
Machine learning and gentrification - emilito
http://urbanspatialanalysis.com/portfolio/predicting-gentrification-using-longitudinal-census-data/
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robotcookies
"The goal here is to use 1990 and 2000 Census data on home prices to predict
home prices in 2010. If those models prove robust, we can use the model to
forecast for 2020."

Selectively picking factors that worked in the past to predict what will
happen in the future is like picking stocks based on what worked in the past-
"Gee, oil and energy companies skyrocketed from 1990 to 2000, so oil and
energy companies will rock from 2010 to 2020!!"

~~~
Waterluvian
The only guarantee you'll ever get is that companies that make lots of money
will succeed more than companies that don't.

When it comes to real estate: location, location, location. On average, you
honestly can't go wrong with anything in the Virgo Supercluster.

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gydfi
Of course the real use case for this kind of thing is property investors
wanting to get in ahead of the wave.

I'm skeptical about the long term prospects for gentrification though, since
there's only so many gentry to go around. Perhaps instead of focusing on
gentrifying places, we should be working on how to gentrify _people_.

~~~
patcon
Re: property investor effect.

Was thinking about this yesterday and wondering if there was some equivalent
of "bitcoin days destroyed" [2] for non-bitcoin title-changes. As in, BDD is a
measure of turnover in Bitcoin-land, where the movement of an asset is
weighted more highly the longer it was at rest. So with Bitcoin, higher values
are good, because they probably mean more hoarded bitcoins are moving.

So the thought was that changes in land title, or long-standing businesses
changing owners, or long-standing tenants moving out -- that these sorts of
changes might be useful in predicting gentrification.

Obviously, a land-title registry on a blockchain would give this for free, but
at least in my city, land titles are very costly to search. But changes in
business ownership (small businesses opening and closing at given addresses)
might be doable for cheap. Could do a community survey of businesses to find
out how long they've been around, then simply track the "permanently_closed"
boolean flag for all businesses within the bounding box, to keep track of when
things are closing.

Anyhow, still need to do some research to find out whether this would be a
useful indicator, but I'm optimistic. Curious about folks' thoughts!

[1]:
[https://en.wikipedia.org/wiki/Community_land_trust](https://en.wikipedia.org/wiki/Community_land_trust)
[2]: [https://bitcoin.stackexchange.com/questions/845/what-are-
bit...](https://bitcoin.stackexchange.com/questions/845/what-are-bitcoin-days-
destroyed)

~~~
whiskers08xmt
I think there's a correlation, but I have a feeling that it's a late
indicator. I think these businesses go out of business after the
gentrification, not before it, though it might still be useful.

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pc86
Emphasis mine:

> Second, as previously mentioned, the predominant pattern over time is
> decline not gentrification. Thus, it is difficult for the model, at least in
> Chicago, to separate a very local phenomenon like gentrification, from a
> more global phenomenon like decline. _Because all cities are modeled
> simultaneously, these predictions are also weighted not only by the Chicago
> trend, but by the trend throughout the sample._

Why would you do that? Housing trends in Philadelphia or NYC have nothing to
do with housing trends in Chicago so modeling all cities simultaneously seems
blatantly wrong.

