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Theoretical, empirical, and operational models in hotel location research
1 Yang Yang, 2 Hao Luo, and 3 Rob Law
1 School of Tourism and Hospitality Management, Temple University,
Philadelphia, PA, 19122, United States
Phone: (01) 215-204-8701; Fax: (01) 215-204-8705
Email: yangy@temple.edu
2 School of Business, Sun Yat-sen University,
Guangzhou, Guangdong, 510275, China.
Phone: (86) 20-8411-2561; Fax: (86) 20-8411-3687
E-mail: luohao6@mail.sysu.edu.cn
3 School of Hotel and Tourism Management, The Hong Kong Polytechnic University,
Hung Hom, Kowloon, Hong Kong, China.
Phone: (852) 3400-2181; Fax: (852) 2362-9362
E-mail: rob.law@polyu.edu.hk
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Theoretical, empirical, and operational models in hotel location research
Abstract: This paper aims to review past literature on hotel location models and evaluate the
state of the art, as well as set out future directions. This study divides hotel location models
into three major categories: theoretical models, empirical models, and operational models.
Four theoretical hotel location models are reviewed and discussed, including the
tourist-historic city model, the mono-centric model, the agglomeration model, and the
multi-dimensional model. Based on previous literature, six empirical models and three
operational models of hotel location are elaborated. Furthermore, some challenges related to
hotel location studies are discussed, and future research directions are provided. In particular,
we advocate the development of more sophisticated hotel location models and the use of
Geographic Information System (GIS) in hotel location analysis.
Keywords: hotel location, empirical models, operational models, literature review
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1. Introduction
Successful investment in the hotel industry hinges greatly on location factors (Kim and
Okamoto, 2006) because ideal location is always associated with larger accommodation
demand (Lockyer, 2005), higher revenue per available room (Sainaghi, 2011), higher
customer satisfaction (Sim, Mak, and Jones, 2006), better performance (Chung and Kalnins,
2001), and lower failure rate (Baum and Mezias, 1992). More importantly, since hotel
location is a long-term fixed investment, a flawed location strategy can be very difficult to
rectify. As a result, there is a huge demand for the analysis of hotel location and the
identification of factors contributing to a superior location. For private hotel investors, the
pattern of hotel location and its evolution provide valuable information on market access to
potential guests and can be further used to understand market competition and equilibrium:
whether the hotel industry is over-supplied within a certain area.
The study of hotel location also facilitates the understanding of urban tourism space and
structure because hotels are the basic facilities that support urban tourism (Rogerson, 2012a)
and their locations influence tourists’ movement within a city (Shoval, McKercher, Ng, and
Birenboim, 2011). Therefore, hotel location research helps governments and authorities
understand the geography of accommodation supplies and contributes to industrial policies
for urban tourism development (Adam, 2013). Moreover, as a major element of “regional life”
and basic urban infrastructure, hotels function in conjunction with other infrastructures in the
city, like convention centers, central business districts (CBDs), transport gateways, and major
tourist attractions. Hence, further knowledge of hotel location provides vital information to
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urban and regional planning efforts, especially those planning projects for service
infrastructure and urban renewal (McNeill, 2008).
The multi-disciplinary nature of hotel location research has resulted in a relatively separate
body of literature which is scattered throughout a diverse mix of academic disciplines, such
as tourism and hospitality management, geography, economics, marketing, finance, and urban
planning. Researchers with different backgrounds tend to over-emphasize the theories and
models of their own disciplines. Therefore, methodological differences and variations can be
observed, albeit somewhat loosely, in different streams of hotel location research. To fill this
research gap, we present a comprehensive retrospective analysis of past research on hotel
location in different disciplines and present recent developments on hotel location modeling
as a unified body of knowledge. The results highlight the advantages and disadvantages of
different theoretical, empirical, and operational models. They also provide valuable guidance
on how to choose the appropriate model or use a combined one to understand specific hotel
location problems for both scholars and practitioners. Moreover, we discuss several
previously overlooked issues with various hotel location models and set out a future research
agenda in this research area.
This study divides previously documented hotel location models into three major categories:
theoretical models, empirical models, and operational models (Fig. 1). Theoretical models are
designed to explain the hotel location process under certain conditions with particular
theories and are generally able to predict future hotel locations. Empirical models employ a
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strategy that explains the hotel location mechanism/pattern based on empirical observations
and summarizes the refined hotel location rule. Finally, operational models indicate how to
apply the pre-existing hotel location rule to make operational hotel location decisions. In the
following part of the paper, different model sub-categories within each of these three models
will be reviewed and discussed.
We also examine different spatial scales of various hotel location models because these scales
lead to different decision-making processes. Basically, we consider three spatial scales of
hotel location analysis, namely, inter-regional, intra-regional, and intra-metropolitan. For
inter-regional studies, the attractiveness of each region to new hotel entries is assessed and
these studies facilitate market entry decision making for hotel investors. For intra-regional
studies, specific locations within a region (like a county, a state, or even a country) are
considered, and city structure factors can be partly overlooked in this broader scale. Finally,
for intra-metropolitan studies, the major approach is to select an appropriate location within a
town, city, or metropolitan area. As a result, city structure, such as CBD location and urban
sprawl, tends to play a crucial role.
(Please place Fig. 1 about here)
Having introduced the research objectives, the remaining parts of this paper are organized as
follows: after the introduction, four types of theoretical hotel location models will be
discussed in Section 2, while six empirical models will be reviewed in Section 3. For
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practitioners, three major operational hotel location models will be presented in Section 4. In
Section 5, the general issues on hotel location modeling will be discussed and future research
directions will be provided. Lastly, in Section 6, final conclusions will be drawn.
2. Theoretical Model
Theoretical models establish the theoretical foundation for the spatial location choice of
hotels. Theories from different disciplines have been used to explain different perspectives on
hotel location. These theories include geographical (Egan and Nield, 2000; Shoval, 2006),
economic (Kalnins and Chung, 2004) and marketing theories (Baum and Haveman, 1997;
Urtasun and Gutiérrez, 2006). We categorize previously documented theoretical models into
four types based on their disciplinary backgrounds, and they are the tourist-historic city
model, the mono-centric model, the agglomeration model, and the multi-dimensional model.
2.1 Tourist-historic city model (THC model)
THC models date back to Ashworth and Tunbridge’s (1990) comprehensive typology of hotel
locations within medium-sized Western European provincial towns. In their work, six types
of location zones were identified, including traditional city gates (A), railway
station/approach roads (B), main access roads (C), “nice” locations (D), transition zones and
urban periphery on motorway (E), and airport transport interchanges (F). These different
zones are associated with different types of hotels. For example, large modern hotels can be
found in type E and type F locations, whereas small and medium hotels dominate type D
locations. They attributed these clusters to the influence of access, land values, environmental
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convenience, historical continuity, and land-use policy.
In tourism and hospitality studies, there is a long tradition of applying the THC model to
investigate hotel location and spatial distribution in tourist-historic cities. Most tourist cities
have been found to exhibit a hotel distribution pattern postulated by the THC model.
Burtenshaw, Bateman, and Ashworth (1991) applied the THC model to explain the typology
of hotel distribution in several European cities. To interpret hotel evolution from a spatial
perspective, Timothy and Wall (1995) studied the accommodation in Yogyakarta, Indonesia
and discovered that the THC model can reasonably explain the location of hotels and predict
the locational classification of accommodations. Furthermore, Oppermann, Din, and Amri
(1996) used this model to discuss the hotel distribution in Kuala Lumpur, Malaysia. In their
study, seven types of location zones were recognized, and the most distinguished was the
“new Central Business District location.” This included large modern hotels and deluxe
shopping centers, which are common in Southeast Asian countries. Rogerson (2012a) also
highlighted the importance of CBD in attracting hotels in three cities of South Africa, and
identified some “nice” locations for hotels as described in the THC model.
In another study by Bégin (2000), it was found that hotel locations in Xiamen, China, in
general, coincided with those described in the THC model. A large number of cheap hotels
were clustered in the historical center and new hotels were constructed in the transition zone
between the old downtown and the emerging CBD. Shoval and Cohen-Hattab (2001)
investigated the location of tourism accommodations in Jerusalem, Israel over the past 150
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years. Focusing on four periods of development, the study confirmed the predictions of the
THC model. It also highlighted other important factors shaping hotel distribution, such as
political upheavals and social and cultural differences between the population groups.
Aliagaoglu and Ugur (2008) found that the results from Dökmeci and Balta (1999) on hotel
location pattern in Istanbul, Turkey confirmed the THC model’s prediction, and both type A
and type E locations in the city were identified.
The value of the THC model lies in its simplicity and briefness to consider major location
hotspots for hotels and the general spatial arrangement within a tourist city. Although it is
very popular in the tourism literature, the THC model is subject to many limitations. First, as
indicated by Ashworth and Tunbridge (2000), the model is taxonomic rather than explanatory.
As such, even though the potential location for hotels within the city can be identified, we do
not understand the exact reason why it is selected. Apart from that, while this model has been
found to be applicable to tourist-historic cities, it may not be appropriate for
non-tourist-historic cities (Aliagaoglu and Ugur, 2008; De Bres, 1994). If it is applicable,
however, then, what improvements or modifications should be made to cater to this new
situation?
2.2 Mono-centric model
The mono-centric model describes the distribution of land use patterns as several
mono-centric rings according to the distance from the city center and emphasizes the
paramount importance of accessibility in shaping this pattern (Alonso, 1964; Von Thünen,
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1826). In the model, it is assumed that an urban area is mono-centric with a single central
point for sprawl, and the bid-rent curve is introduced to depict how much land users’ are
willing to pay for locations with different proximities to the center. Based on the principle of
bid-rent curves, and drawn from Von Thünen’s (1826) land-use model, Yokeno (1968)
proposed a mono-centric model to highlight the possible location of urban hotels. With an
assumption that tourists are willing to pay more in return for easy access to the city center,
the new model suggested that the hotel district is in the center of the city, located between the
city’s innermost CBD and commercial zones (Fig. 2a).
Egan and Nield (2000) derived another mono-centric model from the partial-equilibrium
bid-rent approach, and explained the spatial hierarchy of hotels in terms of the distance to the
city center. Land bid-rent curves highlight the revenue associated with locations, and it is
assumed that hotels’ revenue falls when they move to locations away from the center. In the
model, the location preference of hotels of different levels could be predicted by the shape of
the bid-rent curve associated with them. Luxury hotels (four-/five-star) are expected to have a
very steep and high bid-rent curve and prefer a central location (Fig. 2b). This is because their
higher room rates targeting affluent guests are likely to cover the higher land values
associated with a central location. Conversely, due to insufficient revenue to pay for a central
location, budget hotels choose to either locate at the edge of the city, or select converted
buildings at the edge of the city center. To further validate the generality of Egan and Nield’s
(2000) model, Egan, Chen, and Zhang (2006) tested hotels in three Chinese metropolises:
Beijing, Shanghai, and Shenzhen. Their results suggested that the hotel location in these cities
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generally fit the model well, despite some minor flaws. Many other cities have been found to
contain a spatial hierarchy and concentric arrangement of hotel distribution that is analogous
to Egan and Nield’s (2000) model, such as Cape Town, Durban, and Port Elizabeth in South
Africa (Rogerson, 2012a) and the Kumasi Metropolis in Ghana (Adam, 2013).
(Please place Fig. 2 about here)
In addition, Shoval (2006) demonstrated that Yokeno’s (1968) model was capable of
predicting hotel location in Jerusalem, Israel. He proposed an extended model by recognizing
two geographies of demand for hotels: the hotel area for individual tourism and for organized
tourism (Fig. 2c). Different markets corresponded to different bid-rent curves. In a more
comprehensive empirical study conducted by Yang, et al. (2012), the mono-centric model
was used to explain this spatial hierarchy of hotel distribution in Beijing, China. Based on the
bid-rent analysis, the mono-centric model can also be generalized to study the city with dual
centers, and an overlapped spatial hierarchy of hotel locations to each center has been
identified (Egan, et al., 2006; Lee and Jang, 2011).
In sum, mono-centric models provide a powerful analytical tool, bid-rent analysis, to look
into hotel location and other activities within the scope of the whole city. In general, these
models highlight a centripetal force on upscale hotel locations while a centrifugal force on
downscale ones. Several empirical studies have supported the usefulness of this model in
predicting the spatial arrangement of hotels within a city. However, because of the
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complexity of the hotel location problem, the mono-centric model investigates it under
several oversimplified conditions (Shoval, 2006, p. 63), and some of them have been deemed
too unrealistic for general hotel location cases. For example, it is inappropriate to assume that
the city as a mono-centric one in most situations (Lee and Jang, 2011) and posit the central
location as the major or even the sole preference of hotel guests. Moreover, the mono-centric
model does not adequately capture all aspects of hotel location patterns, and most importantly,
it fails to explain micro-scale hotel agglomeration (Egan, et al., 2006), which has been
accepted as conventional wisdom.
2.3 Agglomeration model
Hotels are not randomly distributed through space. Instead, their locations are usually highly
clustered with other heterogeneous or homogeneous hotels to achieve an agglomeration effect.
Agglomeration effect refers to benefits that the hotel can receive from clustering. In hotel
location research, there are another series of papers that mainly focused on the agglomeration
process of hotel location by underlining hotel co-location (Ingram and Inman, 1996; Kalnins
and Chung, 2004). Unlike the aforementioned models studying the absolute location of hotels
within an area, the agglomeration model specifically sheds light on the relative location of
new hotels and how to locate relative to other hotel incumbents. Canina, Enz, and Harrison
(2005) further discussed reasons for hotel agglomeration from both production and demand
perspectives. Regarding production advantages, agglomeration allows individuals in the
cluster to have exclusive access to resources, and provides greater access to leading suppliers,
special services, or special relationships. Regarding demand advantages, agglomeration
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reduces consumers’ cost of searching.
However, not all hotels can be benefited from agglomeration. Gains and losses from
co-location hinge on the relative strength of agglomeration and competition effects. Some
studies identified an inverted U relationship between the existing number of hotels and new
hotel entrance (Baum and Haveman, 1997; Ingram and Inman, 1996). Ingram and Inman
(1996) argued that the number of new hotels increases as the number of pre-existing hotels
increases and then decreases after certain threshold values as agglomeration proceeds,
because the intense competition between hotels pushes new entrants away (Baum and
Haveman, 1997).
The agglomeration effect is heterogeneous to different types of new hotel entrants, and it has
been found to depend on product heterogeneity between entrants and incumbents. Freedman
and Kosová (2012) revealed that the agglomeration benefits vary across hotels in different
product segments, and new hotels are more likely to choose an area with a higher
concentration of hotels in other segments to seek greater product differentiation. However,
this result is inconsistent with Kalnins and Chung’s (2004) findings, which showed that
although economy and unbranded hotels choose to co-locate with upscale ones, upscale
hotels avoid areas with a large number of other types of hotels. This is because upscale hotels
are more likely to generate spillovers to their neighbors based on their affluent resource
stocks. Canina, Enz, and Harrison (2005) also found that lower-end hotels are more likely to
receive positive spillover effects by co-locating in a cluster with upscale hotels. Enz, Canina,
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and Liu (2008) pointed out that a price premium is associated with locations close to more
upscale and luxury hotels. On the other hand, high-end hotels undergo substantial price
erosion by locating proximate to low-end ones. In another paper co-authored by Tsang and
Yip (2009), they also determined that hotels receive benefits by locating close to upscale
joint-venture hotels.
Hotel size and ownership play important roles in determining hotels’ relative location as well
(McCann and Vroom, 2010; Yang, et al., 2012). Chung and Kalnins (2001) maintained that
by locating surrounded by larger hotels, the revenue performance of small hotels increases. In
terms of ownership related agglomeration, Helmers (2010) found that independent and
franchised new hotels are more likely to cluster with other hotels while company-owned
hotels are not. This result concurs with the findings of Chung and Kalnins (2001), which
discovered that independent hotels in rural markets tend to obtain agglomeration benefits
from co-locating with chain affiliated hotels. Kalnins (2004) explained why new hotel
entrants are less likely to choose to locate nearby properties of the same brand. This is
because entrants could cannibalize the revenues of pre-existing ones. Moreover, Kalnins and
Chung (2006) pointed out the location preference of Gujarati-owned hotels towards other
unbranded Gujarati ones.
The most notable contribution of the agglomeration model is that it acknowledges
agglomeration in explaining the choice of relative location for hotels, which is a commonly
observed situation nowadays. Unlike the THC model and the mono-centric model,
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agglomeration models can be applied to investigate hotel location in intra-metropolitan,
intra-regional, and inter-regional scales. However, these models provide limited information
on choosing absolute location within an area. Therefore, to provide a more comprehensive
and practical solution for hotel location selection, we still need to resort to other absolute
location models after the analysis of relative locations by agglomeration models.
2.4 Multi-dimensional model
When hotels make market entry decisions, they do not only consider the geographical
location but also the product position (Baum and Haveman, 1997). As a result,
multi-dimensional models have been generated to explain hotels’ market entry choice, for
both product and geographical locations. Baum and Haveman (1997) first utilized a
multi-dimensional model to study hotel location in Manhattan, U.S.A from 1898 to 1990.
Their paper assumed that a hotel chooses to agglomerate or differentiate from different
perspectives. That is, the new hotel would choose its positions based on both product and
geographical dimensions that differentiate from, or close to, the existing hotels’. Baum and
Haveman (1997) built up three measures of distance, namely, geographic distance, price
distance, and size distance to reveal the new hotel’s location in three different dimensions.
The results showed that there is a trade-off in multi-dimensional decision making. New hotels
tend to locate geographically close to existing ones that are similar in price dimension and
different in size dimension (Fig. 3a).
Urtasun and Gutiérrez (2006) applied another multi-dimensional model to investigate hotel
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location in Madrid, Spain from 1936 to 1998. They extended Baum and Haveman’s (1997)
model by adding a service dimension, which measure the service diversity that the hotel
provided. The trade-off effects between dimensions were also highlighted. They discovered
that new hotels in Madrid were inclined to locate in close proximity to those of different
prices, similar size, and similar service (Fig. 3b).
(Please place Fig. 3 about here)
Apart from the aforementioned four major theoretical hotel location models, other models
can be found in the past literature. Two other typological theoretical models include the
regional life model (Aliagaoglu and Ugur, 2008), which shows that hotel location is
entangled with other urban elements of regional life, and the Kansas tourism model (De Bres,
1994), which demonstrates a linear orientation of hotel locations to the major streets/roads
and some hotspots near the off ramps of the interstate or highways. Other theoretical models
used in previous research include Porter’s diamond model, which analyzes operating
environment characteristics of firms (Juan and Lin, 2011; Lin and Juan, 2010), the industrial
life cycle model, which highlights spatial patterns in different stages of the life cycle (Sund,
2006), and Dunning’s (1981) eclectic theory, which emphasizes the location advantage as one
of three factors to explain hotel internationalization (Johnson and Vanetti, 2005).
As shown in the body of prior literature, researchers from different disciplines implement
different research philosophies and objectives. For example, human geographers investigate
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hotel location as a part of their endeavor toward understanding localized patterns in the urban
landscape to provide implications for government policies and planning efforts (Bloomfield,
1996; McNeill, 2008), whereas economists develop general hotel location rules that are
transferable to other cases. Some location factors have been recognized across different
models. For instance, the star rating of hotels has been found to be important in depicting the
spatial hierarchy of hotel distribution in the mono-centric models, and it is also significant in
choosing the relative location as suggested by the agglomeration model. The same is true for
CBD. While the THC and mono-centric models highlight its role in attracting hotels, the
agglomeration model explains its attractiveness through urbanized/localized economies.
3. Empirical Model
To better understand the driving forces behind the hotel location decision, substantial
research efforts have spurred a wealth of empirical models. In most studies, qualitative
descriptions, choropleth mapping, and inequity indices were used to describe hotel location
distribution and possible factors shaping the pattern (Bloomfield, 1996; Bull and Church,
1994; Dökmeci and Balta, 1999; Ferreira and Boshoff, 2013; Roehl and Van Doren, 1990;
Rogerson, 2012b, 2013). Apart from these traditional empirical strategies, some more
sophisticated empirical models have emerged to shed light on factors determining hotel
location from observed datasets.
3.1 Spatial statistical model
Spatial statistics includes a set of statistical methods used to investigate the dependence and
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relationship of observations over space. In intra-metropolitan and intra-regional studies, by
treating each hotel location as a single point, point pattern analysis, a well-established spatial
statistical tool, can be used to understand the spatial distribution of these locations. Wall,
Dudycha, and Hutchinson (1985) applied a set of point pattern analysis tools to study the
spatial distribution of accommodations in Toronto, Canada. Quadrat analysis and nearest
neighbor analysis highlighted the clustered pattern of distribution, and standard deviational
ellipses indicated the change in mean center, dispersion, and orientation of the distribution
over periods. Broadway (1993) calculated the geographic mean centers of hotel distribution
in Montreal, Canada over different periods, and highlighted a moderate shift of the center.
Concerning other point pattern analysis tools, Sund (2006) employed the Lorenz curve to
study the inequity of hotel distribution, and Yang and Fik (2011) utilized the K-function to
study hotel clustering at multiple different distances.
In inter-regional hotel location studies, areal spatial statistical methods unveil spatial
dependence of the hotel number/investment across different regions. Luo and Yang (2012)
applied exploratory spatial data analysis (ESDA) to analyze the number of star-rated hotels in
342 Chinese cities and identified major clusters in hotel distribution. In a spatial regression
model of new hotel capacity by Helmers (2010), the spatial autoregressive coefficient was
estimated to be positive and significant, indicating a spatial clustering of new hotel entrants.
3.2 Zoning regression model
The zoning regression model treats the measure of hotel intensity within a particular zone as
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the dependent variable and specifies it as a function of a set of explanatory variables. In this
model, a natural candidate of dependent variable is the number of hotels or new entrants of
each zone (Holl, 2004; Ingram and Inman, 1996). Other measures have also been used, such
as the hotel entry rate (Freedman and Kosová, 2012), the density of new hotel entrants
(Freedman and Kosová, 2012), the number of hotel rooms (Shu and Dai, 2002), and the new
investment in hotels (Kundu and Contractor, 1999; Polyzos and Minetos, 2011; Zhang,
Guillet, and Gao, 2012) in different zones. Helmers (2010) defined the zone via a spatial
weighting matrix in the spatial regression model, which was limited to a zone covered by 15
nearby hotels. In most empirical zoning regression models, linear regression was used to fit
the data. A more sophisticated econometric method is the count data model that treats the
dependent variable (e.g. the number of hotel entrants) as a count number (Holl, 2004; Ingram
and Inman, 1996).
Several shortcomings of zoning regression models are worthwhile to note. First, the
individual characteristics of hotels cannot be fully considered in the model after zonal data
aggregation (Arauzo-Carod, Liviano-Solis, and Manjón-Antolín, 2010). Moreover, the model
sometimes suffers from endogenous aggregation, which arises when the data aggregation of
each zone is not exogenous. This aggregation problem tends to render inconsistent estimates
of econometric models and provide misleading implications.
3.3 Discrete choice model
The discrete choice model explains hotel location choice based on the economic principle of
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utility maximization. It suggests that when hotel investors are facing a spectrum of choices of
different locations, they are going to pick the most desirable location to maximize its
associated utility subject to certain constraints. The utility that the new hotel obtains from
certain locations can be specified as a function of both hotel characteristics (such as star
rating, hotel size, and hotel function) and site attributes (including accessibility,
agglomeration, and environment) (Yang, et al., 2012). Therefore, compared to the zoning
regression model, the discrete choice model is able to account for individual hotel factors in a
more comprehensive way. Yang, et al. (2012) applied an ordered logit model by categorizing
hotels to different ranks according to their proximities to city center. Kalnins and Chung
(2004, 2006) used a conditional logit model to unveil hotel location factors because this
model is able to incorporate a large number of location options covered in the dataset.
3.4 Simultaneous equation model
A simultaneous equation model (SEM) incorporates more than one dependent variable in the
system of equations and consists of multiple equations with other controlling variables to
capture the relationship among multiple dependent variables. Subsequently, it is a natural
candidate to empirically validate the multi-dimensional model by specifying each dimension
as a single equation. To alleviate the inherent endogeneity within the model, two-/three- stage
least square methods are used to estimate the SEM and obtain reliable estimates of hotel
location determinants (Baum and Haveman, 1997; Urtasun and Gutiérrez, 2006).
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3.5 Individual evaluation model
To understand superior locations for new hotels, the individual evaluation model investigates
hotel location factors from the evaluation of individuals, such as hotel investors and potential
hotel guests. Through a survey of leading hotel chains, Johnson and Vanetti (2005) identified
several location advantages of international hotel chains. The size and nature of the city were
found to be the most important factor. They also found that executives from different regions
hold different views towards the importance of various location advantages. Adam and
Amuquandoh (2013a, 2013b) surveyed hotel owners in Ghana and the results highlighted
several location factors, such as economic, neighborhood, and physical site characteristics.
On the other hand, Arbel and Pizam (1977), Tsaur and Tzeng (1996), and Lee, Kim, Kim, and
Lee (2010) shed light on hotel location factors from the demand side and highlighted hotel
location characteristics preferred by customers, such as access to transportation portals and
tourist attractions. Two quantitative decision-making methods have been used to further
understand individual evaluation of hotel location factors: the analytic hierarchy process
(AHP) model (Beedasy and Whyatt, 1999; Newell and Seabrook, 2006) and the modified
Delphi method (Juan and Lin, 2011; Lin and Juan, 2010).
3.6 Hotel success model
A desirable location is always associated with hotel success in terms of several performance
measures. By investigating the factors that influence the performance of pre-existing hotels,
one can identify and predict potential locations for new entrants. The hotel success model
includes regression models used to identify location factors associated with a premium on
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hotel room rate (Enz, et al., 2008; Lee and Jang, 2011; Shoval, 2006), a higher revenue per
available room (Canina, et al., 2005; Chung and Kalnins, 2001; Tsang and Yip, 2009), a
higher profitability (Biemer and Kimes, 1991; Kimes and Fitzsimmons, 1990), and a lower
hotel failure rate (Ingram and Baum, 1997). The choice of performance measure could be
very important if the model aims specifically to obtain a hotel location decision rule. Kimes
and Fitzsimmons (1990) found that the occupancy rate, the total revenue, or total profit are
not appropriate in this situation. Instead, they defined a new profitability measure by adding
depreciation and interest expenses to the total profit and dividing by the revenue.
4. Operational Model
Compared to the numerous studies on theoretical and empirical hotel location models, few
have been concerned with the operational model on hotel location selection. One possible
reason is that most models only look into hotel location from certain perspectives and do not
consider all possible location factors. As a result, these models lack applicability for
operational uses. Even those models covering the comprehensive aspects of factors might be
complicated and difficult to understand for practitioners. The operational hotel location
model applies location decision rules to determine suitable locations for new entrants, and
these rules can be obtained by theoretical and empirical models. Therefore, operational
models transfer knowledge from “scholarly” models to knowledge with greater practical
values to practitioners.
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4.1 Checklist method
Checklist method refers to the systematical evaluation of possible locations based on
pre-specified criteria in a checklist. Several checklists of hotel location can be found in
Medlik (1966), Smith (1995), and Rushmore (2001). Lin and Juan (2010) presented a
checklist for resort park locations in Taiwan. The major criticism of this method comes from
its subjectivity and a lack of generality. The checklist is usually derived from opinions of
experts without rigorous empirical validation and sometimes lacks transferability to consider
localized factors in different environments.
4.2 Statistical prediction
The estimates of some statistical empirical models can be used as the decision rule to
calculate the suitability of potential hotel location, such as the zoning regression model, the
discrete choice model, and the hotel success model. For example, Biemer and Kimes (1991)
proposed a refined hotel location prediction model using the three-step bootstrap procedure
based on an empirical model of profitability. However, since some common pitfalls
associated with statistical empirical models are likely to influence the robustness of results, a
cross-/external validation should be conducted to test the prediction performance and avoid
the over-fitting problem of estimates (Biemer and Kimes, 1991; Kimes and Fitzsimmons,
1990). Smith (1995) introduced residual analysis based on zoning regression models to
evaluate the business potential of locations. The validity of residual analysis relies heavily on
the correct specification of the prediction model, and any mis-specification would result in
misleading implications. Finally, a noticeable advantage of statistical prediction lies in the
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confidence interval that the prediction model generates, which helps to understand the level
of certainty associated with the prediction.
4.3 Geographic Information System (GIS)
GIS is defined as a computerized system used for the storage, retrieval, mapping, and
analysis of geographic data. GIS provides a more efficient decision-making support system
for selecting suitable sites of new hotels by incorporating spatial considerations. Oppermann
and Brewer (1996) presented a conceptual framework of hotel location decision making by
GIS, including data acquisition and data analysis stages. Joerger, DeGloria, and Noden (1999)
provided a detailed example of utilizing GIS for hotel location selection. In their research,
according to the requirements based on soil type, land use type, conservation status, road
accessibility and coast accessibility, a stepwise diagnostic GIS approach was used to narrow
down possible candidates and ultimately to select suitable sites for new hotels. Beedasy and
Whyatt (1999) developed a spatial decision-support system to conduct weighted linear
combination technique to obtain the suitability score of each possible hotel location. Crecente,
Santé, Díaz, and Crecente (2012) utilized GIS to support location selection of thalassotherapy
resorts based on five criteria.
There are still several other operational models facilitating hotel location decision making.
Hobson (1994) demonstrated several location examples which used “Feng Shui,” a set of
traditional Chinese philosophical and religious principles to analyze geographic locations.
Aliouche and Schlentrich (2011) proposed an international expansion assessment model to
24
evaluate possible locations for hotel chain expansion. Moreover, other operational location
models for general tourism facilities can also be used in hotel location evaluation, such as the
LOCAT model (Moutinho and Curry, 1994) and the GIS-supported sustainable tourism
infrastructure planning (STIP) framework (Boers and Cottrell, 2007).
5. Discussion
5.1 Findings of previous research
Table 1 summarizes the surveyed literature (after 1990) with respect to the scale of research,
the research period, the theoretical, empirical, and operational models used, and location
factors highlighted. In total, only 59 published articles that directly pertain to hotel location
analysis were found. Compared to the voluminous literature on other topics in hospitality
management, such as hedonic pricing modeling and hotel efficiency assessment, hotel
location research has attracted only limited attention from scholars over the last two decades.
More importantly, this research is highly scattered throughout a diverse mix of academic
disciplines, leading to substantial heterogeneity in the methodologies utilized in hotel location
research.
(Please place Table 1 about here)
Regarding the research scale, 26 out of 54 papers are intra-metropolitan studies, suggesting
that urban hotel location research dominates the current body of literature. The THC model
was popular in the 1990s, while the agglomeration model of relative hotel location became a
25
new area of study after 2000. The dominant use of qualitative and cartography empirical
methodology has been changed in recent years, and several more sophisticated empirical
models--the discrete choice model and the count data model, for example--have been added
to hotel location analysis. Operational hotel location models, which are essentially paramount
for practitioners, have been overlooked by the past literature. One trend, if any, in the use of
operational models is the emergence of GIS analysis. Along with a wider range of available
geo-coded data and higher computation power, GIS has been, and will continue to be, a
promising tool to evaluate the appropriateness of hotel location alternatives. Regarding
location factors unveiled in past studies, various market access and market potential measures
have been consistently emphasized, such as access to CBD and beach, access to
transportation portals, and local population and income.
5.2 Future research directions
5.2.1 Other location factors
Hotel location pattern is an outcome of local government policies adopted by hotel investors
and urban planners. Under certain policy interventions and restrictions, only a limited number
of alternatives are available for new hotel entrants. On the other hand, along with some policy
supports, new hotels obtain extra benefits when choosing to locate themselves in particular
areas. Therefore, the location decision-making process is not only a result of market forces,
but is also entangled with other factors such as government policies. In the current body of
hotel location literature, little is known about the impact of various government policies on
hotel location. A closer examination of this area is necessary to provide a more
comprehensive picture of hotel location choice. In addition, it is worthwhile to highlight other
26
important hotel location factors that have long been overlooked, but merit in-depth
investigation, such as cultural distance/affiliation of hotel investors, expected long-run risk
associated with the alternative, and accessibility to different market segments. It is crucial for
researchers to develop innovative models that can accommodate these factors in explaining
hotel location choice.
5.2.2 GIS and spatial toolsets
Since hotel location data inherently incorporates geographic information, embracing GIS
technologies provides additional efficiency in data storage, retrieval, analysis, and
visualization. Therefore, working together with more sound theoretical and empirical models,
GIS techniques hold the promise of further improving hotel location decisions. Moreover,
due to the expansion of Internet and the availability of “big data,” GIS location analysis
enters a new phase of sophistication, and web-GIS, which integrates GIS with the internet,
becomes greatly convenient for potential decision-makers. Additionally, we recommend
using the innovative spatial decision-making system, with an integration of various hotel
location models with the web-GIS platform. It would produce high-quality and transferable
outputs on hotel location selection. Therefore, research based on the spatial toolsets deserves
further study.
For empirical models, some statistical/econometric tools of hotel location suffer because they
overlook spatial dependence and spatial heterogeneity (Anselin, 1988). For example, in
empirical regression models, spatial dependence highlights an interrelation between nearby
hotel location outcomes, whereas spatial heterogeneity refers to a variation of regression
27
coefficients over space, which result from the spatial variation of physical and
social-economic factors. To account for these spatial issues, several advanced spatial tools
can be utilized, such as spatial econometric models and geo-statistical models. All these
spatial toolsets have the potential to provide a proliferated agenda for the future.
5.2.3 Agglomeration and competition studies
Past research has paid greater attention on the absolute location of hotels, while limited
concern has been given to hotels’ relative location. Researchers and practitioners do not have
sufficient insight into the agglomeration process of hotels. The agglomeration effect can be
partitioned into urbanization economies and localization economies. The former one refers to
the economic benefits from the inter-sectoral clustering of diverse firms, while the latter
points to the effects stemming from the firms within the same sector (Holl, 2004). The
existing literature simply does not disentangle these two types of economies. Therefore,
future research efforts will be required in this topic. Another area to examine is the field of
imperfect competition. In general, we know little about how investors select hotel locations in
the market structure of monopolistic competition, oligopoly, and monopoly. Imperfect
competition and hotel location require careful analysis and in-depth investigation.
5.2.4 Location choice of chained/franchised hotels
Chained and franchised hotels are likely to utilize different location strategies from the
ordinary ones, and their location choice requires additional considerations on the network
construction of member hotels. In order to take full advantage of market penetration and
avoid cannibalizing existing hotels within the chain, investors should consider that the
location choice of a new property is contingent on the location and pattern of established ones.
To the best of our knowledge, no known research has looked into this sequential location
28
choice of chained/franchised hotels yet, and we advocate for future research on this topic to
provide important insights into hotel chain expansion.
5.2.5 Sophisticated industrial and service location models
The movement towards interdisciplinary study with hybrid methodologies will continue in
the near future. We need to pursue efforts to build and sustain interdisciplinary ties in the area
of hotel location studies. Industrial and service location analysis is concerned with the spatial
location of economic activities in a broader sense. Compared to the location models in other
industries, such as retail, manufacturing, and health care, we can identify significant gaps in
both depth and width. To further improve current hotel location models, researchers can
introduce more sophisticated ideas and insights from industrial and service location models,
such as location allocation models, trade area analysis, models in new economic geography,
and models in spatial industrial organization. However, it is worthwhile to note that some
refinements might be necessary when applying these models outside the hotel industry. This
limitation is due to the fact that hotels are serving a major market consisting of travelers
rather than local residents. In summary, integrating advances from industrial and service
location models present substantial opportunities for additional hotel location research.
6. Conclusion
This review paper presents an important first attempt to understand the current body of hotel
location literature and to identify the advantages and disadvantages of various hotel location
models. We divided all hotel location models into three categories: theoretical, empirical, and
operational. Furthermore, we discussed different model sub-categories within each of these
29
three models. Future research directions are also identified based on this review. We advocate
future studies on some neglected but crucial location factors, further research of the
agglomeration process in hotel location, and the development of more sophisticated spatial
models and decision-making systems to take advantage of an abundance of available
geo-coded data. We wish that this systematic review of hotel location literature will provide
researchers useful information for further development of hotel location models, and, more
importantly, offer practitioners a list of methods to help them in choosing desirable locations.
Nevertheless, it is paramount to concern the limitation of various models. Before embarking
on practical hotel location selection projects, one should keep in mind that all location models
are not a panacea, and they cannot substitute for intelligent decision making. Managerial
insights and judgments are of great importance in interpreting the results of various location
models and translate it to meaningful and refined location strategies (Ghosh and McLafferty,
1987). Moreover, since no single model or method is consistently superior in all situations,
we recommend the application of multiple methods and robustness check of their results to
handle location projects, especially those with extraordinary complexities.
Although all efforts have been made to assemble an exhaustive list of previous hotel location
literature, it is possible that some papers may have been missed due to a segmented body of
hotel location literature across different disciplines. Moreover, we did not cover some papers
investigating hotel location factors with indirect methods. For example, although we
reviewed the hotel success model that identifies location factors contributing to better hotel
30
performances, other studies on hotel pricing models and hotel efficiency models can
incorporate some location measures as well, albeit in a trivial way.
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39
Fig. 1. Research models in hotel location analysis
Provide theoretical insights
Refine location rules
Make operational decisions
Outputs
Operational
Model
Theoretical
Model
Empirical
Model
40
Fig. 2. Spatial arrangement of hotels in mono-centric models
Organized
Tourism
Individual
Tourism
CTD
Budget Hotels
of Type A
Luxury
Hotels
Budget Hotels
of Type B
Business
Hotels
Agriculture
Manufacturing
and Dwelling
Commerce
Hotel
CBD
a. Yokeno’s Model (1964)
b. Egan and Nield’s Model (2000)
c. Shoval’s Model (2006)
41
Fig. 3. Relationships among dimensions in multi-dimensional models
Capacity Distance
Service Distance
Geographic Distance
Price Distance
○+
○+
○+
○+
○+
○+
○+
○+
○-
○-
○-
○-
Size Distance
Geographic Distance
Price Distance
○+
○+
○+
○-
○-
○-
a. Baum and Haveman’s Model (1997)
b. Urtasun and Gutiérrez’s Model (2006)
42
Table 1
Recent literature on hotel location analysis
Studies
Scale, area and period
Theoretic
model
Empirical
model
Operational
model
Location factors
Kimes and
Fitzsimmons (1990)
Intra-regional, 1983 and 1986
HSM
SP
State population per inn, local median income,
and nearby college students
Roehl and Van Doren
(1990)
Inter-regional (USA), -1985
QC
Mobility and affluence
Biemer and Kimes
(1991)
Inter-regional (market area of each inn),
1983-1986
HSM
SP
State population, median income, and room rate
Burtenshaw,
Bateman, and
Ashworth (1991)
Intra-metropolitan (some European cities),
1960-1991
THC model
QC
Access to transport infrastructural, planning
policies, and primary attractions
Broadway (1993)
Intra-metropolitan (Montreal, Canada)
SSM
Subway station, university campus, Olympics,
Expo, and convention center
Bull and Church
(1994)
Inter-regional (sub-regions in the UK),
1981-1989
QC
Local consumption, producer service, and tourist
demand
De Bres (1994)
Intra-metropolitan (six Kansas towns,
USA), 1992
Kansas
tourism
model
QC
Highways
Hobson (1994)
Intra-metropolitan (some Eastern Asian
cities), 1994
“Feng Shui”
Location in relation to its environment such as
roads, valley and sea
Timothy and Wall
(1995)
Intra-metropolitan (Yogyakarta, Indonesia),
1994
THC model
QC
Bloomfield (1996)
Intra-metropolitan (London, Canada),
1959-1995
QC
Massive motorization and traffic growth
Ingram and Inman
(1996)
Inter-regional (around Niagara Falls),
1885-1991
AM
ZRM, HSM
Park development, hotel density and founding,
hotel density, and founding of the competitor
43
Oppermann, Din, and
Amri (1996)
Intra-metropolitan (Kuala Lumpur,
Malaysia), -1995
THC model
QC
Oppermann and
Brewer (1996)
GIS
Site, competition, and demand factors
Tsaur and Tzeng
(1996)
IEM
Convenience of transportation and parking
Baum and Haveman
(1997)
Intra-metropolitan (Manhattan, USA),
1898-1990
MDM
SEM
Price distance, size distance, downtown, hotel
size, hotel price, GNP growth, population, and
local hotel founding
Beedasy and Whyatt
(1999)
Intra-regional (Mauritius)
IEM
GIS
Remoteness from existing tourist zones,
proximity to roads, remoteness from urban,
slope, and elevation
Dökmeci and Balta
(1999)
Intra-metropolitan (Istanbul, Turkey),
-1995
QC
CBD and coastal amenities
Joerger, DeGloria,
and Noden (1999)
Intra-regional (northwestern Costa Rica)
GIS
Soil type, land use type, conservation status,
road accessibility, and coast accessibility
Kundu and
Contractor (1999)
Inter-regional (67 countries), 1988–1990
ZRM
GDP, ratio of exports to GDP, tourism receipts,
and total inward FDI.
Bégin (2000)
Intra-metropolitan (Xiamen, China),
1949-1996
THC model
QC
Special economic zone and urban development
Egan and Nield
(2000)
Intra-metropolitan (major cities in the UK)
MCM
Bid-rent curve
Chung and Kalnins
(2001)
Intra-regional (Texas, USA), 1992
AM
HSM
Share of chained hotels and hotels with different
sizes within the location
Shoval and
Cohen-Hattab (2001)
Intra-metropolitan (Jerusalem, Israel),
1917-1999
THC model
QC
Political upheavals, social and cultural different
between residents
44
Shu and Dai (2002)
(China), 1985-2004
ZRM
GDP, GDP per capita, inbound and domestic
tourist arrivals, tourism revenue, foreign trade,
and freight and passenger turnover
Holl (2004)
Inter-regional (Portugal), 1986-1997
AM
ZRM
Population, access to road, and sectoral diversity
Kalnins and Chung
(2004)
Inter-regional (zip-code units in Texas,
USA), 1992-2000
AM
DCM
Incumbent market share, hotel chain size,
distance to headquarters and owner’s nearest
hotels, and population and income of zip code.
Kalnins (2004)
Intra-regional (Texas, USA), 1990-1999
AM
HSM
Canina, Enz, and
Harrison (2005)
Intra-regional (USA), 2000
AM
HSM
Share of hotels with different sizes and strategies
within the location
Johnson and Vanetti
(2005)
Inter-regional, 2001
Eclectic
Theory
IEM
Size and nature of the city, infrastructure within
the region, and perception of the region
Egan, Chen, and
Zhang (2006)
Intra-metropolitan (Beijing, Shanghai, and
Shenzhen, China)
MCM
QC
Hotel’s bid-rent curve and agglomeration
Kalnins and Chung
(2006)
Intra-regional (Texas, USA), 1990-1999
AM
DCM
Proximate unbranded Gujarati motels, same
owner’s proximate hotels and experience,
distance to nearby hotels, local population,
income and retail outlet growth, and local
unbranded motel rooms
Newell and Seabrook
(2006)
AHP
Volatility of demand, number of visitors, site
attributes, age of target hotel, and current hotel
supply
Shoval (2006)
Intra-metropolitan (Jerusalem, Israel), 1999
MCM
HSM
Hotel market source
Sund (2006)
Intra-regional (Switzerland), 1992-2002
SSM
City with affluent business and leisure tourism,
access to international airports, and proximity to
mountain resorts
45
Urtasun and
Gutiérrez (2006)
Intra-metropolitan (Madrid, Spain),
1936-1998
MDM
SEM
Price distance, size distance, service distance,
zoning, founding time, chain affiliation, and
hotel category
Aliagaoglu and Ugur
(2008)
Intra-metropolitan (Erzurum, Turkey),
1963-2002
Regional
life model
QC
Bus stations and coffee houses
Enz, Canina, and Liu
(2008)
Intra-regional (USA)
AM
HSM
Share of hotel with different strategies, cluster
size, strategy dispersion, and size dispersion
Tsang and Yip (2009)
Intra-metropolitan (Beijing, China),
1998-2001
AM
HSM
Proximity to upscale joint venture hotels
Helmers (2010)
Intra-regional (within Texas, USA),
1997-2005
AM
HSM
Distance to large hotels, metropolitan location,
ownership, luxury rating, local income,
incumbent occupancy, and capacity
Lee, Kim, Kim, and
Lee (2010)
Intra-metropolitan (Seoul, Korea), 2002
IEM
Safety, access to transportation portals, and
connection to area attractions
Lin and Juan (2010)
Porter’s
Diamond
model
IEM
Checklist
Factor endowments, demand conditions, firm
strategy structure and rivalry, related and
supporting industries, government, and chance
Aliouche and
Schlentrich (2011)
Inter-regional (different countries)
International
expansion
assessment
model
Macro- / micro- environmental factors
Juan and Lin (2011)
Porter’s
Diamond
model
IEM
Factor endowment, demand endowment, firm
strategy structure and rivalry, related and
supporting industries, government, and chance
Lee and Jang (2011)
Intra-metropolitan (six cities in USA),
2008
MCM
HSM
Proximity to airport and CBD
46
Novak, Petrić, and
Pranić (2011)
Inter-regional (Croatia), 1997-2007
Regression
explaining
hotels from
different
countries
Foreign direct investment, market
interconnectedness, and tourist flows
Polyzos and Minetos
(2011)
Inter-regional (prefectural level units in
Greece), 1991-1998
ZRM
Natural coastal resource, transportation
infrastructure, population, and policies
Yang and Fik (2011)
Intra-metropolitan (four cities in China),
2010
AM
SSM, ZRM
Agglomeration, access to subway stations, bus
stations, and tourist attractions
Crecente, Santé,
Díaz, and Crecente
(2012)
Intra-regional (211 sites in Spain)
EVAMIX
multicriteria
approach
GIS
Resources, facilities, legislation, containers,
environmental quality, impacts
Freedman and
Kosová (2012)
Inter-regional (counties in USA),
1993-2006
AM
ZRM
Incumbent market share, population, and
employment
Luo and Yang (2012)
Inter-regional (cities in China), 2001-2010
SSM
GDP, foreign investment, and tourist arrivals
Rogerson (2012a)
Intra-metropolitan (three cities in South
Africa), 1990-2010
THC model,
MCM
QC
Beachfront locales
Rogerson (2012b)
Intra-regional (South Africa), 1990-2010
QC
Urban tourism nodes
Yang, Wong, and
Wang (2012)
Intra-metropolitan (Beijing, China), -2004
MCM, AM
DCM
Star rating, opening years, service
diversification, ownership, agglomeration effect,
public service infrastructure, access to road,
subway, and tourism sites
Zhang, Guillet, and
Gao (2012)
Inter-regional (provinces in China),
1990-2009
ZRM
Inbound tourist arrivals and spending, FDI, GDP
per capita, policies, and mega events
Adam (2012)
Intra-metropolitan (Kumasi Metropolis,
Ghana), 2010
MCM
QC
Bid-rent curve
47
Adam and
Amuquandoh
(2013a)
Intra-metropolitan (Kumasi Metropolis,
Ghana), 2010
IEM and
factor
analysis
Economic, neighborhood characteristics,
physical site characteristics, laws and
regulations, social-cultural and transport factors
Adam and
Amuquandoh
(2013b)
Intra-metropolitan (Kumasi Metropolis,
Ghana), 2010
IEM
Economic, neighborhood characteristics,
physical site characteristics, laws and
regulations, social-cultural and transport factors
Ferreira and Boshoff,
2013
Intra-metropolitan (Cape Town),
2007-2010
THC model
QC
CBD, seaboard, and accessibility
Rogerson (2013)
Intra-regional (South Africa), 1990-2010
QC
Market demand
Research scale: Intra-metropolitan (26); Intra-regional (13); Inter-regional (14)
Theoretical model: THC model (7); MCM-Monocentric model (7); AM- Agglomeration model (13); MDM-Multi-dimensional model (2)
Empirical model: QC-Qualitative and cartography (17); SSM-Spatial statistical model (4); ZRM-Zoning regression model (8); DCM-Discrete choice model (3);
SEM-Simultaneous equation model (2); IEM-Individual evaluation model (8); HSM-Hotel success model (11)
Operational model: Checklist (1); SP-Statistical prediction (2); GIS (4)
48
