The increase of the short-term lets market, sparked by the emergence of Airbnb, has led to significant growth in the tourist industry in particular cities and areas.

While this extra activity brings welcome economic growth, it is not without its issues and has caused well-documented housing pressures and gentrification. Studying activity in short term lets - and Airbnb in particular - is difficult. There is little regulation in this area, and therefore no government-available statistics, and also no data made available by Airbnb. Most researchers in this area have either paid for data scraped from the Airbnb website (AirDNA), accessed the limited data openly available (Inside Airbnb), or scraped these data themselves. These data are complex and require considerable estimation when measuring activity and stock levels.

We set up this project to enhance our understanding of the market activities on the Airbnb platform. As well as developing tools for scraping data from the Airbnb website (which we make openly available), we have produced new and more thorough metrics for use with daily scraped data. These will help facilitate comprehensive, reliable and sustainable research to assess Airbnb's impact on the housing markets and provide valuable fine-grained market tracking indices for policy makers and researchers.

This work will not only make carefully designed scraping strategies openly available. It will also provide theoretical and practical guidance for interpreting, modelling and analysing the daily activity trackers derived from the scraped ‘Big Data’ to better monitor the changing temporal and spatial market trends in Scottish cities.

Aims and Objectives

  • Develop, and maintain an open web scraping strategy to monitor Airbnb daily activities in a number of UK cities
  • Provide detailed methods and open code to replicate UBDC’s scraping methods
  • Develop methods and new metrics for measuring Airbnb activity and stock
  • Analyse and publish scientific research on Airbnb stocks, market trends and impact indices to the housing market and neighbourhoods
  • Apply the defined method to build extensive knowledge and better prediction of the market growth in the post-pandemic period
  • Evaluate the impact of localised regulation of the short-term lets market and its impact on Airbnb activity and levels of stock
  • Collaborate with multi-disciplinary research groups and work closely with city stakeholders to explore and demonstrate the value of the data and method for policymaking


Leads: Dr Mark Livingston and Dr Yang Wang

Team: Nikos Ves

Latest Outputs