How did COVID-19 affect tourism occupancy and prices? A spatiotemporal and economic analysis of Madrid and Valencia through Airbnb geospatial data

Abstract

The COVID-19 pandemic has had a significant impact on the tourism industry worldwide. This study aims to understand the early effects of the pandemic on tourist supply and demand by analysing Airbnb's occupancy rates and accommodation prices at both city and neighborhood/district scales in Madrid and Valencia, two Spanish destinations with distinct tourism types. By considering both spatial and statistical analyses at different scales, this study provides valuable insights into the short-term impacts of COVID-19 on the tourism industry in these destinations. The findings reveal a spatial polarisation, with certain areas maintaining higher occupancy rates and prices, suggesting resilience to the crisis, particularly those near green spaces. The analysis further highlights varied effects of the pandemic across different months and neighborhoods/districts. While historic center neighborhoods experienced declines in both occupancy rates and prices, districts with a stronger tourist tradition showed greater resilience. Price rigidity is observed in some urban areas, where occupancy rates decline while prices remain relatively stable or even increase. Two key recommendations are underscored for decision makers: (1) regulating Airbnb should consider neighborhood-specific characteristics and differentiate between types of tourism, establishing minimum standards for housing conditions and the surrounding environment and (2) touristic cities should aim for a polycentric spatial structure by expanding and diversifying tourist areas, avoiding concentration in a single location.

Keywords

• COVID-19
• Airbnb
• Geospatial data
• Statistical analysis
• Tourism dynamics
• Spatiotemporal analysis