Peer-to-peer (P2P) accommodation sharing has become a significant part of the travel and lodging industry, allowing homeowners to engage in entrepreneurial activity via sharing of resources. However, there is limited understanding of how hosts can use listing descriptions to better match their offerings to different consumer segments. The purpose of this paper is to understand the use of listing descriptions by Airbnb hosts and the impact of such descriptions on sales performance.
In this paper, a deep learning-based sentence-level aspect mining approach is used to extract various aspects from host-provided listing descriptions. Then a regression-based approach is used to understand the impact of various aspects of listing descriptions on listing performance.
It was found that aspects for which listing descriptions are the sole source of information have the greatest influence on listing performance. The authors also find that the impact of an aspect on listing performance varies by listing type, and that there is a mismatch between the most included aspects by hosts in their listing descriptions and the most influential aspects that impact sales.
The impact of consumer reviews in the context of Airbnb has been extensively studied. A novel aspect of this study is the exploration of P2P accommodations from a supplier perspective, by understanding the use and impact of host-provided textual descriptions on sales. The findings of this study can help better market properties from a practice perspective and better understand consumer information consumption from a theoretical perspective. The authors also demonstrate a new approach for exploring social phenomena by performing quantitative analysis on textual data using deep-learning and regression-based techniques.
