Effects of external shocks on the Airbnb market – modeling business survival using geocoded open data

Abstract

ABSTRACT This paper seeks to trace the determinants of business survival in the Airbnb market during the latest pandemic. The paper starts with an examination of the key factors determining survival rates of accommodation listings in the Airbnb market during the early shock phase of the pandemic. The analysis is carried out for 10 metropolitan cities all over the world in both 2019 and 2020, so as to investigate the differences in survival probabilities between the pre- and early-pandemic period. We also study the dynamics of the Airbnb market in the late pandemic period using a multinomial logistic regression model. The results show similar patterns as the pre- and early-pandemic periods, indicating a tendency to return to a pre-pandemic state. In particular, in the pandemic time analyses, distance to nature appeared to be positively associated with firm survival, suggesting the importance of a healthy environment for attracting guests during this period. The findings contribute to our understanding of the effects of the pandemic on short-term rental and highlight the role of various critical background factors.