managing tourism products and destinations embedding public good components: a hedonic approach

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Tourism Management 32 (2011) 244–255

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Tourism Management

journal homepage: www.elsevier .com/locate/ tourman

Managing tourism products and destinations embedding public goodcomponents: A hedonic approachq

Ricard Rigall-I-Torrent*, Modest Fluvia 1

Departament d’Economia, Universitat de Girona, Facultat de Ciencies Economiques i Empresarials, Campus Montilivi, 17071 Girona, Catalonia, Spain

a r t i c l e i n f o

Article history:Received 12 May 2009Accepted 21 December 2009

Keywords:BundlingPricingPublic goodsHedonic methodsLocationTourism destinations

q The authors are very grateful to Josep-Maria Espdata and to three anonymous referees for their helpf

* Corresponding author. Tel.: þ34 972 418 781; faxE-mail addresses: [email protected] (R. Rigall-I

edu (M. Fluvia).1 Tel.: þ34 972 418 738; fax: þ34 972 418 032.

0261-5177/$ – see front matter � 2009 Elsevier Ltd.doi:10.1016/j.tourman.2009.12.009

a b s t r a c t

Decision making by tourism firms’ managers and public policymakers is complex for many reasons. Oneof them is that many tourism products embed a combination of multiple public (external to the decisionsof individual firms, related to location and essentially non-rival) and private attributes. Since tourists getsatisfaction from each of the components of the product variety bought, managers face the daunting taskof putting together, promoting and pricing a bundle of heterogeneous components. This paper draws onhedonic pricing literature to obtain insights (beyond making correct pricing decisions) for tourism firms’managers and public policymakers when dealing with products and destinations embedding public goodcomponents. An application to coastal hotels in tourism destinations of Catalonia is presented.

� 2009 Elsevier Ltd. All rights reserved.

1. Introduction

Many products can be understood as bundles of characteristics(Lancaster, 1966; Rosen, 1974). Tourism-related products are noexception. Tourists get satisfaction from each of the components ofa particular product variety bought (see Hasegawa, 2010, fora recent analysis). For instance, hotel customers derive utility fromswimming pools, sports facilities, the quality of food, room services,etc. (see, for instance, Espinet, Saez, Coenders, & Fluvia, 2003;Haroutunian, Mitsis, & Pashardes, 2005; Thrane, 2005). Sincetourism products must be consumed where they are produced, thephysical environment where the production or consumption takesplace also matters. That is, consumers’ choices depend on thespecific combination of public as well as private attributes whichgive rise to the final product. For instance, a run down, insecureenvironment in a tourism resort would surely put off the potentialcustomers of a trendy restaurant or a luxury hotel.

In this paper public can be understood as external to the privatefirms’ decisions, related to location and essentially non-rival. Inparticular, note that public does not mean provided or produced bythe public sector and does not refer only to the so-called publicgoods and services. Public attributes include a location’s cultural

inet for providing them withul advice.: þ34 972 418 032.-Torrent), modest.fluvia@udg.

All rights reserved.

legacy, public safety, degree of preservation of the environment,brand image, public infrastructures, or street cleanliness. All ofthem have a certain degree of non-rivalry (i.e., the cost of additionalusers enjoying public attributes is zero) and of nonexcludability(i.e., after public attributes have been provided, it is not possible toexclude those users who have not purchased the product fromenjoying/suffering them), i.e., they have characteristics of publicgood (Samuelson, 1954). Network externalities (Tirole, 1988) canalso be understood as public attributes, since the satisfactionobtained from a hotel room by tourists depends not only on theattributes embedded in it, but on the availability of complementaryproducts offered by nearby businesses, such as restaurants or pubs.In this setting, identifying the effects of public characteristics on thefinal tourism product marketed by a firm is relevant for manage-ment, since firms need to know how different combinations ofcharacteristics affect the prices they can charge (see Lewbel, 1985).Besides, policymakers, in order to implement appropriate policies,must be aware of the effects that public attributes have on the firmsin their jurisdiction (in a general setting see, for instance, Eppen,Hanson, & Martin, 1991; Green & Krieger, 1985; Guiltinan, 1987;Hanson & Martin, 1990).

Consider a concrete example. Some tourists expect a closematch between their hotel’s private and public characteristics. (Thiscan be summarised as ‘‘a four-star hotel needs a four-star envi-ronment’’.) Those tourists could be considering two hotels, A and B,showing similar prices and identical advertised private character-istics. However, if hotel A was located in a municipality with lots ofhigh quality public characteristics and hotel B was surrounded bya run-down environment, then tourists could be deceived into

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R. Rigall-I-Torrent, M. Fluvia / Tourism Management 32 (2011) 244–255 245

thinking that both hotels represented similar value for money,although they did not. Given this state of affairs, it is important toanswer different questions. For instance, how can firms ascertainthe position of their product with respect to other firms’ competingvarieties on the space of public and private characteristics? Howcan firms design and implement the appropriate promotion,differentiation and pricing strategies? In a nutshell, how cantourism firms’ managers approach the complex task of marketingtourism products embedding public good components?

This paper draws on hedonic pricing literature to provide theanalytical and empirical grounds to answer managerially relevantquestions concerning both public and private characteristics.Studies in areas other than tourism, such as housing markets,usually consider public characteristics (see, for instance, Baranzini,Ramirez, Schaerer, & Thalmann, 2008; Chay & Greenstone, 2005;Hughes & Sirmans, 1992; Polinsky & Shavell, 1976). Althoughhedonic methods have been used in tourism settings, studies in thisarea have mostly centred their attention on private attributes. Anexception is Rigall-I-Torrent and Fluvia (2007), which analyse theeffects of public goods on hotel prices in Catalonia. Nonetheless,that study relies solely on the jurisdiction where hotels are locatedto capture the public attributes of tourism products. Thus, thedifferent public dimensions of interest, such as those considered inthe present paper (exclusivity, complementary products andservices, crowdedness, natural environment, or public safety), arenot disentangled in Rigall-I-Torrent and Fluvia (2007). Besides,previous studies have not taken full advantage of the wholepotential of the methodology. This paper addresses both issues. Todo so, the paper proceeds in 5 sections in addition to this intro-duction. Section 2 briefly sketches the analytical setting underlyinghedonic methods, justifies the utility of hedonic methods, andshows that previous hedonic studies in tourism settings have notaddressed this paper’s topic. Section 3 includes an applicationinvolving coastal hotels in tourism destinations of Catalonia.Section 4 shows how indexes of public and private attributes can becomputed from the hedonic estimates. Section 5 draws differentimplications for managers and policymakers. Finally, the lastsection presents the paper’s main conclusions.

2. Public and private characteristics and the hedonicapproach

2.1. Public and private characteristics

According to Rosen’s (1974) setting, a tourism product can beunderstood as a vector (bundle) of objectively measured public andprivate characteristics or attributes

c ¼�

c1; c2;.; cN; zj1; z

j2;.; zj

M

�; (1)

where cn is the measured value of the private characteristic n, zjm is

the measured value of the public characteristic m for a firm injurisdiction j, and it is assumed that N private and M public char-acteristics exist. For instance, for a hotel the bundle in equation (1)might include category, quality of food, room service, availability ofcar park, sports facilities, swimming pool (c characteristics),cultural legacy, public safety, degree of preservation of the envi-ronment, brand image, public infrastructures, street cleanliness, oravailable amenities (z characteristics). Obviously, firms’ managerscan always determine the private characteristics embedded in theirproduct. When making location decisions, firms can also choose theexact composition of their bundle of public characteristics (that is,firms can ‘shop around’ for the jurisdiction offering the bestcombination of public goods and taxes). However, once the locationdecision has been made, changing the public characteristics

embedded in their product is not in the hands of private firms’managers. Although in this setting firms’ managers face manydifficult challenges, this paper is mainly concerned with strategicdecisions related to setting prices, bundling, promoting, and posi-tioning tourism products. Tactical decisions (such as capacity allo-cation, overbooking, or network management) and real timeconsiderations (such as determining which bookings to accept andwhich to reject) are not taken into account.

In the sequel it is assumed that firms’ managers choose theprivate characteristics and the physical location (that is, the set ofpublic attributes) for their products. When competitors can freelyenter the market, it seems reasonable to assume that a unique priceexists for each of the characteristics (public and private) embeddedin the final product. Then, it is possible to define a price vector

pðcÞ ¼ p�

c1; c2;.; cN; zj1; z

j2;.; zj

M

�(2)

which consumers and firms take as given. Equation (2) simply saysthat the market price of a product depends on the public andprivate characteristics embedded in it. The assumption of a uniqueprice for each attribute is not as strong as it seems. It can beimagined as the outcome of a game in which firms choose theirproducts’ attributes and prices taking into account consumers’tastes and whatever the competitors are currently doing. Indeed, ifcustomers choose their consumption bundle based on a given rule,then the best each firm can do is to set the price that maximizes itsexpected profit given the prices set by the competitors, since insuch circumstances more sophisticated strategies usually cannotyield better results (see Phillips, 2005; Vives, 2001). As the numberof competitors in the market increases (or if the market iscontestable), it is likely that the market will come closer to perfectcompetition (see Ruffin, 1971, for a setting where firms are Cournotcompetitors).

Rosen (1974) shows that when differentiated products are soldin perfectly competitive markets, then the equilibrium priceschedule (2) results from the interaction of consumers and firms.The marginal price of a characteristic is equal to both the averagemarginal willingness to pay of tourists for an additional unit ofcharacteristic embedded in the tourism product and to the amountof money for which firms are willing to embed the characteristic inthe final product (i.e., marginal cost). Hence, from the parameters ofthe hedonic price function it is possible to recover the information(hidden in a product’s overall market price) about the marginalvalue consumers place on characteristics and the marginal costfirms incur to include different characteristics in their product.Notice, however, that regression coefficients capture an averagewillingness to pay only if preferences are homogeneous across theentire population (see, for instance, Chay & Greenstone, 2005). Ifmarket response is a result of preference heterogeneity one mightonly recover an average across subpopulations that sort themselvesaccording to their valuation of specific product characteristics. (Forenlightening accounts of the theoretical groundings behindhedonic methods, see Lars, 2008; Taylor, 2003, 2008.)

2.2. Hedonic methods and management

The previous analysis shows that the information unveiled byhedonic methods provides important insights for answering manyquestions relevant for tourism managers. Traditionally, hedonicmethods have been used for making correct pricing decisions.However, since hedonic methods supply price/quality informationwith a theoretical economic background, they make it possible forthe agents in the market to properly evaluate differentiated prod-ucts on which they can base their production and consumptiondecisions (Kristensen, 1984). Therefore, hedonic methods are

R. Rigall-I-Torrent, M. Fluvia / Tourism Management 32 (2011) 244–255246

a valuable, though often neglected, tool to elicit informationregarding many other managerially relevant variables (see, forinstance, Basu, Mazumdar, & Raj, 2003; Gandal, 1994; Koschat &Putsis, 2002).

This information can be used by policymakers to position theirdestinations and to provide the correct amount of public goods, andfor firms when making location, promotion, pricing, bundling andpositioning decisions. Besides, the product to be analysed by meansof hedonic methods needs not be a real one: it can be a prospectivenew product (i.e., an innovative bundle of attributes not assembledtogether before, see Eppen et al., 1991). Indeed, if products with therelevant characteristics already exist in the market (although withthose attributes not bundled together), then hedonic methodsmake it possible to analyse consumers’ hypothetical marginalvaluation of a prospective bundle of those characteristics.

Furthermore, since hedonic methods are based on linearregression and publicly available information (tour operators’brochures in this paper’s application), these methods are easy andrelatively cheap to implement. The information on consumers’marginal valuation provided by hedonic methods would beexpensive to obtain with other existing marketing research tools.Consider, for instance, the cost of setting up a panel of consumers orof implementing a comprehensive survey (or using contingentvaluation in the case of policymakers). Besides, hedonic methodsrely on market prices, which reflect actual consumer behaviour (i.e.,a sort of revealed preference) and not on the hypothetical will-ingness to pay stated by a consumer faced with an imaginarysituation. Therefore, these methods do not have the potentialshortcomings, associated with people lying or (consciously or not)misrepresenting their preferences, besieging survey-like methods.

Hedonic methods have already been employed for analysing thefactors influencing the prices of different types of tourism products:holiday hotels (Cox & Vieth, 2003; Espinet et al., 2003), luxuryhotels (Hartman, 1989), holiday packages (Aguilo, Alegre, & Riera,2001; Haroutunian et al., 2005; Papatheodorou, 2002; Thrane,2005), bed and breakfast amenities (Monty & Skidmore, 2003), orski resorts (Falk, 2008). However, these studies have centred theirattention on private attributes. Rigall-I-Torrent and Fluvia (2007)analyse the effects of public goods on hotel prices in Catalonia boththeoretically and empirically. Nevertheless, their work relies solelyon the jurisdiction where hotels are located to capture the publicattributes of tourism products. This approach is not satisfactory formanagers and policymakers, since it does not permit to disentanglethe different public dimensions of interest.

The next sections show in detail how in tourism settingshedonic methods can be easily applied by individual firms andpublic policymakers to obtain relevant insights on pricing,promotion, positioning and standing against competing productsand destinations. The process consists of 3 steps:

1. Collecting data (on prices and public and private attributes oftourism products) and using linear regression to computehedonic estimates and decompose the prices of hotels intopublic and private characteristics (Section 3).

2. Using the hedonic estimates in the first step to computeindexes of public and private characteristics (Section 4).

3. Deriving managerially relevant implications from the results insteps 1 and 2 (Section 5).

Before proceeding, it is worthwhile to remark that it could beargued that there is an inherent conflict between the assumptionsof the hedonic method and the uses to which it is being put in thispaper’s context. The hedonic method assumes that the agentsmaking up the market are fully aware of their preference functionsand their cost functions, so that the resulting equilibria can be

interpreted as being marginal values for small changes. If thoseassumptions are correct, it would seem unnecessary for tourismmanagers to differentiate products and brands, for instance, sinceall relevant information would be reflected in market prices andthere would be no opportunities to beat the market. This is true inthe long run for perfectly competitive markets where competitionbetween firms drives economic profits to zero and there is no scopefor a firm outperforming other firms, but not in the short run, whenprofits can be positive (see Rigall-I-Torrent & Fluvia, 2007).

3. Determinants of coastal hotel prices in tourismdestinations of Catalonia

This section uses hedonic methods to analyse the determinantsof coastal hotel prices in tourism destinations of Catalonia. Pricesare decomposed into public and private attributes by means oflinear regression analysis.

3.1. The data

The database employed contains information on prices andprivate characteristics for a sample of 279 coastal hotels in 15jurisdictions of Catalonia (in Spain’s northeast, on the Mediterra-nean coast) for six months (two observations per month) of the year2000. This gives 3208 observations (instead of 279 � 12 ¼ 3348),since some hotels are open only in the summer months. As it is usualin the literature (see, for instance, Espinet et al., 2003; Haroutunianet al., 2005; Thrane, 2005), prices and private characteristics weredrawn from tour operators’ brochures. The sample includes thehotels for which information was available in tour operators’brochures, that is, 60.5% of the hotels in the jurisdictions analysedand the 78.8% of the rooms. The prices used do not include transportto destination or tours at destination. Although it is not possible toconsider discounts on list prices (last minute or based on age or clubmembership, for instance), it is reasonable to assume that brochureprices reflect ‘‘expected’’ prices paid by tourists (subject, of course, todeviations around the expected value).

The choice of private attributes follows the considerations inEspinet (1999) (latter confirmed by Espinet et al., 2003; Har-outunian et al., 2005). For the present analysis this database (whichwas used by Rigall-I-Torrent & Fluvia, 2007) has been expandedwith public attributes which are likely to affect tourists’ satisfac-tion. Information on public attributes has been obtained from theStatistical Institute of Catalonia (Institut d’Estadıstica de Catalunya,2009), local tourism offices, and Espinet (1999). These attributescover a wide range of representative public characteristics whichtourists are likely to value, such as exclusivity (population),complementary products and services (cultural and sports facili-ties, restaurants, marinas), crowdedness (rooms per km2), naturalenvironment (coves), or public safety (local police officers). Thevariables included in the database (and their coded name) arepresented in Table 1. Table 2 provides some descriptive statistics forthe variables at the jurisdiction level.

From the available data it is possible to compute a raw (unad-justed for public and private components) price index of the averageprices of the hotels in the different jurisdictions. This is shown inTable 3. Notice that average prices vary widely among differentmunicipalities. A hotel located in the municipality at the top of theranking inTable 3 has, on average, prices 111.5% higher than a hotel inthe jurisdiction at the bottom. However, when taken at face valuethis ranking is misleading for tourists, hotel managers and publicpolicymakers. Since the public and private characteristics of hotelsdiffer among municipalities, Table 3’s ranking includes heteroge-neous products. Thus, it is not possible to say if, for instance, desti-nation A is cheaper or more expensive than destination B.

Table 1Variables included in the database.

Variable Codedname

Description

Price price Price in euros (V) of a double room (full board)

Variables considered by Rigall-I-Torrent and Fluvia (2007)

Physical location (jurisdictionor municipality) of a hotel

blanes Hotel located in Blanes, dummy variablecalella Hotel located in Calella, dummy variablecambrils Hotel located in Cambrils, dummy variablecasfels Hotel located in Castelldefels, dummy variableestartit Hotel located in l’Estartit (jurisdiction: Torroella de Montgrı), dummy variablelloret Hotel located in Lloret de Mar, dummy variablestfeliu Hotel located in Sant Feliu de Guıxols, dummy variablemalgrat Hotel located in Malgrat de Mar, dummy variablepineda Hotel located in Pineda de Mar, dummy variableplatjaro Hotel located in Platja d’Aro (jurisdiction: Castell-Platja d’Aro), dummy variableroses Hotel located in Roses, dummy variablesalou Hotel located in Salou, dummy variablesitges Hotel located in Sitges, dummy variablesusanna Hotel located in Santa Susanna, dummy variabletossa Hotel located in Tossa de Mar, dummy variable

Category of the hotel star1 one-star hotel, dummy variablestar2 two-star hotel, dummy variablestar3 three-star hotel, dummy variablestar4 four-star hotel, dummy variable

Number of rooms room Total number of rooms in the hotelLocation in front of the beach beach Hotel located in front of the beach, dummy variableRoom services roomserv Availability of room services in the hotel, dummy variableGarden or balcony gardbalc Availability of garden or balcony in the hotel, dummy variableCar park carpark Availability of car park in the hotel, dummy variableSwimming pool swimpool Availability of swimming pool in the hotel, dummy variableSports facilities sports Availability of sports facilities in the hotel, dummy variablePeriod of the year may1 First two weeks of May, dummy variable

may2 Second two weeks of May, dummy variablejune1 First two weeks of June, dummy variablejune2 Second two weeks of June, dummy variablejuly1 First two weeks of July, dummy variablejuly2 Second two weeks of July, dummy variableaugust1 First two weeks of August, dummy variableaugust2 Second two weeks of August, dummy variableseptember1 First two weeks of September, dummy variableseptember2 Second two weeks of September, dummy variableoctober1 First two weeks of October, dummy variableoctober2 Second two weeks of October, dummy variable

Variables new to this study Population in the jurisdiction population De jure inhabitants (in thousands) in 2000Cultural facilities in thejurisdiction

culture Number of cultural facilities per 1000 de jure population in 2001: includes the total numberof archives, museums, theatres, auditoria, and libraries

Sports facilities in thejurisdiction

sports_jur Number of sports facilities per 1000 de jure population in 2001: includes the total number ofsports facilities in the jurisdiction, such as swimming pools, tennis courts, football pitches,golf courses, etc.

Marina in the jurisdiction marina Availability of a marina in 2000, dummy variableRestaurants in the jurisdiction restaurants Number of restaurants per 1000 de jure population in 2000Number of hotel rooms in thejurisdiction

rooms_km2 Total number of hotel rooms per km2 in 1999

Police officers in thejurisdiction

police Local police officers per 100 hotel rooms in 2000

Coves in the jurisdiction coves Proportion of coves over the total number of beaches


3.2. Model specification and estimation

Price differences among locations such as those detected inTable 1 could be attributed to several factors: systematic variationin the degree of competition among locations, in the distributionchannels, or in the climate, for instance. However, this does notseem plausible in this case (see Rigall-I-Torrent & Fluvia, 2007). Thedistinct composition of the public and private characteristicsamong different locations is most likely to explain such differencesin prices. To test this hypothesis different hedonic regressions ofprice on private and public attributes, period of the year and loca-tion can be run. In hedonic regression settings semi-logarithmicspecifications are usual (see Halvorsen & Pollakowski, 1981; Taylor,2003). Drawing on previous analyses (Espinet et al., 2003; Fluvia,

Espinet, & Rigall-I-Torrent, 2005; Rigall-I-Torrent & Fluvia, 2007;Thrane, 2005) and after some trials (see below), this specificationappears as the best for this paper’s sample of data. Thus, a log–linear regression function of the form

log Price ¼ f�

c1; c2;.; cN; zj1; z

j2;.; zj

M ; q�þ u (3)

is estimated, where log Price is the natural logarithm of price, q isa vector of parameters to be estimated, f ð,Þ is a linear function ofthe parameters, and u is a random error term.

Table 4 shows the results of estimating different specificationsof equation (3) by OLS and using White’s (1980) heteroskedasticity-robust estimator of the variance–covariance matrix. (The results areinterpreted and discussed in detail in Sections 3.3 and 3.4.)

Table 2Descriptive statistics at the jurisdiction level of the variables in the database.

Jurisdiction Statistic Price Category Room Beach Roomserv Gardbalc Carpark Swimpool Population Culture Marina Rooms_km2

Police Sports_jur

Coves Restaurants

Blanes Mean 26.53 2.63 171.25 0.25 0.63 1.00 0.88 0.88 30.44 0.13 1.00 105.00 3.90 6.04 0.20 4.83Standarddeviation

10.05 – 90.52 – – – – – 0.00 0.00 – 0.00 0.00 0.00 0.00 0.00

L’Estartit Mean 30.80 1.89 67.56 0.11 0.22 0.78 0.44 0.56 8.45 0.71 1.00 11.10 3.30 22.71 0.57 10.65Standarddeviation

6.75 – 32.92 – – – – – 0.00 0.00 – 0.00 0.00 0.00 0.00 0.00

Lloret de Mar Mean 30.67 2.70 169.38 0.14 0.46 0.89 0.61 0.94 20.05 0.15 1.00 306.20 0.50 17.76 0.55 11.72Standarddeviation

16.23 – 128.28 – – – – – 0.00 0.00 – 0.00 0.00 0.00 0.00 0.00

Platja d’Aro Mean 36.44 3.00 106.79 0.57 0.43 0.93 1.00 0.79 6.69 0.45 1.00 90.30 1.60 34.69 0.63 14.65Standarddeviation

12.78 – 43.64 – – – – – 0.00 0.00 – 0.00 0.00 0.00 0.00 0.00

Roses Mean 34.52 2.69 130.75 0.50 0.38 1.00 1.00 0.81 12.86 0.16 0.00 59.10 1.40 12.52 0.44 11.43Standarddeviation

10.90 – 60.85 – – – – – 0.00 0.00 – 0.00 0.00 0.00 0.00 0.00

Sant Feliu deGuıxols

Mean 34.34 2.36 54.43 0.14 0.43 0.71 0.64 0.43 18.40 0.27 1.00 64.30 3.20 7.01 0.77 3.04Standarddeviation

17.40 – 25.06 – – – – – 0.00 0.00 – 0.00 0.00 0.00 0.00 0.00

Tossa de Mar Mean 35.13 2.38 104.56 0.13 0.50 0.75 0.75 0.63 4.20 0.72 0.00 77.40 0.60 27.89 0.67 29.08Standarddeviation

16.66 – 70.20 – – – – – 0.00 0.00 – 0.00 0.00 0.00 0.00 0.00

Calella Mean 25.09 2.55 165.87 0.26 0.52 0.84 0.45 0.97 13.06 0.31 0.00 826.30 0.50 11.87 0.00 9.04Standarddeviation

10.21 – 94.09 – – – – – 0.00 0.00 – 0.00 0.00 0.00 0.00 0.00

Malgrat deMar


8.49 – 76.52 – – – – – 0.00 0.00 – 0.00 0.00 0.00 0.00 0.00

Pineda deMar


8.13 – 129.92 – – – – – 0.00 0.00 – 0.00 0.00 0.00 0.00 0.00

SantaSusanna


10.83 – 77.16 – – – – – 0.00 0.00 – 0.00 0.00 0.00 0.00 0.00

Cambrils Mean 35.38 3.22 233.67 0.33 0.89 0.89 0.89 1.00 19.94 0.20 1.00 70.70 1.60 8.88 0.00 10.43Standarddeviation

14.20 – 135.02 – – – – – 0.00 0.00 – 0.00 0.00 0.00 0.00 0.00

Salou Mean 31.85 3.05 269.86 0.19 0.88 0.95 0.86 1.00 13.06 0.15 1.00 792.00 0.40 14.32 0.00 18.76Standarddeviation

12.34 – 147.09 – – – – – 0.00 0.00 – 0.00 0.00 0.00 0.00 0.00

Castelldefels Mean 47.38 3.43 64.43 0.29 1.00 1.00 0.86 1.00 45.09 0.09 1.00 55.40 11.00 17.41 0.00 2.33Standarddeviation

8.82 – 25.59 – – – – – 0.00 0.00 – 0.00 0.00 0.00 0.00 0.00

Sitges Mean 50.81 3.00 70.60 0.40 0.80 0.80 0.60 0.70 19.45 0.41 1.00 43.70 3.70 17.28 0.35 7.61Standarddeviation

23.69 – 44.24 – – – – – 0.00 0.00 – 0.00 0.00 0.00 0.00 0.00

Total Mean 31.73 2.73 165.07 0.27 0.56 0.89 0.70 0.88 15.55 0.24 0.66 345.49 1.42 16.68 0.31 11.72Standarddeviation

14.92 – 122.09 – – – – – 7.70 0.18 – 293.56 1.89 7.61 0.29 6.29

Category: 1 ¼ one-star hotel, 2 ¼ two-star hotel, 3 ¼ three-star hotel, 4 ¼ four-star hotel/see Table 1 for a description of the rest of the variables.

Table 3Unadjusted average price index for locations. Lloret de Mar ¼ 100.

Ranking Location Unadjusted price index

1 Castelldefels 166.22 Sitges 163.53 Platja d’Aro 123.94 Cambrils 119.95 Roses 118.76 Tossa de Mar 111.77 Salou 111.58 L’Estartit (Torroella de Montgrı) 105.99 Lloret de Mar 100.010 Santa Susanna 96.011 Sant Feliu de Guıxols 93.612 Blanes 89.413 Calella 83.914 Malgrat de Mar 79.315 Pineda de Mar 78.6


Specification #1 is identical to Rigall-I-Torrent and Fluvia (2007). Itincludes private attributes, period of the year and locationdummies. Location dummies can be interpreted as synthetic indi-cators of public attributes (see Rigall-I-Torrent & Fluvia, 2007).Specification #2 includes public attributes instead of the locationdummies. The estimates of the private attributes and the period ofthe year are robust to the change in the specification. The adjustedR2 indicates that the fit is very good for both specifications (0.8062and 0.7766 for specifications #1 and #2, respectively). Most vari-ables are individually significant at very strong levels and thehypothesis that the slope coefficients in the specifications arejointly zero can be rejected (the p value for the F test is smaller than0.001 for both specifications). Multicollinearity is not a problemsince the mean VIF (variance inflation factor) is 1.55 for specifica-tion #1 and 2.28 for specification #2 and the largest VIF is lowerthan 10 in both cases. Finally, specification #3 excludes both loca-tion dummies and public attributes. Clearly, this specification haslower explanatory power (adjusted R2 is 0.7232) than specifications#1 and #2 and the swimming pool coefficient has the wrong sign.

Table 4OLS estimates of different specifications of the hedonic price model.

Specifications (Dependent variable: log Price)

#1 #2 #3 #4 #5 #6

population �0.0050*** (0.0013) �0.0044** (0.0015)culture 0.0142 (0.0409) 0.0070 (0.0455)marina 0.0651*** (0.0106) 0.0867*** (0.0124)rooms_km2 �0.0001*** (0.0000) �0.0001* (0.0000)police 0.0446*** (0.0052) 0.0516*** (0.0058)sports_jur 0.0003 (0.0008) 0.0030*** (0.0009)restaurants 0.0094*** (0.0009) 0.0080*** (0.0011)coves 0.1110*** (0.0285) 0.1376*** (0.0327)room �0.0003*** (0.0000) �0.0003*** (0.0000) �0.0005*** (0.0000) �0.0002*** (0.0000) �0.0002*** (0.0000) �0.0005*** (0.0000)beach 0.0901*** (0.0092) 0.0835*** (0.0091) 0.0500*** (0.0102) 0.1410*** (0.0116) 0.1446*** (0.0113) 0.1106*** (0.0124)swimpool �0.0167 (0.0121) �0.0212 (0.0138) �0.1252*** (0.0146) 0.0724*** (0.0151) 0.0681*** (0.0161) �0.0366* (0.0163)roomserv 0.1111*** (0.0073) 0.1179*** (0.0075) 0.1277*** (0.0088) 0.2384*** (0.0089) 0.2552*** (0.0093) 0.2831*** (0.0100)gardbalc 0.0432*** (0.0098) 0.0523*** (0.0116) 0.0426** (0.0133) 0.0871*** (0.0111) 0.0908*** (0.0123) 0.0926*** (0.0144)carpark 0.0646*** (0.0073) 0.0556*** (0.0081) 0.0968*** (0.0093) 0.0817*** (0.0087) 0.0731*** (0.0095) 0.1159*** (0.0104)sports 0.0764*** (0.0091) 0.0715*** (0.0094) 0.0767*** (0.0103) 0.1378*** (0.0118) 0.1400*** (0.0123) 0.1528*** (0.0135)star1 �0.1109*** (0.0126) �0.1129*** (0.0148) �0.1007*** (0.0158)star3 0.1499*** (0.0097) 0.1574*** (0.0107) 0.1817*** (0.0120)star4 0.5816*** (0.0194) 0.5971*** (0.0201) 0.6605*** (0.0225)may1 �0.7680*** (0.0160) �0.7646*** (0.0170) �0.7619*** (0.0190) �0.7628*** (0.0194) �0.7602*** (0.0206) �0.7564*** (0.0230)may2 �0.7193*** (0.0156) �0.7161*** (0.0164) �0.7147*** (0.0183) �0.7155*** (0.0192) �0.7130*** (0.0201) �0.7109*** (0.0224)june1 �0.6280*** (0.0146) �0.6254*** (0.0153) �0.6250*** (0.0172) �0.6269*** (0.0179) �0.6251*** (0.0189) �0.6249*** (0.0211)june2 �0.4828*** (0.0138) �0.4829*** (0.0145) �0.4829*** (0.0159) �0.4821*** (0.0170) �0.4822*** (0.0179) �0.4823*** (0.0196)july1 �0.2376*** (0.0138) �0.2376*** (0.0144) �0.2376*** (0.0152) �0.2376*** (0.0170) �0.2376*** (0.0177) �0.2376*** (0.0188)july2 �0.1048*** (0.0138) �0.1048*** (0.0141) �0.1048*** (0.0147) �0.1048*** (0.0168) �0.1048*** (0.0172) �0.1048*** (0.0181)august2 �0.1482*** (0.0132) �0.1482*** (0.0138) �0.1482*** (0.0150) �0.1482*** (0.0166) �0.1482*** (0.0174) �0.1482*** (0.0188)september1 �0.4387*** (0.0141) �0.4387*** (0.0151) �0.4387*** (0.0166) �0.4387*** (0.0177) �0.4387*** (0.0189) �0.4387*** (0.0206)september2 �0.6263*** (0.0150) �0.6264*** (0.0161) �0.6268*** (0.0180) �0.6241*** (0.0183) �0.6242*** (0.0195) �0.6245*** (0.0218)october1 �0.7618*** (0.0160) �0.7578*** (0.0170) �0.7571*** (0.0190) �0.7578*** (0.0194) �0.7548*** (0.0205) �0.7539*** (0.0229)october2 �0.7768*** (0.0165) �0.7696*** (0.0175) �0.7650*** (0.0195) �0.7706*** (0.0200) �0.7634*** (0.0210) �0.7573*** (0.0234)blanes �0.1274*** (0.0159) �0.1931*** (0.0198)estartit 0.2499*** (0.0204) 0.2040*** (0.0245)platjaro 0.1096*** (0.0156) 0.1697*** (0.0184)roses 0.1418*** (0.0179) 0.1058*** (0.0196)stfeliu �0.0231 (0.0236) 0.0208 (0.0291)tossa 0.1548*** (0.0154) 0.1395*** (0.0204)calella �0.1439*** (0.0105) �0.1759*** (0.0146)malgrat �0.2663*** (0.0145) �0.2650*** (0.0193)pineda �0.1649*** (0.0135) �0.2684*** (0.0168)susanna �0.1127*** (0.0136) �0.1209*** (0.0159)cambrils �0.0285 (0.0225) �0.0068 (0.0254)salou �0.0141 (0.0127) �0.0456** (0.0145)casfels 0.1992*** (0.0312) 0.3081*** (0.0300)sitges 0.3419*** (0.0248) 0.4333*** (0.0342)constant 3.5476*** (0.0194) 3.4005*** (0.0348) 3.6398*** (0.0224) 3.4500*** (0.0220) 3.2270*** (0.0364) 3.5513*** (0.0229)

N 3208 3208 3208 3208 3208 3208

Adjusted R2 0.8062 0.7766 0.7232 0.7115 0.6727 0.5953

F 450.31 461.33 480.37 311.97 328.05 341.25pvalue 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

Mean VIF 1.55 2.28 1.64 1.45 2.26 1.55Max VIF 2.81 6.45 2.37 1.83 6.42 1.83

Standard errors (robust to heteroskedasticity) in parentheses/*p < 0.05, **p < 0.01, ***p < 0.001.


As for all hedonic studies applied to hotels, a possible problembesieges the previous specifications (see Thrane, 2005). Sincea hotel’s star rating is a function of its attributes, a specificationerror may arise because the hotel star rating dummies would beendogenous explanatory variables (Thrane, 2005). That is, if a hotelis located in a nice environment and offers interesting restaurantsas well as high quality services, it will earn more stars in thecatalogue than a hotel lacking these attributes (see Aguilo et al.,2001). Thrane (2005) uses a hierarchical regression procedure toestimate different specifications and finds that the hotel star ratingdummies mediate the variables related to having restaurant, TV atthe rooms and the hotel being a resort (i.e., a hotel own by a touroperator). Thrane argues that this shows that although theseattributes do not have direct effects on the package tours’ overallprice in the model that includes the hotel star rating variable, they

have important indirect effects on prices via the hotel star ratingvariable. Specifications #4, #5 and #6 in Table 4 show the effects ofdropping the star rating variables in our sample. Notice that whilethe estimated coefficients of the public attributes and the period ofthe year do not change, those linked to the private attributesincrease. Furthermore, all the coefficients remain significant. Thissuggests that the hotel star rating dummies capture quality factorsnot reflected in the rest of the variables. That is, the quality of roomservices or sports facilities in a four-star hotel is higher than ina one-star hotel. Therefore, omitting the star rating dummies islikely to introduce bias, since then excessive credit is given to theeffects of private attributes (excluding star rating) on price.

All the specifications in Table 4 ignore the possibility of variablesexplicitly interacting multiplicatively. Explicit multiplicative inter-actions could be introduced if, say, the effect of a ‘‘car park’’ on

Table 5OLS estimates of the hedonic price model using different functional forms.

log–linear log–log linear–linear linear–log

population �0.0050*** (0.0013) 0.0735*** (0.0131) �0.0690 (0.0454) 2.2539*** (0.4780)culture 0.0142 (0.0409) �0.0284*** (0.0076) 22.404 (�14.954) �0.8725*** (0.2540)marina 0.0651*** (0.0106) �0.0062* (0.0027) 1.9486*** (0.4003) �0.2126* (0.0915)rooms_km2 �0.0001*** (0.0000) �0.0432*** (0.0106) �0.0026** (0.0009) �0.9581* (0.3956)police 0.0446*** (0.0052) 0.0881*** (0.0160) 1.0005*** (0.1973) 2.6580*** (0.5959)sports_jur 0.0003 (0.0008) 0.1331*** (0.0161) 0.0181 (0.0288) 3.7265*** (0.5708)restaurants 0.0094*** (0.0009) 0.1598*** (0.0156) 0.2672*** (0.0343) 4.8598*** (0.5306)coves 0.1110*** (0.0285) 0.0008 (0.0036) 3.5516*** (�10.740) 0.3005* (0.1320)room �0.0003*** (0.0000) �0.0668*** (0.0080) �0.0134*** (0.0014) �2.8432*** (0.3102)beach 0.0835*** (0.0091) 0.0155*** (0.0019) 3.6595*** (0.3807) 0.7175*** (0.0808)swimpool �0.0212 (0.0138) �0.0039 (0.0028) 0.3399 (0.4833) 0.1722 (0.1051)roomserv 0.1179*** (0.0075) 0.0284*** (0.0016) 3.9466*** (0.2517) 0.9384*** (0.0536)gardbalc 0.0523*** (0.0116) 0.0099*** (0.0023) 1.4215*** (0.3906) 0.2595** (0.0815)carpark 0.0556*** (0.0081) 0.0117*** (0.0017) 1.4719*** (0.2881) 0.2875*** (0.0598)sports 0.0715*** (0.0094) 0.0160*** (0.0020) 2.8599*** (0.4093) 0.6368*** (0.0846)star1 �0.1129*** (0.0148) �0.0296*** (0.0028) �3.1639*** (0.4466) �0.8713*** (0.0883)star3 0.1574*** (0.0107) 0.0313*** (0.0021) 4.2937*** (0.3418) 0.8513*** (0.0683)star4 0.5971*** (0.0201) 0.1281*** (0.0042) 24.3269*** (�10.242) 5.2430*** (0.2159)may1 �0.7646*** (0.0170) �0.1666*** (0.0036) �24.4508*** (0.6883) �5.3225*** (0.1455)may2 �0.7161*** (0.0164) �0.1561*** (0.0035) �23.3802*** (0.6890) �5.0894*** (0.1462)june1 �0.6254*** (0.0153) �0.1364*** (0.0032) �21.2241*** (0.6719) �4.6218*** (0.1423)june2 �0.4829*** (0.0145) �0.1046*** (0.0030) �17.5091*** (0.6663) �3.7944*** (0.1409)july1 �0.2376*** (0.0144) �0.0515*** (0.0030) �9.6227*** (0.6935) �2.0850*** (0.1467)july2 �0.1048*** (0.0141) �0.0227*** (0.0030) �4.5886*** (0.7054) �0.9943*** (0.1497)august2 �0.1482*** (0.0138) �0.0321*** (0.0029) �6.0498*** (0.7065) �1.3109*** (0.1491)september1 �0.4387*** (0.0151) �0.0951*** (0.0031) �15.8756*** (0.6929) �3.4399*** (0.1459)september2 �0.6264*** (0.0161) �0.1358*** (0.0033) �21.0813*** (0.6829) �4.5697*** (0.1435)october1 �0.7578*** (0.0170) �0.1652*** (0.0036) �24.2848*** (0.6938) �5.2891*** (0.1460)october2 �0.7696*** (0.0175) �0.1681*** (0.0037) �24.6490*** (0.7079) �5.3770*** (0.1489)constant 3.4005*** (0.0348) �1.6216*** (0.1622) 32.5282*** (�12.754) �127.6954*** (�66.903)

N 3208 3208 3208 3208

Adjusted R2 0.7766 0.7939 0.7204 0.7349

F 461.33 508.47 217.66 225.20p value 0.0000 0.0000 0.0000 0.0000

Mean VIF 2.27 3.01 2.27 3.01Max VIF 6.10 16.96 6.10 16.96

Standard errors (robust to heteroskedasticity) in parentheses/*p < 0.05, **p < 0.01, ***p < 0.001.


a consumer’s satisfaction was different for two and four-star hotels,that is, if separability among attributes was not appropriate (onthis, see Haroutunian et al., 2005; Ohta & Griliches, 1986). It is alsointeresting to determine whether public and private attributesinteract between them. For instance, the utility derived bycustomers from the presence of a marina might be dependent uponthe category of the hotel they are staying at. However, severalpreliminary trials show that introducing multiplicative interactionscreates a problem of multicollinearity, which is another potentiallyrelevant problem besieging hedonic methods. Nevertheless, asshown in Section 5, relevant interactions between public andprivate attributes can be still derived from the specifications inTable 4.

Summing up, specifications #4, #5 and #6 can be discardedbecause they are likely to produce biased estimates. Specification#3 has lower explanatory power than specifications #1 and #2 andthe swimming pool coefficient has the wrong sign. Finally, speci-fication #1 does not relate the value of location to detailed infor-mation about public attributes available. Therefore, our preferredspecification is #2. A ‘‘poor man’s Box–Cox’’ performed by runninglog–log (adding a small positive number to dummies), linear–linear, and linear–log variations of specification #2 confirms thatthe results are robust across specifications for private characteris-tics and periods of the year, although multicollinearity affectsnegatively the parameter estimates for the public attributes (seeTable 5). Nevertheless, specification #2 presents aggregation bias,since it includes a number of variables measured at the jurisdictionlevel (population, culture, sport facilities, availability of marina,

number of restaurants, number of hotel rooms, police officers, andproportion of coves). Each hotel in the same jurisdiction sharessome common component of variance that is not entirely attrib-utable either to their measured attribute or the regional variables.This leads to a positive correlation of the error terms betweenhotels in the same jurisdiction and to a downward bias of thestandard errors of aggregated variables (Moulton, 1986, 1990).Besides, the empirical model contains repeated observations overtime. Therefore, cluster robust standard errors must be used (Liang& Zeger, 1986; Wooldridge, 2003). Table 6 reports the results ofestimating specification #2 using robust standard errors (White,1980), standard errors clustered by jurisdiction, by time period, andby jurisdiction and time period. When standard errors are clusteredby jurisdiction all public attributes (and some private ones) becomenot significant. Nevertheless, a Wald test rejects the hypothesis thatthe slope coefficients of the public attribute variables are jointlyzero. When standard errors are clustered by time period and byjurisdiction and time period they become significant again (withthe exception of the number of hotel rooms).

3.3. The effect of private attributes on price

The coefficients estimated through log–linear specificationusing standard errors clustered by jurisdiction and time period inTable 6 yield interesting information. For continuous variables,multiplying a variable’s estimated coefficient by 100 gives thepercentage change in price caused by changing that variable by 1%.Thus, increasing the number of rooms of a hotel by 1% brings prices

Table 6OLS estimates of the log-linear hedonic price model under different error structures.

Standard errors

Robust Clustered by jurisdiction Clustered by time period Clustered by jurisdiction and time period

population �0.0050*** (0.0013) �0.0050 (0.0051) �0.0050*** (0.0009) �0.0050* (0.0022)culture 0.0142 (0.0409) 0.0142 (0.2353) 0.0142 (0.0180) 0.0142 (0.0767)marina 0.0651*** (0.0106) 0.0651 (0.0491) 0.0651*** (0.0070) 0.0651*** (0.0181)rooms_km2 �0.0001*** (0.0000) �0.0001 (0.0001) �0.0001* (0.0000) �0.0001 (0.0000)police 0.0446*** (0.0052) 0.0446* (0.0199) 0.0446*** (0.0100) 0.0446*** (0.0094)sports_jur 0.0003 (0.0008) 0.0003 (0.0037) 0.0003 (0.0004) 0.0003 (0.0013)restaurants 0.0094*** (0.0009) 0.0094 (0.0045) 0.0094*** (0.0006) 0.0094*** (0.0015)coves 0.1110*** (0.0285) 0.1110 (0.1278) 0.1110* (0.0489) 0.1110* (0.0525)room �0.0003*** (0.0000) �0.0003** (0.0001) �0.0003*** (0.0000) �0.0003*** (0.0000)beach 0.0835*** (0.0091) 0.0835** (0.0254) 0.0835*** (0.0037) 0.0835*** (0.0081)swimpool �0.0212 (0.0138) �0.0212 (0.0443) �0.0212 (0.0142) �0.0212 (0.0142)roomserv 0.1179*** (0.0075) 0.1179*** (0.0209) 0.1179*** (0.0025) 0.1179*** (0.0067)gardbalc 0.0523*** (0.0116) 0.0523 (0.0273) 0.0523*** (0.0055) 0.0523*** (0.0094)carpark 0.0556*** (0.0081) 0.0556 (0.0276) 0.0556*** (0.0025) 0.0556*** (0.0071)sports 0.0715*** (0.0094) 0.0715* (0.0305) 0.0715*** (0.0050) 0.0715*** (0.0108)star1 �0.1129*** (0.0148) �0.1129*** (0.0264) �0.1129*** (0.0074) �0.1129*** (0.0103)star3 0.1574*** (0.0107) 0.1574*** (0.0325) 0.1574*** (0.0076) 0.1574*** (0.0098)star4 0.5971*** (0.0201) 0.5971*** (0.0870) 0.5971*** (0.0167) 0.5971*** (0.0272)may1 �0.7646*** (0.0170) �0.7646*** (0.0346) �0.7646*** (0.0012) �0.7646*** (0.0324)may2 �0.7161*** (0.0164) �0.7161*** (0.0298) �0.7161*** (0.0013) �0.7161*** (0.0277)june1 �0.6254*** (0.0153) �0.6254*** (0.0247) �0.6254*** (0.0013) �0.6254*** (0.0266)june2 �0.4829*** (0.0145) �0.4829*** (0.0163) �0.4829*** (0.0001) �0.4829*** (0.0247)july1 �0.2376*** (0.0144) �0.2376*** (0.0092) �0.2376*** (0.0000) �0.2376*** (0.0302)july2 �0.1048*** (0.0141) �0.1048*** (0.0101) �0.1048*** (0.0000) �0.1048*** (0.0307)august2 �0.1482*** (0.0138) �0.1482*** (0.0128) �0.1482*** (0.0000) �0.1482*** (0.0274)september1 �0.4387*** (0.0151) �0.4387*** (0.0212) �0.4387*** (0.0000) �0.4387*** (0.0289)september2 �0.6264*** (0.0161) �0.6264*** (0.0228) �0.6264*** (0.0002) �0.6264*** (0.0279)october1 �0.7578*** (0.0170) �0.7578*** (0.0334) �0.7578*** (0.0018) �0.7578*** (0.0320)october2 �0.7696*** (0.0175) �0.7696*** (0.0370) �0.7696*** (0.0017) �0.7696*** (0.0340)constant 3.4005*** (0.0348) 3.4005*** (0.1712) 3.4005*** (0.0220) 3.4005*** (0.0626)

N 3208 3208 3208 3208

Adjusted R2 0.7766 0.7766 0.7766 0.7766

F 461.33 312.02 10 936.78 381.70p value 0.0000 0.0000 0.0000 0.0000

Standard errors in parentheses/*p < 0.05, **p < 0.01, ***p < 0.001.


down by 0.03%. Likewise, the variation in price due to the(dichotomic) characteristic c1 with estimated parameter bb1(keeping the rest of the variables constant) is ðebb1 � 1Þ$100. Thus,the coefficient associated to the variable ‘beach’ says that a hotellocated in front of the beach can set (on average) prices 8.7% higherthan a hotel with otherwise identical private characteristics but notlocated close to the seaside. Likewise, a hotel offering room servicescan set a price, ceteris paribus, 12.5% higher, one having garden orbalcony 5.4% higher, one offering car park can have a price 5.7%higher, and the price of a hotel with sports facilities can be 7.4%higher. On the other hand, having swimming pool does not haveany statistically significant effects on the prices of the hotels in thesample. The category of a hotel heavily influences its price. A one-star hotel must set a price 10.7% lower (on average) than a two-starhotel, whereas 3 and four-star hotels are able to charge, respec-tively, prices 17.0% and 81.7% higher than a two-star hotel withotherwise identical characteristics. Finally, hotel prices differwidely along the season: prices gradually increase as the first twoweeks of August approach (when demand is at the highest inMediterranean destinations) and then gradually decrease.

3.4. The effect of public attributes on price

The regression coefficients of the public attribute variables shedlight on the effects of public characteristics on hotel prices. (Inter-pretation is identical as for private attributes.) Table 6 shows thatfive of the coefficients related to public attributes are significantand have the expected sign. Cultural and sports facilities and the

number of hotel rooms are not statistically significant, but have theexpected sign. The price of a hotel is inversely related to thenumber of inhabitants of the jurisdiction where it is located. Whenthe jurisdiction’s population increases (this can be interpreted asthe jurisdiction losing exclusivity) by 1% the price of hotels goesdown by 0.5%. The presence of complementary products andservices exerts a positive influence on hotel prices. Thus, thepresence of a marina in the jurisdiction increases prices by 6.7% andincreasing the number of restaurants per 1000 inhabitants by 1%pushes prices up by 0.94%. Public safety and the natural environ-ment are also associated with higher hotel prices. Increasing by 1%the number of local police officers per 100 hotel rooms results inprice increases of 4.5%. Finally, when the proportion of coves overthe total number of beaches in the jurisdiction increases by 1% hotelprices go up by 11.1%.

Nevertheless, care should be taken when interpreting theresults for each particular attribute. It must be kept in mind that inour analysis public attributes are assumed to be identical for all thehotels located in the same jurisdiction. However, in this paper only15 jurisdictions are available for the analysis. Since interactions andoverlappings between the relevant public attributes may exist (as itis usually the case in hedonic studies, see Baranzini et al., 2008), it ispossible that we erroneously ascribe the effect on price of someparticular attribute. A bigger sample of jurisdictions might allow toget rid of this potential problem. Alternatively, public attributes canbe collapsed into a synthetic index (this is done in the next section).Be that as it may, the results show that public attributes play animportant role in determining hotel prices.


4. Indexes of private and public characteristics

Studies of tourism prices based on hedonic methods usuallystop with the analysis above. This is unfortunate, since manyadditional relevant insights for managers can be obtained from thehedonic estimates. For instance, Berndt (1991) shows how quality-adjusted price indexes can be constructed from the hedonic esti-mates. In this section we adapt Berndt’s (1991) setting to show howhotel prices can be adjusted for public and private attributes, so thatsynthetic indexes of private and public attributes can be con-structed. This is the second step for deriving managerialimplications.

Table 7Index of private characteristics, IPrC. Lloret de Mar ¼ 100.

Location Index of private characteristics

Castelldefels 136.7Cambrils 123.4Sitges 116.3Salou 113.2Platja d’Aro 111.0Santa Susanna 107.7Malgrat de Mar 102.8Roses 102.6Blanes 101.5Lloret de Mar 100.0Calella 96.8Sant Feliu de Guıxols 96.4Tossa de Mar 95.9Pineda de Mar 91.8L’Estartit (Torroella de Montgrı) 82.7

Table elaborated from the coefficients estimated through log–linear specificationusing standard errors clustered by jurisdiction and time period in Table 6.

4.1. Index of private characteristics

An index of private characteristics (IPrC) can be constructedfrom the estimates in Section 3, which can be succinctly expressedas

log bPh ¼ bg0 þXN

n¼1

bbncn;h þXM

m¼1

b4mzjm (4)

where log bPh is the natural logarithm of the predicted price of hotelh, c1,h, c2,h, ., cN,h are the relevant characteristics or attributes ofhotel h, zj

1; zj2;.; zj

M are the public characteristics of hotels injurisdiction j (notice the absence of subscript h, since all the hotelsin the same jurisdiction have the same public characteristics), andbg0, bb1;.; bbN; b41;.; b4M are the estimated values of the parame-ters. The bb’s refer to private characteristics, the b4’s are theparameters associated to a hotel’s physical location, and bg0 is theconstant term. By subtracting

P b4mzjm (where the limits of

summation are omitted to save space) to both sides of equation (4)and applying the rules of logarithms, for a hotel h located injurisdiction j (4) becomes

logbPh

exp

PMm¼1 b4mzj

m

! ¼ bg0 þXN

n¼1

bbncn;h (5)

sinceP b4mzj

m ¼ log expðP b4mzj

mÞ. Identical conclusions can bereached by considering either side of equation (5). Focusing on theleft-hand side, the price for a hotel adjusted by the public attributesembedded in it, i.e., the net expected effect of private attributes onthat hotel’s price, results. Doing the same for a hotel h’ located ina jurisdiction j’ (notice that it is not necessary that j’ s j) andsubtracting, a comparison of the price of the private characteristicsfor two different hotels located at jurisdictions j and j’ results:

log

bPh0=exp

PMm¼1 b4mzj0

m

!

bPh=exp

PMm¼1 b4mzj

m

! ¼ XN

n¼1

bbncn;h0 �XN

n¼1

bbncn;h_ (6)

Exponentiation of (6) yields

IPrCh

bPh0=exp

PMm¼1 b4mzj0

m

!

bPh=exp

PMm¼1 b4mzj

m

! ¼ exp

0BBB@PN

n¼1bbncn;h0PN

n¼1bbncn;h0

1CCCA; (7)

which gives a quantitative comparison between the private char-acteristics of hotel h and hotel h’ while keeping public attributesconstant. Thus, as remarked by Berndt (1991), a quantity measure is

available for analysing the quality (in terms of private attributes) ofdifferent hotels.

As it is shown in Section 5, interesting implications for both themanagement of private firms and public policy can be obtained byaggregating the previous results regarding individual hotels inorder to obtain an index of private characteristics for the differentmunicipalities. The mean values of the private characteristics forthe hotels in the different jurisdictions can be used. Thus, theaverage index of private characteristics ðIPrCj0 Þ for jurisdiction j’ is

IPrCj0hexp

"Xh˛j0

XN

n¼1

bbncn;h

#,Hj0

!(8)

where Hj0 is the total number of hotels in jurisdiction j’. If juris-diction j is set as the reference, then a ranking of jurisdictionsaccording to the private characteristics of their hotels can beobtained through

IPrCj0

IPrCj¼ exp

0BBB@"P

h˛j0PN

n¼1bbncn;h

#,Hj0"P

h˛jPN

n¼1bbncn;h

#,Hj

1CCCA (9)

The results of this computation using the sample data (and settingLloret de Mar ¼ 100) are shown in Table 7.

4.2. Index of public characteristics

A similar reasoning results in an approximation to the quality ofdifferent jurisdictions in terms of public attributes. To identify theinfluence of public characteristics linked to physical location ona hotel’s final price, it is necessary to consider hotels which haveidentical private characteristics. From (4) the difference in the priceof a hotel h located in jurisdiction j and another hotel h’ located injurisdiction j’ is given by the expression

log bPh0 � log bPh ¼XM

m¼1

b4mzj0m �

XMm¼1

b4mzjm0

bPh0bPh

¼

exp

0BBB@PM

m¼1 b4mzj0mPM

m¼1 b4mzjm

1CCCA; ð10Þ


since c1,h ¼ c1,h’, c2,h ¼ c2,h’, ., cN,h ¼ cN,h’. Whenever all the relevantaspects affecting a product’s price are controlled for in equation (9),then the difference in prices obtained through (10) can be attrib-uted to the difference in the supply of public elements in location jand j’. If, for instance,

P b4mzj0m >

P b4mzjm in equation (10) (where

again the limits of summation are omitted to save space), thena firm in location j’ would be able to set a price on average26664exp

0BBB@PM

m¼1 b4mzj0mPM

m¼1 b4mzjm

1CCCA� 1

37775$100% (11)

higher than a firm in location j for a product with identical privatecharacteristics.

Since public characteristics are identical for all the hotels locatedin the same jurisdiction, by setting a given location as the reference,an index of public characteristics (IPlC) ranking jurisdictionsaccording to the price differentials for a firm which stem from beinglocated at each of the M possible locations can be set up. Indeed, ifjurisdiction j is set as the reference, then (10) becomes

IPlCj0hexp

0BBB@PM

m¼1 b4mzj0mPM

m¼1 b4mzjm

1CCCA_ (12)

Table 8 shows the ranking of jurisdictions in our sample accordingto this index (setting Lloret de Mar ¼ 100).

0

5. Using the hedonic estimates to draw implications formanagers and policymakers

The results obtained in Sections 3 and 4 are valuable, sincethey allow firms’ managers and public policymakers to differen-tiate the two sets of attributes, public and private, embedded inindividual tourism products and thus take decisions accordingly.As shown in Sections 2 and 3, the most obvious use of thoseestimates is for making correct pricing decisions (see also Espinetet al., 2003; Haroutunian et al., 2005; Thrane, 2005). However,hedonic estimates in Section 3 contain much more information ofinterest. This section shows how the estimates and the indexesconstructed in Section 4 can be used in the specification ofdifferent managerially relevant variables at both destination andfirm levels.

Table 8Index of public characteristics, IPlC. Lloret de Mar ¼ 100.

Location Index of public characteristics

Tossa de Mar 125.2Castelldefels 123.6L’Estartit (Torroella de Montgrı) 123.1Platja d’Aro 119.5Sitges 111.5Sant Feliu de Guıxols 109.3Roses 101.4Blanes 100.8Lloret de Mar 100.0Salou 99.7Cambrils 99.3Pineda de Mar 91.8Santa Susanna 91.1Malgrat de Mar 86.4Calella 85.5

Table elaborated from the coefficients estimated through log–linear specificationusing standard errors clustered by jurisdiction and time period in Table 6.

5.1. Positioning of tourism destinations

With the data in Tables 7 and 8 it is possible to map the differentjurisdictions (destinations) on the public–private characteristicsspace. Four quadrants can be drawn by using the average values ofthe indexes of public and private characteristics. This is shown inFig. 1. Three municipalities (Sitges, Platja d’Aro and Castelldefels) inFig. 1 have high values of both indexes. However, there is strongheterogeneity, and each quadrant includes some municipalities. Itis important to notice that most municipalities are located ina quadrant with low (relative to the mean) indexes of private andpublic characteristics. Correlation between both indexes is low(0.146). The mapping in Fig. 1 can be used by public policymakers toascertain the position of their jurisdiction with respect to othercompetitors at a moment in time. Together with the estimates inSection 3.4, Fig. 1 can be used to simulate how the jurisdiction’sstanding would change if the provision of public goods wasincreased by a given amount. For instance, assume that policy-makers in Santa Susanna were considering whether or not to builda marina. Setting to 1 the value of the variable ‘‘marina’’ in equation(10) changes the index of public attributes in that jurisdiction from91.1 to 97.3. By comparing the magnitude of the change in the indexto the cost of changing different public attributes, managers candetermine the most cost-effective way to improve their destina-tion’s standing.

5.2. Location decisions by private firms

When making location decisions, firms can use the mapping inFig. 1 and the results in Section 3.4 to choose the exact compositionof the public characteristics included in their product bundle.Remember that the hedonic estimate of the implicit price fora particular public attribute shows the marginal effect on the priceof hotel rooms of a marginal change in that attribute. Thus, thehedonic estimates provide information to assess the differences inrents among different prospective locations and set the differencesin costs against the possible higher benefits available because ofhigher mark-ups on final prices. For instance, Table 8 says that,given identical private characteristics, a hotel in Tossa de Mar hasprices 25.2% higher than a hotel in Lloret de Mar. Therefore, it is tobe expected that the cost of doing business (which includes wages,property rental costs and local taxes, for instance) in Tossa de Marwill be 25.2% higher than in Lloret de Mar. If costs are, say, only 15%

Tossa de MarCastelldefelsL’Estartit

Platja d’Aro

SitgesSant Feliu de Guíxols

Roses

BlanesLloret de Mar Salou Cambrils

Pineda de Mar Santa SusannaMalgrat de Mar

Calella

8090

100

110

120

13In

dex

of p

ublic

cha

ract

eris

tics

80 100 120 140Index of private characteristics

Horizontal/vertical line: average index of public/private characteristics

Fig. 1. Placement of the tourism supply in the space of private and publiccharacteristics.


higher in the former, then firms will find it more profitable to locatethere rather than in Lloret de Mar. Likewise, in Section 3.4 it hasbeen noticed that, other things being the same, the presence ofa marina in a jurisdiction increases hotel prices by 6.7%. Hence, allother things being equal, if the cost of doing business in a destina-tion with marina is, say, only 5% higher, then this means that thispublic component is not fully accounted for in prices. This can leadto interesting competitive advantages of some firms over others.Similar analyses can be performed for each of the public attributesin Table 6.

5.3. Promotion strategies

Managers can also use the estimates to obtain enlighteninginsights for correctly promoting their products. Consider whichconclusions would be reached if (unadjusted) average prices for thehotels at the different locations were (erroneously) considered. (Foreasier reference, Table 9 brings together the results in Tables 3, 7and 8.) For instance, notice that Calella and Tossa de Mar displayalmost identical values for the index of private attributes (96.8 and95.9, respectively), but very different values for both the index ofpublic characteristics (85.5 and 125.2, respectively) and the unad-justed price index (83.9 and 111.7, respectively). After analysinghotels’ brochures for both destinations, someone, say an externalanalyst, who was ignorant of the differences in public attributesrelated to location would surely reach the conclusion that a hotel inCalella represents much better value for money for tourists thana hotel with identical private characteristics but located in Tossa deMar, since the average unadjusted price for such a hotel in Tossa deMar is 33.1% higher than the unadjusted price for a hotel in Calella.However, this reasoning ignores that the index of public charac-teristics is 46.4% higher in Tossa de Mar. Hence, firms (and publicpolicymakers) in Tossa de Mar should therefore centre theirpromotion efforts in making clear to potential customers that theirproduct variety contains lots of public characteristics, hence thehigher prices charged for similar private characteristics.

5.4. Bundling strategies

Since the hedonic framework is useful to highlight that tourismproducts are bundles of characteristics, hedonic estimates can beused for bundling those characteristics properly. Consider those

Table 9Adjusted and unadjusted price indexes. Lloret de Mar ¼ 100.

Location Unadjusted priceindex (ranking)

Index of privatecharacteristics(ranking)

Index of publiccharacteristics(ranking)

Castelldefels 166.2 (1) 136.7 (1) 123.6 (2)Sitges 163.5 (2) 116.3 (3) 111.5 (5)Platja d’Aro 123.9 (3) 111.0 (5) 119.5 (4)Cambrils 119.9 (4) 123.4 (2) 99.3 (11)Roses 118.7 (5) 102.6 (8) 101.4 (7)Tossa de Mar 111.7 (6) 95.9 (13) 125.2 (1)Salou 111.5 (7) 113.2 (4) 99.7 (10)L’Estartit

(Torroella deMontgrı)

105.9 (8) 82.7 (15) 123.1 (3)

Lloret de Mar 100.0 (9) 100.0 (10) 100.0 (9)Santa Susanna 96.0 (10) 107.7 (6) 91.1 (13)Sant Feliu de

Guıxols93.6 (11) 96.4 (12) 109.3 (6)

Blanes 89.4 (12) 101.5 (9) 100.8 (8)Calella 83.9 (13) 96.8 (11) 85.5 (15)Malgrat de Mar 79.3 (14) 102.8 (7) 86.4 (14)Pineda de Mar 78.6 (15) 91.8 (14) 91.8 (12)

customers who see public and private attributes as complements,i.e., those individuals who think that a four-star hotel deservesa four-star environment. Such tourists would expect a more or lessclose match between public and private attributes. For instance,a tourist weighing up the private attributes of the hotels in Cambrilsand Roses, for instance, could be deceived into thinking that bothlocations offer similar (and balanced, according to her tastes)bundles of public and private attributes, since their unadjustedprice is very similar (111.9 and 118.7, respectively). However, inCambrils the offer of public characteristics is not balanced (thevalue of the index is 99.3) with its supply of private attributes (thevalue of the index is 123.4). A hotel in Roses (whose indexes ofpublic and private characteristics take a value of 101.4 and 102.6,respectively) might represent a better option for that customer.Likewise, Cambrils would be the option of choice for tourists whosee public and private attributes as perfect substitutes, that is, thoseindividuals who are equally happy in destination A, with lots ofprivate goods and few private characteristics, than in destination B,with few public goods and lots of public attributes.

5.5. Provision of public goods

Since, as remarked in Section 2, the hedonic estimate of theimplicit price of a particular public attribute indicates the marginaleffect on the price of hotel rooms of a marginal change in thatattribute, it is possible for policymakers to have an approximateidea of the monetary value that the market attaches to improvingthat indicator. By using data on the total number of hotel rooms(and other tourism-related businesses) policymakers can computethe approximate total change in price that a marginal improvementin each public attribute would have. By adding up the total variationfor each attribute, public policymakers have an estimate of themarket valuation of a marginal variation (improvement or wors-ening) for each attribute. This data can be then compared to the costof marginally changing each attribute and thus obtain the netmarginal value of a particular policy. For instance, remember thatthe presence of a marina in a jurisdiction increases hotel prices by6.7%. Assume that in a given jurisdiction there are 10 identical hotelsand no other tourism businesses with 100 occupied rooms per dayfor 200 days per year. The price of a room is V20 per day. Assumethat the local government is considering whether to build a marinawhich costs V1 million and has an expected service life of 5 years.Ignoring discounting, the expected increase in the hotels’ revenue ifthe marina is built is 0.067�20�10�100� 200� 5¼V1 340 000.Therefore, since the expected increase in hotels’ revenue exceedsthe cost, the marina should be built.

Along these lines, other enlightening insights for firms andpublic policymakers can be retrieved from the hedonic estimates.Those insights may help firms and tourism destinations to identifyand develop competitive advantages. In any case, managers andpolicymakers must bear in mind that competing firms and juris-dictions may respond by changing their own supply of private andpublic attributes, so that the coefficients in the hedonic functionsmay change over time and old competitive advantages may vanishand new ones arise. The discussion in Section 2.2 is also relevanthere.

6. Conclusion

The paper differentiates two sets of attributes embedded intourism products: private and public. The latter are present intourism products not because of direct decisions of the firms. Theycan be understood as non-rival and external to private tourismfirms. Decomposing a product’s price into both kinds of attributeshelps to point out how public and private attributes may interact in


order to create value for customers. As the paper shows, tourismfirms’ management decisions may benefit from considering bothtypes of attributes as an integral part of their products, since bothcomponents are likely to affect the choice by consumers. Indeed,determining the relative contribution of the different public attri-butes on a product’s market price can give important insights tomanagers concerned with their products’ pricing, promotion,positioning and standing against competing products. More to thepoint, knowing the implicit prices of public and private attributescan allow managers to differentiate products and brands; to accu-rately target those consumers who associate price and quality; andto be aware of the complementarity/substitution relationshipsamong different product attributes, for instance. Furthermore,informative insights for public policymakers wishing to fine-tunetheir municipalities’ combination of public attributes and taxes tothe necessities of local firms may be drawn from the paper’sanalysis. Last, but not least, the setting above may also be useful forfostering joint initiatives between the public and the private sectorsso as to implement appropriate policies for managing tourismdestinations. In a nutshell, the insights obtained from hedonicestimates can help firms and destinations to develop competitiveadvantages over other firms and destinations.

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