Opportunities for investment in Airbnb short-term rental properties: Predictive modelling for categorisation and revenue estimation
DOI: https://doi.org/10.3846/ijspm.2026.27872Abstract
This paper discusses the rapidly developing property sector in Edinburgh, Scotland, which in recent years has been shaped by growth in tourism and the expansion of the short-term rental market. The study aims to investigate how Airbnb listings can be categorised into the tiers offering low, medium and high potential earnings for investors. Research methodology includes the K-Means clustering, based on features, such as location, availability, property type, number of bedrooms, bathrooms and price. The paper further examines what variables influence the investors’ revenue streams most. The application of the Random Forest Model to the K-Means clustering results showed that pricing, availability and guest capacity were the principal variables affecting earnings. Incorporating predictive modelling into the market categorisation enabled this study to offer a practical, data-based framework for potential investors who are considering various options for investment in properties with an intent to offer them for short-term rent via Airbnb.
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Airbnb, short-term rentals, earnings potential, investment, predictive modellingHow to Cite
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Copyright (c) 2026 The Author(s). Published by Vilnius Gediminas Technical University.
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