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Demand for Hotel Spending byVisitors to Hong KongRob Law PhD aa Hong Kong Polytechnic University's, Department ofHotel & Tourism Management, Hung Horn , Kowloon,Hong KongPublished online: 20 Oct 2008.

To cite this article: Rob Law PhD (1999) Demand for Hotel Spending by Visitors toHong Kong, Journal of Hospitality & Leisure Marketing, 6:4, 17-29, DOI: 10.1300/J150v06n04_03

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Demand for Hotel Spendingby Visitors to Hong Kong:

A Study of Various Forecasting TechniquesRob Law

ABSTRACT. The accurate forecasting of demand for hotel spending iscrucial for hoteliers, in terms of planning for improving operational effi-ciency, reducing costs, and enhancing service quality. Unfortunately, therehas been no prior study that incorporates formal forecasting techniquesinto the context of hotel spending. This paper reports on a study thatintegrated 8 forecasting techniques into demand for visitors’ spending inHong Kong, measured in visitors’ total hotel bills. Secondary sources ofdata were used to calibrate the forecasting models. Empirical results indi-cated that most of the chosen models succeeded in achieving high direc-tional change accuracy and trend change accuracy. Also, all forecastingmodels reached high correlation coefficients. However, the forecastingmodels attained various levels of mean absolute percentage error andacceptable output range. Overall, the autoregression and neural networkmodel appeared to outperform other models in all dimensions of forecast-ing accuracy. [Article copies available for a fee from The Haworth DocumentDeliveryService:1-800-342-9678.E-mailaddress:getinfo@haworthpressinc.com<Website: http://www.haworthpressinc.com>]

KEYWORDS. Hotel spending, forecasting, Hong Kong, planning

INTRODUCTION

Hong Kong’s tourism industry has undergone significant growth duringthe past 30 years. Visitors came to this former British colony to personally

Rob Law, PhD, is Assistant Professor in the Hong Kong Polytechnic University’sDepartment of Hotel & Tourism Management, Hung Hom, Kowloon, Hong Kong(E-mail: hmroblaw@polyu.edu.hk). His research interests are in hospitality andtourism information technology, tourism demand forecasting, and computer assistedhospitality education.

This research was supported in part by a Hong Kong Polytechnic Universityresearch grant under account number: A-PA96.

Journal of Hospitality & Leisure Marketing, Vol. 6(4) 2000E 2000 by The Haworth Press, Inc. All rights reserved. 17

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JOURNAL OF HOSPITALITY & LEISURE MARKETING18

experience its unique East-meets-West culture, its internationally famous cuisine,and its shopping attractions. Local industries that benefit directly from the tourismindustry include hospitality, food and beverage, the arts, and entertainment. Morespecifically, the hotel industry in Hong Kong depends entirely on a scattered travelmarket for its survival. In Hong Kong, over 95% of hotel business came frominternational visitors (Hong Kong Tourist Association, 1990-1997a). In the period1966 to 1997, the number of hotels in Hong Kong increased from 33 to 66 (HongKong Tourist Association, 1967-1998a). In the same period, hotels in Hong Kongexpanded from 6,089 rooms to 33,425 rooms, representing a 5.5-fold increase. Anatural outcome of this expansion in the hotel industry is the rise in hotel expensesby guests. Figure 1 shows the total amount of visitors’ hotel expenses, measured inreal monetary terms, from 1966 to 1997.

The ability to accurately forecast demand for guest hotel spending wouldbenefit hoteliers to better plan for their businesses, both at the strategic andtactical levels. According to Athiyaman and Robertson (1992), forecasting isa basic requirement of planning. Keiser (1989) argued that forecasting andplanning are necessary to all hospitality functions and to the hotel itself. As a

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FIGURE 1. Total Hotel Expenses for Visitors in Hong Kong (1966-1997)

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Rob Law 19

crucial part of hotel management, forecasting and planning involve decidingwhich business goals are feasible and achievable. Similarly, Mullins (1995)claimed that forecasting and planning in a hotel can provide an operationwith framework and coordination. This also helps hoteliers specify what thehotel enterprise expects, that is, what the hotel’s goals are. In spite of theincreasing importance of hotel spending in keeping the hotel industry finan-cially stable, there are very few published articles that incorporate forecastingtechniques into an analysis of hotel spending. Previous studies on hotelspending have concentrated on analyzing the relationship between demo-graphic variables and hotel expenditures (Clow, Garretson, & Kurtz, 1994;Davis & Mangan, 1992; Lewis, 1985). Although some published articleshave dealt with hotel monetary forecasting (Atkinson, Kelliher, & LeBruto,1997; Ciraldo, 1992; Schwartz & Hiemstra, 1997), these articles are notexactly forecasting hotel spending. The lack of any prior study in forecastingdemand for hotel spending is particularly relevant in the Hong Kong context.In view of this challenge, this research attempts to integrate commonly usedtourism forecasting techniques into visitors’ hotel expenses, and to determinethe forecasting accuracy of these techniques. In other words, the primaryobjective of this research is to provide techniques that are helpful to the HongKong hotel industry in forecasting hotel expenses. This, in turn, helps in-crease the likelihood that the hotel’s objectives will be met. In this paper,demand for guest hotel spending is defined as the total dollar value of thehotel bills paid by visitors in Hong Kong.

The rest of this paper is organized as follows. First, there is methodologysection, in which the data sources are discussed. This section also analyzesthe variables used in this research and the reasons for selecting these vari-ables. As well, this section examines the forecasting techniques used in thisresearch. Next, there is a section that scrutinizes the empirical findings offorecasting outcomes. Forecasting accuracy values in various measurementsare presented and analyzed. The final section presents a summary of thispaper and the contributions of this research. Future research possibilities arealso offered in this section.

METHODOLOGY

Data

In this study, all monetary values are measured in real Hong Kong Dollars(HK$) and adjusted by Hong Kong CPI (1990 = 100). The demand forvisitors’ hotel spending in Hong Kong can be specified in the following form:

Hotel_Expi = f (Arrivalsi, Acc_Ratei, Marketingi, Room_Ratei,Ser_Pricei, Occ_Ratei)

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Where Hotel_Expi = total expenses on hotel bills by visitors in HongKong in Year i

Arrivalsi = Number of visitors in Hong Kong in Year i

Acc_Ratei = Hotel Accommodation Rate in Hong Kong in Year i

Marketingi = Marketing Expenses to promote Hong Kong’s tourismindustry in Year i

Room_Ratei = Average Hotel Room Rate in Hong Kong in Year i

Ser_Pricei = Service Price in Hong Kong in Year i

Occ_Ratei = Hotel Room Occupancy Rate in Hong Kong in Year i

In the above model, Hotel_Exp measures demand for hotel expenses byHong Kong visitors, whereas other variables serve as independent variablesfor the model.

This research used secondary sources of data for forecasting model cal-ibration and accuracy testing. The selection of data was primarily based onthe reliability of sources, the availability of data, and the ability to quantifythe selected variables in the model-calibrating and model-testing stages. Hav-ing searched through various literature and publication channels, hotel ex-penses and relevant data in 1966 to 1997 were selected from the followingsources, and the next subsection will discuss the variables established foranalysis:

S Aggregate Series: National Government (Vol. 1), [CD-ROM] publishedby Data-stream International (December 16, 1998).

S Commissioner of Inland Revenue Annual Review, 1966-1997 publishedby the Census and Statistics Department, Hong Kong Government(1967-1998).

S Hong Kong Consumer Price Index (Annual), 1966-1997 published bythe Census and Statistics Department, Hong Kong Government(1967-1998).

S A Statistical Review of Tourism, 1966-1997 published by the HongKong Tourist Association (1967-1998a).

S Hong Kong Tourist Association Annual Report, 1966-1997 publishedby the Hong Kong Tourist Association (1967-1998b).

S Visitor Arrival Statistics, 1966-1997 published by the Hong Kong Tour-ist Association (1967-1998c).

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Rob Law 21

Variables

Hotel Expenses (Hotel_Exp) is defined as the total hotel bills paid byvisitors in Hong Kong. This variable served as the measurement for demandfor visitors’ hotel expenses in Hong Kong. Previous studies have indicatedthat visitors’ spending is an appropriate measurement of demand for tourism(Pyo, Uysal, & McLellan, 1991; Sheldon, 1994; Smeral, 1988).

Visitor Arrivals (Arrivals) is the number of visitor arrivals in Hong Kong,including visitors from Mainland China. In Hong Kong, over 95% of hotelbusiness comes from visitors (Hong Kong Tourist Association, 1990-1997a).Therefore, more visitor arrivals in Hong Kong mean more hotel business andmore hotel expenses.

Hotel Accommodation Rate (Acc_Rate) is defined as the percentage ofvisitors who had stayed in commercial hotels in Hong Kong. Ryan (1998)indicated that it is common for many visitors to stay with their friends orrelatives instead of in commercial hotels. Naturally, a higher hotel accom-modation rate suggests a higher amount of hotel expenses by visitors.

Marketing Expenses (Marketing) is the amount of money spent by theHong Kong Tourist Association to promote Hong Kong’s tourism industry.Previous studies have demonstrated that a higher level of tourism marketingexpenses can significantly increase the demand for tourism (Crouch, 1993;Witt & Witt, 1995), and therefore the hotel expenses.

Average Room Rate (Room_Rate) is a measurement of average hotel roomrate in Hong Kong. Using this variable as a proxy for the cost of living fortourists in Hong Kong, Law and Au (1999) showed that the average hotelroom rate could affect the demand for travel to Hong Kong. Knutson (1988)claimed that leisure travelers do find room rates and value important whenconsidering initial hotel selection. Accordingly, it is expected that the averageroom rate will negatively influence the amount of hotel expenses.

Service Price (Ser_Price) is defined as the ratio of the world CPI to theHong Kong CPI (1990 = 100). This variable is used as a proxy variable forthe relative prices for purchases. That is, this variable represents all pricesconsidered by a visitor when spending in Hong Kong (excluding hotel ex-penses). In reality, it is close to impossible to collect all real data for tourismprices and CPI values have widely been adopted as a proxy variable fortourism prices (Morley, 1994). Therefore, a higher service price will have anegative influence on tourist arrivals, implying a reduction in total hotelexpenditures.

Room Occupancy Rate (Occ_Rate) is the hotel room occupancy rate inHong Kong. Law (1998) claimed that there is a positive relationship betweenthe number of visitor arrivals and the hotel room occupancy rate in HongKong. In other words, more visitor arrivals are associated with more hotelguests, and therefore a higher amount of hotel expenses.

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Forecasting Techniques

At present, quantitative forecasting techniques mainly consist of causalregression models and time series models (Fretching, 1996; Granger, 1980;Witt & Witt, 1992). Causal regression forecasting models (known as econo-metric models) attempt to model the quantitative relationship between aspecific demand (known as a dependent variable) and its determinants(known as independent variables). In contrast, time series forecasting modelsbuild a relationship for the dependent variable solely based on the variable’spast performance. These causal regression or time series models are thenused, based on their past performance, to forecast or project future perfor-mance. The success of developing a relationship model depends entirely onthe availability of historical data (could be proxy or actual).

In this research, 8 forecasting techniques were used to forecast demand forvisitors’ hotel expenses in Hong Kong. These forecasting techniques in-cluded 2 causal regression models (multiple regression and neural network)and 6 time series models (Naïve 1, Naïve 2, moving average, autoregression,exponential smoothing, and Holt’s exponential smoothing). These commonlyused forecasting techniques included a state-of-the-art model (neural net-work) as well as classical models (others). The main objective of this paper isto investigate the quality of various forecasting techniques in the context ofvisitors’ hotel expenditures in Hong Kong, and to provide suggestions forhoteliers to better plan for their future business. Theoretical concepts and themathematical operations of these forecasting techniques are not covered inthis paper. Interested readers can refer to other references for details of thechosen forecasting methods. Technical details about neural network forecast-ing techniques can be found in Mazanec (1992), Pattie and Snyder (1996),and Zhang, Patuwo, and Hu (1998). Technical descriptions about other fore-casting techniques can be found in Fretching (1996) and Witt and Witt(1992). The next section discusses the forecasting results of these techniques.

EMPIRICAL FINDINGS

This research establishes various forecasting techniques to model the de-mand for hotel spending by visitors to Hong Kong. To date, there exists nopublished article that makes such an attempt. Among the 32 sample entries(1966-1997), 20 were randomly selected for model-calibration, and the restwere used for accuracy testing (validation). Computer programs were imple-mented for the aforementioned forecasting techniques. Table 1 shows theexperimental findings of the 8 different forecasting techniques, and Figure 2provides a graphical presentation of these findings.

In this paper, measurement of the accuracy of the 8 different forecastingmodels is based on directional change accuracy (DCA), trend change accura-

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Rob Law 23

TABLE 1. Experimental Results of Forecasting Visitors’ Hotel Expenses inHong Kong

Year Actual NN MR N1 N2 MV AR ES Holt

1 2.13 2.05 1.32 1.62 2.13 1.41 2.15 1.61 1.752 1.87 2.08 1.78 1.96 1.91 2.04 1.81 1.98 1.803 2.62 2.89 1.63 2.11 2.37 1.98 2.42 2.06 2.204 3.83 4.09 3.56 2.83 3.05 2.52 4.42 2.78 3.205 4.69 5.12 5.43 4.58 5.48 3.75 5.70 4.39 5.426 8.95 7.19 8.17 6.97 8.25 6.19 8.69 6.74 7.697 15.13 14.69 13.87 10.56 11.97 9.61 13.33 10.28 11.548 16.18 16.61 15.20 14.60 12.14 17.03 15.81 15.23 12.399 18.41 18.14 16.79 16.18 17.94 16.11 17.40 15.99 15.33

10 18.80 19.41 18.80 18.41 20.95 16.40 19.09 17.93 19.9311 21.03 20.88 21.27 18.80 19.20 17.80 20.73 18.63 20.3512 18.61 21.18 22.77 23.69 26.68 21.17 21.27 23.06 26.01

All monetary figures are in HK$ Billion.Actual -- actual hotel expenses by visitors.NN-- forecast hotel expenses by visitors using neural network.MR--forecast hotel expenses by visitors using multiple regression.N1-- forecast hotel expenses by visitors using naïve 1.N2-- forecast hotel expenses by visitors using naïve 2.MV-- forecast hotel expenses by visitors using moving average (3).AR-- forecast hotel expenses by visitors using autoregression.ES-- forecast hotel expenses by visitors using exponential smoothing (0.8).Holt-- forecast hotel expenses by visitors using Holt’s exponential smoothing (0.8,0.1).

cy (TCA), mean absolute percentage error (MAPE), acceptable output per-centage (Z), and correlation coefficient ( ). DCA is an indicator of a model’ssuccess in forecasting whether the value of a variable is higher or lower thanits previous value. TCA measures the ability of the model to successfullypredict the changes in a trend. MAPE is a measurement used to computepercentage errors relative to values in the historical data series. Z is used as arelative measurement of the acceptance level. As a reference point for opti-mal experimental outcome, and following a pervious approach adopted byLaw and Au (1999), Z was set at � 15% in this research. measures thecloseness of the observed and estimated hotel expenses by visitors in HongKong. Table 2 shows the empirical results of forecasting accuracy in terms ofDCA, TCA, MAPE, Z, and .

In general, all models were reasonably accurate in terms of DCA andTCA. With the exception of DCA and TCA for moving average and the DCAfor multiple regression, all models achieved over 80% DCA and TCA values,indicating that the models can correctly forecast whether there will be higheror lower hotel expenditure by visitors. Similarly, the models can accurately

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FIGURE 2. Graphical Presentation of the Eight Forecasting Methods

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Multiple Regression

Autoregression

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Rob Law 25

TABLE 2. A Comparison of the Forecasting Accuracy of Various Methods

NH MR N1 N2 MV AR ES Holt

DCA 83.33 75.00 83.33 83.33 66.67 91.67 83.33 83.33TCA 81.82 81.82 81.82 81.82 63.64 90.91 81.82 81.82MAPE 7.17 13.22 15.89 14.04 20.71 7.41 16.72 12.46Ζ 91.67 66.67 58.33 50.00 41.67 83.33 50.00 58.33γ 0.993 0.983 0.958 0.94 0.962 0.990 0.960 0.960

NN -- forecast hotel expenses by visitors using neural network.MR -- forecast hotel expenses by visitors using multiple regression.N1-- forecast hotel expenses by visitors using naive 1.N2-- forecast hotel expenses by visitors using naive 2.MV -- forecast hotel expenses by visitors using moving average (3).AR -- forecast hotel expenses by visitors using autoregression.ES -- forecast hotel expenses by visitors using exponential smoothing (0.8).Holt -- forecast hotel expenses by visitors using Holt’s exponential smoothing (0.8,0.1).

predict when there is a changing trend in relation to visitors’ hotel expenses.In particular, the autoregression model outperforms other models by achiev-ing over 90% accuracy rates for both DCA and TCA. This finding seems tobe consistent with a previous study (Witt & Witt, 1995), which claimed thatautoregression is a good indicator of DCA and TCA. Moving average, withthe worst DCA and TCA indicators in this study, scored accuracy values ofless than 70%. Unfortunately, no prior study exists that provides similarmoving average DCA and TCA indicators for comparison. A future studycould investigate this further.

However, the differences between error magnitudes (MAPE and Z) amongthe forecasting models were fairly big. Both neural network and autoregres-sion models attained less than 8% MAPE values. Moving average scored aMAPE value of over 20%, whereas other models scored MAPE values ofbetween 12.46% and 16.72%. Witt and Witt (1992) classified forecastingmodels with MAPE values of less than 10% as highly accurate forecasting,and forecasting models with MAPE values of between 10% and 20% as goodforecasting. Therefore, all forecasting models (except moving average) fellinto these 2 categories. The findings of this research appear to be consistentwith previous studies, in which autoregression and neural network weresuperior to other forecasting models in terms of MAPE values, while movingaverage attained the lowest forecasting accuracy (Law & Au, 1999; Pattie &Snyder, 1996; Witt & Witt, 1995).

Similar to MAPE, neural network and autoregression both appeared tooutperform other models in terms of Z. Specifically, autoregression and neu-ral network succeeded in achieving over 83% of estimated output falling in

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the � 15% range. Again, this is consistent with previous findings (Law,1998; Law & Au, 1999). Lastly, all forecasting models scored high values,indicating that actual and estimated hotel expenses are closely correlated.Although neural network and autoregression models scored the highestvalues, the difference between values among all models did not seem to belarge.

IMPLICATIONS AND CONCLUSIONS

This research has attempted to compare the accuracy of various forecast-ing techniques in the context of demand for hotel spending by visitors inHong Kong. Secondary sources of data were used for calibration and accura-cy testing of forecasting models. Empirical findings indicated that most fore-casting models could achieve reasonable directional change accuracy andtrend change accuracy results. However, the error magnitude accuracyshowed mixed performance. The autoregression and neural network bothachieved very high accuracy values in terms of mean absolute percentageerror and output acceptance level, whereas other models did not forecast aswell as autoregression and neural network in the MAPE and Z dimensions.Nonetheless, all forecasting models obtained high correlation coefficients. Inshort, autoregression and neural network appeared to be superior to othermodels in terms of all forecasting accuracy dimensions. In particular, autore-gression had the highest values for DCA and TCA, and neural network hadthe best MAPE and Z scores.

This study, albeit limited in scope--both in time frame and variables--islikely to be beneficial to hospitality practitioners, researchers, and policy-makers in Hong Kong and worldwide. Hotel managers can look at the pre-dicted values of guest hotel spending, and can plan for an increase or de-crease in demand. They can then perform a series of actions such as assessingthe current and future positions of the hotel, identifying possible opportuni-ties and risks, and establishing priorities and allocating resources. Similarly,hospitality researchers can confidently apply the forecasting models thatgenerated good forecasting results to their future research into the demand forhotel spending. In particular, autoregression and neural network are the mostreliable forecasting models for visitors’ hotel spending. Likewise, policy-makers in the hotel industry in Hong Kong can plan for hotel developmentprojects and related infrastructure projects. At present, the Hong Kong tour-ism industry is facing a major challenge. Tourist arrivals dropped 11.08% in1997 compared to 1996, and this figure dropped further 8.0% in 1998compared to 1997 (Hong Kong Tourist Association, 1998a). The reasons forthis drop, according to Cheung and Law (1998), were the regional financialturmoil, the loss of political uniqueness after the political hand-over in July

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1997, and the long-lasting problems of over-pricing and over-pollution. As aresult, total hotel expenses by visitors in Hong Kong dropped significantly in1997 and 1998. Specifically, visitors’ total hotel bills in Hong Kong fell16.91% in 1997 compared to 1996, and the corresponding figure in 1998 wasa further 30.04% reduction. Therefore, accurate predictions from forecastingmodels would certainly help people in the Hong Kong hotel industry improvetheir planning and decision making.

While there has been no prior study to forecast visitors’ hotel expendi-tures, this paper should serve as an indicator for future research directions inforecasting demand for international visitor arrivals and their demand forhotel spending. Future research could look into the market segments in termsof hotel expenditures. For instance, different groups of visitors, with differentdemographic characteristics and cultural backgrounds, may display differentcharacteristics in terms of their hotel expenses. It would be useful to forecastthe demand for hotel spending for each of these market segments. Then, onthe basis of the forecast values, hotel managers could plan separately for eachof these market segments. This, in turn, would mean a better quality ofservice for the hotel guests.

Another future research possibility would be to link the forecasting ofvisitors’ hotel expenses with yield management. Yield management is basedon demand and supply, and pricing is the key to profitability. Therefore, a setof demand-forecasting techniques could be established to determine whetherprices should be raised or lowered on the basis of the estimated guest hotelspending. However, the application of yield management is specific to indi-vidual hotels. The transformation of industry-wide hotel expenses forecastingto an individual hotel’s yield management application is very complex, andthis should be done with accurate tools.

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