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The spatial dynamics of urban vegetation and housing prices: Insights from pre- and post-pandemic Chicago using OLS and MGWR models

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  • Dasom Han
  • Chang Gyu Choi

Abstract

This study examines the spatial dynamics of urban vegetation and its impact on housing prices in Chicago, analyzing data from both pre- and post-COVID-19 periods. Employing Ordinary Least Squares (OLS) and Multiscale Geographically Weighted Regression (MGWR) models, we assess how the effects of green spaces on property values vary across different neighborhoods. The OLS model generally indicates a positive correlation between increased vegetation and housing prices. In contrast, the MGWR model reveals that the benefits of urban green spaces to property values are not uniformly distributed and exhibit significant variability. Notably, in some South Side areas of Chicago, increases in green space correlate with declines in property values, a sensitivity that intensified post-pandemic, leading to notable price declines. Conversely, the North Side, characterized as a higher-income area, shows greater resilience to the impacts of both increased green spaces and the COVID-19 pandemic, with less susceptibility to economic downturns. This research underscores the intricate interplay between urban green spaces and economic factors, highlighting how local socio-economic conditions and urban planning strategies can influence the economic benefits of vegetation. The findings provide essential insights for urban policymakers and planners striving to promote sustainable development and equitable economic growth in urban environments.

Suggested Citation

  • Dasom Han & Chang Gyu Choi, 2025. "The spatial dynamics of urban vegetation and housing prices: Insights from pre- and post-pandemic Chicago using OLS and MGWR models," PLOS ONE, Public Library of Science, vol. 20(9), pages 1-21, September.
    • Handle: RePEc:plo:pone00:0330932
    DOI: 10.1371/journal.pone.0330932
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    References listed on IDEAS

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    1. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, December.
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