Recently I have been playing music (hauling instruments), and camping (hauling gear) quite a bit. A second car would have been useful. My wife and I decided it was time to get one, and the practical choice was a minivan.
My goal: to buy a minivan that was the best possible value for the money.
A vehicle is the 2nd largest purchase most folks make; I could save a lot of money by learning about the auto market and car reliability. I thought about this in terms of costs and benefits.
The biggest expense of a minivan is depreciation: the difference between your purchase price, and your eventual sale price. That’s likely to be more expensive than gas, insurance, or repairs.
Right now, cars are overpriced for the same reason houses were in 2007: too many loans. Incentives offered by automakers are sky high, and the resulting auto loan bubble may be bursting, even for used cars.
The price of a car drops 15-25% each year for the first five years. Many cars last 200K miles, or about 17 years at the common 12K-miles-a-year pace. New cars are a bad deal.
Let’s assume cars depreciate 20% a year for ~5 years, and depreciate 10% a year after that. A well-maintained car is as useful on its 10th year as its first, so we end up with this price-to-utility graph:
Assuming a vehicle will last ~17 years, the best deals are between the 6th and 10th years, the ‘middle third’ of its life.
My goal was now more refined: buy a used minivan that’s the best value for the money, one that is likely 6-10 years old.
The benefit of a van for me was obvious: it hauls people, and stuff around. I am not sentimental; a car is a metal box that moves, and fancy features are worth nothing to me.
The cost of a car is money. The benefit is the number of miles it will go before it dies. Ratios are great way to quantify value. In this case, I was looking for Benefit / Cost, or Miles Remaining / Price.
The metric I cared about was Miles Remaining Per Dollar (miles/$). The larger the number, the better the deal.
Not all cars are created equal. Even in the same vehicle segment (minivans), the available choices would differ in price, features, and reliability.
In the past I have had to rely on a composite of opinions about vehicle reliability, because good data wasn’t available. This time was different; I had a quality data source, Dashboard Light.
I started comparisons with a baseline, a ‘quick and easy’ way to do something. In this case, that was looking for a ‘certified’ used version of the most reliable minivan, a Toyota Sienna. The certification gave it a warranty and assurance it was in good repair.
30 seconds of searching found a $22K certified van would last ~143K miles, a.k.a. 6.5 miles-per-dollar.
That was the baseline: 6.5 miles/$
I looked at other minivans:
Going strictly by the numbers, the Dodge van was the best deal. However, other data sources kept reporting Dodge vehicles needing expensive repairs as they aged, so I stuck with the reliable choice.
My goal was now clear: buy a used Toyota Sienna that’s the best value for the money.
Finding a good deal on a used car was like looking for a needle in a haystack.
I wanted to be lazy when searching for a car, so I wrote the math into a Google Spreadsheet, and added every Toyota Sienna for sale within 200 miles of Seattle that had less than 160K miles.
After updating this spreadsheet for a week, I ran a quick check. What age of vehicle should I be expecting to purchase? What price range should I expect to pay?
The best deals were between 7 and 13 years old, meaning they were 2004 to 2010 model year vehicles. There were also many used cars that were a worse deal than the baseline; many were wildly overpriced.
The best deals were between $6K and $15K, with most right above $10K.
With this spreadsheet, I could find that elusive needle, by using math to burn down the haystack.
It was time to find and buy a specific minivan.
The best deal when I started was at an official Toyota dealer, Toyota of Bellevue. It was a 2006 van with 93K miles for $10.2K, meaning it had a miles / $ value of 6.6. That was slightly better than our baseline of 6.5. It also had a clean Carfax.
I went out, took the vehicle for a test drive, and it drove well. I negotiated the sales guy down a few hundred dollars for good measure.
It was extremely helpful to have a list of other deals on a Google sheet, because I could pull it up on my phone during negotiations. It’s hard for a salesman to say, “You’ll never find a deal this good” when I can point to 4 other current listings that are almost as good.
Another useful metric, “Best Deal Price”, showed how the maximum to offer for a vehicle and still have it be the best deal available. This was useful during negotiations.
However, I ran into a catch: the manager refused to allow the vehicle to be inspected by an independent mechanic. Refusing to allow a potential customer to pay for an inspection (in this case, an 11-year-old van) is shady as hell.
The sales folks had lots of ‘reasons’, which boiled down to “trust us because we’re big, and we sell lots of cars”.
I had read in many places to never to buy a car without an independent inspection, so I walked away. Afterwards I kept receiving phone calls & text messages from pushy salesmen, who suggested I was being paranoid.
Lesson: Even the big dealers can be unreasonable or scam-y. Considering the money involved, never trust the seller.
For the next 5 days, I called listing after listing, and encountered the same result: the vans had been sold. Good deals (7 miles / $ and higher) sold within a day or two.
I had to change my strategy. I couldn’t search in the evening and call in the morning; I had to check multiple times a day, and call immediately after finding a good deal.
I found my next good deal several days later, at Rich’s Car Corner, a nearby used-car dealership.
The vehicle was a 2007 Sienna with 84K miles for $8500. In other words, a value of 8.9 miles/$. It was the best deal yet.
An AutoCheck history showed the vehicle had one accident 8 years ago, and had been well-maintained since. That was promising, so I went to the dealership, where there were a few promising signs:
The value was then 8.2 miles/$. That was still the best deal I’d found.
Now came the trickiest part: negotiation. I knew I had a great deal available, using information the dealer didn’t have. That gave me an edge. I was able to negotiate the price down to $8000, arguing that it was an old van that needed hundreds of dollars of repairs.
A Bad Taste
Finally came the challenge I wasn’t prepared for: paperwork scams. The dealer had a pretty bad reputation for selling junkers, but it turns out they also tried to make money in other ways:
Worst of all was an ultimatum: sign an arbitration agreement, “or else we can’t legally transfer the title to you”. Talking around that one was the last hurdle.
The purchase process complete, I had my new van, and a bad taste in my mouth.
I took the van to my favorite mechanic, who came back with bad news. The earlier mechanic had missed that the leaks were symptoms of larger issues. The van needed new front suspension, steering racks, front brakes, and alignment. The repairs cost $3000, so I ended up spending $11000.
After all that, I had purchased a minivan with a value of 6.9 miles / $.
Purchasing a vehicle in a data-driven way was an interative process:
I was able to avoid several traps by knowing they existed, and ways around them:
|Dealers will pressure you; they have quotas to meet||Be prepared to walk away from a bad deal|
|Dealers are more desperate near the end of the month/quarter||Go then, and bargain harder for a deal. You have leverage then|
|Auto loans are a pain, and sometimes predatory for minorities||Save up, and pay in cash|
|Auto loans aren’t available for older (cheaper) cars||Save up, and pay in cash|
|The dealers near you don’t have the best deal||Search widely. Driving 200 miles to buy a car is a cheap way to save \(\)|
|Fancy features are expensive||Buy them later, if you still want them|
Along the way came several interesting lessons:
I’m going to wrap up this long-winded post with one of my favorite quotes:
“At the heart of science is an essential balance between two seemingly contradictory attitudes-an openness to new ideas, no matter how bizarre or counterintuitive they may be, and the most ruthless skeptical scrutiny of all ideas, old and new. This is how deep truths are winnowed from deep nonsense.” - Carl SaganPermalink
Everyone I know has trouble cooking. In my last blog post I looked at the most common ingredients in 24K recipes. I realized that there was a flaw in my previous post: it treated all recipes equally.
Recipes are not created equal. Some are more popular, for good reason. My recipe data set has information on recipe ratings, which are a good proximate metric for popularity. I used the ratings to compute a popularity ‘score’.
Data to the rescue, once again!
Of the ~103 million recipe-ingredient-rating combinations, half of them are in just 25 common ingredients. That’s a smaller number than the 50 ingredients needed in the previous post, probably because popular recipes use more similar ingredients than the average.
Let’s stick with the 50 most common ingredients for now, which cover 61% of 103 million recipe-ingredient-rating combinations.
This is great news for grocery shopping. We can make many popular recipes using the same number of ingredients.
Several common ingredients in the last post aren’t as popular this time around: nutmeg, pecans, potatoes, red bell peppers, thyme, and vinegar.
Conversely, several less common ingredients are more popular now: chili powder, lean beef, margarine, mozzarella cheese, paprika, and chocolate chips.
I love the Pareto Principle, the “law of the vital few”. In this case, 79 ingredients out of 11K recipes cover 70% of the the recipe-ingredient-rating combinations in our data set. That’s only 0.68% of the ingredients in the list. A ‘vital few’, indeed.
Note: red means perishable, blue means nonperishable
This is the second of several posts on food and data, and there is more to come. Stay tuned!Permalink