Keyen Farrell Thesis - Hotel Rates Las Vegas

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


37/39


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.


38/39


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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Cantor, Richard, and Frank Packer. "Determinants and Impact of Sovereign Credit

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Documents

Keyen Farrell Thesis - Hotel Rates Las Vegas