Modelling international monthly tourism demand at the micro destination level with climate indicators and web-traffic data

We investigate if and how climate indicators and web-traffic data may improve the estimates of demand functions’ parameters, considering specific origins and destinations. Overall, augmented demand functions show better fit and more reliable price and income elasticities whether the demand is measured with arrivals or with overnights. However, heterogeneity stemming from the main type of tourism (business vs. cultural vs. sea and sun) affects both the web-based and the climate indicators better describing tourists demand as well as their optimal lags. Our findings highlight the utility of such prompt and territorial detailed information for local policymakers, showing, however, how sensitive different demand segments are to policy intervention.