
Research on the influencing factors of housing rental prices-take Shanghai housing rents as an example
- 1 University of International Business and Economics
* Author to whom correspondence should be addressed.
Abstract
In today’s era, renting offers lower costs and greater flexibility compared to buying, making it a preferred choice for many to meet their accommodation needs. Therefore, identifying factors that affect rental prices is of significant importance both for individuals and governments. Through the establishment of multiple linear regression and stepwise regression models, this paper analyzed 400 samples selected from a dataset of 20,000 rental listings in Shanghai, revealing the relationships between rental prices and key factors in the city. During the process of model establishment and data analysis, variables that significantly impact rent were identified through significance testing. The model indicated a strong relationship between the size of the property and its renovation status with rental prices, while other variables such as proximity to subway stations and the floor level of the property had a weaker impact on rent. These findings can assist individuals in making informed decisions when choosing rental properties. Overall, this research contributes valuable insights into the factors influencing rental prices in Shanghai, enabling better decision-making for both renters and policymakers.
Keywords
Rental price, multiple linear regression, stepwise regression model
[1]. Wang S, Wang Y and Shen Y 2023 The Impact of Supportive Housing Policy Scenarios on Marriage and Fertility Intentions: A Vignette Survey Experimental Study in Shanghai, China. Popul Res Policy Rev, 42, 96.
[2]. Akakuru O C, et al. 2023 Application of artificial neural network and multi-linear regression techniques in groundwater quality and health risk assessment around Egbema, Southeastern Nigeria. Environ Earth Sci, 82, 77.
[3]. Khandaskar S, Panjwani C, Patil D and Bajaj P 2023 House and Rent Price Prediction System using Regression. 2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS), Coimbatore, India, 1733-1739.
[4]. Wen Y D, Wu Y Y and Li H 2024 Institutions, urban space, and residential markets in globalizing Shanghai: A comparative study of housing sale and rental prices, Journal of Urban Affairs.
[5]. Dang G Y and Yang T 2014 Multiple Linear Regression Analysis of Influencing Factors of House Prices in Tangshan City. Journal of Hebei United University (Social Science Edition), 21-25.
[6]. Dai L and Li X T 2019 Analysis of Influencing Factors of Second-hand Housing Prices Based on Multiple Linear Regression Model: Taking a District in Chengdu as an Example. Henan Building Materials, 80-82.
[7]. Zhang Y 2023 Research on the Impact of the Rent in Shanghai Based on Multiple Linear Regression Model. Highlights in Science, Engineering and Technology, 38, 364-369.
[8]. Dai X, Bai X and Xu M 2016 The influence of Beijing rail transfer stations on surrounding housing prices. Habitat International, 55, 79-88.
[9]. Wang N, Wu W and Hu X Y, et al. 2018 The Heterogeneity of the Impact of Major Transportation Facilities on Residential Prices under Urban Crossing Rivers: A Case Study of Binjiang New City in Nanchang City. Urban Studies, 10, 123-130.
[10]. Wang J F 2013 Prediction Model of Commodity Housing Price Based on Multiple Linear Regression. Science and Technology Vision, 210.
Cite this article
Qin,Z. (2024). Research on the influencing factors of housing rental prices-take Shanghai housing rents as an example. Theoretical and Natural Science,38,160-165.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
Disclaimer/Publisher's Note
The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
About volume
Volume title: Proceedings of the 2nd International Conference on Mathematical Physics and Computational Simulation
© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and
conditions of the Creative Commons Attribution (CC BY) license. Authors who
publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons
Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this
series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published
version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial
publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and
during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See
Open access policy for details).