Thursday, July 31, 2014

Buying a Car With Data Science!

Forget Big Data, here's how I used small data and stupid simple tools to make buying a car easier for me to stomach.

When your family's size increases past the ⌊E[#number of children]⌋ in America - you need a bigger vehicle. Given that we now have a family of 3, and that the 3rd child will need a carseat in a few months, it was time to buy a family van.

I'm a big fan of staying out of debt and so I had a fixed budget with which to acquire a vehicle - a used vehicle. A great place to start looking is with the AutoTempest search engine, which aggregates data from lots of different sites. The problem is that it's difficult to know how much you should pay for any given vehicle. If you're buying new you can check something like TrueCar and there's resources like Kelly Blue Book, NADA and Edmunds but from past used buying experience those services tended to underestimate the actual costs with most vehicles I've bought, and while some folks love to negotiate, I find it difficult without any "ground truth" to base my asking price off of.

I toyed around with the idea of doing a full-fledged scrapping of data but it just wasn't worth the time, since I was under a deadline. Instead I took the approach of least resistance and asked my wife to pull together basic info on 20 vehicles matching our criteria - year, milage and asking price. Together we stuck it into a Google Document and plotted the results:

To my surprise and delight, there seemed to be two distinct collections of data - an upper and a lower priced section. Since Google Docs doesn't provide any easy way to put in a regression line I then moved over to Excel and added those in:

The data points highlighted in green were ones that I was considering. Suddenly I now had isolated two vehicles that were "over priced" and ripe for easy negotiation. I also chose the highest price - and lowest milage vehicle - and asked for a significantly lower price on a whim.

The additional data I'd gathered gave me confidence when negotiating. I contacted 2 dealerships with my data and the price I wanted to pay (slightly under the lower price regression).  Ultimately the 1st person I'd talked to accepted my offer, which I new was good from my data and I didn't have to worry about whether or not I should keep negotiating.

What my data DIDN'T do for me:
  • The data didn't impress anyone
  • The data didn't magically make people accept my offers
  • The data didn't make buying a car easy
What my data DID do for me:
  • Took away the awful feeling of not knowing.
  • Gave me confidence when negotiating offers.
  • Let me quickly see which vehicles I should pursue and which to not focus on.
Now I'm driving a baller ... van. Cool indeed.


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