Bhavitha, G., et al. (2020). "House price prediction using advanced regression techniques." JEngSci, 11, 1084.

[10] Tao, R. (2022). "Optimized Housing Price Prediction Based on XGBoost." J Sichuan Univ:Nat Sci Ed, 59, 037001.

[11] Erkek, Mehmet, Çayırlı, Kamil, & Hepsen, Ali. (2020). "Predicting House Prices in Turkey by Using Machine Learning Algorithms." Journal of Statistical and Econometric Methods, 9(4), 31-38.

[12] Chen, T., & Guestrin, C. (2016). "XGBoost: A Scalable Tree Boosting System." In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785-794). ACM. (DOI: 10.1145/2939672.2939785)

[13] Rogozhnikov, A., & Likhomanenko, T.(2017). Infinite Boost: building infinite ensembles with gradient descent. ArXiv, abs/1706.01109.

[14] Vinayak, R.K., & Gilad-Bachrach, R. (2015). DART: Dropouts meet Multiple Additive Regression Trees. ArXiv, abs/1505.01866.

[15] Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, Jake Vanderplas, Alexandre Passos, David Cournapeau, Matthieu Brucher, Matthieu Perrot, and Édouard Duchesnay. (2011). Scikit- learn: Machine Learning in Python. J. Mach. Learn. Res. 12, null (2/1/2011), 2825–2830.