Proceedings of the 4th International Conference on Economic Management and Big Data Applications, ICEMBDA 2023, October 27–29, 2023, Tianjin, China

Research Article

Empirical Research on Population Structure and Housing Demand under the Data Mining Classification Model

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  • @INPROCEEDINGS{10.4108/eai.27-10-2023.2341928,
        author={Panpan  Li and Aifei  Yin and Zhuanping  Du},
        title={Empirical Research on Population Structure and Housing Demand under the Data Mining Classification Model},
        proceedings={Proceedings of the 4th International Conference on Economic Management and Big Data Applications, ICEMBDA 2023, October 27--29, 2023, Tianjin, China},
        publisher={EAI},
        proceedings_a={ICEMBDA},
        year={2024},
        month={1},
        keywords={population structure; housing demand; data mining; classification model; empirical research},
        doi={10.4108/eai.27-10-2023.2341928}
    }
    
  • Panpan Li
    Aifei Yin
    Zhuanping Du
    Year: 2024
    Empirical Research on Population Structure and Housing Demand under the Data Mining Classification Model
    ICEMBDA
    EAI
    DOI: 10.4108/eai.27-10-2023.2341928
Panpan Li1,*, Aifei Yin1, Zhuanping Du1
  • 1: Chongqing college of architecture and technology
*Contact email: 568292716@qq.com

Abstract

Due to the continuous development of socio-economics and gradual adjustments in fertility policies, China's population structure and composition are undergoing profound changes. To precisely explore how these changes impact housing demand, this study specifically selected data from Chongqing City from 2011 to 2020 and employed an advanced data mining classification model for in-depth empirical analysis. Through detailed investigation, we found a significant positive correlation between the urbanization process and average years of education with housing demand. Moreover, as education levels rise, people's economic conditions and housing expectations also increase. These findings offer valuable references for the government and real estate developers in formulating housing policies and adjusting market strategies, emphasizing the need for comprehensive consideration of population structure and educational background in future housing markets.