Proceedings of the 2022 International Conference on mathematical statistics and economic analysis (MSEA 2022)

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Prediction and Analysis of Rental Price using Random Forest Machine Learning Technique Take Shanghai and Wuhan for example

Authors
Chuang Hu1, *, Rui Huang2, Haijian Li3
1School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan, Hubei, 430070, China
2School of economics, Hunan Agricultural University, Changsha, Hunan, 410125, China
3School of Statistics and Mathematics, Zhongnan University of Economics and Law, Wuhan, Hubei, 430073, China
*Corresponding author. Email: 311923@whut.edu.cn
Corresponding Author
Chuang Hu
Available Online 29 December 2022.
DOI
10.2991/978-94-6463-042-8_84How to use a DOI?
Keywords
Rental price; Machine learning; Random forest algorithm
Abstract

With the rapid development of China's real estate market, the real estate industry has become a significant part of Chinese national economy. However, the high housing prices in the first-tier and new first-tier cities have forced many young people to turn their attention to the rental market, setting off an upsurge of housing rental. Based on the random forest model, this paper selects two cities, Shanghai and Wuhan, to study the price trend of the housing rental market and its influencing factors. Finally, it is found that the random forest regression model has no significant effect on the rental forecast in Shanghai. It may be that for a highly modernized first-tier city, the variables selected in this paper are not enough to fully explain the rental price. The prediction effect of rental price in Wuhan is significantly better, among which the characteristics of urban area and housing itself have a great impact on rental price. This research can serve as a reference for future researchers in the housing rental market, while helping landlords and tenants make optimal choices.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2022 International Conference on mathematical statistics and economic analysis (MSEA 2022)
Series
Advances in Computer Science Research
Publication Date
29 December 2022
ISBN
10.2991/978-94-6463-042-8_84
ISSN
2352-538X
DOI
10.2991/978-94-6463-042-8_84How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Chuang Hu
AU  - Rui Huang
AU  - Haijian Li
PY  - 2022
DA  - 2022/12/29
TI  - Prediction and Analysis of Rental Price using Random Forest Machine Learning Technique Take Shanghai and Wuhan for example
BT  - Proceedings of the 2022 International Conference on mathematical statistics and economic analysis (MSEA 2022)
PB  - Atlantis Press
SP  - 587
EP  - 593
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-042-8_84
DO  - 10.2991/978-94-6463-042-8_84
ID  - Hu2022
ER  -