Say Rent to the Tent?

I’m at the age where all my travel plans this year involve wedding-related events. I personally love it. I’m a sap for romance, I love a good open bar, and weddings are endless fonts of quantitative inspiration. (For example, FiveThirtyEight has a fun post about most-played songs at wedding receptions.)

So when my friends Eling and Dan shared their wedding tent rental dilemma, I jumped at the chance to wrap my brain around some data. Here’s the situation, paraphrased from an email from Eling:

Most tent companies require you to order your tent at least five days before the wedding. Tents cost upwards of $1,500. The forecast is looking good as of now, but forecasts can be so unreliable in Miami. What should we be willing to pay to ensure our guests don’t get rained on? Is there a magical formula that can calculate when it’s worth it?

I love this question. There are actually two parts to it. One, how reliable is a 5-day forecast? And, two, how can the couple use the information available to decide on this big ticket item?

For #1, I gathered data about Saturdays (i.e., most popular wedding days) from 2011-2017 in Miami in March. I got data on whether it actually rained from the Weather Underground’s weather history site. The trickier part was figuring out what had been predicted for that day five days earlier. (I’m guessing for-profit weather sites don’t want people criticizing them for poor predictions, so don’t make these easy to mine.) But after some deep Googling just shy of the Dark Web, I found a workable resource from the U.S. National Oceanic and Atmospheric Administration (NOAA). Using their Weather Prediction Center Medium Range Archive, I could look up the date of the Monday that preceded the wedding and see what the predicted precipitation had been for the Miami area.

I kind of love the behavioral economics lessons in this graph. Let’s say you’re a couple who faced a forecast of 0-10% and, therefore, you “gambled” not to rent. On 9 out of 10 of these Saturdays, their gamble worked out. Spend that money on your honeymoon, lovers! But does that mean the  3/29/14 couples who ended up with 0.36 inches of rain make a bad choice if they, like the other 0-10%ers, didn’t rent? Not necessarily! They had the exact same information as the other 0-10% couples. The chips just didn’t fall their way.

I mean, think about it the other way. If the forecast was for 80-90%, most people would be safe and rent a tent. But there are probably couples who “gambled” not to rent, and it turned out not to rain. Does that mean they’re meteorological savants? Unlikely. I would argue that they made a bad choice even though they had a good outcome. There’s a principle in behavioral economics that “one should be judged not by the outcome of a decision, but by the process that led to that decision.”

That brings us back to #2: what is a good process (a “magical formula”) for making this decision? I propose using the tool below, a Tableau interactive which finally puts all those years of econometrics to good use:

Fortunately, March is one of the least rainy months in Miami; so chances are, Eling and Dan will have a clear forecast and will only have to make decisions on the left-hand, sunny-forecast side of this graph. But this “fortune” is also, unfortunately, the grey area. Choosing not to get a tent will always be a gamble.

How willing you are to make this gamble depends on whether you’re willing to accept a little risk. And this risk-averseness is harder to model without doing legit tent consumer research. While I added some guidance into the interactive, I couldn’t figure out a way to make this more cut-and-dried. Sorry, dudes.

Would love to hear your thoughts, dataheads! Is there a way to make the “grey area” more black-and-white? Do you have any good weather data resources?