Airbnb Host Scaling, Seasonal Patterns, and Competition

Abstract

Design/methodology/approach: This is an explorative study based on the city of Milan (Italy). The sample is composed by 31,000 Airbnb hosts. To explore the scaling and seasonal patterns of Airbnb listings, AirDNA daily data were used by the research team, which cover the period of 2014 (from November) to June 2019. Therefore, the data include four completed years (2015–2018) and support a longitudinal analysis.

Purpose: This paper explores the scaling (size) effect, focusing on Airbnb’s host providers, with the aim of understanding the similarities and differences in the seasonal patterns, which is considered as a proxy for competitive threat.

Findings: Empirical findings show progressive dissimilarities when moving from single to multiple listings. There are fewer differences during the seasonal periods that are more centered around leisure clients and higher when Milan works prevalently with business travelers. The evidence supports the scaling effect and its ability to reduce the competitive threat among different hosts.

Originality/value: The previous studies largely distinguish only between single- and multi-listing hosts, ignoring the magnitude of the scaling effect. The findings proposed in this study depict a very different picture, showing a progressive differentiation in the seasonal patterns correlated to the rise in the managed listings. The results, therefore, can significantly change the theoretical knowledge in this field and can re-orient future studies, especially in the sub-field of competition, the determinants of listing performance, and pricing strategies.