Forecasting daily room rates on the basis of an LSTM model in difficult times of Hong Kong : evidence from online distribution channels on the hotel industry

Abstract

Given the influence of the financial-economic crisis, hotel room demand in Hong Kong has experienced a significant drop since June 2019. Given that studies on the room rate aspect remains limited, this study considers the demand for hotel rooms from different categories and districts. This study makes forecast attempts for room rates from mid-October of 2019 to mid-June of 2020, which was a difficult period for Hong Kong owing to the onset of the social unrest and novel coronavirus outbreak. This study develops an approach to the short-term forecasting of hotel daily room rates on the basis of the Long Short-Term Memory (LSTM) model by leveraging the key properties of day-of-week to improve accuracy. This study collects a data set containing 235 hotels of the period from various online distribution channels and generates different time series data with the same day-of-week. This study verifies the proposed model through three baseline models, namely, autoregressive integrated moving average (ARIMA), support vector regression (SVR), and Naïve models. Findings shed light on how to lessen the impact of violent fluctuations by combining a rolling procedure with separate day-of-week time series for the hospitality industry. Hence, theoretical and managerial areas for hotel room demand forecasting are enriched on the basis of adjusting room pricing strategies for hoteliers in improving revenue management and making appropriate deals for customers in booking hotel rooms.