Spanish tourism’s post-pandemic recovery: Insights from a market source-specific approach

Abstract

The recovery of Spain's tourism sector in the post-COVID-19 era has received considerable attention, yet key questions remain regarding structure and resilience of international tourist flows. This study addresses these issues from a demand-side offering a novel contribution that complements recent analyses. Using monthly panel data, we apply Seasonal ARIMA models to simulate a counterfactual scenario in which the COVID-19 pandemic had not occurred. This allows us to quantify missed potential and deviations in actual tourist arrivals. To further analyze international dynamics international tourism flow, we implement k-means cluster analysis, identifying behavioral convergence and divergence across origin markets. Our findings reveal asymmetric recovery patterns: while countries such as the United Kingdom and France exhibit early and strong rebounds, others, like Italy, Portugal, and Nordic countries, show a slowly trajectory even remaining below their expectations. This integrated approach, combining forecasting under uncertainty and market segmentation, provides new insights into the complexity of post-pandemic tourism recovery and supports the development of targeted, resilient strategies for Spain's tourism sector. By reconstructing the expected trajectory of tourism demand while preserving the inherent structure of the time series, this study provides a unique perspective on the enduring effects of COVID-19.