Quantifying New York’s 2009 June gloom using WeatherBill and Wolfram|Alpha

In the northeastern United States, scars are slowly healing from a miserably rainy June — torturous, according to the New York Times. Status updates bemoaned “where’s the sun?”, “worst storm ever!”, “worst June ever!”. Torrential downpours came and went with Florida-like speed, turning gloom into doom: “here comes global warming”.

But how extreme was the month, really? Was our widespread misery justified quantitatively, or were we caught in our own self-indulgent Chris Harrisonism, “the most dramatic rose ceremony EVER!”.

This graphic shows that, as of June 20th, New York City was on track for near-record rainfall in inches. But that graphic, while pretty, is pretty static, and most people I heard complained about the number of days, not the volume of rain.

I wondered if I could use online tools to determine whether the number of rainy days in June was truly historic. My first thought was to try Wolfram|Alpha, a great excuse to play with the new math engine.

Wolfram|Alpha queries for “rain New Jersey June 200Y” are detailed and fascinating, showing temps, rain, cloud cover, humidity, and more, complete with graphs (hint: click “More”). But they don’t seem to directly answer how many days it rained at least some amount. The answer is displayed graphically but not numerically (the percentage and days of rain listed appears to be hours of rain divided by 24). Also, I didn’t see how to query multiple years at a time. So, in order to test whether 2009 was a record year, I would have to submit a separate query for each year (or bypass the web interface and use Mathematica directly). Still, Wolfram|Alpha does confirm that it rained 3.8 times as many hours in 2009 as 2008, already one of the wetter months on record.

WeatherBill, an endlessly configurable weather insurance service, more directly provided what I was looking for on one page. I asked for a price quote for a contract paying me $100 for every day it rains at least 0.1 inches in Newark, NJ during June 2010. It instantly spat back a price: $694.17.

WeatherBill rainy day contract for June 2010 in Newark, NJ

It also reported how much the contract would have paid — the number of rainy days times $100 — every year from 1979 to 2008, on average $620 for 6.2 days. It said I could “expect” (meaning one standard deviation, or 68% confidence interval) between 3.9 and 8.5 days of rain in a typical year. (The difference between the average and the price is further confirmation that WeatherBill charges a 10% premium.)

Below is a plot of June rainy days in Newark, NJ from 1979 to 2009. (WeatherBill doesn’t yet report June 2009 data so I entered 12 as a conservative estimate based on info from Weather Underground.)

Number of rainy days in Newark, NJ from 1979-2009

Indeed, our gloominess was justified: it rained in Newark more days in June 2009 than any other June dating back to 1979.

Intriguingly, our doominess may have been justified too. You don’t have to be a chartist to see an upward trend in rainy days over the past decade.

WeatherBill seems to assume as a baseline that past years are independent unbiased estimates of future years — usually not a bad assumption when it comes to weather. Still, if you believe the trend of increasing rain is real, either due to global warming or something else, WeatherBill offers a temptingly good bet. At $694.17, the contract (paying $100 per rainy day) would have earned a profit in 7 of the last 7 years. The chance of that streak being a coincidence is less than 1%.

If anyone places this bet, let me know. I would love to, but as of now I’m roughly $10 million in net worth short of qualifying as a WeatherBill trader.

Chris Masse has the scoop (once again proving how indispensable he is) on a new real-money prediction market coming soon, one of the few with the CTFC’s blessing to operate in the United States: The American Civics Exchange. Their tag line focuses on the insurance angle: “Your greatest financial risks may be hiding in plain sight — market-based solutions for political risk management”.

Meanwhile, Carlos Saieh, a sharp student in Justin Wolfers’ class where I just gave a guest lecture, found an apparent pricing bug in another insurance-oriented prediction market, WeatherBill (proving how indispensable attentive students with laptops and wifi are):

WeatherBill pricing mistake

Let’s see: for a mere $770, you can purchase a contract that pays out at most $700 in the absolute best case, possibly much less. Hmm, let me think about that one.

Finally, a financial contract that makes mortgage-backed securities look good.

Centrist Messenger How It Works SnippetHere’s a brilliant idea: Centrist Messenger let’s you buy political ads with a money-back guarantee. You pay only if your preferred candidate wins. If the other candidate wins, you get your money back.

Centrist Messenger backs the guarantee with contracts purchased from intrade, in the same way that Priceline backs its “Sunshine Guarantee” with contracts from WeatherBill. (So presumably fully insured ads cost about twice as much as uninsured ads.)

In addition, the ads you buy can’t be too partisan:

Centrist Messages can … make strong advocacy of a position and candidate. However, this advocacy cannot demonize the other side, focus solely on personality, or make false representations of the candidates’ positions.

I’ll add Centrist Messenger to WeatherBill, Priceline, Yoonew, and FirstDIBZ (was TicketReserve) as companies fashioning creative ways to package and sell “markets in uncertainty” in the US amid a challenging legal and regulatory landscape.

What other useful and/or fun ways can you imagine re-packaging gambles as either insurance or contingent goods? Here are some of my own brainstorms:

  • Buy a ticket to a sporting event whose cost is refunded if your team loses.
  • Buy a “streak ticket”: entitles you to a ticket to the next game as long as your team keeps winning. (Variant: “K-loss ticket” entitles you to tickets until your team loses K times.)
  • Buy a “playoff run ticket” which gives you tickets, flights, hotel, etc. for the duration of your team’s playoff run. In other words, as long as your team keeps winning, you keep getting tickets, hotel, and flight to the next game. You may be able to buy this at the beginning of the season cheaply since it’s worth nothing if your team does not make the playoffs.
  • Buy “price drop” insurance: If that precious electronic gadget you just bought (read: iPhone) drops in price within N days, get K times your money back.

WeatherBill let’s you construct an enormous variety of insurance contracts related to weather. For example, the screenshot embedded below shows how I might have insured my vacation at the New Jersey shore:

Read this document on Scribd: WeatherBill Example Contract

For $42.62 I could have arranged to be paid $100 per day of rain during my vacation.

(I didn’t actually purchase this mainly because the US government insists that I am a menace to myself and should not be allowed to enter into such a dangerous gamble — more on this later. And as Dan Reeves pointed out to me, it’s probably not rational to do for small sums.)

WeatherBill is an example of the evolution of financial exchanges as they embrace technology.

WeatherBill can be thought of as expressive insurance, a financial category no doubt poised for growth and a wonderful example of how computer science algorithms are finally supplanting the centuries-old exchange logic designed for humans (CombineNet is another great example).

WeatherBill can also be thought of as a combinatorial prediction market with an automated market maker, a viewpoint I’ll expand on now.

On WeatherBill, you piece together contracts by specifying a series of attributes: date range, place, type of weather, threshold temperature or participation level, minimum and maximum number of bad-weather days, etc. The user interface is extremely well done: a straightforward series of adaptive menu choices and text entry fields guide the customer through the selection process.

This flexibility quickly leads to a combinatorial explosion: given the choices on the site I’m sure the number of possible contracts you can construct runs into the millions.

Once you’ve defined when you want to be paid — according to whatever definition of bad weather makes sense for you or your business — you choose how much you want to be paid.

Finally, given all this information, WeatherBill quotes a price for your custom insurance contract, in effect the maximum amount you will lose if bad weather doesn’t materialize. Quotes are instantaneous — essentially WeatherBill is an automated market maker always willing to trade at some price on any of millions of contracts.

Side note: On WeatherBill, you control the magnitude of your bet by choosing how much you want to be paid. In a typical prediction market, you control magnitude by choosing how many shares to trade. In our own prediction market Yoopick, you control magnitude by choosing the maximum amount you are willing to lose. All three approaches are equivalent, and what’s best depends on context. I would argue that the WeatherBill and Yoopick approaches are simpler to understand, requiring less indirection. The WeatherBill approach seems most natural in an insurance context and the Yoopick approach in a gambling context.

How does the WeatherBill market maker determine prices? I don’t know the details, but their FAQ says that prices change “due to a number of factors, including WeatherBill forecast data, weather simulation, and recent Contract sales”. Certainly historical data plays an important role — in fact, with every price quote WeatherBill tells you what you would have been paid in years past. They allow contracts as few as four days into the future, so I imagine they incorporate current weather forecasts. And the FAQ implies that some form of market feedback occurs, raising prices on contract terms that are in high demand.

Interface is important. WeatherBill shows that a very complicated combinatorial market can be presented in a natural and intuitive way. Though greater expressiveness can mean greater complexity and confusion, Tuomas Sandholm is fond of pointing out that, when done right, expressiveness actually simplifies things by allowing users to speak in terms they are familiar with. WeatherBill — and to an extent Yoopick IMHO — are examples of this somewhat counterintuitive principle at work.

There is another quote from WeatherBill’s FAQ that alludes to an even higher degree of combinatorics coming soon:

Currently you can only price contracts based on one weather measurement. We’re working on making it possible to use more than one measurement, and hope to make it available soon.

If so, I can imagine the number of possible insurance contracts quickly growing into the billions or more with prices hinging on interdependencies among weather events.

Finally, back to the US government treating me like a child. It turns out that only a very limited set of people can buy contracts on WeatherBill, mainly businesses and multi-millionaires who aren’t speculators. In fact, the rules of who can play are a convoluted jumble that I believe are based on regulations from the US Commodity Futures Trading Commission.

Luckily, WeatherBill provides a nice “choose your own adventure” style navigation flow to determine whether you are allowed to participate. Most people will quickly find they are not eligible. (I don’t officially endorse the CYOA standard of re-starting over and over again until you pass.)

Even if red tape locks the average consumer out of direct access, clever companies are stepping in to mediate. In a nice intro piece on WeatherBill, Newsweek mentions that Priceline used WeatherBill to back a “Sunshine Guaranteed” promotion offering refunds to customers whose trips were rained out.

Can you think of other end-arounds to bring WeatherBill functionality to the masses? What other forms of expressive insurance would you like to see?

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