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How to predict gentrification

How to predict gentrification
01 Apr
2019
A socioecological model is predicting the areas of major US cities that are most likely to be affected by gentrification

At the end of 2017, a cafe in the fashionable RiNo (River North) Art District of Denver, Colorado, home to old warehouses and hip new restaurants, caused a furore when it displayed a sign reading ‘Happily Gentrifying The Neighbourhood Since 2014’. Though the owner quickly apologised, saying he had misunderstood the term, the incident sparked an ongoing debate in the city. According to Jeremy Nemeth, associate professor of urban and regional planning at the University of Colorado Denver, the neighbourhood in question has become the ‘epicentre of gentrification’ in Denver, characterised by an influx of white, middle class people and the displacement of the African-American community who have traditionally lived there.

Reacting to this, as well as concerns among his students that the very ideas designed to improve poorer areas of cities, such as bike lanes and green spaces, can end up pushing out the people they are designed to benefit, Nemeth decided to research the issue on a US-wide scale. The result, produced with fellow researcher Alessandro Rigolon, is a socioecological model of gentrification based on 15-years-worth of data that can be used to identify the areas of any US city most vulnerable to gentrification. Nemeth and Rigolon tested the model on five major cities, including New York, Chicago and Los Angeles, identifying hundreds of ‘at-risk’ areas in each.

shutterstock 398550280New York’s DUMBO district has emerged as one of the city’s premier arts districts and is also one of the areas most affected by gentrification

The model is based on a range of factors, dubbed ‘people, place, and policy factors’, which each contribute to gentrification. While many of these seem logical – the most relevant ‘place’ factors that serve to make an area vulnerable are access to jobs, proximity to transit stations and the quality of housing stock – some are less obvious. When it comes to people factors, the study shows that a place with a mixed population of several different races is far more likely to be gentrified than an area with a very high population of black or Latino residents.

Figure 4Using the model, the team were able to produce maps such as this, highlighting at risk areas in Manhattan

Nemeth’s view is that identifying these ‘at-risk’ areas is a crucial first step in designing policies that can prevent gentrification. ‘I am so tired of the argument that this is the market and we have no power,’ he says. ‘We have to understand which places are very vulnerable to this and get ahead of it, otherwise we’ll just be chipping around the edges with little policies.’ He believes that pre-emptive housing policies are particularly important. ‘Gentrification is a housing question at its core. Places that did have subsidised housing, even at relatively low levels, like Chicago and New York, were able to stave off gentrification even though they were vulnerable,’ he explains.

Nemeth admits that the model has the potential to raise tensions within pin-pointed communities, but he’s still comfortable making these identifications. ‘If we don’t talk about it, it’s just going to happen,’ he says. ‘At least if we talk about it, yes there may be fear, but maybe there’s also some encouragement to get up and organise, to talk to elected local officials and to stop it before it takes over.’

 This was published in the April 2019 edition of Geographical magazine

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