Forecasting hospitality industry development in Sharm El Sheikh Hotels based on multi-approach regression modeling

Abstract

The hospitality industry in Sharm El Shiekh City is affected by frequent crises events in the last decade. Despite its difficulty, the forecasting of the hotel occupancy in this important area becomes a necessity to avoid loss of the investments. The forecasting models are based on the regression analysis of the data series. In the present study, time series analysis from two hotels (Hotel A and Hotel B) including occupancy (occupancy %, number of sold rooms, number of guests), financial (total hotel revenue, average room rate, average revenue per guest), quality (service friendliness, food and beverage, cleanliness, entertainment, Wi-Fi) and staff (number of employees and cost per employee training) is used to build regression models with different approaches. The time series data set extends in time interval from January 2013 to December 2021. The linear regression between these different parameters revealed low correlation between the different variables in general. However, there is a higher correlation between the quality parameters and staff cost and number in both hotels. There is also significant correlation between the hotel total revenue and parameters of quality. Multiple regression modeling is used to analyze the relationship between the occupancy versus the quality and staff parameters. The correlation is found to be greater in hotel B. Ten degree polynomial regression is found to be suitable for the nonlinear relation between the occupancy and time. The correlation is equally low in both hotels with medium accuracy and can be used in forecasting the occupancy with caution. In addition, the ten degree polynomial regression is used to forecast the development of guest numbers after Russian -Ukrainian war based on the time series data set including guest numbers that cover the time interval from January 2015 to December may 2022. The correlation is also greater in case of Hotel B and in the same time the Ukrainian guest numbers are higher in this hotel. Accordingly, this hotel has a higher potential to recover in faster rates after the end of the war.