Author(s)

BoHong Wang^1*, YiXuan Guo², ChaoLin Hou¹, ZhiLing Zhang³

Affiliation(s)

¹Finance Management School, Shanghai University of International Business and Economics, Shanghai 201620, China.

²School of Mathematics and Statistics, Wuhan University, Wuhan 430072, Hubei, China.

³School of International Business, Shanghai University of International Business and Economics, Shanghai 201620, China.

Corresponding Author

BoHong Wang

ABSTRACT

Machine learning models supported by big data have been practiced and applied in many ways in recent years, and as a representative technology of artificial intelligence, machine learning models have been proved to be able to perform well in many predictive problems such as economics and management. This paper explores the practice in the problem of residential value assessment by using the more popular machine learning models. The Chain Home platform offers publicly available, granular data on residential property transactions, including variables such as location, area, layout, and pricing. The dataset from November 22, 2024, was selected to provide a consistent time snapshot of the housing market, facilitating reliable model training and evaluation. After that, it further compares the performance of linear regression, random forest algorithm, extreme gradient boosting tree, lightweight gradient boosting tree, classification boosting tree and other algorithms on asset pricing. The empirical results show that the machine learning algorithms can be relatively effective in assessing and pricing residential properties according to their characteristics, and the error between the predicted price and the actual price of the asset appraisal model based on the extreme boosted tree algorithm is much smaller, with an average error of about 17%. This paper attempts to introduce machine learning into the field of asset evaluation, which helps to promote the cross-fertilization research of artificial intelligence and traditional economics problems, and provides reference for promoting the application of artificial intelligence.

KEYWORDS

Asset valuation; XGBoost; Machine learning

CITE THIS PAPER

BoHong Wang, YiXuan Guo, ChaoLin Hou, ZhiLing Zhang. Appication of XGBoost algorithm in housing asset valuation. World Journal of Information Technology. 2025, 3(3): 54-61. DOI: https://doi.org/10.61784/wjit3043.

REFERENCES

[1] Qiu X, Ke X. The Impact of ChatGPT-like Artificial Intelligence on the Asset Appraisal Industry. China Asset Appraisal, 2024(03): 20-26.

[2] Guo F, X Tao. Machine learning and causality in the social sciences: a literature review. Economics, 2023, 23(01): 1-17. DOI:10.13821/j.cnki.ceq.2023.01.01.

[3] Li B, Shao C, Li Y. A Study of Machine Learning-Driven Quantitative Investment in Fundamentals. China Industrial Economy, 2019(08): 61-79. DOI:10.19581/j.cnki.ciejournal.2019.08.004.

[4] Tao X, Guo F. Heterogeneous policy effects assessment with machine learning methods: research progress and future directions. Management World, 2023, 39(11): 216-237. DOI:10.19744/j.cnki.11-1235/f.2023.0127.

[5] Cao S, Jiang W, Wang J, et al. From Man vs. Machine to Man + Machine: The art and AI of stock analyses. Journal of Financial Economics, 2024, 160: 103910-103910.

[6] Kim A, Muhn M, Nikolaev V. Financial statement analysis with large language models. arXiv preprint arXiv:2407.17866, 2024.

[7] Jon K, Jens L, Sendhil M, et al. Prediction Policy Problems. The American economic review, 2015, 105(5): 491-495.

[8] Wang W, Li W, Zhang N, et al. Portfolio formation with preselection using deep learning from long-term financial data. Expert Systems With Applications, 2020, 143: 113042-113042.

[9] Jie X, Yukun Z, Chunxiao X. A review of research on the application of machine learning in financial asset pricing. Computer Science, 2022.

[10] Ma J. Research on the value assessment of data assets in network transaction scenarios based on machine learning. Beijing Jiaotong University, 2024.

[11] Chen T, Guestrin C. XGBoost: A Scalable Tree Boosting System.CoRR, 2016.

[12] Hu J, Szymczak S. A review on longitudinal data analysis with random forest. Briefings in bioinformatics, 2023, 24(2): bbad002.

[13] Prokhorenkova L, Gusev G, Vorobev A, et al. CatBoost: unbiased boosting with categorical features. Advances in neural information processing systems, 2018, 31.

[14] Lu M. Research and design of asset evaluation platform based on big data. Science and Technology for Development, 2019, 15(10): 1093-1105.