Forecasting hotel room demand amid COVID-19

Abstract

The COVID-19 pandemic has hindered international travel considerably, greatly affecting the hotel industry. Hong Kong, as a well-known international tourist destination, has also been hit hard by the crisis. Recovery forecasts for hotel room demand are critical to managing this ongoing crisis. This study employs the autoregressive distributed lag error correction model to generate baseline forecasts of hotel room demand for Hong Kong followed by compound scenario analysis to optimize forecasts considering the pandemic’s impacts. The COVID-19 Travelable Index is designed to group source markets by their pandemic situations, vaccinations, policy responses, and health resilience. To capture pandemic-related uncertainty, this study presents three scenarios describing recovery patterns based on trough duration, the quarter for lifting travel restrictions, and the quarter for returning to baseline forecasts. Hotel demand forecasts geared toward each source market are analyzed, revealing strategies to help hotel businesses manage this crisis.

Recreational travel behavior and COVID-19: Insights from expected utility and the theory of planned behavior

“…In so doing, additional model predictors may include potential travelers’ vaccine status, perceptions of vaccination, age, response efforts of transportation corporations, availability of health care, established restrictions, number of those infected with COVID-19 within the destination, etc. Others have offered these constructs as viable predictors within their works (see Chua et al, 2020; Parady et al, 2020; Shamshiripour et al, 2020; Zhang and Lu, 2021).…”

Tourism forecasting research: a bibliometric visualization review (1999–2022)

“…In the case of the COVID-19 pandemic, the forecasting accuracy of traditional forecasting models has significantly decreased, because they cannot handle exceptional situations. To solve this problem, some scholars use data on confirmed cases of COVID-19 disease, vaccinations and policy responses to construct a COVID-19 indicator and add it to the original tourism forecasting models (Prilistya et al, 2021;Turtureanu et al, 2022;Zhang and Lu, 2022). Most of the existing tourism forecasting articles are postforecasting, and the innovation lies in improving the forecasting accuracy of the model.…”

Forecasting Tourism in the EU after the COVID-19 Crisis

“…Additionally, affective forecasting is researched in the context of travel decision-making and alleviation of a perceived risk ( Karl et al, 2021 ). Zhang and Lu (2022) were among the first to make a contribution regarding the sector forecasting, namely, Hong Kong’s hotel room demand forecasting, by employing the autoregressive distributed lag error correction model for baseline forecasts, followed by compound scenario analysis—all done with the aim of helping the hotel sector during this crisis. Similarly, mixed data sampling (MIDAS) models are found to be effective in monitoring and forecasting COVID-19 impacts on hotel occupancy rates in Macau by Wu et al (2022) , who use high-frequency data: daily visitor arrivals and search queries.…”