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Housing Prices and the Characteristics of Nearby Green Space: Does Landscape Pattern Index Matter? Evidence from Metropolitan Area

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  • Yiyi Chen

    (School of Geography and Planning, Sun Yat-Sen University, Guangzhou 510006, China
    Guangdong Provincial Engineering Research Center for Public Security and Disaster, Guangzhou 510275, China
    Guangdong Key Laboratory for Urbanization and Geo-Simulation, Sun Yat-Sen University, Guangzhou 510275, China)

  • Colin A. Jones

    (The Urban Institute, Heriot-Watt University, Edinburgh EH14 4AS, UK)

  • Neil A. Dunse

    (The Urban Institute, Heriot-Watt University, Edinburgh EH14 4AS, UK)

  • Enquan Li

    (School of Mathematics, University of Birmingham, Birmingham B15 2TT, UK)

  • Ye Liu

    (School of Geography and Planning, Sun Yat-Sen University, Guangzhou 510006, China
    Guangdong Provincial Engineering Research Center for Public Security and Disaster, Guangzhou 510275, China
    Guangdong Key Laboratory for Urbanization and Geo-Simulation, Sun Yat-Sen University, Guangzhou 510275, China)

Abstract

This study aimed to examine the association between housing prices and green space characteristics with a special focus on exploring the effects of the shape pattern index. The research was based on a hedonic price model across two main distance buffers from residential properties to urban green spaces. Green spaces were characterized by size and shape measured by a landscape shape index (LSI). This study was based on 16,222 housing transaction data obtained from the website of real estate agencies during December 2019 in the Metropolitan Area of Beijing. Linear regression and semi-log regression analysis were used to examine the associations between independent housing and neighborhood characteristic variables and housing prices. The results suggested that a one-unit increase in the natural logarithm of the landscape shape index (LSI) can increase housing prices by 4% (5543 CNY ≈ 826 USD). Such marginal effects were more pronounced for residences located close to urban green spaces and tended to decay as the distance from residences to green spaces increased. Additional analysis captured the marginal effects of the natural logarithm of the landscape shape index (LSI > 1.3) on achieving the maximum monetary evaluation of the property. The findings of this study suggest that the effects of specific green space characteristics on housing prices should be taken into account in landscape and urban design.

Suggested Citation

  • Yiyi Chen & Colin A. Jones & Neil A. Dunse & Enquan Li & Ye Liu, 2023. "Housing Prices and the Characteristics of Nearby Green Space: Does Landscape Pattern Index Matter? Evidence from Metropolitan Area," Land, MDPI, vol. 12(2), pages 1-17, February.
    • Handle: RePEc:gam:jlands:v:12:y:2023:i:2:p:496-:d:1070927
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    Cited by:

    1. Shenglin Ben & He Zhu & Jiajun Lu & Renfeng Wang, 2023. "Valuing the Accessibility of Green Spaces in the Housing Market: A Spatial Hedonic Analysis in Shanghai, China," Land, MDPI, vol. 12(9), pages 1-24, August.
    2. Jong-Won Lee & Sang-Woo Lee & Hai Gyong Kim & Hyun-Kil Jo & Se-Rin Park, 2023. "Green Space and Apartment Prices: Exploring the Effects of the Green Space Ratio and Visual Greenery," Land, MDPI, vol. 12(11), pages 1-13, November.

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