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Daily Accessed Street Greenery and Housing Price: Measuring Economic Performance of Human-Scale Streetscapes via New Urban Data

Author

Listed:
  • Yu Ye

    (College of Architecture and Urban Planning, Tongji University, Shanghai 200092, China)

  • Hanting Xie

    (School of Design, University of Pennsylvania, Philadelphia, PA 19104, USA)

  • Jia Fang

    (College of Architecture and Urban Planning, Tongji University, Shanghai 200092, China)

  • Hetao Jiang

    (College of Architecture and Urban Planning, Tongji University, Shanghai 200092, China)

  • De Wang

    (College of Architecture and Urban Planning, Tongji University, Shanghai 200092, China)

Abstract

The protective effects of street greenery on ecological, psychological, and behavioral phenomena have been well recognized. Nevertheless, the potential economic effect of daily accessed street greenery, i.e., a human-scale and perceptual-oriented quality focusing on exposure to street greenery in people’s daily lives, has not been fully studied because a quantitative measuring of this human-scale indicator is hard to achieve. This study was an attempt in this direction with the help of new urban data and new analytical tools. Shanghai, which has a mature real estate market, was selected for study, and the housing prices of 1395 private neighborhoods in its city center were collected. We selected more than forty variables that were classified under five categories—location features, distances to the closest facilities, density of facilities within a certain radius, housing and neighborhood features, and daily accessed street greenery—in a hedonic pricing model. The distance and density of facilities were computed through a massive number of points-of-interest and a geographical information system. The visible street greenery was collected from Baidu street view images and then measured via a machine-learning algorithm, while accessibility was measured through space syntax. In addition to the well-recognized effects previously discovered, the results show that visible street greenery and street accessibility at global scale hold significant positive coefficients for housing prices. Visible street greenery even obtains the second-highest regression coefficient in the model. Moreover, the combined assessment, the co-presence of local-scale accessibility and eye-level greenery, is significant for housing price as well. This study provides a scientific and quantitative support for the significance of human-scale street greenery, making it an important issue in urban greening policy for urban planners and decision makers.

Suggested Citation

  • Yu Ye & Hanting Xie & Jia Fang & Hetao Jiang & De Wang, 2019. "Daily Accessed Street Greenery and Housing Price: Measuring Economic Performance of Human-Scale Streetscapes via New Urban Data," Sustainability, MDPI, vol. 11(6), pages 1-21, March.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:6:p:1741-:d:216284
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    Cited by:

    1. Teng Zhong & Guonian Lü & Xiuming Zhong & Haoming Tang & Yu Ye, 2020. "Measuring Human-Scale Living Convenience through Multi-Sourced Urban Data and a Geodesign Approach: Buildings as Analytical Units," Sustainability, MDPI, vol. 12(11), pages 1-19, June.
    2. Liv Osland & John Östh & Viggo Nordvik, 2022. "House price valuation of environmental amenities: An application of GIS‐derived data," Regional Science Policy & Practice, Wiley Blackwell, vol. 14(4), pages 939-959, August.
    3. Qiwei Song & Yifeng Liu & Waishan Qiu & Ruijun Liu & Meikang Li, 2022. "Investigating the Impact of Perceived Micro-Level Neighborhood Characteristics on Housing Prices in Shanghai," Land, MDPI, vol. 11(11), pages 1-21, November.
    4. Yonglin Zhang & Xiao Fu & Chencan Lv & Shanlin Li, 2021. "The Premium of Public Perceived Greenery: A Framework Using Multiscale GWR and Deep Learning," IJERPH, MDPI, vol. 18(13), pages 1-16, June.
    5. Zhaoya Gong & Qiwei Ma & Changcheng Kan & Qianyun Qi, 2019. "Classifying Street Spaces with Street View Images for a Spatial Indicator of Urban Functions," Sustainability, MDPI, vol. 11(22), pages 1-17, November.
    6. Yang, Linchuan & Chau, K.W. & Wang, Xu, 2019. "Are low-end housing purchasers more willing to pay for access to basic public services? Evidence from China," Research in Transportation Economics, Elsevier, vol. 76(C).
    7. Yan Liu & Linchuan Yang & Kwong Wing Chau, 2020. "Impacts of Tourism Demand on Retail Property Prices in a Shopping Destination," Sustainability, MDPI, vol. 12(4), pages 1-14, February.
    8. Gao, Qishuo & Shi, Vivien & Pettit, Christopher & Han, Hoon, 2022. "Property valuation using machine learning algorithms on statistical areas in Greater Sydney, Australia," Land Use Policy, Elsevier, vol. 123(C).
    9. 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.

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