A Spatial Autocorrelation Approach for Examining the Effects of Urban Greenspace on Residential Property Values
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This paper presents spatially explicit analyses of the greenspace contribution to residential property values in a hedonic model. The paper utilizes data from the housing market near downtown Los Angeles. We first used a standard hedonic model to estimate greenspace effects. Because the residuals were spatially autocorrelated, we implemented a spatial lag model as indicated by specification tests. Our results show that neighborhood greenspace at the immediate vicinity of houses has a significant impact on house prices even after controlling for spatial autocorrelation. The different estimation results from non-spatial and spatial models provide useful bounds for the greenspace effect. Greening of inner city areas may provide a valuable policy instrument for elevating depressed housing markets in those areas.
KeywordsHousing value Urban greenspace Hedonic pricing model Spatial dependence
We would like to thank the anonymous reviewers for their comments that helped improve the paper. All omissions or errors remain our own.
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