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Understanding the Effects of Influential Factors on Housing Prices by Combining Extreme Gradient Boosting and a Hedonic Price Model (XGBoost-HPM)

Author

Listed:
  • Sheng Li

    (Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources, Shenzhen 518034, China
    Shenzhen Municipal Planning & Land Real Estate Information Centre, Shenzhen 518034, China)

  • Yi Jiang

    (Shenzhen Municipal Planning & Land Real Estate Information Centre, Shenzhen 518034, China)

  • Shuisong Ke

    (Shenzhen Municipal Planning & Land Real Estate Information Centre, Shenzhen 518034, China)

  • Ke Nie

    (Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources, Shenzhen 518034, China
    Shenzhen Research Center of Digital City Engineering, Shenzhen 518034, China)

  • Chao Wu

    (School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
    Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province, Nanjing University of Posts and Telecommunications, Nanjing 210023, China)

Abstract

The characteristics of housing and location conditions are the main drivers of spatial differences in housing prices, which is a topic attracting high interest in both real estate and geography research. One of the most popular models, the hedonic price model (HPM), has limitations in identifying nonlinear relationships and distinguishing the importance of influential factors. Therefore, extreme gradient boosting (XGBoost), a popular machine learning technology, and the HPM were combined to analyse the comprehensive effects of influential factors on housing prices. XGBoost was employed to identify the importance order of factors and HPM was adopted to reveal the value of the original non-market priced influential factors. The results showed that combining the two models can lead to good performance and increase understanding of the spatial variations in housing prices. Our work found that (1) the five most important variables for Shenzhen housing prices were distance to city centre, green view index, population density, property management fee and economic level; (2) space quality at the human scale had important effects on housing prices; and (3) some traditional factors, especially variables related to education, should be modified according to the development of the real estate market. The results showed that the demonstrated multisource geo-tagged data fusion framework, which integrated XGBoost and HPM, is practical and supports a comprehensive understanding of the relationships between housing prices and influential factors. The findings in this article provide essential implications for informing equitable housing policies and designing liveable neighbourhoods.

Suggested Citation

  • Sheng Li & Yi Jiang & Shuisong Ke & Ke Nie & Chao Wu, 2021. "Understanding the Effects of Influential Factors on Housing Prices by Combining Extreme Gradient Boosting and a Hedonic Price Model (XGBoost-HPM)," Land, MDPI, vol. 10(5), pages 1-15, May.
    • Handle: RePEc:gam:jlands:v:10:y:2021:i:5:p:533-:d:556560
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    References listed on IDEAS

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    2. Chibuzor N. Obiora & Ali N. Hasan & Ahmed Ali, 2023. "Predicting Solar Irradiance at Several Time Horizons Using Machine Learning Algorithms," Sustainability, MDPI, vol. 15(11), pages 1-17, June.
    3. Asad Aziz & Muhammad Mushahid Anwar & Hazem Ghassan Abdo & Hussein Almohamad & Ahmed Abdullah Al Dughairi & Motrih Al-Mutiry, 2023. "Proximity to Neighborhood Services and Property Values in Urban Area: An Evaluation through the Hedonic Pricing Model," Land, MDPI, vol. 12(4), pages 1-12, April.
    4. Qinyu Cui & Yiting Huang & Guang Yang & Yu Chen, 2022. "Measuring Green Exposure Levels in Communities of Different Economic Levels at Different Completion Periods: Through the Lens of Social Equity," IJERPH, MDPI, vol. 19(15), pages 1-26, August.
    5. Nuri Hacıevliyagil & Krzysztof Drachal & Ibrahim Halil Eksi, 2022. "Predicting House Prices Using DMA Method: Evidence from Turkey," Economies, MDPI, vol. 10(3), pages 1-27, March.

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