HOT NEW RESEARCH COLUMN
A QUICK TOUR OF TOURISM FORECASTING Paul Goodwin
INTRODUCTION s the world’s largest industry, it is responsible for the biggest movement of people across the globe. National economies depend on it. Environmentalists decry the pollution it causes. Nationalists fear it for the threat it poses to their cultures, while others extol its ability to enhance understanding between peoples. In the last twenty years its growth has been astonishing.
A
It is, of course, tourism, and forecasting can potentially play an important role in enhancing its benefits and mitigating some of its problems. But whether you run a hotel in Sydney or are an economic planner in
Singapore, how do you accurately forecast something that is so new in its scale and scope and so dependent on factors like fashion, the weather, exchange rates, natural disasters, economic well being and people’s perception of security issues? STUDIES IN TOURISM FORECASTING In a recent paper, Haiyan Song and Gang Li (2008) reviewed research into tourism forecasting that had been published in 121 articles since the year 2000. They found that tourism forecasting researchers have to rely predominantly on secondary data, collected by governments or other agencies. Because of the nature of these data, tourism demand is usually measured
Paul Goodwin is Foresight’s Research Column editor. His earlier columns covered new-product forecasting (Spring 2008), new approaches to supermarket forecasting (Summer 2007), and recent studies of forecasting know-how, training, and information sharing (Spring 2007).
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by the numbers of tourists arriving at particular destinations from particular departure points, though a few researchers have used tourist expenditure as a measure of demand. Tourists may leave home with a sense of romance and adventure, but most studies that have investigated the causes of variations in this demand find that the main drivers are hard economic factors, like the income of tourists, prices in the destination country relative to those at home, tourism prices in competing destinations, and exchange rates. The most striking feature of research into tourism forecasting is the variety of methods that researchers have tested. Approaches include relatively simple time series extrapolative methods like exponential smoothing, the more sophisticated seasonal ARIMA models, complex econometric models, artificial intelligence methods (like neural networks), and even technical analysis, which is most commonly used in stock market forecasting. Research into the use of judgmental forecasting methods has been less common.
of knowing when a change of trend is likely, there has been a limited amount of research into the prediction of turning points. However, some studies suggest that the use of leading indicators is effective. For example, the cycle of tourism demand from a particular country might lag behind the economic cycle in that country, so an economic downturn can be indicative of a fall in tourists from that country a few periods later. On seasonality, researchers have investigated whether tourism seasonal patterns are deterministic (in which case simply using dummy variables in forecasting models is usually sufficient) or whether they are stochastic and hence subject to changes over time. They have found conflicting results. However, there is some evidence that seasonal patterns exhibit meanreverting behavior in that, despite sometimes straying from established patterns, they tend to return to these patterns in the long term.
The authors conclude that there is no exclusive winning method in tourist demand forecasting. The relative accuracy of approaches depends on factors like how far ahead one is forecasting and whether it is for monthly, quarterly or annual demand. However, there is some evidence that modern econometric methods, such as those based on time-varying parameters or autoregressive distributed lags, tend to perform well. Alternatively, a combination of methods is also worth considering; for example, a simple average of the forecasts of several different methods. Because the forecaster’s eggs are not all in the same basket, combining reduces the risk that the forecasts from a particular method will be disastrously wrong.
Natural and man-made crises and disasters are perhaps the most salient influences on tourism demand. London theatres suffered as the number of American tourists fell in the wake of the first Gulf War. The 2004 tsunami devastated tourist areas of Thailand, Sri Lanka, and Indonesia, while Taiwan and other countries saw a drop in tourism as a result of the SARS epidemic in 2003. To estimate the effects of these types of events, researchers usually employ historical data to forecast what tourism numbers would have been, had the event not occurred. The difference between this forecast and the actual demand is taken as an estimate of the event’s impact. Of course, this only works if the demand has been affected by one crisis or disaster at a particular time. It cannot separate the effects of several simultaneous crises.
FACTORS AFFECTING TOURISM DEMAND Tourism demand is characterized by sudden directional changes in trends, seasonality and the impacts of events such as wars and natural disasters, all of which make the forecasting process more challenging. Despite the importance to businesses and macroeconomic planners
Moreover, estimating the effects of an event after the fact is obviously less desirable than being prepared for possible disasters in advance. For this purpose Song and Li recommend that planners use a framework like that suggested by Prideaux et al. (2003). This framework employs a range of tools, such as risk assessment,
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scenario planning, historical analysis, and the Delphi approach to handle different types of unexpected events according to the level of uncertainty associated with them. The framework allows planners to draw on the strengths of complementary methods, both quantitative and judgmental, in order to generate a range of scenarios derived from different assumptions. FORECASTING HOTEL VISITS Song and Li’s review focuses on tourism forecasting at a high level – total numbers of tourists arriving in particular countries or regions. However, some recent papers have also looked at forecasting at the level of the individual hotel, though, of course, not all people staying at hotels are tourists. For example, Carrie Queenan and her co-researchers (Queenan et al., 2007) looked at solutions to the problem of forecasting the number of higher-value and lower-value customers at a hotel/casino, given that the booking limits for these types of customers will constrain the historical data needed for accurate forecasts. (We don’t know what demand might have been for these categories of customers if we had set different limits.) In another recently published paper, Sedat Yüksel (2007) describes a method for forecasting monthly demand at a five-star hotel in Ankara and compares it with established methods like moving averages, the Holt-Winters method, and ARIMA models. Yüksel’s approach involved a Delphi panel which used the Analytic Hierarchy Process (AHP) to make judgmental adjustments to the forecasts of the HoltWinters method, which they found to be the most accurate quantitative method. The AHP, designed by Thomas Saaty (1980), allows people to estimate weights that reflect the relative importance of different factors that might affect demand, such as exchange rates, the hotel’s marketing activities, and events taking place in Ankara.
structured method for eliciting these judgments. The author argued that the process helped hotel managers avoid crises arising from demand fluctuations in their business and allowed the forecasts to accommodate change. Surprisingly, however, forecast accuracy was apparently tested only on one month’s demand data, so no firm conclusions can be drawn about the process’ effectiveness. Moreover, it seems that any potential benefits would be bought at a fairly high cost, with panels of experts having to be available to make a large number of repeated judgments on a monthly basis. If the thought of all that estimation makes you feel tired, then perhaps it’s time to book a holiday.
REFERENCES Prideaux, B., Laws, E. & Faulkner, B. (2003). Events in Indonesia: Exploring the limits of formal tourism trend forecasting methods in complex crisis situations, Tourism Management, 24, 475-487. Queenan, C.C., Ferguson, M., Higbie, J. & Kapoor, R. (2007). A comparison of unconstraining methods to improve revenue management systems, Production and Operations Management, 16, 729-746. Saaty, T.L. (1980). The Analytic Hierarchy Process, New York: McGraw Hill. Song, H. & Li, G. (2008). Tourism demand modelling and forecasting: A review of recent research, Tourism Management, 29, 203-220. Yüksel, S. (2007). An integrated forecasting approach to hotel demand, Mathematical and Computer Modelling, 46, 1063-1070.
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Thus the forecasting process integrated a variety of different approaches: a quantitative forecasting method, the judgments of groups of experts, and a
Paul Goodwin The Management School, University of Bath
[email protected] Summer 2008 Issue 10 FORESIGHT
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