New  design  anticipates  U.S.  communities  most likely  to  gentrify,  prescribes  effective  anti-displacement  policies

New design anticipates U.S. communities most likely to gentrify, prescribes effective anti-displacement policies

By Anne Rowe for DPS board, February 24, 2019

A sociological design of gentrification. Credit: Journal of Urban Affairs

A new research study design enables city planners, policymakers and community leaders to much better focus resources to limitation gentrification in susceptible areas throughout the U.S.

By analyzing the “people, location and policy” aspects that determine whether a community will gentrify or not, the model uses a better understanding of what fosters gentrification and what limits it. This process reveals the roles that federal government and policy can proactively play in restricting its most damaging effects.

“This model is a brand-new method of thinking about what affects gentrification and how to avoid it,” stated research study co-author Jeremy Németh, Ph.D., associate teacher of Urban and Regional Preparation at the University of Colorado Denver. “This research study debunks the argument that gentrification is an uncontrollable repercussion of market forces, and outlines particular strategies where neighborhoods have real power to limitation it.”

Public companies, nonprofits and city federal governments with minimal resources can use publicly available information to model gentrification probability, develop early warning systems and then establish prevention strategies for their neighborhoods.

The research study, “Toward a socio-ecological model of gentrification: How people, place, and policy shape community modification,” is released in the Journal of Urban Affairs.

“We’re offering the design as a tool for city governments and anti-gentrification actors to be more proactive in targeting shown interventions in the most vulnerable neighborhoods,” stated Németh, who co-authored the paper with Alessandro Rigolon, assistant professor of Leisure, Sport, and Tourism at the University of Illinois at Urbana-Champaign.

Predicting gentrification

The researchers checked the predictive gentrification design in the five most populated U.S. areas: Chicago, Los Angeles, New York City, San Francisco and Washington, D.C.

Three “place” aspects—access to tasks, proximity to transit stations and the quality of housing stock—emerged as strong predictors of a community’s possibility to gentrify across all regions. As they greatly influence these location aspects, this points to the critical role city organizers play in shaping gentrification forces.

The variety of a area is the “people” aspect with the greatest predictive value, the research study discovered.

“We understand from years of research on implicit predisposition that if a community has a very high share of Black or Latinx residents, it is much less likely to gentrify than one with a mix of a number of racial or ethnic groups,” said Németh. He and Rigolon said they weren’t shocked by the finding that racial/ethnic diversity is a strong predictor of gentrification.

Proven policy interventions

Although these elements weren’t evaluated in this national-level research study, numerous recent studies in California have shown that local “policy” methods tested to slow gentrification include lease controls, neighborhood land trusts, and anti-eviction regulations.

This first-of-its-kind research study uses neighborhoods a design to identify the areas most vulnerable to gentrification and a roadmap to carry out proven anti-gentrification methods prior to it’s too late.

For this research study, gentrification is specified as the influx of middle- and upper-class residents in a spatially concentrated style, which typically results in the displacement of veteran homeowners, who disproportionately are badly informed, lower-income individuals of color.

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More info:
Alessandro Rigolon et al. Towards a socioecological model of gentrification: How people, location, and policy shape area modification, Journal of Urban Affairs (2019). DOI: 10.1080/07352166.2018.1562846

Provided by:
University of Colorado Denver