The claim that successful urban revitalization results in “gentrification” and is therefore a bad thing is one of those hypothetical objections to placemaking strategies that isn’t based on hard data, a phenomenon about which I wrote as an obstacle to the implementation of placemaking strategies. For the most part, the objection to gentrification by “advocates” for lower-income people is an objection to hypothetical “displacement” of existing residents by new, higher income folks. To put it as plainly as I can, while those advocates (and journalists) often point to anecdotal evidence of low-income people being forced from their homes by avaricious landlords, I have seen no reliable aggregate data in support of that theory.
I’m a student of the strategies for social change that are about bringing people together – not driving them apart. To Dr. King, integration was not only about bringing economic benefit and political power to dispossessed people, but also creating a society where everyone, regardless of background, is treated with equal concern and respect. Social integration is a good in and of itself, creating communities that honor difference. In addition, it seems likely to me, based on observation and experience, that housing and educating low-income people in the same communities as higher income people may well provide those lower-income people with the tools for economic advancement. Segregating and concentrating low-income people seems to lead to higher levels of social dysfunction.
The Furman Center’s most recent “State of New York Housing and Neighborhoods in 2015” (http://furmancenter.org/files/sotc/NYUFurmanCenter_SOCin2015_9JUNE2016.pdf) demonstrates that in New York City displacement through gentrification is not a significant effect, but also tells us some interesting things about the dynamics of improving neighborhoods. I find the Furman Center to be an indispensable, and non-ideological, source of measurable information about real estate trends. They are also quite careful about drawing conclusions from the data – and not confusing causes and effects.