The application of machine learning in house price prediction
Research Article
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The application of machine learning in house price prediction

Zihao Chen 1*
1 Huanggang Middle School, Huanggang City, Hubei Province, China, 430071
*Corresponding author: 13477672699@189.cn
Published on 27 November 2025
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AORPM Vol.4 Issue 3
ISSN (Print): 3029-0899
ISSN (Online): 3029-0880
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Abstract

In recent years, with the significant impact of housing price changes on economic and social stability, scientifically predicting housing price trends can prompt local governments to formulate policies related to housing prices, and investors can make investments based on the corresponding housing prices. At the same time, homebuyers can also make housing purchase plans according to housing prices. Traditional methods for predicting housing prices are primarily based on experience or simple statistical models, which cannot reasonably consider complex factors. This paper investigates the efficacy of machine learning methodologies in the prediction of housing market valuations. Firstly, it discusses the characteristics and value of supervised, unsupervised, and decision tree algorithms in the machine learning field when applied to housing price prediction. Secondly, it explores the reasons affecting housing prices, such as economic characteristics like national income, regional features, and the influence of supply and demand. Then, it applies a public housing price dataset to establish a decision tree for predicting housing prices. Based on basic features, it expands the feature library. It seeks suitable feature combinations to analyze housing prices better and draw conclusions on the value of machine learning methods in housing price prediction. This article summarizes successful experiences from existing application cases and studies the problems and deficiencies in the application process.

Keywords:

machine learning, housing price prediction, linear regression, financial prediction, data modeling

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Chen,Z. (2025). The application of machine learning in house price prediction. Advances in Operation Research and Production Management,4(3),79-83.

References

[1]. Chen, X., Chen, Y., Wang, Z., et al. (2024). Research on the influencing factors of housing price differentiation between cities.Economic Theory and Economic Management, 44(2), 49.

[2]. Zhang, J., & Guo, H. (2024). A review and outlook on batch appraisal of real estate driven by data.Journal of Xihua University (Philosophy and Social Sciences Edition), 43(3), 13-27.

[3]. Zhou, L., & Zhao, M. (2022). Analysis of housing price prediction based on several machine learning models.National Circulation Economy.

[4]. Wang, Y. (2024). Research on housing price prediction based on machine learning.Finance, 14, 1552.

[5]. Mo, P., & Lin, G. (2024). Research on second-hand housing transaction price prediction based on the XGBoost model.Advances in Applied Mathematics, 13, 4417.

[6]. Zhu, Y., Zheng, L., & Zhu, W. (2024). Construction of urban housing price prediction model from a spatiotemporal perspective: A case study of Beijing.Statistics and Application, 13, 276.

[7]. Wang, Y., & Chen, M. (2025). Research on housing rent prediction in Chengdu based on machine learning.Computer Science and Application, 15, 138.

[8]. Li, J., Li, Z., Chen, J., et al. (2022). Forecasting tourism demand in Beijing based on online search data.Hans Journal of Data Mining, 12, 133.

[9]. Zhang, X., & Hu, S. (2024). Comparison of machine learning-based monetary policy models and traditional rule-based models.World Economic Research, 13, 222.

[10]. Qin, Q., Chen, Y., Xiang, Z., et al. (2022). An improved SVR-SARIMAX hybrid sales prediction model for excavators.Construction Machinery & Equipment, 53(9).

[11]. Li, X., & Ding, Z. (2024). Hybrid strategy improved Harris hawk optimization algorithm.Journal of Yunnan University (Natural Science Edition), 47(1), 60-69.

[12]. Wang, X., Chen, X., Lin, X., et al. (2024). Design and implementation of a housing price prediction system based on deep learning.Computer and Digital Engineering, 52(9), 2572-2567.

[13]. Zheng, Y. (2024). Real estate price analysis and prediction based on GM(1, 1) model: A case study of Shanghai.Modern Management, 14, 1359.

[14]. Shang, J., Li, W., Xi, L., et al. (2024). Agricultural product price prediction based on EMD-PSO-ARIMA model.Hubei Agricultural Sciences, 63(8), 121.

[15]. Wang, Y. (2024). Research on housing price prediction based on machine learning.Finance, 14, 1552.

References

[1]. Chen, X., Chen, Y., Wang, Z., et al. (2024). Research on the influencing factors of housing price differentiation between cities.Economic Theory and Economic Management, 44(2), 49.

[2]. Zhang, J., & Guo, H. (2024). A review and outlook on batch appraisal of real estate driven by data.Journal of Xihua University (Philosophy and Social Sciences Edition), 43(3), 13-27.

[3]. Zhou, L., & Zhao, M. (2022). Analysis of housing price prediction based on several machine learning models.National Circulation Economy.

[4]. Wang, Y. (2024). Research on housing price prediction based on machine learning.Finance, 14, 1552.

[5]. Mo, P., & Lin, G. (2024). Research on second-hand housing transaction price prediction based on the XGBoost model.Advances in Applied Mathematics, 13, 4417.

[6]. Zhu, Y., Zheng, L., & Zhu, W. (2024). Construction of urban housing price prediction model from a spatiotemporal perspective: A case study of Beijing.Statistics and Application, 13, 276.

[7]. Wang, Y., & Chen, M. (2025). Research on housing rent prediction in Chengdu based on machine learning.Computer Science and Application, 15, 138.

[8]. Li, J., Li, Z., Chen, J., et al. (2022). Forecasting tourism demand in Beijing based on online search data.Hans Journal of Data Mining, 12, 133.

[9]. Zhang, X., & Hu, S. (2024). Comparison of machine learning-based monetary policy models and traditional rule-based models.World Economic Research, 13, 222.

[10]. Qin, Q., Chen, Y., Xiang, Z., et al. (2022). An improved SVR-SARIMAX hybrid sales prediction model for excavators.Construction Machinery & Equipment, 53(9).

[11]. Li, X., & Ding, Z. (2024). Hybrid strategy improved Harris hawk optimization algorithm.Journal of Yunnan University (Natural Science Edition), 47(1), 60-69.

[12]. Wang, X., Chen, X., Lin, X., et al. (2024). Design and implementation of a housing price prediction system based on deep learning.Computer and Digital Engineering, 52(9), 2572-2567.

[13]. Zheng, Y. (2024). Real estate price analysis and prediction based on GM(1, 1) model: A case study of Shanghai.Modern Management, 14, 1359.

[14]. Shang, J., Li, W., Xi, L., et al. (2024). Agricultural product price prediction based on EMD-PSO-ARIMA model.Hubei Agricultural Sciences, 63(8), 121.

[15]. Wang, Y. (2024). Research on housing price prediction based on machine learning.Finance, 14, 1552.

Cite this article

Chen,Z. (2025). The application of machine learning in house price prediction. Advances in Operation Research and Production Management,4(3),79-83.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Journal: Advances in Operation Research and Production Management

Volume number: Vol.4
Issue number: Issue 3
ISSN: 3029-0880(Print) / 3029-0899(Online)

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