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Tropical Vegetation and Residential Property Value: A Hedonic Pricing Analysis in Singapore

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  • Belcher, Richard N.
  • Chisholm, Ryan A.

Abstract

Effective urban planning depends on knowing homebuyers' preferences for neighbourhood features that provide different amenities, such as managed parks and trees. As the expansion of tropical urban areas into biodiversity hotspots is predicted to more than double by 2030, knowing homebuyers utility from different vegetation types can contribute to global biodiversity conservation strategies. We used the hedonic pricing method to estimate the economic value of managed, spontaneous and high conservation value vegetation to Singapore public housing using a mixed effects model. On average vegetation had positive effects on property selling price, accounting for 3% of the average property's value, or a total of S$179 million for all public housing apartments sold over 13 months. These effects were almost entirely driven by managed vegetation, which had positive marginal effects on price for 98.1% of properties. The estimated marginal effects of high conservation value vegetation were mostly negative (90.5%% properties), but positive for properties without much managed vegetation nearby. The estimated marginal effects of spontaneous vegetation were mixed and mostly small. To reconcile the goals of protecting high conservation value vegetation and maximising homeowner utility, new public housing developments should contain more managed vegetation but be away from high conservation value vegetation.

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  • Belcher, Richard N. & Chisholm, Ryan A., 2018. "Tropical Vegetation and Residential Property Value: A Hedonic Pricing Analysis in Singapore," Ecological Economics, Elsevier, vol. 149(C), pages 149-159.
  • Handle: RePEc:eee:ecolec:v:149:y:2018:i:c:p:149-159
    DOI: 10.1016/j.ecolecon.2018.03.012
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    6. Zhijiao Qin & Yan Yu & Dianfeng Liu, 2019. "The Effect of HOPSCA on Residential Property Values: Exploratory Findings from Wuhan, China," Sustainability, MDPI, vol. 11(2), pages 1-18, January.
    7. 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.
    8. Chad M. Botes & Alberto M. Zanni, 2021. "Trees, ground vegetation, sidewalks, cycleways: users’ preferences and economic values for different elements of an urban street—a case study in Taipei," Environmental Economics and Policy Studies, Springer;Society for Environmental Economics and Policy Studies - SEEPS, vol. 23(1), pages 145-171, January.
    9. Dongwon Ko & Seunghoon Park, 2024. "Investigating the Correlation between Air Pollution and Housing Prices in Seoul, South Korea: Application of Explainable Artificial Intelligence in Random Forest Machine Learning," Sustainability, MDPI, vol. 16(11), pages 1-23, May.
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