# Company Valuations Implied by my Valuations Bible: Are Snap, Netflix, Square and Twitter Grossly Overvalued?

Applying the Gross Margin Multiple Method to Public Company Valuation

In my last two posts I’ve laid out a method to value companies not yet at their mature business models. The method provides a way to value unprofitable growth companies and those that are profitable but not yet at what could be their mature business model. This often occurs when a company is heavily investing in growth at the expense of near-term profits. In the last post, I showed how I would estimate what I believed the long-term model would be for Tesla, calling the result “Potential Earnings” or “PE”. Since this method requires multiple assumptions, some of which might not find agreement among investors, I provided a second, simplified method that only involved gross margin and revenue growth.

The first step was taking about 20 public companies and calculating how they were valued as a multiple of gross margin (GM) dollars. The second step was to determine a “least square line” and formula based on revenue growth and the gross margin multiple for these companies. The coefficient of 0.62 shows that there is a good correlation between Gross Margin and Revenue Growth, and one significantly better than the one between Revenue Growth and a company’s Revenue Multiple (that had a coefficient of 0.36 which is considered very modest).

Where’s the Beef?

The least square formula derived in my post for relating revenue growth to an implied multiple of Gross Margin dollars is:

GM Multiple = (24.773 x Revenue growth percent) + 4.1083

Implied Company Market Value = GM Multiple x GM Dollars

Now comes the controversial part. I am going to apply this formula to 10 companies using their data (with small adjustments) and compare the Implied Market Value (Implied MKT Cap) to their existing market Cap as of several days ago. I’ll than calculate the Implied Over (under) Valuation based on the comparison. If the two values are within 20% I view it as normal statistical variation.

Table 1: Valuation Analysis of 10 Tech Companies

• * Includes net cash included in expected market cap
• *** Uses 1/31/18 year end
• **** Growth rate used in the model is q4 2017 vs q4 2016.  See text

This method suggests that 5 companies are over-valued by 100% or more and a fifth, Workday, by 25%. Since Workday is close to a normal variation, I won’t discuss it further. I have added net cash for Facebook, Snap, Workday and Twitter to the implied market cap as it was material in each case but did not do so for the six others as the impact was not as material.

I decided to include the four companies I recommended, in this year’s top ten list, Amazon, Facebook, Tesla and Stitchfix, in the analysis. To my relief, they all show as under-valued with Stitchfix, (the only one below the Jan 2 price) having an implied valuation more than 100% above where it currently trades. The other three are up year to date, and while trading below what is suggested by this method, are within a normal range. For additional discussion of these four see our 2018 top Ten List.

Digging into the “Overvalued” Five

Why is there such a large discrepancy between actual market cap and that implied by this method for 5 companies? There are three possibilities:

1. The method is inaccurate
2. The method is a valid screen but I’m missing some adjustment for these companies
3. The companies are over-valued and at some point, will adjust, making them risky investments

While the method is a good screen on valuation, it can be off for any given company for three reasons:  the revenue growth rate I’m using will radically change; a particular company has an ability to dramatically increase gross margins, and/or a particular company can generate much higher profit margins than their gross margin suggests. Each of these may be reflected in the company’s actual valuation but isn’t captured by this method.

To help understand what might make the stock attractive to an advocate, I’ll go into a lot of detail in analyzing Snap. Since similar arguments apply to the other 4, I’ll go into less detail for each but still point out what is implicit in their valuations.

Snap

Snap’s gross margin (GM) is well below its peers and hurts its potential profitability and implied valuation. Last year, GM was about 15%, excluding depreciation and amortization, but it was much higher in the seasonally strong Q4. It’s most direct competitor, Facebook, has a gross margin of 87%.  The difference is that Facebook monetizes its users at a much higher level and has invested billions of dollars and executed quite well in creating its own low-cost infrastructure, while Snap has outsourced its backend to cloud providers Google and Amazon. Snap has recently signed 5-year contracts with each of them to extend the relationships. Committing to lengthy contracts will likely lower the cost of goods sold.  Additionally, increasing revenue per user should also improve GM.  But, continuing to outsource puts a cap on how high margins can reach. Using our model, Snap would need 79% gross margin to justify its current valuation. If I assume that scale and the longer-term contracts will enable Snap to double its gross margins to 30%, the model still shows it as being over-valued by 128% (as opposed to the 276% shown in our table). The other reason bulls on Snap may justify its high valuation is that they expect it to continue to grow revenue at 100% or more in 2018 and beyond. What is built into most forecasts is an assumed decline in revenue growth rates over time… as that is what typically occurs. The model shows that growing revenue 100% a year for two more years without burning cash would leave it only 32% over-valued in 2 years. But as a company scales, keeping revenue growth at that high a level is a daunting task. In fact, Snap already saw revenue growth decline to 75% in Q4 of 2017.

Twitter is not profitable.  Revenue declined in 2017 after growing a modest 15% in 2016, and yet it trades at a valuation that implies that it is a growth company of about 50%. While it has achieved such levels in the past, it may be difficult to even get back to 15% growth in the future given increased competition for advertising.

Netflix

I recommended Netflix in January 2015 as one of my stock picks for the year, and it proved a strong recommendation as the stock went up about 140% that year. However, between January 2015 and January 2018, the stock was up over 550% while trailing revenue only increased 112%.  I continue to like the fundamentals of Netflix, but my GM model indicates that the stock may have gotten ahead of itself by a fair amount, and it is unlikely to dramatically increase revenue growth rates from last year’s 32%.

Square

Square has followed what I believe to be the average pattern of revenue growth rate decline as it went from 49% growth in 2015, down to 35% growth in 2016, to under 30% growth in 2017. There is no reason to think this will radically change, but the stock is trading as if its revenue is expected to grow at a nearly 90% rate. On the GM side, Square has been improving GM each year and advocates will point out that it could go higher than the 38% it was in 2017. But, even if I use 45% for GM, assuming it can reach that, the model still implies it is 90% over-valued.

Blue Apron

I don’t want to beat up on a struggling Blue Apron and thought it might have reached its nadir, but the model still implies it is considerably over-valued. One problem that the company is facing is that investors are negative when a company has slow growth and keeps losing money. Such companies find it difficult to raise additional capital. So, before running out of cash, Blue Apron began cutting expenses to try to reach profitability. Unfortunately, given their customer churn, cutting marketing spend resulted in shrinking revenue in each sequential quarter of 2017. In Q4 the burn was down to \$30 million but the company was now at a 13% decline in revenue versus Q4 of 2016 (which is what we used in our model). I assume the solution probably needs to be a sale of the company. There could be buyers who would like to acquire the customer base, supplier relationships and Blue Apron’s understanding of process. But given that it has very thin technology, considerable churn and strong competition, I’m not sure if a buyer would be willing to pay a substantial premium to its market cap.

An Alternative Theory on the Over Valued Five

I have to emphasize that I am no longer a Wall Street analyst and don’t have detailed knowledge of the companies discussed in this post, so I easily could be missing some important factors that drive their valuation.  However, if the GM multiple model is an accurate way of determining valuation, then why are they trading at such lofty premiums to implied value? One very noticeable common characteristic of all 5 companies in question is that they are well known brands used by millions (or even tens of millions) of people. Years ago, one of the most successful fund managers ever wrote a book where he told readers to rely on their judgement of what products they thought were great in deciding what stocks to own. I believe there is some large subset of personal and professional investors who do exactly that. So, the stories go:

• “The younger generation is using Snap instead of Facebook and my son or daughter loves it”
• “I use Twitter every day and really depend on it”
• “Netflix is my go-to provider for video content and I’m even thinking of getting rid of my cable subscription”

Once investors substitute such inclinations for hard analysis, valuations can vary widely from those suggested by analytics. I’m not saying that such thoughts can’t prove correct, but I believe that investors need to be very wary of relying on such intuition in the face of evidence that contradicts it.

# The Valuation Bible – Part 2: Applying the Rules to Tesla and Creating an Adjusted Valuation Method for Startups

This post is part 2 of our valuation discussion (see this post for part 1).  As I write this post Tesla’s market cap is about \$56 billion. I thought it would be interesting to show how the rules discussed in the first post apply to Tesla, and then to take it a step further for startups.

Revenue and Revenue Growth

Revenue for Tesla in 2017 was \$11.8 billion, about 68% higher than 2016, and it is likely to grow faster this year given the over \$20 billion in pre-orders (and growing) for the model 3 coupled with continued strong demand for the model S and model X. Since it is unclear when the new sports car or truck will ship, I assume no revenue in those categories. As long as Tesla can increase production at the pace they expect, I estimate 2018 revenue will be up 80% – 120% over 2017, with Q4 year over year growth at or above 120%.

If I’m correct on Tesla revenue growth, its 2018 revenue will exceed \$20 billion. So, Rule Number 1 from the prior post indicates that Tesla’s high growth rates should merit a higher “theoretical PE” than the S&P (by at least 4X if one believes that growth will continue at elevated rates).

Calculating TPE

Tesla gross margins have varied a bit while ramping production for each new model, but in the 16 quarters from Q1, 2014 to Q4, 2017 gross margin averaged 23% and was above 25%, 6 of the 16 quarters. Given that Tesla is still a relatively young company it appears likely margins will increase with scale, leading me to believe that long term gross margins are very likely to be above 25%. While it will dip during the early production ramp of the model 3, 25% seems like the lowest percent to use for long term modeling and I expect it to rise to between 27% and 30% with higher production volumes and newer factory technology.

Tesla recognizes substantial cost based on stock-based compensation (which partly occurs due to the steep rise in the stock). Most professional investors ignore artificial expenses like stock-based compensation, as I will for modeling purposes, and refer to the actual cost as net SG&A and net R&D. Given that Tesla does not pay commissions and has increased its sales footprint substantially in advance of the roll-out of the model 3, I believe Net SG&A and Net R&D will each increase at a much slower pace than revenue. If they each rise 20% by Q4 of this year and revenue is at or exceeds \$20 billion, this would put their total at below 20% of revenue by Q4. Since they should decline further as a percent of revenue as the company matures, I am assuming 27% gross margin and 18% operating cost as the base case for long term operating profit. While this gross margin level is well above traditional auto manufacturers, it seems in line as Tesla does not have independent dealerships (who buy vehicles at a discount) and does not discount its cars at the end of each model year.

Estimated TPE

Table 1 provides the above as the base case for long term operating profit. To provide perspective on the Tesla opportunity, Table 1 also shows a low-end case (25% GM and 20% operating cost) and a high-end profit case (30% GM and 16% operating cost).  Recall, theoretic earnings are derived from applying the mature operating profit level to trailing and to forward revenue. For calculating theoretic earnings, I will ignore interest payments and net tax loss carry forwards as they appear to be a wash over the next 5 years. Finally, to derive the Theoretic Net Earnings Percent a potential mature tax rate needs to be applied. I am using 20% for each model case which gives little credit for tax optimization techniques that could be deployed. That would make theoretic earnings for 2017 and 2018 \$0.85 billion and \$1.51 billion, respectively and leads to:

• 2017 TPE=\$ 56.1 billion/\$0.85 = 66.0
• 2018 TPE= \$ 56.1 billion/1.51 = 37.1

The S&P trailing P/E is 25.5 and forward P/E is about 19X. Based on our analysis of the correlation between growth and P/E provided in the prior post, Tesla should be trading at a minimum of 4X the trailing S&P ratio (or 102 TP/E) and at least 3.5X S&P forward P/E (or 66.5 TP/E). To me that shows that the current valuation of Tesla does not appear out of market.  If the market stays at current P/E levels and Tesla reaches \$21B in revenue in 2018 this indicates that there is strong upside for the stock.

Table 1: Tesla TPE 2017 & 2018

The question is whether Tesla can continue to grow revenue at high rates for several years. Currently Tesla has about 2.4% share of the luxury car market giving it ample room to grow that share. At the same time, it is entering the much larger medium-priced market with the launch of the Model 3 and expects to produce vehicles in other categories over the next few years. Worldwide sales of new cars for the auto market is about 90 million in 2017 and growing about 5% a year. Tesla is the leader in several forward trends: electric vehicles, automated vehicles and technology within a car. Plus, it has a superior business model as well. If it reaches \$21 billion in revenue in 2018, its share of the worldwide market would be about 0.3%. It appears poised to continue to gain share over the next 3-5 years, especially as it fills out its line of product.  Given that it has achieved a 2.4% share of the market it currently plays in, one could speculate that it could get to a similar share in other categories. Even achieving a 1% share of the worldwide market in 5 years would mean about 40% compound growth between 2018 and 2022 and imply a 75X-90X TP/E at the end of this year.

The Bear Case

I would be remiss if I omitted the risks that those negative on the stock point out. Tesla is a very controversial stock for a variety of reasons:

1. Gross Margin has been volatile as it adds new production facilities so ‘Bears’ argue that even my 25% low case is optimistic, especially as tax rebate subsidies go away
2. It has consistently lost money so some say it will never reach the mature case I have outlined
3. As others produce better electric cars Tesla’s market share of electric vehicles will decline so high revenue growth is not sustainable
4. Companies like Google have better automated technology that they will license to other manufacturers leading to a leap frog of Tesla

As they say, “beauty is in the eyes of the beholder” and I believe my base case is realistic…but not without risk. In response to the bear case that Tesla revenue growth can’t continue, it is important to recognize that Tesla already has the backlog and order momentum to drive very high growth for the next two years. Past that, growing market share over the 4 subsequent years to 1% (a fraction of their current share of the luxury market) would generate compound annual growth of 40% for that 4-year period. In my opinion, the biggest risk is Tesla’s own execution in ramping production. Bears will also argue that Tesla will never reach the operating margins of my base case for a variety of reasons. This is the weakness of the TPE approach: it depends on assumptions that have yet to be proven. I’m comfortable when my assumptions depend on momentum that is already there, gross margin proof points and likelihood that scale will drive operating margin improvements without any radical change to the business model.

Applying the rules to Startups

As a VC I am often in the position of helping advise companies regarding valuation. This occurs when they are negotiating a round of financing or in an M&A situation.  Because the companies are even earlier than Tesla, theoretic earnings are a bit more difficult to establish. Some investors ignore the growth rates of companies and look for comps in the same business. The problem with the comparable approach is that by selecting companies in the same business, the comps are often very slow growth companies that do not merit a high multiple. For example, comparing Tesla to GM or Ford to me seems a bit ludicrous when Tesla’s revenue grew 68% last year and is expected to grow even faster this year while Ford and GM are growing their revenue at rates below 5%. It would be similar if investors compared Apple (in the early days of the iPhone) to Nokia, a company it was obsoleting.

Investors look for proxies to use that best correlate to what future earnings will be and often settle on a multiple of revenue. As Table 2 shows, there is a correlation between valuation as a multiple of revenue and revenue growth regardless of what industry the companies are in. This correlation is closer than one would find by comparing high growth companies to their older industry peers.

Table 2: Multiple of Revenue and Revenue Growth

However, using revenue as the proxy for future earnings suffers from a wide variety of issues. Some companies have 90% or greater gross margins like our portfolio company Education.com, while others have very low gross margins of 10% – 20%, like Spotify. It is very likely that the former will generate much higher earnings as a percent of revenue than the latter. In fact, Education.com is already cash flow positive at a relatively modest revenue level (in the low double-digit millions) while Spotify continues to lose a considerable amount of money at billions of dollars in revenue. Notice, this method also implies that Tesla should be valued about 60% higher than its current market price.

This leads me to believe a better proxy for earnings is gross margin as it is more closely correlated with earnings levels. It also removes the issue of how revenue is recognized and is much easier to analyze than TPE. For example, Uber recognizing gross revenue or net revenue has no impact on gross margin dollars but would radically change its price to revenue. Table 3 uses the same companies as Table 2 but shows their multiple of gross margin dollars relative to revenue growth. Looking at the two graphs, one can see how much more closely this correlates to the valuation of public companies. The correlation coefficient improves from 0.36 for the revenue multiple to 0.62 for the gross margin multiple.

Table 3: Multiple of Gross Margin vs. Revenue Growth

So, when evaluating a round of financing for a pre-profit company the gross margin multiple as it relates to growth should be considered. For example, while there are many other factors to consider, the formula implies that a 40% revenue growth company should have a valuation of about 14X trailing gross margin dollars.  Typically, I would expect that an earlier stage company’s mature gross margin percent would likely increase. But they also should receive some discount from this analysis as its risk profile is higher than the public companies shown here.

Notice that the price to sales graph indicates Tesla should be selling at 60% more than its multiple of 5X revenue. On the other hand, our low-end case for Tesla Gross Margin, 25%, puts Tesla at 20X Gross Margin dollars, just slightly undervalued based on where the least square line in Table 3 indicates it should be valued.

# The Valuation Bible

After many years of successfully picking public and private companies to invest in, I thought I’d share some of the core fundamentals I use to think about how a company should be valued. Let me start by saying numerous companies defy the logic that I will lay out in this post, often for good reasons, sometimes for poor ones. However, eventually most companies will likely approach this method, so it should at least be used as a sanity check against valuations.

When a company is young, it may not have any earnings at all, or it may be at an earnings level (relative to revenue) that is expected to rise. In this post, I’ll start by considering more mature companies that are approaching their long-term model for earnings to establish a framework, before addressing how this framework applies to less mature companies. The post will be followed by another one where I apply the rules to Tesla and discuss how it carries over into private companies.

Growth and Earnings are the Starting Points for Valuing Mature Companies

When a company is public, the most frequently cited metric for valuation is its price to earnings ratio (PE). This may be done based on either a trailing 12 months or a forward 12 months. In classic finance theory a company should be valued based on the present value of future cash flows. What this leads to is our first rule:

Rule 1: Higher Growth Rates should result in a higher PE ratio.

When I was on Wall Street, I studied hundreds of growth companies (this analysis does not apply to cyclical companies) over the prior 10-year period and found that there was a very strong correlation between a given year’s revenue growth rate and the next year’s revenue growth rate. While the growth rate usually declined year over year if it was over 10%, on average this decline was less than 20% of the prior year’s growth rate. What this means is that if we took a group of companies with a revenue growth rate of 40% this year, the average organic growth for the group would likely be about 33%-38% the next year. Of course, things like recessions, major new product releases, tax changes, and more could impact this, but over a lengthy period of time this tended to be a good sanity test. As of January 2, 2018, the average S&P company had a PE ratio of 25 on trailing earnings and was growing revenue at 5% per year. Rule 1 implies that companies growing faster should have higher PEs and those growing slower, lower PEs than the average.

Graph 1: Growth Rates vs. Price Earnings Ratios

The graph shows the correlation between growth and PE based on the valuations of 21 public companies. Based on Rule 1, those above the line may be relatively under-priced and those below relatively over-priced. I say ‘may be’ as there are many other factors to consider, and the above is only one of several ways to value companies. Notice that most of the theoretically over-priced companies with growth rates of under 5% are traditional companies that have long histories of success and pay a dividend. What may be the case is that it takes several years for the market to adjust to their changed circumstances or they may be valued based on the return from the dividend. For example, is Coca Cola trading on: past glory, its 3.5% dividend, or is there something about current earnings that is deceptive (revenue growth has been a problem for several years as people switch from soda to healthier drinks)? I am not up to speed enough to know the answer. Those above the line may be buys despite appearing to be highly valued by other measures.

Relatively early in my career (in 1993-1995) I applied this theory to make one of my best calls on Wall Street: “Buy Dell sell Kellogg”. At the time Dell was growing revenue over 50% per year and Kellogg was struggling to grow it over 4% annually (its compounded growth from 1992 to 1995, this was partly based on price increases). Yet Dell’s PE was about half that of Kellogg and well below the S&P average. So, the call, while radical at the time, was an obvious consequence of Rule 1. Fortunately for me, Dell’s stock appreciated over 65X from January 1993 to January 2000 (and well over 100X while I had it as a top pick) while Kellogg, despite large appreciation in the overall stock market, saw its stock decline slightly over the same 7-year period (but holders did receive annual dividends).

Rule 2: Predictability of Revenue and Earnings Growth should drive a higher trailing PE

Investors place a great deal of value on predictability of growth and earnings, which is why companies with subscription/SaaS models tend to get higher multiples than those with regular sales models. It is also why companies with large sales backlogs usually get additional value. In both cases, investors can more readily value the companies on forward earnings since they are more predictable.

Rule 3: Market Opportunity should impact the Valuation of Emerging Leaders

How the rules can be applied to companies that are pre-profit

As a VC, it is important to evaluate what companies should be valued at well before they are profitable. While this is nearly impossible to do when we first invest (and won’t be covered in this post), it is feasible to get a realistic range when an offer comes in to acquire a portfolio company that has started to mature. Since they are not profitable, how can I apply a PE ratio?

What needs to be done is to try to forecast eventual profitability when the company matures. A first step is to see where current gross margins are and to understand whether they can realistically increase. The word realistic is the key one here. For example, if a young ecommerce company currently has one distribution center on the west coast, like our portfolio company Le Tote, the impact on shipping costs of adding a second eastern distribution center can be modeled based on current customer locations and known shipping rates from each distribution center. Such modeling, in the case of Le Tote, shows that gross margins will increase 5%-7% once the second distribution center is fully functional. On the other hand, a company that builds revenue city by city, like food service providers, may have little opportunity to save on shipping.

• Calculating variable Profit Margin

Once the forecast range for “mature” gross margin is estimated, the next step is to identify other costs that will increase in some proportion to revenue. For example, if a company is an ecommerce company that acquires most of its new customers through Facebook, Google and other advertising and has high churn, the spend on customer acquisition may continue to increase in direct proportion to revenue. Similarly, if customer service needs to be labor intensive, this can also be a variable cost. So, the next step in the process is to access where one expects the “variable profit margin” to wind up. While I don’t know the company well, this appears to be a significant issue for Blue Apron: marketing and cost of goods add up to about 90% of revenue. I suspect that customer support probably eats up (no pun intended) 5-10% of what is left, putting variable margins very close to zero. If I assume that the company can eventually generate 10% variable profit margin (which is giving it credit for strong execution), it would need to reach about \$4 billion in annual revenue to reach break-even if other costs (product, technology and G&A) do not increase. That means increasing revenue nearly 5-fold. At their current YTD growth rate this would take 9 years and explains why the stock has a low valuation.

• Estimating Long Term Net Margin

Once the variable profit margin is determined, the next step would be to estimate what the long-term ratio of all other operating cost might be as a percent of revenue. Using this estimate I can determine a Theoretic Net Earnings Percent. Applying this percent to current (or next years) revenue yields a Theoretic Earnings and a Theoretic PE (TPE):

TPE= Market Cap/Theoretic Earnings

To give you a sense of how I successfully use this, review my recap of the Top Ten Predictions from 2017 where I correctly predicted that Spotify would not go public last year despite strong top line growth as it was hard to see how its business model could support more than 2% or so positive operating margin, and that required renegotiating royalty deals with record labels.  Now that Spotify has successfully negotiated a 3% lower royalty rate from several of the labels, it appears that the 16% gross margins in 2016 could rise to 19% or more by the end of 2018. This means that variable margins (after marketing cost) might be 6%. This would narrow its losses, but still means it might be several years before the company achieves the 2% operating margins discussed in that post. As a result, Spotify appears headed for a non-traditional IPO, clearly fearing that portfolio managers would not be likely to value it at its private valuation price since that would lead to a TPE of over 200. Since Spotify is loved by many consumers, individuals might be willing to overpay relative to my valuation analysis.

Our next post will pick up this theme by walking through why this leads me to believe Tesla continues to have upside, and then discussing how entrepreneurs should view exit opportunities.

SoundBytes

I’ve often written about effective shooting percentage relative to Stephen Curry, and once again he leads the league among players who average 15 points or more per game. What also accounts for the Warriors success is the effective shooting of Klay Thompson, who is 3rd in the league, and Kevin Durant who is 6th. Not surprisingly, Lebron is also in the top 10 (4th). The table below shows the top ten among players averaging 15 points or more per game.  Of the top ten scorers in the league, 6 are among the top 10 effective shooters with James Harden only slightly behind at 54.8%. The remaining 3 are Cousins (53.0%), Lillard (52.2%), and Westbrook, the only one below the league average of 52.1% at 47.4%.

Table: Top Ten Effective Shooters in the League

*Note: Bolded players denote those in the top 10 in Points per Game

# Ten Predictions for 2018

In my recap of 2017 predictions I pointed out how boring my stock predictions have been with Tesla and Facebook on my list every year since 2013 and Amazon on for two of the past three years. But what I learned on Wall Street is that sticking with companies that have strong competitive advantages in a potentially mega-sized market can create great performance over time (assuming one is correct)! So here we go again, because as stated in my January 5 post, I am again including Tesla, Facebook and Amazon in my Top ten list for 2018. I believe they each continue to offer strong upside, as explained below. I’m also adding a younger company, with a modest market cap, thus more potential upside coupled with more risk. The company is Stitch Fix, an early leader in providing women with the ability to shop for fashion-forward clothes at home. My belief in the four companies is backed up by my having an equity position in each of them.

I’m expecting the four stocks to outperform the market. So, in a steeply declining market, out-performance might occur with the stock itself being down (but less than the market). Having mentioned the possibility of a down market, I’m predicting the market will rise this year. This is a bit scary for me, as predicting the market as a whole is not my specialty.

We’ll start with the stock picks (with January 2 opening prices of stocks shown in parenthesis) and then move on to the remainder of my 10 predictions.

1. Tesla stock appreciation will continue to outpace the market (it opened the year at \$312/share).

The good news and bad news on Tesla is the delays in production of the Model 3. The good part is that we can still look forward to massive increases in the number of cars the company sells once Tesla gets production ramping (I estimate the Model 3 backlog is well in excess of 500,000 units going into 2018 and demand appears to be growing). In 2017, Tesla shipped between 80,000 and 100,000 vehicles with revenue up 30% in Q3 without help from the model 3. If the company is successful at ramping capacity (and acquiring needed parts), it expects to reach a production rate of 5,000 cars per week by the end of Q1 and 10,000 by the end of the year. That could mean that the number of units produced in Q4 2018 will be more than four times that sold in Q4 2017 (with revenue about 2.0-2.5x due to the Model 3 being a lower priced car). Additionally, while it is modest compared to revenue from selling autos, the company appears to be the leader in battery production. It recently announced the largest battery deal ever, a \$50 million contract (now completed on time) to supply what is essentially a massive backup battery complex for energy to Southern Australia. While this type of project is unlikely to be a major portion of revenue in the near term, it can add to Tesla’s growth rate and profitability.

2. Facebook stock appreciation will continue to outpace the market (it opened the year at \$182/share).

3. Amazon stock appreciation will outpace the market (it opened the year at \$1188/share).

Amazon, remarkably, increased its revenue growth rate in 2017 as compared to 2016. This is unusual for companies of this size. In 2018, we expect online to continue to pick up share in retail and Amazon to gain more share of online. The acquisition of Whole Foods will add approximately \$4B per quarter in revenue, boosting year/year revenue growth of Amazon an additional 9%-11% per quarter, if Whole Foods revenue remains flattish. If Amazon achieves organic growth of 25% (in Q3 it was 29% so that would be a drop) in 2018, this would put the 3 quarters starting in Q4 2017 at about 35% growth. While we do expect Amazon to boost Whole Foods revenue, that is not required to reach those levels. In Q4 2018, reported revenue will return to organic growth levels. The Amazon story also features two other important growth drivers. First, I expect the Echo to have another substantial growth year and continue to emerge as a new platform in the home. Additionally, Amazon appears poised to benefit from continued business migration to the cloud coupled with increased market share and higher average revenue per cloud customer. This will be driven by modest price increases and introduction of more services as part of its cloud offering. The success of the Amazon Echo with industry leading voice technology should continue to provide another boost to Amazon’s revenue. Additionally, having a large footprint of physical stores will allow Amazon to increase distribution of many products.

4. Stitch Fix stock appreciation will outpace the market (it opened the year at \$25/share and is at the same level as I write this post).

Stitch Fix is my riskiest stock forecast. As a new public company, it has yet to establish a track record of performance that one can depend upon. On the other hand, it’s the early leader in a massive market that will increasingly move online, at-home shopping for fashion forward clothes. The number of people who prefer shopping at home to going to a physical store is on the increase. The type of goods they wish to buy expands every year. Now, clothing is becoming a new category on the rapid rise (it grew from 11% of overall clothing retail sales in 2011 to 19% in 2016). It is important for women buying this way to feel that the provider understands what they want and facilitates making it easy to obtain clothes they prefer. Stitch Fix uses substantial data analysis to personalize each box it sends a customer. The woman can try them on, keep (and pay for) those they like, and return the rest very easily.

5. The stock market will rise in 2018 (the S&P opened the year at 2,696 on January 2).

While I have been accurate on recommending individual stocks over a long period, I rarely believe that I understand what will happen to the overall market. Two prior exceptions were after 9/11 and after the 2008 mortgage crisis generated meltdown. I was correct both times but those seemed like easy calls. So, it is with great trepidation that I’m including this prediction as it is based on logic and I know the market does not always follow logic! To put it simply, the new tax bill is quite favorable to corporations and therefore should boost after-tax earnings. What larger corporations pay is often a blend of taxes on U.S. earnings and those on earnings in various countries outside the U.S. There can be numerous other factors as well. Companies like Microsoft have lower blended tax rates because much of R&D and corporate overhead is in the United States and several of its key products are sold out of a subsidiary in a low tax location, thereby lowering the portion of pre-tax earnings here. This and other factors (like tax benefits in fiscal 2017 from previous phone business losses) led to blended tax rates in fiscal 2015, 2016 and 2017 of 34%, 15% and 8%, respectively. Walmart, on the other hand, generated over 75% of its pre-tax earnings in the United States over the past three fiscal years, so their blended rate was over 30% in each of those years

Table 1: Walmart Blended Tax Rates 2015-2017

The degree to which any specific company’s pre-tax earnings mix changes between the United States and other countries is unpredictable to me, so I’m providing a table showing the impact on after-tax earnings growth for theoretical companies instead. Table 2 shows the impact of lowering the U.S. corporate from 35% to 21% on four example companies. To provide context, I show two companies growing pre-tax earnings by 10% and two companies by 30%. If blended tax rates didn’t change, EPS would grow by the same amount as pre-tax earnings. For Companies 1 and 3, Table 2 shows what the increase in earnings would be if their blended 2017 tax rate was 35% and 2018 shifts to 21%. For companies 2 and 4, Table 2 shows what the increase in earnings would be if the 2017 rate was 30% (Walmart’s blended rate the past three years) and the 2018 blended rate is 20%.

Table 2: Impact on After-Tax Earnings Growth

As you can see, companies that have the majority of 2018 pre-tax earnings subject to the full U.S. tax rate could experience EPS growth 15%-30% above their pre-tax earnings growth. On the other hand, if a company has a minimal amount of earnings in the U.S. (like the 5% of earnings Microsoft had in fiscal 2017), the benefit will be minimal. Whatever benefits do accrue will also boost cash, leading to potential investments that could help future earnings.  If companies that have maximum benefits from this have no decline in their P/E ratio, this would mean a substantial increase in their share price, thus the forecast of an up market. But as I learned on Wall Street, it’s important to sight risk. The biggest risks to this forecast are the expected rise in interest rates this year (which usually is negative for the market) and the fact that the market is already at all-time highs.

6. Battles between the federal government and states will continue over marijuana use but the cannabis industry will emerge as one to invest in.

7. At least one city will announce a new approach to Urban transport

Traffic congestion in cities continues to worsen. Our post on December 14, 2017 discussed a new approach to urban transportation, utilizing small footprint automated cars (one to two passengers, no trunk, no driver) in a dedicated corridor. This appears much more cost effective than a Rapid Bus Transit solution and far more affordable than new subway lines. As discussed in that post, Uber and other ride services increase traffic and don’t appear to be a solution. The thought that automating these vehicles will relieve pressure is overly optimistic. I expect at least one city to commit to testing the method discussed in the December post before the end of this year – it is unlikely to be a U.S. city. The approach outlined in that post is one of several that is likely to be tried over the coming years as new thinking is clearly needed to prevent the traffic congestion that makes cities less livable.

8. Offline retailers will increase the velocity of moving towards omnichannel.

Retailers will adopt more of a multi-pronged approach to increasing their participation in e-commerce. I expect this to include:

• An increased pace of acquisition of e-commerce companies, technologies and brands with Walmart leading the way. Walmart and others need to participate more heavily in online as their core offline business continues to lose share to online. In 2017, Walmart made several large acquisitions and has emerged as the leader among large retailers in moving online. This, in turn, has helped its stock performance. After a stellar 12 months in which the stock was up over 40%, it finally exceeded its January 2015 high of \$89 per share (it reached \$101/share as we are finalizing the post). I expect Walmart and others in physical retail to make acquisitions that are meaningful in 2018 so as to speed up the transformation of their businesses to an omnichannel approach.
• Collaborating to introduce more online/technology into their physical stores (which Amazon is likely to do in Whole Foods stores). This can take the form of screens in the stores to order online (a la William Sonoma), having online purchases shipped to your local store (already done by Nordstrom) and adding substantial ability to use technology to create personalized items right at the store, which would subsequently be produced and shipped by a partner.

9. Social commerce will begin to emerge as a new category.

Many e-commerce sites have added elements of social, and many social sites have begun trying to sell various products. But few of these have a fully integrated social approach to e-commerce. The elements of a social approach to e-commerce include:

• A feed-based user experience
• Friends’ actions impact your feed
• Following trend setters to see what they are buying, wearing, and favoring
• Following particular stores and/or friends

I expect to see existing e-commerce players adding more elements of social, existing social players improving their approach to commerce and a rising trend of emerging companies focused on fully integrated social commerce.

10. “The Empire Strikes Back”: automobile manufacturers will begin to take steps to reclaim use of its GPS.

It is almost shameful that automobile manufacturers, other than Tesla, have lost substantial usage of their onboard GPS systems as many people use their cell phones or a small device to run Google, Waze (owned by Google) or Garmin instead of the larger screen in their car. In the hundreds of times I’ve taken an Uber or Lyft, I’ve never seen the driver use their car’s system. To modernize their existing systems, manufacturers may need to license software from a third party. Several companies are offering next generation products that claim to replicate the optimization offered by Waze but also add new features that go beyond it like offering to order coffee and other items to enable the driver to stop at a nearby location and have the product prepaid and waiting for them. In addition to adding value to the user, this also leads to a lead-gen revenue opportunity. In 2018, I expect one or more auto manufacturers to commit to including a third-party product in one or more of their models.

Soundbytes

Tesla model 3 sample car generates huge buzz at Stanford Mall in Menlo Park California. This past weekend my wife and I experienced something we had not seen before – a substantial line of people waiting to check out a car, one of the first Model 3 cars seen live. We were walking through the Stanford Mall where Tesla has a “Guide Store” and came upon a line of about 60 people willing to wait a few hours to get to check out one of the two Model 3’s available for perusal in California (the other was in L.A.). An hour later we came back, and the line had grown to 80 people. To be clear, the car was not available for a test drive, only for seeing it, sitting in it, finding out more info, etc. Given the buzz involved, it seems to me that as other locations are given Model 3 cars to look at, the number of people ordering a Model 3 each week might increase faster than Tesla’s capacity to fulfill.

# Re-cap of 2017 Top Ten Predictions

I started 2017 by saying:

When I was on Wall Street I became very boring by having the same three strong buy recommendations for many years…  until I downgraded Compaq in 1998 (it was about 30X the original price at that point). The other two, Microsoft and Dell, remained strong recommendations until I left Wall Street in 2000. At the time, they were each well over 100X the price of my original recommendation. I mention this because my favorite stocks for this blog include Facebook and Tesla for the 4th year in a row. They are both over 5X what I paid for them in 2013 (\$23 and \$45, respectively) and I continue to own both. Will they get to 100X or more? This is not likely, as companies like them have had much higher valuations when going public compared with Microsoft or Dell, but I believe they continue to offer strong upside, as explained below.

Be advised that my top ten for 2018 will continue to include all three picks from 2017. I’m quite pleased that I continue to be fortunate, as the three were up an average of 53% in 2017. Furthermore, each of my top ten forecasts proved pretty accurate, as well!

I’ve listed in bold the 2017 stock picks and trend forecasts below, and give a personal evaluation of how I fared on each. For context, the S&P was up 19% and the Nasdaq 28% in 2017.

1. Tesla stock appreciation will continue to outpace the market. Tesla, once again, posted very strong performance.  While the Model 3 experienced considerable delays, backorders for it continued to climb as ratings were very strong. As of mid-August, Tesla was adding a net of 1,800 orders per day and I believe it probably closed the year at over a 500,000-unit backlog. So, while the stock tailed off a bit from its high (\$385 in September), it was up 45% from January 3, 2017 to January 2, 2018 and ended the year at 7 times the original price I paid in 2013 when I started recommending it. Its competitors are working hard to catch up, but they are still trailing by quite a bit.
2. Facebook stock appreciation will continue to outpace the market. Facebook stock appreciated 57% year/year and opened on January 2, 2018 at \$182 (nearly 8 times my original price paid in 2013 when I started recommending it). This was on the heels of 47% revenue growth (through 3 quarters) and even higher earnings growth.
3. Amazon stock appreciation will outpace the market. Amazon stock appreciated 57% in 2017 and opened on January 2, 2018 at \$1,188 per share. It had been on my recommended list in 2015 when it appreciated 137%. Taking it off in 2016 was based on Amazon’s stock price getting a bit ahead of itself (and revenue did catch up that year growing 25% while the stock was only up about 12%). In 2017, the company increased its growth rate (even before the acquisition of Whole Foods) and appeared to consolidate its ability to dominate online retail.
4. Both online and offline retailers will increasingly use an omnichannel approach. Traditional retailers started accelerating the pace at which they attempted to blend online and offline in 2017. Walmart led, finally realizing it had to step up its game to compete with Amazon. While its biggest acquisition was Jet.com for over \$3 billion, it also acquired Bonobos, Modcloth.com, Moosejaw, Shoebuy.com and Hayneedle.com, creating a portfolio of online brands that could also be sold offline. Target focused on becoming a leader in one-day delivery by acquiring Shipt and Grand Junction, two leaders in home delivery. While I had not predicted anything as large as a Whole Foods acquisition for Amazon, I did forecast that they would increase their footprint of physical locations (see October 2016 Soundbytes). The strategy for online brands to open “Guide” brick and mortar stores ( e.g. Tesla, Warby Parker, Everlane, etc.) continued at a rapid pace.
5. A giant piloted robot will be demo’d as the next form of entertainment. As expected, Azure portfolio company, Megabots, delivered on this forecast by staging an international fight with a giant robot from Japan. The fight was not live as the robots are still “temperamental” (meaning they occasionally stop working during combat). However, interest in this new form of entertainment was incredible as the video of the fight garnered over 5 million views (which is in the range of an average prime-time TV show). There is still a large amount of work to be done to convert this to an ongoing form of entertainment, but all the ingredients are there.
6. Virtual and Augmented reality products will escalate. Sales of VR/AR headsets appear to have well exceeded 10 million units for the year with some market gain for higher-end products. The types of applications have expanded from gaming to room design (and viewing), travel, inventory management, education, healthcare, entertainment and more. While the actual growth in unit sales fell short of what many expected, it still was substantial. With Apple’s acquisition of Vrvana (augmented reality headset maker) it seems clear that Apple plans to launch multiple products in the category over the next 2-3 years, and with Facebook’s launch of ArKIT, it’s social AR development platform, there is clearly a lot of focus and growth ahead.
7. Magic Leap will disappoint in 2017. Magic Leap, after 5 years of development and \$1.5 billion of investment, did not launch a product in 2017. But, in late December they announced that their first product will launch in 2018. Once again, the company has made strong claims for what its product will do, and some have said early adopters (at a very hefty price likely to be in the \$1,500 range) are said to be like those who bought the first iPod. So, while it disappointed in 2017, it is difficult to tell whether or not this will eventually be a winning company as it’s hard to separate hype from reality.
8. Cable companies will see a slide in adoption. According to eMarketer, “cord cutting”, i.e. getting rid of cable, reached record proportions in 2017, well exceeding their prior forecast. Just as worrisome to providers, the average time watching TV dropped as well, implying decreased dependence on traditional consumption. Given the increase now evident in cord cutting, UBS (as I did a year ago) is now forecasting substantial acceleration of the decline in subscribers. While the number of subscribers bounced around a bit between 2011 and 2015, when all was said and done, the aggregate drop in that four-year period was less than 0.02%. UBS now forecasts that between the end of 2016 and the end of 2018 the drop will be 7.3%. The more the industry tries to offset the drop by price increases, the more they will accelerate the pace of cord cutting.
9. Spotify will either postpone its IPO or have a disappointing one. When we made this forecast, Spotify was expected to go public in Q2 2017. Spotify postponed its IPO into 2018 while working on new contracts with the major music labels to try to improve its business model. It was successful in these negotiations in that the labels all agreed to new terms. Since the terms were not announced, we’ll need to see financials for Q1 2018 to better understand the magnitude of improvement. In the first half of the year, Spotify reported that gross margins improved from 16% to 22%, but this merely cut its loss level rather than move the company to profitability. It has stated that it expects to do a non-traditional IPO (a direct listing without using an investment bank) in the first half of 2018. If the valuation approaches its last private round, I would caution investors to stay away, as that valuation, coupled with 22% gross margins (and over 12% of revenue in sales and marketing cost to acquire customers), implies net margin in the mid-single digits at best (assuming they can reduce R&D and G&A as a percent of revenue). This becomes much more challenging in the face of a \$1.6 billion lawsuit filed against it for illegally offering songs without compensating the music publisher. Even if they managed to successfully fight the lawsuit and improve margin, Spotify would be valued at close to 100 times “potential earnings” and these earnings may not even materialize.
10. Amazon’s Echo will gain considerable traction in 2017. Sales of the Echo exploded in 2017 with Amazon announcing that it “sold 10s of millions of Alexa-enabled devices” exceeding our aggressive forecast of 2-3x the 4.4 million units sold in 2016. The Alexa app was also the top app for both Android and iOS phones. It clearly has carved out a niche as a new major platform.

Stay tuned for my top 10 predictions of 2018!

SoundBytes

• In our December 20, 2017 post, I discussed just how much Steph Curry improves teammate performance and how effective a shooter he is. I also mentioned that Russell Westbrook leading the league in scoring in the prior season might have been detrimental to his team as his shooting percentage falls well below the league average. Now, in his first game returning to the lineup, Curry had an effective shooting percentage that exceeded 100% while scoring 38 points (this means scoring more than 2 points for every shot taken). It would be interesting to know if Curry is the first player ever to score over 35 points with an effective shooting percentage above 100%! Also, as of now, the Warriors are scoring over 15 points more per game this season with Curry in the lineup than they did for the 11 games he was out (which directly ties to the 7.4% improvement in field goal percentage that his teammates achieve when playing with Curry as discussed in the post).

# Ending the Year on a High Note…or should I say Basketball Note

## Deeper analysis on what constitutes MVP Value

In my blog post dated February 3, 2017, I discussed several statistics that are noteworthy in analyzing how much a basketball player contributes to his team’s success. In it, I compared Stephen Curry and Russell Westbrook using several advanced statistics that are not typically highlighted.

The first statistic: Primary plus Secondary Assists per Minute a player has the ball. Time with the ball equates to assist opportunity, so holding the ball most of the time one’s team is on offense reduces the opportunity for others on the team to have assists. This may lead to fewer assisted baskets for the whole team, but more for the individual player. As of the time of the post, Curry had 1.74 assists (primary plus secondary) per minute he had the ball, while Westbrook only had 1.30 assists per minute. Curry’s efficiency in assists is one of the reasons the Warriors total almost 50% more assists per game than the Thunder, make many more easy baskets, and lead the league in field goal percentage.

The second statistic: Effective Field Goal Percentages (where making a 3-point shot counts the same as making 1 ½ 2-point shots). Again, Curry was vastly superior to Westbrook at 59.1% vs 46.4%. What this means is that Westbrook scores more because he takes many more shots, but these shots are not very efficient for his team, as Westbrook’s shooting percentage continued to be well below the league average of 45.7% (Westbrook’s was 42.5% last season and is 39.6% this season to date).

The third statistic: Plus/Minus.  Plus/Minus reflects the number of points your team outscores opponents while you are on the floor.  Curry led the league in this in 2013, 2014, and 2016 and leads year-to-date this season. In 2015 he finished second by a hair to a teammate. Westbrook has had positive results, but last year averaged 3.2 per 36 minutes vs Curry’s 13.8. One challenge to the impressiveness of this statistic for Curry is whether his leading the league in Plus/Minus is due to the quality of players around him. In refute, it is interesting to note that he led the league in 2013 when Greene was a sub, Durant wasn’t on the team and Thompson was not the player he is today.

The background shown above brings me to today’s post which outlines another way of looking at a player’s value. The measurement I’m advocating is: How much does he help teammates improve? My thesis is that if the key player on a team creates a culture of passing the ball and setting up teammates, everyone benefits. Currently the value of helping teammates is only measured by the number of assists a player records. But, if I’m right, and the volume of assists is the wrong measure of helping teammates excel (as sometimes assists are the result of holding the ball most of the time) then I should be able to verify this through teammate performance. If most players improve their performance by getting easier shots when playing with Westbrook or Curry, then this should translate into a better shooting percentage. That would mean we should be able to see that most teammates who played on another team the year before or the year after would show a distinct improvement in shooting percentage while on his team. This is unlikely to apply across the board as some players get better or worse from year to year, and other players on one’s team also impact this data. That being said, looking at this across players that switch teams is relevant, especially if there is a consistent trend.

To measure this for Russell Westbrook, I’ve chosen 5 of the most prominent players that recently switched teams to or from Oklahoma City: Victor Oladipo, Kevin Durant, Carmelo Anthony, Paul George and Enes Kantor. Three left Oklahoma City and two went there from another team. For the two that went there, Paul George and Carmelo Anthony, I’ll compare year-to-date this season (playing with Westbrook) vs their shooting percentage last year (without Westbrook). For Kantor and Oladipo, the percentage last year will be titled “with Westbrook” and this year “without Westbrook” and for Durant, the seasons in question are the 2015-16 season (with Westbrook) vs the 2016-17 season (without Westbrook).

Shooting Percentage

Given that the league average is to shoot 45.7%, shooting below that can hurt a team, while shooting above that should help. An average team takes 85.4 shots per game, so a 4.0% swing translates to over 8.0 points a game. To put that in perspective, the three teams with the best records this season are the Rockets, Warriors and Celtics and they had first, second and fourth best Plus/Minus for the season at +11.0, +11.0 and +5.9, respectively. The Thunder came in at plus 0.8. If they scored 8 more points a game (without giving up more) their Plus/Minus would have been on a par with the top three teams, and their record likely would be quite a bit better than 12 and 14.

Curry and His Teammates Make Others Better

How does Curry compare? Let’s look at the same statistics for Durant, Andrew Bogut, Harrison Barnes, Zaza Pachulia and Ian Clark (the primary player who left the Warriors). For Barnes, Bogut, Pachulia and Durant I’ll compare the 2015 and 2016 seasons and for Clark I’ll use 2016 vs this season-to-date.

So, besides being one of the best shooters to play the game, Curry also has a dramatic impact on the efficiency of other players on his team. Perhaps it’s because opponents need to double team him, which allows other players to be less guarded. Perhaps it’s because he bought into Kerr’s “spread the floor, move the ball philosophy”. Whatever the case, his willingness to give up the ball certainly has an impact. And that impact, plus his own shooting efficiency, clearly leads to the Warriors being an impressive scoring machine. As an aside, recent Warrior additions Casspi and Young are also having the best shooting percentages of their careers.

Westbrook is a Great Player Who Could be Even Better

I want to make it clear that I believe Russell Westbrook is a great player. His speed, agility and general athleticism allow him to do things that few other players can match. He can be extremely effective driving to the basket when it is done under control. But, he is not a great outside shooter and could help his team more by taking fewer outside shots and playing less one/one basketball. Many believed that the addition of George and Anthony would make Oklahoma City a force to be reckoned with, but to date this has not been the case. Despite the theoretic offensive power these three bring to the table, the team is 24th in the league in scoring at 101.8 per game, 15 points per game behind the league leading Warriors. This may change over the course of the season but I believe that each of them playing less one/one basketball would help.

# Using Technology to Revolutionize Urban Transit

Worsening traffic requires new solutions

As our population increases, the traffic congestion in cities continues to worsen. In the Bay Area my commute into the city now takes about 20% longer than it did 10 years ago, and driving outside of typical rush hours is now often a major problem. In New York, the subway system helps quite a bit, but most of Manhattan is gridlocked for much of the day.

The two key ways of relieving cities from traffic snarl are:

1. Reduce the number of vehicles on city streets
2. Increase the speed at which vehicles move through city streets

Metro areas have been experimenting with different measures to improve car speed, such as:

1. Encouraging carpooling and implementing high occupancy vehicle lanes on arteries that lead to urban centers
2. Converting more streets to one-way with longer periods of green lights
3. Prohibiting turns onto many streets as turning cars often cause congestion

No matter what a city does, traffic will continue to get worse unless compelling and effective urban transportation systems are created and/or enhanced. With that in mind, this post will review current alternatives and discuss various ways of attacking this problem.

Ride sharing services have increased congestion

Uber and Lyft have not helped relieve congestion. They have probably even led to increasing it, as so many rideshare vehicles are cruising the streets while awaiting their next ride. While the escalation of ridesharing services like Uber and Lyft may have reduced the number of people who commute using their own car to work, they have merely substituted an Uber driver for a personal driver. Commuters parked their cars when arriving at work while ridesharing drivers continue to cruise after dropping off a passenger, so the real benefit here has been in reducing demand for parking, not improving traffic congestion.

A simple way to think about this is that the total cars on the street at any point in time consists of those with someone going to a destination plus those cruising awaiting picking up a passenger. Uber does not reduce the number of people going to a destination by car (and probably increases it as some Uber riders would have taken public transportation if not for Uber).

The use of optimal traffic-aware routing GPS apps like Waze doesn’t reduce traffic but spreads it more evenly among alternate routes, therefore providing a modest increase in the speed that vehicles move through city streets. The thought that automating these vehicles will relieve pressure is unrealistic, as automated vehicles will still be subject to the same movement as those with drivers (who use Waze). Automating ridesharing cars can modestly reduce the number of cruising vehicles, as Uber and Lyft can optimize the number that remain in cruise mode. However, this will not reduce the number of cars transporting someone to a destination. So, it is clear to me that ridesharing services increase rather than reduce the number of vehicles on city streets and will continue to do so even when they are driverless.

Metro rail systems effectively reduce traffic but are expensive and can take decades to implement

Realistically, improving traffic flow requires cities to enhance their urban transport system, thereby reducing the number of vehicles on their streets. There are several historic alternatives but the only one that can move significant numbers of passengers from point A to point B without impacting other traffic is a rail system. However, construction of a rail system is costly, highly disruptive, and can take decades to go from concept to completion. For example, the New York City Second Avenue Line was tentatively approved in 1919. It is educational to read the history of reasons for delays, but the actual project didn’t begin until 2005 despite many millions of dollars being spent on planning, well before that date. The first construction commenced in April 2007. The first phase of the construction cost \$4.5 billion and included 3 stations and 2 miles of tunnels. This phase was complete, and the line opened in January 2017. By May daily ridership was approximately 176,000 passengers. A second phase is projected to cost an additional \$6 billion, add 1.5 more miles to the line and be completed 10-12 years from now (assuming no delays). Phase 1 and 2 together from actual start to hopeful finish will be over two decades from the 2005 start date…and about a century from when the line was first considered!

Dedicated bus rapid transit, less costly and less effective

Most urban transportation networks include bus lines through city streets. While buses do reduce the number of vehicles on the roads, they have several challenges that keep them from being the most efficient method of urban transport:

1. They need to stop at traffic lights, slowing down passenger movement
2. When they stop to let one passenger on or off, all other passengers are delayed
3. They are very large and often cause other street traffic to be forced to slow down

One way of improving bus efficiency is a Dedicated Bus Rapid Transit System (BRT). Such a system creates a dedicated corridor for buses to use. A key to increasing the number of passengers such a system can transport is to remove them from normal traffic (thus the dedicated lanes) and to reduce or eliminate the need to stop for traffic lights by either altering the timing to automatically accommodate minimal stoppage of the buses or by creating overpasses and/or underpasses. If traffic lights are altered, the bus doesn’t stop for a traffic light but that can mean cross traffic stops longer, thus increasing cross traffic congestion. Elimination of interference using underpasses and/or overpasses at each intersection can be quite costly given the substantial size of buses. San Francisco has adopted the first, less optimal, less costly, approach along a two-mile corridor of Van Ness Avenue. The cost will still be over \$200 million (excluding new buses) and it is expected to increase ridership from about 16,000 passengers per day to as much as 22,000 (which I’m estimating translates to 2,000-3,000 passengers per hour in each direction during peak hours). Given the increased time cross traffic will need to wait, it isn’t clear how much actual benefit will occur.

Will Automated Car Rapid Transit (ACRT) be the most cost effective solution?

I recently met with a company that expects to create a new alternative using very small automated car rapid transit (ACRT) that costs a fraction of and has more than double the capacity of a BRT.  The basic concept is to create a corridor similar to that of a BRT, utilizing underpasses under some streets and bridges over other streets. Therefore, cross traffic would not be affected by longer traffic light stoppages. Since the size of an underpass (tunnel) to accommodate a very small car is a fraction of that of a very large bus, so is the cost. The cars would be specially designed driverless automated cars that have no trunk, no back seats and hold one or two passengers. The same 3.5 to 4.0-meter-wide lane needed for a BRT would be sufficient for more than two lanes of such cars. Since the cars would be autonomous, speed and distance between cars could be controlled so that all cars in the corridor move at 30 miles per hour unless they exited. Since there would be overpasses and underpasses across each cross street, the cars would not stop for lights. Each vehicle would hold one or two passengers going to the same stop, so the car would not slow until it reached that destination. When it did, it would pull off the road without reducing speed until it was on the exit ramp.

The company claims that it will have the capacity to transport 10,000 passengers per hour per lane with the same setup as the Van Ness corridor if underpasses and overpasses were added. Since a capacity of 10,000 passengers per hour in each direction would provide significant excess capacity compared to likely usage, 2 lanes (3 meters in total width instead of 7-8 meters) is all that such a system would require. The reduced width would reduce construction cost while still providing excess capacity. Passengers would arrive at destinations much sooner than by bus as the autos would get there at 30 miles per hour without stopping even once. This translates to a 2-mile trip taking 4 minutes! Compare that to any experience you have had taking a bus.  The speed of movement also helps make each vehicle available to many more passengers during a day. While it is still unproven, this technology appears to offer significant cost/benefit vs other alternatives.

Conclusion

The population expansion within urban areas will continue to drive increased traffic unless additional solutions are implemented. If it works as well in practice as it does in theory, an ACRT like the one described above offers one potential way of improving transport efficiency. However, this is only one of many potential approaches to solving the problem of increased congestion. Regardless of the technology used, this is a space where innovation must happen if cities are to remain livable. While investment in underground rail is also a potential way of mitigating the problem, it will remain an extremely costly alternative unless innovation occurs in that domain.