Nowcasting Gentrification: Using Yelp Data to Quantify Neighborhood Change

Edward L. Glaeser; Hyunjin  Kim; Michael  Luca

Article | American Economic Association Papers and Proceedings | May 2018

Nowcasting Gentrification: Using Yelp Data to Quantify Neighborhood Change

by Edward L. Glaeser, Hyunjin Kim and Michael Luca

Abstract

Data from digital platforms have the potential to improve our understanding of gentrification and enable new measures of how neighborhoods change in close to real time. Combining data on businesses from Yelp with data on gentrification from the Census, Federal Housing Finance Agency, and Streetscore (an algorithm using Google Streetview), we find that gentrifying neighborhoods tend to have growing numbers of local groceries, cafés, restaurants, and bars, with little evidence of crowd-out of other types of businesses. For example, the entry of a new coffee shop into a zip code in a given year is associated with a 0.5% increase in housing prices. Moreover, Yelp measures of local business activity provide leading indicators for housing price changes and help to forecast which neighborhoods are gentrifying.

Keywords: Forecasting Models; Simulation Methods; Regional Economic Activity: Growth, Development, Environmental Issues, and Changes; Geographic Location; Local Range; Transition; Data and Data Sets; Measurement and Metrics; Economic Growth; Forecasting and Prediction;

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Citation:

Glaeser, Edward L., Hyunjin Kim, and Michael Luca. "Nowcasting Gentrification: Using Yelp Data to Quantify Neighborhood Change." American Economic Association Papers and Proceedings 108 (May 2018): 77–82.

This case explores how neuroscientist Mariam Chammat helped set up the first behavioral insights team at the center of the French government, and encouraged French administrations to innovate and create policy initiatives based on psychological theories of influence and persuasion. Students are asked to assess 35 projects ripe for behavioral intervention and pick the winning proposals.

Dominant platform businesses often develop products in adjacent markets to complement their core business. One common approach used to gain traction in these adjacent markets has been to pursue a tying strategy. For example, Microsoft pre-installed Internet Explorer into Windows, and Apple set Apple Maps as the iOS default. Policymakers have raised concerns that dominant platforms may be leveraging their market power to gain traction for lower quality products when they use a tying strategy. In this paper, we empirically explore this question by examining Google’s decision to tie its new reviews product to its search engine. We experimentally vary the reviews displayed above Google’s organic search results to show either exclusively Google reviews (Google’s current tying strategy) or reviews from multiple platforms determined to be the best-performing by Google’s own organic search algorithm. We find that users prefer the version that does not exclude competitor reviews. Furthermore, looking at observational data on user traffic to Yelp from search engines, we find that Google’s exclusion of downstream competitors may have been effective. The share of Yelp’s traffic coming from Google has declined over this period, relative to traffic from Bing and Yahoo (which do not exclude other companies’ reviews), and Google reviews has grown quicker than Yelp and TripAdvisor during the period in which they excluded these (and other) reviews providers. Overall, these results shed light on platform strategy: tying has the potential to facilitate entry in complementary markets, even when the tied product is of worse quality than competitors.

Working Paper | HBS Working Paper Series | 2018

Product Quality and Entering Through Tying: Experimental Evidence

Hyunjin Kim, Michael Luca, Hyunjin Kim and Michael Luca

Keywords: Market Entry and Exit; Market Platforms; Competitive Strategy; Product Marketing; Quality;

Citation:

Kim, Hyunjin, and Michael Luca. "Product Quality and Entering Through Tying: Experimental Evidence." Harvard Business School Working Paper, No. 19-045, October 2018. (Revised December 2018. Forthcoming in Management Science.) View Details

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Nowcasting Gentrification: Using Yelp Data to Quantify Neighborhood Change

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