Does it make sense for OEMs to be investing in AVs and urban mobility products?

TLDR: AVs and robot taxi services are not a near-term (3-7 year) threat to existing OEM business lines, but they are a longer-term (7+ year) threat to OEMs; and this is why they are investing so heavily in AV technology.


Investments being made by large car manufacturer (OEMs) in self-driving autonomous vehicles (AVs) and urban mobility solutions are receiving a lot of attention these days, and for good reason. AVs promise significant societal benefits and economic opportunity, but I want to contemplate and deconstruct why OEMs are making these investments.  What is the business rationalle.

The going sentiment or explanation seems to be A) that AVs and these urban mobility solutions threaten the OEMs existing business and/or B) that the OEMs’ ability to design, manufacture, and distribute vehicles makes them a natural necessity in the future value chain of AVs. While I agree with the latter explanation, I think there is room to elaborate  on the former.

 

Vehicle sales will not decline significantly over near-term (3-7 years) because of robot taxi services.

First, the majority of cars and trucks are sold to consumers and businesses located outside dense urban city centers. Second, as I’ve explained in a previous post, robot taxi fleets will be economically constrained–over the near-term–to being deployed within dense urban city centers. As a result, over the near-term, if anywhere, robot taxi services will reduce car ownership rates within dense urban centers, but car ownership rates in urban centers are already low, therefore little impact will be made on the number of vehicles sold by OEMs. Simply put, car ownership is already low in the areas/populations wherein AV-based robot taxi services will be launched over the next 3-7 years. It won’t be until robot taxi services slowly make their way out to lower density suburban areas that car ownership there starts to be impacted.

I would suggest that vehicle sales by volume are declining not because of ride-hailing services, but rather vehicle build quality is improving. Cars are being built better and are lasting longer, and hence need to be replaced less frequently. Again, for most people living in the suburban US, ride-hailing is not a substitute for car ownership. And for most people living in urban environment, car-sharing (eg. GetAround) and short-term rental (eg. ZipCar) services are more likely to be a substitute* for car ownership than ride-hailing service is. Sure, hailing a ride is better than renting a car for two hours in most situations (that’s why ZipCar’s business is declining significantly), so ride-hailing services might be substitute for some urban market segments but I would argue that most car owners in urban areas own their car because they have consistent weekly inter-city transportation needs. Most people decide to buy/lease a vehicle because they have consistent transportation needs that cannot be solved with a better alternative. Getting to/from work or shuttling the kids to/from five days a week, for example.

The same holds true for suburban car owners. If you live outside dense urban areas and decide to buy/lease a vehicle, it is because you have consistent transportation needs that cannot be solved with a better alternative. Getting to/from work or shuttling the kids to/from five days a week, for example. For most people, car ownership is a better alternative for these consistent trips than Uber and Lyft.

As a result, I would argue that car ownership has not been impacted significantly by ride-hailing services–in neither urban nor suburban population.

Ride-hailing platforms have become a complementary transportation solution; not a substitute. They have become a substitute only for traditional taxi services. For urban dwellers, it complements mass-transit utilization. For suburban dwellers, it complements car ownership, as a solution for infrequent trip needs such as getting to/from the airport or nights on the town to avoid drinking and driving.

Although I don’t think robot taxi fares will come down to the level of mass-transit fares, the already low car ownership rates in urban populations means that I see robot taxi trips being used by consumers as a substitute for an occasional mass-transit trip; not permanent car ownership.

It’s a long-term play for OEMs.

Robot taxi services are not a near-term threat to OEMs, but I do think they are a long-term threat (7+ years). For the reasons stated above and in this post, robot taxi ride fares over the near-term will remain prohibitively high for suburban populations, where car ownership is highest. Eventually, in the longer-term (7+ years), the technology and economic constraints will change such that serving more sparsely populated areas can be done profitably, and robot taxi platforms will start serving these areas.

This is why I think hundred-year-old OEMs are investing in urban mobility and AV tech. OEMs don’t and shouldn’t care about being relevant over the next decade; they’re aiming to be relevant over the next 100 years.

 

*According to Jeffrey Rifkin, “Some 800,000 individuals in the U.S. are now using car-sharing services. Each car-share vehicle eliminates 15 personally owned cars.”   

fundamentally flawed bank underwriting

A lot of friends, who weren’t bankers, have asked me to explain the whole financial crisis thing to them.  So far, I do not know of anyone who has made the explanatory point I share below; but I am sure someone has.  Regardless of whether someone has published this explanation, one thing about the financial crisis really has me confounded.

I just find it hard to believe that professional bankers with decades of banking experience didn’t realize they were helping “blow up the bubble” and push home prices higher with the generous liquidity they were providing to home buyers.  Lenders providing financing to consumers enabled consumers to then make  higher bids on real estate properties, and these generous bids then become the sales prices at which the property was sold, which is then fed back into the bankers financing decisions in the form of “comp” values, which bankers use to determine how much they can lend against the property, which is the collateral securing the loan.

So what’s the solution?   Shouldn’t real estate lending practices simply be a simple function of the borrower’s ability to pay the proposed mortgage; not on a forecasted sales price the property could be expected to sell for at some point in the future?   That is, bankers can simply look at the borrowers income and expenses and determine how much the borrower can afford in terms of monthly mortgage payments; and then back into how much the borrower can afford to borrow given the market interest rates and time duration of the loan.

Had lending practices been based on these exogenous means for affording real estate properties, I think it would have been very easy to see the “bubble” forming, because a “safe demand” (i.e. people’s ability to afford mortgage payments) for real estate properties could have been measured by measuring people’s income and wealth, minus the value of the real estate properties owned by people.

Paradoux of Choice

In our quest to maximize freedom, we maximize choice, but is this maximization of choice leading to maximization of happiness?

In his TedTalk, Psychologist Barry Swartz helps me understand why I hate large restaurant menus.  He articulates nicely, ideas that strongly suggest maximizing choice does not maximize our happiness; rather it is in fact reducing our happiness!   I don’t think there is anything more important to ponder than things that directly affect our collective (he mentions the possibility of Pareto Inefficient economies) and individual  happiness, and I highly recommend watching the entire 19 minute TedTalk (below); but some of the major takeaways are below.

The cost of choices includes:

  • paralysis, the resulting procrastination, and the resulting consequences of not taking action create huge costs for individuals
  • the opportunity costs of not choosing another available choice, subtracts from the satisfaction of making the choice that I made
  • with so many choices, we expect one of those choices to be a perfect fit; and high expectations that prevent us from being presently surprised and “The key to happiness is low expectations!”

Some takeaways:

  • “Everything was better back when things were worse”
  • “…pretty confident we have long since passed the point [number of choices] where choices are adding to our welfare”

Bad credit application methodology

Whenever I recognize where a part of world is such that we can’t access something when we most need that something, and–worse–that we have best access to that thing when we least need it, I am confused. This kind of situation seems wrong.

So this brings me to a small example of such a paradox: the credit credit card application process. I don’t understand why the consumer credit application process is not more sophisticated in the sense that credit card companies can issue credit cards soley to refinance (a.k.a transfer) a balance from another card, allowing the individual who most needs a lower interest rate to gain access to that lower rate. this I what I wanted to do with one of the banks that i do business with a couple of days ago. Unfortunately, for me and the bank that could have not only gained my business but taken it away a competitor, the customer service representative who was taking my application was unable to specify anywhere in my application that the application was not be submitted to increase my total credit, but rather that it was being used merely to replace an existing credit account. Thus, although the risk profile would have actually improved upon securing a less expensive (i.e. lower interest and financing fees) credit account, the bank denied my application, because it could only assume that I was applying for the credit card because my other two cards were maxed out.

To a degree, I understand why lenders have to charge higher interest rates to those borrowers with more risky profiles; but one could argue that this model actually causes that which it wishes to avoid: unpaid/uncollectible loans. Why? because the borrower ran out of cash before he was able to fix his situation either with more income or lower expenses. Had he a lower rate, he might have been able to make a couple more monthly payments; and these two months might have given him the time he needed to make adjustments to his life and expenses.

I worked in the high-yield lending business for 3.5 years, and I saw predatory lending at it’s worse, so I know that more often than not, the reason lenders charge higher interest and fees to certain borrowers is because they can; not because it is neccessarilly right. For example, I have seen lenders create a lending product that frequently took advantage of borrowers ignorance and literally was lending the borrowers money back to the borrower.

A bank needs to create a credit product that gives it the opportunity to refinance other credit card balances while keeping the risk profile of the borrower in check by requiring the borrower close the card account from which the balance was transferred, and maybe even prohibit that the borrower not open any new lines of credit until the borrowers financial situation markedly improves. I think this could be a win-win opportunity for both th lender, borrower, and society.

Redfin

Use Redfin.com to find bargain home prices:

  • Use the Redfin search window to find a neighborhood by name or ZIP code. On the “Overview of homes for sale” page that appears, click “reduced listings” to show a list of price-reduced homes in the area.
  • On Redfin, at the bottom of each home’s listing page, see “listing price history,” showing the dates and amounts of any price reductions.
  • Farther down the same page, you’ll find a bar graph labeled “Should I wait for a price reduction?” This tells you how long homes are staying on the market in the neighborhood and at what point sellers are dropping their prices. You can get an idea if the time is right, given local trends, to make a lower bid. (Read more about this tool here.)