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Published on 24 June 2024
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Zhu,Z. (2024). The research on factors influencing housing prices-take Beijing as an example. Theoretical and Natural Science,38,208-212.
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The research on factors influencing housing prices-take Beijing as an example

Zining Zhu *,1,
  • 1 Hengshui Tai Hua Middle School

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2753-8818/38/20240560

Abstract

Housing price is not only related to the economic situation of individuals and families, but also has a profound impact on the whole social economy. For many families, their home is their biggest asset. A rise in house prices can increase household net assets. A fall in house prices could lead to a decline in assets. This paper aims to study the influencing factors of housing prices. The data in this paper take Beijing from May to July 2014 as an example. First of all, the article mentions some papers about housing prices, then the author uses scatter plot and linear regression to fix the model. In the results, it can be seen that sqft-living, sqft-above and view have a relatively high correlation with housing prices. Finally, the specific correlation data table is listed. The final conclusion gives the results of the research in this paper and the authors of the articles mentioned. Housing price is not only related to the economic situation of individuals and families, but also has a profound impact on the whole social economy.

Keywords

Housing prices, multiple linear regression, influencing factors

[1]. Wu Z K, Tang W G and Wu B 2007 Using the Priority Factor Method to Analyze the Impact of House Price Factors on Buyers’ Orientation. Journal of Tianjin University of Commerce, 27(3).

[2]. Hu Q 2013 Analysis of housing price factors based on the SVAR model. Times Finance, 2017.

[3]. Yang Dianxue, Zhang Zhimin. An empirical study on incorporating housing price factors into China’s CPI. Statistics&Information Forum, 28(3).

[4]. Lv C Y, Liu Y X and Wang L D 2022 Analysis and Forecast of Influencing Factors on House Prices Based on Machine Learning. Proceedings of 3rd International Symposium on Information Science and Engineering Technology, 117-121.

[5]. Yan Z Yand Zong L 2020 Spatial Prediction of Housing Prices in Beijing Using Machine Learning Algorithms. In Proceedings of the 2020 4th High-Performance Computing and Cluster Technologies Conference & 2020 3rd International Conference on Big Data and Artificial Intelligence. Association for Computing Machinery, New York, NY, USA, 64-71.

[6]. Peng Z, Huang Q and Han Y C 2019 Model Research on Forecast of Second-Hand House Price in Chengdu Based on XGboost Algorithm. 2019 IEEE 11th International Conference on Advanced Infocom Technology (ICAIT). IEEE.

[7]. Pan J, et al. 2023 Analysis and prediction of second-hand housing prices in Qingdao based on integrated algorithms. Advances in Applied Mathematics, 12(4), 1671-1682.

[8]. Wang X J 2019 Research on the impact of second-hand housing prices in Chongqing. Journal of Langfang Normal University (Natural Science Edition), 19(3).

[9]. Zheng Y F 2007 Research on the spatial difference of housing prices in different urban areas of Hangzhou. Economic Forum, 20, 32-34.

[10]. Wang N, 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.

Cite this article

Zhu,Z. (2024). The research on factors influencing housing prices-take Beijing as an example. Theoretical and Natural Science,38,208-212.

Data availability

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

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About volume

Volume title: Proceedings of the 2nd International Conference on Mathematical Physics and Computational Simulation

Conference website: https://www.confmpcs.org/
ISBN:978-1-83558-461-3(Print) / 978-1-83558-462-0(Online)
Conference date: 9 August 2024
Editor:Anil Fernando
Series: Theoretical and Natural Science
Volume number: Vol.38
ISSN:2753-8818(Print) / 2753-8826(Online)

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