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Assessing the Effects of Subjective and Objective Measures on Housing Prices with Street View Imagery: A Case Study of Suzhou

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  • Jin Zhu

    (School of Geography Science and Geomatics Engineering, Suzhou University of Science and Technology, Suzhou 215009, China
    State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China)

  • Yao Gong

    (School of Geography Science and Geomatics Engineering, Suzhou University of Science and Technology, Suzhou 215009, China)

  • Changchang Liu

    (School of Geography Science and Geomatics Engineering, Suzhou University of Science and Technology, Suzhou 215009, China)

  • Jinglong Du

    (School of Geography Science and Geomatics Engineering, Suzhou University of Science and Technology, Suzhou 215009, China)

  • Ci Song

    (State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    University of Chinese Academy of Sciences, Beijing 100101, China)

  • Jie Chen

    (State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    University of Chinese Academy of Sciences, Beijing 100101, China)

  • Tao Pei

    (State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    University of Chinese Academy of Sciences, Beijing 100101, China
    Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China)

Abstract

The price of a house is affected by both the subjective and objective factors of the street environment in a neighborhood. However, the relationships between these factors and housing prices are not fully understood. Street view imagery (SVI) has recently emerged as a new data source for housing price studies. The SVI contains both objective and subjective information and can be used to extract objective measurements describing the physical environment and subjective measurements depicting human perceptions. Compared to conventional methods, there is consistency between subjective and objective information extracted from SVIs, and the two types of information are acquired from the perspective of the human visual perceptual system. Therefore, using both objective and subjective information extracted from street view images to study their relationship with housing prices has several advantages. In this study, focusing on the city of Suzhou, China, we extracted subjective perception and objective view indices from SVIs and systematically assessed their effects on housing prices. The global ordinary least squares (OLS) regression model and the local geographically weighted regression (GWR) model were used to model the correlations between these measures and housing prices. The OLS reveals that overall objective measures have stronger explanatory power, and built environment factors have a greater impact on housing prices. GWR shows that subjective factors can explain more variance in housing prices on the local scale and that home buyers care more about the subjective perceptions of the neighborhood’s surroundings. The map of the GWR local coefficients demonstrates that the perception indicators have both positive and negative effects on housing prices in different places. In addition, a Monte Carlo test was performed to verify the spatially varying relationships between these measures. Our findings provide important references for urban designers and guide various applications, such as safe neighborhood design and sustainable city planning.

Suggested Citation

  • Jin Zhu & Yao Gong & Changchang Liu & Jinglong Du & Ci Song & Jie Chen & Tao Pei, 2023. "Assessing the Effects of Subjective and Objective Measures on Housing Prices with Street View Imagery: A Case Study of Suzhou," Land, MDPI, vol. 12(12), pages 1-25, November.
    • Handle: RePEc:gam:jlands:v:12:y:2023:i:12:p:2095-:d:1285245
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    References listed on IDEAS

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    1. Hu, Lirong & He, Shenjing & Han, Zixuan & Xiao, He & Su, Shiliang & Weng, Min & Cai, Zhongliang, 2019. "Monitoring housing rental prices based on social media:An integrated approach of machine-learning algorithms and hedonic modeling to inform equitable housing policies," Land Use Policy, Elsevier, vol. 82(C), pages 657-673.
    2. Rencai Dong & Yonglin Zhang & Jingzhu Zhao, 2018. "How Green Are the Streets Within the Sixth Ring Road of Beijing? An Analysis Based on Tencent Street View Pictures and the Green View Index," IJERPH, MDPI, vol. 15(7), pages 1-22, June.
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