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Housing Market Activity is Associated with Disparities in Urban and Metropolitan Vegetation

  • K. Arthur Endsley
  • Daniel G. Brown
  • Elizabeth Bruch


In urban areas, the consistent and positive association between vegetation density and household income has been explained historically by either the capitalization of larger lawns and lower housing densities or landscaping and lifestyle districts that convey prestige. Yet cities with shrinking populations and rising land burdens often exhibit high vegetation density in declining neighborhoods. Because the observed associations do not directly address the causal connection between measures of social privilege and vegetation in urban landscapes, it is difficult to understand the forces that maintain them. Here, we compare patterns of household income with new measures derived from housing market data and other parcel-level sources—sale prices, tax foreclosures, new housing construction, demolitions, and the balance of construction and demolition. Our aim is to evaluate whether these spatially, temporally and semantically finer measures of neighborhood social conditions are better predictors of the distribution of urban vegetation. Furthermore, we examine how these relationships differ at two scales: within the City of Detroit and across the Detroit metropolitan area. We demonstrate, first, that linear relationships between income or home values and urban vegetation, though evident at broad metropolitan scales, do not explain recent variations in vegetation density within the City of Detroit. Second, we find that the real estate and demolition records demonstrate a stronger relationship with changes in vegetation density than corresponding changes in US Census measures like income, which suggests they hold at least as much interest for understanding how the relationships between biophysical changes and neighborhood change processes come about.


land cover urban vegetation vegetation cover vegetation change urbanization shrinking cities urban ecology social stratification remote sensing 



This study was enabled by a grant from the Sloan and Moore Foundation and the University of Michigan’s MCubed Diamond Big Data Challenge 3. Drs. Jeff Morenoff and Chris Ruf at the University of Michigan provided early input to the project. The authors would also like to thank Dr. Eric Seymour at Brown University for his help in geocoding the real estate data. Finally, we would like to thank the anonymous reviewers and the subject-matter editor for their constructive feedback.


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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School for Environment and SustainabilityUniversity of MichiganAnn ArborUSA
  2. 2.School of Environmental and Forest SciencesUniversity of WashingtonSeattleUSA
  3. 3.Population Studies Center, Institute for Social ResearchUniversity of MichiganAnn ArborUSA
  4. 4.Department of SociologyUniversity of MichiganAnn ArborUSA
  5. 5.Center for the Study of Complex SystemsAnn ArborUSA
  6. 6.Santa Fe InstituteSanta FeUSA

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