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A Spatial Autocorrelation Approach for Examining the Effects of Urban Greenspace on Residential Property Values

Abstract

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.

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Notes

  1. 1.

    Delinquent tax properties could be an opportunity for open space, but municipalities such as city of Los Angeles historically have been reluctant to do so (Harnik 2000).

  2. 2.

    Although studies on park impacts date back as early as 1939 (Herrick 1939 as cited in Crompton 2004), the analysis then was limited to data availability and computation capability.

  3. 3.

    The vacant lot value is based on the transaction data of this neighborhood in 2000. The improvement cost is based on 2006 data for a 0.32 acre pocket park construction project in Los Angeles County.

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Acknowledgements

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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Correspondence to Christina Q. Li.

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Conway, D., Li, C.Q., Wolch, J. et al. A Spatial Autocorrelation Approach for Examining the Effects of Urban Greenspace on Residential Property Values. J Real Estate Finan Econ 41, 150–169 (2010). https://doi.org/10.1007/s11146-008-9159-6

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Keywords

  • Housing value
  • Urban greenspace
  • Hedonic pricing model
  • Spatial dependence