A review of research on tourism demand forecasting: Launching the Annals of Tourism Research Curated Collection on tourism demand forecasting

This study reviews 211 key papers published between 1968 and 2018, for a better understanding of how the methods of tourism demand forecasting have evolved over time. The key findings, drawn from comparisons of method-performance profiles over time, are that forecasting models have grown more diversified, that these models have been combined, and that the accuracy of forecasting has been improved. Given the complexity of determining tourism demand, there is no single method that performs well for all situations, and the evolution of forecasting methods is still ongoing.This article also launches the Annals of Tourism Research Curated Collection on tourism demand forecasting, which contains past and hot off the press work on the topic and will continue to grow as new articles on the topic appear in Annals.     

Forecasting international city tourism demand for Paris: Accuracy of uni- and multivariate models employing monthly data

The purpose of this study is to compare the predictive accuracy of various uni- and multivariate models in forecasting international city tourism demand for Paris from its five most important foreign source markets (Germany, Italy, Japan, UK and US). In order to achieve this, seven different forecast models are applied: EC-ADLM, classical and Bayesian VAR, TVP, ARMA, and ETS, as well as the naïve-1 model serving as a benchmark. The accuracy of the forecast models is evaluated in terms of the RMSE and the MAE. The results indicate that for the US and UK source markets, univariate models of ARMA(1,1) and ETS are more accurate, but that multivariate models are better predictors for the German and Italian source markets, in particular (Bayesian) VAR. For the Japanese source market, the results vary according to the forecast horizon. Overall, the naïve-1 benchmark is significantly outperformed across nearly all source markets and forecast horizons.     

Density tourism demand forecasting revisited

This study used scoring rules to evaluate density forecasts generated by different time-series models. Based on quarterly tourist arrivals to Hong Kong from ten source markets, the empirical results suggest that density forecasts perform better than point forecasts. The seasonal autoregressive integrated moving average (SARIMA) model was found to perform best among the competing models. The innovation state space models for exponential smoothing and the structural time-series models were significantly outperformed by the SARIMA model. Bootstrapping improved the density forecasts, but only over short time horizons.This article also launches the Annals of Tourism Research Curated Collection on Tourism Demand Forecasting, a special selection of research in this field.     

A piecewise linear approach to modeling and forecasting demand for Macau tourism

In this paper, we construct and use a piecewise linear method to model and forecast, on a monthly basis, the demand for Macau tourism. Data over the period January 1991–December 2005 and a seasonally adjusted series for tourism demand are used. The study examines 4 forecasting horizons ranging from 6 to 24 months in advance. Mean absolute percentage errors and root mean square errors are adopted as criteria for evaluating the accuracy of the forecasting exercises. Finally, the forecasts of piecewise linear model are compared with those of autoregressive trend model, seasonal autoregressive integrated moving average and its arch-rival fractionally integrated autoregressive moving average models. The piecewise linear model is more accurate than the three benchmark models tested and the improvement is practically significant.     

Impact of financial/economic crisis on demand for hotel rooms in Hong Kong

The main objectives of this study are (1) to identify the factors that influence the demand for hotel rooms in Hong Kong and (2) to generate quarterly forecasts of that demand to assess the impact of the ongoing financial/economic crisis. The demand for four types of hotel room from the residents of nine major origin countries is considered, and forecasts are generated from the first quarter of 2009 to the fourth quarter of 2015. Econometric approaches are employed to calculate the demand elasticities and their corresponding confidence intervals, which are then used to generate interval demand predictions. The empirical results reveal that the most important factors in determining the demand for hotel rooms in Hong Kong are the economic conditions (measured by income level) in the origin markets, the price of the hotel rooms and the ‘word of mouth’ effect. Demand for High Tariff A and Medium Tariff hotel rooms is estimated to have experienced negative annual growth in 2009 due to the influence of the financial/economic crisis, whereas that for High Tariff B hotel rooms is thought to have grown in 2009 after having decreased in 2008. The demand for tourist guesthouse rooms is expected to be the least affected by the crisis. Overall demand is predicted to recover gradually from 2010 onwards.     

Progress in tourism demand research: Theory and empirics

To explore recent progress in tourism demand research, we comprehensively survey current studies in the leading tourism and hospitality journals, asking six evaluative questions about the scientific merits of the studies and three explorative questions about emerging areas in the literature. The examination identifies potential flaws and their consequences in the field of tourism demand. A theoretical foundation is recommended for future tourism demand studies with a view to reduce bias in the empirical analysis of tourism demand. Several emerging areas of analysis in the field of tourism demand are recognized and discussed. Our study provides critical insights that will enable future tourism demand research to generate more reliable, impactful information than in the past.     

Modelling the interdependence of tourism demand: The global vector autoregressive approach

This study develops a global vector autoregressive (global VAR or GVAR) model to quantify the cross-country co-movements of tourism demand and simulate the impulse responses of shocks to the Chinese economy. The GVAR model overcomes the endogeneity and over-parameterisation issues found in many tourism demand models. The results show the size of co-movements in tourism demand across 24 major countries in different regions. In the event of negative shocks to China’s real income and China’s tourism price variable, almost all of these countries would face fluctuations in their international tourism demand and in their tourism prices in the short run. In the long run, developing countries and China’s neighbouring countries would tend to be more negatively affected than developed countries.     

Spatial-temporal forecasting of tourism demand

This study conducts spatial-temporal forecasting to predict inbound tourism demand in 29 Chinese provincial regions. Eight models are estimated among a-spatial models (autoregressive integrated moving average [ARIMA] model and unobserved component model [UCM]) and spatial-temporal models (dynamic spatial panel models and space-time autoregressive moving average [STARMA] models with different specifications of spatial weighting matrices). An ex-ante forecasting exercise is conducted with these models to compare their one-/two-step-ahead predictions. The results indicate that spatial-temporal forecasting outperforms the a-spatial counterpart in terms of average forecasting error. Auxiliary regression finds the relative error of spatial-temporal forecasting to be lower in regions characterized by a stronger level of local spatial association. Lastly, theoretical and practical implications are provided.This article also launches the Annals of Tourism Research Curated Collection on Tourism Demand Forecasting, a special selection of research in this field.     

Academic tourism demand in Galicia,Spain

The last few decades have witnessed a dramatic increase in the mobility of higher education students. When fulfilling certain conditions, this type of mobility can actually be considered a type of tourist activity. This paper justifies the choice of the term “academic tourism” to describe such a form of tourism. Further to this, its primary purpose is to identify the main determinants that drive the demand of academic tourism in Galicia. An empirical analysis has been carried out using a dynamic panel data model by a generalized method of moments (GMM). Contrary to what can be observed in other types of tourism, the results suggest that academic tourism depends mainly on determinants that are not strictly economic; namely, the relevance of the habits and preferences of students, the potential for differential attractiveness of the University of Santiago de Compostela, and the significant impact of the Erasmus programme. In light of these results, policy implications are then discussed.     

Measuring price elasticities of demand for outbound tourism using competitiveness indices

The real exchange rate (REX) has long been used as the proxy for prices in tourism demand models. However it has limitations, particularly when it comes to models of outbound tourism. As an alternative, a price competitiveness index (PCI) is developed and used as a proxy for prices in a model of outbound tourism from Australia. Results obtained show that while REX is statistically insignificant and yields a price elasticity of −0.002, PCI is significant and generates a price elasticity of −1.07. The results obtained show that PCI outperforms REX as the preferred price variable in modelling outbound demand on both theoretic and empirical grounds. Furthermore, this index can be used to monitor the inter-temporal competitiveness of a destination.     

The determinants of Chinese visitors to Australia: A dynamic demand analysis

Although China has progressively become an important inbound tourism market for Australia, its demand elasticities have been little studied to date. This study examines the determinants of Chinese visitors to Australia using a dynamic time-series estimator. Interesting findings include a high income elasticity as a source of the continuous doubledigit growth rates in Chinese arrivals that Australia has experienced over the past two decades, together with relatively high total trip price elasticities for both short run and long run. A trend of Chinese outbound to Australia is also identified. From a policy perspective, the results confirm that keeping a low cost of visiting Australia, both ground and travel costs, is a good strategy to secure greater numbers of Chinese tourists.     

Demand elasticity estimates for New Zealand tourism

Demand elasticities for New Zealand tourism are estimated for 16 different international visitor segments. Segments are differentiated by origin, purpose of visit, and travel style. Elasticities for both international visitor arrivals and on-the-ground expenditure per arrival are estimated for each segment using time-series data. In general, on-the-ground consumption per arrival is more price sensitive than number of arrivals, and Asian market segments are found to be more price sensitive, both in terms of arrivals and on-the-ground expenditure, compared to international visitors from other regions. An application of the results is presented giving the total effect of exchange rate changes on expenditure by international visitors in New Zealand, and management implications are discussed.     

A meta-analysis of international tourism demand forecasting and implications for practice

Numerous studies on tourism forecasting have now been published over the past five decades. However, no consensus has been reached in terms of which types of forecasting models tend to be more accurate and in which circumstances. This study uses meta-analysis to examine the relationships between the accuracy of different forecasting models, and the data characteristics and study features. By reviewing 65 studies published during the period 1980–2011, the meta-regression analysis shows that the origins of tourists, destination, time period, modeling method, data frequency, number of variables and their measures and sample size all significantly influence the accuracy of forecasting models. This study is the first attempt to pair forecasting models with the data characteristics and the tourism forecasting context. The results provide suggestions for the choice of appropriate forecasting methods in different forecasting settings.     

Forecasting tourism demand with ARMA-based methods

The forecast of tourism volume in the form of arrivals is of special importance for tourism and other hospitality industries because it is an indicator of future demand, thereby providing basic information for subsequent planning and policy making. In this paper, three univariate ARMA-based models are applied to tourism demand, as represented by the number of world-wide visitors to Hong Kong, Japan, Korea, Taiwan, Singapore, Thailand, the Philippines, Australia and New Zealand. The study employs both monthly and quarterly time series generated from nine principal tourist destinations in Asian-Pacific region in the forecasting exercise to ensure the reliability of the forecasting evaluation. Forecasting performance based on disaggregated arrival series in a particular destination is examined as well. The general impression is that the ARMA-based models perform very well and in some cases the magnitude of mean absolute percentage error is lower than 2% level.     

Medical tourism development in Hong Kong: An assessment of the barriers

The aim of this study is to determine the factors influencing the development of medical tourism in Hong Kong. A qualitative research method was adopted to collect data from representatives of private and public hospitals, government bodies, and medical institutions. The results reveal that policies and regulations, government support, costs, capacity problems, and the healthcare needs of the local community are the main barriers to the development of such tourism. Several strategies for lifting these barriers are suggested, such as new promotional activity policies, government action to encourage investment in the medical tourism market, and cooperative efforts by the hospitality sector and medical institutions to develop medical tourism products.     

Global Financial/Economic Crisis and Tourist Arrival Forecasts for Hong Kong

This paper examines the impact of the global financial/economic crisis on the demand for Hong Kong tourism by residents of 10 major source markets for the period 2009–2012. To capture the influence of this crisis, the Autoregressive Distributed Lag Model (ADLM) is used to calculate the demand elasticities, and four scenarios (ranging from the most pessimistic to the most optimistic) are created to examine the possible impacts of changes in source market income levels and the price of tourism on the demand for Hong Kong tourism in these markets. The demand elasticities reveal that the economic conditions in the source markets are the most significant determinants of demand for Hong Kong tourism. In the most pessimistic scenario, total tourist arrivals to Hong Kong are projected to reach 27.6 million in 2009 and 26.0 million in 2012, whereas in the most optimistic scenario, these numbers are 30.7 million in 2009 and 33 million in 2012. In all of the scenarios, tourist arrivals from the long-haul markets are expected to suffer more losses relative to the short-haul markets during the 2009–2012 forecasting period. The forecasts also indicate that the market shares of the source markets will change slightly over this period, with Mainland China, Taiwan and Japan constituting the dominant markets for Hong Kong tourism.     

A travel demand model for Mainland Chinese tourists to Hong Kong

The purpose of the study was to determine what exogenous variables best explained the travel demand for Mainland Chinese tourists to Hong Kong. The 12 years (1984–1995) annual time series data of ‘number of Mainland Chinese tourist arrivals’, ‘China disposable income per capita’, ‘consumer price indices in Hong Kong and China’ and ‘exchange rates’ was used to develop a travel demand model. Seven exogenous variables were selected for the model through a literature review. An OLS multiple regression analysis was performed to identify the ‘best’ subset of seven exogenous variables to determine the demand model. The results showed that travel demand for Mainland Chinese tourists to Hong Kong could be explained by ‘disposable income per capita’ and ‘relaxation of visa requirements’.     

Optimal targeting of latent tourism demand segments

This paper identifies for the first time the optimal target markets employing the latent tourism demand expenditure, a novel concept in tourism literature. The study quantifies latent tourism demand between each pair of origin-destination through distinguishing by type of tourism and seasonality. It works with market shares that are estimated via a fractional regression model. Moreover, latent demand is clustered using a market segmentation approach based on a latent class regression. Finally, the optimal target markets are chosen depending on the expected latent tourism expenditure. The result has clear policy implications in terms of which markets are promoted, the optimal channels of communication and the maximum budget for each marketing campaign.     

The impact of the Asian financial crisis on Japanese demand for travel to Hong Kong: A study of various forecasting techniques

The Asian financial crisis has drawn worldwide attention because of its significant economic impact on local economics, especially on the economy of a tourism‐dependent destination. Unfortunately, there have been very few articles about the relationship of the Asian financial crisis and tourism demand forecasting. This relative lack of prior studies on the Asian financial crisis and tourism demand forecasting is particularly true in the context of Hong Kong. This article reports on a study that utilized officially published data to test the accuracy of forecasts of Japanese demand for travel to Hong Kong, measured in terms of the number of Japanese tourist arrivals. Seven commonly‐used tourism forecasting techniques were used to determine the forecasting accuracy. The quality of forecasting accuracy was measured in five dimensions. Experimental results indicated mixed results in terms of forecasting accuracy. Overall, artificial neural network outperformed other techniques in three of the five dimensions.     

Travel demand and behavior of university students in Hong Kong

University students represent an emerging market for the travel industry. This paper reports the findings of a study on the travel behavior and travel patterns of Hong Kong university students. Using a questionnaire, a survey was conducted to identify student travel motives, their travel planning process and travel preferences. A total of 264 university students in Hong Kong were interviewed and data analyzed. The results indicate that to “Experience new and different styles”, “Take it easy and relax”, “Go to places I haven't been before” and “Outstanding scenery” were perceived as important motives in students' travel decision-making. Four factors with 24 attributes were identified. These factors were “Social relationships”, “Obtain new experience”, “Relax and enjoy” and “Cost of travel”. Based on the findings of the study, it was suggested that modifications to the existing tourism products and services and new product development were necessary in order to meet the demand of this emerging market.