Suppliers of tourist services continuously generate big data on ask prices. We suggest using this information, in the form of a price index, to forecast the occupation rates for virtually any time-space frame, provided that there are a sufficient number of decision makers “sharing” their pricing strategies on the web. Our approach guarantees great transparency and replicability, as big data from OTAs do not depend on search interfaces and can facilitate intelligent interactions between the territory and its inhabitants, thus providing a starting point for a smart decision-making process. We show that it is possible to obtain a noticeable increase in the forecasting performance by including the proposed leading indicator (price index) into the set of explanatory variables, even with very simple model specifications. Our findings offer a new research direction in the field of tourism demand forecasting leveraging on big data from the supply side.

旅游服务供应商不断生成有关要价的大数据。我们建议以价格指数的形式使用此信息来预测几乎任何时空范围内的占用率，前提是有足够的决策者在网络上“共享”其定价策略。我们的方法保证了高度的透明度和可复制性，因为来自OTA的大数据不依赖于搜索界面，并且可以促进领土与其居民之间的智能互动，从而为明智的决策流程提供了起点。我们表明，即使使用非常简单的模型规格，也可以通过将建议的领先指标（价格指数）包括在解释变量集中来获得显着的预测性能增长。