  There is a rumbling debate over the impact of gentrification: presumed
gentrifiers have been the target of protests and attacks in some cities, while
they have been welcome as generators of new jobs and taxes in others. Census
data fails to measure neighborhood change in real-time since it is usually
updated every ten years. This work shows that Airbnb data can be used to
quantify and track neighborhood changes. Specifically, we consider both
structured data (e.g. number of listings, number of reviews, listing
information) and unstructured data (e.g. user-generated reviews processed with
natural language processing and machine learning algorithms) for three major
cities, New York City (US), Los Angeles (US), and Greater London (UK). We find
that Airbnb data (especially its unstructured part) appears to nowcast
neighborhood gentrification, measured as changes in housing affordability and
demographics. Overall, our results suggest that user-generated data from online
platforms can be used to create socioeconomic indices to complement traditional
measures that are less granular, not in real-time, and more costly to obtain.
