You’re looking to lease a car and you stop by your local dealership to test drive the one you want, but then it comes to the negotiation part and you’re a bit lost. Most likely the salesperson will present to you a paper with four boxes in it with numbers that don’t exactly make much sense, but he tells you that he can put you in the car for $x per month.
You look at this paper and the numbers aren’t really connected in any way and you’re not sure what you’re negotiating, except maybe the final monthly lease price.
You really only need to ask the salesperson for three things:
I recently helped a friend out with a lease negotiation and it got frustrating because every time I asked the salesperson for those three things, he would disappear for 10 minutes and come back with a lower monthly payment amount. I would then remind him that he said he would go and get the numbers I asked for, and so he would go back and leave us sitting there for 10 minutes and come back with another price.
Finally it took some getting agitated before he came back with the numbers I asked for.
Here’s what those numbers mean:
Now that you’ve insisted on these thee numbers from the salesperson, you can figure out how much you’re paying for the lease (and also understand if they’re trying to take advantage of you).
First, the sale price of the car. For example, you found a car that shows an MSRP of $25,000 in the window. Of course you shouldn’t pay this amount. In my experience, it’s not too hard to get a final sales price of 10% (or more) below that number. So in this example, we negotiate a price of $22,500.
You’ve also got the residual value, which is set by the financing company. In our example, let’s say the residual value is $15,000.
A lease means that you’re paying for the difference between the sale price and the residual value, because the residual value is how much the car is worth at the end of the lease. In this case, you’re paying $7,500 over three years (or however long the lease is). $7,500 divided by 36 months is about $208 per month to lease this car. (As a side note, it’s usually a bad idea to get a lease longer than 36 months).
But wait, this is where the money factor comes into play. What does that weird small decimal mean? For example, .00350. How do you put that into an easier-to-understand form? Multiply it by 2400. Therefore, a money factor of .00350 is the equivalent of an 8.4% interest rate. Wow, that’s high! Particularly with car purchase deals these days of 0%. This is why you need to understand the money factor and push back when it’s too high.
In the example above, when I was helping my friend with a car lease, the initial money factor we were quoted was .00275. That sounds low, right? Well that works out to an interest rate of 6.6%. That still high. Just by questioning that number we were able to get the dealership to reduce it to .001375, or the equivalent of 3.3%.
Now we’re talking. How does this relate to the lease monthly payment? Keep in mind that you’re going to apply the interest rate to the entire value of the vehicle, not just the amount you’re paying to lease it.
For a rough way of figuring the interest portion of your lease payment, do the following:
That’s our lease payment! $311.13.
But what if we negotiate down the money factor? Let’s try the money factor that I was recently able to get for my friend:
Just by negotiating down our money factor we reduced our payment by $51.57. You can see how much of a difference that makes.
So, next time you want to lease a car, insist on knowing the sale price, money factor, and residual value. You now know how to do the math and understand exactly what the dealership is selling you. Good luck with your next car lease!
The National Transit Database site has been updated with February 2018 data. In looking at most cities in Michigan, transit ridership continued to decline. For instance, February ridership in Grand Rapids, Detroit, and Kalamazoo all declined by over 8%. Ann Arbor saw a decline of about 2%, and Lansing saw an increase of just over 1%.
The Detroit Free Press also ran a story today, using my NTD site, and pointed out that the Detroit People Mover costs over $25 million to operate (and consequently loses nearly $10 per rider).
The Mackinac Center has been running a series of stories, using my site and its data.
Oh, and the Detroit People Mover’s ridership declined about 40% in February. Interestingly, the Detroit People Mover’s average trip length is 1.4 miles. The average cost to taxpayers of each trip is $9.95. How much is a 1.4 mile uber ride?
I created a new site that helps visualize data from the National Transit Database, which historically has made its data very difficult to parse.
For a little background: all transit agencies which receive federal funding must report a certain amount of data to the Federal Transit Administration’s National Transit Database. The FTA publishes two series of data: first, a spreadsheet of monthly ridership data, which usually lags by about two months. This spreadsheet has limited financial, ridership, and vehicle data for the each agency’s fiscal year, usually about one-and-a-half to two years prior to the present date. Second, the full NTD, which reports a high level of data about each agency, spread out across about 20 excel spreadsheets. The full NTD for each year (2016 is the latest available) contains financial (operating and capital), ridership, fuel/energy usage, and vehicle data.
In the past, when comparing different modes of transit and their financial costs, it has taken a lot of time and effort to just parse the spreadsheets and find the data you need.
My own National Transit Database site, which went live this week, is a start in parsing that data and making it more available. It’s meant to be user-friendly and visually informative.
For instance, take a look at the page for the Interurban Transit Partnership (The Rapid), in Grand Rapids, Michigan. I created a chart that shows overall ridership across all modes of transit provided by The Rapid, as well as breakdowns for each mode.
For each individual mode of transit (for instance, bus, bus rapid transit, demand-response, etc) there is a tab with financial data. This tab pulls from both the monthly ridership spreadsheet and the full NTD data for the latest fiscal year (in this instance, 2016). Included is ridership, the number of passenger miles, average trip length, total operating spending, total fares received, total depreciation, and a breakdown of the cost of providing each ride and the total amount of subsidy required to provide that ride.
For the depreciation number, I had to estimate the amount of depreciation attributed to each transit mode because for some reason the NTD spreadsheets don’t break down the depreciation amount on a per-mode basis. Oddly, that’s one of the few bits of data in the NTD that isn’t broken down by mode. You can review the depreciation data in the Operating Expense Reconciliation spreadsheet. Therefore, I estimated each mode’s share of depreciation by allocating the depreciation amount based on the number of trips each mode represents as a portion of the total number of trips provided by that transit agency. Adding depreciation gives a much more accurate picture of the cost of providing a service because that includes a fair cost of the capital portion of each mode’s cost. Simply referring to the operating cost per ride (as many transit agencies do, including the NTD) paints an inaccurate picture.
One important thing to note, when reviewing the data, is that overall public transit ridership seems to have begun a decline in 2014 for many, if not most, transit agencies. Randal O’Toole has been reporting on this trend over at his Antiplanner blog.
The final item I’d like to point out is the monthly ridership change data. Simply presenting raw monthly ridership numbers would be very noisy and not very helpful or informative. Instead, it’s helpful to see how ridership is changing over time, even though it has a lag of a couple of months.
I plan to add more features, such as combined UZA ridership numbers, much more financial data, energy usage data, capacity usage data, and more. Feel free to contact me if you have suggestions.
A federal judge unsealed the source code for a software program that was used to compare DNA samples in New York City’s crime lab.
In July 2016, Judge Valerie Caproni of the Southern District of New York determined in U.S. v. Johnson that the source code of the Forensic Statistical Tool, a genotyping software, “is ‘relevant … [and] admissible’” at least during a Daubert hearing—a pretrial hearing where the admissibility of expert testimony is challenged. Caproni provided a protective order at that time.
This week, Caproni lifted that order after the investigative journalism organization ProPublica filed a motion arguing that there was a public interest in the code. ProPublica has since posted the code to the website GitHub.