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8/9/2019 Keyen Farrell Thesis - Hotel Rates Las Vegas
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Keyen Farrell copyright 2010 , All rights reserved Page 1
The Determinants of Hotel Rates inLas Vegas, Nevada
Senior Economics ThesisKeyen Farrell
This study attempts to examine the determinants of hotel rates in Las Vegas, Nevada.Published prices are analyzed for 112 Las Vegas lodging properties. Regression analysisis used to estimate implicit prices for several hotel amenities. In addition, the effect ofdistance from various points of interest on rates is examined. Finally, this paper examinesthe extent to which tourism ratings contain information over and above that which ispublicly available.
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unique to the location of the property. This paper estimates implicit prices for several site
and situation attributes.
Specifically, this paper estimates implicit prices for thirteen site attributes. These
include the number of rooms, pools, and restaurants as well as the number of stars
awarded to a property. Other site attributes tested include the presence of a full-service
spa, complimentary high speed internet, and complimentary breakfast. Still other site
variables examined are the availability of room service, on-site shopping, on-site
entertainment, existence of a casino, existence of complimentary transportation to the Las
Vegas Strip, and complimentary transportation to McCarran International Airport.
In addition to the thirteen site attributes, implicit prices are estimated for three
situation variables. These variables denote the distance from the Las Vegas Strip,
distance from McCarran International Airport, and whether or not the property is located
on the Strip.
A second aim of this paper is to compare the power of properties Automobile
Club of America (AAA) ratings to predict room rates to the power of the other site and
situation variables to predict room rates. The motivation for this aim comes from Cantor
and Packer (1994). Though they examine credit ratings, and not tourism ratings, they find
startling evidence that credit ratings contain information over and above that which is
publicly available. It is suspected that other ratings systems, such as tourism ratings may
behave in a similar manner, and part of this paper explores the issue.
The results of this paper will be of particular value to hotel managers. Proper
knowledge of the implicit prices of hotel attributes can enable hotel managers to boost
profits by charging prices that accurately reflect the value of the amenities featured in the
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lodging establishment. By the same token, such knowledge can increase guest
satisfaction by revealing which hotel characteristics provide the most utility to guests so
that properties can offer them.
Relevant Literature:
A sizable body of literature has accumulated which addresses both directly and
indirectly the topic of this paper. Some papers quantitatively seek to determine the value
of a hotel room from the propertys attributes while other papers rely on surveys to
determine the general attributes most valuable to guests. Bull (1994) seeks to determine
the value of a lodging propertys location through regression analysis. He hypothesizes
that there is a value to specific advantages which one location might have in distance
from the city center, beaches, or other points of interest. Hotel managers should thus
charge higher room rates in desirable locations. His paper builds a methodology for
formally determining the value of a lodging propertys location. The author uses hedonic
analysis in order to derive implicit prices for several lodging attributes expected to affect
room rates.
The author examines 15 motels located along a 3.5km stretch of highway in
Ballina, Australia. Ballina is a coastal town and popular beach destination. A river flows
through the city perpendicular to the ocean before emptying into the ocean. The highway
consists of two roads, one which runs parallel to the ocean and another that runs parallel
to the river. The city center is located at the corner where the ocean, river, and two
highways converge. This location is also where the ocean beaches are found. As a result,
locations closer to the city center/beach area are more desirable.
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The author includes two situation attributes. The first is distance from the city
center/beach area. The second is a side dummy variable equaling one if the hotel is on
the river side and zero if otherwise. Three hotels face the river side and it is postulated
that hotels facing the river command a higher price. Three other variables are included to
indicate site attributes. They are, number of rating stars, age of the property, and presence
of a restaurant. Room rate is then regressed on the five total explanatory variables. Age
and side are dropped from the specification due to low correlations, and the equation is
rerun with the remaining three variables.
The remaining three coefficients are significant and have the expected sign. The
study finds that an additional star is worth $14-16 dollars per night in the sample (p.13).
A restaurant on the property adds around $6-10 per night, and each kilometer of distance
from the city center/ocean area reduces room rates by $3-6, ceteris paribus (p.13).
In their study, White and Mulligan (2002) use hedonic analysis to estimate
published prices for 600 lodging properties belonging to six national chains. As in Bull
(1994), OLS regression is used to estimate the effect of site and situation variables on
room rates. Site attributes refer to amenities and other characteristics of the property such
as number of rooms, availability of complimentary breakfast, etc. Situation variables
refer to characteristics of the location, area, or surrounding market. There are five dummy
site variables to control for each of the six budget lodging chains in the sample. Four
additional dummy site variables expected to affect room rates are also included in the
model. These variables are, the existence of a pool, existence of a spa, complimentary
breakfast, and number of rooms. It is expected that hotels with more rooms are likely
newer and offer more amenities like valet parking. Each of these four site variables is
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expected to have a positive effect on room rates. Several situation variables are also
included in the model. These include two dummy situation variables denoting interstate
location and urban location. Finally, median family income is added to the specification
as a proxy for the higher operating costs that hotels in high-income areas face.
The authors find that breakfast has the largest per-unit effect on room rates,
decreasing the average room rate by $4.14 per night (p.538). The presence of a spa
increases room rates by $3.53 (p.538) per night in the sample. A one-room increase in
hotel size increases the price of an overnight stay by approximately nine cents. All
coefficients except the pool coefficient are significant though the sign of the breakfast
coefficient is unexpected.
In regards to the situation variables, an increase in median family income has a
positive effect on room rates, as expected. Properties in urban locations also have higher
room rates, ceteris paribus. In terms of the interstate variable, properties along an
interstate charge less per night, all else constant, than properties not located on an
interstate. This is expected due to higher noise levels.
While hedonic estimates are desirable since they produce a quantitative estimate
of the implicit value of each attribute, much of the hospitality literature utilizes surveys to
qualitatively approximate the value guests assign to various lodging attributes. Mayo
(1974) uses a self-report questionnaire to examine the determinants of motel choice at
twenty-four locations spread equally throughout the United States. Seven hundred and
forty-eight travelers responded to a questionnaire administered en-route to avoid any
potential recall bias.
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While the relative importance of lodging characteristics varies among guests, four
main attributes stand out as consistently desirable among guests. The first is the hotels
aesthetics, which encompasses attributes such as dcor and attractiveness of the property.
Second is the motels proximity to tourist attractions. The remaining attributes that are
significant determinants of motel choice are the availability of a pool and on-site dining.
The paper makes another important contribution to our understanding of traveler
behavior through its emphasis on the value of advertising. The study finds that
advertising increases guest confidence in the establishment and increases the likelihood
of a booking. The perceived accommodation quality that travelers associate with a
nationwide advertising campaign underscores the important role that perceived quality
plays in determining traveler preferences. Hotel ratings such as the (AAA) Diamond
Awards similarly affect perceived accommodation quality, and it is likely that guests
prefer a favorably-rated property.
Another relevant finding is the strong preference for large chain accommodations
among vacationers. This suggests that larger properties may be preferable to smaller
properties. The author finds that two particular perceived attributes of large properties are
most desirable to travelers. First, travelers perceive accommodations as standardized in
large chains, and feel they know what to expect. Secondly, they assume large hotels to be
newer and offer more modern accommodation which is viewed as superior. Surprisingly,
the travelers reported that their income level did not have a large impact on their choice
of accommodation. This suggests that infrequent travelers are willing to splurge for
lodging priced high relative to their income if the property offers desired characteristics.
That is, lodging has a surprisingly low income elasticity.
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In another paper, Cadotte and Turgeon (1988) study the main components of
guest satisfaction. Their work applies to the purposes of this paper, because guests will
likely pay a higher price to stay at a property displaying the characteristics most
important to guest satisfaction. The authors survey executives from 260 lodging
establishments representing 280,000 rooms. The sample consists of a broad nationwide
cross-section of lodging establishments covering properties of all sizes, occupancies, and
room rates.
The major finding emerging from the paper is the importance of staff service to
the user experience. Next to the price of the room itself, guest complaints most frequently
regard the speed and quality of service. Similarly, guest compliments most frequently
concern the helpful attitude of employees. Admittedly, the criteria are imperfect and the
interviews conducted with hotel executives may not communicate guest preferences in an
entirely accurate manner. However, hotel executives consistently reported guests
overwhelming desire for good service. The finding indicates that the human element
plays a critical role in the guest experience. It appears that the value of a hotel room is not
solely a function of physical hotel attributes. Thus measures that account for the type and
quality of service such as tourism rating systems are useful in understanding the price
travelers are willing to pay for accommodation at a given establishment.
Arbel and Pizam (1977) adopts a more focused approach by examine the
importance to guests of a single attribute: location. The authors examine urban tourists
willingness to use accommodations located outside of a city center. The authors seek to
determine the extent to which a trade-off exists between distance from the city center and
hotel rates. They conducted interviews with 300 foreign, English-speaking tourists
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staying at least one night in Tel Aviv Israel. The purpose of the interviews was to
approximate tourists willingness to stay outside of the city center.
Arbel and Pizam find that 76.3% of tourists do not require a reduction in room
rate to stay at a hotel up to fifteen minutes from the city center (p.20). However, for
properties located thirty minutes from the city center, 61.4% of tourists said that a
reduction in room rate was necessary to compensate for the longer travel time (p.20). The
authors are surprised by the relative insensitivity of guests to the distance of their
accommodations from the city center. They conclude that there is a considerable market
of tourists who are willing to pay the same rates that city center hotels charge while
staying at distant properties, especially those within 15 minutes from the city center.
Yet as one would expect, they find that distance flexibility decreases as distance
from the city center increases. That is, as distance increases by equal amounts, guests
require an increasing percentage reduction in room rates. For instance, of the respondents
who said they required a rate reduction to induce them to stay at a hotel 15 minutes from
the center, the mean required reduction was 4% (p.21). This is considerably less than the
12% mean rate reduction required to induce travelers to stay 30 minutes from the city
center (p.21).
Finally, Cantor and Packer (1996) provides additional insights that are relevant to
this paper and the hospitality industry in general. Interestingly, sovereign credit ratings
can be seen as analogous to tourism ratings such as the AAA Diamond Awards. In their
paper, Cantor and Packer examine the ability of published rating criteria to predict
sovereign credit ratings. They regress both Moodys and Standard and Poors sovereign
credit ratings for forty-nine countries on eight separate rating criteria. The eight criteria
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expected to influence a countrys credit risk are, per capita income, GDP growth,
inflation, fiscal balance, external balance, external debt, an indicator for economic
development, and an indicator for default history. All criteria with the exception of GDP
growth, fiscal balance, and external balance are significantly correlated with both
agencies ratings, and the eight criteria explain around 90 percent of the variation in
credit ratings (p.41).
However, the finding that is of most relevance to this paper is the superior power
of credit ratings over standard sovereign risk indicators to predict relative spreads. The
authors examine whether the rating itself or the eight aforementioned sovereign risk
indicators is a better predictor by regressing sovereign bond spreads on the respective
proxy. They find that the eight risk indicators can only predict 86% of the variation in
spreads while ratings themselves explain 92%. This finding suggests that ratings contain
information additional to that which is publicly available. The authors suggest that
difficulty in quantifying the criteria as well as the lack of information regarding the
respective weights assigned to the published criteria likely contribute to the difference in
predictive power.
This finding has important implications for the lodging industry, where
establishments live and die by tourism ratings. While the ratings criteria of agencies such
as that of AAA are publicly available, no indication of the methodology or weights
assigned to each criterion is provided. Additionally, the detailed nature of the rating
criteria makes it difficult to replicate tourism ratings from individual lodging attributes.
Rating agencies such as AAA inspect minute lodging details such as the build quality of
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pool furniture, making it very difficult to quantify tourism rating criteria. This difficulty
is similar to that encountered with quantifying sovereign credit rating criteria.
Moreover, tourism rating agencies assess lodging attributes not readily visible to
the public such as the hotel kitchen. In this sense, tourism ratings contain information not
publicly available. Indeed part of this paper is devoted to examining the existence of
information in tourism ratings that is over and above that which is contained in readily
observable lodging attributes.
The Model:
RATE = 0 + 1ROOMS + 2STARS + 3NUMREST + 4POOLS+ 5CASINO + 6SPA+ 7INTERNET + 8BREAKFAST + 9ROOMSERVE + 10SHOWS + 11SHOPS +12STRIP+ 13AIRTRANS + 14STRIPTRANS + 15AIRDIST + 16STRIPDIST
The Dependent Variable:
RATE is the dependent variable used in the model. RATE denotes the published
one night, per-room rate of a standard room at a given lodging property. The standard
room rate was chosen as the rate for the dependent variable because it was the most
widely published rate. Additionally, the vast majority of Las Vegas properties offer
standard rooms. More importantly, however, the size of standard rooms is relatively
uniform, making comparisons of rooms across properties more meaningful. A great deal
of variation exists in suite accommodations, which makes suite comparisons across
properties problematic.
The Independent Variables:
The fully-specified model contains thirteen separate site variables. ROOMS
denotes the number of standard rooms contained in the lodging property. The ROOMS
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variable excludes suites since the dependent variable is expressed in dollars per standard
room per night. The expected effect of ROOMS on RATE is ambiguous due to
competing effects. First, larger properties are expected to offer more amenities such as
concierge services and valet parking, beyond those represented by other site variables in
the model. While it is likely not worthwhile for small hotels to invest in items such as
concierge and valet services, larger properties are more likely to make these investments
since there is a greater number of potential users. In addition to providing a wider range
of amenities, White and Mulligan (2002) suggests that larger properties are likely newer.
In general, newer properties are styled to reflect the tastes of the modern guest and are
more comfortable. Larger properties are expected to be more desirable, all else fixed,
since they offer a wider range of amenities and are likely newer. It is expected that guests
will pay more for a newer hotel with a wider range of amenities. In the presence of these
two effects alone, an increase in the number of standard rooms would be expected to have
a positive effect on the dependent variable.
However, a supply effect exerts an opposite effect on the dependent variable. It is
expected that large properties will decrease room rates to fill their rooms. Since larger
properties contain more rooms, they have a larger supply of rooms than smaller
properties. In order to reach the same occupancy rate as a smaller hotel, it is expected that
a large property has to decrease rates relative to smaller properties to increase the
quantity of rooms demanded by guests.
Additionally, economies of scale are also expected to decrease room rates. Larger
establishments are expected to have lower total average costs than smaller
establishments. For example, a large establishment can have one maintenance department
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for many more rooms than a small establishment containing many fewer rooms. Large
hotels also enjoy considerable administrative savings over smaller properties. It is likely
that the same standard computer system can check-in many more guests at a larger hotel
than a smaller hotel with little or no additional costs to the large hotel. The savings
enjoyed by large establishments decrease the operating costs per room. The lower costs
allow managers who set prices as a markup over costs to in turn lower prices. The
presence of a supply effect and economies of scale are expected to cause an increase in
the number of rooms to have a negative effect on the dependent variable. However, the
cumulative effect of ROOMS on RATE is not known due to the aforementioned
competing effects.
STARS is another site variable included in the model. STARS denotes the
number of AAA Diamonds awarded to the property, ranging from one to five diamonds.
In this paper, each AAA Diamond is considered to be one star. There are 122 AAA rated
properties within 15 miles of downtown Las Vegas. A propertys AAA rating is based
upon 27 separate criteria. These criteria consider the external structure and hotel grounds,
public spaces such as the lobby area, restaurants, guestrooms, and level of service.
Each additional star indicates more and better amenities as well as enhanced
service. Since this is desirable to guests, it is expected that an increase in the number of
stars (e.g. AAA Diamonds) will have a positive effect on the dependent variable. It is
also expected that the positive effect of STARS on RATE is strengthened by the positive
endorsement that comes with a favorable AAA rating. That is, guests are more inclined to
book a room at a property backed by a trusted agency such as AAA. Thus, favorably-
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rated properties are expected to command a price premium over comparable properties
that lack the endorsement of a favorable AAA rating.
Admittedly there is some overlap between criteria measured by STARS and the
other site variables. As discussed, the age of the property is presumed to be captured by
the ROOMS variable. For instance, a number of AAA criteria examine the quality of the
propertys construction, and newer hotels will likely receive higher ratings for better
construction. Thus STARS is expected to be positively correlated with ROOMS. While
some correlation is expected between STARS and each of the other twelve site variables,
the AAA criteria examine far more attributes than the other site attributes. The criteria
also examine attributes represented by site variables in the model in far greater detail. For
instance, while the POOLS variable simply indicates the number of pools located on a
property, the AAA criteria looks deeper, rating the quality of pool furniture and the
presence of a full-time professional attendant.
Even more importantly, STARS is affected by the level and quality of service. No
other variable in the model explicitly contains information on service provided by staff.
For instance, while NUMREST denotes the number of restaurants, unlike STARS, it is
not affected by the level of service at each restaurant. Cadotte and Turgeon (1988)
suggests the importance of non-physical attributes like quality of service as a component
of guest satisfaction. Therefore it is reasonable to include STARS in the model.
NUMREST is a site variable denoting the number of restaurants located on the
lodging property. Mayo (1974) finds that the presence of a restaurant is an important
criterion for most travelers when selecting a lodging property. More restaurants offer
guests more varied dining options. As the number of restaurants increases, the property is
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able to cater to a wider range of diners tastes. Additionally, more dining options allow
for flexibility in guest budgets. This is another attractive feature of having multiple
restaurants located on-site. Since more restaurants offer guests more flexibility in both
the type of food they consume and the price they pay, an increase in the number of
restaurants is expected to have a positive effect on the dependent variable.
CASINO is another site variable used to denote the presence of a casino.
CASINO is a dummy variable equal to one if the property has an on-site casino and zero
if otherwise. Gaming is a source of entertainment for guests, and casinos are a profit
center for the property as well. In 2005, 86% of visitors to Las Vegas engaged in some
form of gambling (Las Vegas Visitor Profile). It is expected that guests are willing to pay
a higher price to stay at a property with a casino than a comparable property lacking a
casino since guests derive enjoyment from an on-site casino. Guests at properties lacking
a casino incur costs both in terms of lost leisure and transportation fees if they wish to
locate a casino. This leads to an expected positive effect of the existence of a casino on
room rates.
However, since casinos are also a source of revenue for properties that feature
them, it is expected that hotel managers may reduce hotel rates in order to draw potential
gamblers onto the property. The expected effect of a casino on the dependent variable is
ambiguous as a result of these two competing effects.
POOLS is a site variable used to denote the number of pools located on the
property. Pools are a source of enjoyment for guests, and it is expected that guests are
willing to pay more to stay at a property with a pool than a comparable property that
lacks a pool. Indeed Mayo (1974) suggests that pools play an important role in the
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selection of accommodation. Additionally, as the number of pools increases, guests have
more options in terms of what size and type of pool they would like to use. This
flexibility is considered a desirable attribute. An increase in the number of pools is
expected to have a positive effect on the dependent variable as a result of the increased
flexibility associated with additional pools.
SPA is another site variable denoting the presence of an on-site spa. SPA is a
dummy variable equal to one if the property offers a spa and zero if otherwise. Some
properties offer salon services only, but for the purposes of this paper, the property must
feature a full-service spa complete with massage services to be considered as having a
spa. Full-service spas offer many more amenities than simple salons, including different
massage treatments in addition to the services offered by simple salons. The wide array
of services offered by full-services spas is considered to be a desirable attribute. It is
expected that guests are willing to pay more to stay at a property with a full-service spa
than a comparable property lacking a full-service spa. Thus, the existence of a spa is
expected to have a positive effect on the dependent variable.
INTERNET is a site variable denoting the presence of complimentary in-room
high-speed internet access. INTERNET is a dummy variable equal to one if the property
offers complimentary in-room high-speed internet access and zero if otherwise. It is
expected that guests are willing to pay a higher rate for the added convenience of in-room
high-speed internet access. Thus the existence of complimentary in-room high-speed
internet is expected to have a positive effect on the dependent variable.
BREAKFAST is a dummy site variable equal to one if the property offers
complimentary breakfast and zero if otherwise. It is expected that guests are willing to
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pay more to stay at a property offering complimentary breakfast because of the
convenience of being able to eat on the premises as well as the savings over a paid
breakfast. Thus, the existence of complimentary breakfast is expected to have a positive
effect on the dependent variable.
ROOMSERVE is a dummy site variable equal to one if the property offers
twenty-four hour full room service and zero if otherwise. It is expected that guests are
willing to pay a higher price to stay at a property offering the option of having food
delivered from the kitchen at all hours of the day. If the NUMREST variable equals zero,
then ROOMSERVE will also equal zero since properties without a restaurant cannot
offer room service. The existence of room service is expected to have a positive effect on
the dependent variable due to the added convenience associated with twenty-four hour
room service.
SHOWS is a dummy site variable equal to one if the property offers free on-site
entertainment and zero if otherwise. To be considered as offering on-site entertainment,
the property must have a dedicated entertainment venue such as an arena or stage. For the
purposes of this paper, properties offering entertainment in venues not dedicated to
entertainment, such as bars, are not considered to offer on-site entertainment.
Entertainment can take many forms including comedians and singers, and these
entertainers are expected to make the property more desirable than a comparable property
that does not offer free entertainment. It is expected that guests are willing to pay a higher
rate for the utility derived from free on-site entertainment. Thus, the existence of a
dedicated entertainment venue is expected to have a positive effect on the dependent
variable.
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SHOPS is a dummy site variable equal to one if the property contains one or more
shops and zero if otherwise. Most properties contain gift shops and many contain liquor
stores. However, for the purposes of this paper, gift shops and liquor stores have no
impact on the SHOPS variable. Only the presence of more substantial and higher-end
stores is considered to have a sizable positive impact on guest utility. Guests save travel
time by being able to shop on the premises. As a result, it is expected that guests are
willing to pay a higher rate for the convenience of on-site shopping. Thus the existence of
on-site shopping is expected to have a positive effect on the dependent variable. The
desert heat is expected to increase the value of the option to shop without leaving the air-
conditioned premises, strengthening the positive effect of SHOPS on the dependent
variable.
While site variables describe attributes unique to the property itself such as
amenities, situation variables describe attributes pertaining to the location of the property.
STRIP is a dummy situation variable equal to one if the property is located anywhere
along the Las Vegas Strip and zero if otherwise. The Strip constitutes a four mile stretch
of Las Vegas Boulevard South stretching from the Stratosphere Hotel at the northern end
to the Mandalay Bay Hotel on the southern end. This is an iconic part of Las Vegas, and
many famous properties line the Strip. Additionally, many properties on the Strip offer
entertainment and may draw guests from other properties just to view the entertainment.
The Strip is the hub of activity in Las Vegas. The presence of many shows,
restaurants, and hotels within close proximity to one another is a desirable attribute of the
Strip. Additionally, the Las Vegas Monorail stops at seven stations along the Strip and
links many properties on the Strip, making hotel rooms on the Strip even more desirable.
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Additionally, it could be argued that aircraft noise at properties located near the
airport would make these properties less desirable and in turn reduce hotel rates. If this
were true, an increase in the AIRDIST variable would have a positive effect on the
dependent variable which could potentially offset the positive effect previously
discussed. However, most guest activities are carried out indoors due to the desert heat,
so it is not expected that aircraft noise will create disutility for guests staying near
McCarran International Airport. Thus the original negative effect of an increase in
distance from the airport on room rates is expected.
AIRTRANS is a dummy site variable equal to one if the property provides a
complimentary airport shuttle and zero if otherwise. Guest staying at a property that does
not offer a complimentary airport shuttle incur search costs in locating a taxi service as
well as the explicit cost of the airport taxi fare itself. Since the guest staying at a property
offering free airport shuttle service incurs neither of these costs, it is expected that guests
are willing to pay more for a property offering free airport shuttle service, ceteris paribus.
Thus the existence of a complimentary airport shuttle is expected to have a positive effect
on the dependent variable. Additionally, it is suspected that the addition of the
AIRTRANS variable to the model could make the AIRDIST coefficient less significant
since the hotel assumes the explicit costs of transportation, yet the guest still faces the
implicit cost of lost leisure in traveling to and from the airport even if the hotel pays the
actual costs.
The Data Set:
The sample consists of 112 AAA Diamond-rated properties located within 15
miles of downtown Las Vegas that are open for business and offer standard rooms. There
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are 122 AAA Diamond-rated properties within this range. However several were closed
for renovation and several properties contained only suite accommodations. This left 112
AAA Diamond-rated properties located within 15 miles of downtown Las Vegas that
were open for business and that offered standard rooms. Thus the study sample consists
of 112 separate properties. Room rate data was collected from published standard room
rates found on the AAA travel website. An attempt was made to obtain information
regarding the actual rate charged by each property, but hotel confidentiality prohibited
the release of such internal information. Table I contains descriptive statistics for room
rates.
The independent variables were collected from both the AAA travel website as
well as Vegas.com, a travel website that provides detailed profiles for nearly all Las
Vegas lodging properties. In addition to the dependent variable, the STARS, ROOMS,
and STRIP variables were collected from the AAA travel website. The variables,
NUMREST, CASINO, POOLS, SPA, INTERNET, BREAKFAST, ROOMSERVE,
SHOWS, SHOP, STRIPTRANS, and AIRTRANS were all collected from the hotel
profiles found on the Vegas.com website. The STRIPDIST and AIRDIST variables were
collected using Google Maps. Table II contains a description of each variable.
While the websites condensed data into an easily accessible form, there is
admittedly a great deal of variability in the quality of each hotel amenity. The quality of
every amenity is important because the quality determines the utility derived and hence
the amount guests are willing to pay for the amenity. The difficulty of controlling for the
quality of amenities denoted by the independent variables manifested itself mainly
through the POOLS, SHOP and NUMREST variables. In regard to the number of pools,
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no attempt was made to control for the size of the pool. Very few properties publish the
square footage of their pools. As a result, the POOL variable does not differentiate
between large and small pools. For example, the 8,000 foot-long lazy river pool that
snakes around the MGM Grand property was given the same weight as a pool a fraction
of its size such as those found at smaller establishments. All but one property contained a
pool.
In regards to shopping, huge variation exists in the quality of shops in the sample.
One glaring example is the contrast between the Maserati Dealership located in the Wynn
Resort and the Bass Pro Shop found at the Silverton Resort. The shops in the sample sell
extremely diverse baskets of goods, and this paper makes no attempt to weight the quality
of goods sold in hotel shops.
A similar problem exists with the NUMREST data. While the variable denotes the
number of restaurants, it does not differentiate the quality of each restaurant. Two
properties illustrate the uneven quality of restaurants particularly well. In the sample, the
Medici Caf located at The Ritz Carlton Las Vegas received the same weight as the
McDonalds located at the Circus Circus Resort. No feasible methodology was found for
weighting the quality of restaurants, and there is no widespread restaurant equivalent of
the AAA Diamond Award. While AAA does provide separate ratings for some
restaurants, it rates far fewer restaurants than hotels, and relying on published ratings
would have reduced the sample size by an unacceptable degree. As a result, no attempt
was made to weight the varying quality of hotel restaurants.
It should be noted, however, that restaurants are indeed a component of the AAA
Diamond criteria for lodging establishments as well. This allows the STARS variable to
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control for at least some of the variation in restaurant quality across the sample. Indeed it
is expected that the STARS variable captures much of the overall differences in the
quality of amenities across properties. As discussed in the previous section, the AAA
criteria look deeper than many of the independent variables in the model.
The inability to control for the quality of amenities across hotels was much less of
an issue with the SPA, INTERNET, BREAKFAST, ROOMSERVE, SHOWS, CASINO,
AIRTRANS and STRIPTRANS variables. The amenities denoted by these variables are
of far more uniform quality than those indicated by the POOLS, SHOPS, and NUMREST
variables. As discussed, to be classified as a having a spa, the property must offer full-
service massage treatments. In regards to the internet variable, there is almost no
variation the quality of complimentary high-speed internet across establishments
although speed may be slightly affected by the establishments choice to use cable or
DSL modems.
There is similarly very little variation in the quality of continental breakfasts, as
this item is largely uniform across establishments. Most continental breakfasts consist of
cereal, toast, coffee, fruit, and other basic items. In addition, it is believed that there is
little variation in the quality of room service. While the quality of restaurant food might
affect the utility one receives from room service, the majority of the guests utility comes
from the ability to consume restaurant food in the room, and this convenience factor does
not vary across establishments offering room service.
In addition, the quality of shows across properties is considered to be relatively
constant since the property must have a dedicated entertainment venue to be classified as
offering shows. In regards to the CASINO variable, all but one casino featured slot
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machines as well as table games. Additionally, all but one casino was at least 20,000
square feet and all but four were over 40,000 square feet. The large size of most casinos
in the sample and the availability of slots as well as table games at all but one of the
casinos suggests that the quality of the gaming experience is relatively constant across
establishments.
Lastly, the quality of AIRTRANS and STRIPTRANS is considered to be
reasonably constant across establishments. Complimentary shuttles are a fairly
standardized from of transportation. Admittedly, some establishments likely run shuttles
more or less frequently than others. Additionally some hotels might provide more or
fewer drop-off and pick-up points than others when providing Strip transportation. While
detailed route and schedule information regarding airport and Strip transportation was not
available, it is reasonable to expect little variation in the quality of complimentary airport
and Strip transportation.
The two distance variables, STRIPDIST and AIRDIST, were collected using a
mapping utility provided by Google Maps. The distance from the hotel to the Strip was
calculated using Google driving directions. The hotel address was inputted as the from
address, and the Mirage hotel (the most desirable address on the Strip for reasons stated)
was entered as the to address. Thus the STRIPDIST variable indicates the distance of a
one-way trip from the hotel to the Strip. It was unclear if some Las Vegas roads are one-
way streets. If this is the case, the return distance would likely vary for some of the
properties. Although this difference is likely small, the possibility of a discrepancy should
be noted, as guests who must return to their properties each evening are affected by the
travel times for traveling not just to, but also from the Strip.
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The AIRDIST variable was also calculated using driving directions provided by
Google. The hotel address was inputted as the from address and McCarran International
Airport was entered as the to address. Thus the AIRDIST variable indicates the distance
of a one-way trip from the hotel to the airport. As such it indicates the distance guests
must travel to return to the airport at the end of their stay. The potential presence of one-
way streets might cause the airport-to-hotel distance to differ slightly from the hotel-to-
airport distance in the same manner as the STRIPDIST variable. Table I contains
descriptive statistics for the independent variables.
Multicollinearity is likely a problem with some of the data. Table III contains a
simple correlation matrix for each variable in the model. An inspection of the simple
correlation matrix reveals high correlations between many of the variables. Indeed
several correlation coefficients are in excess of .70. STRIPDIST and AIRDIST have a
correlation coefficient of .91, the highest of the sample. The high correlation between the
two distance variables is the result of McCarran International Airports close proximity to
the Las Vegas Strip.
Variance Inflation Factors (VIFs) were also calculated to assess the degree of
multicollinearity among the data. Table IV contains VIFs for each explanatory variable.
As expected, STRIPDIST and AIRDIST have the highest VIFs, both in excess of five.
VIFs over five indicate severe multicollinearity. ROOMS, SHOWS, and SHOPS also
have VIFs in excess of five. Several other variables have VIFs approaching five.
Multicollinearity exists among the site variables including ROOMS, SHOWS, and
SHOPS, because properties that have amenities such as shows and shops are larger
establishments. These large establishments typically offer several of the amenities
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expected to influence hotel rates such as the existence of shows and shops. For instance,
if a property is large enough to have a shopping arcade, it likely also has a dedicated
entertainment venue. In the same vein, one would not likely find a large establishment
with only one pool or restaurant. Large establishments usually have several pools and
several restaurants. In sum, the multicollinearity observed in the site variables is the
result of large establishments typically offering more than one of the amenities measured
by each variable in the model. Moreover, the quantities of each amenity (such as the
number of restaurants or pools) will likely increase together as the size of the
establishment increases.
Results:
Log and semi-log forms were tested for each equation. Regression results are
summarized in Table V. In the fully specified model, the semi-log form produces a
slightly better fit, though the increase in goodness-of-fit is only modest. In all other
equations, the semi-log form produces slightly poorer fits. Additionally, while there are
some differences in the significance of coefficients between the linear and semi-log
forms, there are no major overall differences in significance across forms.
Equation (1) tests the fully-specified model. When fully specified, the linear form
of the model explains 66% of the variation in room rates. This indicates that the fully-
specified form produces a good fit of the data. STARS, NUMREST, and SHOPS have
the expected sign and are significant at the 1% level using a one-sided t-test. ROOMS
and CASINO are significant at the 1% level using a two-sided t-test. AIRTRANS is also
significant at the 1% level using a one-sided t-test, though it enters with an unexpected
sign. INTERNET and STRIPTRANS have the expected sign and are significant at the
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10% level using a one-sided t-test. The remaining variables, POOLS, SPA,
BREAKFAST, ROOMSERVE, SHOWS, STRIP, AIRDIST, and STRIPDIST, are not
statistically significant. The lack of significance of these variables makes it not possible
to reject the null hypothesis that these coefficients are equal to zero.
The existence of a casino has the largest overall effect on room rates. In the fully-
specified model, the existence of a casino decreases room rates by $70.60 per night,
ceteris paribus. This suggests that hotel managers do indeed decrease rates to draw
potential gamblers onto the property. The existence of on-site shopping and the number
of stars awarded to the property have the largest positive effects on room rates. The
existence of on-site shopping increases room rates by an average of $45.16, ceteris
paribus. Each additional star increases room rates by an average of $38.41 per night,
ceteris paribus. This papers indication of the importance of shopping is supported by
visitor behavior. In 2006, the average visitor to Las Vegas made shopping expenditures
of approximately $206 (Las Vegas Visitor Profile). This is almost as large as the average
food expenditures of $260 (Las Vegas Visitor Profile). Clearly shopping is central to the
Las Vegas experience.
Each additional room decreases hotel rates by approximately 2 cents per night,
ceteris paribus. The mean size of hotels in the sample is 808 rooms, meaning that
managers in the sample discount rooms by $16.16 on average. The negative sign of
ROOMS suggests that the aforementioned supply effect and existence of economies of
scale prevail in the relationship between the number of rooms and hotel rates.
Each additional restaurant has a modest positive effect on room rates, increasing
room rates by $4.89, ceteris paribus. The existence of complimentary internet and strip
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transportation have a larger positive effect on room rates, increasing rates by $12.51 and
$16.92 respectively. Surprisingly, the there is no statistically significant difference in
hotel rates between on-Strip and off-Strip properties in the fully-specified model. The
STRIP variable is only significant at the 12% level. The difference between the STRIP
and STRIPTRANS coefficients indicates that the value of being permanently located on
the STRIP compared to being transported to the Strip free of charge is $2.75 per night.
However, this result is only marginally significant due to the statistical significance of the
STRIP variable at only the 12% level.
Another surprising result is the negative coefficient of AIRTRANS. This result is
even more surprising given that AIRTRANS is significant at all levels. The presence of a
complimentary airport shuttle decreases hotel rates by $25.06, ceteris paribus. White and
Mulligan (2002) suggests that budget establishments are willing to take a loss on some
amenities in order to attract a wider customer base. Therefore it is possible that budget
establishments are willing to assume the cost of free airport transportation in order to
compete with more expensive establishments. However, complimentary Strip
transportation increases room rates by $16.92, and this result is statistically significant. If
the explanation put forth in White and Mulligan (2002) were correct, one might expect a
free Strip shuttle to have a negative effect on room rates as well. Yet the STRIPTRANS
variable has the positive effect on room rates predicted by the original underlying theory.
Thus, the negative sign of AIRTRANS remains puzzling.
The semi-log form of equation (1) provides a slightly better fit. NUMREST is no
longer significant at the 1% level, and is only significant at the 10% level. The SHOWS
variable becomes statistically significant in the semi-log form. Additionally, the
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INTERNET variable is no longer significant in the semi-log form, and the significance of
the AIRTRANS variable decreases from the 1% to the 5% level. The significance of the
STRIPTRANS variable increases from the 10% to the 5% level.
Equation (2) tests the fully specified model without STARS, henceforth referred
to as the core model. The core model is considered to be the most natural framework for
testing the effect of each variable on RATE because of the tendency for STARS to
capture many of the attributes described by the other variables. With the exception of the
STRIPDIST variable, the magnitude of every coefficient increases when STARS is
excluded from the model. AIRTRANS and STRIPDIST lack the expected sign, although
only the AIRTRANS result is significant.
Among the statistically significant results, each additional room reduces room
rates by 3 cents, ceteris paribus. The mean size of hotels in the sample is 808 rooms,
meaning that managers in the sample discount rooms by $24.24 on average. Each
additional restaurant increases room rates by $5.86. The mean number of restaurants in
the sample is 3.77, meaning that restaurants increase room rates by $22.09 in the sample,
on average. Each additional pool increases room rates by $6.51, ceteris paribus. The
mean number of pools in the sample is 1.77, meaning that pools in the sample increase
room rates by $11.52 on average. The existence of a casino still has the largest effect on
the dependent variable, reducing room rates by $80.46, ceteris paribus. The existence of
complimentary high-speed internet increases room rates by $19.26, ceteris paribus. The
existence of complimentary breakfast has a similar effect, increasing room rates by
$17.31, ceteris paribus.
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The existence of on-site shopping has a large and statistically significant effect on
room rates. On-site shopping increases room rates by $63.85, ceteris paribus. It is also
evident that properties located on the Las Vegas Strip charge a considerable premium
over comparable properties not located on the Strip. On average, on-Strip properties
charge $30.31 more per night than comparable properties not situated on the Strip. The
large and significant increase in room rates associated with a Strip location seems to
contradict the small and insignificant effect of distance from the Strip on room rates. If
transportation costs and lost leisure create the value of a Strip location, then distance
from the Strip should also affect room rates. The large and significant effect of a Strip
location on room rates suggests that guests value lodging properties on the Strip for their
association with a famous and iconic part of America. Thus the Strip has a value separate
from the convenience offered by its close proximity to many attractions.
A second result suggests that the Strip has its own intrinsic value separate from
the added convenience of a Strip location. The difference between the STRIP and
STRIPTRANS coefficients indicates that the value of being permanently located on the
STRIP as opposed to being transported to the Strip for free is $2.74 per night. Thus even
when guests have the option of free transportation to the Strip, they prefer a Strip
location. Admittedly guests value avoiding the hassle associated with taking a shuttle to
the Strip, yet at least some of the difference between STRIP and STRIPTRANS likely
reflects the intrinsic value of a Strip location. Unlike in the fully-specified model, this
result is statistically significant.
A third result provides still more evidence that part of a Strip propertys value
does not come from its closeness to attractions. The existence of complimentary shuttle
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service to the Strip increases room rates by $27.57 in the core model, ceteris paribus. Yet
the distance of the property from the Strip has no significant effect on room rates. One
would expect the distance from the Strip to have a significant negative effect on room
rates if the Strip were so valuable for its added convenience. Thus, this seemingly
contradictory effect also suggests that a portion of a Strip propertys value is not derived
from its easy access to other attractions.
As in the fully-specified model, the existence of a complimentary airport shuttle
has a statistically significant effect on room rates, though it enters with the opposite sign.
The existence of complimentary airport shuttle service decreases room rates by $33.19,
ceteris paribus.
As expected, the coefficients become more significant with the removal of
STARS from the model, since the other variables pick up the variation in the dependent
variable previously captured by STARS. The significance of INTERNET and
STRIPTRANS both increase when STARS is removed from the model. In the linear core
model, the variables ROOMS, NUMREST, CASINO, and SHOPS have the same
significance levels as in the fully-specified linear model. POOLS, BREAKFAST, and
STRIP are not significant in the fully-specified linear model and become significant in
the linear form of the core model. STRIPDIST, AIRDIST, ROOMSERVE, SHOWS, and
SPA are not significant in the core or fully-specified model. While surprising, the lack of
significance of AIRDIST and STRIPDIST is consistent with some of the literature.
Indeed Arbel and Pizam (1977) finds that 76.3% of tourists do not require a reduction in
cost to stay at a hotel up to fifteen minutes from the city center (p.20).
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The high mobility of visitors provides an alternate explanation for the lack of
significance of the STRIPDIST variable. According to the 2006 Las Vegas Visitor
Profile, the average guest visits 6.2 casinos during their stay in Las Vegas (Las Vegas
Visitor Profile). The high mean number of casinos visited suggests a high degree of
visitor mobility. It is possible that the considerable degree of casino-to-casino traveling
done by visitors once they arrive at the Strip diminishes the relative disutility of traveling
to the Strip. If guests staying off the Strip visited only one casino upon arriving at the
Strip, they would likely be more concerned about the travel time to the Strip. Yet it is
known that the average guest visits 6.2 casinos (Las Vegas Visitor Profile). Since the
drive to the Strip is only one part of the average guests travels, they are likely less
concerned with the hassle of reaching the Strip. Finally, AIRTRANS is the only variable
that experiences a decrease in significance when STARS is removed from the model.
Equation (3) excludes the AIRDIST and STRIPDIST variables from the core
model. There is almost no change in the magnitude of the coefficients or significance
which is expected due to the lack of statistical significance of the AIRDIST and
STRIPDIST variables. Only the significance of the AIRTRANS and STRIPTRANS
variables increases slightly.
Equation (4) excludes the STRIPDIST variable from the core model. Equation (5)
excludes AIRDIST from the core model. There are no major changes in the magnitude or
significance of the coefficients in either equation compared to the core model. Equation
(6) incorporates the slope dummy STRIPDIST*STRIPTRANS. The
STRIPDIST*STRIPTRANS coefficient indicates that in the linear model, each additional
mile of distance from the Strip increases room rates by $5.06 if the hotel offers free
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shuttle service to the Strip. However, the result is not significant. The STRIP and
STRIPTRANS variables are no longer significant with the addition of the
STRIPDIST*STRIPTRANS variable to the core model.
Equation (7) incorporates the slope dummy AIRDIST*AIRTRANS into the core
model. The coefficient of the slope dummy variable indicates that an additional mile of
distance from McCarran International Airport decreases room rates by 62 cents if the
hotel offers free airport shuttle service, ceteris paribus. However, the result is not
statistically significant.
Equation (8) excludes the AIRTRANS and STRIPTRANS variables from the core
model. There are no major changes in significance and only modest increases in the
magnitude of the coefficients as compared to the core model.
Equation (9) excludes the AIRTRANS variable from the core model. The
significance of STRIP decreases from the 5% to the 10% level, and STRIPTRANS is no
longer significant in equation (9). Equation (10) excludes the STRIPTRANS variable
from the core model. ROOMSERVE and SHOWS are not statistically significant in the
core model, but are significant in equation (10). STRIP is no longer significant in
equation (10). There are modest changes in the magnitudes of the coefficients in
equations (9) and (10) compared to the core model.
Equation (11) excludes ROOMS from the core model. The ROOMS variable is
excluded due to the high degree of multicollinearity between ROOMS and the other
variables. POOLS, which was significant at the 5% level in the core model, is no longer
significant in equation (11). BREAKFAST and STRIP are also no longer significant in
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equation (11). The significance of the SHOPS variable decreases from the 1% to the 5%
level. The significance of STRIPTRANS increases from the 5% to the 1% level.
In addition to examining the main determinants of hotel rates, this paper seeks to
compare the power of STARS to predict room rates to the power of the other independent
variables to predict room rates. Cantor and Packer (1996) compares the ability of
sovereign credit ratings to standard sovereign risk indicators in predicting relative
spreads. As discussed, they determine that the credit rating itself is a better predictor of
credit spreads than its publicly disclosed components.
This paper seeks to determine if the AAA Diamond rating as measured by the
STARS variable, contains information that is over and above that which is contained in
readily observable lodging attributes. In equation (12), STARS and logSTARS are
regressed on the core model. Regression results are summarized in Table VI. In the linear
model, readily observable hotel attributes explain approximately 54% of the variation in
the STARS variable. In equation (13), RATE and logRATE are regressed on STARS.
The semi-log form of equation (13) produces the best fit, explaining 48% of the variation
in room rates. The semi-log form of the core model explains 52% of the variation in room
rates (Table V, equation 2). Thus, readily observable lodging attributes are only a
marginally better predictor of room rates. Nonetheless, it is not possible to conclude that
the AAA Diamond ratings contain information that is over and above that which is
publicly available.
Conclusions:
Several findings of this paper stand out. Perhaps the most remarkable finding is
the magnitude of the negative effect of the existence of a casino on hotel rates. In 2006,
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the average Las Vegas gambler had a gambling budget of approximately $652 (Las
Vegas Visitor Profile). In a town where 88% of visitors gamble, gaming constitutes a
huge source of revenue (Las Vegas Visitor Profile). The findings of this paper underscore
gamblings value to hotels as a source of revenue. Indeed managers are willing to
discount room rates by over $80 per night to draw potential gamblers onto their property.
The high mean number of casinos visited per stay in Las Vegas (6.2) suggests the
existence of significant competition among properties to attract gamblers (Las Vegas
Visitor Profile). Reducing room rates is a major way in which hotels compete for
gamblers.
This paper also highlights the importance of shopping as a guest activity. The
existence of on-site shopping is an extremely valuable attribute, increasing room rates by
$63.85 on average (Table V, equation 2). This suggests that in a town synonymous with
gambling, many visitors take to the boutiques and shops, not simply the slots. Developers
considering hotel construction in the Las Vegas area should examine the feasibility of
incorporating a shopping arcade into the complex. In addition to the increase in room
rates associated with on-site shopping, hotels can earn rents from shop tenants.
Other findings of this paper have important implications for managers of smaller
establishments. Providing high-speed internet and complimentary breakfasts are two
particularly low-cost ways in which managers can increase revenues. As seen in the core
model (Table V, equation 2), complimentary high-speed internet increases room rates by
$19.26 while complimentary breakfast increases room rates by $17.31. The payback
period for installing high-speed internet is likely very short given the small investment
required to install high-speed internet relative to the large increase in hotel rates it
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produces. Similarly, a continental breakfast can be provided for much less than the
increase in room rates of $17.31 that it produces. Thus complimentary internet and
complimentary breakfasts represent significant profit opportunities for managers at
establishment that do not currently offer these amenities.
Another notable result is the remarkable ability of AAA lodging ratings alone to
predict room rates. The specification including all of the variables excluding STARS
(Table V, equation 2), can only explain 4% more of the variation in room rates than a
specification including only STARS (Table VI, equation 13). While the individual hotel
attribute variables are better predictors of hotel rates, they are only marginally better.
While surprising, this result is not as dramatic as the finding in Cantor and Packer (1996)
that individual credit rating criteria actually explain 6% less of the variation in credit
spreads than credit ratings alone (p.44).
While this paper makes important contributions to the current body of literature,
there are several opportunities for further research. One particular finding that deserves
further study is the insignificance of the distance variables. Neither distance from the
Strip nor distance from the airport has a significant effect on room rates. This remains
puzzling given that costs incurred in terms of transportation fees and lost leisure increase
as the distance from the Strip increases. The expected negative effect of the distance
variables on room rates was considered one of the most theoretically sound relationships
in the paper. Further study is needed to explain the apparent lack of a statistical
relationship between hotel rates and a lodging propertys distance from the Las Vegas
Strip and McCarran International Airport.
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Additional study is also needed to address the difficulty this paper encounters in
controlling for the varied quality of amenities. Future work should refine the
measurement of variables to account for differences in quality. For example, the size of
hotel pools as well as the type of hotel dining, instead of simply the number of pools and
restaurants should be taken into account. Moreover, the quality of goods for sale in hotel
shops should be assessed. More refined measurement of the variables would yield more
conclusive results.
Another shortcoming of this paper is its failure to incorporate a proxy for
monopoly power into the model. Other authors have examined the presence of monopoly
power in the lodging industry. Mulligan and White (2002) does so with a variable
denoting the proportion of rooms controlled by each hotel in its zip code. They determine
that the degree of monopoly power does have a statistically significant effect on room
rates. This paper attempted to construct a similar variable. A private travel research
company supplied data for the number of rooms in each Las Vegas zip code, but the
census data was incomplete and unusable. A more complete specification of the model
would include a proxy for monopoly power.
While this paper sheds light on many of the underlying determinants of hotel
rates, there is much left to be done. Future work must expand and refine the model used
in this paper in order to gain a richer understanding of the determinants of hotel rates.
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