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Accommodation determinants of seasonal patterns
Annals of Tourism Research, Vol. 34, No. 2, pp. 422–436, 2007 0160-7383/$ - see front matter Ó 2007 Elsevier Ltd. All rights reserved. Printed in Great Britain
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doi:10.1016/j.annals.2006.10.002
ACCOMMODATION DETERMINANTS OF SEASONAL PATTERNS Javier Capo´ Parrilla Antoni Riera Font Jaume Rossello´ Nadal Universitat de les Illes Balears, Spain Abstract: Seasonal demand variations represent a central theme in both academic research and in policymaking and management. The literature review shows general agreement that certain characteristics such as weather, school holidays, and special events influence the demand. This article explores an alternative vision, analyzing the supply determinants of seasonality related to accommodation services as a representative sector of tourism. The Balearic Islands of Spain are used as a typical Mediterranean destination where demand fluctuation is high. Results of the study show that establishments with a higher level of services measured in terms of star category tend to have a longer opening period during the year. Keywords: Accommodation, discrete choice models, seasonality, Balearics. Ó 2007 Elsevier Ltd. All rights reserved. Re´sume´: De´terminants d’he´bergement et tendances saisonnie`res. Les variations de la demande saisonnie`re constituent un the`me central dans la recherche acade´mique et dans la prise de de´cisions et la gestion. Un examen de la litte´rature montre un accord ge´ne´ral que certaines caracte´ristiques telles que climat, vacances scolaires et e´ve´nements spe´ciaux influencent la demande. Cet article examine une autre vision en analysant les de´terminants de l’offre avec la saisonnalite´ en ce qui concerne les services d’he´bergement comme secteur repre´sentatif du tourisme. Les ˆıles Bale´ares en Espagne sont pre´sente´es comme un destination me´diterrane´enne typique ou` la fluctuation de la demande est e´leve´e. Les re´sultats de l’e´tude montrent que les e´tablissements avec un plus haut niveau de services mesure´ en termes du syste`me d’e´toiles ont tendance a` avoir une pe´riode d’ouverture plus longue pendant l’anne´e. Mots-cle´s: he´bergement, mode`les de choix discret, saisonnalite´, Bale´ares. Ó 2007 Elsevier Ltd. All rights reserved.
INTRODUCTION It is often argued that seasonality in tourism is caused by two basic elements. The first relates to regular temporal variations in natural phenomena, particularly those associated with climate (temperature, rainfall, snow, sunlight, and the like) and the period of the year.
Javier Capo´ Parrilla, Antoni Riera Font and Jaume Rossello´ Nadal are all associate researchers at the university’s Center for Economic Research, members of the CSIC Associate Unit through the IMEDEA, and lecturers in the Department of Applied Economics, University of the Balearic Islands in Spain (Email). They share interests in environmental economics, tourism economics, economic growth, and econometric modeling. 422
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Tourists have specific preferences, which make it necessary to distinguish between different purposes of tourism (such as sun and sea, hiking, ski vacations, and conferences). The second cause depends on social factors and policies concerning specific customs and legislated holidays. They include school schedules, industrial and public holidays, festivals (religious and cultural), and other events that, even today, are usually based on historic conventions. For the tourism industry, annual demand fluctuation is a major problem, not only because of declining returns on investment and problems caused by recruiting and employing full- and part-time staff (Ashworth and Thomas 1999; Bar-On 1993, 1999; Krakover 2000), but also because of the temporal effects related to the efficiency and the capacity of the facilities (Sutcliffe and Sinclair 1980) and to the management of public goods and services such as infrastructures, public safety, public health (Murphy 1985), and natural resources (Manning and Powers 1984). On the other hand, when the number of tourists exceeds the capacities at accommodation and facilities, a reduction in the quality of the services can occur owing to congestion, overbooking, or saturation with subsequent reduced satisfaction level. It is not surprising that coping with seasonality has been one of the most public and private issues studied for years and one of the most recurrent issues in the literature (Baum and Lundtorp 2001; Koenig and Bischoff 2004a). Butler (1994) highlighted the fact that demand fluctuation is a difficult problem to overcome, arguing that despite the efforts to reduce peaks, monthly seasonality has, in fact, increased in some destinations with the rapid development of tourism, as Bar-On (1975) found. In this context, Yacoumis (1980) studies different alternatives for tackling demand fluctuations in Sri Lanka. Aguilo´ and Sastre (1984) look at international arrivals and activity rates for the Balearic Islands and compare different inequality measures. Soesilo and Mings (1986) analyze the problem in a US city exploring how specific types of businesses respond to demand fluctuations. Snepenger, Houser and Snepenger (1990) report on changes faced by tourism businesses in Alaska by examining the factors related to seasonal levels. Donatos and Zairis (1991) and Drakatos (1987) investigate the temporal tourism patterns in the main Greek islands in order to examine the possibilities of extending the high period. More recently, Uysal, Fesenmaier and O’Leary (1994) conduct another study on demand variations in the United States using a standard quarterly calendar breakdown to study the concentration of travel time. Owens (1994) concludes that a multiple-segment customer strategy seems most appropriate for Canada given that the largest percentage of sales is in summer and that specialized resorts are more vulnerable in uncertain economic periods. Janiskee (1996) analyzes the temporal aspects of community festivals in the United States as tourism demand generators, showing that many festivals at the national scale are held when the weather is suitable. Rossello´, Riera and Sanso´ (2004), using a regression analysis of the seasonal distribution of the Balearic arrivals, find that as income grows and relative prices fall, annual demand fluctuations tend to be less acute, but when the nominal
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exchange benefits tourists, they tend to be higher. Koenig and Bischoff (2004b) analyze the seasonal variations in occupancy rates for the accommodation sector in Wales, discussing several concrete pointers for tailoring marketing strategies to the requirements of different accommodation segments. SooCheong applies financial portfolio theory to France and describes the ‘‘seasonal demand efficient frontier’’ (2004:833), suggesting that marketers should select a mix of tourism segments that fall along a demand–risk target. Therefore, it is possible to identify four basic supply and demand strategies by reducing demand seasonality, its repercussion from a demand perspective, supply seasonality, and negative effects from the supply perspective. Strategies for Reducing Seasonality From a demand perspective for reducing losses, a distinction between policies designed to lessen seasonality and policies designed to reduce their negative effects can be made. Therefore, the ability to extend the season, or to introduce additional season(s), has probably been one of the most tested within the former. Events and festivals are one of the most common single options within this strategy (Baum and Hagen 1999; Getz 1991, 1997). In this sense, Baum and Hagen also discuss the identification of new market segments as another policy to increase demand outside the peak season. New or alternative sources of demand for existing products and facilities can include senior citizens, incentive and conference market and other business people, short-break holidaymakers, and affinity groups, as these are the most able and willing to travel in the shoulder or off-peak seasons (McEnnif 1992). In the same way, another of the most popular strategies has been the pricing policy. Contra-seasonal pricing is practiced widely to reduce the temporal spreading of tourism demand (Butler and Mao 1997). Thus, O’Driscoll (1985) addresses the recurrent problem of annual demand fluctuation in tourism from the United States to Europe, suggesting that promotional pricing can generally be effective in increasing off-peak tourism. The latter policies, designed to reduce the negative impacts of overcrowding, focus on decreasing relative demand in the peak season and can include an increase in travel or accommodation prices, or the introduction of entrance fees to attractions or protected natural areas during the primary tourism season(s) (Weaver and Oppermann 2000). Allock (1994) also suggests the spatial redistribution of demand at peak times through the development of circuits of attractions, twin attractions, or two-center holiday—such as one week spent at the seaside and one in the countryside—to take tourists away from congested or ecologically sensitive areas. From a supply perspective, strategies for reducing annual demand fluctuations are more limited and focused exclusively on policies oriented to reducing negative effects. Weaver and Oppermann explore ways of expanding current capacity as a means of dealing with high peak-season demand. This might, for instance, take the form of creating new facilities or utilizing external resources such as schools and
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universities on a temporary basis. There are obvious dangers if such measures are considered in isolation from other developments, as the increased supply might result in an overloading at other existing facilities (Mathieson and Wall 1982). It also has to be borne in mind that, if the capacity is expanded on a permanent basis, this strategy can lead to an increased problem of underutilization of facilities in the off-peak periods. On the other hand, from the supply perspective, another strategy to reduce the underutilization of resources and facilities is the closure of tourism establishments (completely or partly) in the low period. This radical measure for reducing costs is generally employed only when it is not possible to substantially increase demand outside the peak period. However, none of these supply strategies treat the problems mentioned above concerning staff, traffic congestion, deterioration of natural resources, infrastructure efficiency, and management of public services. In this context, the present study focuses on the accommodation supply to expand the peak period as a new policy oriented to reducing seasonality instead of only lessening negative effects. In this way, at the same time, it explores a psychographic dimension of the tourist trying to identify which hotel supply factors are more related to low-period tourists, some of whose characteristics may differ from the high-period ones, as many other studies have found (Calantone and Johar 1984; Spotts and Mahoney 1993). Although the analysis of the supply in the tourist segment is often limited because of the large gaps in the empirical evaluation of parts of the industry (Sinclair and Stabler 1997), it can be argued that accommodation is a predominant part of the product mix and consequently its analysis can usually be extrapolated to the whole. Thus, considering the Balearic Islands hotels as a case study, factors that determine the opening function pattern of the establishments are analyzed with the objective of finding which their characteristics are positively related with longer (or shorter) annual opening periods. Results can be useful, for the private sector improving the efficiency of its establishments, and for the public policymaking to establish incentives for infrastructures to help reduce annual demand fluctuations. Suggested policies derived from this study could be complementary and be combined with others focused on demand. THE OPENING FUNCTION It is often argued that one of the most general distinctions of accommodation supply is the high level of fixed costs and the high level of exit barriers (Bull 1995), characteristics that result, in the short run, in very low elasticity. For most temperate climate destinations of the northern hemisphere with high annual fluctuations, the alternatives for accommodation establishments in the low period are limited, temporary closing being one of the most used. In this context, although tourism demand, especially the modeling of it, has been a most popular area in the literature, surprisingly little attention is paid to supply. As a notable exception, Borooah (1999)
SEASONAL ACCOMMODATION DETERMINANTS
426
analyzes the supply of hotel rooms in Queensland using quarterly time series. He estimates the strength of the relationship among the number of rooms and earnings per occupied unit, occupancy rates, and the real rate of interest. His major conclusion is that the supply of rooms is strongly responsive to increases in earnings (per occupied unit) but is less influenced by increases in the occupancy rate or by changes in the interest rate. In any case, the review of the literature does not provide any theoretical framework that describes the opening behavior of the accommodation establishments throughout the year. For this reason, before evaluating the modeling alternatives that can be estimated econometrically, a simple theoretical model is presented for placing the opening function into context. The Theoretical Model It can be argued from a general point of view that the annual benefits function of a given tourism establishment is generated by the following expression: P i ¼ f ðq i ; s i ; xÞ
ð1Þ
where Pi represents the annual profit of the establishment i; qi is a vector for the major characteristics of the establishment; si represents the annual opening period (for simplicity measured in months); and x is a vector for other external factors related to profit but not directly controlled by managers (such as demand evolution or entertainment supply location). In this way, it is assumed that the annual profit of the establishment (Pi) can be explained, fundamentally, by qi, si and x. Given the difficulty of observing Pi and, consequently, of estimating f, it is assumed that each manager selects a given number of opening months in order to maximize the establishment profits, in the sense that each manager decides to open his/her establishment one month more only if expected net profits during this month are positive. Thus, the longer the opening period, the more expected months with a positive profit. This way, it is presumed that s i presents a direct relationship with the maximum establishment expected profits and, accordingly, it can be determined by: s i ¼ g ðq i ; xÞ
s i
ð2Þ
Because and some of qi and x are observable variable vectors, it is possible to establish a functional relationship between s i and the observables qi and x in order to quantify the influence of these variables on the opening period of the establishment. In this way, the main problem that must be solved arises from the fact that the objective variable, the profits, is a non-observed latent continuous variable; therefore s i , a limited and discrete variable, is taken as their projection. Since contracts of employment and other supply services frequently have a monthly periodicity, the opening period of accommodation establishments is usually determined by complete months. In destinations where the annual demand fluctuations are important, it is possible
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to simplify the definition of the opening period by distinguishing three seasons: high, medium, and low. Whatever the measurement alternative (months or seasons), in order to estimate econometrically the establishments’ opening function of Eq. (2), the characteristics of the issue that must be considered in the first stage are that the dependent variable is limited and discrete and consequently the use of ordinary least squares is inappropriate. In particular, for the case of the number of months, because the variable takes values like 12, 11, 10, 9, and the like, it is necessary to appeal to the ordered discrete choice models that have been developed in order to produce efficient estimations within this context. On the other hand, for the case of the opening season, because the dependent variable has only two possibilities (open or closed) for each season, efficient estimations can be originated through the binary choice model (Greene 2003). In both cases, the initial implicit hypothesis considers that the profits latent variable (Pi) is a linear function of the characteristics of the establishments (qi) and other location variables (x). Thus, for the modeling exercise that considers the number of months s i as the dependent variable the link with the profit function (Pi) can be described through i parameters (ci) that can be estimated and whose function is to establish the correspondence between the probability function and s i . In the case of the season, the implicit hypothesis is similar but simpler because the latent variable s i in expression 4 can only take two values, 1 if the establishment is opened in the specified season and 0 in any other case. The Balearics Study Hotel establishments in the Balearic Islands were chosen to evaluate the model. Reasons for this choice are several. First is the high experience of the economic agents derived from the maturity of the destination. Second is the importance of tourism sector to the economy of the islands. Third is the high seasonality level manifested, with openings of rooms lower than 20% in winter months versus nearly 100% in summer. Other characteristics are valid whatever the period in the year. There is a clear predominance of mass tourism of the sun and sand variety, coming mainly (about 70%) from the United Kingdom and Germany and a high repeat rate, with only about 25% visiting for the first time. Also, in the last few years, a new tendency of shortening the average length of stay has appeared. In this way, the Balearics can be understood as an avant-garde Mediterranean destination that has represented a development model for many other warm regions in the world. The early development of mass tourism in the islands during the 60s and its positive consequences on residents’ personal incomes, motivated other destinations around the world to copy the formula of package tours including accommodation. All these reasons explain why many international hotel chains with a high presence in the Mediterranean and the Caribbean (such as Sol Melia`, Barcelo´, Riu) have their origin in the Balearics.
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In this context, it is not surprising that the most popular accommodation alternative in the Balearics will be hotels, which in 2005 accounted for 74.2% of the total recorded nights including domestic tourism. From an economic point of view, the hotel sector of the islands represented 16.4% of the GDP of the Balearics in 2004. The total number of hotel beds is about 420,000 and they are classified in two main categories: gold and silver. There are different hotel qualities, ranging from one to five stars in the case of gold establishments, and from one to three stars in the case of silver. The more stars, the higher quality and more services (heated swimming pool, jacuzzi, sauna, internet service in rooms, cre`che, hairdressing salon, massage service, and the like). Nowadays, the gold category is the most important, representing 75.0% of the hotel beds of the Balearics in 2005. Within this category, the three-star establishments are the most popular, with 55.6% of the beds. However, these have not been constant characteristics of the accommodation supply. During the last few years there has been a progressive transformation of low-quality establishments to high-quality ones. In the first stage, during the 80s, there was a massive transformation of silver establishments into gold. In the second stage, from the 90s to the present, public administration has promoted a restructuring policy in order to raise the quality of establishments, principally in terms of stars. Reasons for the public incentives that promote the improved quality of the establishments can be found in the belief that this is a way to differentiate the Balearic tourism product from traditionally centered ‘‘sun and sand’’ resorts, that have increased considerably during the last few years in the Mediterranean, and to achieve an increase in the mean daily tourist expenditure. Consequently, an additional objective of this study is to evaluate if this kind of public policy can help to reduce annual demand fluctuations, too. Study Results In order to find a relationship between the opening period of Balearic hotels and their characteristics, this study is based on a database from the Economic Research Center that was compiled using information in the Official Hotels Guide of Spain and direct interviews of some establishment managers. The hotels database was elaborated, taking into account all hotels in the regional government register in 2003 (1,317), and including a random sample of 217 establishments. However, because a number of the registers lack information about some of the desired variables, the sample was restricted to 198 (15% of the hotel population). For each establishment of the sample, the database includes information for 130 variables. These can be classified, first, as location ones (island, municipality, population, distance of the establishment from the nearest urban center, to the beach, to the nearest golf course, to the sea, and more). Second are the establishment characteristics (stars,
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N=198
90 78
80 70 60
47
50 40 29
30
18
20 10 10 1
2
4
5
6
7
10
11
0 6
7
8
9
12
Figure 1. Number of Opening Months of the Establishments’ in the Sample
24-hour room service, room heating, mini-bar, year of construction, year of the last restoration, number of rooms, sports facilities, size of building, garden area, price of a double room, opening seasons, number of opening months during the last year, and the like). Unfortunately, promotional efforts were not included in the database. In this way, it is possible to observe two variables with information about the opening behavior of the hotels. The first refers to the number of opening months during 2003 with distribution as shown in Figure 1. The second contains information from the Official Hotels Guide about the seasons in which the establishment regularly opens, if it offers services all year round, only in the medium and the high seasons, or only in high one. Traditionally, high, medium, and low season refers to particular months (June, July, August, and September for the high; March, April, May, and October for the medium; and January, February, November, and December for the low). Results show that all the establishments of the sample were open during the high season 2003, about 63% also in the medium and only 28% in the low one. According to the establishment opening behavioral model described above, different models were estimated in the first stage including all the large set of explanatory variables in the hotel establishments’ database. The ‘‘general-to-specific strategy’’ (Charemza and Deadman 1992:80–109; Hendry 1995:266) was applied in order to reduce the number of explanatory variables to be included in the final specifications. In order to guarantee the stability and the robustness of the results, different initial groups of variables were tested. However, because of the high correlation among them, only a very small set related to establishment characteristics was included in the final specifications, making the interpretation of the results complicated in all the cases.
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SEASONAL ACCOMMODATION DETERMINANTS
From a tactical point of view, a feasible way to overcome the problem is the use of principal component analysis to obtain artificial factors that are orthogonal with respect to each other. The idea is to obtain a low number of factors that could summarize the initial set of information. Once the factors are interpreted, they can be introduced in the regression analysis as independent variables with the guarantee that no multicollinearity problems will arise. However, in the case of the hotel establishments, it is usual that the number of stars and belonging to the gold or the silver categories, without being an exact linear combination of other variables, summarize, in fact, the principal characteristics of an accommodation establishment, making the interpretation of the results easier than using principal component analysis. In this respect, prior research (Aguilo´, Alegre and Riera 2001; Papatheodorou 2002; Sinclair, Clewer and Pack 1990; Thrane 2005) and conventional wisdom suggest that the star rating of a hotel to a large extent is a function of its both objective and subjective attributes. In this sense, as Table 1 shows, the category and the number of stars are clearly related to the incidence of the main set of the characteristics according to the rule that the more services, the higher category and stars. Thus, in order to obtain an unbiased result of the seasonality determinants, three different models (Table 2) were estimated in a second stage including the category (gold and silver) and the number of stars (from 1 to 5) as summarizing variables for the establishment characteristics jointly with other location and structural variables such as the island where the establishment is situated (Mallorca, Menorca, and Pitiusas), if the property is located in the main city of each island (Palma, Maho´ or Eivissa), if it is situated near the beach and/or near urban concentrations and, finally, the age of the building and the date of the building restoration. The reference establishment is a five-star golden hotel located in Mallorca (the largest island), not in the capital or in an urban concentration, but near the beach. Thus, OLOGIT refers to the ordered discrete choice model through a logistics distribution function where the dependent variable is the number of opening months; LOGIT-L refers to a logit binary choice model where the dependent variable is 1 if the establishment is open in the above low season and 0 otherwise; and finally LOGIT-M refers also to a logit binary choice model where the dependent variable is 1 if the establishment is open in the above low and medium seasons and 0 otherwise. Results can be qualified as acceptable given the joint significance of the variables and the high forecasting power of the logit models. Parameter estimations show that the establishments located on the smaller islands (Menorca and Pitiusas) present a lower opening period whatever the model used. Only for the low-season case and for the Pitiusas islands results are not significant, probably because of too few hotels. At the same time it is shown how the establishments located in the capitals of each one of the islands (capital), also present a high opening period. This circumstance can be related to the main propensity for shopping in poorer weather typical of the winter. In reference to other location variables, results show a negative effect on the opening period when the establishment is close to the beach (beach) and a
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Table 1. Incidence and Magnitude of Some Key Characteristics by Categories Characteristics and Services
All Silver sample 1*
Silver 2*
Silver 3*
Gold 1*
Gold 2*
Gold 3*
Gold 4*
Gold 5*
Establishments within the sample Web-site Private parking Restaurant Garden Central heating Air Conditioner Meeting room Satellite dish Internet service Cre`che Hairdressing salon Laundry Massage service Jacuzzi Sauna Heated swimming pool Outside swimming pool Rent a car service Strongbox Minibar 24 hours services Room restaurant service Alarm clock service TV in the room Internet in the room Hairdryer in the room Establishment age in years Years since the last improvement High season double room price US$ Mid season price double room US$ Low season double room price US$ Breakfasta US$ Meetings rooms M2 of sport facilities M2 of garden N° of restaurants
217
23
13
3
8
25
87
45
13
80.2% 42.9% 74.7% 82.0% 74.2% 64.5% 49.8% 81.6% 64.1% 15.7% 9.2% 53.9% 19.8% 27.2% 31.3% 33.2% 73.3% 77.9% 82.0% 21.7% 6.5% 27.6% 79.7% 74.7% 18.0% 41.0% 32.7 5.9
43.5% 13.0% 34.8% 56.5% 47.8% 34.8% 21.7% 65.2% 26.1% – – 21.7% – – – – 34.8% 52.2% 65.2% 8.7% – 17.4% 34.8% 30.4% 13.0% 13.0% 39.6 7.6
38.5% 7.7% 46.2% 46.2% 30.8% 30.8% 30.8% 53.8% 30.8% – – 30.8% – – – – 30.8% 30.8% 61.5% – – 7.7% 30.8% 30.8% – 15.4% 43.5 9.1
66.7% – 66.7% 100.0% 66.7% 100.0% – 100.0% 66.7% – – – – – – – 66.7% – 66.7% – – – 100.0% 66.7% – – 50.0 4.0
62.5% – 62.5% 50.0% 37.5% – 37.5% 50.0% 50.0% – – 37.5% – – – – 37.5% 62.5% 75.0% – – 12.5% 75.0% 37.5% – – 38.2 7.9
76.0% 40.0% 68.0% 68.0% 40.0% 40.0% 28.0% 84.0% 44.0% 8.0% – 40.0% 4.0% 8.0% 4.0% 4.0% 56.0% 80.0% 72.0% – – 20.0% 72.0% 60.0% 16.0% 24.0% 49.2 5.4
87.4% 41.4% 81.6% 90.8% 81.6% 67.8% 42.5% 83.9% 74.7% 21.8% 4.6% 52.9% 8.0% 19.5% 24.1% 29.9% 81.6% 83.9% 87.4% 9.2% – 19.5% 92.0% 83.9% 8.0% 24.1% 33.82 5.35
93.3% 60.0% 84.4% 93.3% 95.6% 95.6% 80.0% 91.1% 73.3% 17.8% 17.8% 77.8% 44.4% 60.0% 71.1% 64.4% 93.3% 88.9% 86.7% 53.3% 6.7% 44.4% 88.9% 95.6% 35.6% 91.1% 27.2 5.5
100.0% 92.3% 92.3% 92.3% 100.0% 84.6% 84.6% 76.9% 100.0% 30.8% 53.8% 84.6% 92.3% 84.6% 76.9% 100.0% 92.3% 84.6% 92.3% 76.9% 69.2% 61.5% 84.6% 92.3% 53.8% 92.3% 31.6 4.8
124.35
61.39
64.33
93.78
77.73
98.15
106.02 173.76
353.75
98.84
51.81
55.22
78.90
59.96
77.46
80.14
139.79
305.09
79.71
46.64
46.92
69.40
50.31
59.58
60.27
111.80
259.58
8.94 1.71 1 202 5 663 0.95
6.52 1.14 25 215 0.39
6.55 1.25 50 373 0.53
8.29 2.33 258 616 0.67
6.14 1.50 – 162 0.67
7.08 1.63 300 1951 0.65
8.12 1.54 1329 5221 0.98
11.36 2.05 1659 6343 1.19
20.33 3.31 2 406 31 066 2.21
Note: Characteristics refer to the average within each group.
positive effect when close to urban concentrations (central). Clearly, these results accord with the most popular tourist activities in the high and the low periods (Aguilo´, Bardolet and Sastre 2004). In relation to the category of the establishments, with the five-star hotels as the reference, it is shown that the lower the grade of the establishment, the greater the propensity to have a shorter opening period. In this sense, although it cannot be assumed statistically, through a
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SEASONAL ACCOMMODATION DETERMINANTS
Table 2. Estimation Results (Hotel Category) Variable
OLOGIT
LOGIT-L
LOGIT-M
gold1 gold2 gold3 gold4 silver1 silver2 silver3a menorca pitiusas capital beach central years constant
3.70a 3.00a 2.49a 1.15 2.32b 2.77a 2.90c 4.09a 3.09a 3.97a 1.07a 0.12a 0.03b c5 = 9.23; c6 = 7.71; c7 = 2.75; c8 = 1.66; c9 = 1.30; c10 = 0.62; c11 = 0.41; c12 = 0.16
1.96 3.52b 2.75a 1.51 1.51 1.86 – 3.79a 0.94 4.80a 1.53a 0.22a 0.03 0.144
3.96b 3.60c 2.83 0.58 3.41c 2.57 – 3.43a 3.27a 2.57b 1.19b 0.09 0.03c 3.098c
198 0.19 – 129.45a 270.17
198 0.43 87% 96.25a 62.66
198 0.30 74% 79.94a 92.34
N Pseudo R2 % Clasif. Correc. LR Chi2 Log Likelihood a, b
and c denote significance at 1%, 5% and 10%, respectively. d silver3 could not be used in the case of the binary models because of the low number of observations included in this category.
Wald test, that the four-star hotels (gold4) have a different opening period from the five-star ones, a negative sign from the parameter in all the three estimated models is obtained. In addition, as smaller categories are considered (gold3, gold2 and gold1), the corresponding parameter becomes more negative and more statistically significant, a characteristic that shows the existence of a clear relationship between the category of an establishment and its yearly opening behavior. Concerning the establishments included in the silver category (silver1, silver2 and silver3), results illustrate the same conclusions, showing that the lower the category of the establishment, the greater propensity it has of having a shorter opening period. Finally, it is also important to highlight the positive sign for the age of the building (years) in all the models, although only in the ordered logit model the parameter is significant at 5% level. A possible reason that could explain this result is that the years variable is collecting, in fact, the experience in management of the establishment which confers to the establishment a greater ability (reputation, repeat tourists, specialized entertainment) to expand the opening period. However, because management variables have not been included, results must be interpreted carefully.
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Policy Implications The results obtained show that the conventional categories used to describe different market segments in the accommodation business of the Balearics summarize the quantity and the quality of its services. In this sense, the gold category is characterized by bigger and, in general, more modern establishments with a higher level of services. The silver category includes more old-fashioned establishments with fewer rooms and services. Within both categories, establishments can be ranged again by the number of stars, in the sense that the more stars, the more quality and quantity of services. Through the identification of the relationship between different hotel characteristics and services and the yearly opening behavior of the establishment, it seems clear that properties with a longer opening period during the year are those with a high level of services. In this way, public policies that stimulate the transformation of the lower category hotels into higher ones (assuming that the higher the category the higher the level of services) could probably be able to capture consumers willing to travel in the low period and, consequently, would reduce yearly demand fluctuation in the destination. On the other hand, it has been shown that the location of the establishments is another important factor related to a longer or a shorter opening period. Therefore, results can be useful in planning new tourism areas close to historic centers, subject to ecological criteria. For the private sector, results can help to justify marketing strategies oriented to the increase of quality and quantity of services related to the expanding the high period and improving business efficiency. Further, from a psychographic perspective, it is shown that low-period tourists, unlike highperiod ones, are motivated by different factors than the traditional weather and beach. It will depend on the hotel marketing strategies to attract this kind of tourist. CONCLUSION Although seasonality is one of the most well-known and characteristic phenomena of the tourism market, there is a lack of knowledge of the factors that explain yearly demand fluctuations beyond the climatic or institutional and social. Consequently, measures to reduce seasonality have been among the more explored tactics by both public administrations and the private sector. From a demand perspective, the most popular tactics for reducing yearly peaks and troughs have been the organization of special events and festivals, the identification of new market segments, and promotional pricing. From a supply perspective, the tactics include expanding the current capacity to deal with peak-period demand and closing enterprises during the lows. However, these tactics do not eliminate the problems associated with seasonality. In contrast to the existing literature, this work has dealt with the problem with a focus on the accommodation sector. Because of limited literature describing the foundations of the openings of these
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establishments, a conceptual framework has been derived. In order to evaluate the model, the case study of the hotels of the Spanish Balearics has been analyzed, to identify the characteristics that influence the opening period. Thus, different discrete choice models have been estimated, evidencing that higher quality services in a hotel positively affect its opening period. Location, closer to urban areas and in Mallorca (the island with better transport connections with the continent during winter), is also related to a longer opening period. For the private sector, results can help justify quality service investments related to expanding the high period and increasing efficiency through lower fluctuation. For public administration, results can justify restructuring industry policies to promote transforming lower-quality hotels into higher and converting them from the silver to the gold category. Moreover, it has shown that other characteristics related to location are also determinants for a longer (or shorter) opening period. They cannot be used as instrument policies in the short term but can be useful in the long term to plan new tourism developments in warm Mediterranean regions. Future research will have to respond to such questions as which connections among hotel facilities and categories determine the seasonal behavior of tourists, and what the importance of image is, as opposed to the specific facilities, in explaining consumers’ behavior when they choose a particular accommodation. REFERENCES Aguilo´, E., E. Bardolet, and A. Sastre 2004 La Despesa Turı´stica (The Tourist Expenditure). Palma de Mallorca: Government of the Balearic Islands. Aguilo´, E., and A. Sastre 1984 La Medicio´n de la Estacionalidad del Turismo: El caso de Baleares. Estudios Turı´sticos 81:79–88. Aguilo´, P., J. Alegre, and A. Riera 2001 Determinants of the Price of German Tourists Packages on the Island of Mallorca. Tourism Economics 7:59–74. Ashworth, J., and B. Thomas 1999 Patterns of Seasonality in Employment in Tourism in the UK. Applied Economics Letters 6:735–739. Allock, J. 1994 Seasonality. In Tourism Marketing and Management Handbook, S. Witt and L. Moutinho, eds., pp. 86–92. New York: Prentice Hall. Bar-On, R. 1975 Seasonality in Tourism: A Guide to the Analysis of Seasonality and Trends for Policy Making. London: The Economist Intelligence Unit, Technical Series No. 2. 1993 Seasonality. In VNR’s Encyclopedia of Hospitality and Tourism, M. Khan, M. Olsen and T. Var, eds., pp. 705–734. New York: Van Nostrand Reinhold. 1999 The Measurement of Seasonality and its Economic Impacts. Tourism Economics 5:437–458. Baum, T., and L. Hagen 1999 Responses to Seasonality: The Experiences of Peripheral Destinations. International Journal of Tourism Research 1:299–312. Baum, T. and S. Lundtorp, eds. 2001 Seasonality in Tourism. Oxford: Elsevier.
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Borooah, V. 1999 The Supply of Hotel Rooms in Queensland, Australia. Annals of Tourism Research 26:985–1003. Bull, A. 1995 The Economics of Travel and Tourism. Melbourne: Longman. Butler, R. 1994 Seasonality in Tourism: Issues and Problems. In Tourism: the State of the Art, A. Seaton, C. Jenkins, R. Wood, P. Dieke, M. Bennet, L. Maclellan and R. Smith, eds., pp. 332–339. Chichester: Wiley. Butler, R., and B. Mao 1997 Seasonality in Tourism: Problems and Measurement. In Quality Management in Urban Tourism, P. Murphy, ed., pp. 9–23. Chichester: Wiley. Calantone, R., and J. Johar 1984 Seasonal Segmentation of the Tourism Market Using a Benefit Segmentation Framework. Journal of Travel Research 22(2):14–24. Charemza, W., and D. Deadman 1992 New Directions in Econometric Practice. Cheltenham: Edward Elgar. Donatos, G., and P. Zairis 1991 Seasonality of Foreign Tourism in the Greek Island of Crete. Annals of Tourism Research 18:515–519. Drakatos, C. 1987 Seasonal Concentration of Tourism in Greece. Annals of Tourism Research 14:582–586. Getz, D. 1991 Festivals, Special Events and Tourism. New York: Van Nostrand Reinhold. 1997 Event Management and Event Tourism. New York: Cognizant. Greene, W. 2003 Econometric Analysis. Englewood Cliffs: Prentice Hall. Hendry, D. 1995 Dynamic Econometrics. Oxford: Oxford University Press. Janiskee, R. 1996 The Temporal Distribution of America’s Community Festivals. Festival Management and Event Tourism 3:129–137. Koenig, N., and E. Bischoff 2004a Seasonality Research: The State of Art. Working papers of the School of Business and Economics: University of Wales Swansea. 2004b Analyzing Seasonality in Welsh Room Occupancy Data. Annals of Tourism Research 31:374–392. Krakover, S. 2000 Partitioning Seasonal Employment in the Hospitality Industry. Tourism Management 21:461–471. Manning, R., and L. Powers 1984 Peak and Off-peak Use: Redistributing the Outdoor Recreation/Tourism Load. Journal of Travel Research 23(2):25–31. Mathieson, A., and G. Wall 1982 Tourism: Economic, Physical and Social Impacts. Essex: Longman. McEnnif, J. 1992 Seasonality of Tourism Demand in the European Community. EIU Travel and Tourism Analyst 3:67–88. Murphy, P. 1985 Tourism: A Community Approach. London: Methuen. O’Driscoll, T. 1985 Seasonality in the Trans-Atlantic Vacation Market. Annals of Tourism Research 12:109–110. Owens, D. 1994 The All-season Opportunity for Canada’s Resorts. The Cornell Hotel and Restaurant Administration Quarterly 35(5):28–41. Papatheodoru, A. 2002 Exploring Competitiveness in the Mediterranean Resorts. Tourism Economics 8:133–150.
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Rossello´, J., A. Riera, and A. Sanso 2004 The Economic Determinants of Seasonal Patterns. Annals of Tourism Research 31:697–711. Sinclair, M., A. Clewer, and A. Pack 1990 Hedonic Prices and the Marketing of Package Holidays: The Case of Tourist Resorts in Malaga. In Marketing Tourism Places, G. Ashworth and B. Goodall, eds., pp. 85–103. London: Routledge. Sinclair, M., and M. Stabler 1997 The Economics of Tourism. London: Routledge. Snepenger, D., B. Houser, and M. Snepenger 1990 Seasonality of Demand. Annals of Tourism Research 17:628–630. Soesilo, J., and R. Mings 1986 Assessing the Seasonality of Tourism. Visions in Leisure and Business 6(2):25–38. SooCheong, J. 2004 Mitigating Tourism Seasonality. A Quantitative Approach. Annals of Tourism Research 31:819–836. Spotts, D., and E. Mahoney 1993 Understanding the Fall Tourism Market. Journal of Travel Research 32(2):3–15. Sutcliffe, C., and M. Sinclair 1980 The Measurement of Seasonality within the Tourist Industry: An Application to Tourist Arrivals in Spain. Applied Economics 12(4):429–441. Thrane, C. 2005 Hedonic Price Models and Sun-and-Beach Package Tours: The Norwegian Case. Journal of Travel Research 43(3):302–308. Uysal, M., D. Fesenmaier, and J. O’Leary 1994 Geographic and Seasonal Variation in the Concentration of Travel in the United States. Journal of Travel Research 32(3):61–64. Weaver, D., and M. Oppermann 2000 Tourism Management. Brisbane: Wiley. Yacoumis, J. 1980 Tackling Seasonality: The Case of Sri Lanka. Tourism Management 1:84–98.
Submitted 15 February 2005. Resubmitted 14 May 2006. Resubmitted 19 July 2006. Resubmitted 27 July 2006. Final version 30 August 2006. Accepted 11 October 2006. Refereed anonymously. Coordinating Editor: Stephen L. J. Smith
www.elsevier.com/locate/atoures
doi:10.1016/j.annals.2006.10.002
ACCOMMODATION DETERMINANTS OF SEASONAL PATTERNS Javier Capo´ Parrilla Antoni Riera Font Jaume Rossello´ Nadal Universitat de les Illes Balears, Spain Abstract: Seasonal demand variations represent a central theme in both academic research and in policymaking and management. The literature review shows general agreement that certain characteristics such as weather, school holidays, and special events influence the demand. This article explores an alternative vision, analyzing the supply determinants of seasonality related to accommodation services as a representative sector of tourism. The Balearic Islands of Spain are used as a typical Mediterranean destination where demand fluctuation is high. Results of the study show that establishments with a higher level of services measured in terms of star category tend to have a longer opening period during the year. Keywords: Accommodation, discrete choice models, seasonality, Balearics. Ó 2007 Elsevier Ltd. All rights reserved. Re´sume´: De´terminants d’he´bergement et tendances saisonnie`res. Les variations de la demande saisonnie`re constituent un the`me central dans la recherche acade´mique et dans la prise de de´cisions et la gestion. Un examen de la litte´rature montre un accord ge´ne´ral que certaines caracte´ristiques telles que climat, vacances scolaires et e´ve´nements spe´ciaux influencent la demande. Cet article examine une autre vision en analysant les de´terminants de l’offre avec la saisonnalite´ en ce qui concerne les services d’he´bergement comme secteur repre´sentatif du tourisme. Les ˆıles Bale´ares en Espagne sont pre´sente´es comme un destination me´diterrane´enne typique ou` la fluctuation de la demande est e´leve´e. Les re´sultats de l’e´tude montrent que les e´tablissements avec un plus haut niveau de services mesure´ en termes du syste`me d’e´toiles ont tendance a` avoir une pe´riode d’ouverture plus longue pendant l’anne´e. Mots-cle´s: he´bergement, mode`les de choix discret, saisonnalite´, Bale´ares. Ó 2007 Elsevier Ltd. All rights reserved.
INTRODUCTION It is often argued that seasonality in tourism is caused by two basic elements. The first relates to regular temporal variations in natural phenomena, particularly those associated with climate (temperature, rainfall, snow, sunlight, and the like) and the period of the year.
Javier Capo´ Parrilla, Antoni Riera Font and Jaume Rossello´ Nadal are all associate researchers at the university’s Center for Economic Research, members of the CSIC Associate Unit through the IMEDEA, and lecturers in the Department of Applied Economics, University of the Balearic Islands in Spain (Email
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Tourists have specific preferences, which make it necessary to distinguish between different purposes of tourism (such as sun and sea, hiking, ski vacations, and conferences). The second cause depends on social factors and policies concerning specific customs and legislated holidays. They include school schedules, industrial and public holidays, festivals (religious and cultural), and other events that, even today, are usually based on historic conventions. For the tourism industry, annual demand fluctuation is a major problem, not only because of declining returns on investment and problems caused by recruiting and employing full- and part-time staff (Ashworth and Thomas 1999; Bar-On 1993, 1999; Krakover 2000), but also because of the temporal effects related to the efficiency and the capacity of the facilities (Sutcliffe and Sinclair 1980) and to the management of public goods and services such as infrastructures, public safety, public health (Murphy 1985), and natural resources (Manning and Powers 1984). On the other hand, when the number of tourists exceeds the capacities at accommodation and facilities, a reduction in the quality of the services can occur owing to congestion, overbooking, or saturation with subsequent reduced satisfaction level. It is not surprising that coping with seasonality has been one of the most public and private issues studied for years and one of the most recurrent issues in the literature (Baum and Lundtorp 2001; Koenig and Bischoff 2004a). Butler (1994) highlighted the fact that demand fluctuation is a difficult problem to overcome, arguing that despite the efforts to reduce peaks, monthly seasonality has, in fact, increased in some destinations with the rapid development of tourism, as Bar-On (1975) found. In this context, Yacoumis (1980) studies different alternatives for tackling demand fluctuations in Sri Lanka. Aguilo´ and Sastre (1984) look at international arrivals and activity rates for the Balearic Islands and compare different inequality measures. Soesilo and Mings (1986) analyze the problem in a US city exploring how specific types of businesses respond to demand fluctuations. Snepenger, Houser and Snepenger (1990) report on changes faced by tourism businesses in Alaska by examining the factors related to seasonal levels. Donatos and Zairis (1991) and Drakatos (1987) investigate the temporal tourism patterns in the main Greek islands in order to examine the possibilities of extending the high period. More recently, Uysal, Fesenmaier and O’Leary (1994) conduct another study on demand variations in the United States using a standard quarterly calendar breakdown to study the concentration of travel time. Owens (1994) concludes that a multiple-segment customer strategy seems most appropriate for Canada given that the largest percentage of sales is in summer and that specialized resorts are more vulnerable in uncertain economic periods. Janiskee (1996) analyzes the temporal aspects of community festivals in the United States as tourism demand generators, showing that many festivals at the national scale are held when the weather is suitable. Rossello´, Riera and Sanso´ (2004), using a regression analysis of the seasonal distribution of the Balearic arrivals, find that as income grows and relative prices fall, annual demand fluctuations tend to be less acute, but when the nominal
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exchange benefits tourists, they tend to be higher. Koenig and Bischoff (2004b) analyze the seasonal variations in occupancy rates for the accommodation sector in Wales, discussing several concrete pointers for tailoring marketing strategies to the requirements of different accommodation segments. SooCheong applies financial portfolio theory to France and describes the ‘‘seasonal demand efficient frontier’’ (2004:833), suggesting that marketers should select a mix of tourism segments that fall along a demand–risk target. Therefore, it is possible to identify four basic supply and demand strategies by reducing demand seasonality, its repercussion from a demand perspective, supply seasonality, and negative effects from the supply perspective. Strategies for Reducing Seasonality From a demand perspective for reducing losses, a distinction between policies designed to lessen seasonality and policies designed to reduce their negative effects can be made. Therefore, the ability to extend the season, or to introduce additional season(s), has probably been one of the most tested within the former. Events and festivals are one of the most common single options within this strategy (Baum and Hagen 1999; Getz 1991, 1997). In this sense, Baum and Hagen also discuss the identification of new market segments as another policy to increase demand outside the peak season. New or alternative sources of demand for existing products and facilities can include senior citizens, incentive and conference market and other business people, short-break holidaymakers, and affinity groups, as these are the most able and willing to travel in the shoulder or off-peak seasons (McEnnif 1992). In the same way, another of the most popular strategies has been the pricing policy. Contra-seasonal pricing is practiced widely to reduce the temporal spreading of tourism demand (Butler and Mao 1997). Thus, O’Driscoll (1985) addresses the recurrent problem of annual demand fluctuation in tourism from the United States to Europe, suggesting that promotional pricing can generally be effective in increasing off-peak tourism. The latter policies, designed to reduce the negative impacts of overcrowding, focus on decreasing relative demand in the peak season and can include an increase in travel or accommodation prices, or the introduction of entrance fees to attractions or protected natural areas during the primary tourism season(s) (Weaver and Oppermann 2000). Allock (1994) also suggests the spatial redistribution of demand at peak times through the development of circuits of attractions, twin attractions, or two-center holiday—such as one week spent at the seaside and one in the countryside—to take tourists away from congested or ecologically sensitive areas. From a supply perspective, strategies for reducing annual demand fluctuations are more limited and focused exclusively on policies oriented to reducing negative effects. Weaver and Oppermann explore ways of expanding current capacity as a means of dealing with high peak-season demand. This might, for instance, take the form of creating new facilities or utilizing external resources such as schools and
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universities on a temporary basis. There are obvious dangers if such measures are considered in isolation from other developments, as the increased supply might result in an overloading at other existing facilities (Mathieson and Wall 1982). It also has to be borne in mind that, if the capacity is expanded on a permanent basis, this strategy can lead to an increased problem of underutilization of facilities in the off-peak periods. On the other hand, from the supply perspective, another strategy to reduce the underutilization of resources and facilities is the closure of tourism establishments (completely or partly) in the low period. This radical measure for reducing costs is generally employed only when it is not possible to substantially increase demand outside the peak period. However, none of these supply strategies treat the problems mentioned above concerning staff, traffic congestion, deterioration of natural resources, infrastructure efficiency, and management of public services. In this context, the present study focuses on the accommodation supply to expand the peak period as a new policy oriented to reducing seasonality instead of only lessening negative effects. In this way, at the same time, it explores a psychographic dimension of the tourist trying to identify which hotel supply factors are more related to low-period tourists, some of whose characteristics may differ from the high-period ones, as many other studies have found (Calantone and Johar 1984; Spotts and Mahoney 1993). Although the analysis of the supply in the tourist segment is often limited because of the large gaps in the empirical evaluation of parts of the industry (Sinclair and Stabler 1997), it can be argued that accommodation is a predominant part of the product mix and consequently its analysis can usually be extrapolated to the whole. Thus, considering the Balearic Islands hotels as a case study, factors that determine the opening function pattern of the establishments are analyzed with the objective of finding which their characteristics are positively related with longer (or shorter) annual opening periods. Results can be useful, for the private sector improving the efficiency of its establishments, and for the public policymaking to establish incentives for infrastructures to help reduce annual demand fluctuations. Suggested policies derived from this study could be complementary and be combined with others focused on demand. THE OPENING FUNCTION It is often argued that one of the most general distinctions of accommodation supply is the high level of fixed costs and the high level of exit barriers (Bull 1995), characteristics that result, in the short run, in very low elasticity. For most temperate climate destinations of the northern hemisphere with high annual fluctuations, the alternatives for accommodation establishments in the low period are limited, temporary closing being one of the most used. In this context, although tourism demand, especially the modeling of it, has been a most popular area in the literature, surprisingly little attention is paid to supply. As a notable exception, Borooah (1999)
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analyzes the supply of hotel rooms in Queensland using quarterly time series. He estimates the strength of the relationship among the number of rooms and earnings per occupied unit, occupancy rates, and the real rate of interest. His major conclusion is that the supply of rooms is strongly responsive to increases in earnings (per occupied unit) but is less influenced by increases in the occupancy rate or by changes in the interest rate. In any case, the review of the literature does not provide any theoretical framework that describes the opening behavior of the accommodation establishments throughout the year. For this reason, before evaluating the modeling alternatives that can be estimated econometrically, a simple theoretical model is presented for placing the opening function into context. The Theoretical Model It can be argued from a general point of view that the annual benefits function of a given tourism establishment is generated by the following expression: P i ¼ f ðq i ; s i ; xÞ
ð1Þ
where Pi represents the annual profit of the establishment i; qi is a vector for the major characteristics of the establishment; si represents the annual opening period (for simplicity measured in months); and x is a vector for other external factors related to profit but not directly controlled by managers (such as demand evolution or entertainment supply location). In this way, it is assumed that the annual profit of the establishment (Pi) can be explained, fundamentally, by qi, si and x. Given the difficulty of observing Pi and, consequently, of estimating f, it is assumed that each manager selects a given number of opening months in order to maximize the establishment profits, in the sense that each manager decides to open his/her establishment one month more only if expected net profits during this month are positive. Thus, the longer the opening period, the more expected months with a positive profit. This way, it is presumed that s i presents a direct relationship with the maximum establishment expected profits and, accordingly, it can be determined by: s i ¼ g ðq i ; xÞ
s i
ð2Þ
Because and some of qi and x are observable variable vectors, it is possible to establish a functional relationship between s i and the observables qi and x in order to quantify the influence of these variables on the opening period of the establishment. In this way, the main problem that must be solved arises from the fact that the objective variable, the profits, is a non-observed latent continuous variable; therefore s i , a limited and discrete variable, is taken as their projection. Since contracts of employment and other supply services frequently have a monthly periodicity, the opening period of accommodation establishments is usually determined by complete months. In destinations where the annual demand fluctuations are important, it is possible
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to simplify the definition of the opening period by distinguishing three seasons: high, medium, and low. Whatever the measurement alternative (months or seasons), in order to estimate econometrically the establishments’ opening function of Eq. (2), the characteristics of the issue that must be considered in the first stage are that the dependent variable is limited and discrete and consequently the use of ordinary least squares is inappropriate. In particular, for the case of the number of months, because the variable takes values like 12, 11, 10, 9, and the like, it is necessary to appeal to the ordered discrete choice models that have been developed in order to produce efficient estimations within this context. On the other hand, for the case of the opening season, because the dependent variable has only two possibilities (open or closed) for each season, efficient estimations can be originated through the binary choice model (Greene 2003). In both cases, the initial implicit hypothesis considers that the profits latent variable (Pi) is a linear function of the characteristics of the establishments (qi) and other location variables (x). Thus, for the modeling exercise that considers the number of months s i as the dependent variable the link with the profit function (Pi) can be described through i parameters (ci) that can be estimated and whose function is to establish the correspondence between the probability function and s i . In the case of the season, the implicit hypothesis is similar but simpler because the latent variable s i in expression 4 can only take two values, 1 if the establishment is opened in the specified season and 0 in any other case. The Balearics Study Hotel establishments in the Balearic Islands were chosen to evaluate the model. Reasons for this choice are several. First is the high experience of the economic agents derived from the maturity of the destination. Second is the importance of tourism sector to the economy of the islands. Third is the high seasonality level manifested, with openings of rooms lower than 20% in winter months versus nearly 100% in summer. Other characteristics are valid whatever the period in the year. There is a clear predominance of mass tourism of the sun and sand variety, coming mainly (about 70%) from the United Kingdom and Germany and a high repeat rate, with only about 25% visiting for the first time. Also, in the last few years, a new tendency of shortening the average length of stay has appeared. In this way, the Balearics can be understood as an avant-garde Mediterranean destination that has represented a development model for many other warm regions in the world. The early development of mass tourism in the islands during the 60s and its positive consequences on residents’ personal incomes, motivated other destinations around the world to copy the formula of package tours including accommodation. All these reasons explain why many international hotel chains with a high presence in the Mediterranean and the Caribbean (such as Sol Melia`, Barcelo´, Riu) have their origin in the Balearics.
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In this context, it is not surprising that the most popular accommodation alternative in the Balearics will be hotels, which in 2005 accounted for 74.2% of the total recorded nights including domestic tourism. From an economic point of view, the hotel sector of the islands represented 16.4% of the GDP of the Balearics in 2004. The total number of hotel beds is about 420,000 and they are classified in two main categories: gold and silver. There are different hotel qualities, ranging from one to five stars in the case of gold establishments, and from one to three stars in the case of silver. The more stars, the higher quality and more services (heated swimming pool, jacuzzi, sauna, internet service in rooms, cre`che, hairdressing salon, massage service, and the like). Nowadays, the gold category is the most important, representing 75.0% of the hotel beds of the Balearics in 2005. Within this category, the three-star establishments are the most popular, with 55.6% of the beds. However, these have not been constant characteristics of the accommodation supply. During the last few years there has been a progressive transformation of low-quality establishments to high-quality ones. In the first stage, during the 80s, there was a massive transformation of silver establishments into gold. In the second stage, from the 90s to the present, public administration has promoted a restructuring policy in order to raise the quality of establishments, principally in terms of stars. Reasons for the public incentives that promote the improved quality of the establishments can be found in the belief that this is a way to differentiate the Balearic tourism product from traditionally centered ‘‘sun and sand’’ resorts, that have increased considerably during the last few years in the Mediterranean, and to achieve an increase in the mean daily tourist expenditure. Consequently, an additional objective of this study is to evaluate if this kind of public policy can help to reduce annual demand fluctuations, too. Study Results In order to find a relationship between the opening period of Balearic hotels and their characteristics, this study is based on a database from the Economic Research Center that was compiled using information in the Official Hotels Guide of Spain and direct interviews of some establishment managers. The hotels database was elaborated, taking into account all hotels in the regional government register in 2003 (1,317), and including a random sample of 217 establishments. However, because a number of the registers lack information about some of the desired variables, the sample was restricted to 198 (15% of the hotel population). For each establishment of the sample, the database includes information for 130 variables. These can be classified, first, as location ones (island, municipality, population, distance of the establishment from the nearest urban center, to the beach, to the nearest golf course, to the sea, and more). Second are the establishment characteristics (stars,
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N=198
90 78
80 70 60
47
50 40 29
30
18
20 10 10 1
2
4
5
6
7
10
11
0 6
7
8
9
12
Figure 1. Number of Opening Months of the Establishments’ in the Sample
24-hour room service, room heating, mini-bar, year of construction, year of the last restoration, number of rooms, sports facilities, size of building, garden area, price of a double room, opening seasons, number of opening months during the last year, and the like). Unfortunately, promotional efforts were not included in the database. In this way, it is possible to observe two variables with information about the opening behavior of the hotels. The first refers to the number of opening months during 2003 with distribution as shown in Figure 1. The second contains information from the Official Hotels Guide about the seasons in which the establishment regularly opens, if it offers services all year round, only in the medium and the high seasons, or only in high one. Traditionally, high, medium, and low season refers to particular months (June, July, August, and September for the high; March, April, May, and October for the medium; and January, February, November, and December for the low). Results show that all the establishments of the sample were open during the high season 2003, about 63% also in the medium and only 28% in the low one. According to the establishment opening behavioral model described above, different models were estimated in the first stage including all the large set of explanatory variables in the hotel establishments’ database. The ‘‘general-to-specific strategy’’ (Charemza and Deadman 1992:80–109; Hendry 1995:266) was applied in order to reduce the number of explanatory variables to be included in the final specifications. In order to guarantee the stability and the robustness of the results, different initial groups of variables were tested. However, because of the high correlation among them, only a very small set related to establishment characteristics was included in the final specifications, making the interpretation of the results complicated in all the cases.
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From a tactical point of view, a feasible way to overcome the problem is the use of principal component analysis to obtain artificial factors that are orthogonal with respect to each other. The idea is to obtain a low number of factors that could summarize the initial set of information. Once the factors are interpreted, they can be introduced in the regression analysis as independent variables with the guarantee that no multicollinearity problems will arise. However, in the case of the hotel establishments, it is usual that the number of stars and belonging to the gold or the silver categories, without being an exact linear combination of other variables, summarize, in fact, the principal characteristics of an accommodation establishment, making the interpretation of the results easier than using principal component analysis. In this respect, prior research (Aguilo´, Alegre and Riera 2001; Papatheodorou 2002; Sinclair, Clewer and Pack 1990; Thrane 2005) and conventional wisdom suggest that the star rating of a hotel to a large extent is a function of its both objective and subjective attributes. In this sense, as Table 1 shows, the category and the number of stars are clearly related to the incidence of the main set of the characteristics according to the rule that the more services, the higher category and stars. Thus, in order to obtain an unbiased result of the seasonality determinants, three different models (Table 2) were estimated in a second stage including the category (gold and silver) and the number of stars (from 1 to 5) as summarizing variables for the establishment characteristics jointly with other location and structural variables such as the island where the establishment is situated (Mallorca, Menorca, and Pitiusas), if the property is located in the main city of each island (Palma, Maho´ or Eivissa), if it is situated near the beach and/or near urban concentrations and, finally, the age of the building and the date of the building restoration. The reference establishment is a five-star golden hotel located in Mallorca (the largest island), not in the capital or in an urban concentration, but near the beach. Thus, OLOGIT refers to the ordered discrete choice model through a logistics distribution function where the dependent variable is the number of opening months; LOGIT-L refers to a logit binary choice model where the dependent variable is 1 if the establishment is open in the above low season and 0 otherwise; and finally LOGIT-M refers also to a logit binary choice model where the dependent variable is 1 if the establishment is open in the above low and medium seasons and 0 otherwise. Results can be qualified as acceptable given the joint significance of the variables and the high forecasting power of the logit models. Parameter estimations show that the establishments located on the smaller islands (Menorca and Pitiusas) present a lower opening period whatever the model used. Only for the low-season case and for the Pitiusas islands results are not significant, probably because of too few hotels. At the same time it is shown how the establishments located in the capitals of each one of the islands (capital), also present a high opening period. This circumstance can be related to the main propensity for shopping in poorer weather typical of the winter. In reference to other location variables, results show a negative effect on the opening period when the establishment is close to the beach (beach) and a
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Table 1. Incidence and Magnitude of Some Key Characteristics by Categories Characteristics and Services
All Silver sample 1*
Silver 2*
Silver 3*
Gold 1*
Gold 2*
Gold 3*
Gold 4*
Gold 5*
Establishments within the sample Web-site Private parking Restaurant Garden Central heating Air Conditioner Meeting room Satellite dish Internet service Cre`che Hairdressing salon Laundry Massage service Jacuzzi Sauna Heated swimming pool Outside swimming pool Rent a car service Strongbox Minibar 24 hours services Room restaurant service Alarm clock service TV in the room Internet in the room Hairdryer in the room Establishment age in years Years since the last improvement High season double room price US$ Mid season price double room US$ Low season double room price US$ Breakfasta US$ Meetings rooms M2 of sport facilities M2 of garden N° of restaurants
217
23
13
3
8
25
87
45
13
80.2% 42.9% 74.7% 82.0% 74.2% 64.5% 49.8% 81.6% 64.1% 15.7% 9.2% 53.9% 19.8% 27.2% 31.3% 33.2% 73.3% 77.9% 82.0% 21.7% 6.5% 27.6% 79.7% 74.7% 18.0% 41.0% 32.7 5.9
43.5% 13.0% 34.8% 56.5% 47.8% 34.8% 21.7% 65.2% 26.1% – – 21.7% – – – – 34.8% 52.2% 65.2% 8.7% – 17.4% 34.8% 30.4% 13.0% 13.0% 39.6 7.6
38.5% 7.7% 46.2% 46.2% 30.8% 30.8% 30.8% 53.8% 30.8% – – 30.8% – – – – 30.8% 30.8% 61.5% – – 7.7% 30.8% 30.8% – 15.4% 43.5 9.1
66.7% – 66.7% 100.0% 66.7% 100.0% – 100.0% 66.7% – – – – – – – 66.7% – 66.7% – – – 100.0% 66.7% – – 50.0 4.0
62.5% – 62.5% 50.0% 37.5% – 37.5% 50.0% 50.0% – – 37.5% – – – – 37.5% 62.5% 75.0% – – 12.5% 75.0% 37.5% – – 38.2 7.9
76.0% 40.0% 68.0% 68.0% 40.0% 40.0% 28.0% 84.0% 44.0% 8.0% – 40.0% 4.0% 8.0% 4.0% 4.0% 56.0% 80.0% 72.0% – – 20.0% 72.0% 60.0% 16.0% 24.0% 49.2 5.4
87.4% 41.4% 81.6% 90.8% 81.6% 67.8% 42.5% 83.9% 74.7% 21.8% 4.6% 52.9% 8.0% 19.5% 24.1% 29.9% 81.6% 83.9% 87.4% 9.2% – 19.5% 92.0% 83.9% 8.0% 24.1% 33.82 5.35
93.3% 60.0% 84.4% 93.3% 95.6% 95.6% 80.0% 91.1% 73.3% 17.8% 17.8% 77.8% 44.4% 60.0% 71.1% 64.4% 93.3% 88.9% 86.7% 53.3% 6.7% 44.4% 88.9% 95.6% 35.6% 91.1% 27.2 5.5
100.0% 92.3% 92.3% 92.3% 100.0% 84.6% 84.6% 76.9% 100.0% 30.8% 53.8% 84.6% 92.3% 84.6% 76.9% 100.0% 92.3% 84.6% 92.3% 76.9% 69.2% 61.5% 84.6% 92.3% 53.8% 92.3% 31.6 4.8
124.35
61.39
64.33
93.78
77.73
98.15
106.02 173.76
353.75
98.84
51.81
55.22
78.90
59.96
77.46
80.14
139.79
305.09
79.71
46.64
46.92
69.40
50.31
59.58
60.27
111.80
259.58
8.94 1.71 1 202 5 663 0.95
6.52 1.14 25 215 0.39
6.55 1.25 50 373 0.53
8.29 2.33 258 616 0.67
6.14 1.50 – 162 0.67
7.08 1.63 300 1951 0.65
8.12 1.54 1329 5221 0.98
11.36 2.05 1659 6343 1.19
20.33 3.31 2 406 31 066 2.21
Note: Characteristics refer to the average within each group.
positive effect when close to urban concentrations (central). Clearly, these results accord with the most popular tourist activities in the high and the low periods (Aguilo´, Bardolet and Sastre 2004). In relation to the category of the establishments, with the five-star hotels as the reference, it is shown that the lower the grade of the establishment, the greater the propensity to have a shorter opening period. In this sense, although it cannot be assumed statistically, through a
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SEASONAL ACCOMMODATION DETERMINANTS
Table 2. Estimation Results (Hotel Category) Variable
OLOGIT
LOGIT-L
LOGIT-M
gold1 gold2 gold3 gold4 silver1 silver2 silver3a menorca pitiusas capital beach central years constant
3.70a 3.00a 2.49a 1.15 2.32b 2.77a 2.90c 4.09a 3.09a 3.97a 1.07a 0.12a 0.03b c5 = 9.23; c6 = 7.71; c7 = 2.75; c8 = 1.66; c9 = 1.30; c10 = 0.62; c11 = 0.41; c12 = 0.16
1.96 3.52b 2.75a 1.51 1.51 1.86 – 3.79a 0.94 4.80a 1.53a 0.22a 0.03 0.144
3.96b 3.60c 2.83 0.58 3.41c 2.57 – 3.43a 3.27a 2.57b 1.19b 0.09 0.03c 3.098c
198 0.19 – 129.45a 270.17
198 0.43 87% 96.25a 62.66
198 0.30 74% 79.94a 92.34
N Pseudo R2 % Clasif. Correc. LR Chi2 Log Likelihood a, b
and c denote significance at 1%, 5% and 10%, respectively. d silver3 could not be used in the case of the binary models because of the low number of observations included in this category.
Wald test, that the four-star hotels (gold4) have a different opening period from the five-star ones, a negative sign from the parameter in all the three estimated models is obtained. In addition, as smaller categories are considered (gold3, gold2 and gold1), the corresponding parameter becomes more negative and more statistically significant, a characteristic that shows the existence of a clear relationship between the category of an establishment and its yearly opening behavior. Concerning the establishments included in the silver category (silver1, silver2 and silver3), results illustrate the same conclusions, showing that the lower the category of the establishment, the greater propensity it has of having a shorter opening period. Finally, it is also important to highlight the positive sign for the age of the building (years) in all the models, although only in the ordered logit model the parameter is significant at 5% level. A possible reason that could explain this result is that the years variable is collecting, in fact, the experience in management of the establishment which confers to the establishment a greater ability (reputation, repeat tourists, specialized entertainment) to expand the opening period. However, because management variables have not been included, results must be interpreted carefully.
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Policy Implications The results obtained show that the conventional categories used to describe different market segments in the accommodation business of the Balearics summarize the quantity and the quality of its services. In this sense, the gold category is characterized by bigger and, in general, more modern establishments with a higher level of services. The silver category includes more old-fashioned establishments with fewer rooms and services. Within both categories, establishments can be ranged again by the number of stars, in the sense that the more stars, the more quality and quantity of services. Through the identification of the relationship between different hotel characteristics and services and the yearly opening behavior of the establishment, it seems clear that properties with a longer opening period during the year are those with a high level of services. In this way, public policies that stimulate the transformation of the lower category hotels into higher ones (assuming that the higher the category the higher the level of services) could probably be able to capture consumers willing to travel in the low period and, consequently, would reduce yearly demand fluctuation in the destination. On the other hand, it has been shown that the location of the establishments is another important factor related to a longer or a shorter opening period. Therefore, results can be useful in planning new tourism areas close to historic centers, subject to ecological criteria. For the private sector, results can help to justify marketing strategies oriented to the increase of quality and quantity of services related to the expanding the high period and improving business efficiency. Further, from a psychographic perspective, it is shown that low-period tourists, unlike highperiod ones, are motivated by different factors than the traditional weather and beach. It will depend on the hotel marketing strategies to attract this kind of tourist. CONCLUSION Although seasonality is one of the most well-known and characteristic phenomena of the tourism market, there is a lack of knowledge of the factors that explain yearly demand fluctuations beyond the climatic or institutional and social. Consequently, measures to reduce seasonality have been among the more explored tactics by both public administrations and the private sector. From a demand perspective, the most popular tactics for reducing yearly peaks and troughs have been the organization of special events and festivals, the identification of new market segments, and promotional pricing. From a supply perspective, the tactics include expanding the current capacity to deal with peak-period demand and closing enterprises during the lows. However, these tactics do not eliminate the problems associated with seasonality. In contrast to the existing literature, this work has dealt with the problem with a focus on the accommodation sector. Because of limited literature describing the foundations of the openings of these
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establishments, a conceptual framework has been derived. In order to evaluate the model, the case study of the hotels of the Spanish Balearics has been analyzed, to identify the characteristics that influence the opening period. Thus, different discrete choice models have been estimated, evidencing that higher quality services in a hotel positively affect its opening period. Location, closer to urban areas and in Mallorca (the island with better transport connections with the continent during winter), is also related to a longer opening period. For the private sector, results can help justify quality service investments related to expanding the high period and increasing efficiency through lower fluctuation. For public administration, results can justify restructuring industry policies to promote transforming lower-quality hotels into higher and converting them from the silver to the gold category. Moreover, it has shown that other characteristics related to location are also determinants for a longer (or shorter) opening period. They cannot be used as instrument policies in the short term but can be useful in the long term to plan new tourism developments in warm Mediterranean regions. Future research will have to respond to such questions as which connections among hotel facilities and categories determine the seasonal behavior of tourists, and what the importance of image is, as opposed to the specific facilities, in explaining consumers’ behavior when they choose a particular accommodation. REFERENCES Aguilo´, E., E. Bardolet, and A. Sastre 2004 La Despesa Turı´stica (The Tourist Expenditure). Palma de Mallorca: Government of the Balearic Islands. Aguilo´, E., and A. Sastre 1984 La Medicio´n de la Estacionalidad del Turismo: El caso de Baleares. Estudios Turı´sticos 81:79–88. Aguilo´, P., J. Alegre, and A. Riera 2001 Determinants of the Price of German Tourists Packages on the Island of Mallorca. Tourism Economics 7:59–74. Ashworth, J., and B. Thomas 1999 Patterns of Seasonality in Employment in Tourism in the UK. Applied Economics Letters 6:735–739. Allock, J. 1994 Seasonality. In Tourism Marketing and Management Handbook, S. Witt and L. Moutinho, eds., pp. 86–92. New York: Prentice Hall. Bar-On, R. 1975 Seasonality in Tourism: A Guide to the Analysis of Seasonality and Trends for Policy Making. London: The Economist Intelligence Unit, Technical Series No. 2. 1993 Seasonality. In VNR’s Encyclopedia of Hospitality and Tourism, M. Khan, M. Olsen and T. Var, eds., pp. 705–734. New York: Van Nostrand Reinhold. 1999 The Measurement of Seasonality and its Economic Impacts. Tourism Economics 5:437–458. Baum, T., and L. Hagen 1999 Responses to Seasonality: The Experiences of Peripheral Destinations. International Journal of Tourism Research 1:299–312. Baum, T. and S. Lundtorp, eds. 2001 Seasonality in Tourism. Oxford: Elsevier.
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Submitted 15 February 2005. Resubmitted 14 May 2006. Resubmitted 19 July 2006. Resubmitted 27 July 2006. Final version 30 August 2006. Accepted 11 October 2006. Refereed anonymously. Coordinating Editor: Stephen L. J. Smith