The leading indicators for hotel demand are the length of stay, demand for hotel rooms, occupancy of the hotel, spending at certain activities or overall local spending, volume of the visitors, and others. These leading indicators for the demand for hotels are different from but associated with each other. The hotel rooms’ demand is already presented from commercial sources. Forecasting the rates of the occupancy and the arrivals of the hotel guest in the future is considered to be an essential feature of the management of hotel revenue. There are several methods for forecasting the arrivals and rates of occupancy. However, these methods lack the independent variables because they are dependent on the consistent pattern of tourist activities. The tourism and travel associated shocks, drastic economic changes, and on and off situations are the factors that reduce the forecasting accuracy. Given the possibility of the inaccuracy in forecasting, it is suggested that hotel managers employ use web traffic data as a means to reduce errors in forecasting (Yang et al 2014).
Consumer surplus is calculated using the demand models. The functional form can have considerable effects on the final outcome. These functional forms include log linear model, which are appealing because the log linear provides demand function that is non-linear. The temperature in Table 1, the dummy variable for Europe, and the Gross Domestic Product was tested in every regression. Research finding shows that the coefficient of the temperature shows that the individuals from tropical states tend to visit the Great Barrier Reef. It is also suggested that the colder the state is colder, the more individuals it attracts. In this regard, the best model that fits the data is the travel cost model because it fits the main objective of the report, which is to examine both the domestic and international travel to the Great Barrier Reef (Carr et al n.d).
References
Carr, L. & Mendelsohn, R. (n.d). Valuing Coral Reefs: A Travel Cost Analysis of the Great
Barrier Reef [PDF Document]. Retrieved from Yale School of Forestry & Environmental Studies
Website: https://environment.yale.edu/files/biblio/YaleFES-00000272.pdf
Yang, Y.; Pan, B. & Song, H. (2014). Predicting hotel demand using destination marketing
organizations' web traffic data. Journal of Travel Research, 53(4), pp. 433-447
