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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 analysis is used to estimate ...

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 analysis is used to estimate implicit prices for several hotel amenities. In addition, the effect of distance from various points of interest on rates is examined. Finally, this paper examines the extent to which tourism ratings contain information over and above that which is publicly available. Written by Keyen Farrell - http://keyenfarell.com

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Keyen Farrell Thesis - Hotel Rates Las Vegas Document Transcript

  • 1. The Determinants of Hotel Rates in Las Vegas, Nevada<br />Senior Economics Thesis<br />Keyen Farrell<br />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 analysis is used to estimate implicit prices for several hotel amenities. In addition, the effect of distance from various points of interest on rates is examined. Finally, this paper examines the extent to which tourism ratings contain information over and above that which is publicly available.<br />Introduction:<br />In the year 2000, lodging became a nearly $100 billion industry in the United States (History of Lodging). Yet despite the importance of lodging to the U.S. economy, surprisingly little information exists regarding the determinants of hotel rates. Numerous studies such Mayo (1974) have attempted to examine the value to guests of various lodging attributes using willingness-to-pay surveys and self-report questionnaires. Other scholars such as Arbel and Pizam (1977) have sought to isolate the effect of only location on hotel rates using self-report questionnaires. <br />Yet while much survey data exists, there is a clear paucity of econometric studies concerning the determinants of hotel rates. Hedonic analysis is especially well-suited in the lodging industry for its ability to tease-out implicit prices for individual hotel attributes. <br />White and Mulligan (2002) make a strong contribution to the understanding of the determinants of hotel prices with their hedonic analysis of published prices of 600 lodging properties in four southwestern U.S. states. Bull (1994) follows a more focused approach, primarily examining the effect of location on room rates in a coastal Australian town.<br />In this paper, standard room rates for 112 Las Vegas lodging properties are analyzed. Las Vegas is an ideal testing ground for hedonic analysis due to the large variation in amenities and room rates across properties. This paper follows the methodology set forth by Mulligan and White (2002). The authors disaggregate determinants of hotel rates into site and situation attributes. Site attributes describe characteristics unique to the property itself. Situation attributes describe characteristics unique to the location of the property. This paper estimates implicit prices for several site and situation attributes.<br />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. <br />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.<br />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. <br />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 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. <br />Relevant Literature:<br />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 property’s 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 property’s 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 property’s location. The author uses hedonic analysis in order to derive implicit prices for several lodging attributes expected to affect room rates. <br />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. <br />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. <br />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). <br />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 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. <br />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. <br />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. <br />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. <br />While the relative importance of lodging characteristics varies among guests, four main attributes stand out as consistently desirable among guests. The first is the hotel’s aesthetics, which encompasses attributes such as décor and attractiveness of the property. Second is the motel’s proximity to tourist attractions. The remaining attributes that are significant determinants of motel choice are the availability of a pool and on-site dining. <br />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.<br />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.<br />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. <br />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. <br />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 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. <br />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. <br />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). <br />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 Moody’s and Standard and Poor’s sovereign credit ratings for forty-nine countries on eight separate rating criteria. The eight criteria expected to influence a country’s 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).<br />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. <br />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 pool furniture, making it very difficult to quantify tourism rating criteria. This difficulty is similar to that encountered with quantifying sovereign credit rating criteria. <br />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.<br />The Model:<br />RATE = β0 + β1ROOMS + β2STARS + β3NUMREST + β4POOLS+ β5CASINO + β6SPA + β7INTERNET + β8BREAKFAST + β9ROOMSERVE + β10SHOWS + β11SHOPS + β12STRIP + β13AIRTRANS + β14STRIPTRANS + β15AIRDIST + β16STRIPDIST<br />The Dependent Variable:<br />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. <br />The Independent Variables:<br /> The fully-specified model contains thirteen separate site variables. ROOMS denotes the number of standard rooms contained in the lodging property. The ROOMS 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. <br />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. <br />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 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.<br />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 property’s 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. <br />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-rated properties are expected to command a price premium over comparable properties that lack the endorsement of a favorable AAA rating. <br />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 property’s 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. <br />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. <br />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 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.<br />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. <br />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. <br />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 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. <br />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. <br />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.<br />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 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. <br />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. <br />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.<br />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. <br />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. It is expected that guests are willing to pay a higher price for a property situated on the Strip compared to a comparable property not located on the Strip. As a result, a Strip location is expected to have a positive effect on the dependent variable. <br />STRIPDIST is a situation variable denoting the distance, in miles, of the property from the most desirable part of the Las Vegas Strip. Upon inspection of a map of the Las Vegas Strip, it was noted that a portion of the Strip between Sands Avenue and Flamingo Road contained an unusually high concentration of resorts. Harrah’s, Treasure Island, The Venetian, and The Mirage are all located in this region. In addition, the Wynn Resort and Casino, arguably the most publicized resort to recently open in Las Vegas is located adjacent to the Mirage. There is also a large mall complex situated next to the Mirage property. Since the Mirage is at the center of this concentration of properties, STRIPDIST was calculated as the distance from a given property to The Mirage Resort. <br />This concentration of properties is expected to be the most desirable location on the Strip since it contains the highest concentration of gaming, entertainment, and shopping. As the distance from this locus of properties increases, guests must travel farther to take advantage of the high concentration of leisure activities it offers. The quantity of leisure lost as well as transportation costs increases as the distance from the Strip (e.g. the Mirage Resort) increases. As a result, the rate guests are willing to pay decreases as the property’s distance from the Strip increases. That is, guests require compensation in the form of a lower room rate to be situated farther from the Strip. The compensation required increases as the distance from the Strip increases. Thus an increase in the STRIPDIST variable is expected to have a negative effect on the dependent variable. <br />STRIPTRANS is a dummy site variable equal to one if a property not located on the Strip offers complimentary shuttle service to the Strip. STRIPTRANS is only useful when looking at properties not situated on the STRIP since properties located on the Strip obviously do not provide transportation to the Strip. Only some properties not located on the Strip provide complimentary transportation to the Strip. Properties that offer free shuttle service to the Strip provide added convenience over properties that do not offer free shuttle service. Guests who stay at properties not offering free shuttle service and wish to access the Strip incur search costs in locating a taxi service as well as the explicit cost of the taxi fare itself. Since guests staying at a property offering free shuttle service to the Strip incur neither of these costs, it is expected that guests are willing to pay more for a property offering free shuttle service to the Strip, ceteris paribus. Thus, the existence of complimentary shuttle service to the Strip is expected to have a positive effect on the dependent variable. <br />AIRDIST is a situation variable that denotes the distance in miles from McCarran International Airport. The costs incurred by guests both in taxi fares and lost leisure increase as the distance of the property from the airport increases. Thus, an increase in distance from McCarran International Airport is expected to have a negative effect on the dependent variable. McCarran International Airport is the eighth busiest airport in the nation. Surprisingly however, in 2005, 54% of visitors reported that they arrived by ground transportation, and 46% reported that they arrived by air (Las Vegas Visitor Profile). Admittedly, a sizable portion of visitors do arrive by ground transportation, but nearly half still arrive by air, making the distance of the property from the airport a concern for nearly half of the visitors to Las Vegas. <br />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.<br />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. <br />The Data Set:<br />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 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. <br />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. <br />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, 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. <br />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. <br />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 Café located at The Ritz Carlton Las Vegas received the same weight as the McDonald’s 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. <br />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 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.<br />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 establishment’s choice to use cable or DSL modems. <br />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 guest’s utility comes from the ability to consume restaurant food in the room, and this convenience factor does not vary across establishments offering room service. <br />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 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. <br />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. <br />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. <br />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.<br />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 Airport’s close proximity to the Las Vegas Strip. <br />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 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. <br />Results: <br />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. <br />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 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. <br />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 paper’s 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.<br />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. <br />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 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. <br />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.<br />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 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.<br />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. <br />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. <br />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. <br />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. <br />A third result provides still more evidence that part of a Strip property’s value does not come from its closeness to attractions. The existence of complimentary shuttle 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 property’s value is not derived from its easy access to other attractions. <br />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. <br />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). <br />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 guest’s 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. <br />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.<br />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 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. <br />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. <br />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. <br />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. <br />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 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. <br />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. <br />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. <br /> Conclusions:<br />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, 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 gambling’s 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.<br />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. <br />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 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. <br />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). <br />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 property’s distance from the Las Vegas Strip and McCarran International Airport.<br />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.<br />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.<br />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.<br />Bibliography<br />Arbel, Avner, and Abraham Pizam. " Some Determinants of Urban Hotel Location: the Tourists' Inclinations." 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