# The Price of Skiing

December 17, 20097

Last year I was a senior Economics major at Colby College.  This meant that I had to do a final project that involved a lot of my own research and said something new about what I found.  I decided to do my project on skiing.  Specifically, I wanted to see if there was any relationship between the quantifiable factors that describe a given cross-country ski center and the price that that ski center charges.  Without getting too deep into the details of the project, this article seeks to describe what I did and what I found.

I started off with a sample of cross-country ski centers.  I decided to use all the centers I could find in New England and New York.  I then dropped the ones that run without charging for trail passes.  This left me with 80 different centers across the northeast.  I then came up with any variable I felt may play a role in the pricing of a center.  There were 12 variables I came up with.  I collected data on all 12 for all 80 ski centers.  There were two types of variables.  The first group were normal ones that lend themselves to being quantified, including total kms, average annual snowfall, average local income, other centers within 20 miles, other centers within 100 miles, population within 20 miles, and population within 100 miles.

The second group of variables accounts for things that are harder to quantify.  They are just answers to yes or no questions, such as, “is there a retail shop?”  If yes, then that center has a “1” in that category.  If no, the center has a “0”.  There were five of these “amenity variables”.  They were: is there a high-quality grooming machine (Pisten Bully, Bombardier, etc. ), is there a retail shop, are there lessons offered, is there on-site lodging, and is there a downhill resort nearby.  With those twelve statistics as well as the price of each center established, I was ready to dive into the analysis.

The first thing I did was add two more variables that were manipulations of the variables I already had.  One was the population within 20 miles multiplied by the local income per capita.  This was an attempt to have a variable that represents the total amount of money within 20 miles.  The second manipulation was a variable that I called “quality factor.”  It was the five amenity variables added together.  So if a center has a Pisten Bully, offers rentals, lessons, and lodging, and has a downhill resort in town, then that center gets a 5 for the “quality factor”.

The next part is kind of fun, in a sort of “I’m a lame economics nerd” kind of way.  You can try it yourself and see what you come up with.  Which of these variables do you think determine the price?  All of them?  Some of them?  None of them?  There’s a lot more that goes into pricing, so maybe this whole thing won’t work.  Or maybe, all of these variables help to perfectly determine price for all the centers.

As it turns out, three variables are pretty good at determining price.  I’d suggest you try again to guess what they are.  Here’s the answer.  The most important determinants of price are: total kilometers (classic+skate), number of centers within 20 miles, and the “quality factor” that I described.  Those three are consistently significant, and all the others are consistently terrible.  After some further analysis, I found that having a downhill resort in town actually had no effect, so the final equation I came up with used three independent variables, including total kms, centers within 20 miles, and a quality factor that is lessons+retail+grooming+lodging.  That equation looks like this:

Price=8.683+.024 x Total Km+.534 x Centers w/in 20 miles+1.025 x Quality Factor

This means that a center can charge (or rather, does charge) \$8.68 even if they offer nothing.  For each extra kilometer of skiing they have, classic or skate, they can charge another 2.4 cents.  Then if they count up all the other centers within 20 miles of their own, they can charge an extra 53.4 cents for each one.  Then if they have a big grooming machine, lodging, lessons, or a retail shop, they can charge an extra dollar and 2.5 cents.

This all makes sense.  The number of kilometers has a direct effect on both how nice it is to ski at a place and also how much work it takes to maintain the trails.  Both of these should drive the price up.  The number of centers within 20 miles seems to work as a good measure for how popular skiing is in that area and how good the local terrain and snow might be.  Where skiing seems to be best in the Northeast, there are many ski centers-places like Stowe, VT and Jackson, NH.  So in those places, prices can be higher.  Finally, for each of the 4 amenities I mentioned, there is a value there that makes the ski center more accessible and higher-quality.  The services also increase the amount of work it takes to run the center.  This will increase the price as well.

The most important question to ask now is whether or not the equation worked.  Sure, there were a few significant variables and things seem to make sense, but how good is the equation at actually predicting the price?  The answer, predictably, is that the equation is ok, but not perfect.  The range of prices is from 8 to 22 dollars.  The equation is off by an average of \$2.30.  It predicted over a quarter of the center’s prices within a dollar, almost half within 2 dollars, and almost three quarters within 3 dollars.

The final question to answer here is what does all of this mean?  First, this formula is probably a pretty good resource for ski centers to use.  Second, it shows how centers can change their prices if it changes what they offer.  Finally, and most importantly, it shows a lot about the cross-country skiing experience.  It shows what is important (kms, lessons, lodging, grooming, etc.) and what is not (snowfall, local income, population, etc.)  Take from that what you will.  My most important conclusion comes from two findings.  First, the centers within 20 miles is important.

It’s not competition that drives price down though, it’s an indication of a good skiing culture and good conditions.  Where the people like to ski, there are ski areas.  And where people like to ski, the prices can be higher.  The second important finding is that income and population do not matter.  It does not matter if there are few people with low per-capita income.  If the people like to ski, they will ski.  Just as Charlie Yerrick, director of ski operations at Trapp Family Lodge, told me in an email, “[Price is determined more by the local culture than the income.  People who like skiing ski whether they have money or not.

MTB Thesis

Matt Briggs hails from Eastern Massachusetts where he started skiing as a freshman at Concord-Carlisle High School and joined the CSU club team as a junior. Matt was a 4-year carnival skier at Colby. His career highlights include two top-40s at nationals in 2008, and ten top-10s and three top-5s in EISA carnival racing, as well as a win and three 2nds in Eastern Cups. He is happy to continue skiing in New England with the Craftsbury Green Racing Project. This year he hopes to qualify for the US squad for U23 World Championships. Matt is also a part owner of a company, lobsterjoke.com, which is the world’s leading lobster-related joke company.

#### Matthew Briggs

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• December 17, 2009 at 12:46 pm

Great work Matt! Now I can determine if the local ski place is over/under charging me.

I’d be curious about how much different skiers would be willing to pay. It seems like \$15-20 is the going rate for a day pass, but I wonder if value is lost by not having a more flexible rate, like a 1/2 day pass or discount Tuesdays. As a group, I bet the 35+ adults are price insensitive and would pay more, but people in their 20s are broke and can’t afford to ski as much. How to capture that value?

• December 17, 2009 at 1:40 pm

Thanks Matt! I was just complaining to myself this morning about Prospect Mtn.’s rate climbing each year with no improved services. Your tables in the pdf reinforces my frustration, as they come in as one of the most over priced areas.

I live 2 miles from Maple Corners in MA. You have their snowfall at 45 and list Notchview and Prospect as 30. This is nowhere near reality as Prospect is in a honey hole and get nearly twice what Maple sees. You should double check your snowfall data.

Great overall job however and a good source for picking new places to ski.

• December 17, 2009 at 2:56 pm

Good project and well executed, Matt! You’ve approached the manner in the way of standard economics, as taught in universities. A different and I think more accurate basic way of determining prices would require access to information that owners are not usually willing to cough up. It would be the following:

Price =
Costs of land, equipment, buildings, materials, utilities, taxes,
and such divided by units of business expected (skiers, ski days,
sales/day) per time period – short or over an appropriate life span
+ Cost of labor (wages, benes, employee taxes)
+ Unpaid value added by labor (basis of gross profit – approach not taught in school)
– Interest paid on credit (if any) and marketing costs
+/- supply & demand factors (vary over time and add/subtract to sum of other factors) and any ownership idiosyncracies.

I think what you have called quality is typically built into fixed/material costs and labor costs, with some influence of and on supply/demand.

• December 17, 2009 at 3:03 pm

Sorry, correction: Strike “Interest paid on credit (if any),” and add marketing costs to the top Costs category.

• December 17, 2009 at 3:19 pm

Maybe I just missed this, but did you take rental equipment into account? It seems like that would be an important variable. Cool analysis.

• December 18, 2009 at 1:22 am

Excellent work Matt! Thanks for sharing this. Your thesis was a great read (in the opinion of a fellow “econ geek”).

I would like to make a couple of comments. First – I didn’t see that you took lighted trails into account as a “determinant of demand” variable. Some of your low quality/ high price ski centers could perhaps justify their rates if they are near population centers and offer night skiing to folks after work.

Also – you did not include skiing venues that you don’t have to pay to ski at. I understand your logic. But, it does seem that free skiing areas near pay-to-ski areas might be a significant variable. If a pay-to-ski skiing center borders a free, or cheap, skiing area (like publicly groomed park trails, school groomed free trails or groomed snowmobile trails) then this is competition that should drive the ski center’s rates down. If the rate to ski at such centers is high, then it should show up as overpriced using your equation with the free-skiing-nearby variable.

Your equation is a great rule of thumb for day pass price-reasonability determination. What would be interesting is to find the average season pass cost per day pass cost for all of your xc skiing centers. Then that multiplier could be used to determine what ski centers offer the best season pass deals (assuming some ‘x’ number of skiing days per year).

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