Identifying potential scam listings on Airbnb
Identifying potential scam listings on Airbnb

A network of hosts and reviewers on Airbnb (Image by author)

I ntroduction

I recently worked on a project using data on Airbnb listings in London. The initial aim of my project was to use machine learning to predict rental prices based on features of the property, its location and reviews associated with the listing (more on this in a later post). Whilst researching the use of Airbnb in London, I came across a WIRED article published in February 2020, which inspired me to look at the data in another way.

WIRED article which inspired the project (Source: WIRED)

First, some background. In 2015, a law was introduced which allows Londoners to let their home on a short-term basis without the need for planning permission, for up to 90 nights a year. This policy was aimed at benefitting residents, allowing them to earn extra income from their home, and not at providing opportunities for the commercial sector. However, there are growing concerns that short-term letting platforms are becoming increasingly commercialised and are removing housing stock from the long-term rental market, exacerbating London’s housing shortage.

A Greater London Authority report from February 2020 showed the average revenue per night from an Airbnb listing to be £109 versus £58 for a long-term tenancy. There is an obvious incentive for hosts to exceed the 90-night limit, as they can get a far better return than they would on the long-term rental market. The same report estimated that as many as 23% of listings in London may be in breach of the 90-night limit (as of May 2019). Airbnb, which accounts for 65% of the short-term letting market in London, introduced a cap on its website in 2017, which automatically makes ‘entire home’ listings unavailable when the 90-night limit is reached (unless there are relevant planning permissions in place to rent it for a longer period). However hosts can easily bypass the cap by re-listing properties with different addresses or photos or by creating multiple host accounts, to avoid algorithms designed to detect duplicates. In the industry, this is referred to as ‘systemising’ listings.

The WIRED article mentioned above describes in great detail an Airbnb scam which the journalist uncovered in London. This particular scam centres around a de facto hotel built for short-term rentals which breaches both Airbnb policies and the 90-night law. There are duplicate listings for apartments, most listings use the same photos or mirror image photos and some of the listings don’t exist. Guests often complain of being put into a different apartment to the one they booked and sometimes apartments are double-booked. Many of the reviews and host profiles are fake or misleading.

The article identifies a network of connected host accounts behind 200 ‘systemised’ listings. Listings hosted by these accounts have many fake reviews from a small group of reviewers. All of these accounts are essentially one person, or at least one company.
['Julia Mcaleenan']
['Data Science', 'Airbnb', 'Network Analysis']