18 October 2021. Monday. 3PM. London Time. Hybrid (zoom and facet-to-face). Room Colln CG15.
Title Nowcasting Gentrification Using Airbnb Data
Speaker Giovanni Quattrone, Department of Computer Science, Middlesex University, London
Abstract Census data fails to measure neighbourhood 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 neighbourhood 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 neighbourhood 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.
Bio Giovanni Quattrone is a Senior Lecturer in Computer Science at Middlesex University in London. He is also a honorary member of the Department of Computer Science at the University College London and King’s College London. Prior to joining Middlesex University Giovanni was a freelancer data scientist working for a number of industries (including Rolls-Royce) and a Research Fellow at University College London, UK. Giovanni’s area of work is applied data science with specific expertise on social/urban computing and computer-supported cooperative work. Giovanni published more than 60 peer-reviewed papers in the field, also receiving best paper awards in top venues.
Meeting ID: 668 413 8396