Understanding patterns and competitions of short‐ and long‐term rental markets: Evidence from London

Abstract

Abstract In this article, we compare short‐term rental (STR) and long‐term rental (LTR) price patterns in London using one of the most popular STR platforms, Airbnb, and the LTR platform, Zoopla property website. This research aims to enhance our understanding of both LTR and STR price patterns; as well as STR dynamics specifically, using predictive modeling to analyze how the patterns might evolve. We used the coefficient of variation and correlation analysis to examine the rental price patterns of both short‐ and long‐term markets. Then we developed a rent‐based gravity model to predict STR price pattern that is sensitive to the changes in visits to tourist destinations. Based on our analysis, we concluded that: (1) STR prices tend to be higher overall with an indication of higher volatility (less stability) compared to LTR; (2) there is statistical evidence supporting the arguments that STR and LTR markets are indeed in competition; and (3) the proposed gravity model provides a robust prediction of the STR pattern with a characteristic that higher‐priced short‐term properties are found to be geographically concentrated in the core city areas and those surrounding residential areas with easy access to popular tourist attractions.

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