Original Research

Forecasting tourist arrivals in South Africa

Andrea Saayman, Melville Saayman
Acta Commercii | Vol 10, No 1 | a141 | DOI: https://doi.org/10.4102/ac.v10i1.141 | © 2010 Andrea Saayman, Melville Saayman | This work is licensed under CC Attribution 4.0
Submitted: 06 December 2010 | Published: 06 December 2010

About the author(s)

Andrea Saayman, North-West University, South Africa
Melville Saayman, North-West University, South Africa

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Purpose: The aim of this paper is to model and forecast tourism to South Africa from the country's main intercontinental tourism markets. These include Great Britain, Germany, the Netherlands, the United States of America and France.

Problem investigated: Tourism to South Africa has grown substantially since the first democratic elections in 1994. It is currently the third largest industry in the country and a vital source of foreign exchange earnings. Tourist arrivals continue to grow annually, and have shown some resilience to a number of emerging market crises, including the terrorist attacks in the USA. Business success, marketing decisions, government's investment policy as well as macroeconomic policy are influenced by the accuracy of tourism forecasts, since the tourism product comprises a number of services that cannot be accumulated. Accurate forecasts of tourism demand are paramount to ensure the availability of such services when demanded. In addition, the seasonal nature of tourism leads to a pattern of excess capacity followed by shortage in capacity.

Method: Since univariate time series modelling has proved to be a very successful method for forecasting tourist arrivals, it is also the method employed in this paper. The naïve model is tested against a standard ARIMA model, as well as the Holt-Winters exponential smoothing and seasonal-non-seasonal ARIMA models. Forecasting accuracy is assessed using the mean absolute percentage error, root mean square error and Theill's U of the various models. Monthly tourist arrivals from 1994 to 2006 are used in the analysis, and arrivals are forecasted for 2007.

Findings: The results show that seasonal ARIMA models deliver the most accurate predictions of arrivals over three time horizons, namely three months, six months and 12 months.

Value: This paper is the first tourist arrivals forecast using South African data for the country as a whole, and therefore it forms an interesting case study as a long haul and growing tourist destination.

Conclusion: The univariate forecasts provide fairly accurate forecasts of tourist arrivals in South Africa, especially over the short run. As such, it is understandable why it remains a popular approach to forecast tourist arrivals. However, this method does not make provision for assessing the influence of external events and therefore its policy application is limited.


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