Covid-19 International Dashboard April 21

The Covid-19 case rate for 55 selected countries and the EU including Apple Mobility Trends for driving. All data is 7-day average.

Red line is case rate in cases/day starting at 30 cases/day.

Black line is Stringency Index which is inverse of Apple Mobility Trends (lower bound means no change in mobility, upper bound is zero mobility).

Color coding: 

  • Green: Late Stage (>50% post-peak)
  • Blue: Middle-Late Stage (~50% post-peak)
  • Orange: Middle-Early Stage (peaked)
  • Pink: Early Stage [or indeterminate] (pre-peak)

Download link for full resolution:

Sources: Data is sourced from the European Centre for Disease Prevention and Control (ECDC) via Our World In Data. Mobility data is sourced from Apple Mobility Trends which tracks change in Apple Maps routing requests.

Covid-19 US Dashboard April 20 (Updated)

The Covid-19 case rates for 48 selected US States or Territories. All data is 7-day average.

Case rate is cases/day starting at 30 cases/day.

Link to full-size image:

Color coding: 

  • Green: Late Stage (>50% post-peak)
  • Blue: Middle-Late Stage (~50% post-peak)
  • Orange: Middle-Early Stage (peaked)
  • Pink: Early Stage [or indeterminate] (pre-peak)

Sources: US States data is sourced from US CDC via The New York Times.

Covid-19 International Dashboard April 20

The Covid-19 case rate for 55 selected countries and the EU including Apple Mobility Trends for driving. All data is 7-day average.

Red line is case rate in cases/day starting at 30 cases/day.

Black line is Stringency Index which is inverse of Apple Mobility Trends (lower bound means no change in mobility, upper bound is zero mobility).

Color coding: 

  • Green: Late Stage (>50% post-peak)
  • Blue: Middle-Late Stage (~50% post-peak)
  • Orange: Middle-Early Stage (peaked)
  • Pink: Early Stage [or indeterminate] (pre-peak)

Download link for full resolution:

Sources: Data is sourced from the European Centre for Disease Prevention and Control (ECDC) via Our World In Data. Mobility data is sourced from Apple Mobility Trends which tracks change in Apple Maps routing requests.

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Lasts Longer (2.0)

About a year and a half ago in Lasts Longer I noted that “keeping iPhones in use” became a top priority for Apple’s design for sustainability. I considered this provocative because it prioritizes usage and users over units sold, in contrast to how the company was perceived by investors. This new focus was reinforced by the company’s dropping of unit shipments data and the addition of Services data including subscriptions count and gross margins.

Apple effectively transformed itself into a services company with customer acquisition, subscriptions (measured by average revenue per user) and retention/satisfaction as key operating metrics. The 1.5 billion active devices and ~1 billion active iPhones and 500 million paid subscriptions are the new data points rather than units sold.

To succeed with this new Lasts Longer strategy, one of the objectives should be that devices become more durable and their durability should in-turn result in more minutes of use and thus more return on initial purchase price. But how can we measure this? We don’t have precise data on the use time. We have some anecdotes but no official data. We do know the iOS operating system is now designed to work on older hardware but that does not give us a measure of what is in use.

Luckily we now have an indicator that can help: Bank My Cell published a new data set on various used phone prices. This is a gold mine.

The data consists of prices for about 220 used phone models that changed hands in 2019. The prices are given for the beginning of the year (earliest date when the model was sold) and the end of the year (latest date when the model was sold.)

It’s thus possible to see how various models from a number of companies changed in price during the year. The date of initial release of the phones is also given (though initial selling price isn’t. It’s a bit tricky to determine initial selling price because the model may have been sold for an extended period of time and discounted through that period.)

What we do have allows comparisons between different manufacturers to see, especially for recent (late) models, just how big the drops have been in prices.

I recommend reading the original post to see some of the comparisons between brands. In addition I chose to illustrate some of the differences with the graphs below.

These graphs show phone model prices at the beginning of 2019 (blue) vs. the end of 2019 (red). The models are not individually identifiable but they are shown by the year of introduction. So, in the case of the iPhone, the 2019 cohort (those released around September 2019) are the top line, then the 2018 group is shown in the second line and on down. Thus the oldest iPhones traded during 2019 were iPhones 5, initially released in 2012.

To read the graph, you can scan each line to see the gap between the red and blue triangles to see how much the phones roughly fell in value. The comparison above is between Apple, Google and Samsung.

Here are some patterns I noticed:

  • Google phones had larger drops during the year than equivalent iPhones. Just how much the drops were is listed by the original post: “Google lost an average of -51.68% between Jan-Dec 2019. The Pixel 3 (2018) was the fastest depreciating and highest value loss at -56.70% (-$267)”.
  • The overall picture of Samsung’s phones is hard to discern. Flagship Android phones ($700+) dropped in value roughly twice as fast as Apple’s. But the the overall picture is messy. Budget phones are mixed with premium and the average pricing seems to quickly go near zero.
  • iPhones have a more valuable “mid-range” for models more than 2 years old. Android phones fall hard in the mid range and cluster around zero quickly. This is the heart of the “old but valuable” proposition.

Depreciation rates might seem academic but depreciation is an indicator of a real perception of value and that in itself is a reflection of utility. Users are likely to pay for a used product only if they think it can still be useful in proportion to the price. New product buyers might pay for other things like prestige, status signaling or just wanting to be first. But used buyers are more mindful of what the product can do–they are less likely to benefit from signaling.

Analogously, the used car market often shows the underlying quality or reliability of a car model. If a luxury car plummets in value it means that it becomes a huge burden to late usage, usually due to servicing costs. In previous decades luxury cars were also durable cars but with complexity they became money pits.

In the used phone market hardware repair and maintenance are less consequential but there is a concern for software support, security and privacy. The serviceability of the battery, the camera quality and the fit and finish of the body matter.

Buyers are not stupid. The market speaks words of wisdom. A phone that is worth more will reflect more inherent utility. Remember that a phone is unlocked more than 80 times a day. If it has 4 years of use then it get unlocked 116,000 times. That’s not “uses” but unlocks. Actual usage in terms of taps, swipes, or glances could be triple that figure. There are phones on the list that are 7 years old. It’s not unreasonable to assume that a moderately well used iPhone has enabled a quarter million interactions.

Remember that phones are used all day, every day. At home and at work. In cars and in planes.

Even on weekends, and on holidays. And even when you’re “social distancing”.

In fact, the more physically isolated you are the more likely you are to rely on the phone as your social lifeline. In difficult times the product people turn to first and last isn’t one of luxury or frivolity. It’s a product that keeps you informed, connected, lets you help others and may even keep you alive.

It’s in difficult times that the true character of a product shines through. The smartphone, derided, mocked and blamed for all kinds of societal ills is what we turn to first to avoid getting ill.

Carry On

Exactly one month ago Apple reported their highest quarterly revenue ever. They also guided to growth of between 8.6% and 15.5% into the current (1st calendar) quarter. This guidance is illustrated as the right-most bar in the following graph:

Note that the growth is relative to the year-ago quarter. The quarter was almost a third over by the time the issuance of guidance but less than three weeks later the company withdrew their guidance. The company did not issue new guidance.

The reasons given were both a restricted supply and a disruption in demand due to the COVID-19 viral outbreak. That outbreak paralyzed China and in the 10 days since has come to threaten the world.

Apple was the first company to warn about the impact of the virus on its business but not the last. The market reaction was muted. Mysteriously, the market seems to be reacting to the outbreak at this later time even though the dynamics of the epidemic were foreseeable.

The question of impact on the business is still open but I’d like to reflect a bit on the impact of any number of possible disasters or “acts of God” on any business.

The greatest catastrophes in history were wars and pandemics. In the 20th century in particular there were two world wars and one large pandemic in 1918. Add to that a depression and you would think that century was cursed.

And yet, these crises merely delayed technologies. They did not eliminate them or the companies which introduced them. For example, the introduction of Television was delayed by WWII and the adoption of the automobile was paused. At the same time new innovations were introduced including plastics, radar and microwaves and jumps in manufacturing productivity. The 1918 flu was followed by the roaring 20’s and WWII by the post-war boom years. The 20th century came to be celebrated as the most innovative time in history, a time when standards of living and prosperity exploded.

It’s not prudent to ignore a pandemic but it’s not prudent to contemplate an apocalypse will follow. Demand deferred is not demand destroyed. Civilization is fundamentally able to absorb these shocks and it’s useful to look to history to see exactly how we managed to do so.

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Analyst, analyze yourself

It’s common knowledge that sell-side analyst price targets for stocks are not taken seriously. The evidence is simply that no public accounting exists for their historic performance. It would be trivial to grade performance but it seems nobody bothers to do it.

It seems strange given the attention paid and repetition of these targets in financial media. When a “note” is published with an opinion and a target price the stock price can and does actually react. But why should it if there is no accountability?

Well, not quite zero accountability. Philip Elmer-DeWitt publishes analyst estimates (example) diligently. These estimates are presumed to be 12 month targets, meaning that at the time when the estimate is published the target price is assume to be reached 12 months hence. He does grade the performance of these same analysts (and more) in predicting quarterly earnings and revenue estimates for the just-ended quarter.

But an estimate for what *just* happened in the last 90 days is a lot easier than predicting how markets will react to all the information about a company 12 months into the future.

So how hard can it be to measure performance on price targets vs. just-ended quarterly estimates?

Here is my modest attempt: I note that Apple’s share price today is trading around $298 per share. I also note that we can recover the estimates from analysts exactly a year ago. At that time Apple issued a rare warning that its own estimates for the fourth calendar quarter 2018, issued about 60 days earlier would be quite a bit different than what will be released (in 3 weeks).

This compelled all the analysts to re-set their targets at the same time. What we ended up is a whole batch of January 3rd 2019 estimates for, presumably, January 3rd 2020.

Well, January 3rd 2020 is tomorrow. How did the estimates hold up?

The following graph tells the story:

Apple Share Price vs. a sampling of Analyst Estimates, Past and Present.

The green line in the graph represents the closing share price at weekly intervals (from about October 2016 until last week.) The blue dots represent various estimates. Note that they are 12 months since their issuance and that since estimates can come at any time the are not easily clustered.

That is except last year and the “big reset” when the estimates all were issued on the same day. I highlighted the range with a vertical line. Note that the closing price last week was well above the highest estimate and that the lowest estimate ($140 is less than 50% of the current price).

This is quite a big fail. Errors of 50 for a 12 month time frame are egregious.

A few additional observations:

The spread or variance of estimates is enormous. Far wider than what was the case 3 or 4 years ago. (I look forward to getting more data to perform this variance analysis). It’s peculiar to me that a larger, more stable business, as Apple is today, is more difficult to predict. To see even today a range in targets between $150 to $350 is bizarre.

Another observation is that very, very few targets were “accurate”. That is anywhere near what actually happened. To find these rare successes, look for blue dots placed directly or near the green line. The chances of a correct target is less than what can be attributed to chance. Again, I look forward to a larger data set to see if there is any set of analysts that perform particularly well or particularly poorly.

Again, we’re not describing here predictions about the stock market in general, or of macroeconomic indicators. We’re talking about predicting one single company’s stock price in a 12 month period. A company that receives a great deal of scrutiny and which publishes a surprisingly large amount of data about itself.

Now I don’t want to suggest that there is a better way to predict share prices. I will not try to do so because although it might be easy to predict earnings based on overall company strategy, processes and team (all well known) the share price is that data multiplied by a random number called “sentiment”. That sentiment can be summarized as the P/E ratio which wanders around aimlessly. I don’t think I have any way to predict sentiment one second into the future, never mind one year.

But even if I don’t think there is a method to the madness of sentiment, it seems dozens of people find it necessary to claim that there is one. But if you do, then analyze yourself. What did you learn from the mistakes? What is the theory you’re employing?

And if you don’t analyze yourself, then don’t expect to be taken seriously.

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iPods Pro

At time of writing (December 12, 2019) AirPods Pro delivery wait time is over 4 weeks. It’s been like this since they shipped. I tried stores in several countries and although units can make an appearance on a shelf, they sell out immediately.

AirPods are part of the “Wearables & Home” category for Apple which used to be called Other Products and include also the Watch, iPod, Apple TV, Beats and HomePod (among other items.) iPod revenues were broken out as recently as 2015 but as the graph below shows, there have been no specifics on any product in “Wearables & Home” since then.

Wearables, Home and Accessories in a historic perspective with the iPod.

It therefore falls upon us to estimate how much of the entire category is any one product. It’s been very difficult as the only clues lie in growth rates, sometimes cited for the Apple Watch and sometimes for “Wearables” alone. As far as I can tell from the available clues, the split is roughly as shown between the Orange and Green areas. Orange reflects estimate for Watch and Green everything else.

This analysis helped me conclude the Apple Watch overtook the historic “peak iPod” which occurred in the fourth quarter of 2007 at $4 billion. My Watch revenue estimate was $4.2 billion in the fourth quarter of 2018. This conclusion was confirmed by statements from the Company.

The problem lately has been that AirPods have become huge unto themselves. There is literally no information about AirPods sales as a product category. The only option is to guess Watch and subtract it from Wearables and then guess again the portion of “non-Watch Wearables & Home” that is AirPods.

Looking forward to the next quarter, I am expecting a 51% increase y/y for Wearables and 24% growth in Watch. This results in a Watch revenues about $5.2 billion and non-Watch $5.7 billion. Now if we assume $1.7 billion for non-Watch-non-AirPods (i.e. Apple TV, HomePod, Beats, iPod, other) then this quarter AirPods will have overtaken peak iPod.

Remember that iPod was the phenomenon which reset all expectations for Apple. It caused Apple to cease calling itself Apple Computer. It (at least psychologically) laid the foundation for iPhone and everything else that followed. In 2005 and through 2007 Apple was “the iPod company”. I remember people working in a large search engine company calling Apple “that media company” as a result of over-intellectualizing iTunes.

(One more footnote on the AirPods Pro is that at $250 a pair and $300 for Apple Watch, throwing in a case puts these attached accessories for an iPhone at roughly the same average selling price for the iPhone of a few years ago.)

For the AirPods to overtake the iPod highlights just what a phenomenal category Wearables has become. In combination with Home and other accessories the category is going to decidedly overtake the Mac, having already passed the iPad.

And so it goes, something dismissed as inconsequential–”does not move the needle”–ends up becoming a massive force of change. The iPod was that, the original Apple II, the Mac and yes, also the iPhone. It’s the asymmetry of humility that this happens over and over again.

To hear more about the profundities of AirPods Pro tune in to the next Critical Path podcast.