Shabrina, Zahratu;
Morphet, Robin;
(2022)
Understanding patterns and competitions of short- and long-term rental markets: Evidence from London.
Transactions in GIS
, 26
(7)
pp. 2914-2931.
10.1111/tgis.12989.
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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.
| Type: | Article |
|---|---|
| Title: | Understanding patterns and competitions of short- and long-term rental markets: Evidence from London |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1111/tgis.12989 |
| Publisher version: | https://doi.org/10.1111/tgis.12989 |
| Language: | English |
| Additional information: | This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third-party material in this article are included in the Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
| Keywords: | Social Sciences, Geography, TO-PEER ACCOMMODATION, AIRBNB, TRANSPORT, MODELS, ACCESSIBILITY, LOCATION, TOURISM |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment > Centre for Advanced Spatial Analysis |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10165968 |
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