Forecasting tourism demand for South Africa

Abstract

Tourism is currently the third largest industry within South Africa. Many African countries, including South Africa, have the potential to achieve increased economic growth and development with the aid of the tourism sector. As tourism is a great earner of foreign exchange and also creates employment opportunities, especially low–skilled employment, it is identified as a sector that can aid developing countries to increase economic growth and development. Accurate forecasting of tourism demand is important due to the perishable nature of tourism products and services. Little research on forecasting tourism demand in South Africa can be found. The aim of this study is to forecast tourism demand (international tourist arrivals) to South Africa by making use of different causal models and to compare the forecasting accuracy of the causal models used. Accurate forecasts of tourism demand may assist policy–makers and business concerns with decisions regarding future investment and employment. An overview of South African tourism trends indicates that although domestic arrivals surpass foreign arrivals in terms of volume, foreign arrivals spend more in South Africa than domestic tourists. It was also established that tourist arrivals from Africa (including the Middle East), form the largest market of international tourist arrivals to South Africa. Africa is, however, not included in the empirical analysis mainly due to data limitations. All the other markets namely Asia, Australasia, Europe, North America, South America and the United Kingdom are included as origin markets for the empirical analysis and this study therefore focuses on intercontinental tourism demand for South Africa. A review of the literature identified several determinants of tourist arrivals, including income, relative prices, transport cost, climate, supply–side factors, health risks, political stability as well as terrorism and crime. Most researchers used tourist arrivals/departures or tourist spending/receipts as dependent variables in empirical tourism demand studies. The first approach used to forecast tourism demand is a single equation approach, more specifically an Autoregressive Distributed Lag Model. This relationship between the explanatory variables and the dependent variable was then used to ex post forecast tourism demand for South Africa from the six markets identified earlier. Secondly, a system of equation approach, more specifically a Vector Autoregressive Model and Vector Error Correction Model were estimated for each of the identified six markets. An impulse response analysis was undertaken to determine the effect of shocks in the explanatory variables on tourism demand using the Vector Error Correction Model. It was established that it takes on average three years for the effect on tourism demand to disappear. A variance decomposition analysis was also done using the Vector Error Correction Model to determine how each variable affects the percentage forecast variance of a certain variable. It was found that income plays an important role in explaining the percentage forecast variance of almost every variable. The Vector Autoregressive Model was used to estimate the short–run relationship between the variables and to ex post forecast tourism demand to South Africa from the six identified markets. The results showed that enhanced marketing can be done in origin markets with a growing GDP in order to attract more arrivals from those areas due to the high elasticity of the real GDP per capita in the long run and its positive impact on tourist arrivals. It is mainly up to the origin countries to increase their income per capita. Focussing on infrastructure development and maintenance could contribute to an increase in future tourist arrivals. It is evident that arrivals from Europe might have a negative relationship with the number of hotel rooms available since tourists from this region might prefer accommodation with a safari atmosphere such as bush lodges. Investment in such accommodation facilities and the marketing of such facilities to Europeans may contribute to an increase in arrivals from Europe. The real exchange rate also plays a role in the price competitiveness of the destination country. Therefore, in order for South Africa to be more price competitive, inflation rate control can be a way to increase price competitiveness rather than to have a fixed exchange rate. Forecasting accuracy was tested by estimating the Mean Absolute Percentage Error, Root Mean Square Error and Theil’s U of each model. A Seasonal Autoregressive Integrated Moving Average (SARIMA) model was estimated for each origin market as a benchmark model to determine forecasting accuracy against this univariate time series approach. The results showed that the Seasonal Autoregressive Integrated Moving Average model achieved more accurate predictions whereas the Vector Autoregressive model forecasts were more accurate than the Autoregressive Distributed Lag Model forecasts. Policy–makers can use both the SARIMA and VAR model, which may generate more accurate forecast results in order to provide better policy recommendations.