Time series modeling of tourist accommodation demand in Kenya

Abstract

Tourism is a very important sector in the world economy and contributes significantly to foreign exchange earnings. Earnings from tourism in Kenya increased annually from Kenya Shillings 24.3 billion in 2001 to 73.7 billion in 2010 (ROK, 2012). The number of tourists coming into the country increased from 1,146,102 in the year 2003 to 1,822,885 in the year 2011. The major tourist zones in Kenya are: Nairobi, Beach, Mombasa, Coast Hinterland, Maasailand, Nyanza basin, Western, Central and North (ROK, 2012). These can further be reduced to three: Nairobi, Coastal and Others. Tourism in Kenya relies on many other sectors and industries, one of which is the hotel and accommodation. In order to enable these related industries match the specific accommodation needs for the tourists arriving in the country, there is needed a model that can forecast the accommodation demands by the tourists. This will make it possible for the hotel industry players to respond in good time to the anticipated changes in demand over time and also to maximize returns on investments.

Seasonal variations are important in tourism and hospitality demands. The Box-Jenkins models for time series analysis allow the analyst to forecast future values of a series with only the past period’s data, without having some related variable’s data (Shumway and Stoffer, 2011). The authors therefore focused on the Box-Jenkins models to generate a forecasting model using quarterly data on bed occupancy by tourists visiting Kenya from 1974 to 2011. The SARIMA (1,1,2)(1,1,1)[4] model was found to be suitable for forecasting future quarterly demand on tourist accommodation in Kenya. This model shall therefore be useful to the tourism and related industries in forecasting future demands and maximize their returns on investment.

Keywords: Tourist Accommodation Demand, Kenya Tourism Accommodation, Tourism Accommodation Modeling