We suggest a nonlinear time series methodology to model the (last-minute) price adjustments that hotels active in the online market make to adapt their early-booking rates in response to unpredictable fluctuations in demand. We use this approach to reverse-engineer the pricing strategies of six hotels in Milan, Italy, each with different features and services. The results reveal that the hotels’ ability to align last-minute adjustments with early-booking decisions and account for stochastic demand seasonality varies depending on factors such as size, star rating, and brand affiliation. As a primary empirical finding, we show that the autocorrelations of the first four moments of the last-minute price adjustment can be used to gain crucial insights into the hoteliers’ pricing strategies. Scaling up this approach has the potential to equip policymakers in smart destinations with a reliable and transparent tool for the real-time monitoring of demand dynamics.

我们建议采用非线性时间序列方法来模拟在线市场上活跃的酒店为调整提前预订率以应对不可预测的需求波动而进行的（最后一刻）价格调整。我们使用这种方法对意大利米兰的六家酒店的定价策略进行逆向工程，每家酒店都有不同的功能和服务。结果显示，酒店将最后一刻的调整与提前预订决策结合起来并考虑随机需求季节性的能力因规模、星级和品牌关系等因素而异。作为主要的实证发现，我们表明，最后一刻价格调整的前四个时刻的自相关性可用于获得对酒店经营者定价策略的重要见解。扩大这种方法有可能为智慧目的地的政策制定者提供可靠、透明的工具来实时监控需求动态。