Airbnb property performance and guest reviews datasets, made available through our partnership with AirDNA, are already supporting research into how short-term rentals shape neighbourhoods, housing markets and local economies across the UK.
The AirDNA datasets provide detailed information on more than 1.7 million UK Airbnb listings, drawing on over 92 million recorded stays and 31 million guest reviews from 2014 to the present.
Available at daily, monthly and yearly timeframes, the data allow researchers to examine short-term rental activity at different spatial and temporal scales.
These new datasets build on a growing body of research using HASP resources to explore the impacts of short-term rentals beyond headline tourism statistics.
Exploring neighbourhood change in second tier cities
Researchers at the University of Liverpool are examining how platforms such as Airbnb contribute to the spatial expansion of tourism beyond city centres. Building on earlier studies focused on London, their work is now exploring second tier cities, where short-term rentals are increasingly embedded in residential neighbourhoods.
By analysing the location and growth of short-term lets, the research explores how tourism activity intersects with everyday neighbourhood life – including potential impacts on housing availability, community cohesion and local services.
The new AirDNA datasets offer additional opportunities to extend this work by linking property performance and guest activity with neighbourhood level change over time.
Understanding offshore ownership and rental markets
At the LMU Munich Center for Economic Studies, researchers are combining property listings data from Zoopla and Airbnb to investigate offshore ownership of UK real estate. The project examines whether, and to what extent, offshore investment influences both long-term and short-term rental markets.
By comparing ownership patterns across platforms, the researchers are able to assess how different forms of property investment interact with housing supply and rental availability.
Access to detailed Airbnb property performance data enables further exploration of how offshore owned properties are used, including their role in the short-term rental sector.
Informing debate on proposed tourist levies
Researchers at the University of Leeds are analysing data on short‑term rental properties to understand how prices affect how often homes are booked. Using machine learning methods, the team are exploring “what‑if” scenarios to see how different tourist taxes might change the short‑term rental market across the UK.
Assessing economic and social impacts of short-term rentals
At University College London (UCL), researchers are using Airbnb listings and performance data from AirDNA to explore how the growth of the short-term rental market affects the economy.
Their work considers impacts on housing markets and rental availability, examines whether employment linked to short-term rentals represents genuinely new economic activity or displaces existing jobs, and explores how the presence of short-term lets influences community dynamics and social cohesion.
The inclusion of guest review data alongside property performance metrics also enables researchers to connect economic indicators with visitor experience and perceptions of place.
Enabling responsible, place-based research
By providing access to short-term rental data at multiple time scales, alongside full guest reviews as an add on dataset, the new AirDNA release supports a wide range of research into housing, tourism, neighbourhood change and sustainability
Researchers can explore the metadata and apply for access via the HASP Data Catalogue, with support available from the HASP team for project scoping and responsible data use.
Accessing the data
All AirDNA datasets are available via our Safeguarded data service:
- AirDNA Airbnb Property Performance – Daily
- AirDNA Airbnb Property Performance – Monthly
- AirDNA Airbnb Property Performance – Yearly
- AirDNA Airbnb Guest Reviews
Detailed information about what each dataset contains is provided in the Data Dictionary, which is available in the above metadata listings.
Researchers who are unsure which version of the data best fits their project, or who want advice on combining datasets, can contact the HASP team for support.
