You know it when you see it, or perhaps smell it. Gentrification is that new dog park. It’s the Starbucks on the corner, the yoga studio, and the gradual rise in police presence. But it’s surprisingly hard to track the exact moment when a critical mass of more affluent people move into a neighborhood and tip property values up—the simplest, if not the most universally agreed upon, definition of the “G” word. Traditional public data sources can fail to pick up the rapid transformation that can occur in a community, since their records are usually updated on multi-year cycles. And government registries usually catalogue businesses in broad categories—you’re not going to find artisanal donut parlors or motorcycle lifestyle shops grouped together by the Census Bureau. A new working paper shows how review data can be used to quantify and track neighborhood change, putting a hard spine on what can otherwise be a soft science. Matching up a massive trove of business and service listings from the uber-popular reviews site against changes in housing prices and demographics, they found that reviews appears to work as a real-time forecaster of neighborhood change. You just have to look at the right types of listings.

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