Valuing access to urban greenspace using non-linear distance decay in hedonic property pricing
Artikel i vetenskaplig tidskrift, 2022

Modelling walking distance enables the observation of non-linearities in hedonic property pricing of accessibility to greenspace. We test a penalized spline spatial error model (PS-SEM), which has two distinctive features. First, the PS-SEM controls for the presence of a spatially autocorrelated error term. Second, the PS-SEM allows for continuous non-linear distance decay of the property price premium as a function of walking distance to greenspaces. As a result, compared with traditional spatial econometric methods, the PS-SEM has the advantage that data determines the functional form of the distance decay of the implicit price for greenspace accessibility. Our PS-SEM results from Oslo, Norway, suggest that the implicit price for greenspace access is highly non-linear in walking distance, with the functional form varying for different types of greenspaces. Our results caution against using simple linear distances and assumptions of log or stepwise buffer-based distance decay in property prices relative to pedestrian network distance to urban amenities. The observed heterogeneity in the implicit property prices for walking distance to greenspace also provides a general caution against using non-spatial hedonic pricing models when aggregating values of greenspace amenities for policy analysis or urban ecosystem accounting purposes.

Urban planning

Urban ecosystem accounting

Hedonic pricing method (HPM)

Urban ecosystem services valuation

Penalized spline spatial error model (PS-SEM)

Environmental justice


Edyta Łaszkiewicz

Uniwersytet Lodzki

Axel Heyman

Chalmers, Arkitektur och samhällsbyggnadsteknik, Stadsbyggnad

Xianwen Chen

Inland Norway University of Applied Sciences

Norwegian Institute for Nature Research

Zofie Cimburova

Norwegian Institute for Nature Research

Megan Nowell

Norwegian Institute for Nature Research

David N. Barton

Norwegian Institute for Nature Research

Ecosystem Services

2212-0416 (ISSN)

Vol. 53 101394


Annan data- och informationsvetenskap

Sannolikhetsteori och statistik




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