Research on the Influencing Factors of Rental House Prices

Xiaoyang Mao

doi:10.62051/cs0pd728

Authors

Xiaoyang Mao

DOI:

https://doi.org/10.62051/cs0pd728

Keywords:

Rental price; Linear regression; Pearson correlation coefficient.

Abstract

Since the 1990s, China's real estate market has gradually recovered, and has developed into a large and mature trading market today, as the housing price increase, the demand of housing rental also increases a lot. Based on this this article aims to identify the factors that have an impact on rental price. The method of multi-linear regression is used to analyze the factors with over 10,000 samples in Guangzhou from May 2020 to June 2020 from the website of LianJia, one of the biggest real estate agencies in China. The dataset contains 10 variables in total, this research uses VIF, Pearson correlation coefficient to narrow the variables down to District, Rent Type, Area, Elevator Flag and Buld Floor these five variables and considered that they have a significant linear relationship with rental price. By focusing on these variables this study provides a view of the relationship of demand and supply of China’s renting market.

Downloads

Download data is not yet available.

References

[1] W. Y. Zhang, People Living in rental residential housing in China 2017-2023, Statista, (2022).

[2] Y. Wang, et al. Spatial Differentiation and Influencing Factors Dataset of Housing Rents in Guangdong-Hongkong-Macao Greater Bay Area, Journal of Global Change Data & Discovery, 6 (1) (2022) 37-44.

[3] Y. Wang, et al. The core influencing factors of housing rent difference in Guangzhou’s urban district, Acta Geographica Sinica, 76(8) (2021).

[4] L. Q. Gu, On the perfection of China’s Long-term Rental System, Frontiers in Business, Economics and Management, (2022).

[5] T. Hu, The rise of China’s rental housing market, Jones Lang LaSalle, (2018).

[6] D. G. Wang, S. M. Li, Socio-economic differentials and stated housing preferences in Guangzhou, China, Habitat International, (2006). DOI: https://doi.org/10.1016/j.habitatint.2004.02.009

[7] Y. X. Xu, S. J. He, J. X. Qian, An investigation on the emerging housing rental market in a studentified village in Guangzhou: a new institutional economics perspective. University at Albany, (2020).

[8] Y. L. Ouyang, K.Y. Sun, Empirical Evidence Based on Geographic Regression Discontinuity Analysis of Housing in Guangzhou School District. International Conference on Finance, Investment and Business Analysis (FIBA 2022), (2022). DOI: https://doi.org/10.54691/bcpbm.v26i.1935

[9] Y. Gao, C. H. Wang, H. H. Qiao, P. Yin, The spatial differentiation pattern and influencing factors of housing prices in Shanghai's tourism and accommodation industry, Geography, 42 (8) (2022) 11.

[10] C. Y. Zhou, C. Y. Li, Research on Regional Second hand Housing Price Prediction Method Based on PSO-LSTM, Modern Information Technology, (2024).