Lead times in flux: Analyzing Airbnb booking dynamics during global Upheavals (2018–2022)

Short-term changes in booking behaviors can significantly undermine naive forecasting methods in the travel and hospitality industry, especially during periods of global upheaval. Traditional metrics like average or median lead times capture only broad trends, often missing subtle yet impactful distributional shifts. In this study, we introduce a normalized L1 (Manhattan) distance to measure the full distributional divergence in Airbnb booking lead times from 2018 to 2022, with particular emphasis on the COVID-19 pandemic. Using data from four major U.S. cities, we find a two-phase pattern of disruption: a sharp initial change at the pandemic's onset, followed by partial recovery but persistent divergences from pre-2018 norms. Our approach reveals shifts in travelers' planning horizons that remain undetected by conventional summary statistics. These findings highlight the importance of examining the entire lead-time distribution when forecasting demand and setting pricing strategies. By capturing nuanced changes in booking behaviors, the normalized L1 metric enhances both demand forecasting and the broader strategic toolkit for tourism stakeholders, from revenue management and marketing to operational planning, amid continued market volatility. • Normalized L1 metric reveals subtle, full-distribution shifts in booking lead-time, beyond mean or median. • COVID-19 split: four U.S. cities show abrupt lead-time collapse at onset, then partial rebound diverging from 2018. • Toy example shows moderate lead-time shifts can mislead naive “pickup” forecasts, urging distribution-aware models. • Analytic bound links forecast error to normalized L1 distance, giving practitioners a quick risk gauge. • Year-over-year patterns still evolving post-pandemic, stressing continuous monitoring and adaptive booking strategy.