Measuring Gentrification: Using Yelp Data to Quantify Neighborhood Change

Edward L. Glaeser; Hyunjin  Kim; Michael  Luca

Working Paper | 2018

Measuring Gentrification: Using Yelp Data to Quantify Neighborhood Change

by Edward L. Glaeser, Hyunjin Kim and Michael Luca

Abstract

We demonstrate that data from digital platforms such as Yelp have the potential to improve our understanding of gentrification, both by providing data in close to real time (i.e., nowcasting and forecasting) and by providing additional context about how the local economy is changing. Combining Yelp and Census data, we find that gentrification, as measured by changes in the educational, age, and racial composition within a zip code, is strongly associated with increases in the numbers of grocery stores, cafes, restaurants, and bars, with little evidence of crowd-out of other categories of businesses. We also find that changes in the local business landscape is a leading indicator of housing price changes and that the entry of Starbucks (and coffee shops more generally) into a neighborhood predicts gentrification. Each additional Starbucks that enters a zip code is associated with a 0.5% increase in housing prices.

Keywords: Geographic Location; Local Range; Transition; Data and Data Sets; Measurement and Metrics; Forecasting and Prediction;

Language: English

Format: Print

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Citation:

Glaeser, Edward L., Hyunjin Kim, and Michael Luca. "Measuring Gentrification: Using Yelp Data to Quantify Neighborhood Change." NBER Working Paper Series, No. 24952, August 2018.

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Measuring Gentrification: Using Yelp Data to Quantify Neighborhood Change

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