Using GIS and landscape metrics in the hedonic price modeling of the amenity value of urban green space: A case study in Jinan City, China [An article from: Landscape and Urban Planning]

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Description:
Urban green spaces have important amenity values that include provision of leisure opportunities and aesthetic enjoyment. However, most of these values lack a market price. Consequently, they are usually ignored or underestimated by urban planning policy-makers, with the result that remnant urban green spaces are being gradually encroached upon by urban sprawl. As a result, quantitative information regarding the implicit, non-market price benefits from urban green space is urgently required. Properties bought and sold on the market are compound commodities that embody amenity values and people are willing to pay to live in the proximity of local amenity environment. Thus hedonic models, which use such properties as proxies, can often be employed to quantify environmental amenities. In China, residential housing reform (in place since 1998) has terminated the traditional residential welfare system, and made it possible to quantify the monetary value of green space amenities based on hedonic pricing models. This study was conducted in Jinan City, and will help address the previous absence of the application of hedonic price models to the valuation of urban green space amenities in mainland China. GIS and landscape metrics were used in determining hedonic price model variables. As expected, the results proved that the hedonic pricing model performed well using this approach, and accordingly it was further improved. Results also confirmed the positive amenity impact of proximate urban green spaces on house prices, and highlighted the preferences of homeowners in Jinan City. Green space amenity variables that were statistically significant at the 5% level included the size-distance index of scenery forest, accessibility to park and plaza green space types, and the percentage of urban green space. In addition, land-use patch richness, the location sector and the education environment also proved to be highly significant variables. The results of the study should provide insights to policy-makers involved in urban planning.