Handbook on Tourism Forecasting Methodologies

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Permission to photocopy this material in Spain must be obtained through: CEDRO, Centro Español de ...... the different facets of tourism demand, ranging from asking experts to give their best 'guesstimates' to ...... Regulatory Push & Pull (RPP).

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Handbook on Tourism Forecasting Methodologies

Copyright © 2008 World Tourism Organization and European Travel Commission Photo on cover and CD: copyright © iStockphoto.com/Murat Giray Kaya

Handbook on Tourism Forecasting Methodologies ISBN: 978-92-844-1238-9 (UNWTO) ISBN: 978-92-990050-0-2 (ETC)

Published by the World Tourism Organization and the European Travel Commission Printed by the World Tourism Organization, Madrid, Spain First printing 2008 All rights reserved

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The designations employed and the presentation of material in this publication do not imply the expression of any opinions whatsoever on the part of the Secretariat of the World Tourism Organization or the European Travel Commission concerning the legal status of any country, territory, city or area, or of its authorities or concerning the delimitation of its frontiers or boundaries.

World Tourism Organization Calle Capitán Haya, 42 28020 Madrid, Spain Tel.: (+34) 915 678 100 Fax: (+34) 915 713 733 Website: www.unwto.org Email: [email protected]

European Travel Commission 19A Avenue Marnix (PO Box 25) 1000 Brussels, Belgium Tel.: (+32) 2 - 548 90 00 Fax: (+32) 2 - 514 18 43 Website: www.etc-corporate.org Email: [email protected]

All UNWTO and ETC joint publications are protected by copyright. Therefore and unless otherwise specified, no part of a UNWTO and ETC publication may be reproduced, stored in a retrieval system or utilized in any form or by any means, electronic or mechanical, including photocopying, microfilm, scanning, without prior permission in writing. UNWTO and ETC encourage dissemination of their work and is pleased to consider permissions, licensing, and translation requests related to UNWTO and ETC publications. Permission to photocopy this material in Spain must be obtained through: CEDRO, Centro Español de Derechos Reprográficos Calle Monte Esquinza, 14 28010 Madrid, Spain Tel.: (+34) 91 308 63 30, Fax: (+34) 91 308 63 27 [email protected]; www.cedro.org For authorization of the reproduction of UNWTO works outside of Spain, please contact one of CEDRO’s partner organizations, with which bilateral agreements are in place (see: http://www.cedro.org/ingles_funcion_internacional.asp) For all remaining countries as well as for other permissions, requests should be addressed directly to the World Tourism Organization. For applications see: http://www.unwto.org/pub/rights.htm

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Table of Contents



Foreword.............................................................................................................................

vii



Acknowledgements...........................................................................................................

ix

1

What Is Tourism Forecasting and How to Do It?............................................................. 1.1 What Is Forecasting?.................................................................................................... 1.2 What to Forecast?........................................................................................................ 1.3 Quantitative versus Qualitative Forecasting................................................................... 1.4 Time Scale, Time Series and Data Collection................................................................

1 1 1 2 3

2 Basic Descriptions of Forecasting Methods................................................................... 2.1 Simple Extrapolative Methods...................................................................................... 2.2 No-change Extrapolation Method................................................................................. 2.2.1 General Description.......................................................................................... 2.2.2 Step-by-step Guide......................................................................................... 2.2.3 Technical Details and Mathematics................................................................... 2.2.4 Simple Worked Example.................................................................................. 2.3 Single Moving Average Extrapolation Method............................................................... 2.3.1 General Description.......................................................................................... 2.3.2 Step-by-step Guide......................................................................................... 2.3.3 Technical Details and Mathematics................................................................... 2.3.4 Simple Worked Example.................................................................................. 2.4 Exponential Smoothing Extrapolation Method.............................................................. 2.4.1 General Description.......................................................................................... 2.4.2 Step-by-step Guide......................................................................................... 2.4.3 Technical Details and Mathematics................................................................... 2.4.4 Simple Worked Example.................................................................................. 2.5 Decomposition Methods.............................................................................................. 2.5.1 General Description.......................................................................................... 2.5.2 Step-by-step Guide to Removing Seasonality Using Decomposition................ 2.5.3 Technical Details and Mathematics................................................................... 2.5.4 Simple Worked Example.................................................................................. 2.6 Simple Extrapolative Methods – Conclusions............................................................... 2.7 Advanced Extrapolative Methods................................................................................. 2.8 Autoregressive Moving Average (ARMA) Method.......................................................... 2.8.1 General Description.......................................................................................... 2.8.2 Technical Details and Mathematics................................................................... 2.9 Causal Models.............................................................................................................

5 5 6 6 6 6 7 7 7 8 8 9 9 9 10 10 12 13 13 13 13 14 16 16 17 17 17 18

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Handbook on Tourism Forecasting Methodologies

2.10 Linear Regression......................................................................................................... 2.10.1 General Description.......................................................................................... 2.10.2 Step-by-step Guide......................................................................................... 2.10.3 Technical Details and Mathematics................................................................... 2.10.4 Example of Linear Regression Using Time as the Independent Variable . ......... 2.11 Multiple Regression Method......................................................................................... 2.11.1 General Description.......................................................................................... 2.11.2 Step-by-step Guide......................................................................................... 2.11.3 Technical Details.............................................................................................. 2.11.4 Concise Example of a Multiple Regression Model............................................ 2.12 Structural Econometric Methods.................................................................................. 2.12.1 General Description.......................................................................................... 2.12.2 Step-by-step Guide......................................................................................... 2.12.3 Technical Details.............................................................................................. 2.13 Qualitative Forecasting Methods................................................................................... 2.14 Jury of Executive Opinion............................................................................................. 2.14.1 General Description.......................................................................................... 2.14.2 Step-by-step Guide......................................................................................... 2.15 The Delphi Method....................................................................................................... 2.15.1 General Description.......................................................................................... 2.15.2 Step-by-step Guide......................................................................................... 2.16 Scenario Planning........................................................................................................ 2.16.1 General Description.......................................................................................... 2.16.2 Step-by-step Guide......................................................................................... 2.17 Mixtures of Methods..................................................................................................... 2.18 Comparing the Performance of Different Methods........................................................ 2.18.1 Error Magnitude Accuracy................................................................................ 2.18.2 Directional Change Accuracy........................................................................... 2.18.3 Trend Change Accuracy...................................................................................

18 18 19 19 20 21 21 22 23 23 26 26 26 27 27 28 28 28 28 28 29 30 30 31 31 31 32 33 33

3 Choosing a Forecasting Methodology............................................................................. 3.1 Getting Started............................................................................................................. 3.2 Resource Constraints................................................................................................... 3.3 Choosing an Appropriate General Methodology........................................................... 3.4 Choosing a Specific Forecasting Method..................................................................... 3.4.1 Categories of Methodologies........................................................................... 3.4.2 Using the Decision Matrix................................................................................. 3.4.3 Key to User Requirements in the Decision Matrix.............................................

35 35 35 37 38 38 39 39

4

41 42 43 44 45 46

Case Studies....................................................................................................................... 4.1 German National Tourist Board’s World Cup Forecast.................................................. 4.2 VisitBritain’s International Passenger Forecast, 2006.................................................... 4.3 Kwa-Zulu-Natal 5-year Demand Forecast..................................................................... 4.4 Romanian Domestic Tourism Forecast......................................................................... 4.5 TRC New Zealand’s 6-year Tourism Activity Forecast...................................................

Table of Contents

4.6 Namibia TB’s 15-year Tourism Growth Forecast........................................................... 4.7 Namibia TB’s 15-year Employment Growth Forecast.................................................... 4.8 UNWTO/Fundación Premio Arce’s International Arrivals Forecast for 2006................... 4.9 VisitScotland’s International Tourism Forecast............................................................... 4.10 Tourism Research Australia’s 10-year Tourism Growth Forecast................................... 4.11 VisitBritain’s Terrorism Impact Forecast......................................................................... 4.12 Japan Travel Bureau Foundation’s Outbound Tourism Demand Forecast...................... 4.13 Tourism Authority of Thailand’s International Tourism Forecast...................................... 4.14 Canadian Tourism Industry Forecast(s) – Inbound, Outbound, Domestic and Industry Profits...................................................................................................... 4.15 Airbus’ Traffic Growth Forecast..................................................................................... 4.16 Pacific Asia Travel Association’s Forecasts of Tourism Demand.................................... 4.17 Hungarian NTO’s Short-term Forecast.......................................................................... 4.18 VisitScotland’s Avian Flu Scenarios Forecast................................................................ 4.19 VisitScotland’s Climate Change and Tourism Scenarios Forecast................................. 4.20 Austrian National Tourist Office’s Tourism Forecasting Techniques................................ 4.21 CONSAVE 2050’s Scenario Forecasting on Aviation and Emissions ............................

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v

48 49 50 55 56 58 59 60 61 62 63 66 68 69 70 71

List of Boxes, Figures and Tables ....................................................................................

73

List of Acronyms ...............................................................................................................

75

Glossary..............................................................................................................................

77

Bibliography . .....................................................................................................................

81

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Foreword

“Prediction is very difficult, especially if it’s about the future.” Niels Bohr, Nobel Prize winning physicist

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The aim of forecasting is to predict the future. Sometimes this is easy to do, such as when events are regular and follow simple patterns. We all know that the sun will come up tomorrow or that if we heat water it will boil. But, there are many things that we cannot predict with any accuracy such as what the weather will be like next month. This is because predicting the future becomes much harder when we start to look at more complicated processes or if we want very specific answers to events that are influenced by several different factors. Nearly everything associated with the economy (including tourism demand) comes into this final category of things that are very hard to predict accurately due to the large number of different factors involved. Unfortunately, it is often the very specific predictions that are most important to help plan effectively for the future. For businesses like tourism, that provide services for clients, it is extremely important to estimate what the demand will be. For instance, having a good idea of how many people will be visiting a hotel, city, or country, in a given month, season, or year, will help people to plan much more effectively. If demand is predicted to increase, more staff can be hired, more excursions arranged, accommodation capacity increased etc. As everybody who works in the tourism industry knows, demand for products and services can be affected by an incredible number of different factors – world economy, fuel prices, tourist infrastructure, hotel prices, natural disasters etc. Because of all of these factors, tourism demand, in all of its different forms, is one of the most difficult variables to predict. There is a huge number of different ways to predict the different facets of tourism demand, ranging from asking experts to give their best ‘guesstimates’ to highly complicated computer programs that can teach to make more accurate forecasts. This handbook aims to be a simple guide to the complex world of tourism forecasting. In the following chapters, the basic forecasting techniques will be explained, their advantages and disadvantages discussed, and examples described. The handbook also includes an excel file where the following methodologies are further explained and exemplified: •

Simple Linear Regression



Decomposition



Differencing



No-change models



Moving Average models



Single Exponential Smoothing

The handbook will contribute to the further development and understanding of tourism forecasting in general, will encourage more organizations and individuals to engage in the forecasting process, and will enhance the strategic planning and sustainable development of tourism.

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Handbook on Tourism Forecasting Methodologies

This handbook is the third in a series of joint collaborations between the European Travel Commission (ETC) and the World Tourism Organization (UNWTO) in the area of methodological manuals. The first two, Evaluating NTO Marketing Activities and Tourism Market Segmentation, have been very well received by the international tourism industry and we hope that this handbook will likewise make its contribution to the international tourism community.

Leslie Vella Chairman, ETC Market Intelligence Group

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and John Kester Chief, Market Trends, Competitiveness and Trade in Tourism Services, UNWTO

Acknowledgements

This handbook has been prepared by Murray Simpson and Richard Ladle of Sustainable Solutions Worldwide and the Oxford University Centre for the Environment (OUCE) on commission to the European Travel Commission (ETC) and the World Tourism Organization (UNWTO). Appreciation is expressed to Ross Macculloch of Tourismate, Alan Wilson of Oxford Economic Forecasting and Alistair Hunt of Metroeconomica for their valuable contributions. The report, which forms part of ETC’s ongoing Market Intelligence Programme, was carried out under the supervision of Mr Bill Richards of the European Travel & Tourism Action Group (ETAG), on behalf of ETC’s Market Intelligence Group, and by UNWTO Market Trends, Competitiveness and Trade in Tourism Services.

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The members of the ETC Market Intelligence Committee who contributed to this exercise are: Mr Leslie Vella (Chairman), Malta, Ms Lisa Davies (ETC Executive Unit), Ms Gaëlle Berréhouc, France, Mr Christian Ørsted Brandt and Mr Robin Rich, Denmark, Mr David Edwards, United Kingdom, Ms Carola Seseña del Moral, Spain, Ms Sandra Carvão, Mr John Kester and Mr Augusto Huescar (UNWTO), Mr Brian Maher, Ireland, Ms Carla Matta, Sweden, Mr Bill Richards (ETAG), Ms Judit Sulyok, Hungary, Mr Tom Ylkänen, Finland, and Mr Jernej Zajec, Slovenia. The World Tourism Organization and the European Travel Commission are extremely grateful to all of the case study contributors for their extremely valuable collaboration; VisitBritain, the German National Tourist Board, the Kwa-Zulu-Natal Tourism Authority, the Romanian National Institute of Research Development in Tourism, the Tourism Research Council New Zealand, the Namibia Tourist Board, Fundación Premio Arce – Universidad Politécnica de Madrid, VisitScotland, Tourism Research Australia, the Japan Travel Bureau Foundation, the Tourism Authority of Thailand, the Conference Board of Canada, Airbus, the Pacific Asia Travel Association (PATA), the Hungarian National Tourist Office, the Austrian National Tourist Office and Deutsches Zentrum für Luft- und Raumfahrt (DLR).

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Chapter 1 What Is Tourism Forecasting and How to Do It?

1.1 What Is Forecasting? Forecasting can be defined as a prediction of a future event. The most familiar kinds of forecasts are the weather forecasts in the newspapers or on the television or the radio every day. Those working in business will also be familiar with economic forecasts. These are very important for investors and governments who are trying to maximize income generation or financially plan for the future. Forecasting is also highly important for the tourism industry, which needs accurate predictions of demand so that it can plan effectively from season to season, year to year. If a bad year for tourist arrivals is predicted, then a tourism operator may want to reduce casual staff and reduce the scale and extent of his operation. When a good year is expected, he may want to take on new staff, make more beds available and increase the frequency of excursions.

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Stated simply, accurate tourism demand forecasts improve the efficiency of businesses, increase profits and strengthen economies. But how can one predict the future demand for tourism in a world that is so complicated and when there are so many factors that can influence the number of tourists visiting a country, a region, a town, a resort, or even a hotel. Even in a year when everything else is stable, (which never happens), a single chance event like a tsunami or a terrorist attack can have a strong influence on tourism demand. Luckily, even though the world is an uncertain, and sometimes dangerous, place people still want to go on holiday, and experience has shown that it is often possible to make quite accurate forecasts about many important components of tourism demand.

1.2 What to Forecast? For people working in the tourism industry, the most useful information is normally how many people will be visiting an area or staying at an accommodation unit and how long will they be staying. Closely related to this are forecasts about what the tourists will do while they are staying in an area/resort/hotel. How many excursions might they go on? How much money will they be spending on excursions/food/ nightlife? Finally, it may also be useful to know if and how tourists are changing. For instance, if the average age is going down and the average-spend on nightlife is going up, then a destination may want to change its advertising strategy to attract a more mature type of tourist. Alternatively, it may want to increase the range of excursions and attractions that appeal to a younger more adventurous clientele. Tourism forecasting is also extremely useful for those working in promoting and managing the tourism industry. Indeed, National Tourism Organizations (NTOs) and government departments with a tourism remit often have extensive forecasting programmes that encompass a much greater range of subjects than international arrivals or visitor spend. For instance, effective management of tourism at a national level requires a good understanding of the influence of global economic cycles or disease pandemics on tourism flows. Forecasting can also be used to improve planning for sporting and cultural events of international importance, such as the Olympic Games or the Football World Cup. The elements of tourism demand that are forecasted are known as ‘variables’ because their quantities vary in time and space. The essence of forecasting is that these variables (e.g. visitor numbers, visitor spend, hotel occupancy rates, etc) are dependent upon other factors (also variables) and that it is the consistent relationship between these variables that allows to make a forecast.

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Handbook on Tourism Forecasting Methodologies

The variable that one is interested in is generally known as the dependent variable and the variables used to predict the value of the dependent variable are known as independent or predictor variables. All forecasting methods are just different ways to make predictions about the value of dependent variables using one or a number of independent variables. The techniques range from applying very simple mathematical techniques using standard spreadsheets, all the way through to the application of advanced and highly sophisticated commercially available software. Table 1.1 Examples of commonly used dependent variables (= what is forecasted) and independent variables (= what is used to make the forecasts) Dependent variables

Independent variables

Total arrivals

Time

International tourist arrivals

Economic variables

Domestic tourist arrivals

Demographic variables

Overnight stays

Cost of travel

Same-day visitors

Tourist taxes

Excursions sold

Market variables

Passenger seats occupied

Political variables

Visitors spend

Climate

Tourism contribution to GDP

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Hotel occupancy rate

1.3 Quantitative versus Qualitative Forecasting Not all of the forecasting techniques to estimate the future value of the dependent variable use mathematics. Because the real world is so complicated, it is sometimes better to use expert opinions rather than resort to simple relationships that could be upset by a factor (independent variable) that has not been measured. This sort of educated guesswork does not use numbers and is therefore referred to as qualitative forecasting (methods 2.13 to 2.16; case studies 4.2 to 4.6 and 4.17 to 4.21). Qualitative methods, such as the opinions of juries of experts’, are very useful to quantify risks of complicated or unpredictable events such as terrorism or changes in the global economic markets. However, in many, if not in most situations, those working in tourism are very interested in numbers and precise predictions about tourism demand and, because of this, quantitative forecasting is perhaps more typical (methods 2.1 to 2.12; case studies 4.1 to 4.16). Both quantitative and qualitative methods can be used to make predictions about dependent variables and the choice of method often depends upon exactly what one is trying to predict (the dependent variable), the level of precision required, and sometimes the timeframe of the forecast. There are two main types of quantitative methods that are covered in this handbook: •

Extrapolative methods where a historical sequence is ‘extrapolated’ into the future. For the sake of simplicity, extrapolative methods are further divided into: –– simple extrapolative methods – that can be easily adopted with commercially available software and –– advanced extrapolative methods – which require a more in depth understanding of statistics and normally require specialized software packages. Since this handbook is aimed primarily at individuals and organizations who are new to tourism forecasting more emphasis is placed on the simple methods.

What is Tourism Forecasting and How to Do It?



3

Causal methods where the mathematical relationship between tourism demand and another factor, for example currency exchange rate, is used to predict the future.

It is important to remember that the aim of forecasting is not to come up with a perfect prediction of tourism demand (which is impossible) but, instead, to predict the most probable level of demand. Good qualitative or quantitative forecasting should be able to give reasonably accurate information on what will happen to tourism demand if different tourism policies are adopted – e.g. tourist taxes – or if circumstances change significantly – e.g. if the price of air transport goes up considerably.

1.4 Time Scale, Time Series and Data Collection

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A time series can be defined as a collection of observations/measurements of a variable obtained through repeated measurements over time. In the case of tourism forecasting, the most relevant type of time series is that of the dependent variable – e.g. some measurement of tourism demand. It is important to note that the time series measurements have to be collected at regular time intervals and that irregularly collected information or single observations have very little use for forecasting. There is no consensus on what is the ‘right’ time-interval for collecting data. It depends on what variable you are trying to forecast and the time-scale one is interested in. Time is the most important independent variable in tourism forecasting. Indeed, most of the simple quantitative tourism forecasting methods use past-trends in tourism demand – known as a time series or time series data – to predict what the future trends will be. For instance, if tourism demand has risen by between 3 and 4% for the last ten years, it makes sense that, in the absence of unforeseen events, it might increase by a similar amount next year. Of course, a very constant rise or decrease in demand is the exception rather than the rule, and most of the dependent variables show more complicated patterns. Fortunately, there are more complicated quantitative methods to deal with factors like seasonal fluctuations, cycles and other patterns of change in tourism demand over time. Time series data is therefore essential for most types of tourism demand forecasting. However, not all techniques require the same length of time series and, with the simplest techniques such as the nochange method (see section 2.2) you might only need the value for the previous time period. For more complex forecasting methods, it may be desirable to have a time series that stretches back several years. There is no minimum length for a time series as such, but the accuracy of the forecast may be adversely affected if the time series is too short. Another important element of data collection is the frequency with which data is collected. This depends on the type of forecast being made and could be annual, quarterly, and monthly or even weekly depending on available resources and the level of precision required for the forecast. For most tourism operators (at whatever level), the most useful forecasts concern tourism demand for the following season or year. Fortunately, short forecasting time-frames like this also give the most accurate forecasts. Although different forecasting methodologies vary in their ability to produce accurate forecasts for different points in the future, it is generally true that all methods give less accurate predictions for events in the distant future. This is because there are always unpredictable factors such as natural catastrophes or blips in the global economic cycle that, over the long-term, will reduce the accuracy of any forecasting methodology. It is also interesting to note, especially for those working on tight budgets, that it is often the simplest quantitative methods that give the most accurate results for short-term forecasts. It is only when trying to achieve medium- and long-term forecasts that simple methods begin to break down and more complex forecasting techniques need to be adopted.

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Chapter 2 Basic Descriptions of Forecasting Methods

Tourism forecasting can be very complicated and is often done by teams of experts working in government research departments, universities, or consultancies who specialize in economic forecasts. But, useful and accurate tourism demand forecasting can still be done even if these resources or expertise are not available. This section contains an outline of the most common forecasting methods used by tourism practitioners. There are, of course, many excellent sources of reference (see box 2.1) for those wanting a more indepth discussion of the mathematics and underlying principles of tourism forecasting. However, the following account of tourism forecasting methods aims to give an easily understandable overview of tourism forecasting methods with a minimum jargon and mathematical formulae. The methods are all cross-referenced to real life case studies (section 4).

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Box 2.1 Further reading on tourism forecasting Frechtling, D. C. (2001), Forecasting Tourism Demand: Methods and Strategies, ButterworthHeinemann – a relatively comprehensive text with clear examples of how to perform many of the simple quantitative forecasting techniques using commercially available spreadsheet software. Frechtling’s book is aimed at individuals with little background in forecasting and is essential reading for anyone who wishes to explore the basic techniques in more detail. Frechtling, D. C. (1996), Practical Tourism Forecasting, Butterworth Heinenman, Oxford – the predecessor to Frechtling (2001), but still a very useful guide to tourism forecasting. Song, H. and Witt, S. F. (2000), Tourism Demand Modelling and Forecasting: Modern Econometric Approaches, Pergamon – a thorough and highly technical account of econometric approaches to tourism forecasting. The stated aim is to present recent advances in econometric modelling methodology at a level that is accessible to non-specialists, although those without a mathematical or economics background will find it hard going. Witt, S. F. and Witt, C. A. (1992), Modeling and Forecasting Demand in Tourism, Academic Press, London – a slightly dated, but still definitive guide to tourism demand forecasting. Wong, K. F. and Song, H. (2002), Tourism Forecasting and Marketing, Hayworth Hospitality Press, New York – an edited series of papers on tourism forecasting. The book addresses econometric and time series approaches to forecasting, focusing on the concepts, model specification, data analysis, and methodologies used in day-to-day tourism planning.

2.1 Simple Extrapolative Methods Simple extrapolative methods, as the name suggests, are a family of techniques that use simple mathematical extrapolations (projections into the future) to produce forecasts of tourism demand. Generally, they are most effective for short range forecasts, simple situations, and as a point of reference for comparing the results of more sophisticated models of forecasting. Even though there are many quantitative forecasting methods with different levels of sophistication available, many organizations still use simple extrapolation of time series data, often in combination with qualitative (expert) judgments (case studies 4.2 to 4.6).

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Handbook on Tourism Forecasting Methodologies

2.2 No-change Extrapolation Method 2.2.1 General Description Extrapolation is the term given to any extension of a trend into the future. In the case of tourism forecasting, the aim is to extend a historical trend in the dependent variable – e.g. overnight stays, visitor’s arrivals or spend, etc – into the (often near) future, so that one can plan more effectively. Extrapolation works best when the historical trend is very clear and consistent. For instance, if by looking at the last five years of international tourist arrivals one discovers that they have been growing at between 1.5 and 2.5%, then the most simple extrapolation would be to assume that this trend will continue, and that next year there will be 1.5 – 2.5% more international tourist arrivals than this year. This is the most basic kind of extrapolation of a historical data set (time series), and is often referred to as the no-change or naïve method. Simple extrapolation methods are often called univariant, because they are only interested in changes in one factor, our dependent variable, over time. For this reason, they are also often referred to as time-series models.

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No-change models are used very frequently in tourism demand forecasting (case study 4.1) and, surprisingly, they often give the most accurate calculations. Forecasters will often make several forecasts based on the range of variation over several previous years. By doing this, they can give pessimistic and optimistic estimates of tourism demand based on the lowest and highest growth figures. In the above example, a growth in international tourist arrivals of 1.5% would represent the most pessimistic extrapolation based on the historical data.

2.2.2 Step-by-step Guide Stage 1: Obtain figures (data) for current and previous time period for the dependent variable of interest – e.g. international tourist arrivals. Stage 2: Calculate percentage change from previous time period or estimate percentage change based on published sources – e.g. use UNWTO regional forecast for tourism growth. Stage 3: Apply percentage change to current time period to create forecast of the value of the dependent variable for the next time period.

2.2.3 Technical Details and Mathematics The naïve model is also sometimes referred to as a random walk model because it assumes that there is no pattern or trend in the series. Without a trend, the best prediction will be to assume that the last value is the best predictor of the next value. Conversely, in the absence of a trend, historical values are of no utility in predicting future values and are therefore not used at all in the prediction. There are at least three common ways (below) for calculating a naïve forecast and these can be easily entered into any commercially available software package. •

Equation 2.1a assumes that there is no change at all between time periods.



Equation 2.1b assumes that growth rates remain unchanged from one time period to the next.



Equation 2.1c is identical to 2.1a, but takes into account seasonality by using the value for the same time period (e.g. month) in the following year. This is often better than a straight naïve model because most tourist destinations show some degree of seasonal changes in tourism demand.

Basic Descriptions of Forecasting Methods

7

However, rather than use this final equation it may be preferable to remove the effects of seasonality through decomposition before applying the forecasting methods. A simple worked example is given in section 2.2.4.

Box 2.2 Equations for no-change models Equation 2.1a Ft = At – 1 Equation 2.1b Ft = At – 1 × (At – 1/At – 2) Equation 2.1c Ft = At – m Where:

F = forecast value



A = actual value



t = some time period



m = number of periods in a year

2.2.4 Simple Worked Example Scenario: International tourist arrivals (July 2000 – 2004): Delivered by http://www.e-unwto.org Georgios Drakopoulos (307-99-294) Tuesday, March 15, 2011 5:40:51 AM

International tourist arrivals, world (thousand) July 2000

80,737

July 2001

82,504

July 2002

82,490

July 2003

80,048

July 2004

87,708

Source: UNWTO

Under a no-change model, the prediction for July 2005 would be as follows: No-change Model 2.1a: Ft = At – 1 = 87,708 No-change Model 2.1b: Ft = At – 1 × (At – 1/At – 2) = 87,708 × (87,708/80,048) = 96,101 The actual value for July 2005 was: 93,392 NB: In the above example, data for July only was used in order to remove the effects of seasonality.

2.3 Simple Moving Average Extrapolation Method 2.3.1 General Description Even though no-change models are often successful, there are good reasons to use more complex forecasting techniques. For instance, tourism demand rarely follows predictable patterns over long

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periods of time. A more common situation is that there are both long and short-term trends in tourism demand and that many factors may exert an influence on demand from one year to the next. In situations where there are longer-term trends – e.g. over several years/seasons, then simple extrapolative methods can still be used as long as the trend is not too complicated. For instance, if there has been a slow but steady decline in international arrivals over the last 5 years, it may be more accurate to assume that the most recent growth, i.e. for this year, will be closest to the situation that we will see the following year. There are, however, drawbacks to using the current years figures to estimate what will happen to tourism demand next year. What happens if this year was unusually good (or unusually bad)? Untypical events are, sadly, very common in the tourism industry but fortunately it is possible to reduce the ‘influence’ of an unusual year on our forecast while still using a simple extrapolative method. The simplest way to do this is to use an average of the last several values in our time-series. This should give a much more typical value that is less affected by the last value in the time series. The further back one goes in time to create the average, the more ‘typical’ the value is, and the less affected by unusual values in the dependent variable. Unfortunately, as already seen, the further back in time, the less likely it is that the values reflect what will happen next year/season. For this reason, it is typical to create an average from just the last few values in the time-series of the dependent variable. One could, for instance, use the last three years data for every new forecast. By doing this, the average ‘moves’ along one time period for every successive forecast and this technique is referred to as the simple movingaverage or SMA method.

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2.3.2 Step-by-step Guide Stage 1: Obtain figures (data) for current and several previous time periods for the dependent variable of interest – e.g. international tourist arrivals. Stage 2: Calculate average percentage change using several previous time periods and the current time period. Stage 3: Apply average percentage change to current time period to create forecast of value of dependent variable for the next (future) time period.

2.3.3 Technical Details and Mathematics The SMA method uses a simple arithmetic mean to estimate central tendency. The equation (below) can be easily entered into a standard spreadsheet package such as Microsoft Excel©.

Box 2.3 Equation for simple moving average model Equation 2.2 Ft = (At – 1 + At – 2 + At – 3)/n Where:

F = forecast value



A = actual value



t = some time period



n = number of past time periods

Basic Descriptions of Forecasting Methods

9

The SMA method assumes that the number of visitors during a time period can be forecast by calculating the average number of visitors over the last x months. Normally 3 or 6 month moving averages are used.

2.3.4 Simple Worked Example Scenario: International tourist arrivals (July 2002 – 2004): International tourist arrivals, world (thousand) July 2002

82,490

July 2003

80,048

July 2004

87,708

Source: UNWTO

Under a simple moving average (SMA) model, the prediction for July 2005 would be as follows: SMA Model 2.2: Ft = (At – 1 + At – 2 + At – 3)/n

= (82,490 + 80,048 + 87,708)/3



= 83,415

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The actual value for July 2005 was: 93,392 NB: In this example, two previous time periods and the ‘current’ time period are used.

2.4 Exponential Smoothing Extrapolation Method 2.4.1 General Description As discussed, it is often the case that the last measurement of the dependent variable in a time series is likely to be of most use when trying to forecast what the next value might be. Older values are likely to be of less importance and, by logically, very old values (say from 20 years ago) are likely to be of no value at all in helping forecast future trends. The simplest way to deal with this is, by either using a moving average (SMA method) of the last few values, or by only using the last value (no-change method). However, there are also more sophisticated extrapolative methods to deal with this problem. One solution is to assume that the importance of past values of the dependent value to the new forecast will decrease in a constant manner. This is what the exponential smoothing method does – it is very similar to the SMA method, but instead of creating a simple average it gives more weight to the most recent measurements. This should be better than the no-change model because it takes into consideration the values from several previous time periods. It should also be better than a simple moving average because older values have less influence on the forecast. Exponential smoothing is heavily used in the tourism industry, and like the other extrapolative methods is good for short-term forecasts, but less effective for longer term forecasting.

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2.4.2 Step-by-step Guide Stage 1: Obtain figures (data) for current and several previous time periods for the dependent variable of interest – e.g. international tourist arrivals. Stage 2: Calculate the weighted (smoothed) average percentage change using several previous time periods and the current time period. Weight time periods by exponentially devaluing each period starting with the current value and working backwards. Stage 3: Apply the smoothed percentage change to current time period to create forecast of value of dependent variable for the next (future) time period. Stage 4: Repeat stages 1 to 3, keeping the number of time periods used to create the smoothed average constant.

2.4.3 Technical Details and Mathematics

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Exponential smoothing methods seek to isolate trends from irregular (stochastic) variation. To perform single exponential smoothing, or the closely related technique of double exponential smoothing, seasonality has to be removed through decomposition. Furthermore, exponential smoothing only works effectively on data where the moving average is stationary (= time series which has a constant mean and variance over time). For this reason, like with the Autoregressive Moving Average (ARMA) methods (see section 2.8), trends in the mean may have to be removed through differencing. In exponential smoothing, a new estimate for the dependent variable is the combination of the estimate for the present time period plus a portion of the random error generated in the present time period. When used for forecasting, exponential smoothing uses weighted averages of past data. The effect of recent observations is expected to decrease exponentially over time. It is one of the most common and powerful methods of forecasting used for all kinds of ‘one step ahead’ forecasting in practical situations. It is heavily used for short-term forecasts in the tourism industry, but it is not generally considered appropriate for long-term or even medium-term forecasting. The key question in exponential smoothing is how much ‘weight’ to attach to the current period, the previous period, and earlier periods when estimating the value of the next period. In spreadsheets, such as Microsoft Excel, a single smoothing parameter (alpha) is estimated but specialist statistical software, such as SPSS© allow up to four weighting parameters to be assigned: Parameter 1: Alpha – Varies from 0 (old observations count as much as the most recent observation) to 1 (the most recent observation is used exclusively). Parameter 2: Gamma – If there is a trend in the series then gamma is used to weight the value of each preceding observation. Once again, it varies from 0 (all observations count equally) to 1 (the trend is based only on the most recent observations in the series). Parameter 3: Phi – Used instead of gamma if the trend is disappearing (dying out or damping). A Phi of 1 uses all observations to estimate a trend towards dying out whereas Phi of nearer to .9 responds more rapidly to observations that the trend is dying out. Parameter 4: Delta – Used if the data shows seasonality or cycles. A value of 1 indicates that all observations count equally, while a delta of 1 estimates seasonality primarily from more recent observations. Single extrapolation smoothing: In the simplest situation where there is no trend (observations vary randomly around the mean of the time series), seasonality or damping, only alpha needs to be set. Even alpha is not required if adjacent data points do not cluster together (autocorrelation). Assessment of

Basic Descriptions of Forecasting Methods

11

autocorrelation, trends, damping and seasonality can be easily performed in statistical programs such as SPSS©. Many modern statistical packages will also help you to estimate smoothing parameters and refine your exponential smoothing model to give you the best fit.

Box 2.4 Simplified equation for single exponential smoothing Equation 2.3 Ft = α × At – 1 + (1 – α) × Ft – 1 Where:

F = forecast value



A = actual value



t = some time period



α = smoothing constant between 0 and 1

Double exponential smoothing: When dealing with time series that shows simple increasing or decreasing (linear) trends over time single exponential smoothing may not be able to capture the variability accurately, and it may be necessary to use double exponential smoothing (DES). DES computes a smoothed level and trend at each data point and the forecast is made by using the last point in the data series to forecast one or two points ahead in the future. As is typical with models of this type there have been several different DES models proposed for tourism forecasting, but the simplest and easiest to apply is probably Brown’s one-parameter adaptive method.

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Box 2.5 Brown’s one-parameter adaptive method for double exponential smoothing Level:

α At + (1 – A)(Lt – 1 + bt – 1)

Trend:

bt = α (Lt – Lt – 1) + (1 – α) bt – 1

Forecast:

Ft + h = Lt + hbt

Where:

L = level of the series



α = level and trend smoothing constant between 0 and 1



A = actual value



b = trend of the series



t = some time period



h = number of time periods ahead to be forecast

The forecast is obtained by multiplying the trend (bt) by the number of steps ahead that you want to forecast (h) added to the base value (Lt). In order to start a DES forecast, you first need to make initial estimates for the level (L) and the trend (b) of the series. One common way to do this is as follows: Level initialization:

L1 = A1

Trend initialization: b1 = A2 – A1 The advantages of DES are that, even though it is still relatively simple, it can still capture linear trends up or down, and can forecast several periods ahead. However, it cannot track non-linear trends, it fails to simulate stepped series, it cannot deal effectively with seasonality, and it does not incorporate causal relationships (like all time series methods).

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Exponential smoothing can be performed with widely available spreadsheets such as Microsoft Excel, but is normally done on statistical software such as SPSS©. Furthermore, a degree of familiarity with more sophisticated mathematical methods is required to effectively estimate the smoothing parameters. For this reason, only a simple worked example is presented here – for more detailed information on this method, please refer to Frechtling (2001).

2.4.4 Simple Worked Example

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Scenario: International tourist arrivals (December 2004 to July 2005): International tourist arrivals, world (thousand)

Exponentially smoothed forecast

December 2004

69,404



January 2005

69,309

69,404

February 2005

67,244

69,319

March 2005

70,971

67,452

April 2005

64,672

70,619

May 2005

67,718

65,266

June 2005

66,537

67,473

July 2005



66,631

1) See Decomposition methods (section 2.5) regarding the methodology to obtain seasonally adjusted data, as well as the accompanying Excel spreadsheet. Source: UNWTO

In this example: alpha (α) = 0.9 Initialization = First actual value is used as first forecast value Under a single exponential smoothing model (SES), the seasonally adjusted prediction for July 2005 would be as follows: SMA Model: Ft = α × ALt – 1 + (1 – α) × Ft – 1 = 0.9 × 66,537 + (1 – 0.9) × 67,473

= 66,631

The above figure can be converted into a ‘real’ number by removing the effects of adjustment for seasonality. In this case, we need to multiply the prediction by the seasonal adjustment factor for July 2005 (= 1.4465 for this data set). Seasonal adjustment factors can be calculated relatively simply through decomposition methods (see section 2.5 and the worked example in the Excel ‘Decomposition’ worksheet’). = 66,631 × 1.4465 = 96,382 The actual value for July 2005 was: 93,392 NB: In the above example, seasonally adjusted data has been used. A simpler, but possibly less precise estimate could be gained by using yearly data. The more complicated example has been used in order to illustrate the more commonly used technique.

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13

2.5 Decomposition Methods 2.5.1 General Description Another potential problem with simple extrapolative methods is that tourism demand often shows distinct trends within a year (seasonality). Most destinations have low and high seasons that are often related to prevailing weather conditions and tourism demand may show distinct patterns from one month or even week to the next. The simplest way of dealing with this is to split the historical data (time series) into months or seasons, and then use a simple extrapolative method based on the historical trends for the time period one is interested in. Seasonality is just one feature of historical trends in tourism demand data. The time series may also show distinct cycles in addition to many other irregular variations caused by all the different ‘events’ that can influence whether a tourist comes to particular destination at a particular time of year. For this reason, tourism demand forecasters often try to decompose their time series into its constituent parts to make analysis easier. It is often possible to partition the effects of seasonality, long-term trends, cycles, and all of that irregular unpredictable variation. Once decomposition of the time series has taken place, and the effects of season and any cycles have been removed, then a variety of forecasting methods, such as the simple extrapolative techniques outlined above, can be used to make the forecast.

2.5.2 Step-by-step Guide to Removing Seasonality Using Decomposition

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Stage 1: Obtain figures (data) for current and several previous time periods for the dependent variable of interest – e.g. international tourist arrivals. Stage 2: Isolate the seasonal and irregular factors through the ratio-to-moving-averages method. Stage 3: Produce a seasonally adjusted series. Stage 4: Forecast the trend-cycle series using an appropriate forecasting method (e.g. SMA, SES or regression).

2.5.3 Technical Details and Mathematics Decomposition is a mathematical term for separating (decomposing) a time series into its constituent parts. Statisticians refer to these parts as trends, cyclical elements, seasonal elements, and irregular effects. A forecaster is normally interested in the irregular component of a time series, which is often obscured by the trends, and cycles that are common in tourism data. If these can be removed, then it is often possible to directly compare data or use the ‘de-trended’ and ‘de-cycled’ data in other forecasting models. Thus, classical decomposition method assumes that there are at least 4 factors influencing the underlying trend in the time series: •

The trend component (T) in a time series is the long-run general movement caused by factors such as long-term economic trends, demography, weather, etc. It can often be approximated by a linear (straight line) model.



The cyclical component (C) is wave-like movement around the central trend that may vary in amplitude and duration but often lasts for several years. Can be driven by factors such as long term economic cycles.

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Handbook on Tourism Forecasting Methodologies



The seasonal variations (S) are patterns repeated over fixed intervals of anything up to a year. They are often the result of weather (wet and dry seasons, summer and winter) and man-made conventions such as holidays.



The error term (e) or irregular component is simply the residual component of a time series that is not explained by T, C, and S.

There are several ways to apply decomposition, but one of the most widely used is known as the ‘ratioto-moving average classical decomposition method’. This method assumes that the components have a multiplicative relationship1 with each other:

Box 2.6 Equation for removing seasonality through decomposition Equation 2.4 At = Tt.Ct.St.et

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Where:

A = actual value in time series



T = the trend component



C = the cyclical component



S = the seasonal component



e = the error term



t = time period (usually a month or a quarter)

Of course this looks disarmingly simple but the real challenge is to accurately estimate each of these components so that an effective forecasting model can be developed.

2.5.4 Simple Worked Example The below tables give an example of the easiest way to adjust for seasonality through a ratio to moving-average method. The practical steps are straightforward and easy to follow using a spreadsheet package such as Microsoft Excel – see in accompanying spreadsheet the ‘Decomposition Model’. Stage 1: Calculate the 12-month moving average. Stage 2: Calculate the cantered 12-month moving average (see below). This is the average of the first two values of the 12-month moving average calculated in stage 2. Stage 3: Calculate the seasonal ratios. This is the raw (actual) value divided by the cantered 12-month moving average calculated in stage 2. Stage 4: Calculate raw seasonal factor. This is the average of the monthly seasonal ratios. Stage 5: Calculate the seasonal adjustment factor (SAF) by multiplying the seasonal factor obtained in stage 4 by the adjustment factor (= 12/sum of the raw seasonal factors). Stage 6: Calculate the seasonally adjusted series by dividing the raw (actual) monthly values by the seasonal adjustment factor (SAF).

1

Other models assume that the components have an additive relationship

Basic Descriptions of Forecasting Methods

Scenario: International tourist arrivals (January 1989 to December 1990):

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Month

International tourist arrivals, world (thousand)

Stage 1

Stage 2

Stage 3

Stage 4

Stage 5 (SAF)

Seasonally adjusted figures

January 1989

22,815

0.689

0.687

33,228

February 1989

23,211

0.702

0.700

33,178

March 1989

29,769

0.807

0.804

37,015

April 1989

29,761

0.980

0.977

30,473

May 1989

34,945

1.005

1.002

34,892

June 1989

37,303

34,260

1.089

1.111

1.107

33,692

July 1989

53,981

34,418

1.568

1.541

1.536

35,151

August 1989

54,783

34,477

1.589

1.580

1.574

34,804

September 1989

41,807

34,699

1.205

1.222

1.217

34,344

October 1989

31,302

35,023

0.894

0.916

0.913

34,291

November 1989

24,349

35,279

0.690

0.718

0.715

34,054

December 1989

26,212

35,515

0.738

0.772

0.769

34,070

January 1990

24,591

34,186

35,678

0.689

0.689

0.687

35,815

February 1990

25,208

34,334

35,920

0.702

0.702

0.700

36,034

March 1990

29,198

34,501

36,170

0.807

0.807

0.804

36,306

April 1990

35,665

34,453

36,394

0.980

0.980

0.977

36,519

May 1990

36,806

34,945

36,629

1.005

1.005

1.002

36,750

June 1990

41,581

35,100

36,711

1.133

1.111

1.107

37,556

July 1990

55,364

35,457

36,578

1.514

1.541

1.536

36,052

August 1990

57,330

35,572

36,518

1.570

1.580

1.574

36,422

September 1990

45,060

35,785

36,395

1.238

1.222

1.217

37,017

October 1990

34,045

36,056

36,277

0.938

0.916

0.913

37,295

November 1990

26,993

36,284

36,255

0.745

0.718

0.715

37,751

December 1990

29,204

36,504

36,232

0.806

0.772

0.769

37,960

1989

1990

Average 1989 – 1990 (raw seasonal factor)

Seasonal adjusted factor (SAF)

January

0.689

0.689

0.687

February

0.702

0.702

0.700

March

0.807

0.807

0.804

April

0.980

0.980

0.977

May

1.005

1.005

1.002

June

1.089

1.133

1.111

1.107

July

1.568

1.514

1.541

1.536

15

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Handbook on Tourism Forecasting Methodologies

1989

1990

Average 1989 – 1990 (raw seasonal factor)

Seasonal adjusted factor (SAF)

August

1.589

1.570

1.580

1.574

September

1.205

1.238

1.222

1.217

October

0.894

0.938

0.916

0.913

November

0.690

0.745

0.718

0.715

December

0.738

0.806

0.772

0.769

12.043

12.001

Sum =

Note: The above figures differ slightly from those in the accompanying Excel Worksheet because the example is (necessarily) only a subset of the entire dataset.

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2.6 Simple Extrapolative Methods – Conclusions Although simple extrapolative methods have many drawbacks, especially if the historical trend of the dependent variable is complex, they are very easy to use and have good predictive power, especially for short-term forecasts. They are also commonly used as a control method when assessing whether other forecasting methods are worth using. If a more complex method cannot forecast more accurately than a no-change model, then there is no point in using it. Generally, in any kind of forecasting, the simplest model, making the fewest assumptions and giving the best results should be the one that is used. Simple extrapolative methods are also the most widely used (see case studies in section 4) of the available quantitative forecasting methods for the following reasons: a) First, they can generally be performed using spreadsheet programs that come as standard with most PCs. b) Second, they are straightforward and easy to understand. c) Third, in most circumstances they produce robust forecasts that often outperform more sophisticated methods. d) Finally, they can be easily combined with qualitative methods, such as experts’ judgment or juries of expert opinion to produce more realistic forecasts.

2.7 Advanced Extrapolative Methods The various simple extrapolative methods discussed above are generally not very good at dealing with situations where tourism demand does not follow a simple trend over time. Of course, the time series can be decomposed first, but this is only one of the ways to deal with issues such as seasonality, cycles and complex trends. There are also several more sophisticated models that have been developed for use in tourism demand forecasting.

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17

2.8 Autoregressive Moving Average (ARMA) Method 2.8.1 General Description The most commonly used of these more complex models is called the Box-Jenkins or Autoregressive Moving Average (ARMA) method. This forecasting method seeks to find the best combination of two forecasting methods (Autoregression and Moving Average). A detailed treatment of the underlying mathematical assumptions and technical aspects of more sophisticated techniques, such as ARMA and econometric models, are beyond the scope of this handbook. Suffice to say, using ARMA and associated forecasting methods effectively and appropriately require a certain level of expertise but should not be beyond anybody with a reasonably level of training in mathematics.

2.8.2 Technical Details and Mathematics ARMA or Box-Jenkins forecasting models find the best combination of two forecasting methods (Autoregression and Moving Average) and their associated parameters. ‘Best’ in this case means the model that is the most accurate for simulating the historical data and, by extrapolation, should be the best at predicting future trends.

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Limitations: ARMA models can only deal with time series that are stationary in the means and variances. If the data series does not conform to this, then differencing must be used to achieve stationarity. Differencing is a simple way to remove the influence of seasonality on a time series and thereby reveal any underlying patterns/trends that might be hidden by the regular fluctuations caused by high and low seasons. Time series can be differenced using any time period, but the procedure is always the same: •

a first difference is calculated by subtracting the first value in the time series from the second value, and the second from the third, etc.;



this new series is then examined for a stationary mean;



if a stationarity is still not achieved the first differenced series can itself be differenced and so on.

Models such as ARMA and ARIMA require the time series to show stationarity or they do not give accurate forecasts. ARMA models are a form of general linear model (GLM) that use historical time series data to construct a mathematical function. They work best in situation where the data shows stable trend conditions. They combine three types of processes to generate the forecast: •

autoregression;



differencing to strip off the integration of the series;



moving averages.

Each of these three processes responds to random disturbances in different ways. Lags of the differenced series appearing in the forecasting equation are called ‘autoregressive’ terms, lags of the forecast errors are ‘moving average’ terms, and a time series which needs to be differenced to be made stationary is said to be an ‘integrated’ version of the stationary series.

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Handbook on Tourism Forecasting Methodologies

2.9 Causal Models So far, only forecasting methods that consider the behaviour of the dependent variable through time have been described. This sort of single or univariant model is often perfectly adequate for many forecasting situations. However, there are another class of models that work by analyzing the relationships between the dependent variable and one or more independent variables. This could potentially give a more accurate forecast, especially if the relationship with another variable, e.g. currency exchange rates, is strong and there is a good understanding of how this second variable may change. In the case of many economic variables most governments produce detailed forecasts that could be very useful for forecasting tourism demand.

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The most commonly used method for quantifying the relationship between two variables is known as Linear Regression where the relationship between a dependent and one or more independent variables can be represented by an equation that describes a straight (linear) line. Linear regression can be done very easily on spreadsheet packages such as Microsoft Excel. Even more complicated forecasting methods, often referred to as structural or econometric models (see case studies 4.9 to 4.11 and 4.14 to 4.16), can be constructed that quantify the relationship between variables that are both independent and dependent. Like the advanced extrapolative methods discussed above, structural models do require a degree of mathematical and technical expertise that is beyond the scope of this handbook. The book by Song and Witt (see bibliography) gives an excellent account of the development and uses of these more sophisticated techniques. There are very good reasons to use causal models as opposed to univariant analysis. For a start, if one knows the relationship between several independent variables and tourism demand, one can start to create a variety of forecasts for different potential scenarios. For instance, if one understands the relationship between tourist taxes and tourism demand and also knows that the government is thinking of increasing the level of airport departure tax next year, then its is possible to see how this might influence demand. Forecasts that take into account the actions of competitors (companies, regions or countries) on tourism demand thus providing valuable background information that can form the basis of important policy and management decisions can also be developed. Causal models can be really useful. But, like all other forms of tourism forecasting, they may have difficulty capturing the complexities of the real world and the more sophisticated methods require a great deal of knowledge and expertise. One of the biggest problems with linear regression and structural models is that it is by no means obvious which variables should be put into the forecast, and it can also require considerable skill to interpret the results when using many independent and dependent variables. However, despite some of these potential difficulties, causal models are extremely useful and add another dimension to tourism demand forecasting. They are also very commonly used and, because they vary so much in complexity and sophistication, can be conducted at some level whatever the level of resources at your disposal.

2.10 Linear Regression 2.10.1 General Description Linear regression takes two basic forms in tourism forecasting: •

In the first form, the relationship between two variables in space is quantified, but taking the data from the same year. For instance, one could quantify (regress) the relationship between number of visitors and exchange rates for all the countries in Eastern Europe.



Alternatively, one could look at the same relationship in a single country but use data collected over the last twenty years.

Basic Descriptions of Forecasting Methods

19

Both forms of analysis have advantages and disadvantages, and the decision of which method to use depends on many factors – not least the aims of the study. Generally speaking, looking at the relationship of tourism demand to another variable over different countries can be problematic because there are many possible confounding factors that make interpretation of the results problematic. This leads to situations where one might find a strong relationship between two variables over time, but no relationship between the same two variables in space when time is kept constant. At its simplest, linear regression can be used in the same way as the extrapolative methods using time as the independent variable. This is sometimes referred to as simple or univariant regression. The basis of linear regression is to (mathematically) fit a straight line to a time series data. This would be quite difficult, but not impossible, to do by hand on a piece of paper but very simple to do on a spreadsheet such as Microsoft Excel. The results of the regression can be most easily represented on a simple scatter graph (see section 2.10.4) where the independent variable (in this case time) is on the horizontal or X-axis and the dependent variable (tourism demand) is on the vertical or Y-axis. The regression equation quantifies the relationship between the data points on the X- and the Y-axis – it tells the predicted value of the dependent variable for every possible value of the independent variable.

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The regression equation relating the X (independent) and Y (dependent) variables is also referred to as the regression line or the line of best fit because it is the line that describes the midpoint through all of the data points. Another possible forecast could be obtained by connecting the earliest value to the final value (the end-point line). This is also problematic, not least because the values of these two data points can vary considerably over time. Like the no-change simple extrapolative method a single ‘unusual’ year could cause huge errors in forecasting accuracy, and for this reason regression lines are the preferred method of forecasting. Although simple linear regressions are designed to predict straight line relationships there are ways to deal with trends that deviate from this pattern (non-linear trend). The easiest ‘trick’ for doing this is to change a non-linear relationship into a linear one by mathematically manipulating (transforming) one or both of the variables. Common transformations that can be applied to curved (non-linear) data are logarithmic, parabolic and sine transformations. Knowing which transformation to use requires some experience, but can also be arrived at by a trail and error process of performing the transformation and then inspecting the resulting scatter graph to see whether the relationship is now straight.

2.10.2 Step-by-step Guide Stage 1: Obtain figures (data) for current and several previous time periods for the dependent variable of interest – e.g. international tourist arrivals. Stage 2: Use a spreadsheet or similar computer program to regress the dependent variable against the time periods for which you have the data. Transform the data if necessary. Stage 3: Apply the equation that describes the line of best fit to your next time period to generate the forecast. Stage 4: Forecast the trend-cycle series using an appropriate forecasting method – e.g. SMA or regression.

2.10.3 Technical Details and Mathematics Regression analysis is concerned with how one or more variables affect the dependent (forecast) variable. There are two main types of regression that are, mathematically, very closely related:

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simple linear regression (one independent variable and one dependent variables) and



multiple regression (two or more independent variables and one dependent variable).

Structural models are also similar to linear regression models in as much as they consist of a series of linked regression equations that contain variables that are both dependent and independent variables. The complexities of these techniques are covered in many excellent statistical textbooks (e.g. Witt & Witt, 1991), and are beyond the scope of this handbook. However, the basic principles are as follows:

Box 2.7 Equation for simple linear regression model Equation 2.5 Y = a + b1X1 + e Where:

Y = the dependent (forecast) variable



a = the intercept constant

b = slope coefficient



X = independent variable

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e = residual

The skill of regression analysis is not in the mathematics, which is relatively straightforward and can be done using commercially available software, but rather in teasing out the relationships between variables. For instance, a significant correlation between two variables does not necessarily indicate that the variables have a causal relationship (although they might). Experts at regression analysis are able to choose the ‘best’ independent variable for the regression model, spot ‘suspect’ correlations, and identify the most realistic model out of a series of superficially similar alternatives. In addition to this, there are many different forms of regression analysis that are appropriate under different circumstances and to become fully proficient at using the multiplicity of techniques takes a considerable time.

2.10.4 Example of Linear Regression Using Time as the Independent Variable Scenario: International tourist arrivals (July 1994 – 2004): International tourist arrivals, world (thousand) July 1994

64,261

July 1995

64,749

July 1996

65,807

July 1997

68,868

July 1998

70,906

July 1999

75,118

July 2000

80,737

July 2001

82,504

July 2002

82,490

July 2003

80,048

July 2004

87,708

Source: UNWTO

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21

Under a simple linear regression extrapolation the forecast is as follows:

International arrivals (thousands)

90.000 y = 2,414.4 x + 60,350 85.000 80.000 75.000 70.000 65.000 60.000 1992

1994

1996

1998

2000

2002

2004

2006

Year

Linear regression equation

y = 2,414.4 x + 60,350 (when x = 12)



= 89,323

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The actual value for July 2005 was: 93,392 In the example above, ‘x’ in the equation represents the time period. The equation (y = 2,414.4 + 60,350) can be derived very easily using Microsoft Excel by clicking on any of the data points and adding a trend line, or by choosing ‘Regression’ from the data analysis tool pack1. A full example is available in the ‘Simple Linear Regression’ worksheet in the accompanying excel spreadsheet. An alternative to using annual data for the same month would be to use seasonally adjusted data (see Decomposition Method above). 1

This is the statistical function in Excel. It generally comes as standard, but often needs to be installed.

NB: For the purposes of clarity yearly data has been used. It would be equally, if not more valid, to use seasonally adjusted data (see excel spreadsheet example).

2.11 Multiple Regression Methods 2.11.1 General Description Unfortunately, simple linear regression is not very useful beyond analyzing time series trends, as tourism demand is affected by many variables, not just one. This means trying to understand tourism demand just on the basis of one other variable is too simplistic and would certainly create inaccurate and unreliable predictions. The way to deal with this problem is to include many more factors in the regression analysis – known as the Multiple Regression forecasting method. Multiple regression is itself a specific form of a more general statistical procedure known as General Linear Modelling (GLM) and takes many different forms. Performing multiple regression analysis is now relatively straightforward thanks to the wide availability of statistical software packages such as SPSS. However, the real skill of using a multiple regression is not about doing the calculations, but about the choice of independent variables, selecting the most appropriate version of the model, and interpreting the results.

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The general consensus about multiple regression models for forecasting tourism demand is that: •

they typically do better than other models (except autoregressive models such as ARMA or ARIMA) when forecasting over time horizons of two years or more;



when forecasting over shorter time horizons (e.g. one year of less) they are typically out-performed by simple extrapolative methods;



the main advantage of multiple regression models may not be in their forecasting accuracy but in their ability to help practitioners to understand the relationships between tourism demand and other socio-economic variables;



the choice of ‘best’ multiple regression model must be related to the specific forecasting scenario under investigation.

2.11.2 Step-by-step Guide As Frechtling (2001) very succinctly states – “multivariate regression forecasting modelling is a complex activity and difficult to do well”. The main difficulties lie, not so much in the mathematics which, though sophisticated, can be learnt relatively quickly, but in the skill required to identify the relevant explanatory variables and to evaluate the validity of the different potential models that are statistically significant.

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Stage 1: Put together time series data for independent variable – e.g. tourist arrivals. Stage 2: Identify likely causal factors – e.g. economic growth in main markets, exchange rate, climate, etc. It is worth noting that if a variable has not changed very much over the time series then it is unlikely to provide any explanatory power and should be removed from the analysis. Stage 3: Put together equivalent data for factors identified in stage 2. Stage 4: Use regression software to see whether some or all of independent variables help to explain the dependent variable. Stage 5: Decide on the ‘best’ explanatory equation. This is not just a matter of choosing the model that forecasts most accurately, but is also about evaluating the models validity in the light of your expectations about how the different variables should relate to each other. Stage 6: Use the explanatory equation with existing data and/or projections for independent variables to produce forecast for dependent variable.

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2.11.3 Technical Details The general form of the multivariate linear regression model is as follows:

Box 2.8 Equation for a multiple regression model Equation 2.6 Y = a + b1X1 + b2X2 + ... bnXn + e Where:

Y = the dependent (forecast) variable



a = the intercept constant





b = slope coefficient



X = independent variable



n = number of independent variables e = residual

Once again, the skill is mainly in determining the most appropriate variables to include within the model and the most appropriate form of multivariate regression model to use. A full discussion of multivariate regression models is beyond the scope of this handbook.

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2.11.4 Concise Example of a Multiple Regression Model For the sake of simplicity, multiple regression modelling can be split into three different stages: 1) identification of potential explanatory variables; 2)

model building and statistical analysis; and

3)

assessment of model validity and accuracy.

The first stage involves making decisions about the range and number of explanatory variables to enter into the model. It is, of course, tempting to put all of the available variables on into the model and to use whichever ones are significant. However, this would be unwise because, sooner of later, variables will come up that are correlated by chance rather than because they have a causal relationship with tourism demand. For this reason, potential explanatory variables that one might have a strong reason to suspect may play a major role in determining tourism demand should be chosen. Fortunately, these variables are well known so, for the novice forecaster, there are plenty of examples and alternatives to choose from. Variables that influence tourism demand are often split into three different categories: push factors, pull factors and resistance factors: •

Push factors are those characteristics that encourage people to leave home and go on holiday.



Pull factors are those that are thought to attract a tourist to a particular location.



Resistance factors are those could put a person off visiting a destination or country.

A list of commonly used push, pull and resistance factors is given in box 2.9

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Box 2.9 Commonly used push, pull and resistance factors 1.

Push factors – Population size – Income trends – GNI, GDP, etc. – Education distribution – Age distribution – Leisure time – Family structure – Weather (home)

2.

Pull factors – Friends/relatives – Climate (at the destination) – Commercial ties – Social/cultural ties

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– Marketing – Special events – Habit 3.

Resistance factors – Prices – products, necessities, exchange rates, etc. – Competitors actions – Supply capacities – Distance/travel time – Hidden taxes and terminal fees – Threats to personal safety

Example of multiple regression: Impact of climate on world tourism demand (from Lise and Tol, 20021) Lise and Tol attempted to quantify the impact of climatic variables such as temperature on tourism demand at a global, regional and national (Netherlands) scale using regression techniques. In this account of their study, focus will be on their attempts to model the impacts of climate on world tourism demand using a multivariate modelling technique. It should be noted that this is somewhat unusual since most regression models for tourist demand include economic variables. Rationale for model: Since tourism is volatile and highly situation specific it is likely to be responsive to climate change. Data: 17 years of data (1980 – 1996) for 210 countries. For the analysis all of the data was pooled together and treated as cross-section data, giving a total of 1,730 observations.

Basic Descriptions of Forecasting Methods

Estimated Model: LNARRIVALS = β0 + β1YEAR + β2AREA + β4POPDEN + β5COAST + β6GDPPC + β7TW + β8TW2 + β9PS + β10PS2 + error LNARRIVALS = Natural logarithm of tourist arrivals Variable definitions Variable

Definition

Area

Land surface area per country km2

Coast

Total length of coast in destination country (km)

GDPPC

Country per capita purchasing power (PPP) income (US$ day)

Arrivals

Arrivals per country

POPDEN

Population density (number per km2)

TW

Average day and night temperature of the warmest month (°C)

PS

Average cumulative precipitation in June, July, August (cm/summer)

Year

Year of observation

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Model choice: As the authors clearly point out, other socioeconomic explanatory variables could easily have been added such as crime rates or exchange rates but such data is hard to interpret. Furthermore, the aim of this study was to look at tourist sensitivity to climatic variables. Multiple Regression Model Results: As with many multiple regression models, relating to tourism demand the explanatory power4 is relatively low (R2 = 0.5), but the statistically significant estimates of the parameters are plausible and stable over the sample, suggesting that the findings are robust. Regression results Value

Significance

– 1.00

< 0.050

Year

0.40

< 0.001

Area

0.30

< 0.050

POPDEN

0.30

< 0.001

Coast

7.90

< 0.001

GDPPC

2.10

< 0.001

TW

0.44

< 0.001

TW2

– 1.04

< 0.001

PS

– 0.08

< 0.010

PS2

0.04

< 0.050

Observations

1,686

R2

0.50

Constant

1 Lise, W. and Tol, R. S. J. (2002), ‘Impact of Climate on Tourism Demand’, Climatic Change, 55, pp. 429–449 2 The explanatory power of a regression is measured by the R2 statistic which explains ‘goodness of fit’ of the regression line and describes the percentage of variation in the dependent variable that is explained by the independent variable(s). The R2 measure may vary from zero to one.

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2.12 Structural Econometric Methods 2.12.1 General Description Even multiple regression models may not be sophisticated enough to accurately quantify the relationships between tourism demand and the factors that may influence, or be influenced, by it. This latter point is important since the tourism demand variable itself (say visitor arrivals) may influence future tourism demand. In a sense, tourism demand may act as both a dependent (forecast) variable and an explanatory (independent) variable. A simple example of this is when high tourism demand ‘encourages’ hoteliers to raise room rates increasing their profits that, may in turn, fund increased development or marketing. The tourism boom may also encourage the local government to increase taxes on hoteliers. The whole system obviously cannot be thought of in terms of a single dependent variable that is influenced to different degrees by a series of independent but possibly interacting explanatory variables. Structural econometric models have been developed to try to capture some of this complexity.

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These models are called structural econometric models because they seek to quantify these complicated systems of feed-back loops and cross-dependencies that together determine current and future tourism demand. They try to capture, in mathematical terms, the important structures of economic systems and other elements of the real world and are usually based on linked systems of multiple regression models. These techniques require expert advice and the forecasting may have to be sub-contracted to a consultancy that specializes in this sort of sophisticated forecasting. An interesting, and potentially important, new method that is closely related to structural econometric models has recently been used to address tourism demand forecasting problems. Artificial Neural Networks (ANN’s) are computer programs that mimic the structure of the brain and are designed to solve problems such as tourism demand where there are multiple inputs and outputs that show complex quantitative relationships. Research in this area is still in its infancy and it remains to be seen whether ANN’s will be superior to structural econometric models in their ability to accurately predict the multiple and complex facets of tourism demand. In summary, causal models are important and widely used in tourism forecasting, and come in many different forms encompassing wide range of related methodologies. Unfortunately, apart from simple linear regression that has limited uses, they are nearly all complex and costly to create. These models may also require a large amount of econometric data that may not always be readily available. The exact amount of data required will be dependent upon on a number of different factors such as the complexity of the final model, the initial number of economic metrics incorporated into the model, and the spatio-temporal scale of the model.

2.12.2 Step-by-step Guide This is a more complex process than using a single econometric regression to forecast with, and is less easy to set out in a series of discrete steps. For each equation within the model the steps are effectively the same as for the regression equation, that is: Stage 1: Put together time series data for independent variable – e.g. tourist arrivals. Stage 2: Identify likely causal factors – e.g. economic growth in main markets, exchange rate, etc.. Stage 3: Put together equivalent data for factors identified in stage 2. Stage 4: Use regression software to see whether some or all of independent variables help to explain dependent variable. Stage 5: Decide on the ‘best’ explanatory equation.

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27

Stage 6: Use the explanatory equation with existing data and/or projections for independent variables to produce forecast for dependent variable. However, there are some important preliminary steps in building a structural model. In particular, it is important to consider which variables one wants to have equations for in the model. Clearly, this needs to include the tourism series that ultimately is to be forecast, but it is also likely to include a number of the drivers that appear as explanatory variables in other equations. On the other hand, there is no point for forecasting purposes in including a variable that might be an important driver, but which it is impossible to predict in its own right (e.g. terrorist attacks or natural disasters). One other consideration in the choice of variables might be whether the model is also wanted as a tool for ‘what-if’ simulations to analyze the potential impact of alternative assumptions about world developments, policy changes or any other factor. If so, it is important to think about what effects can sensibly be built into a structural model and what effects are simply too unpredictable or involve making the model much more complex and potentially less robust.

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Stage 6 is also slightly more complex than with a single equation since most structural models have a degree of simultaneity in them – i.e. variable A depends partly on variable B – but variable B also depends partly on variable A. For example, the number of visitor arrivals is affected by the exchange rate since this affects how expensive it is to visit a country compared with alternative destinations. But at the same time, the exchange rate may be affected by the number of visitor arrivals since this affects the demand for the currency. Model solution software is available to handle this simultaneity straightforwardly – e.g. by a process of iteration, but it means that spreadsheet packages, for example, are not as simple a way of producing a forecast with a structural model as with a single equation.

2.12.3 Technical Details Structural econometric models are made up of any number of simultaneous multiple regression equations. The mathematics for this are very complicated and we recommend to consult a specialist textbook, such as Song and Witt (2000). The same variable can appear in more than one of these simultaneous equations as either an independent or a dependent variable in a more accurate representation of how the real world tourism demand actually operates. This blurring of variable types necessitates a new type of terminology. •

Endogenous variables: all variables that appear within the structure of the econometric model. That is, all variables which appear on the left-hand side of one of the multiple regression equations.



Exogenous variables: all variables that are determined by relationships that are not captured within the structure of the model but rather by external forces. They never appear on the left-hand side of one of the multiple regression equations because they are not influenced by any other variable within the econometric model.

Structural econometric models are thus composed of systems of simultaneous equations that are needed for determining the value of at least one of the endogenous variables. This method suffers from similar problems to that encountered in the more straightforward linear regression method in as much as great technical and analytical skill is needed to successfully solve these systems of equations in a way that provides a realistic forecast.

2.13 Qualitative Forecasting Methods An alternative way of capturing the complexity of the real world in forecasting is to use a more powerful computer: the human brain. An experienced and educated member of the tourism industry or an expert on economics may be able to provide a forecast that captures many of the important features of econometric models and some of the complexity that no mathematical equation can contain.

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Indeed, an experienced individual or a panel of experts may be able to factor in imponderables such as terrorism and changes in public opinion that could never be adequately captured by any sort of computer program or statistical test. For this reason, and because it is often cheaper and easy to perform a qualitative forecast or a combination of simple extrapolative forecasting and qualitative judgment many tourism companies are moving towards the use of qualitative forecasts. The simplest kind of qualitative method for tourism forecasting is simply to ask an expert or a group of experts to give their judgment on what is likely to happen. Often, qualitative forecasting can be used to adjust very simple extrapolative methods and this can be as simple as a senior executive adjusting a nochange model up or down based on their understanding of wider events that could influence tourism demand. However, there are much more objective ways of producing qualitative forecasts. For a start, one person is often biased in his views or opinions and it stands to reason that a consensus of two or more experts is more likely to give a good forecast. But how many experts are needed and how can a consensus be reached?

2.14 Jury of Executive Opinion

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2.14.1 General Description The number of experts used for qualitative forecasting varies hugely, and is usually influenced by the size of the organization involved and their willingness to consult the wider forecasting community. Once the group of experts has been identified, there are two main ways to extract a consensus from them. The first, and the simplest, is known as a Jury of Executive Opinion (case study 4.16) and in its basic form uses a meeting, or a series of meetings, to achieve consensus on the forecast. The second method is known as the Delphi technique and is covered in more detail below. Juries of executive opinion are most useful in situations where there is not very much information on past trends, causal relationships have not been identified, or where a major event, such as a natural disaster, has occurred that makes all previous forecasts redundant. As mentioned above, the size of the jury is completely flexible and can often contain only two people.

2.14.2 Step-by-step Guide Stage 1a: Identify individuals within the organization, or in other organizations, with experience and a good understanding of the changing trends in tourism demand – this is your jury. Stage 1b: If appropriate, run a quantitative forecast that the jury can use as a basis for their discussions. Stage 2: Organize a series of meetings that last until the jury reaches a consensus on the forecast.

2.15 The Delphi Method 2.15.1 General Description Even though several experts might be involved in generating a forecast for a jury of executive opinion, there is still considerable opportunity for judgmental bias and the accuracy of the forecast ultimately

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29

depends upon the knowledge, experience and skill of the people who make up the jury. Furthermore, in small juries a single dominant individual might exert undue influence making the consensus less consensual than is ideal. One solution to this dilemma would be to try to reach consensus without meetings, or indeed, without the various experts ever meeting each other. This is exactly how our second qualitative technique, the Delphi method, works. Delphi studies can use a series of structured survey questionnaires to generate a forecast consensus among a group of experts and can be used for all types of forecasts. Delphi could be used for everything from predicting the probability of a terrorist attack to the likely value of next years’ national tourist revenue. Whatever the nature of the forecast, it is important to have a clear definition of the problem under investigation – e.g. the effect of international terrorism on tourism to Egypt over the next 5 years. Panellists can then be identified, contacted, and the initial survey constructed. Questionnaire design normally goes through at least two stages: •

An initial or draft survey is issued that allows the coordinator to identify keys issues and to develop the main questionnaire. This can then be distributed to the panellists and the responses collected and analyzed.



The results of this process are then fed back into reformulated questionnaires that are redistributed and then this process is repeated until a consensus is reached among the panellists (see figure 2.1).

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The number of times the process is repeated will depend upon the response rate and the speed with which consensus is reached. One of the important features of Delphi is that panellists are anonymous so that any answers given are, hopefully, largely free of bias.

2.15.2 Step-by-step Guide Figure 2.1 Step-by-step guide to the Delphi forecasting process Define problem

Identify panel

Develop initial survey Test/analyse Formulate questionnaire Test/analyse Reformulate questionnaire

No

Consensus reached?

Test/analyse

Yes

Forecast

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Handbook on Tourism Forecasting Methodologies

2.16 Scenario Planning 2.16.1 General Description Delphi and Juries of Executive Opinion are very flexible, and can be used in a wide range of situations, but are generally limited in scope and are similar to most quantitative methods in that they deal with only one possible future. Qualitative techniques can also be used to distinguish between multiple, plausible and uncertain futures. This technique is referred to as Scenario Planning (see case studies 4.18 to 4.21) and can be applied to virtually any area of tourism forecasting. Scenario Planning works by constructing alternative possible futures, or scenarios, for the tourism industry based upon information from a wide variety of sources, including experts’ opinion. Scenario Planning deals with one of the biggest problems of many quantitative methods: the reliance on a single forecasting point with a range of uncertainty (figure 2.2).

The central objective of Scenario Planning in tourism is therefore to create a realistic set of possible futures, ideally with estimated probabilities of occurrence, to help organizations respond appropriately if and when one of these scenarios occurs. The fact that many of the scenarios probably will not occur is in a sense unimportant since the very act of looking at different possible futures may make an organization more flexible and able to adapt in the face of change. Figure 2.2 The inability of traditional forecasting techniques to deal with the increasing range of uncertainty as we get further from the present

x y z

Present

Time

Range of uncertainty

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Scenario Planning is more than just another forecasting method – it is also a powerful agent for organisational change and is much more management focused than other forecasting methods. If fully implemented, Scenario Planning can change the nature of forecasting from one-off or periodic ‘events’ into a continuous process of learning, adapting and adjusting. According to some practitioners, Scenario Planning, unlike most other forecasting methodologies, is not even aimed at pinpointing future events but rather seeks to understand and predict the forces that push and pull the future in different directions. If these forces are visible and understandable, then organizations will be better able to respond to them if and when they occur.

Future

Figure 2.2 shows the inability of traditional forecasting techniques to deal with the increasing range of uncertainty as one gets further from the present. The forecasting point (denoted by x) represents one of many possible futures (e.g. forecasting points y and z) that might be more accurately represented through a forecasting technique such as Scenario Planning.

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31

In summary, Scenario Planning is rather different to forecasting in several fundamental ways. Most obviously, it is not trying to make accurate predictions of the future. It encourages flexibility and adaptive change in the face of the huge uncertainty that looms over all attempts to predict future tourism demand with a high degree of accuracy and precision.

2.16.2 Step-by-step Guide Stage 1: Set timeframe − e.g. 5 years, 10 years, etc.. Stage 2: Identify driving forces that may influence tourism demand. Stage 3: Construct alternative scenarios. Stage 4: Present scenarios to key experts within or outside the organization. Stage 5: Begin process of (continuously) reconstructing scenarios as future events unfold. Stage 6: Engage forecasters to quantify effects of each scenario – usually through structural econometric models.

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2.17 Mixtures of Methods Individuals and organizations doing practical tourism forecasting do not usually use a single method and there is no limit to the number of potential combinations of methods that can be used for forecasting. At one end of the spectrum, a simple no-change (quantitative) forecast can be adjusted up or down on the basis of the (qualitative) opinion of one or more experts. At the other extreme, custom-built causal models can be built to give a quantitative dimension to the (qualitative) scenarios identified in a Scenario Planning exercise. There is no one best combination but reading through the case studies on section 4 should give a reasonable feel for what is most commonly used.

2.18 Comparing the Performance of Different Methods Most small to medium sized organizations working in the tourism sector will not have the time or the resources to adopt multiple forecasting techniques. For this reason some simple guidelines have been included in section 3 of this handbook to help choose the most appropriate forecasting method for each particular situation. However, sometimes one may find oneself in the fortunate position of having several alternative techniques to choose from. In this case, how to decide which one is giving the best performance and is the most appropriate one to use? There are obviously many pragmatic considerations such as cost and management objectives to take into account (see section 3.), but there are also quantitative criteria that can be use in model evaluation: •

Robustness: How much is the method affected by outliers in the data. A robust model is one that does not give undue weight to unusual events such as a world cup year or a flower festival. Of course, these factors are often hard to factor into a model and this is why qualitative techniques are often used to adjust forecast values.



Simplicity: Although it is not always the case, the most appropriate model and the most robust model, is also the simplest one. One of the reasons for this is that with each additional factor there is a greater probability of introducing a new source of error.

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Accuracy: How close is the forecast to the actual figures for the element of tourism demand of interest? Frechtling (2001) identifies three different dimensions of accuracy: error magnitude accuracy, directional change accuracy and trend change accuracy are briefly discussed below.

2.18.1 Error Magnitude Accuracy Error magnitude accuracy is the most commonly used measure of the accuracy of a particular forecasting technique. This is defined mathematically as: et = At – Ft Where: t = some time period

e = forecast error



A = actual value of variable being forecast



F = forecast value For instance, the value of error magnitude occurrence (et) for the No Change model (see equation 2.1b on page 7) is:

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et = 93,392 (real value) – 96,101 (forecasted value) = 2,709 When the forecast is higher than the actual value than the error will be positive, if the forecast is an underestimate than the error will be negative. The above method is merely the simplest of several other ways to assess error magnitude accuracy (for a more complete discussion refer to Frechtling (2001). Many of these rely on the calculation of percentage error rather than absolute error. The most widely used of these is the mean absolute percentage error normally referred to by its acronym, MAPE: MAPE = 1/n × (et/At) × 100 Where: n = number of time periods

t = some time period



e = forecast error (see above error magnitude occurrence)



A = actual value of variable being forecast For instance, the value of MAPE for the No-change model (equation 2.1b on page 7) is: = 1/3 × (Error magnitude accuracy/real value) × 100 or = 1/3 × (2,709/93,392) × 100 = 0.97% NB: In the above equation, 1 is divided by 3 (1/3) as three time periods have been used for the forecast.

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MAPE gives a measure of the percentage error of a particular forecasting method/model over a specified period of time. Although it is difficult to ascribe significance to the values of MAPE, as a rule of thumb values of less than 10% can be considered as highly accurate forecasting while greater than 50% is probably unacceptably poor.

2.18.2 Directional Change Accuracy It is often the case that the most important information that we require about the future is not so much accurate estimates of numbers but simply whether the number of tourist arrivals will go up or down during a specified time period. It is therefore interesting to assess the ability of different models to accurately predict the direction of change in tourism demand. Directional change accuracy (DCA) is sometimes referred to as tracking error and can be simple calculated using the following equation: DCA = (ΣFDt/ΣADt) × 100

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Where: DCA = % directional change accuracy

FD = directional change accuracy forecast



AD = directional change actually occurring



t = some time period



2.18.3 Trend Change Accuracy Trend change accuracy is closely related to directional change accuracy but is concerned with the ability of a method/model to predict the time period when the forecast variable changes direction. For instance, when tourist arrivals begin to decline after a long period of growth. For this reason, trend change accuracy is often also referred to as turning point accuracy. Normally, the turning point (trend change) is defined as at least two consecutive time periods that show a different trend to the preceding two time periods. For this reason, at least four consecutive data points are required to detect a turning point – two showing an upward trend followed by two with a downward trend or vice-versa. Turning point accuracy can be easily quantified as the either the frequency of turning points (or nonturning points) that have been correctly predicted. In some ways turning point accuracy is the most important type of accuracy for a business because they directly impact on financial decisions.

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Chapter 3 Choosing a Forecasting Methodology

3.1 Getting Started The selection of appropriate methodologies for use in tourism forecasting is by no means easy – for a start there is a bewildering array of alternatives to choose from. Anyway, there is no one correct choice of forecasting model and even the experts can disagree. That being said, there are some general guidelines that can be followed and having a broad grasp of the advantages and disadvantages of the various methodologies will help negotiating the way through the minefield that is tourism demand forecasting. Choosing an appropriate methodology is not always straightforward. For instance, it may not always be as simple as choosing which method will give the best forecast but rather which is the best method given the time, money and/or expertise available.

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If an organization has never done any forecasting there are at least two stages that need to be considered to choose a methodology: Stage 1: Assess the resources available. Identify of the various economic, practical, and knowledge-based constraints and assess of how these might limit the choice of forecasting method. Stage 2: Choose a suitable methodology given the constraints identified previously (stage 1). Identify the methodology based on the strengths, weaknesses and characteristics of the different types of tourism demand forecasting models.

3.2 Resource Constraints Given a surplus of time and money, any organization could produce state-of-the-art quantitative forecasts for every possible future scenario in their comprehensive Scenario Planning exercise. In an ideal world, one could hire a team of highly competent, technically gifted researchers who devote all of their time to producing accurate forecasts using the most up-to-date techniques, specially developed software, and the latest computing hardware. Unfortunately, for most small or medium sized organizations working in the tourism industry this is a mildly diverting fantasy. The typical situation is that in many tourism organizations forecasting may not be a priority, it may be poorly funded, it may be left to staff that have many other responsibilities or it may not be done at all. Furthermore, the software and computing resources may be limited to commercially available spreadsheets and computers with limited processing power. If this is the case, still a variety of useful and straightforward forecasting methods that will generate important information can be applied. Different forecasting methods require different levels of resources (see box 3.1 for a simple description of the relative costs of the different methodologies described in section 2).

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Box 3.1 Relative resource requirements of different forecasting methodologies Resource requirements Staff

Data/ information

Time

Computer software

Simple extrapolative

low

low

low

low

Advanced extrapolative

medium

low

medium

medium

Causal – regression

medium

medium

medium

medium/high

Causal – econometric

high

high

high

high

Qualitative – jury of opinion

low

medium

low

low

Qualitative – Delphi

medium

medium

high

medium

Qualitative – scenario planning

high

high

high

low

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In most small to medium sized organizations, the main constraints on forecasting will be: •

Expertise and knowledge



Financial



Time and personnel

Of these, financial is probably the biggest constraint, and one that directly effects other constraints. With enough money an organization can buy in expertise from the many economic international and national private consultancies that specialize in forecasting. By doing this, it will be getting a premier service that is neatly tailored to the organizations’ needs. However, these services do not come cheap and it may be that the organization does not require either the level of sophistication or detail that they will provide. Also, wanting to do repeat forecasts using a consultancy may not give the flexibility and speed of response desired. In summary, choice of forecasting methodology is inevitably a compromise between time, available budget, and the amount of expertise available. This can be represented by a simple decision-tree (figure 3.1). Since most forecasting requires data, or at least information, the first decision to make is whether all the needed information is available. If not, it may be needed to get it from other sources or to start collecting it – an expensive and time-consuming pursuit. If one is confident to have enough resources, it is still necessary to calculate whether the organization or its staff can devote the required time to the forecasting project. If the answer is no, maybe it might be better to hire one of the many consultancies that work in this area. If the organization does have the time and the commitment, the final decision rests on budget and on the level of expertise available in the organization. It goes without saying that a low budget and no expertise leave few choices but to implement a simple (although potential effective) quantitative or qualitative method. If the organization has a generous budget, but little expertise, a consultant may well be the best short-term solution with staff training and recruitment a better long-term solution if forecasting and Scenario Planning are likely to play an important role in the future of the organization. If expertise is already available, then the choices will be once again dictated by available budget. With a low budget the choice may be restricted to non-specialist software (spreadsheets etc.) and to less intensive qualitative methods that rely on the collation of opinions of those who are inside the organization. With a larger budget there is no methodology that is beyond grasp.

Social Networking and User-generated Content

37

Figure 3.1 A simple decision tree diagram for deciding upon the most appropriate forecasting strategy to adopt

Data available

No

Survey/ review

Yes

Time available

Options 3

No Yes

Consultant

Low budget + Low expertise Budget + Low expertise Low budget + Expertise Budget + Expertise

Consultant

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Options 2 Options 1 Options 1: Most appropriate technique to gain best forecast Options 2: Most appropriate technique using commercially available software Options 3: Simple technique using spreadsheets or qualitative judgement Consultant: Seek technical assistance from forecasting consultancy

3.3 Choosing an Appropriate General Methodology The decision that has to be made is, whether there is sufficient data for the type of forecast chosen. If this data is not available, then it is necessary to collect it, buy it, contract a consultant to collect it, review publicly available material or, more commonly, some combination of these methods. Data is obviously not such a problem for extrapolative methods, but can be especially problematic for causal methods such as structural econometric models. Assuming that the organization has both time and data available, the type of forecasting to be chosen will be primarily influenced by available budget and expertise. If there is a reasonable budget, but no real expertise, there are many consultancies that specialize in forecasting that can be contracted to create forecasts using a range of the most appropriate techniques. A more typical situation is that an organization will have some budget and relatively low specific expertise in tourism forecasting. Under this scenario forecasting should be possible but might be restricted to simple extrapolative or causal methods and commercially available software (option 3 in figure 3.1).

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Handbook on Tourism Forecasting Methodologies

Options obviously increase if the organization has some experience in tourism forecasting. Under this circumstance (option 2 in figure 3.1) it may still be restricted to standard software, but may be able to implement more advanced extrapolative methods or causal methods. The organization may have both abundant expertise and a generous budget for forecasting. If this is the case, then it already has a good basic grasp of what is available. In this circumstance, the organization can develop its own models and/or software and implement the most appropriate methodology for the time-frame and variables that it wishes to forecast.

3.4 Choosing a Specific Forecasting Method After using the decision tree (figure 3.1) one should know by now generally what sort of techniques are available, but still needs to make the crucial decision about what specific methodologies to adopt.

3.4.1 Categories of Methodologies In this handbook, forecasting techniques have been divided into extrapolative methods (basic and advanced), causal models, and qualitative methods (table 3.1). Combinations of these forecast methods, especially qualitative with basic forms of extrapolation or causal methods are also common.

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Table 3.1 Characteristics of the major categories of forecasting methods Extrapolation

Causal

Qualitative

Expertise needed

low/medium

medium/high

low/medium

Type of data

time series

varied

expert experiential knowledge

Time series data dependent

high/medium

high

low

Internal expertise needed

medium

high

low

Appropriate forecast horizon

short

short to Long

long

Ease of implementation

easy

medium to difficult

easy

Development cost

low

high

low except for price of experts

Accuracy

high for limited use

medium

medium

Robustness

high: forecasts affected by extreme values

medium: highly dependent on data and model parameters

low: highly dependent on knowledge of experts

Ease of interpretation

low explanatory ability.

low for econometric and medium for regression

high

Maintenance cost

low

high

low

Flexibility

limited

dependent on model.

high

Cost savings from results

low

effective planning tool if well constructed

medium/high

Best suited for?

simple situations and short-term forecasts

understanding the various quantitative relationships between tourism demand and one or more variables

complex situations with a wide variety of predictive scenarios

Social Networking and User-generated Content

39

3.4.2 Using the Decision Matrix One way to help on the way through the labyrinth of potential forecasting methods at disposal is by constructing a decision matrix. A decision matrix is simply a table that can be used to choose the most appropriate forecasting methodology based on individual requirements. Looking up the requirements that most closely match ones’ own in the left-hand column of the table labelled ‘User Requirements’ and moving across the row until the method that gives the best result for one’s requirement(s). The number of symbols (*) in the box indicates how the method performs against your requirements: •

one symbol: weak or low;



two symbols: moderate or medium and



three symbols: strong or high.

The selected methodologies are indicative of popular methodologies used by tourism forecasters around the world. There is a description below each methodology, which briefly identifies its main features, what it is best suited for and its data requirements.

3.4.3 Key to User Requirements in the Decision Matrix The decision matrix contains information on the following characteristics of each method:

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User’s requirements: •

Versatility/flexibility: how readily can this methodology be adapted to different forecasting requirements?



Accuracy: how correct has this method proven to be in the past?



Affordability: what are the relative costs of adopting this method?



Upgradeability: can the effectiveness of this methodology be improved, for example with more data, investment, or more advanced computer software?



Ease of use: is this methodology quickly and/or easily implemented without considerable investment in training, resources or technology?



Ease of interpretation: can results from this methodology be easily understood?



Credibility: do researchers and users feel the results from this methodology are trustworthy?



Speed: how fast can this methodology produce results?



Incorporating expert opinion: how much judgmental expertise is required to produce forecasting outputs from this methodology? For example, are there any additional factors that are not necessarily reflected in data?



Maintenance: the extent/cost of input of resources required to enable this methodology to perform over time.



Staffing demand: what are the staffing resource demands of the project?



Popularity of use: how frequently is this methodology used?



User satisfaction: this is based on stakeholder responses and indicates how satisfied the users are with forecasting outputs from this methodology.

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Handbook on Tourism Forecasting Methodologies

Table 3.2 Forecast method decision matrix Methodology

Simple Advanced Causal extrapolative extrapolative regression

Causal structural

Qualitative jury/Delphi

Qualitative scenario planning

Features

simplicity of use

accuracy – simulating historical data to future

understanding impacts of variables

policy development based on economic impacts

flexibility

flexibility

Best suited for

time series predictions

strategy development based on forecasts

interdepenassessing interrelation- dencies of ships between variables variables

simple tourism demand

complex and uncertain futures; future proofing

Data requirements

strong time series data

strong time series data

push, pull and resistance factors

expertise/ experience dependent

expertise and information to develop scenarios

push, pull and resistance factors

Users’ requirements Versatility/ flexibility

*

*

**

**

***

***

***

***

**

**

**

N/A

Affordability

*

**

**

***

*

***

Upgradeability

*

*

**

**

**

***

Ease of use

***

**

*

*

***

**

Ease of interpretation

***

**

**

*

***

***

**

**

**

***

**

***

***

**

**

*

***

*

Incorporating expert opinion

*

*

**

**

***

***

Maintenance

*

**

**

***

*

***

Staffing demand

*

**

**

***

**

***

Popularity of use

***

**

*

**

***

*

User satisfaction

**

**

**

**

**

***

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Accuracy

Credibility Speed

Key: high * * *

medium **



low *

Chapter 4 Case Studies

The following section gives an outline of case studies from around the world. These examples were chosen to be illustrative of the commonly used methods and should give a clear indication of how organizations deal with the complexities of tourism demand forecasting. It should be noted, however, that this is not an exhaustive list and there are as many different strategies to deal with tourism forecasting as there are organizations.

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All of the case studies follow a similar format and include the following categories: •

Organization: the name of the company or organization doing the forecasting.



Type of organization: National Tourism Organization (NTO), private company, etc.



Purpose of the forecast: what were the aims and objectives of the forecast? Why was the forecast needed?



Methodology: description of the methods used grouped into the categories as described in section 2.



Overview of methodology: a concise description of the data collection, analysis and forecasting strategies employed by the organization.



Summary of results: description of the main findings of the forecasting project.

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Handbook on Tourism Forecasting Methodologies

4.1 German National Tourist Board’s World Cup Forecast Organization: German National Tourist Board (GNTB). Type of organization: National Tourism Organization (NTO). Purpose of forecast: To assess the benefits of the 2006 FIFA World Cup Germany for the German travel industry. Methodology: Extrapolative Methods (Simple). Overview of methodology: This forecast is based on the experience of the 2000 European Football Championship in Belgium and the Netherlands, when accommodation overnight stays reached for an average of 1.7 overnight stays per ticket sold. Summary of results: The GNTB predicted that the FIFA World Cup will increase the number of overnight stays by around 1.7% for 2006 as a whole, according to a projection on the figures from a recent comparable event made by the Netherlands Tourism Board (NTB) on the European Football Championship 2000. Netherlands

Germany

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4,844,800 overnight stays

Assumption: 64 matches with an average 44,000 spectators/tickets

5,504,000 overnight stays

Overnight stays per ticket: 1.72 Source: NTB European Football Championship 2000

Assumption: all matches are sold out

2,816,000 tickets

Source: Own calculation, GNTB Market Research according to NTB/MeerWarde 2000 FIFA figures.

3,200,000 tickets

Case Studies

43

4.2 VisitBritain’s International Passenger Forecast, 2006 Organization: VisitBritain. Type of organization: National Tourism Organization (NTO). Purpose of forecast: To provide a context for Business Planning within the organization, and to provide a benchmark for the wider tourism industry. The forecast is a key input to media releases and provides 2006 inbound volume and value forecasts, and revised 2005 estimates. Methodology: Extrapolative methods + Qualitative methods.

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Overview of methodology: •

Based on the International Passenger Survey (IPS) data, the average annual growth rate for the period 1994 – 2004 specific to visits and spend for each country (or group of countries) was used to produce forecasts for 2006.



Monthly IPS data from 1978 to 2004 was analyzed to calculate the percentage of full-year visits and spend that has accrued by each month of the year. The ‘minimum to maximum’ amount of visits and spend that had accrued by August showed ranges of 66 – 71% and 65 – 70%, respectively. These figures were then used as the basis for estimating full year figures for 2005.



A ‘forecast envelope’ (likely range of forecasts and context) was produced around the central forecasts.



The methodology included consultation with overseas managers and British Airways economists to check that forecasts are broadly in line with their own expectations for 2006.

Summary of results: •

The volume of inbound tourism to reach 29.3 million visits in 2005, an increase of 5.5% on 2004. A further increase of 4.4% is forecast for 2006, taking the total number of visits to 30.6 million.



The value of inbound tourism receipts is forecast to grow by 6.2% in 2005 to £ 13.9 billion and by a further 4.3% in 2006 to £ 14.5 billion.



The strongest growth in 2006 is expected to come from Asia, Central Europe and emerging markets.



Inbound visits for business and to visit friends and relatives (VFR) are forecast to grow more strongly than holiday visits in 2006.

These forecasts are dependent on normal circumstances prevailing and do not factor in unexpected shocks caused by terrorism, health scares and other crises. During 2005 London has been affected by international terrorism, and a slowdown in the rate of growth for inbound visits from a small number of markets as a consequence was expected.

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4.3 Kwa-Zulu-Natal 5-year Demand Forecast Organization: Kwa-Zulu-Natal (KZN) Tourism Authority. Type of organization: Regional Tourism Organization. Purpose of forecast: Assess the increased volume, spend and direct employment attributable to KZN visitors over next 5 years. Methodology: Extrapolative methods + Qualitative methods.

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Overview of methodology: A series of key assumptions were made and influence factors allowed for: •

South African Tourism (SAT), World Tourism Organization (UNWTO) and World Travel and Tourism Council (WTTC) projections are reasonable.



Dube Trade Port and King Shaka international airport to be established in 2009.



Broad assumptions from previous strategy were accepted.



2 million additional domestic trips over 10 years in the past.



Impact of increased visitation due to Football World Cup in South Africa in 2010.



Spend: inflation base of 5% + 1.



Spread: growth strategy.



Seasonality: SAT index trend past 4 years 1.1 + 0.1.



2004 direct employment base 20% of SAT’s estimate of 539,000.



2004 total employment base 20% of WTTC’s estimate of 1.1 million.



WTTC’s projection of 39% increase in South Africa’s direct tourism employment by 2010.



WTTC’s projection of 37% increase in South Africa’s total tourism employment by 2010.



WTTC projection that ZAR 66,000 of total GDP will be required to generate 1 direct or indirect job in RSA in 2010.

Summary of results: Macro targets

2004

2005

2006

2007

2008

2009

2010

Domestic arrivals (thousand)

11,400

11,600

11,800

11,950

11,100

12,300

12,500

1,300

1,360

1,400

1,460

1,500

1,600

1,960

524

583

614

646

680

715

753

5,038

5,289

5,554

5,832

6,123

6,429

6,751

Direct contribution to KZN GGP (ZAR, billion)

13

14

15

16

18

19

20

Total contribution to KZN GGP (ZAR, billion)

118

20

21

23

25

27

32

Total employment (thousand)

140

148

154

159

165

173

194

Foreign arrivals (thousand) Domestic per trip per person (ZAR) Foreign spend per trip per person (ZAR)

Case Studies

45

4.4 Romanian Domestic Tourism Forecast Organization: National Institute of Research Development in Tourism, Romania. Type of organization: Research Institute. Purpose of forecast: To forecast the level of domestic tourism in Romania in 2005 – arrivals of Romanian tourists in accommodation establishments. Methodology: Extrapolative methods + Qualitative methods.

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Overview of methodology: •

The forecast for the entire year (2005) was based on existing data for the first half of 2005 to which was added the estimation for the second semester of 2005.



The estimation for the second semester was based on the opinion of representatives of travel agencies who informed the press media in Romania that the number of tourists is expected to decrease by 10% compared with the similar period last year. (This expected decrease is mainly due to bad weather in the summer season and in particular floods that occurred in Romania in 2005).



The number of tourists in Romania in the second half of 2004 was 2,520,428.



The expected decrease of 10% was applied to the 2004 figure giving an estimated total of 2,268,385 tourists for the second half of 2005.



This estimation of 2,268,385 was then added to the actual number of tourists for the first half of 2005 (1,836,206) giving a total of 4,104,591.

Summary of results: •

The forecast total of domestic tourists for Romania for the year 2005 was 4,104,591.



The above 2005 forecast figure compared to the number of domestic tourists in 2004 (4,279,023) shows a drop of almost 4.1% in Romanian domestic tourism.

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4.5 TRC New Zealand’s 6-year Tourism Activity Forecast Organization: Tourism Research Council New Zealand (TRCNZ). Type of organization: Research Council for the Ministry of Tourism. Purpose of forecast: To produce accurate and reliable forecasts of tourism activity in New Zealand that can be used with confidence by tourism stakeholders for 2005 to 2011. Methodology: Extrapolative Methods + Qualitative Methods. Overview of methodology: •

The visitor arrival forecasts are produced using three stages: Stage 1: Development of statistical models using historical data to forecast visitor arrivals from New Zealand’s 23 largest inbound markets, segmented by origin and purpose. Stage 2: Panel of experts reviews the preliminary forecasts and makes consensus adjustments where necessary.

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Stage 3: Forecasts presented to the TRCNZ for final acceptance.



The short-term forecasts where generated using historical monthly arrivals patterns and the annual arrivals forecasts.



Assumptions: –– Historical relationships can be strong but there is no guarantee they will continue into the future. –– Unanticipated events are capable of creating short-term deviations from long term-trends that are difficult to model or predict. –– Models used are demand models. It is assumed that the key supply variables such as air travel, accommodation, and transport capacity, and attractions/facilities can and will adjust to changes in demand.



The following factors may affect the accuracy of the forecasts: –– Data limitations. –– Past relationships between variables may not hold in the future. –– Political and/or economic factors may affect international travel.

Summary of results: •

International visitors reached an all time high or 2.3 million in 2004. Total arrivals are expected to reach 2.5 million in 2006 increasing further to 3.2 million by 2011. This represents a total increase over the forecast period of 37.5% and an average increase of 4.7% per annum.



International visitors spent a total of 44.6 million nights in New Zealand in 2004. Total nights are expected to reach 47.6 million in 2006 increasing to 59.5 million by 2011.Total increase of 33.3%, average of 4.2% per annum.



The average length of stay in 2004 was 19.1 nights. This is expected to fall slightly to 18.6 nights in 2006 and further to 18.5 nights by 2011.



International visitors spent a total of NZ$ 6.3 billion in New Zealand in 2004. International tourism expenditure is expected to reach NZ$ 6.9 billion in 2006, increasing further to NZ$ 9.6 billion by 2011.

Case Studies

47

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These forecasts, along with the wider information base, contribute to quality policy, business, planning, and investment decision-making.

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4.6 Namibia TB’s 15-year Tourism Growth Forecast Organization: Namibia Tourism Board (NTB). Type of organization: National Tourism Organization (NTO). Purpose of forecast: To provide a forecast on tourism growth for 2004 – 2020. Methodology: Extrapolative methods + Qualitative methods. Overview of methodology: •

The World Tourism Organization (UNWTO) forecast an average annual growth rate (AAG) of 7.3% for international arrivals to Southern Africa to the year 2020.



For domestic arrivals, for which no forecasts exist, it was estimated that 10% of the Population (2001 Census) use commercial accommodation establishments. Arrivals for this market were estimated to grow in line with the average annual demographic growth rate (2.6%), plus a modest increase of 0.5% for each 5-year period (i.e. 10.5% in 2010; 11% in 2015 and 11.5% in 2020) in consideration of improving economic conditions for the population in general over the planned period (UNWTO, Tourism 2020 Vision).



The following parameters were retained from the 2002 NTB Visitor Exit Survey: purpose of visit (e.g. holiday), utilization of commercial accommodation, and length of stay (nights).

Summary of results:

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Projected International Tourist Arrivals, 2004 – 2020* Year

Holiday

Business

VFR

Total

2003

452,245

206,115

96,640

755,000

2004

485,259

221,161

103,695

810,115

2005

520,683

237,306

111,264

869,253

2006

558,693

254,630

119,387

932,709

2007

599,477

273,217

128,102

1,000,797

2008

643,239

293,162

137,453

1,073,855

2009

690,195

314,563

147,488

1,152,246

2010

740,580

337,526

158,254

1,236,360

2015

1,053,345

480,072

225,089

1,758,505

2020

1,498,197

682,818

320,149

2,501,164

* For clarity, only forecasts for 2005-2010 have been shown in their entirety.



The average length of stay for domestic arrivals has been estimated at 10 days.



The number of persons-per-hotel-room, has been estimated at 1.7 for international tourists and VFR and slightly lower (1.5) for business travellers generally using a larger share of double rooms for single occupancy. For domestic arrivals, it has been estimated initially at 2.1 and reduced to 1.9 from 2010 onwards.



Occupancy rates for accommodation establishments, unusually low at 50.5% for the base-year 2003, have been progressively increased by 1% per year to 52% in 2005; 57% in 2010; 62% in 2015 and by 0.5% per year thereafter, reaching 65% in 2020.

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4.7 Namibia’s 15-year Employment Growth Forecast Organization: Namibia Tourism Board (NTB). Type of organization: National Tourism Organization (NTO). Purpose of forecast: To provide a forecast on employment growth in the tourism and accommodation sector for 2004 – 2020. Methodology: Extrapolative methods + Qualitative methods.

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Overview of methodology: •

Use of tourism forecasted growth (see case study 4.6).



Labour market demand depends mainly on two variables: the creation of new jobs due to the expansion of the sector and the replacement of staff due to attrition. The new jobs created by expansion of the hotel sector are closely related to the number of hotel rooms planned and this is itself dependent on the projection of visitor arrivals, their utilization of commercial accommodation, length of stay, bed occupancy, and hotel occupancy rates in general.



The Employee/Room ratio, a common metric to assess staffing levels in the hotel industry, depends on the age of the hotel, its layout and the level of technology used. Based on the results of the Manpower Survey carried out for a large cross-section of the industry, it was calculated at 0.925 per room for the sub-sector as a whole, taking into account the preponderance of small and low class hotels (80% of enterprises) and the fact that security personnel is sub-contracted. This ratio was reduced by 0.05 per year, starting in 2005, to account better management and increased staff productivity, but this will depend also on policy decisions and their implementation concerning education and training for the Hotel, Catering and Tourism (HCT) sector. The ratios used are therefore 0.9 for 2010, 0.875 in 2015 and 0.85 in 2020.

Summary of results: •

Direct employment creation in other tourist related enterprises (restaurants, travel agencies and tour guiding services), which represent together about 27.5% of the labour market, was assumed to increase in direct proportion to the number of total overnight stays and hotel workers. The combined effect of the model assumptions kept the estimates of employment creation on the conservative side, a precautionary measure taken to offset planning for over-capacity in the training system. This also in consideration of the unemployment rate (30%) reported by the 2001 Population and Housing Census.



On these assumptions, overall employment growth in the hotel, catering and tourism (HCT) sector for the period to 2005 – 2020 is estimated to grow from 24,150 persons in 2003 to 51,000 in 2020, with an average annual increase of 4.5%.



Estimated net employment growth will be 6,450 new jobs created between 2006 and 2010, 7,400 jobs created between 2011 and 2015 and 11,000 new jobs created between 2016 and 2020.

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4.8 UNWTO/Fundación Premio Arce’s International Tourist Arrivals Forecast for 2006 Organization: Fundación Premio Arce/Universidad Politécnica Madrid and World Tourism Organization (UNWTO). Type of organization: Research institution and intergovernmental organization. Purpose of forecast: To assess world tourism prospects at different time scales for 2004, 2005 and 2006. Methodology: Extrapolative method (Advanced).

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Overview of methodology: •

A monthly time series of international tourist arrivals worldwide from 1989 until 2005 was used in this study. The number of data was 204.



Initially, data from 1989 until 2002 was used to identify an advanced extrapolative stochastic model. First, a decomposition method was applied to the original series, and then the residual series was modelled (ARFIMA).



The criteria to choose this modelling technique was: 1) short- middle-term forecast were the objectives of this modelling; 2) causal models should not be used due to the complex structure of tourism market at world level; 3) the short number of data available constrains the use of other techniques.



The model enabled forecasts to be estimated for the next 12 months from the last data point used in the modelling. For example, forecast of 2004 was based on a time series from 1989 until 2003, forecast of 2005 was based on a time series from 1989 until 2004 (table 4.1).



The model was used to forecast in one-year time too. For example, forecast of 2004 was based on a yearly time series from 1989 until 2002, forecast of 2005 was based on a time series from 1989 until 2003 (table 4.2).



All the forecast were done on monthly basis and monthly, quarterly and annual forecast errors were calculated (table 4.1 and 4.2).



To visual compare the real data with the model estimation both time series were plotted for the forecast in the next 12 months (figure 4.1) and forecast in one year (figure 4.2).



The same model with the feedback data of 2004 and 2005 was applied to forecast 2006 (figure 4.3) on a monthly basis (table 4.3).



S.A.S. software was used in modelling and forecast this time series.

Summary of results: •

Forecasting the next 12 months to time series end (table 4.1) shows excellent results based on the low error values. Except April and May of 2004, the rest of the months have errors less than 5%. For quarterly and annually forecast values the errors are in a range of 0.3 and 2.5% in absolute terms.



The forecast results showed in table 4.1 are plotted to visually confirm these results.



Forecast in one year to time series end (table 4.2) shows much less accuracy. In an extrapolative stochastic model is normal to have more errors when more time pass from the end of the data time series.



However, two situations can be differentiated in each year. Based on the historical data until 2002, the model could reflect quite well the annual forecast of 2004, pointed up the fact that the error

Case Studies

51

was only 2.5%. Going to the quarterly forecast, the forecast error estimated in the last quarter suggests that some factor that was not reflected in 2002 is changing market trend. Based in the historical data until 2003, the model could not reflect the world tourism scenario in any scale time studied. •

Errors higher than a certain value can be used to identify when a change appears in the market. This is quite evident when the forecast in one year and the data time series are plotted together (figure 4.2).



Forecast of 2006 is represented graphically in figure 4.3 and numerically in table 4.3. A calculation of coefficient of variation shows that, with an 85% confidence interval, an increment of 4.6% is expected referred to the 2005 number of international tourist arrivals worldwide.

Forecast for 2004 and 2005 based on a time series until 2003 and 2004, respectively Year

Delivered by http://www.e-unwto.org Georgios Drakopoulos (307-99-294) Tuesday, March 15, 2011 5:40:51 AM

2004

2005

Month

Data

Forecast

Forecast (%) Monthly

Quarterly

Annually

– 2.5

– 1.6

January

48,995

48,364

– 1.3

February

49,205

48,183

– 2.1

March

53,743

54,585

1.6

April

59,352

54,881

– 7.5

May

63,506

59,867

– 5.7

June

68,401

66,882

– 2.2

July

86,870

84,998

– 2.2

August

86,938

88,650

2.0

September

68,721

68,885

0.2

October

63,263

62,203

– 1.7

November

50,631

50,487

– 0.3

December

55,360

54,615

– 1.3

January

51,774

52,490

1.4

February

50,215

52,300

4.2

March

61,548

60,155

– 2.3

April

59,633

56,689

– 4.9

May

67,475

64,236

– 4.8

June

72,053

70,612

– 2.0

July

92,139

89,044

– 3.4

August

91,003

91,053

0.1

September

73,143

71,971

– 1.6

October

65,974

66,311

0.5

November

53,000

54,267

2.4

December

58,315

58,696

0.7

– 1.7

– 0.8

– 0.7

– 2.4

0.3

– 1.1

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Handbook on Tourism Forecasting Methodologies

The forecast for 2004 is based in a data series with 2003 included and the forecast for 2005 is based in a data series with 2004 included DATA FRCST

100,000

Tourists (thousands)

90,000

Feedback model estimation with 2004 data

80,000

Months with a monthly error higher than 5%.

70,000

60,000

50,000

40,000 01 02 03 04 05 06 07 08 09 10 11 12 01 02 03 04 05 06 07 08 09 10 11 12

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2004-2005 Note: In these cases the estimation is calculated for the next 12 months at the end of the series.

Forecast for 2004 and 2005 based on a time series until 2002 and 2003, respectively Year

2004

Month

Data

Forecast in 1 year

Forecast (%) Monthly

January

48,995

48,876

– 4.3

February

49,205

47,401

– 3.7

March

53,743

57,288

6.6

April

59,352

57,556

– 3.0

May

63,506

63,352

– 0.2

June

68,401

68,429

0.0

July

86,870

85,655

– 1.4

August

86,938

86,646

– 0.3

September

68,721

66,946

– 2.6

October

63,263

58,327

– 7.8

November

50,631

46,386

– 8.4

December

55,360

50,368

– 9.0

Quarterly –1.0

– 0.5

– 6.7

Annually – 2.6

Case Studies

Year

Month

2005

Data

Forecast in 1 year

53

Forecast (%) Monthly

Quarterly

Annually

– 10.6

– 10.4

January

51,774

47,848

– 7.6

February

50,215

47,016

– 6.4

March

61,548

52,548

– 14.6

April

59,633

52,195

– 12.5

May

67,475

56,770

– 15.9

June

72,053

64,161

– 11.0

July

92,139

82,189

– 10.8

August

91,003

86,135

– 5.3

September

73,143

65,985

– 9.8

October

65,974

59,431

– 9.9

November

53,000

47,788

– 9.8

December

58,315

51,741

– 11.3

– 10.4

-10.2

DATA FRCST in 1 year

100,000

90,000 Tourists (thousands)

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The forecast for 2004 is based in a data series with 2002 included and the forecast for 2005 is based in a data series with 2003 included

Feedback model estimation with 2003 data

80,000

This point defines the moment when the system is influenced by some factor not contemplated before.

70,000

60,000

50,000

40,000 01 02 03 04 05 06 07 08 09 10 11 12 01 02 03 04 05 06 07 08 09 10 11 12

2004-2005 Note: In these cases, the estimation is calculated for the next 24 months at the end of the series and the last 12 forecasted values are plotted.

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Handbook on Tourism Forecasting Methodologies

The forecast for 2006 is based in a data series with 2005 included. In these cases, the estimation is calculated for the next 12 months at the end of the series DATA FRCST

100,000

Tourists (thousands)

90,000

80,000

70,000

60,000

50,000

40,000 01 02 03 04 05 06 07 08 09 10 11 12 01 02 03 04 05 06 07 08 09 10 11 12

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2004-2006 Note: The model used is an advanced extrapolative stochastic model.

Forecast for 2006 based on a time series from 1989 until 2005 2006

Forecast international tourist arrivals, world (thousand)

January

54,926

February

54,284

March

62,816

April

63,937

May

70,290

June

75,007

July

94,445

August

93,790

September

75,787

October

69,297

November

56,458

December

61,541

Case Studies

55

4.9 VisitScotland’s International Tourism Forecast Organization: VisitScotland. Type of organization: Regional Tourism Organization. Purpose of forecast: To assess Scotland’s economic prospects for international tourism up to 2008. Methodology: Causal methods.

Delivered by http://www.e-unwto.org Georgios Drakopoulos (307-99-294) Tuesday, March 15, 2011 5:40:51 AM

Overview of methodology: •

A Structural Time Series equation (model) was created for tourism demand in Scotland.



The model took account of the effects on tourism demand of the key economic drivers of demand, seasonal changes in demand and intervention variables for one-off events such as major unanticipated changes in exchange rates, political changes or sporting events.



The model enabled forecasts to be estimated for the main international markets for Scotland.



Both arrivals and expenditure were considered as each has somewhat different impacts on and implications for the destination.



The forecasts were based on long periods of quarterly data and could be provided for up to 30 years ahead, although obviously confidence limited the longer term forecasts whereas shorter term forecasts tended to give more confidence as to their reliability.



Software was used which enabled easy selection of markets and time periods as well as for changes in the economic drivers of tourism demand.



Where data was unavailable or unreliable, forecasts were adjusted using secondary sources, historic data and information drawn from other organizations such as VisitBritain, Australian Forecasting Council and organizations in Sweden, Spain, Japan and Russia.

Summary of results: •

Using the model, and taking into account the range of variable factors above, the value of international tourism in Scotland was expected to grow from £ 0.97 billion in 2005 to £ 1.04 billion in 2008.



The US market into Scotland was expected to be £ 300 million in 2005 and rise to £ 367 million in 2008 accounting for exchange rate forecasts, strong GDP growth and more direct flights.



The Australian market was expected to grow from £ 54 million to £ 71 million driven by a strong housing market, equity release and a stable economy.



The German market into Scotland was expected to grow in 2005 though slow economic growth may reduce spending through to 2008.



Spain and Sweden represented an increased opportunity due to flights and growing economies.

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4.10 Tourism Research Australia’s 10-year Tourism Growth Forecast Organization: Tourism Research Australia (TRA). Type of organization: National Tourism Organization (NTO). Purpose of forecast: To provide a forecast on tourism growth to 2014. Methodology: Causal methods.

Delivered by http://www.e-unwto.org Georgios Drakopoulos (307-99-294) Tuesday, March 15, 2011 5:40:51 AM

Overview of methodology: •

Forecasts for inbound, outbound, domestic travel, and direct export earnings were produced using an iterative process.



The first iteration involves the TRA Forecasting Unit estimating activity and expenditure using econometric models. These models provide forecasts based on price, income and seasonality, as well as significant events in major markets.



The second iteration involves a subcommittee, the Tourism Forecasting Committee/TFC Technical Committee) made up of senior researchers and economists (usually from TFC-member organizations) as well as independent advisors, reviewing the model based forecasts and applying qualitative adjustments. Any adjustments are made by consensus.



The third and final iteration involves industry and government experts (TFC) reviewing the forecasts. Again, any adjustments are made by consensus.



The TFC forecasts represent the most likely outcome given past trends, current information, and the impact of policy and industry changes. Thus, it is important to note that the TFC produces ‘forecasts’ as distinct from ‘targets’, where the latter are developed for business planning purposes as levels to aspire to in terms of planning and performance management.

Forecasts for tourism activity are largely built around assumptions regarding future economic activity. Summary of results: •

International visitor arrivals growth in 2006 was expected to slow moderately but forecast to remain robust with growth of 5.6% to around 5.9 million. Over the full forecast period to 2014, the number of visitors was forecast to grow at an average annual rate of 5.6% to reach 9 million.



In real terms, the value of inbound tourism is forecast to increase from AU$ 18.4 billion in 2005 to AU$ 32.2 billion in 2014. This represents an annual average growth of 6.4%.

Tourism forecasts, 2000 – 2014 Year

Inbound arrivals (thousand)

Domestic visitor nights (thousand)

Outbound departures (thousand)

TIEV (real) (AU$ billion)

TDEV (real) (AU$ billion)

2000

4,931

293,384

3,498

18.1

59.4

2001

4,855

289,644

3,443

18.8

58.4

2002

4,839

298,657

3,461

18.4

58.1

2003

4,744

294,111

3,388

16.9

55.9

2004

5,215

296,878

4,369

17.5

55.3

2005

5,575

297,484

4,662

18.5

56.4

2006

5,925

300,484

4,850

19.6

57.0

2007

6,292

303,303

5,055

20.7

58.0

Case Studies

Year

Inbound arrivals (thousand)

Domestic visitor nights (thousand)

Outbound departures (thousand)

TIEV (real) (AU$ billion)

TDEV (real) (AU$ billion)

2008

6,661

306,171

5,237

21.8

58.9

2009

7,051

309,065

5,393

23.2

59.9

2010

7,454

311,993

5,557

24.6

60.7

2011

7,874

314,948

5,718

26.1

61.4

2012

8,324

317,548

5,879

27.9

62.1

2013

8,798

320,169

6,024

29.9

62.4

2014

9,297

322,812

6,164

32.1

62.4

6.8

0.9

3.2

6.3

1.1

Average annual growth rate 2005 – 2014 (%)

TIEV = Total Inbound tourism economic value

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TDEV = Total domestic tourism economic value

57

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Handbook on Tourism Forecasting Methodologies

4.11 VisitBritain’s Terrorism Impact Forecast Organization: Tourism Industry (Emergency) Response Group (TIER)/VisitBritain. Type of organization: National Tourism Organization (NTO). Purpose of forecast: To provide robust estimates and forecasts of the impact of the 7 and 21 July terrorist incidents in the capital on London and the United Kingdom’s visitor economies. Methodology: Causal methods. Overview of methodology: •

Time series data with daily/weekly periodicity were identified and correlated with aggregate tourism trends which were then used to forecast overall impact.



A comprehensive search was made for evidence of the scale of the impact on tourism and the rate of recovery.



The relationships between these indicators and visitor numbers and spending were estimated and used to quantify the impacts of the incidents on the economy.



Data for the economic tracking study was drawn from a variety of indicators, including accommodation, attractions, travel and enquiries.

Summary of results:

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Impacts: •

Spending by international visitors in 2005 was predicted to be £ 750 million less than previously forecast. £ 500 million of this loss would occur in London. This compares with an earlier estimate of losses of £ 300 million (United Kingdom) / £ 150 million (London) made after 7 July but before the incident on 21 July.



Visits to London museums during July declined on average by 17.8%. Early indications for August suggested that overall visits had fallen by 20 – 25%.



Estimated losses were supported by recent United Kingdom inbound figures showing a fall of 7.4% for businesses in its membership in July 2005 compared with the previous year and forward bookings were down 9.3%.

Looking Forward: •

Forecasts are dependent on normal circumstances prevailing and do not factor in unexpected shocks caused by terrorism, health scares and other crises. During 2005, London was affected by international terrorism; a slowdown in the rate of growth for inbound visits from a small number of markets as a consequence was expected. The impact was anticipated to lessen within a few months.



According to the research, despite the substantial losses incurred by the industry, foreign visitor numbers and spending in the United Kingdom and London is still expected to end at 3.5 – 4% higher than last year. This is due to the record levels of visitors and spending Britain and London experienced in the first six months of 2005.



Barring any further shocks, and with collaborative marketing activities in place, growth in inbound tourism should rebound in 2006 and rise by over 10% and by 8.5% in 2007.

Case Studies

59

4.12 Japan Travel Bureau Foundation’s Outbound Tourism Demand Forecast Organization: Japan Travel Bureau Foundation (JTBF). Type of organization: Tourism Research Institute (not-for-profit-based). Purpose of forecast: To provide forecasts on outbound tourism demand for 2006. Methodology: Causal methods.

Delivered by http://www.e-unwto.org Georgios Drakopoulos (307-99-294) Tuesday, March 15, 2011 5:40:51 AM

Overview of methodology: •

There is a strong correlation between the number of Japanese outbound travelers and GDP. The outbound volume can be forecasted almost solely on GDP.



The exchange rate of ¥ also affects the outbound demand.



To forecast the outbound volume, multiple regression equation is used with GDP and the exchange rates of ¥ / US$ as independent variables, and the quarterly number of outbound travellers as a dependent variable.



Since the quarterly number of outbound travellers varies widely depending on season, moving average of each quarter is used as a variable.



The outbound volume has been stagnating since the latter half of the 1990s. Hence, the latest data available is used for multiple regression equation since 1996, which was the turning point year for the Japanese outbound market. Thus, parameters for the equation are updated every year.



Until the mid 90s, when the outbound volume could be forecasted exponentially, each variable was used logarithmically in the same model.



Economic growth rate for the next year released by government bodies and economic research institutes, as well as exchange rates, are used to compute the expected number of outbound volume.

Summary of results: •

According to Economic Forecast for FY2006, the economic growth rate will record 1.9%, and the exchange rate of 118 ¥ / US$ is expected.



Based on these figures, the number of outbound travellers is expected reach 17.9 million, a 3.6% gain from the previous year. However, JTBF announced 17.9 million travellers with a 3.4% growth rate.

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Handbook on Tourism Forecasting Methodologies

4.13 Tourism Authority of Thailand’s International Tourism Forecast Organization: Tourism Authority of Thailand (TAT). Type of organization: National Tourism Organization (NTO). Purpose of forecast: To estimate Thailand’s tourism growth in terms of the number of international tourist arrivals and tourism revenue for 2006 – 2007. The international tourist forecast is used as information for developing tourism marketing strategies. Methodology: Causal methods. Overview of methodology: •

Multiple regression analysis was used to predict the number of international tourist arrivals for Thailand for the years 2006 – 2007. It was based on international tourist data for the past 10 years (1994 – 2004) and also adjusted by economic factors as well as social and political factors such as epidemic, crisis, consumer trends, and government policy. As a result, the first draft of forecast tourist number was made and presented for review from TAT experts, comprising tourism analysts, researchers and marketing practitioners.



The second draft of forecast tourist number was sent to 22 managers of TAT overseas offices for their considerations, which were based on current market situations including airlines’ seat capacity, political and social updates on key generating markets. Private organizations such as the Thai Hotel Association (THA) and Thailand Tourism Council were then asked for their opinions on the final draft of projected tourist number.

Delivered by http://www.e-unwto.org Georgios Drakopoulos (307-99-294) Tuesday, March 15, 2011 5:40:51 AM

Summary of results: International tourist arrivals to Thailand with trends and forecast 2000 – 2007 Year

Tourist

Average length of stay (days)

Average expenditure

Number (million)

Change (%)

Value (million bath)

Change (%)

2000 (1)

9.51

+ 10.82

7.77

3,861.19

+ 4.23

285,272

+ 12.75

2001 (1)

10.06

+ 5.82

7.93

3,748.00

– 2.93

299,047

+ 4.83

2002 (1)

10.80

+ 7.33

7.98

3,753.74

+ 0.15

323,484

+ 8.17

2003 (1)

10.00

– 7.36

8.19

3,774.50

+ 0.55

309,269

– 4.39

2004 (1)

11.65

+ 16.46

8.13

4,057.99

+ 7.51

384,360

+ 24.28

2005 (2)

11.60

– 0.44

8.15

3,890.00

– 4.14

368,000

– 4.26

2006 (3)

13.80

+ 18.97

8.20

4,300.00

+ 10.54

486,300

+ 32.15

2007 (3)

14.70

+ 6.52

8.22

4,500.00

+ 4.65

544,000

+ 11.87

(1) Actual figure (2) Trend (3) Target

Person/day (bath)

Change (%)

Revenue

Case Studies

61

4.14 Canadian Tourism Industry Forecast(s) – Inbound, Outbound, Domestic and Industry Profits Organization: Conference Board of Canada. Type of organization: Not-for-profit research organization. Purpose of forecast: To provide a comprehensive overview of the Canadian tourism industry over the next five years. Semi-annual forecasts using monthly and quarterly data. Methodology: Causal methods.

Delivered by http://www.e-unwto.org Georgios Drakopoulos (307-99-294) Tuesday, March 15, 2011 5:40:51 AM

Overview of methodology: •

Econometric forecasts and market share analysis of visits and expenditures for all major international inbound, outbound and domestic travel markets were generated by purpose of trips and mode and transportation.



Domestic and foreign travel demand was also generated in concordance with the tourism spending categories reported by Canada’s Tourism Satellite Account.



Refinement of econometric based travel forecasts involving consultation with key industry stakeholders.



Origin-destination visits/spending forecasts were generated for each Canadian province as well as for eight major Canadian cities.



Tourism industry profitability, GDP and employment forecasts utilized demand side travel forecasts in conjunction with supply side forecasts. Profitability forecasts for the tourism industry account for price increases as well as major cost components including labour, material, and capital costs for each sector of the Canadian tourism industry.



Shock minus control analysis was used to isolate the tourism impact of major events and policy changes on an as-needed basis.

Summary of results: •

Overall, overseas arrivals to Canada expected to increase 11% between 2006 and 2008.



Domestic spending on tourism related goods and services expected to grow 8.3% between 2006 and 2008.



Largely due to the Western Hemisphere Travel Initiative (WHTI) and the likely passport requirement for US travellers, profits in the Canadian tourism industry are expected to fall from C$ 1.5 billion in 2006 to C$ 1.4 billion in 2007 and C$ 1.3 billion in 2008. Fortunately, the 2010 Winter Olympics in Vancouver is expected to increase tourism spending significantly, and help restore profitability in the industry.

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Handbook on Tourism Forecasting Methodologies

4.15 Airbus’ Traffic Growth Forecast Organization: Airbus. Type of organization: Aircraft Manufacturer. Purpose of forecast: To model major traffic flow from 2004 to 2023. Methodology: Causal methods.

Delivered by http://www.e-unwto.org Georgios Drakopoulos (307-99-294) Tuesday, March 15, 2011 5:40:51 AM

Overview of methodology: •

Airbus models each major traffic flow. Airbus analyses and forecasts a total of 140 distinct domestic, regional and intercontinental passenger sub-markets, and a total of 145 directional air cargo sub-markets. This is achieved using the latest projections of economic growth and other indices (including oil prices) from the Global Insight Forecasting Group. The cargo forecast also incorporates analysis of historical multidirectional exports and imports by country and commodity.



Each traffic forecast model differs for each individual flow, depending on the best fit of the different sets of economic and air transport variables. These models, although based on econometric modelling techniques, also integrate various analyses of the regional and structural changes that are expected to influence the dynamics and development of the current and future air transport system.



The growing importance of the low-cost carriers (LCC) in the United States of America and Europe, as well as in Asia and the Pacific, is an example of a development that must be taken into account when forecasting traffic. In order to reflect these new market developments as accurately as possible in the forecast, analysis must be carried out to measure LCC traffic stimulation or diversion from network carriers, the new route potential for LCCs and the subsequent growth potential of these airlines through the development of either new or existing routes.



Other structural changes that must also be considered include the pace of liberalization of markets to and from developing countries, the growing importance of regional airlines, increasing environmental and congestion constraints, and the subsequent impact on airports or regions.

Summary of Results: The results of careful analysis in these areas allow Airbus forecasters to develop every traffic flow forecast, taking into account the precise circumstances prevailing on every flow and region studied. The combination of these individual ‘bottom-up’ traffic forecasts is then compared to a ‘top-down’ global forecast in order to confirm the initial findings. In the absence of any major exogenous disruption, outside the normal business or economic cycles, traffic for the next 20 years is anticipated to grow at an average pace of 5.3% per annum. Annual average growth rate, by region of airline registration (%) Region of airline registration

2004 – 2013

2014 – 2023

20 years

Europe

5.8

4.6

5.2

North America

4.8

3.5

4.2

Latin America

5.3

4.5

4.9

Middle East

10.7

3.6

7.1

Africa

5.3

3.8

4.5

China

9.1

7.4

8.2

Asia and the Pacific

6.7

5.3

6.0

World

6.0

4.6

5.3

Case Studies

63

4.16 Pacific Asia Travel Association’s Forecasts of Tourism Demand Organization: Pacific Asia Travel Association (PATA). Type of organization: Not-for-profit Travel and Tourism Trade Association. Purpose of forecast: To identify various levels of growth/contraction in 40 key origin/destination markets within the Asia Pacific arena. Methodology: Structural Integrated Time-series Econometric Analysis (SITEA) as developed by Professor Lindsay W. Turner and Stephen F. Witt. Overview of methodology: Causal methods. A time-series approach has the advantage of overcoming the problems associated with spurious regression, and will tend in many cases to generate accurate forecasts. But such an approach by itself is limiting, especially in understanding the economic forces that are behind the changes in the flow series. It is also clear, at least to some degree, that a non-quantitative approach needs to be incorporated.

Delivered by http://www.e-unwto.org Georgios Drakopoulos (307-99-294) Tuesday, March 15, 2011 5:40:51 AM

The SITEA model uses a time-series approach as a starting point to overcome the problem of nonstationarity. In this process, the underlying series is fitted to form a mathematical projection of the seasonal, cyclical and trend components, where present, and then the influence of economic variables is added sequentially to adjust these time-series components. Further, dummy variables can be added in as independent variables under the judgment of the forecaster to account for special economic/political influences that cause changes in the series. The decision to keep or remove each additional variable is based upon the variable having the correct sign according to economic theory (for example, price should be negative and income positive) and the level of statistical significance, based upon a 0.05 acceptance level. Because the process has the potential to include a large number of parameters in any one model, the number of economic variables added is limited to three and dummy variables to two, and no more than two, cycles are interpreted initially in the series. These restrictions stop any model from becoming overparameterised, leading to questionable results, or simply no model solution occurring. Limiting the number of economic variables is an advantage from a practical forecasting point of view, because the forecasting task is already so large. Each forecast series is treated as a separate modelling exercise and as such this creates a huge forecasting exercise for this study. Forecasts are completed for 40 countries involving 588 annual arrivals forecast series and 569 quarterly forecasts used to derive annual arrival forecast series, giving a total of 1,157 individual forecast analyses. Additionally, from a theoretical point of view it is possible to limit the number of independent variables, because previous research has clearly defined the most important economic measures influencing tourist arrivals. The specific economic measures used here are: •

income: per capita personal disposable income;



price: origin-destination country exchange rate adjusted by destination CPI divided by origin CPI;



economy air fares.

These variables may be substituted if data is not available by: •

income: per capita GDP;



price: destination CPI divided by origin CPI;



exchange rate: between origin and destination currencies;



air fare index.

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Additionally, various country-specific dummy variables and dampening parameters have been added to allow for various specific measures for individual markets, for example political changes in Sri Lanka and Nepal. For many forecast series the variables could not be obtained for the specific countries over the required time-frame, so a set of substitute variables was tested for significance as a backup set of variables. In some cases, no economic measure was found to be significant, and the forecast process reverts to a pure time-series model. Occasionally, only a dummy variable is significant and the forecast process then reverts to a pure timeseries model with one or two dummy variables. In the situation where source countries are grouped together (for example, ‘other countries’), no economic variables are used, and only rarely a dummy variable, because the origin countries are either unknown (or not fully known), or because an average of measures would not be reliable as an economic indicator. In the SITEA model, a combination of time-series and econometric modelling is used, and the problem regarding non-stationarity (and spurious regression) is overcome.

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However, the problem of being able to forecast the economic measures remains, so these are separately forecast using time-series methods. The final forecasts for arrivals for each origin-destination pair, including groups of source countries and ‘other countries’ are done quarterly where data permits, and annually otherwise. The annual forecasts from quarterly data are the sum of the individually forecast quarters. The ‘Total’ forecasts for each world region and the ‘Grand Total’ are the sum of the forecasts for the relevant countries. The forecasting process is iterative in that different combinations of economic and dummy variables are possible, and an iterative decision process to derive the optimum model based on statistical measures of fit is required to reach a decision on the best model. The final forecasts generated by the quantitative process were then assessed by independent experts as to likely inaccuracies in the forecasts given other ‘non- quantifiable‘ or other unmeasured factors that experts considered may raise or lower numbers. Depending upon these assessments further analysis or direct change was carried out or comments were added to the forecast chapters. The expert opinion phase of the analysis is a critical phase. Having now undertaken a rigorous set of expert opinion stages in the analysis, the value of this form of analysis is clearer and has been found to reduce errors significantly in the forecasts across all countries. The major expert opinion phases are: Stage 1: Adjustment by researchers alone for oddities, model misspecifications and clearly unrealistic forecasts. Stage 2: Consideration of forecasts by individual experts and incorporation of the results of a survey of NTOs regarding the effect of recent events. Stage 3: Examination of the latest arrivals data (e.g. sample from the forecasting model) from NTOs and accommodation projections. Comparison of trends from the latest figures with the forecasts by several experts. These stages are spread over a four month period from when the forecasts were originally completed to when they are about to be published. Apart from building in expert opinion, they also incorporate use of the latest data. A degree of error does occur where countries measure arrivals by nationality and not residency because an assumption is made that all nationals have come from their place of residence.

Case Studies

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Summary of results: •

The forecasts for calendar year 2005 were released at the 54th PATA Annual Conference in Macao, China, in April 2005 and covered the period 2005 – 2007.



These forecasts covered 40 Asia Pacific destinations, providing counts for international arrivals, and where data existed, departure forecasts and tourism receipts forecasts.



International visitor arrivals to each destination were forecast by country of origin as measured by each respective destination in their official statistics (country of nationality or country of origin).



While full-year confirmed arrivals are not available (at the time of writing) for all destinations for which forecasts were produced for calendar year 2005, initial findings are extremely interesting.



More than half (27) of the 40 destinations covered have provided preliminary results for 2005 and at the aggregate level the difference between the forecast volume of international arrivals and the figures to date differ by only 1.4%.



In terms of best Forecast/Actual performance, Papua New Guinea and Hawaii, have so far produced the narrowest margins, although even China with an actual inbound volume of 120 million visitors, still came in with a very close actual to forecast result, (see below).



As expected, the worst forecast to actual performances came from, in particular, those destinations directly affected by the 24 December 2004 tsunami, where lingering effects continue to make themselves felt.



Undoubtedly, there will also be wins and losses for various destinations at the source market level, but on average the SITEA model has provided a consistently accurate series of forecasts with respect to – at least – the direction of the expected outcome (gain/loss) and relative magnitude.

Forecast accuracy for South East Asian countries Destination Papua New Guinea

Actual (2005)

Forecast (2005)

Difference (%)

69,250

69,381

– 0.2

7,379,635

7,406,494

– 0.4

Macao, China

18,711,187

19,020,952

– 1.6

Hong Kong, China

23,359,417

23,770,431

– 1.7

3,915,324

3,874,953

1.0

120,000,000

117,185,674

2.4

Taiwan, Province of China

3,378,118

3,294,176

2.5

Vietnam

3,467,758

3,355,805

3.3

Hawaii

India China

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4.17 Hungarian NTO’s Short-term Forecast Organization: Hungarian National Tourist Office (HNTO). Organization Type: National Tourism Organization (NTO). Purpose of forecast: The purpose of the monthly updated, short term forecast is to provide actual market information for the key stakeholders. Methodology: Qualitative methods. Overview of methodology: •

The forecast includes a three month forecast of the occupancy of the three main hotel chains, the booking figures of the air carriers and main tour operators and qualitative information/analysis about the most important source markets.



The hotel chains provide their own booking figures for the HNTO.



The market analysis contains information about the following important source markets: Germany, United Kingdom, Austria, Italy, United States of America, the Netherlands, Spain, Poland, Switzerland and Russia. The most important factors influencing travel decision and behaviour during the forecasted period are: public holidays, weather conditions, big sport events hosted by the source markets, opening of new air connections, and trends in the travel behaviour.



The forecast is based on qualitative expert opinion and incorporates the expectations (i.e. booking trends) of carriers (airlines, bus companies, train companies) and tour operators.

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Summary of results: for the period October – December 2005: Source market

Forecast

Germany

– increase of the number of visitors – above average increase of arrivals at hotels – new air connections with the lake Balaton – decrease the demand for bus trips

United Kingdom

– increasing popularity of Central and Eastern Europe – boom of air capacity/connections – main benefit for 3 to 5 star hotels in Budapest – increasing popularity of city-breaks – good results in the meetings industry segment

Austria

– record number of visitors in 2005 – popularity of ‘Spa and Wellness’ – new air connections (Vienna – Pécs)

Italy

– public holidays affecting positively the booking figures for Hungary

United States of America

– increase of the number of European trips – high fuel prices affecting travelling costs – the senior segment is the most affected by higher prices

The Netherlands

– popularity of last-minute trips and city-breaks – special offers for Christmas and New Year’s Eve – new low cost air connection (Amsterdam – Budapest)

Spain

– increasing air capacity motivating individual travellers – special offers for Christmas and New Year’s Eve

Case Studies

Source market

Forecast

Poland

– good macroeconomic performance – popularity of ‘Spa and Wellness’ – new air connections (Warsaw – Budapest)

Switzerland

– popularity of ‘Spa and Wellness’

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– negotiations for opening new air connection

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4.18 VisitScotland’s Avian Flu Scenarios Forecast Organization: VisitScotland. Type of organization: Regional Tourism Organization. Purpose of forecast: To examine the consequences and strategies for the Scottish tourism industry should an Avian Flu Pandemic occur in Scotland. Methodology: Qualitative methods (Scenario Planning).

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Overview of methodology: Steps Involved in Scenario Planning: •

Frame the issues – scenarios need to have a clear focus, purpose and scope.



Identify participants and solicit input – from within the organization and externally from public and private sector stakeholders.



Draw a picture of what is known – to outline the trends, key themes and relationships evident in the issue(s) being addressed.



Add uncertainties to the picture – including unknown environmental factors, such as the impact of events and tipping points.



Sketch out possible paths – with reference to the uncertainties and trends, a number of possible and plausible future paths can be highlighted.



Test for plausibility – the multiple scenarios developed need to be tested for internal consistency, logic and causal relationships.



Anticipate interactive dynamics – anticipation of how different actors may react during the scenarios and how competitors will also respond.



Formulate strategies – how the organization(s) will cope with potential changes and what actions they need to take.

Summary of results: In this scenario, Avian Flu has mutated into a form readily transferable from human to human. Although it has not yet developed into pandemic proportions, it has become a major cause for concern. The impact on the Scottish economy equates to a minor shock and overall impact has been negligible, and measured over a two-year time span the summary results were as follows: •

A small change in tourism, generating only a 0.3% change in output and a ₤ 3.6 billion impact on GDP, equivalent to only a minor growth in tourism employment.



A corresponding decline of 0.3% in output in all other sectors of the economy, leading to a net loss of 3,180 FTE jobs.



International tourism decreases significantly, particularly the American market, but daytrips and domestic tourism increase, compensating for the loss in overseas markets.



The tourism economy remains relatively robust in this scenario, with any drop in overseas visitors compensated by domestic tourism, as many British residents decide to holiday in the United Kingdom, boosting the domestic market and creating opportunities for those tourism businesses able to adapt to meet the needs of this market.



Although some businesses suffer from loss of trade, those that have developed contingency plans to react to the changing business environment may actually benefit from this scenario.

Case Studies

69

4.19 VisitScotland’s Climate Change and Tourism Scenarios Forecast Organization: VisitScotland. Type of organization: Regional Tourism Organization. Purpose of forecast: To understand the impact of Climate Change on Scottish Tourism. Methodology: Qualitative methods (Scenario Planning).

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Overview of methodology: •

The Intergovernmental Panel on Climate Change (IPCC) Third Assessment Report outlined the potential evidence on climate change. It presented an increasing body of observations that showed a warming world and changes in global and regional climate systems. It also predicted potentially significant changes in climate in future years. Making future predictions of climate change in the United Kingdom was taken forward by the United Kingdom Climate Impacts Programme (UKCIP), which provides tools and guidance on assessing impacts and developing adaptation responses.



UKCIP has published a range of climate change scenarios for the United Kingdom. The UKCIP02 scenarios summarized changes already occurring in the United Kingdom. It also presented four alternative climate change scenarios for the next century (for low, medium-low, medium-high and high emissions scenarios) for three time periods – 2011 to 2040 (2020s), 2041 to 2070 (2050s) and 2071 to 2100 (2080s).



The scenarios provided estimated future changes in climate variables, with associated levels of confidence.



Research as part of the Scottish Executives Adaptive Strategies programme for public sector organizations addressed the question of what adaptive strategies should public sector organizations adopt based on the impact of climate change.



A Mapping Exercise was undertaken to measure and assess such change.

Summary of results: For Scotland, it is likely that there will be warmer, wetter and cloudier winters, and warmer, drier summers. Therefore, the likely impacts and key effects of climate change on Scottish tourism to 2080 include: •

Higher temperatures and a longer season will present opportunities for the Scottish tourism industry.



Winter sports in Scotland will be increasingly at risk from shorter seasons and even more unreliable snow cover.



The potential impacts on ecosystems could affect the natural beauty of certain hot-spots with distinctive landscapes (e.g. in the highlands).



Pressure of increased tourism on tourist hot-spots will require careful management and consultation with local communities.



There could be impacts on marine transport to the islands and for winter visitors in general from increases in extreme weather.

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4.20 Austrian National Tourist Office’s Tourism Forecasting Techniques Organization: Austria National Tourist Office (ANTO). Type of organization: National Tourism Organization (NTO). Purpose of forecast: To assist the local and international tourism industry. Methodologies: Quantitative and Qualitative methods (Scenario Planning). Overview of methodologies:

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Quantitative Methods: Destination Country Portfolio Analysis •

The Austrian National Tourist Office’s Source Market Analysis provides the local and international tourism industry with a strategy tool on which, among others, the resource allocation for individual source markets can be based. Presently 25 source markets are covered (21 in Europe and 4 overseas).



The analysis for a specific destination country is based on the systematic compilation or assessment of approximately 60 economic and tourism indicators per source market over the course of three to four years. Forecasts (causal) are provided for example for the expenditures on outbound trips.



For more methodological details please refer to Smeral, E. (2007), ‘Evaluating leisure time travel source markets: an innovative guide for National Tourist Organizations for future competitiveness’, in Weiermair, K. et al., Time Shift. Impact on Leisure and Tourism, Berlin, ESV, pp. 83 – 90.

Qualitative Methods: Scenario techniques: The Austrian National Tourist Office has been working with scenario techniques for several years. In the past scenarios were compiled on the topics of city tourism in Austria as well as summer and winter tourism in Austria. In 2005, the ANTO has started another scenario process to investigate “The future consumer”. Scenario steps: Step 1: Environmental analysis: • collect of influencing factors (as for example: traffic, demographics etc.); • evaluate the situation of these factors and expected future development; • evaluate the developing options on the basis of incidence rates. Step 2: Development of scenarios: • bundle two of these clear developing factors with other projections; • name the bundles; • create the scenarios and tell stories of the future, once stories make scenarios more feasible and concrete. Step 3: Development of recommendations – in this step recommendations should be made. These recommendations should be necessary to reach or to avoid the scenarios.

Case Studies

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4.21 CONSAVE 2050’s Scenario Forecasting on Aviation and Emissions Organization: Deutsches Zentrum für Luft- und Raumfahrt (DLR). Project: Constrained Scenarios on Aviation and Emissions (CONSAVE 2050). Type of organization: National Research Institute, Project funded by the European Commission (EC). Purpose of forecast: To develop scenarios on aviation and emissions which address the key aspects of aviation constrains to research community and stakeholders. Methodology: Qualitative and Quantitative methods (Scenario Planning).

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Overview of Methodology: •

CONSAVE 2050 was started in September 2002 as an EC Accompanying Measure Project. The project consists of developing scenarios on aviation and emissions which address the key aspects of interest to stakeholders, specifically the aviation industry, policy makers, climatologists and transport researchers. The main focus is on the year 2050, with a look at shorter term (2025) and longer term (2100) developments relevant to aviation industry planning and climate models respectively. CONSAVE 2050 includes constraining conditions plus the latest ’background’ data on external influences on land and air transport, hence setting the framework for the long term development in aviation.



Scenario selection: four CONSAVE scenarios with alternative ‘philosophies’ were designed, to be able to cover a broad range of possible futures and to allow for a ‘pure’ discussion of the key study questions, in particular those related to future challenges and constraints for aviation. The four scenarios are qualitatively described by storylines and assumptions and are quantified for the key descriptors, calculated with the AERO-model, using scenario-specific sets of model inputs. They were eventually labelled as: –– ‘Unlimited Skies’ (ULS) – global, dominant actor: market –– ‘Regulatory Push & Pull’ (RPP) – global, dominant actor: policy –– ‘Fractured World’ (FW) – regional, dominant actors: depending on regions –– ‘Down to Earth’ (DtE) – global, dominant actor: society



Each CONSAVE Aviation Scenario is consistently derived from a related CONSAVE Background Scenario. The CONSAVE Background Scenarios were quantified for GDP, population, and key energy issues, applying the respective figures calculated for the ‘partner’ scenarios in the IPCC/ SRES exercise (on the basis of a total of six reviewed quantification models).

CONSAVE Scenario

Consistent IPCC 2000 scenario

Unlimited Skies (ULS)

IPCC/SRES A1G-

Regulatory Push & Pull (RPP)

IPCC/SRES A1T

Fractured World (FW)

IPCC/SRES A2

Down to Earth (DtE)

IPCC/SRES B1

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Scenarios overview: The main characteristics and assumption of the four scenarios were: Assumptions for 2020/2050

Unlimited Skies (ULS)

Population (billion) World GDP (US$, trillion) Energy availability

Peak of world oil production (incl. artificial oil) Energy use (Exajoule)

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Energy price (1990 = 1)

Regulatory Push & Pull (RPP) 7.5 to 8.7

57 to 180 available

57 to 171 available

Fractured World (FW)

Down to Earth (DtE)

8.2 to 11.3

7.5 to 8.7

40 to 82

53 to 136

dependent upon region; scarcity after 2050 expected

available scarcity after 2050 expected

2080

2050

2020

2020

700 – 1,350

610 – 1,100

600 – 970

580 – 810

1.5 – 2

2–4

4–8

2–4

Environment

no catastrophic change

Technology development

significant change; main problems 2052 – 2058

little change

some alarming, but no catastrophic change

dynamism of technological innovation is broad-based; communication and transportation growth

heterogeneous partly incompatible

rapid diffusion of post-fossil technologies

Political development

market philosophy

emission regulations

regional differences

pollution sources tightly controlled

Citizen’s values

global, pragmatic solutions

regulatory approach to environment

autarky, regional orientation

environmental and safety concerns

Customer preferences

convenient and flexible service and mobility

cheap and environmentally okay

security concerns

stigmatization of fast/international patterns

Aircraft technology

new very large aircraft available

like ULS, plus hydrogen powered

different standards

introduction of hydrogen powered

Safety and security

high standards

high standards (regulation)

high effort to ensure security

high standards

Market development

deregulation, strong competition

controlled liberalization, medium competition

dominance of national carriers

decrease in the number of airlines

Air transport supply and demand

constraints

capacity regulated

depending to regions

no constraints, but low profitability

Aviation costs

lower specific costs

lower specific costs

higher (security and standards)

higher specific costs

Full report available at www.dlr.de/consave/

List of Boxes, Figures and Tables

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Boxes Box 2.1

Further reading on tourism forecasting...............................................................................

5

Box 2.2

Equations for no-change models........................................................................................

7

Box 2.3

Equation for simple moving average model........................................................................

8

Box 2.4

Simplified equation for single exponential smoothing........................................................

11

Box 2.5

Brown’s one-parameter adaptive method for double exponential smoothing......................

11

Box 2.6

Equation for removing seasonality through decomposition.................................................

14

Box 2.7

Equation for simple linear regression model.......................................................................

20

Box 2.8

Equation for a multiple regression model...........................................................................

23

Box 2.9

Commonly used push, pull and resistance factors..............................................................

24

Box 3.1

Relative resource requirements of different forecasting methodologies...............................

36

Figure 2.1

Step-by-step guide to the Delphi forecasting process..........................................................

29

Figure 2.2

The inability of traditional forecasting techniques to deal with the increasing range fof uncertainty as we get further from the present...............................................................

30

Figure 3.1

A simple decision tree diagram for deciding upon the most appropriate forecasting



strategy to adopt................................................................................................................

37

Table 1.1

Examples of commonly used dependent variables (= what is forecasted) and independent variables (= what is used to make the forecasts)......................................

2

Table 3.1

Characteristics of the major categories of forecasting methods...........................................

38

Table 3.2

Forecast method decision matrix........................................................................................

40

Figures

Tables



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List of Acronyms

ANN

Artificial Neural Network

ARIMA

Autoregressive Integrated Moving Average

ARMA

Autoregressive Moving Average

AUD

Australian dollar

CAD

Canadian dollar

DES

Double Exponential Smoothing

ETC

European Travel Commission

GDP

Gross Domestic Product

GLM

General Linear Model

HCT

Hotel, Catering and Tourism sector

HNTO

Hungarian National Tourist Office

IPCC

Intergovernmental Panel on Climate Change

LCC

Low Cost Carriers

NTB

Namibia Tourism Board

NTO

National Tourism Organization

SAT

South African Tourism

SMA

Simple Moving Average

SPSS©

Statistical Package for the Social Sciences

TIER

Tourism Industry (Emergency) Response Group

TFC

Tourism Forecasting Council (Australia)

TRA

Tourism Research Australia

TRCNZ

Travel Research Council of New Zealand

UNWTO

World Tourism Organization

WTTC

World Travel and Tourism Council

ZAR

South African Rand

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Glossary

Term

Definition

Artificial Neural Network

Computer programs that mimic the structure of the brain and are designed to solve problems such as tourism demand, where there are multiple inputs and outputs that show complex quantitative relationships.

Autocorrelation

In a time series, autocorrelation is a measure of the influence of one data point on the one closest to it. It means that there is a cross-correlation of a signal with itself and that the observations are therefore not independent.

Autoregression

A form of regression model where the dependent variable is the current value and the independent variables are N previous values of the time series.

Autoregressive Moving Average

Sometimes called Box-Jenkins model, this is an advanced extrapolative method for forecasting using time series data.

Causal factors/variables

Factors/variables that have a causal relationship with the variable being forecast.

Causal model

A forecasting technique that uses the causal (mathematical) relationship between one or more variables and the variable being forecast to make a quantitative forecast.

Confounding Factors

Any factor that is not considered in the analysis, but which influences the variable being forecast.

Control method

Normally a simple extrapolative method, such as a no-change model, that is used to compare the forecasting performance of other, more sophisticated, forecasting methods.

Correlation

A statistical association between two or more variables.

Cross dependency

See autocorrelation.

Damping

The progressive diminution with time of certain quantities characteristic of a phenomenon.

Decomposition

A method of breaking down the pattern of a time series into its constituent parts: season, cycle, trend, and random components.

Delphi method

A qualitative forecasting method that uses a series of anonymous attitude surveys of a panel of experts to gradually refine a tourism demand forecast.

Demographic variable

Variables that relate to the human population, such as age structure, sex ratio, etc.

Dependent variable

The variable to be forecast based on its time series (extrapolative methods) or the variable determined by one or more independent variables in a regression equation.

Econometric model

See structural econometric methods.

Economic cycle

Predictable and long-term pattern changes in the income of large economic entities such as nations.

Economic variables

Variables that relate to economics such as tax levels, exchange rates, etc.

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Term

Definition

Endogenous variables

All variables that appear within the structure of the econometric model.

Error

The difference between a computed, estimated, or measured value and the true, specified, or theoretically correct value.

Exogenous variables

All variables that are determined by relationships that are not captured within the structure of the model, but rather by external forces.

Explanatory equation

An equation, such as a regression equation, that is used to predict the value of a variable.

Exponential smoothing

A widely used simple extrapolative forecasting technique.

Extrapolation

Estimation of a variable outside of its observed range by assuming that the unknown value can be logically derived from the known values.

Extrapolative methods (also known as ‘time series methods’)

Are any method that uses a time series to extrapolate future values of the tourism demand variable of interest.

Forecasting

The process of using historical information to predict the future.

Forecasting point

The exact point in time that relates to a particular forecast.

Future-proofing

The process of making structural adjustments to a business or organization to reduce the impact of a range of possible futures.

General Linear Model

A flexible mathematical modelling technique that is often used as an alternative to multiple regression methods.

Independent variable

A variable that affects the dependent variable in a regression model but is not affected by it.

Interdependencies of variables

Variables that affect each other’s values. Sometimes it is difficult to identify whether two variables are inter-dependent and, if undetected, this can cause problems for certain forms of regression analysis.

Jury of executive opinion

A group of individuals, often within the same organization, who form a committee (jury) for the purpose of tourism forecasting.

Line of best fit

The regression line that minimizes the distance between all the points on a scattergraph.

Linear

A straight line trend.

Linear regression

A widely used simple extrapolative or causal method for tourism forecasting.

Moving average

The average value of a variable over a specified time period.

Multiple regression

A widely used causal method for tourism forecasting that relates several related variables to the variable of interest. See general linear model.

Naïve method

See no-change method.

No-change method

A simple extrapolative forecasting method that assumes that the value or rate from the preceding time period will remain unchanged.

Non-linear trend

Any trend that cannot be approximated by a straight line.

Political variables

Variables that relate to politics such as change of government or the introduction of a new policy that may influence the tourism industry.

Predictor variable

See dependent variable.

Qualitative forecasting

A type of forecasting that uses some form of expert opinion.

Quantitative forecasting

A type of forecasting that quantifies the relationship between the tourism demand variable of interest (dependent variable) and one or more factors.

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Glossary

79

Term

Definition

Range

The difference between the minimum and the maximum forecast.

Regression line

See line of best fit.

SAS

Statistical Analysis System.

Scatter-graph

A graph which illustrates the relationship between an independent variable and a dependent variable.

Scenario Planning

A qualitative forecasting technique that creates a realistic set of possible futures, ideally with estimated probabilities of occurrence, to help an organization respond appropriately if and when one of these scenarios occurs.

Seasonality

Cyclicality in a variable from one season to the next.

Simple moving average method

A simple extrapolative method of tourism forecasting that uses the average value of the dependent value from several previous time periods to predict the future value.

Simultaneity

A system of equations or a set of simultaneous equations that share variables.

Stationarity

A stochastic process whose joint distribution of observations is not a function of time.

Structural Econometric methods

A causal forecasting method in which linked multivariate equations are used to model the relationship between multiple dependent and independent variables simultaneously.

Time series

A sequence of measurements of a variable at different points in time.

Time series data

Data collected at regular intervals in time

Transformed variable

A variable that has been mathematically altered (normally to make it more amenable to statistical analysis).

Trend

The pattern of change of a variable over time.

Univariant

Referring to a single variable.

Variable

A quantity that can assume any of a set (range) of values.

Weighted variable

A variable that has been adjusted in relation to another factor or variable.

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Bibliography

Agarwal, V. B. and Yochum, G. R. (1998), ‘Tourism and Advertising: Evidence from Virginia Beach’, International Advances in Economic Research, 4, pp. 384 – 397. Archer, B. H. (1994), ‘Demand Forecasting and Estimation’, in Ritchie, J. R. B. and Goeldner, C. R. (eds.), Travel, Tourism, and Hospitality Research: A Handbook for Managers and Researchers, John Wiley & Sons, New York, pp. 105 – 114. Athiyaman, A. and Robertson, R. W. (1992), ‘Time Series Forecasting Techniques: Short-term Planning in Tourism’, International Journal of Contemporary Hospitality Management, 4, pp. 8 – 11. Bar-On, R. R. V. (1989), Travel and Tourism Data: A Comprehensive Research Handbook on the World Travel Industry, The Oryx Press, Phoenix. Blake, A. et al. (2004), ‘Tourism in Scotland: The Moffat Model for Tourism Forecasting and Policy in Complex Situations 2004/2’ (Online), paper published by the Tourism and Travel Institute, available: http://www. nottingham.ac.uk/~lizng/ttri/Pdf/2001%204.PDF

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Crouch, G. I. (1994b), ‘The Study of International Tourism Demand: A Survey of Practice’, Journal of Travel Research, 32, pp. 41 – 57. Crouch, G. I. et al. (1992), ‘Marketing International Tourism to Australia: A Regression Analysis’, Tourism Management, 13, pp. 196 – 208. Dharmaratne, G. S. (1995), ‘Forecasting Tourist Arrivals in Barbados’, Annals of Tourism Research, 22, pp. 804 – 818. Dilek, O. and Fergus, B. (2002), ‘Provider – User Deference’s in Perceived Usefulness of Forecasting Formats’, International Journal of Management Science, 32, pp. 31 – 39 Divisekera, S. (2003), ‘A Model of Demand for International Tourism’, Annals of Tourism Research, 30, pp. 31 – 49. Downward, P. and Lumsdon, L. (2001), ‘The Demand for Day-visits: An Analysis of Visitor Spending’, Tourism Economics, 6, pp. 251 – 261. Edgell, D. L. and Smith, G. (1993), ‘Tourism Milestones for the Millennium: Projections and Implications of International Tourism for the United States through the Year 2000’, Journal of Travel Research, 32, pp. 42 – 47. Edwards, A. D. and Graham, A. (1997), International Tourism Forecasts to 2010, Lebhar-Friedman, London. Enders, W. and Sandler, T. (1992), An Econometric Analysis of the Impact of the Terrorism on Tourism, Kyklos, 45, pp. 531 – 554. Faulkner, B. and Valerio, P. (1995), ‘An Integrative Approach to Tourism Demand Forecasting’, Tourism Management, 16, pp. 29 – 37. Frechtling, D. C. (1992), ‘The US Tourism Industry’s View of EC ’92’, Tourism Management, 13, pp. 37 – 40. Frechtling, D. C. (1996), Practical Tourism Forecasting, Butterworth-Heinemann, Boston. Frechtling, D. C. (2001), Forecasting Tourism Demand: Methods and Strategies, Butterworth-Heinemann, Oxford. Frechtling, D. C. (2003), ‘Tourism Demand Modelling and Forecasting: Modern Econometric Approaches’, Journal of Travel Research, 41, pp. 332 – 334. Fritz, R. G. et al. (1984), ‘Combining Time-series and Econometric Forecasts of Tourism Activity’, Annals of Tourism Research, 11, pp. 219 – 230.

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