THE DETERMINANTS OF SKI RESORT SUCCESS

A THESIS

Presented to

The Faculty of the Department of Economics and Business

The Colorado College

In Partial Fulfillment of the Requirements for the Degree

Bachelorof Arts

By

Ezekiel Anouna

May/2010 THE DETERMINANTS OF SKI RESORT SUCCESS

Ezekiel Anouna

May, 2010

Economics

Abstract

As the economy is in a decline, fewer people are willing to pay for luxuries such as vacations. Thus, the ski resort industry is suffering. This thesis reveals an opportunity m the growth of free skiing and a demand for more difficult terrain. In this paper, data is collected from nearly all Colorado ski resorts to form a regression model explaining resort success. Regression analysis is conducted to discover what aspects of a ski resort contribute to success. Primarily, skier visits from the 2008-2009 ski season are_useclas the dependant variable in the regression model to measure resort success. Additionally, hedonic pricing theory is applied to test lift ticket price as a dependant variable. The paper finds thatresort size, and possibly terrain park features are related to resort success. The hedonic pricing regression finds that bowl skiing, and lack of crowds, increase consumer willingness to pay for expensive lift tickets.

KEYWORDS: (ski resort, terrain park, hedonic pricing) ON MY HONOR, I HAVE NEITHER GIVEN NOR RECEIVED UNAUTHORIZED AID ON THIS THESIS

Signature I would like to thank my thesis advisor, Esther Redmount, for her support and consistent efforts to make sure I was on track. I would like to thank my family for supporting me in so many ways throughout my college career. I could not have done any of this without any of you.

TABLE OF CONTENTS

ABSTRACT "> ACKNOWLEDGEMENTS iv 1 INTRODUCTION 1

2 LITERATURE REVIEW 7 2.1 Marketing 8 2.2 Product Quality 10 2.3 Pricing Strategy 14

3 METHODOLOGY 18 3.1 Data 18 3.2 Regression Model 19 3.3 Regression Method 36

4 REGRESSION RESULTS AND ANALYSIS 43 4.1 Regression 1 43 4.2 Regression 2: The Hedonic Pricing Model 60 4.3 Discussion 66

5 CONCLUSION 68 LIST OF TABLES

3.1 County Income 36

3.2 Variable Titles and Measurement Units 37

3.3 Raw Data 40

4.1 Equation 1 Regression Output 46

4.2 Equation 2 Regression Output 46

4.3 Equation 1 White Test Output 49

4.4 Equation 2 White Test Output 49

4.5 Equation 2 White Correction Output 50

4.6 Correlation Matrix 1 52

4.7 Correlation Matrix 2, Adjusted to Fix Multicollinearity 54

4.8 Equation 3 Regression Output 56

4.9 Equation 3 White Test Output 57

4.10 Equation 3 White Correction Output 58

4.11 Correlation Matrix 3 62

4.12 Hedonic Pricing Regression Output 64

4.13 Equation 4 White Test Output 66 LIST OF FIGURES

1.1 Skier Visits by Region (millions) 2

4.1 Equation 1 Histogram - Normality Test 47

4.2 Equation 2 Histogram - Normality Test 47

4.3 Residuals vs. VISITf Graph 1 48

4.4 Equation 3 Histogram - Normality Test 56

4.5 Residuals vs. VISIT Graph 2 57

4.6 Equation 4 Histogram - Normality Test 65

4.7 Residuals vs. PRICE Graph 65 CHAPTER 1

INTRODUCTION

The skiing andsnowboarding culture is a staple of the Colorado way of life according to not only a mass of Coloradoans, but also to many abroad. Colorado accounts for approximately 20 percent of ski and snowboard visits in the US every year .

One ski visit, according to the industry, is defined as every one day that a skier or snowboarder usesa ski ticket or ski pass to ski or snowboardon a mountain. Therefore, one in five skiers or snowboarders chooses a Colorado ski area as his or her preferred destination every trip to a mountain. In fact, annual ski visits to Colorado account for approximately the same amount of ski visits to Vermont, Utah, and New York combined every year, as seen in FIGURE 1.1.

1 Colorado Ski Country USA, "Colorado Ski Country USA Facts and Stats"; available from http://media-coloradoski.com/CSCFacts/; internet; accessed 1 November

2009.

2 Ibid Figure 1.1

Skier Visits by Region (millions)

^em 11.85

A Colorado Vermont New York 33.15 ^Hi Utah HHk 4 * Rest of USA

Colorado ski resorts have been responsible for attracting tourists to the state every winter for over a century.In 1914, Howelsen Hill Ski Area was established in Steamboat

Springs, Colorado, and is currently the oldest Colorado ski resort in continuous use3.

Since then, 26 additional Colorado ski resorts continue to gross large amounts of tourism

SkiTown.com, "Howelsen Hill"; available from http://www.coloradoskicountry.com/overview.cfm/col8/HowelsenHill; internet; accessed 16 November 2009. revenue for the state4. With eight regional airports, including Aspen, Durango, Eagle,

Grand Junction, Gunnison, Hayden (Steamboat), Montrose and Telluride, that offer more than 8.6 million passenger seats annually, over half of Colorado's ski areas are located within 35 miles of an airport5. Additionally, the industry employs over 30,000 people annually, more than any private sector employer in Colorado6. In fact, the Colorado ski industry contributes approximately $2.6 billion annually to Colorado's tourism industry7.

Even with total skier visits down 5.5% in the US over the most recent 2008-09' ski season, Colorado still saw almost 12 million skier visits to its resorts' slopes8. Thus, ski resorts play an important role in not only Colorado's culture, but also its economy.

The unfortunate truth is, though, that the current poor economy is having a negative effect on the ski resort industry as fewer vacationers per year are choosing expensive vacations such as those to ski resorts. In the light of a poor economy in North

America, ski resort revenues are falling. In effect, it is necessary for ski resorts to develop effective strategies for adapting to this problem by maximizing the experience

4 SkiTown.com, "Colorado Ski Resort Guide"; available from http://www.coloradoskicountry.com/; internet; accessed16 November 2009.

5 Colorado Ski Country USA, "Colorado Ski Country USA Facts and Stats"; available from http://media-coloradoski.com/CSCFacts/; internet; accessed 1 November 2009.

6 Ibid

7 Ibid

8 Ibid they deliver to potential customers, without dramatically increasing costs and prices.

There is an additional environmental factor contributing to the decrease in resort revenue according to some research on climate change9. This mounting evidence regarding climate change should not be ignored.

The question, therefore, is where is there room for improvement in order to combat such increasingly problematic conditions in the industry? In this paper, I identify an opportunity in the growth of extreme skiing and snowboarding. Freeskiing is a form of extreme skiing that is performed in terrain parks, areas of a mountain devoted to

freestyle jumping, grind rails, and other freestyle features used for the purpose of

performing tricks. Extreme skiing also can be performed on terrain that is either very

steep, very dangerous, or both. This type of extreme skiing is sometimes known as big

mountain skiing or backcountry skiing if the terrain is veryhard to access. These styles

of skiing andsnowboarding have quickly become popular among the youth culture in

snow sports. Additionally, the fact that all major ski and snowboard manufacturers now

produce a multitude of equipment specifically for this kind of skiing and snowboarding

demonstrates how rapidly its popularity has grown. While extreme-specific equipment

was rare and very expensive before the millennium, a decade later, it is now some of the

most popularly purchased equipment, produced in a varying range of quality and price.

9 Charles Shih, Sarah Nicholls, and Donald F. Holecek. 2009. Impact of Whether on Downhill Ski Lift Ticket Sales. Journal of Travel Research 47, no. 3 (02) : 359-372. Extreme skiing andsnowboarding has clearly become a dominant, and permanent part of skiing and snowboarding.

Ski resorts could take advantage of the continual growth in the presence of extreme skiing andsnowboarding to develop focused strategies aimed at capitalizing on this growth. The research I conduct in this paper aims to provide statistically significant evidence that suggests such strategies could be effective by testing to discover weather the increased presence of more difficult terrain has an effect on resort success or not. I hypothesize that both the percentage of expert/extreme terrain, and the presence of terrain parks at a resort will be statistically significant and positively correlated with ski visits to ski resorts in Colorado. Ultimately, I argue that if the presence of extreme terrain and park features is related to ski resort success, than ski resorts in Colorado with more difficult skiing will seea greater amount of skier visits proportional to resort size, annually.

I will perform regression analyses using ski visits, and lift ticket price as my dependant variables, explained by a number of different physical resort characteristics, which will be my independent variables in my models. The most important independent variables in my model will be the percentage of resort acreage deemed "expert terrain," and the number of available features in terrain parks, since these variables are critical to what I try to prove in my thesis. Other independent variables I would expect to be significantly correlated to skier visits are annual natural snowfall, annual manmade snowfall, annual resort advertising budget, number of ski and snowboard instructors employed, presence of real estate, transportation time to reach a resort, and ticket price.

In this study, data is collected on ski resorts in Colorado either through resort websites, Colorado ski resort collaboration websites, such as ColoradoSki.com and

Media-ColoradoSki.com, or by contacting private resort companies, such as VailResorts

Inc., by phone, email, or in person.

Establishing that there is a significant correlation between the percentage of difficult terrain at a resort and the amount of ski visits it receives annually has several implications. Firstly, if the correlation is significantly positive, this study could actas motivation for ski resorts to take advantage of the opportunity to open new more difficult terrain, and build more terrain parks in order to attract more visitors. If the correlation is

significantly negative, resorts could spend less time on their terrain parks and more

difficult runs in order to spend more time with other aspects of the resort. If this is still the case and my findings reveal a more positive correlation with less difficult terrain, there would be reason to focus efforts elsewhere on the mountain. Finally, if there is no

significant correlation at all between difficulty of runs and annual ski visits, the study

will also focus on other aspects that may be significant to the success of a resort.

Although the goal of this thesis is to establish a connection between advanced skiing

opportunities and resort success, I include other variables that may provide valuable

information to the broader vacation industry, and I apply economic theories to in ways

they have not yet been applied. CHAPTER 2

LITERATURE REVIEW

There has been a rapid growth in the number of Colorado ski resorts since their conception in Colorado about a century ago. While the supply of ski terrain increased quickly, the demand for ski resorts also increased, but at a slower rate. Although the supply of ski terrain continues to increase in Colorado ski resorts almost every year, the demand has recently been on the decline. Ski resort revenues are falling as fewer ski enthusiasts and vacationers decide to purchase lift tickets at skiresorts. In effect, it is beneficial toski resort managements to take advantage of the research and findings of existing literature in order to adapt to the current problems facing them. The existing literature, in addition to this paper, can potentially help ski resorts maximize the experience they deliver to potential customers without dramatically increasing costs and prices.

Literature regarding ski resorts certainly exists, but is relatively rare compared to other similar subjects such as sports, or vacationing. And there is very little to be found specifically concerning the impact of terrain difficulty on resort success. However, the existing literature does generally focus on some form of success or another. The literature reveals that skier visits and resort success are related to differentfactors such as annual snowfall, number of lifts, skiable terrain, and much more that will be discussed later in this chapter. Additionally, the majority of literature focuses on resort business strategies. These strategies seem to fall mostly in the categories of ticket pricing, advertising strategy, and maximizing the best resort experience to the consumer.

Although more research is certainly required,the literature on the subject that currently exists provides helpful insight asa foundation for resorts to continue their respective adapting strategies.

Marketing

Much of the existing literature regarding the success of ski resorts focuses on marketing strategy. A good marketing strategy should lead to higher amounts of customer loyalty and higher return rates of former customers (Clark and Maher 2007;

Echelberger and Shafer 1970; Ferrand and Vecchiatini 2008;Frochot and Kreziak 2008;

Gachen 2002; McCune 1994; Tsiotsou 2006). Although there is much overlap in the marketing theory behind the literature, each contributor varies in conclusion. The themes that stand out as most successful methods of marketing include direct personal relationship development, and portraying an effective image of the resort.

For example, Clark and Maher (2007) explore the relationship between organizationally related factors and consumer attitudinal loyalty in the resort industry using data collected from one ski resort. Using a qualitative survey to question the resorts customers, they found that trust, commitment, satisfaction, past behavior towards customers, and value predict 60% of the variation in attitudinal loyalty. Essentially, they found that vacation marketers are building consumer loyalty by developing relationships with customers. This research indicates thatresort marketers need to segment customers, and create strategies in order to manipulate factors that are most important to each

segment.

Similarly, Ferrand and Vecchiatini (2008) created a questionnaire to examine how

ski resort image, in addition to ski and non-ski activities affect customer satisfaction.

Using structural equation modeling, they found that image, and ski and non-ski attributes

all had a statistically significant impact on customer satisfaction, as predicted. Against

their predictions though, projected ski resort image increased consumer satisfaction more

that the actual skiing activities and other activities provided by the resort. This research

implies that a quality resort management team would be wise to pay as equal, if not

greater, attention to its marketing strategy as the amenities it provides to its consumers.

Frochot and Kreziak (2008) take the study of projected image a step further.

They created a test asking focus groups to measure their appeal to different images of

resorts in order to assess what images were most effectivein appealing to ski resort

visitors. A commonly more appealing image was that of a church in an old mountain

village, or a ski instructor tutoring a child. Themes such as nature, such as a frozen

stream, appealed far less according to the focus groups. This studyrevealed that the most 10 effective image positioning was related to the size of the resort. Thus, smaller resorts would get poor results using the same imaging strategy as a large resort, and vise versa.

Marketing is clearly a necessary variable in measuring the success of a ski resort.

Instead of copying what may have worked for the most successful of resorts, the existing research indicates marketing can beused to its maximum potential in the industry if resorts can identify their respective consumer bases, and use them as a starting point to develop marketing strategy1. However, there is still a need for research regarding the

communication and delivery of marketing efforts .

Product Quality

The quality of product that is measured in the literature is always defined

differently according to each model. Nonetheless, product quality remains perhaps the

most important aspect of resort success most likely because this is the aspect of running a

resort that resort management will aim to maximize. The best-quality product the resort

delivers is ultimately the most valuable ski and non-ski experience according to the

customer (Closser 1980; Echelberger and Shafer 1970; Ferrand and Vecchiatini 2002;

Matzler et. all 2008; McCune 1994; Olson et. all 1999, Tsiotsou 2006; Wheaton 2005).

1 Rodoula Tsiotsou. 2006. Using visit frequency to segment ski resorts customers. Journal of Vacation Marketing 12, no. 1 (01): 15-26.

2 Isabelle Frochot, Dominique Kreziak. 2008. Customers' perceptions of ski resorts' images: Implications for resorts' positioning strategies. Tourism & Hospitality Research

8, no. 4 (10): 298-308. 11

"The easiest way to increase revenue is to get each Sunday River skier to ski here just one more day each year, and to do that you need to give them a great skiing experience so they want to come back," says Leslie Otten regarding his management strategy towards his company's ski resort3.

Although conducted decades ago, Echelberger and Shafer (1970) provide a detailed function to explain resort success, measured by visitor days:

Total visitor days = age of ski area + miles of novice trails + miles of

intermediate trails + miles of expert trails + lift capacity + slope exposure

+ snow making capacity + number of days in operation + total advertising

budget + average driving time + number of ski instructors + percent of

slopes groomed + percent of slopes rolled + percent of advertisement

(magazines, radio, TV) + percent of advertisement (brochures, leaflets).

Ten years later, Closser (1980) examines how to best appraise the value of a ski resort.

After subtracting some of Echelberger and Shafer's (1970) independent variables and adding some of his own, he measures resort value as a function of snowmaking capabilities, grooming capabilities, chairlift capacity, site ownership, size, location, and lodging (real estate). After conducting his research, his calculations lead him to the new conclusion that real estate in or around a resort is closely intertwined with the success of the resort. Thus, more lodging leads to a more successful ski resort. Success is also dependant on uncontrollable factors such as weather and the economy according to

Closser (1980).

3 Jenny C. McCune. 1994. A downhill battle: Ski resorts fight for survival. Management review 83, no. 2 (02) : 38. 12

Tsiotsou (2006) adds one more significant variable to the model: ski experience.

Tsiotsou's research revealed that more experienced skiers were more likely to return to a resort repeatedly.

Klenosky, Gengler, and Mulvey (1993) include another significant variable: distance from home. I will use a slightly different variable in my econometric analysis to measure for similar results. I will consider the distance needed to be driven to a resort, and will discuss this variable further in later chapters.

Mulligan and Llinares (2003) examine the ski resort industry in detail.

Specifically, they examine the impact of new technology, in the form of high-speed

chairlifts, on the success of a resort. They explain that when a resort is not reaching its

full capacity, resorts may opt to increase visitor attraction by installing the new lifts, or

replacing old ones. However, since high-speed chairlifts decrease uphill capacity filled,

or the percentage of a chairlift filledat one time, sometimes a new chairlift can bean

unnecessary expense if there are not enough visitors to fill uphill capacity. They thus

conclude that the addition of a new chairlift at one resort decreases the propensity of

other local ski resorts to add one. This may account for the reason a resort such as Vail

will have over 3 times the amount of open chairlifts as a nearby resort such as Arapahoe

Basin4.

4 VailSki Resort, "Vail. Like Nothing On Earth"; available from http://www.vail.com/; internet; accessed16 November 2009. 13

Perdue (2002) examines season ski pass pricing wars between Colorado ski resorts. He points out the important differentiation between a pass holder, and the ticket holder, since ticket prices will not effect pass purchasers and pass prices won't effect ticket purchasers. He finds that pass holders and ticket holders generally hold different

values regarding what in a ski resort will deliver the maximum utility. For example, their

findings indicate pass holders consider terrain and snow conditions far more valuable to a

ski resort than ticket holders. Expectedly, ticket purchasers valued the family

experience, food, friendly staff, and lodging as the more valuable aspects of a resort.

Perdue also found that in either case, if something one sub-group rated more important

was not satisfactory at one resort, a close cheaper resort was an attractive option. For

example, if the sub-group concerned primarily with snow conditions deemed the

conditions on a certain day to be unsatisfactory, it was not uncommon for its members to

ski at a cheaper or less crowded resort. The consideration of subgroups is not necessary

in this paper, however, since ski visits are the same whether recorded on a pass or ski

ticket.

Another noteworthy study was that of Olson, Slater, and Anthony (1999). They

conducted a case study of Vail Resorts of Colorado. Their study demonstrates how an

innovative management team created a ski experience that was not exclusive to only

skiing. Vail Resorts expanded into the real estate industry, and developed the Vail

Valley to include many other activities besides snow sports to become, and remainone of

the world's most successful ski resort companies. 14

This paper tends to fall under this category of maximizing product quality.

Essentially, I use econometric analysis to predict how the difficulty of terrain at a ski resort impacts how visitors to ski resorts view resort quality.If one difficulty of terrain is

considered better quality than another, the results in this paper will show a statistical

significance of that terrain difficulty level.

Pricing Strategy

Pricing strategy expectedly has influence on the demand for tickets to a ski

resort just as price does for any good that is not completely inelastic. A resort such as

Vail charges $85 for a one-day adult ski ticket5. A resort such as Arapahoe Basin, on the

other hand, only charges $49 for an adult ski ticket6. While it is not uncommon for skiers

and snowboarders to prefer Arapahoe Basin specifically because of this price difference,

Vail still sees far more ski visits per yearthan Arapahoe Basin. Yes, Vail is farlarger,

but is this the onlyreason for its superior popularity? I submit that the answer to that

question is no. Thus, a better question is, what differentiates one resort from another to

the point where they can charge such higher prices? In the most basic sense, what makes

one resort better than another in the eyes of visitors to ski resorts?

5 Vail Ski Resort, "Ski Lift Tickets - Vail Lift Tickets"; available from http://www.vail.com/plan-your-trip/lift-tickets/lift-tickets-explorer.aspxiinternet;

accessed 16 November 2009.

6 Arapahoe Basin, "Arapahoe Basin Ski Area | Tickets and Passes"; available from http://www.arapahoebasin.com/ABasin/tickets/default.aspx; internet; accessed 16

November 2009. 15

The concept of hedonic pricing applies to ski resort ticket pricing. However, while the theory behind hedonic pricing assumes that there is perfect competition, which there is certainly not in the Colorado ski resort industry, the concept still proves useful in

explaining how resorts may carry out pricing strategy7. Rosen (1974) explains, "When

goods can be treated as tied packages of characteristics, observed market prices are also

comparable on those terms." A ski day can certainly be expressed as a "tied package of

characteristics," since it is an experience that delivers many differing values to each

differentresort visitor. Rosen continues, "The economic content of the relationship

between observed prices and observed characteristics becomes evident once price

differences among goods are recognized as equalizing differences for the alternative

packages they embody." Thus, Rosen states that price differences simply equalize the

net utility to the end consumer. Rosen uses the housing market as an example. He

explains that the price of a house can be seen as a function of a number of different

variables such as size, number of bedrooms, number of bathrooms, and much more.

Hedonic pricing Rosen's method for assigning a price value to each variable.

More literature concerning hedonic pricing discusses the concept of "implicit

price8." Taylor (2003) indicates that increased price must be associated with an increase

7 Sherwin Rosen. 1974. Hedonic Prices and Implicit Markets: Product Differentiation in Pure Competition. The Journal of Political Economy 86, (1) : 34-51.

8 Laura Taylor. "The Hedonic Method," in A Primer on Nonmarket Valuation, ed. Patricia Champ, Kevin Boyle and Thomas Brown (Dondrecht, The Netherlands: Kluwer Academic Publishers, 2003), 310. 16

in favorable characteristics. I can indirectly apply this concept to my thesis. Replacing

price with utility to the consumer, I assume an "implicit utility," in that increased skier

visits to one resort must similarly be associated with an increase in favorable

characteristics. For example, higher annual, natural snowfall, a universal favorable

characteristic of ski resorts, should result in a higher utility to ski resort visitors, and in

effect higher annual visits.

Although pricing strategy is not my major concern in this paper, the concepts

can still be applied when slightly tweaked. Instead ofan interest in pricing strategy, I am

interested in the utility delivered to the consumer by resorts. My first regression models measure utility through skier visits to resorts. If skiers visit one resort more than another, the resort can be said to offer more utility to the consumer. My hedonic pricing models will measure utility monetarily. If a resort can charge more for a lift ticket than another, and still see more skier visits in a year, the resort can be said to offer more utility.

Similar to Rosen's (1974) housing example, I essentially attempt to discover which factors, or independent variables, contribute to the utility offered by Colorado resorts in my hedonic pricing model.

Conclusion

While the subject of how to achieve maximum success in the vacation resort industry has been researched relatively thoroughly, there are still areas that have not been covered, and there are opportunities that have yet to be examined. The last decade has 17 seen the emergence of the extreme skiing and snowboarding culture. Thenumber of terrain parks and extreme terrain runs are noticeably increasing rapidly. More analysis

on the growth of this affinity for more difficult terrain, much of which I aim to contribute

in this paper, must be conducted and analyzed so that resorts may better take advantage

of this rapidly growing ski and snowboard movement if its growth is, in fact, beneficial

to the resorts. I use the existing literature to provide a framework to develop a model of

my own that explains skier visits to resorts, and later, one that explains ski resort pricing

strategy. I test the models using different equations in regression analyses. CHAPTER 3

METHODOLOGY

In this chapter, I describe the methodology employed during this study. I

examine the factors that influence success at Colorado ski resorts, and discuss the

methods I used in the econometric regression analysis. I explain resort success as a

function of a number of different variables that contribute to the attractiveness of a resort

to their visitors. I include my personal expectations of how each variable will relate to

skier visits. Using quantitative econometric analysis, I determine which of these factors

are statistically significant. Each variable is additionally statistically compared to each

other to determine their effects, if any, on one another.

Data

Most of the data collected for this analysis comes from Colorado Ski Country

USA (CSCUSA). Established in 1963, CSCUSA's mission is to promote skiing in

Colorado and the Rocky Mountain Region in the face of east coast and European competition. After approximately 40 years in operation, CSCUSA considers itself the

18 19

"marketing, communications and publicpolicy arm" for 22 ski and snowboard resorts in

Colorado1. These ski resorts will make up the majority of my sample population for the data I use in this study.

Several resorts, such as Breckenridge, Beaver Creek, Keystone, and Vaildid not have data as readily available. Thus, the data for these resorts was collected partially from their respective websites. Members of the Vail Ski Patrol offered the rest of the data needed for this study.

The data is compiled at the beginning of the 2009-2010 ski season, which is as current as it canbe for this study, and I will use the data from the end of the 2008-2009 ski season for my ski visit numbers since ski visits are still in the process of compiling this 09-10 season.

Regression Model

Based on a review of the existing related literature, the data available, and the thesis I adopt in this paper, this study employs the following regression model for ski visit generators at Colorado ski resorts:

Colorado Ski Country USA, "Colorado Ski Country USA Overview"; available from http://www.coloradoski.com/Company/; internet; accessed 30 November 2009. 20

Skier visits = Natural snowfall + Manmade snowfall + Number of

chairlifts + Number of trails + Amount of skiable terrain + Steepness +

% of beginner terrain + % of intermediate terrain + % of expert terrain +

Availability of bowl skiing + Availability of backcountry skiing + Ticket

price + Number of terrain park features + Distance from Denver + Resort

Atmosphere (Destination Resort?) + Local Wealth

A more accurate model might include a variable accounting for advertisement.

Surely, a highly marketed resort would attract far more visitors than a resortthat does not market at all. However, resorts are unwilling to relay information concerning the allocations of their budgets.

Another variable that would improve the model would account for the amenities found in the resorts town, or amenities offered on the mountain itself. This sort of variable, however, is veryhard to measure, and would require qualitative research regarding factors such as food, shopping, available entertainment, real estate, local atmosphere, and so much more. Itis, perhaps,a point for future research. For the purpose of this paper, I use a "Destination Resort" dummy variable, which I discuss in detail later.

Skier Visits

Skier visits is my dependant variable. Since ski resorts are hesitant to give out

figures regarding their revenues, profits, and other financials, this variable is the best to 21

measure resort success. One "skier visit" is defined by the ski resort industry as one skier, snowboarder, snowshoer, or other snowsport enthusiast visit to the mountain per day. Ski resorts can measure this through their ticket and pass swiping system. If anyone wants to ride a chairlift up the mountain, they must first have a lift ticket or pass be scanned at the bottom of the mountain prior to riding the lift up. The scanner electronically registers that ticket or pass-holder as a skier visit. If the ticket or pass is scanned again on the same day, the scanner will not register any more visits than the first, thus registering one skier visit per visitor per day no matter how long the visitor decides to spend on the mountain.

While one-full-day-ticket-holders arethe most profitable ski visitors to resorts, it is not realistic for resorts to only sell one-full-day tickets for many reasons. Visitors who live in close proximity to resorts will commonly ski the entire season, but generally not willing to pay the full-day ticket price every time they visit. Thus, resorts offer ski passes, which ultimately discounts a day of skiing significantly, allowing these enthusiasts to ski more. Passes can come in the form of anywhere between a 2-day pass to an unlimited full season pass to the rare unlimited lifetime pass. Additionally, many do not want to ski a full day. Thus, resorts offer discounted half-day tickets. Although resorts make less money on these options than the full-day ticket, it is necessary to

provide these options to their consumers. 22

No matter which option a visitor chooses, the electronic ticket scanners register one skier visit without taking into account the monetary value of that visit to the resort.

The variable, skier visits, is perhaps even a better measure of resort success than a variable measured monetarily since in essence, this economic study aims to find out why visitors choose certain resorts over others, and not why visitors pay the amount they do

for their visit.

Natural Snowfall

Natural snowfall is an important independent variable in explaining resort

success, and in effect skier visits. A multitude of studies (Closser 1980; Echelberger and

Shafer 1970; Ferrand and Vecchiatini 2002; Matzler et. all 2008; McCune 1994; Olson

et. all 1999, Tsiotsou 2006; Wheaton 2005) have found the amount of natural snowfall at

resorts to be significantly, positively correlated with dependant variables such as resort

attractiveness, profitability, ticketprice, and even skier visits. Each of these dependent

variables is a form of measuring resort success. Therefore, I also expect the amount of

natural snowfall to be statistically, significantly correlated to my dependant variable,

skier visits.

There are several reasons why natural snowfall would attract more visitors to

resorts. Primarily, a ski resort absolutely needs snowfall in order to operate. Although a

resort could produce an abundance of manmade snow, it would be far too costly to solely

rely on manmade snow. Thus, a resort with very little natural snowfall would not be able 23

to maintain much skiable terrain, and such a resort would likely fail. However, a resort that receives a fair amount of natural snowfall every year can develop a proper base of compact snow, which can generally sustain hordes of skiers, snowboarders, and others for a ski season.

Secondly, on the extreme end of the spectrum, resorts that receive the most natural snowfall are conducive to powder skiing. Powder skiing is a phenomenon that attracts masses of snowsport enthusiasts. Ski areas such as Silverton and Wolf Creek are famous for their powder skiing. The last ten years has seen an average of 400 feet and

465 feet per year respectively2. In effect, the resorts have earned international acclaim, and are currently widely desired destinations for ski enthusiasts.

It's clear that powder skiing, and in effect, a desire for natural snowfall is increasing in its already great popularity. Stephen Drake, founder of DPS Skis, a powder ski company, says, "the market for free-ride and powder skis has been growing every year... The last six or seven years have shown dramatic growth3.

In my regression analysis, the amount of natural snowfall is measured by the annual natural snowfall infeet at each resort, averaged over the last 10 ski seasons.

Colorado Ski Country USA, "Colorado Ski Country USA Facts and Stats"; available from http://media-coloradoski.com/CSCFacts/; internet; accessed 1 November 2009.

3 The New York Times, "Now for Regular Skiers, the Ultimate Terrain"; available fromhttp://www.nytimes.com/2009/12/13/travel/13headsup.html?_r=l&emc=etal; internet; accessed 15 December 2009. 24

While simply using the previous season's natural snowfall would most likely work, some resorts had bad seasons and some had great seasons. Thus, an average will givea better idea of the resorts' reputations for natural snowfall from the consumers' standpoint.

Manmade Snowfall

Manmade snowfall is a variable for similar reasons as natural, with the exception that manmade snow will never produce powder, and instead producesan icier skiing surface than natural snow. It is still an important variable to include, though.

Manmade snow allows for more skier visits because it allows a resort to open a resort sooner at the beginning of the ski season and close the resort later at the end. Therefore, more open ski days mean more days to accumulate skier visits. For example, snow- making machines have the ability to spread manmade snow over 380 acres of Copper

Mountain, which is expected to be openfrom November 6 to April 18. Conversely, a resort such as Powderhorn only has the potential to cover 25 acres of its terrain, and is only expected to be openfrom December 17 to March 284. While snowmaking ability is not the exclusive reason for differences in season length, it certainly helps resorts such as

Copper prolong their seasons, and accumulate skier visits.

I measure manmade snowfall in snowmaking acres. I expect that this variable will have a slight positive correlation with skier visits.

Colorado Ski Country USA, "Colorado Ski Country USA Facts and Stats"; available from http://media-coloradoski.com/CSCFacts/; internet; accessed 1 November 2009. 25

Number of Chairlifts

The number of chairlifts at a resort can make a resort more attractive to the consumer for multiple reasons. First and most important, itsis a good measure of how crowded a resort is. Resorts add chairlifts in order to reduce traffic in lift lines. The more lifts there are, the less people there will be in every lift line. A common complaint of many resorts is that they are too crowded. In other words, waiting in lift lines takes too long. This mentality can resultin the decision to visit another, less crowded resort, even if the distance is greater, orthe snow or terrain quality is inferior.

Secondly, the number of chairlifts should provide an adequate measure of resort size. After all, the more terrain a resort opens, the more lifts a resortwill need to access that terrain.

In this study the number of skiable acres at a resort is divided by the number of chairlifts to measure the crowdedness of a resort, specifically. Less acres per chairlift indicates a less crowded resort since theoretically, a wait toride a lift should be far shorter when the availability of other liftsis increased. For these reasons in addition to the findings in past research, I expect that the number of acres per chairlift at a resortwill have a statistically significant, positive correlation with skier visits.

Number of Trails

The number of ski trails should similarly act as a decent measure of size in the regression analysis. However, I include this variable primarily for another purpose. The 26 number of trails is included as a measure of the potential variability in terrain at ski

resorts. I expect that the more trails a resort has,the greater potential for terrain

versatility. Thus, I predict that there will be a positive correlation with the number of

trails at a resort and skier visits since it would be logical for large groups or vacationing

families to choose resorts with differing terrain to suit all member preferences.

Amount of Skiable Terrain

In this study, I consider skiable terrain to be any terrain on the mountain opened

by a resortfor skiing and snowboarding. While this could also be considered a fine

measure of the potential for terrain versatility, I use skiable terrain as a variable in my

regression analysis purely to measure size against skier visits. According to simple logic

and past literature, skiable terrain will surely have a strong significant statistical

correlation with skier visits. I measure skiable terrain in acreage, and expect that more

acreage of skiable terrain will attract more skier visits annually.

Steepness

The steepness of a resort mountain refers to the average steepness of its total

skiable terrain. This can be considered one aspect of mountain difficulty. The steeper

the terrain, the more difficult it is. Therefore, it is likely that skiers and snowboarders

seeking difficult terrain aresimilarly attracted to steeper terrain. In this study, I measure

steepness by dividing total vertical feet of a ski mountain by total skiable terrain. I 27

expect that if my hypothesis is correct, steepness will be positivelycorrelated with skier visits.

Percentage of Beginner Terrain

The percentage of beginner terrain at a resort is the percentage of its total terrain that it considers easy enough for beginning skiers and snowboarders. Signs featuring a green circle designate beginner terrain. This terrain is usually very smooth and flat. It is mostly used by first-time skiers and snowboarders, and during the learning process.

More experienced skiers and snowboarders usually stay away from beginner terrain when at all possible. Once people are at a skill where they can stay off beginner terrain, they generally do, and there are far more experienced skiers that visit resorts than beginners. I therefore assume that there is a low demand for beginner terrain. For this reason, I expect that the percentage of beginner terrain will have a negative correlation with skier visits.

Percentage of Intermediate Terrain

The percentage of intermediate terrain at a resort is the terrain that the resort considers more challenging than beginner terrain, butnot hard enough to be for expert skiers and snowboarders only. It is designated by a blue square. This terrain is generally steeper than beginner terrain. Additionally, it may not be groomed and maintained as carefully, leaving it less smooth than beginner terrain. The extra challenge, however, I predict is enough to attract more skier visits. It is ideal terrain for families, large groups, 28

and the recreational skier or snowboarder, and even the most experienced skiers and boarders are frequently seen on this type of terrain. Therefore, I expect that intermediate terrain will be positively, and significantly correlated with skier visits.

Percentage of Expert Terrain

This independent variable is perhaps the most revealing in the context of this study. The percentage of extreme terrain at a resort is the terrain the resort considers only for expert skiers and snowboarders. It is designated by either a single black diamond, or double black diamond if the terrain is extra perilous. On the easiest of expert terrain, it is common for the terrain to be very steep. The snow is generally far from smooth. It is common for this terrain to be located in dense trees, or to be riddled with obstacles, such as boulders and logs. Mogul runs, ski runs that have developed large, closely-knit mounds of snow, are almost always designated as black diamonds.

Double black diamonds are runs that are sure to be steep, and very challenging to traverse. Sometimes signs will even read, "Extreme Terrain" over the double black diamonds. In these situations, it is not common to find large cliffs or snow cornices, sometimesmandatory to jump off in order to get down a run. The most experienced skiers tend to gravitate toward expert terrain because, since it is the most challenging, it is also considered by many to be the most exciting. While it is not the most skied terrain when compared to intermediate terrain, I expect that the percentage of expert terrain will have a statistically significant, positive correlation with skier visits, because I expect that 29

the most avid ski enthusiasts who most commonly frequent ski resorts have the highest demand for expert and extreme terrain.

Availability of Bowl Skiing

Bowls have always been popular among skiers and snowboardersof varying skill levels. Bowls are vast expanses of mountain, usually free of trees and rocks. They appeal to so many people because they are rarely crowded and are widely varying in terrain, therefore appealing to nearly all skill levels. However, bowls are mostly designated as expert terrain. Thus, if my analysis finds that bowl skiing is, as I expect, positively correlated with skier visits at a significant level, this variable will serve as evidence supporting my thesis. Availability of bowl skiing is a dummy variable.If a resort hasbowls, it is assigned a "1," and if not, a "0."

Availability of Backcountry/Big Mountain Skiing

The terms, "backcountry" and "big mountain" are used to describe areas that are either not easily reached or out of bounds. At resorts, this terrain is usually only accessible by hiking to it, riding in a cat, a large vehicle designed for maintaining ski mountains, or by helicopter. The terrain is so appealing because it is rarelyskied, leaving large amounts of fresh powder, and the terrain is generally challenging. In the sample I use for this study, some of the resorts more noteworthy for their backcountry skiing include Silverton and Telluride, which both offer heli-skiing. 30

This type of skiing is greatly increasing in popularity. Chris Owens, an owner of EpicQuest, an adventure tour company focusing on heli-skiing, says, "The market for big mountain skiing in remote locations is growing in a big way... If you can ski the blues and blacks at your resorts, you can ski remote backcountry.5" If Owens is correct, ski resort management could absolutely find an opportunity in increasing the amount of backcountry terrain or putting forth time and money to expand current backcountry programs and operations.

This independent variable is a dummy variable. If a resort currently offers backcountry skiing, it is assigned a "1," and if not, "0." It is hard to say if there will be a significant correlation between the availability of backcountry skiing and skier visits to resorts because it is such a fledgling branch of the ski resort industry. However, due to its growth in popularity, and keeping true to my hypothesis that more challenging terrain will attract more skiers, I expect that the correlation will be positive.

Ticket Price

I use single full-day ticket price as a measure for this independent variable.

According to hedonic pricing, a highly priced ticket represents a more valuable

5 The New York Times, "Now for Regular Skiers, the Ultimate Terrain"; available fromhttp://www.nytimes.com/2009/12/13/travel/13headsup.html?_r=l&emc=etal; internet; accessed 15 December 2009. 31 experience to the consumer6. I believe that this concept generally holds true forin the context of ski resort experiences. For example, the Aspen resorts, consisting of Aspen

Mountain, the Highlands, Buttermilk, and Snowmass, charge $78 per ticket because the experience offers so much. One $78 ticket grants access to anyof the four mountains.

Additionally, Aspen is a luxurious town that offers plenty of amenities. Echo Mountain asks $45 for a day of skiing. However, the experience is much more limited. The Aspen mountains offer more than 50 times the skiable acreage than Echo Mountain. Also,

Aspen offers a wide array of terrain at all skill levels while Echo limits itself to solely terrain park free skiing. Juxtapositions such as that of Aspen to Echo indicate that ticket price will actually have a significant positive correlation with skier visits. Essentially, I expect that more skier visits will represent visitors who were willing to pay more for a better experience.

In Chapter 4,1 test hedonic pricing models. I replace skier visits with ticket price as a dependant variable. In these models, the expectations regarding which variables will impact resort pricing strategy changes from which would impact the amount of skier visits in a year. For example, I expect that the difficulty of terrain will not have the same weight in the hedonic pricing models as my primary regression models. I believe amenities such as availability of terrain park features, annual snowfall, and local wealth will hold more weight in a hedonic pricing model. While results will differ, both sets of models willessentially be testing for what factors influence the success of a ski resort.

Sherwin Rosen. 1974. Hedonic Prices and Implicit Markets: Product Differentiation in Pure Competition. The Journal ofPolitical Economy 86, (1) : 34-51. 32

Number of Terrain Park Features

A terrain park feature canbe one of many different items. Large snow jumps are perhaps the most common in current terrain parks. Grind rails, metal rails or boxes that skiers and boarders slide down, are probably the most common after jumps. Another feature is the half-pipe, a large half-tube made of snow. There are still many other creativefeatures that canbe found in terrain parks. I include all features in measuring this variable.

The use of terrain parks has grown rapidly since their appearance less than two decades ago. Terrain park events have been included into the Olympic Games.

Increasing amounts of free-skiing and boarding companies have been established, many growing quite successfully and rapidly. Most importantly,the skiing and snowboarding youth continue togravitate toward terrain parks. While it is not clear whether resorts continually add new features to their parks every year to satisfy the demand, or whether terrain park popularity continues to grow because resorts are increasing the amount of features, it is clear that there is opportunity in this branchof skiing and snowboarding culture. There is a lack of existing research on terrain parks as of yet, but due to the rapid growth in their popularity, I expect that there will be a positive correlation between terrain park features and skier visits. This variable, along with my variable measuring expert and extreme terrain, is oneof the variables that is most relevant to my hypothesis

since terrain park features, after all, can be considered the most extreme of terrain, and 33 the regression results can be valuable to the ski resort industry if thevariable is significantly correlated with skier visits.

Distance from Denver, Colorado

I predict that the amount of time needed to travelto a resort is negatively

correlated with a resort's skier visits. This concept is hard to measure, but the distance

from a resort to Denver in miles is a fine measurement for several reasons. Firstly, most

out-of-town Colorado skierswill have to come through Denver international Airport. It

is logicalto assume that these vacationers may choose to visit a resort over another if the

latter is significantlyfarther away. Additionally, day skiers who do not spend the night

in the location they ski will most likely frequent a closer resort rather than one that is

farther. Since Denver is Colorado's largest city, and since it is located within 300 miles

of all the resorts in the sample excluding3,1 thought it appropriate for this variable. I

expect that this variable will be negatively correlated with skier visits.

Destination Resort

A destination location is defined by the travel industry as a location that is

generally an upscale area of tourism, attractive not only because of the quality of the

primary product that it offers, but also because of its other available amenities, or

activities, as opposed to its convenience of distance, or price. A destination ski resort

does not necessarily sell the skiing experience. Rather, it sells the experience ofbeing at 34 the destination itself with skiing being one of its primary features. For these reasons, destination resorts tend to sell tickets at a higher rate.

For example, a resort such as Arapahoe Basin is popular simply because there exists good skiing. The only stores and restaurants are located right at the bottom of the

ski resort and only offer Arapahoe Basin goods. There are no restaurants, entertainment

opportunities, lodging, or any other activities offered at Arapahoe Basin. It is not a

destination resort. On the other hand, Aspen Mountain is very much so a destination

resort. It offers an abundance of fine dining opportunities, inexpensive to glamorous

stores, multiple entertainment venues, lodging for all price ranges, and much more.

This variable is a reasonable substitute for some variables that could have helped

form a more solid and statistically efficient regression model. For example, previous

literature accounts for customer's satisfaction with lodging facilities, food, and resort

atmosphere7. Since this information was not readily available, a destination resort

dummy variable may provide good insight into the effects of the amenities available at

ski resorts on skier visits.

It is safe to assume that destination resorts will be positively and significantly

correlated with skier visits because vacationers enjoy theextraamenities. These extras

should attract more vacationers. Additionally, they should attract visitors to stay longer,

and in effect, ski or snowboard more days on the mountain.

7 Richard Perdue. 2002. Perishability, Yield Management, and Cross-Product Elasticity: A Case Study of Deep Discount Season Passes in the Colorado Ski Industry. Journal of Travel Research 41, no. 1 : 15-22. 35

Local Wealth

Wealth around a ski resort can play an critical factor in its success. Elementary microeconomic theory states that some population's willingness to pay for some good will increase with higher wealth. According to this theory, if a population surrounding a ski resort earns more than one surrounding another ski resort, the population with higher wealth will purchase more ski tickets when priced equally. While not priced exactly equally, the highest ticket price in the data set differs by just over $50from thelowest, while income levels differ by tens of thousands of dollars. Thus, it is safe to assume microeconomic theory canbe applied in this context, considering ticket prices are roughly equal to each other relativeto income levels.

I use the counties in which the resorts are located as my populations for my data.

The U.S. Census Bureau provides two sets of data that are helpful. One is median household income as of 2008. The other is per capita money income as of 1999. There are multiple reasons why I opted to use median household income. Firstly, the data is far more recent, and much more could have changed since 1999 than 2008. Secondly, the per capita income data shows little diversity ranging roughly from $20,000 to $30,000.

The regression analysis would potentially reveal less about the wealth variableas opposed to median household income data, which ranges roughly from $40,000 to

$70,000. Finally, it makes more sense to include household income because per capita income canbe skewed by factors such as children, who are mostly unemployed. The resorts included in this study are located in 13 of these counties. I assigned a 1, 2, or 3 to each county based on its median household income. A median household income of

$40,000 to $50,000 is assigned a 1, a median household income of $50,001 to $60,000 is 36 assigned a 2, and a median household income of $60,001 and above is assigned a 3. All income data and income group assignments can be seen in Table 3.1. I predict that the resorts that belong to group 3 will attract the higher numbers of skier visits while the resorts that belong to group 1 will receive the lower. Thus, I expect that skier visits will be positively correlated, and statistically significant to my local wealth independent variable.

TABLE 3.18

County Income

Median Per Capita Household Money Regression County Income Income Value Pitkin $72,088 $40,811 3 Boulder $66,760 $28,976 3 Summit $64,813 $28,676 3 Routt $63,085 $28,792 3 San Miguel $61,074 $35,329 3 Grand $58,895 $25,198 2 Clear Creek $57,227 $28,160 2 Larimer $56,701 $23,689 2 La Plata $56,427 $21,534 2 Lake $48,280 $18,524 1 Gunnison $46,972 $21,407 1 Chaffee $45,916 $19,430 1 San Juan $40,252 $17,584 1

Regression Method

The following regression model is ideal to test all independent variablesagainst my dependant variable,skier visits:

Q U.S. Census Bureau, "State and CountyQuick Facts"; available from http://quickfacts.census.gov/qfd/states/08000.html; internet; accessed 20 December 2009. 37

VISIT = Bo + fii BEG + B2 INT + B3 EXP + B4 NAT + B5 MAN + B6 LIFT

+ B7 TRAIL + B8 ACRES + B9 STEEP + &l0 BOWL + Bn BC + 6,2 PRICE

+ B13 PARK + BM DENVER + B15 DEST + Rl6 WEALTH

TABLE 3.2 provides a list ofeach variable, it's unit of measurement, and its corresponding abbreviated title in the equation.

TABLE 3.2

Variable Titles and Measurement Units

Variable Regression Equation Title Skier visits (#) VISIT Beginner terrain (%) BEG Intermediate terrain (%) INT Expert terrain (%) EXP Natural snowfall (feet) NAT Snowmaking acreage (acres) MAN Crowdedness (acres/lift #) LIFT Diversity of terrain (#) TRAIL Size (acres) ACRES Steepness (vert/acres) STEEP Availability of bowl-skiing (0/1) BOWL Backcountry skiing (0/1) BC Ticket price ($) PRICE Terrain park features (#) PARK Proximity to Denver (miles) DENVER Destination Resort (0/1) DEST County wealth (1/2/3) WEALTH

Unfortunately, this equation is impossible to use when running a regression. The independent variables, BEG, INT, and EXP all add up to %100 of a resort's terrain.

Thus, it is mathematically impossible to runa proper regression without dropping atleast 38

oneof the three terrain difficulty variables. I therefore develop various other regression equations in order to analyze my thesis. Chapter 4 discusses these equations, and analyzes the conducted regressions.

Additionally, I test hedonic pricing models in Chapter 4. In these equations, I test the majority of my independent variables against PRICE instead of VISIT. VISIT is treated as an independent variable. The main goal of these models remains essentially the same: to discover the impacts of terrain difficulty on resort success, but in these models, success is measured by the amount a resort is able to charge for a ticket instead of the amount of skiers it attracts. Hedonic pricing theory implies that the full price of a lift ticket is actually the price for the complete utility offered by purchasing that ticket. A consumer of Vail Mountain is not only purchasing a ride up their chairlift. Rather, he or she is purchasing the opportunity to ski the largest expanse of skiable terrain in Colorado, the opportunity to ski Vail's infamous Back Bowls, the assurance of fresh snowfall, and much more. Thus, in my hedonic pricing model, lift ticket price can be thought as a function of all aspects of purchasing that ticket that will offer utility to the consumer. My model is as follows:

PRICE = Bo + Bi BEG + B2 INT + B3 EXP + 64 NAT + B5 MAN + B6 LIFT

+ B7 TRAIL + B8 ACRES + B9 STEEP + Bio BOWL + Bu BC + B,2 VISIT

+ B13 PARK + BM DENVER + fi15 DEST + Bl6 WEALTH 39

Again, I test modified versions of the model for several reasons discussed in detail in

Chapter 4. All of the raw data used in my regression analyses can befound in TABLE

3.3. 40

TABLE 3.39

Raw Data

VISIT BEG INT EXP NAT MAN LIFT Ski Area (#) (O/o) (%) (%) (feet) (acres) (#) Arapahoe Basin 409810 10 30 60 350 125 7

Aspen Hiqhlands 199430 18 30 52 300 110 5

Aspen Mountain 304052 0 48 52 300 210 8 Beaver Creek 815350 19 43 38 310 605 17 Breckenridge 1630000 14 31 55 300 540 30 Buttermilk 126976 35 39 26 200 108 9 Copper Mountain 873039 21 25 54 285 380 22

Crested Butte 358735 23 57 20 250 300 16

Durango Purgatory 239845 23 51 26 250 250 10 Echo Mountain 30208 20 60 20 215 60 3 Eldora 255119 20 50 30 300 500 12 Howelsen 14680 25 20 55 150 50 4 Keystone 921069 19 32 49 230 662 20 Loveland Ski Area 313564 13 41 46 400 160 10 Monarch 165724 14 28 58 350 0 7 Powderhorn 69760 20 50 30 250 25 4 Silverton 6101 0 0 100 400 0 1 Ski Cooper 54998 30 40 30 260 0 5 Snowmass 733597 6 50 44 300 230 21 SolVista 63540 50 30 20 220 245 5 Steamboat 959603 14 42 44 349.9 375 18 Sunlight 69393 20 55 25 250 21 3 Telluride 419476 23 36 41 309 220 18 Vail 1569788 18 29 53 346 390 31 Winter Park/ Mary Jane 947331 8 37 55 327.7 299 24 Wolf Creek 178517 20 35 45 465 0 7

9 Colorado Ski Country USA, "Colorado Ski Country USA Facts and Stats"; available from http://media-coloradoski.com/CSCFacts/; internet; accessed 1 November

2009. 41

TABLE 3.3 - Continued

TRAIL ACRES VERT BOWL BC Ski Area (#) (acres) (feet) (O/l) (O/l) Arapahoe Basin 105 900 2270 1 0 Aspen Highlands 118 1028 3635 1 1 Aspen Mountain 76 673 3267 1 1 Beaver Creek 148 1815 4040 1 0 Breckenridge 155 2358 3400 1 0 Buttermilk 44 470 2030 0 0 Copper Mountain 127 2465 2601 1 1 Crested Butte 121 1125 3062 1 0 Durango Purgatory 85 1200 2029 0 1 Echo Mountain 15 85 600 0 0 Eldora 53 680 1600 1 0 Howelsen 15 25 440 0 0 Keystone 135 3148 3128 0 0 Loveland Ski Area 92 1365 2410 1 0 Monarch 64 800 1170 1 1 Powderhorn 43 1600 1650 0 0 Silverton 69 1819 3287 1 1 Ski Cooper 26 400 1200 1 1 Snowmass 91 3132 4406 1 0 SolVista 33 406 1000 0 0 Steamboat 165 2965 3668 1 1 Sunlight 70 470 2010 0 0 Telluride 118 2000 4425 1 1 Vail 193 5289 3450 1 1 Winter Park/ Mary Jane 142 3078 3060 1 0 Wolf Creek 77 1600 1604 1 0 42

TABLE 3.3 - Continued

PRICE PARK DENVER DEST WEALTH Ski Area ($) (#) (miles) (O/l) (1/2/3) Arapahoe Basin 51 17 68 0 3 Aspen Highlands 78 0 220 1 3 Aspen Mountain 78 10 220 1 3 Beaver Creek 98 30 110 1 3 Breckenridge 93 120 90 1 3

Buttermilk 78 61 220 1 3 Copper Mountain 72 77 75 1 3 Crested Butte 69 16 230 1 1 Durango Purgatory 45 20 330 1 2 Echo Mountain 45 40 37 0 2 Eldora 53 10 45 0 3 Howelsen 15 3 165 1 3 Keystone 93 100 70 1 3 Loveland Ski

Area 50 8 53 0 2 Monarch 49 11 157 0 1 Powderhorn 45 11 250 0 1 Silverton 125 0 360 1 1 Ski Cooper 36 0 130 0 1 Snowmass 78 25 220 1 3 SolVista 44 0 78 0 2 Steamboat 74 17 157 1 3 Sunlight 39 10 160 1 2 Telluride 76 14 330 1 3 Vail 98 55 100 1 3 Winter Park/ Mary Jane 72 25 67 0 2 Wolf Creek 45 0 246 1 1 CHAPTER 4

REGRESSION RESULTS AND ANALYSIS

Regression 1

I introduce two initial equations to test. Both test all but one of the independent variables against skier visits. They both must exclude one of the terrain difficulty variablessince including all of them renders a regression equation impossible to test, as their sum equals 100%. Thus, Equation 1 excludes the INT variable while Equation 2 excludes the BEG variable. In this way, each variable is still tested, and both equations

still test the EXP variable, which is most relevant to this thesis. The equations are as

follows:

Equation 1

VISIT = Bo + Bi INT + B2 EXP + B3 NAT + B4 MAN + B5 LIFT + B6 TRAIL +

B7 ACRES + B8 STEEP + B9 BOWL + Bi0 BC + Bn PRICE + Rl2 PARK + Bn DENVER

+ BM DEST + B15 WEALTH

43 44

Equation 2

VISIT = flo + Bi BEG + 1J2 EXP + B3 NAT + B4 MAN + B5 LIFT + U6 TRAIL +

fi7 ACRES + J58 STEEP + 69 BOWL + B10 BC + Bn PRICE + Rn PARK + Ru DENVER

+ 614 DEST + 6)5 WEALTH

Equation 1 results in a high R-squared value of 0.95. This means roughly 95% of skier visits are supposedly explained by all the independent variables in theequation.

However, the majority of the independent variables remain statistically insignificant. My

EXP variable, which I expected to be positively and significantly correlated, is insignificant with a t-stat of -0.34. However, the PARK variable, which is the other relevant independent variable to my hypothesis, is positively correlated with skier visits, and statistically significant with a t-stat of 2.84. The only other significantly correlated variable to skier visits is ACRES, which has the highest t-stat of 3.29. PARK and

ACRES are both significant at a 5% confidence level. Another noteworthy variable is my TRAIL variable, which has a t-stat of 1.89, witha very low probability value of

.0082, meaning at a 10% confidence level, this variable would be significantlycorrelated.

The results ofEquation 2 are quite similar, which makes sense since only the

BEG terrain variable is substituted for the INT variable. Yet again, the R-squared value is 0.95. ACRES and PARK are again the only two significantly correlated variables, with t-stats of 3.29 and 2.84 respectively, both identical to their counterparts in the first regression equation. The TRAIL variable is also significant at the 10% confidence level.

Again, my EX variable is insignificant, though, with a t-stat of-0.4. 45

Firstly, these regression results indicate the obvious: the larger a ski resort is, the more skier visits it will receive. It also indicates, as I proposed in my hypothesis, that the more terrain park features a resort offers, the more skier visits it will receive.

Additionally, the TRAIL variable, which I use to measure diversity of runs at a resort, may play a part in attracting skier visits. The results indicate that the difficulty of terrain does not, however, play a significant role in attracting skier visits. In fact, none of the other independent variables come close to explaining skier visits. The majority receives very high output probability values. The EXP and INT probability values in the Equation

1 output are 0.74 and 0.99 respectively, and the EXP and BEG probability values in the

Equation 2 output are 0.7 and 0.99. This suggests an extremely low likelihood of these variables having any impact on how many skier visits a resort receives annually in the context of Equations 1 and 2. Many other independent variables also had very high probability values, as seen in TABLE 4.1 and TABLE 4.2. 46

TABLE 4.1 Equation 1 Regression Output

Variable Coefficient Std. Error t-Statistic Prob.

C -324736.8 458746.6 -0.707878 0.4952 INT 13.17494 4615.421 0.002855 0.9978 EX -2029.682 6016.658 -0.337344 0.7428 NAT 60.27184 1180.074 0.051075 0.9603 MAN 217.1764 328.7300 0.660653 0.5238 LIFT 20.86286 283.7858 0.073516 0.9428 TRAIL 3399.136 1797.687 1.890839 0.0879 ACRES 161.8554 49.26720 3.285257 0.0082 STEEP 24554.48 24620.45 0.997321 0.3421 BOWL 208912.9 143921.9 1.451572 0.1773 BC -5506.921 87553.54 -0.062898 0.9511 PRICE -1785.317 3626.358 -0.492317 0.6331 PARK 6058.990 2134.910 2.838054 0.0176 DENVER -34.96080 727.3619 -0.048065 0.9626 DEST -92847.25 151444.1 -0.613079 0.5535 WEALTH 16254.22 68527.07 0.237194 0.8173

R-squared 0.952901 Mean dependent var 451142.5 Adjusted R-squared 0.882252 S.D. dependent var 463908.6 F-statistic 13.48785 Durbin-Watsoni stat 1.590158 Prob(F-statistic) 0.000109

TABLE 4.2 Equation 2 Regression Output

Variable Coefficient Std. Error t-Statistic Prob.

C -323419.3 466334.0 -0.693536 0.5038 BEG -13.17494 4615.421 -0.002855 0.9978 EX -2042.857 5070.748 -0.402871 0.6955 NAT 60.27184 1180.074 0.051075 0.9603 MAN 217.1764 328.7300 0.660653 0.5238 LIFT 20,86286 283.7858 0.073516 0.9428 TRAIL 3399.136 1797.687 1.890839 0.0879 ACRES 161.8554 49.26720 3.285257 0.0082 STEEP 24554.48 24620.45 0.997321 0.3421 BOWL 208912.9 143921.9 1.451572 0.1773 BC -5506.921 87553.54 -0.062898 0.9511 PRICE -1785.317 3626.358 -0.492317 0.6331 PARK 6058.990 2134.910 2.838054 0.0176 DENVER -34.96080 727.3619 -0.048065 0.9626 DEST -92847.25 151444.1 -0.613079 0.5535 WEALTH 16254.22 68527.07 0.237194 0.8173

R-squared 0.952901 Mean dependent var 451142.5 Adjusted R-squared 0.882252 S.D. dependent var 463908.6 F-statistic 13.48785 Durbin-Watsoni stat 1.590158 Prob(F-statistic) 0.000109 47

This high number of insignificant variables, combined with the very high R-

squared values, in each regression output indicates a problem with the models. A

histogram normality test conducted on each regression reveals a Jarque-Bera value of .77,

far less than 5.9. FIGURE 4.1 and FIGURE 4.2 illustrate these tests. The results suggest

that my distribution is normal. Other problems are far more likely, though.

FIGURE 4.1 Equation 1 Histogram - Normality Test

Series: Residuals 7- Sample 1 26 Observations 26 6- Mean 8.12e-11 5- Median 4689.126 Maximum 262311.7 4- Minimum -2022140 Std. Dev, 100679.1 3- Skewness 0.353951 Kurtosis 3456432 2- Jarque-Bera 0.788575 1 - Probability 0.680936

0- •200000-100000 1000002000003O0D0O

FIGURE 4.2 Equation 2 Histogram - Normality Test

Series: Residuals 7- Sample 1 28 Observations 26 6- Mean 1.31e-10 5- Median 4689.126 Maximum 262311.7 4- Minimum -202214.0 Std. Dev. 100679.1 3- Skewness 0.353951 Kurtosis 3.458432 2- Jarque-Bera 0.788575 1 - Probability 0.680936

0 -200000-100000 100000 200000 300000 48

Heteroskedasticity causes the standard errors of coefficients to be unreliable, and

effect, for t-stats to be unreliable. Heteroskedasticity is, thus, likely since even though in both regressions resulted in high R-squared values, so few t-stats are even close to implying significant correlations. A plot of residuals against fixed VISIT values, as seen in FIGURE 4.3, reveals pointsgenerally centered about the residual value of 0, but with points straying out as VISIT values increase. This indicates a potential problem with

heteroskedasticity.

FIGURE 4.3

Residuals vs. VISITT Graph 1

1,600,000

1,200,000-

800,000-

> 400,000-

-400,000-400,000-2007)00 0 20oToOO400,000

RESID 49

A White Test conducted on the regression output from Equation 1, as seen in

TABLE 4.3, indicates no problem with heteroskedasticity. With 15 degrees of freedom at the 5% confidence level, the observed R-squared value of 20.59 is well below the relevant x* critical point of 25. Although Equation 1 remains homoskedastic, a high multicollinearity is still likely.

TABLE 4.3 Equation 1 White Test Output

F-statistic 2.534934 Prob. F(15,10) 0.0708 Obs*R-squared 20.58604 Prob. Chi-Square(15) 0.1506 Scaled explained SS 3.740252 Prob. Chi-Square(15) 0.9985

Equation 2, though, tests positive for heteroskedasticity. With only 11 degrees of freedom at the 5%, the observed R-squared value is 21.77 is higher than 19.7, the x critical point. TABLE 4.4 displays the White Test output. This indicates that error variance is not constant. The given standard errors of coefficients and the t-stats are not, in this case, reliable.

TABLE 4.4 Equation 2 White Test Output

F-statistic 3.429648 Prob. F(15,10) 0.0273 Obs*R-squared 21.76855 Prob. Chi-Square(15) 0.1140 Scaled explained SS 3.955101 Prob. Chi-Square(15) 0.9979 50

The statistical computer software, EViews, corrects this with a White correction.

TABLE 4.5 displays the corrections. R-squared stays high at.95, and the t-stat values for

ACRES and TRAILS increase to 4.07 and 2.01 respectively, so that TRAILS is

significantly correlated with skier visits at the 5% confidence level. However, the t-stat

value associated with PARK drops to 1.6 with a probability value of. 14, meaning it only

becomes significant at approximately a 15% confidence level. Even though R-squared

stays high,the majority of independent variables remain insignificant. Thus, a problem

with the data is still likely.

TABLE 4.5 Equation 2 White Correction Output

Variable Coefficient Std. Error t-Statistic Prob.

c -323419.3 451418.5 -0.716451 0.4901 BEG -13.17494 5341.461 -0.002467 0.9981 EX -2042.857 5204.408 -0.392524 0.7029 NAT 60.27184 960.1895 0.062771 0.9512 MAN 217.1764 285.0757 0.761820 0.4638 LIFT 20.86286 269.2792 0.077477 0.9398 TRAIL 3399.136 1688.304 2.013344 0.0718 ACRES 161.8554 39.77281 4.069499 0.0023 STEEP 24554.48 24521.99 1.001325 0.3403 BOWL 208912.9 186691.1 1.119030 0.2893 BC -5506.921 66383.83 -0.082956 0.9355 PRICE -1785.317 3477.389 -0.513407 0.6188 PARK 6058.990 3794.740 1.596681 0.1414 DENVER -34.96080 851.0207 -0.041081 0.9680 DEST -92847.25 156601.6 -0.592888 0.5664 WEALTH 16254.22 60691.28 0.267818 0.7943

R-squared 0.952901 Mean dependent var 451142.5 Adjusted R-squared 0.882252 S.D. dependent var 463908.6 S.E. of regression 159187.6 Akaike info criterion 27.06881 Sum squared resid 2.53E+11 Schwarz criterion 27.84303 Log likelihood -335.8946 Hannan-Quinn criter. 27.29176 F-statistic 13.48785 Durbin-Watson stat 1.590158 Prob(F-statistic) 0.000109 51

Multicollinearity in both regression equations makes sense. Many of the independent variables can be associated. For example, while the TRAIL variable is supposed to measure variety in terrain, it clearly also doubles as a measure of size.

Therefore, the TRAIL and ACRES variables have the potential to have a strong positive correlation. A variable such as EXP similarly has the potential to be strongly correlated with BEG, although negatively, because if a mountain harbors a large percentage of difficult terrain, it is very likely that the percentage of beginner terrain is small. Running a correlation matrix confirms high multicollinearity. This means the standard errors of the coefficients are inflated, and the calculated t-stats are artificially decreased. The EXP variable, measuring the percentage of the most difficult terrain is correlated with several other independent variables, namely the BEG and INT variables, which makes sense since greater percentages of expert terrain will automatically lower beginner and intermediate terrain. Several variables, such as ACRES, TRAIL, and MAN, are correlated probably due to size. Obviously ACRES measures size. The amount of trails will generally be higher at a larger ski resort, and there will be more need for man-made

snow at a larger ski resort. TABLE 4.6 displays all correlations, with the most significant

correlations in bold. Itis important not to discount the first regression attempts, however.

Although correlations exist, there are only several that are strong, 52

Table 4.6

Correlation Matrix 1

BEG INT EXP NAT MAN LIFT TRAIL ACRES VISIT BEG 1.00 INT 0.11 1 .00 EXP -0.67 -0 .81 1 .00 NAT -0.55 -0 .28 0 .53 1 .00 MAN -0.02 0 .08 -0 .05 -0 .06 1 .00 LIFT -0.40 -0,.57 0,.66 0 .38 -0 .26 1 .00 TRAIL -0.35 -0 .12 0 .29 0 .41 0.66 -0 .05 1 .00 ACRES -0.34 -0 .20 0 .35 0 .38 0 .52 0 .12 0. 81 1,.00 STEEP 0.17 -0 .07 -0 .05 -0 .57 -0 .31 -0 .17 -0 .51 -0,.52 BOWL -0.52 -0 .21 0 .47 0 .73 0 .16 0 .13 0 .51 0..36 BC -0.19 -0 .32 0 .35 0 .23 -0 .13 0 .26 0 .21 0..19 PRICE -0.43 -0 .35 0 .51 0 .33 0,.49 0 .49 0. 64 0. 62 PARK -0.01 -0 .07 0 .06 -0 .20 0. 63 -0 .19 0 .46 0.,45 DENVER -0.14 -0 .11 0 .16 0 .10 -0..40 0 .48 -0 .11 -0. 06 DEST -0.14 -0 .15 0 .20 -0 .02 0..26 0 .14 0,.43 0. 32 WEALTH -0.06 -0 .03 0 .06 -0 .21 0.,57 -0 .37 0,.40 0. 77

STEEP BOWL BC PRICE PARK DENVER DEST WEALTHr VISIT BEG INT EXP NAT MAN LIFT TRAIL ACRES STEEP 1.00 BOWL -0.41 1. 00 BC -0.18 0. 36 1. 00 PRICE -0.47 0. 41 0. 25 1 .00 PARK -0.20 -0. 10 -0. 15 0 .44 1.,00 DENVER -0.02 -0. 02 0. 40 0 .17 -0.,36 1..00 DEST 0.09 0. 04 0. 24 0 .48 0.,30 0.,49 1.00 WEALTH 0.17 0. 05 -0. 01 0 .28 0.,44 -0.,79 0.37 1 nn 53

To correct for multicollinearity, there is a combination of changes to the original data. First, some independent variables are dropped from the regression. TRAIL is dropped because it acts too much as a second measurement of size. ACRES measures

size better than any other variable, and TRAIL is correlated with numerous other

independent variables. For similar reasons, I drop MAN, the variable that measures the

acreage of man-made snowfall. It also has too much potential to be a measure of resort

size, since greater acreage will almost always equate to more acres that need man-made

snow. MAN is additionally almostcompletely insignificant according to the output from

the first regressions. Although none of the terrain difficulty variables seem to beof any

significance according to the first regression analyses, I keep EXP in my equation

because it is the terrain variable most relevant to my hypothesis, and because it's

probability value, roughly .7, from the first two regression outputs is significantly more

promising than those of the BEG and INT variables, at .99. Thus, I drop BEG and INT,

which are both correlated with several other independent variables. Although it seems

logical that greater amounts of natural snowfall would have quite a significant effect on

skier visits, the NAT independent variable has almost no correlation according to the

regression outputs in TABLE 4.1 and TABLE 4.2. In both regressions, its t-stat is close

to 0 and its probability values of roughly .95 indicate that it has almost no impact on skier

visits. Since it is also highly correlated with other independent variables, dropping NAT

is reasonable. Finally, I drop PRICE. PRICE becomes more important in the following

hedonic pricing regression equations, but according to the regression outputs, in this case,

PRICE impacts skier visits very little. 54

The independent variables that remain still must be tweaked in order to be rid of high multicollinearity. By taking the logarithm of all LIFT data entries, I am able to decrease their values and range, thereby weakening the variable's correlation with some of the other independent variables. Similarly, squaring all values of the STEEP variable has a similar effectin decreasing several correlations. A combination of dropping, squaring, and taking the logarithm of several independent variables results in the correlation matrix displayed in TABLE 4.7. Only the variables, STEEP, and LIFT are correlated to a significant degree.

TABLE 4.7

Correlation Matrix 2, Adjusted to Fix Multicollinearity

EXP LIFT ACRES STEEP BOWL BC PARK DENVER DEST WEALTH EXP 1.00 LIFT 0.45 1.00 ACRES 0.35 0.41 1.00 STEEP 0.07 -0.67 -0.35 1.00 BOWL 0.47 0.30 0.36 -0.36 1.00 BC 0.35 0.30 0.19 -0.18 0.36 1.00 PARK 0.06 -0.10 0.45 -0.16 -0.10 -0.15 1.00 DENVER 0.16 0.40 -0.06 -0.01 -0.02 0.40 -0.36 1.00 DEST 0.20 0.09 0.32 0.12 0.04 0.24 0.30 0.49 1.00

WEALTH 0.06 -0.34 0.27 0.17 0.05 -0.01 0.44 -0.29 0.37 1.00

Although several variables are no longer part of the regression, they were dropped either because they held so little significance, or because they were too similar in measurement to another variable, namely ACRES. The following equation is still an accurate representation of what factors contribute to attracting skier visitsat a Colorado resort: 55

Equation 3

VISIT = Bo + Bi EXP + B2 LIFT + 63 ACRES + B4 STEEP + B5 BOWL + B6 BC +

B7 PARK + B8 DENVER + B9 DEST + Bio WEALTH

Again, the regression results for Equation 3 reveal that total acreage and the availability of terrain park features are positively correlated with skier visits, and statistically significant at the 5% level. As displayed in TABLE 4.8, the t-stat for

ACRES is 5.24, and 2.42 for PARK. In this case, the availability of bowl skiing is also positively correlated, but only significant at the 10% confidence level. BOWL has a t- stat of 1.87, and a probability value of 0.08. The majority of the other variables still hold little significance, though. However, R-squared, although slightly lower than its counterparts in the regression outputs from Equations 1 and 2, remains high at 0.92.

Thus, according to Equation 3, 92% of skier visits to resorts in Colorado can be explained by the independent variables included in Equation 3. However, the high R-squared value combined with the low number of significant independent variables still suggests a problematic regression.

Again, a histogram-normality test, illustrated by FIGURE 4.4, reveals that the

distribution remains normal, as the Jarque-Bera value of .96 remains below 5.9. Since

multicollinearity is now not a threat to the regression, heteroskedasticity is likely a

problem again. 56

TABLE 4.8 Equation 3 Regression Output

Variable Coefficient Std. Error t-Statistic Prob.

C -1240.441 606751.2 -0.002044 0.9984 EX -2395.065 5425.629 -0.441435 0.6652 LIFT -75139.05 316594.5 -0.237335 0.8156 ACRES 224.1212 42.80415 5.235968 0.0001 STEEP 345.9718 2020.479 0.171233 0.8663 BOWL 273449.6 - 146503.8 1.866502 0.0816 BC -8160.817 84151.21 -0.096978 0.9240 PARK 5564.179 2302.915 2.416146 0.0289 DENVER -629.9585 642.3339 -0.980734 0.3423 DEST 50347.00 131235.7 0.383638 0.7066 WEALTH 38214.65 58249.38 0.656053 0.5217

R-squared 0.927012 Mean dependent var 451142.5 Adjusted R-squared 0.878353 S.D. dependent var 463908.6 S.E. of regression 161801.6 Akaike info criterion 27.12224 Sum squared resid 3.93E+11 Schwarz criterion 27.65451 Log likelihood -341.5891 Hannan-Quinn criter. 27.27551 F-statistic 19.05130 Durbin-Watson stat 1.815891 Prob(F-statistic) 0.000001

FIGURE 4.4 Equation 3 Histogram - Normality Test

Series: Residuals Sample 1 26 Observations 28

Mean B.16e-11 4- Median -3672.783 Maximum 326746.5

3- Minimum -206716.0 Std.Dev. 125331.0 Skewness 0.471673 Kurtosis 2.996498

Jarque-Bera 0.964072 1-1 Probability 0.617525

-200000 200000 57

FIGURE 4.5 plots the residuals from the regression against values of the

dependant variable. The points on the graph tend to disperse outward from 0 on the

RESID axis, suggesting a problem with heteroskedasticity.

FIGURE 4.5 Residuals vs. VISIT Graph 2

2,000,000-1

1,600,000-

1,200,000-

OT

800,000-

400,000-

-400,000-200,000 0 200,000 400,000

RESID

A White Test confirms heteroskedasticity. As seen in TABLE 4.9, with a small2 degrees

of freedom, the observed R-squared value of 21.09 is far greater than the %2 critical point

of 5.99. Thus, both standard errors of coefficients and t-stats are still unreliable.

TABLE 4.9 Equation 3 White Test Output

F-statistic 6.440246 Prob. F(10,15) 0.0007 Obs*R-squared 21.08831 Prob. Chi-Square(10) 0.0205 Scaled explained SS 7.006748 Prob. Chi-Square(10) 0.7248 58

TABLE 4.10 displays the regression output provided by EViews after correcting for heteroskedasticity with the White Correction. R-squared yet again stays high at .92.

As seen in the table, though, the PARK variable drops in significance, with a t-stat of only 1.51. With a probability value at. 15, PARK now becomes statistically significant at approximately the 15% confidence level. ACRES still remains highly correlated with the dependant VISIT variable with a t-stat 5.23. The availability of bowl skiing also drops in significance, and becomes statistically significant at over the 15% confidence level.

TABLE 4.10 Equation 3 White Correction Output

Variable Coefficient Std. Error t-Statistic Prob.

c -1240.441 522235.5 -0.002375 0.9981 EX -2395.065 4091.562 -0.585367 0.5670 LIFT -75139.05 256433.5 -0.293016 0.7735 ACRES 224.1212 42.81565 5.234562 0.0001 STEEP 345.9718 1742.974 0.198495 0.8453 BOWL 273449.6 185162.4 1.476809 0.1604 BC -8160.817 83618.33 -0.097596 0.9235 PARK 5564.179 3673.161 1.514820 0.1506 DENVER -629.9585 664.9170 -0.947424 0.3584 DEST 50347.00 100482.8 0.501051 0.6236 WEALTH 38214.65 35886.31 1.064881 0.3038

R-squared 0.927012 Mean dependent var 451142.5 Adjusted R-squared 0.878353 S.D. dependent var 463908.6 S.E. of regression 161801.6 Akaike info criterion 27.12224 Sum squared resid 3.93E+11 Schwarz criterion 27.65451 Log likelihood -341.5891 Hannan-Quinn criter. 27.27551 F-statistic 19.05130 Durbin-Watson stat 1.815891 Prob(F-statistic) 0.000001

From a statistical standpoint, this final regression indicates that, at the 5%

confidence level, only the total amount of skiable terrain offered at a ski resort can help

predict how many skier visits a resort will see in a year. Even though 92% of skier visits 59 can be explained by the independent variables in Equation 3 according to the regression output, it is hard to discount some of the less significantvariables.

My hypothesis is split in two parts. Given the findings of this study, I am unable to support one part of my hypothesis, suggesting more difficult terrain increases skier visits. However, even though terrain park features only are statistically significant around a 15% confidence level, it would be wrong to discard the variable completely. It is safe to say that ski resort visitors choose a resortfor other reasons than size. The

PARK variable is initially statistically significant, but belonging to a regression with potentially problematic multicollinearity. Inthe third regression equation, PARK is still one of the more correlated variables. Thus, while the PARK variable may not strongly support the thesis of this paper, it certainly should not be completely discounted as contributing evidence. Whether more skiers visit because there are more features at one resort, or whether resorts put up more featuresas more and more skiers visit, is still a question.

Additionally, factors such as the availability of bowl skiing, should not be discounted. At just over a 15% confidence level, BOWL is significantly, positively correlated with skier visits, according to the regression. It is, then, not irrational to agree that skiers and snowboarders are attractedto bowl skiing.

The findings of this paper certainly warrant further study. Perhaps the next step is to collect data from a wider range of subjects. This study was deliberately conducted in the context of the Colorado ski resort industry, specifically. A broader array of resorts

around the country and around the world could provide more definitive patterns in data, which could help clarify and build on the findings of the proposed models and findings in 60 this paper. With more research, and more analysis, the difficulty of terrain may yet play a role in the success of a ski resort, too.

Regression 2: The Hedonic Pricing Model

Hedonic pricing has been used in the past to describe the housing industry, and any industry where competition is near perfect. In this section, I apply hedonic pricing concepts to my regression analysis. In theory, whichever variables are positively correlated and significant are aspects of a mountain that attract a consumer to pay more for his or her lift ticket. Conversely, a variable that is negatively correlated, and significant, prevents the consumer from wanting to pay more for a lift ticket. In essence, in this context, hedonic pricing theory implies that the consumer is paying a certain price for not only the lift ticket, but also for whichever aspects, or independent variables, are included in a day of skiing.

I submit that testing a hedonic pricing model can provide evidence toward enforcing my proposed thesis. The hedonic pricing model, in the context of this paper, tests forresort success just as the preceding regression equations did. In this case, the dependant variable is PRICE. I propose that consumers pay more for tickets at resorts with more difficult terrain, or more terrain park features.

Some resorts see less annual snowfall, have less terrain, build fewer lifts, and are still able to charge more for a day on the mountain. Silverton, for example, charges $125 for a day on the mountain. Yet Silverton is 360 miles away from Denver, does not offer terrain park features, and makes no man-made snow. However, Silverton Mountain is deemed, 100% expert terrain. Therefore, Silverton has the highest percentage of extreme 61

terrain out of the sample population, and it also charges the highest. Silverton may be an outlier, but perhaps not coincidence. I test a hedonic pricing regression equation, which aims to discover which aspects of a ski resort allow its management to charge more for a day of skiing, and which hinder the ability. I test the following equation:

Equation 4

PRICE = Bo + Bi EXP + B2 LIFT + B3 ACRES + B4 STEEP + B5 BOWL + B6 BC +

B7 PARK + B8 DENVER + B9 DEST + B,o WEALTH + Bn NAT

The independent variables that explain PRICE are the same as in Equation 3.

However, I add the NAT variable back into the equation because I suspect high amounts

of natural snowfall to be an extra that people will pay more forin a lift ticket. All the

essential variables that visitors would want in their experience witha day of skiing still

remain from Equation 3. I predict that EXP, the percentage of expert terrain will be

positively correlated with price because ski enthusiasts who are more advanced may pay

more to ski terrain at their level than beginners might pay at their level. I expect STEEP

to be positively correlated with PRICE for the same reason. I predict that the LIFT

variable will have a positive correlation with price because less crowded lift lines is an

amenity for which some would likely pay extra. I expect the amount of acres at a resort

to be positively correlated with price because a larger mountain implies more diversity

and less crowds, both considered benefits at a ski resort. I predict that the availability of

bowl and backcountry skiing will both be positively correlated with pricesince they are 62

both relatively rare extras not available at all resorts. I expect terrain park features to be

positively correlated with price because they also add to the ski experience of many, such

as the growing number of skiers and snowboarders who are starting to ski solely in the

terrain parks. Distance from Denver should be negatively correlated. It makes sense that

skierswill pay to drive fewer hours to a resort. Finally, I expect WEALTH to be

positively correlated with PRICE, not because it is a variable falls under the category of

an extraor amenity, as a hedonic pricing theorist may suggest, but for simple microeconomic reasons. A town with higher income will pay more to ski at their mountain than a less wealthy town. WEALTH is the only independent variable in the equation I do not consider relevant to hedonic pricing. It is included simply because it is likely to be a determinant of PRICE.

Multicollinearity is problematic early on in this paper,so variables are tweaked to prevent it. The only necessary change is that all natural snowfall values are squared.

TABLE 4.11displays the correlation matrix with the more relevant correlations in bold.

TABLE 4.11 Correlation Matrix 3 EXP LIFT ACRES STEEP BOWL EXP 1.00 LIFT 0.66 1.00 ACRES 0.35 0.12 1.00 STEEP -0.05 -0.17 -0.52 1.00 BOWL 0.47 0.13 0.36 -0.41 1.00 BC 0.35 0.26 0.19 -0.18 0.36 PARK 0.06 -0.19 0.45 -0.20 -0.10 DENVER 0.16 0.48 -0.06 -0.02 -0.02 DEST 0.20 0.14 0.32 0.09 0.04 WEALTH 0.06 -0.37 0.27 0.17 0.05 NAT 0.53 0.39 0.33 -0.48 0.67 63

TABLE 4.11 - Continued Correlation Matrix

BC PARK DENVER DEST WEALTH NAT EXP LIFT ACRES STEEP BOWL BC 1.00 PARK -0.15 1.00 DENVER 0.40 -0.36 1.00 DEST 0.24 0.30 0.49 1.00 WEALTH -0.01 0.44 -0.29 0.37 1.00 NAT 0.18 -0.23 0.12 -0.01 -0.25 1.00

The regression output, as seen on TABLE 4.12, displays a few significant

results. The intercept coefficient happens to be significant with a t-stat of 1.99. LIFT,

thevariable measuring crowdedness is positively correlated with PRICE, and statistically

significant with a t-stat of 3.28. The availability of bowl skiing has a t-stat of 2.41,

signifying its significant positive correlation with ticket price. The PARK variable is

positively correlated with PRICE, butnot significant. This, perhaps, is recognition of

terrain park attractiveness, but is only strong enough to be significant at roughly a 20%

confidence level since terrain parks represent such a small part of every mountain.

Another noteworthy variable is WEALTH. It is statistically significant at the 10% level

with a t-stat of 1.84. STEEP is surprisingly, negatively correlated, although insignificant

until around the 20% confidence level. The independent variables explain 86% of ticket price. 64

FIGURE 4.12 Hedonic Pricing Regression Output

Variable Coefficient Std. Error t-Statistic Prob.

C 33.22739 16.63548 1.997381 0.0656 EX -0.125928 0.359772 -0.350023 0.7315 LIFT 0.049484 0.015079 3.281586 0.0055 ACRES 0.002135 0.003105 0.687683 0.5029 STEEP -2.136768 1.516571 -1.408947 0.1807 BOWL 21.01419 8.731969 2.406580 0.0305 BC -3.576893 6.505651 -0.549813 0.5911 PARK 0.192340 0.148349 1.296543 0.2158 DENVER -0.002200 0.050208 -0.043813 0.9657 DEST 11.19873 9.982910 1.121790 0.2808 WEALTH 8.338605 4.528967 1.841172 0.0869 NAT -0.000146 0.000115 -1.274650 0.2232

R-squared 0.863073 Mean dependent var 65.34615 Adjusted R-squared 0.755488 S.D. dependent var 24.66892 F-statistic 8.022238 Durbin-Watson stat 2.342645 Prob(F-statistic) 0.000264

The output looks to be without problem. A histogram normality test, seen in

FIGURE 4.6, reveals a Jarque-Bera value of 0.8, far less than 5.9. Therefore, the distribution is normal. Additionally, as seen in FIGURE 4.7, a residual versus PRICE plot shows little pattern, indicating no heteroskedasticity. An analysis of an EViews- generated White Test, seen in TABLE 4.13, confirms the absence of heteroskedasticity since the observed R-squared value, 10.54, is less than 11.1, the x2 critical point associated with 5 degrees of freedom at the 5% confidence level. 65

FIGURE 4.6 Equation 4 Histogram - Normality Test

8 Series: Residuals

7- Sample 1 26 Observations 26

6- Mean 7.98e-15 5- Median 1.085548 Maximum 16.44398 4- Minimum -19.40627 Std.Dev. 9.128383 3- Skewness -0.039121 Kurtosis 2.142647 2- Jarque-Bera 0.802941 1- Probability 0.669335

0- -20 -15 -10 10 15 20

FIGURE 4.7 Residuals vs. PRICEGraph 66

TABLE 4.13 Equation 4 White Test Output

F-statistic 0.867149 Prob. F(11,14) 0.5876 Obs*R-squared 10.53607 Prob. Chi-Square(11) 0.4829 Scaled explained SS 1.745299 Prob. Chi-Square(11) 0.9992

Discussion

Although all regression equations in this paper aim to explain, ultimately, what

aspects of a ski resort are critical for success, the two methods of regression resulted in

different answers to this question. When measuring success as the number of skier visits

a ski resort attracts in a year, total skiable acreage is the only statistically significant variable at the 5% confidence level. This would imply that Colorado ski resorts would do well to open as much land for skiing as possible. Basically, the results suggest that chances are, space to ski will be filled. Although the evidence is not statistically strong, it is noteworthy that the number of terrain park features also possibly relates to the amount of skiers a resort attracts.

When measuring resort success as the amount of money it can successfully charge in the hedonic pricing model, the crowdedness of resorts, and availability of bowl skiing.

Othernoteworthy variables from this model include, again, the amount of terrain park features, with a significant, positive correlation at roughlya 20% confidence level. Local income level is, as expected, positively correlated with ticket price, and significant on the

10% level. These variables' correlation to price makes sense. People commonly go where there are no crowds. Especially in Colorado, home to world-famous back-bowl 67

skiing, it is not surprising that people will pay a higher priced ticket if they have the opportunity to ski bowls. Finally, although it remains a reach to call a variable significant at a 20% significance level, it is still possible that the presence of terrain park skiing impacts a consumer's willingness to pay for higher-priced tickets. I suspect that as the youth who have made the freeskiing sport what it is today grow older, this variable will impact the resort success. In the context of hedonic pricing strategy, as these skiers and snowboarders grow older, and asthe sport grows,ahigh presence of terrain park features will become a desired amenity at many skiresorts, thereby allowing resorts to charge for the extra utility created by higher amounts of features. The hedonic model is the better model. The results are more congruent with the more logical expectations, and an analysis of the statistics can, in this case, beof use in decision making, and hopefully problem solving given the poor economy. CHAPTER 5

CONCLUSION

The goal of this thesis is to expose an opportunity in the ski industry, which is suffering currently. The opportunity lies in the growth of extreme skiing and freeskiing.

As these branches of the sport grow, resorts may bewise to take advantage of being on the forefront of the sport by developing new features, and investing in projects to either create new, "extreme terrain" ski runs, or terrain parks.

The study examines nearly every ski resort in Colorado. Data concerning many critical aspects of each resort is acquired, and utilized in regression analyses to answer the question, "Why do people go, andpay to ski at the resorts that they do?" I propose that if a resort's terrain difficulty is related to why visitors skiat the resort, than the presence of the most difficult terrain, from extreme to expert to park feature skiing, will be highly correlated with resort success, whether measured by annual skier visits or by the amount a resort may successfully charge for one lift ticket.

The results of the study emphasize several findings supported by statistical evidence. There is one statistically significant result from my initial multivariate regression: the size of a ski resort, measured in acres in the regression, is highly

68 69

correlated with annual skier visits. This is a conclusion that is rather obvious, and unfortunately, it is not incredibly helpful information since the amount of skiers at a resort and the resort'ssize are causal of one another.

The Hedonic Pricing regression is far more effective in acquiring results.

Hedonic Pricing theory requires thinking in a different way. Instead of predicting which factors would attract skiers, one must predict what exactly a skier is agreeing to pay for when he or she buys his lift ticket. This way of thinking provided a framework to develop a more effective regression equation. Results included a significant positive correlation between ticket price, and both the availability of bowl skiing and crowd control on mountains.

The study falls short of providing the strongest evidence supporting my hypothesis, and in effect, confirming that the opportunity I identify would be significantly beneficial. However, there are some points that support my hypothesis. While it is impossible to say I discovered any hard evidence that my thesis was correct, in analyzing multiple regression outputs, the availability of park features always remained on the edge of having a significant statistical correlation with resort success. A rejection of the null hypothesis in this situation is not reasonable, but the PARK variable's consistent position of significance around the 20% confidence level should be considered grounds for additional research.

The study examines nearly every ski resort in Colorado in detail. Future research, sufficient time and resources, could be conducted on ski resorts outside of Colorado.

Data not included in this study that could be helpful includes information concerning resort marketing strategies, facility and equipment maintenance, and even qualitative 70 research directly from consumers. This is the first paper to focus especially on terrain difficulty. It is, to the best of my knowledge, the only paper to even consider the impact of terrain parks on ski resorts in any way.

This study is also the first to apply hedonic pricing theory to a skiing. The vast majority of hedonic pricing literature applies to real estate and the housing market. I apply hedonic pricing theory to ski resorts and the factors that contribute to their success.

In this paper, the hedonic pricing model actually is less problematic than the standard multivariate model, and produces better results. This study provides an example of how one economic theory, even if generally used in a completely different field than what is familiar or what is being studied, canbe applied to any field if thought out carefully. WORKS CONSULTED

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