The Optimization house price prediction model using gradient boosted regression trees (GBRT) and xgboost algorithm

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Putri Susi Sundari
Mahardika Khafidz Putra


In this rapidly advancing technological era, the demand for the real estate industry has also increased, including in the field of house price prediction. House prices fluctuate every year due to several factors such as changes in land prices, location, year of construction, infrastructure developments, and other factors. Numerous studies have been conducted on this issue. However, the challenge lies in building a proven accurate and effective model for predicting house prices with the abundance of features present in the dataset. The objective of this research is to develop a predictive model that can accurately estimate house prices based on relevant features or variables. The researcher utilizes ensemble learning techniques, combining the Gradient Boosted Regression Trees (GBRT) and XGBoost algorithms. The dataset used in this article is titled "Ames Housing dataset" obtained from Kaggle. The predictive model is then evaluated using the Root Mean Squared Error (RMSE) method. The RMSE result from a previous study that used the combination of Lasso and XGBoost was 0.11260, while the RMSE result from this research is 0.00480. This indicates a decrease in the RMSE value, indicating a lower level of error in the model. It also means that the combination of GBRT and XGBoost algorithms successfully improves the prediction accuracy of the previous research model.

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D. R. Damayanti, S. Wicaksono, M. F. Al Hakim, J. Jumanto, S. Subhan, and Y. N. Ifriza, “Rainfall Prediction in Blora Regency Using Mamdani’s Fuzzy Inference System,” J. Soft Comput. Explor., vol. 3, no. 1, pp. 62–69, Mar. 2022, doi: 10.52465/joscex.v3i1.69.

S. Lu, Z. Li, Z. Qin, X. Yang, and R. S. M. Goh, “A hybrid regression technique for house prices prediction,” in 2017 IEEE international conference on industrial engineering and engineering management (IEEM), IEEE, 2017, pp. 319–323, doi:

M. Thamarai and S. P. Malarvizhi, “House Price Prediction Modeling Using Machine Learning.,” Int. J. Inf. Eng. Electron. Bus., vol. 12, no. 2, 2020, doi:

A. P. Singh, K. Rastogi, and S. Rajpoot, “House Price Prediction Using Machine Learning” in 2021 3rd International Conference on Advances in Computing, Communication Control and Networking (ICAC3N), IEEE, 2021, pp. 203–206. Available online:

Q. Zhang, “Housing price prediction based on multiple linear regression,” Sci. Program., vol. 2021, pp. 1–9, 2021. DOI:

Y. Huang, Y. Liu, C. Li, and C. Wang, “GBRTVis: online analysis of gradient boosting regression tree,” J. Vis., vol. 22, pp. 125–140, 2019, doi:

A. F. Mulyana, W. Puspita, and J. Jumanto, “Increased accuracy in predicting student academic performance using random forest classifier,” J. Student Res. Explor., vol. 1, no. 2, pp. 94–103, Jul. 2023, doi: 10.52465/josre.v1i2.169.

N. Ragapriya, T. A. Kumar, R. Parthiban, P. Divya, S. Jayalakshmi, and D. R. Raman, “Machine Learning Based House Price Prediction Using Modified Extreme Boosting,” Asian J. Appl. Sci. Technol., vol. 7, no. 1, pp. 41–54, 2023. Available online:

N. Chen, “House Price Prediction Model of Zhaoqing City Based on Correlation Analysis and Multiple Linear Regression Analysis” Wirel. Commun. Mob. Comput., vol. 2022, 2022. ,doi:

C. H. R. Madhuri, G. Anuradha, and M. V. Pujitha, “House price prediction using regression techniques: A comparative study” in 2019 International conference on smart structures and systems (ICSSS), IEEE, 2019, pp. 1–5, doi:

B. Afonso, L. Melo, W. Oliveira, S. Sousa, and L. Berton, “Housing prices prediction with a deep learning and random forest ensemble” in Anais do XVI encontro nacional de inteligência artificial e computacional, SBC, 2019, pp. 389–400, doi:

A. Amalia, M. Radhi, S. H. Sinurat, D. R. H. Sitompul, and E. Indra, “PREDIKSI HARGA MOBIL MENGGUNAKAN ALGORITMA REGRESSI DENGAN HYPER-PARAMETER TUNING” J. Sist. Inf. dan Ilmu Komput. Prima (JUSIKOM PRIMA), vol. 4, no. 2, pp. 28–32, 2021, doi:

T. Chen, H. Shang, and Q. Bi, “A prediction method of five-axis machine tool energy consumption with GBRT algorithm” in 2019 IEEE 5th International Conference on Mechatronics System and Robots (ICMSR), IEEE, 2019, pp. 34–39, doi:

Y. Wang and Y. Tang, “A recommendation algorithm based on item genres preference and GBRT” in Journal of Physics: Conference Series, IOP Publishing, 2019, p. 12053, doi:

P. Nie, M. Roccotelli, M. P. Fanti, Z. Ming, and Z. Li, “Prediction of home energy consumption based on gradient boosting regression tree” Energy Reports, vol. 7, pp. 1246–1255, 2021, doi:

R.-T. Mora-Garcia, M.-F. Cespedes-Lopez, and V. R. Perez-Sanchez, “Housing Price Prediction Using Machine Learning Algorithms in COVID-19 Times” Land, vol. 11, no. 11, p. 2100, 2022, doi:

M. Jain, H. Rajput, N. Garg, and P. Chawla, “Prediction of house pricing using machine learning with Python” in 2020 International conference on electronics and sustainable communication systems (ICESC), IEEE, 2020, pp. 570–574, doi:

W. F. Abror, A. Alamsyah, and M. Aziz, “Bankruptcy Prediction Using Genetic Algorithm-Support Vector Machine (GA-SVM) Feature Selection and Stacking” J. Inf. Syst. Explor. Res., vol. 1, no. 2, Jul. 2023, doi: 10.52465/joiser.v1i2.180.

C. Fan, Z. Cui, and X. Zhong, “House prices prediction with machine learning algorithms” in Proceedings of the 2018 10th International Conference on Machine Learning and Computing, 2018, pp. 6–10, doi:

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