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Ryan Walsh

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Fox School of Business

Using Data to Predict Neighborhood Change and Gentrification

April 12, 2019 By Ryan Walsh 1 Comment

In a class post earlier this semester, I pointed out a couple of ways that data could be used to improve our everyday lives. In my own personal everyday life, I’ve been looking into buying a house. The main issue that I’ve run into so far in this process is that any of the available homes in the neighborhoods that I’d like to live in are a little (and sometimes very) out of my price range. This issue got me to start thinking that maybe I should take a slightly more open-minded approach, expand my options a bit and further my search out to some spots where the homes would be slightly more affordable.

One of my main goals in buying a house is to find something that will increase in value over time, and one of the best ways to do this would be to buy in an up-and-coming neighborhood before it becomes the hip and trendy place to live, which inspired me to ask: is it possible to use data to predict what the next up-and-coming place will be?

As it turns out–somewhat obviously–this is something that people have already been doing for a while, especially people working in real estate. Every bit of data involved in real estate and city planning, from building permits to regional mortgage lending characteristics, can be used to predict development and the change in housing prices.

Researchers from the consulting firm Urban Spatial, led by Ken Steif, director of the Urban Spatial Analytics grad program at the University of Pennsylvania, recently unveiled a model to help provide policymakers with a prospective look at future gentrification based largely upon U.S. Census data. The goal of this model was largely to help cities looking to stop displacement as investment increases.

Recently, Seattle decided to put permit data to the test, building a platform identifying new real estate projects, and giving a glimpse into what could be possible for certain neighborhoods. They even included a comment section to help gauge public sentiment.

So while data “can” help predict where the next hot spot could be, its practical use at the moment mainly lies with city planners and other policymakers, which is certainly fair.

 

Sources:

  • Next City: “New Model Could Help Cities Predict Gentrifying Neighborhoods“
  • Urban Spatial: “Predicting gentrification using longitudinal census data“
  • Washington Post: “How to predict rising home prices, neighborhood change and gentrification“
  • UNC: “Predicting Gentrification in the Era of Big Data“

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Filed Under: Uncategorized Tagged With: analytics, city planning, data, housing, innovation, predictive analytics, targeting, technology

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Comments

  1. Angelo markel says

    November 30, 2019 at 6:00 am

    I thought there are not a useful tips i will find which i can utilize in my real life. But i was wrong & i have experienced here a genuine

    Reply

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