Social network of peer-to-peer accommodations for a visual decision support system in tourism: The case of the Canary Islands

Abstract

The peer-to-peer accommodation market has experienced significant growth in recent years, leading to increased competition and offer heterogeneity. This scenario presents challenges for investors and stakeholders, required to value the importance of differentiation and accommodations' typology to ensure favorable profits and social impact. In this work, we examine the touristic accommodation market in the Canary Islands using real data from Airbnb and applying a novel network-based visual methodology. The data analysis methodology involves the creation and visualization of a network that places accommodations based on their similarity. Using community detection algorithms, we identify accommodation typologies, perform a descriptive analysis of the resulting clusters, and evaluate economic and exogenous variables. Nine accommodation types are found having key differentiating characteristics such as guest capacity, number of properties owned by the host, and managerial aspects (for example, cancellation policy). Clusters with higher economic benefits (characterized by a large capacity) are placed on the periphery of the visual map in contrast to common accommodation types, located in the center; thus showing the importance of differentiation. The accommodations' typologies are not specific to a particular island, but are homogeneously distributed in the Canaries archipelago. The results emphasize the managerial advantage of this decision support system for investors and tourist managers in making informed strategic decisions.