Aswath Damodaran, Uber’s Valuation and Ratchets

The last time I’d written about Aswath Damodaran’s comments on Uber’s valuation, it was regarding his “fight” with Uber investor Bill Gurley, and whether his valuation was actually newsworthy.

Now, his latest valuation of Uber, which he concludes is worth about USD 28 Billion, has once again caught the attention of mainstream media, with Mint writing an editorial about it (Disclosure: I write regularly for Mint).

I continue to maintain that Damodaran’s latest valuation is also an academic exercise, and the first rule of valuation is that “valuation is always wrong”, and that we should ignore it.

However, in the context of my recent piece on investor protection clauses in venture investments (mainly ratchets), it is useful to look at Damodaran’s valuation of Uber, and how it compares to Uber’s valuation if we were to account for investor protection clauses.

“True value” of Indian unicorns after accounting for investor protection. Source: Mint

When Uber raised $3.5 Billion from Saudi Arabia’s Public Investment Fund earlier this year, the headline valuation number was $62.5 Billion. Given the late stage of investment, it is unlikely that the investor would have done so without sufficient downside protection – at the very least, they would want a “full ratchet” (if the next investment happens at a lower valuation, then they get additional shares to compensate for their loss). This is a conservative assumption since late stage (“pre-IPO”) investments usually have clauses more friendly to the investor, usually incorporating a minimum “guaranteed return”.

Plugging these numbers into the model I’ve built (pre-money valuation of $59 Billion and post-money valuation of $62.5 billion), the valuation of the put option written by existing investors in favour of Uber comes to around $1.28 Billion. Accounting for this option, the total value of the company comes out to $39.6 Billion.

Damodaran’s valuation, based on his views, principles and numbers, is $28 Billion. Assuming that investors and management of Uber are aware of the downside protection clauses and its impact on the company’s valuation, Damodaran’s valuation is not that much of a discount on Uber’s true valuation!

Why Uber/Ola is Nehruvian

According to Ramachandra Guha’s India After Gandhi, the ostensible reason for India adopting a statist/socialist/planned approach was the scarcity of capital.

With capital being scarce in the newly independent country, Jawaharlal Nehru had reasoned that in order for the country to develop, whatever capital existed had to be deployed in the most productive manner possible. A free market for capital would end up deploying capital where it wasn’t required the most, denying more critical sectors of capital. A planned economy, on the other hand, would result in more efficient usage of capital.

While India has developed significantly in the 70 years since independence, it is still not completely out of the woods. Poverty remains high and India’s per capita income is at the lower end of the spectrum. Thus, while capital may not be as scarce a resource as it was in 1950, effective deployment of capital is still necessary to ensure India’s continued economic growth.

From this perspective, think of the car. When at rest, it is adding no economic value apart from making itself available to its owner (and its owner alone) at a point of time when the latter needs it. From this perspective, the economic value that the parked car adds is almost entirely in terms of “option value”.

A parked car also consumes valuable economic resources, with the most important being the real estate it stands on. This particular resource is so important that it forms an important form of urban regulation in most markets (a building or a business needs to have a certain minimum number of parking spaces and so on).

Moreover, the two common axes on which the value of a car is evaluated are age and distance travelled. Considering that the car adds economic value only in terms of the latter – when it helps transport someone, depreciation of the car in terms of age is entirely uncompensated. On this account, too, a parked car is a dead weight loss.

It is not hard to see, thus, that a parked car is an enormous waste of capital; capital that an emerging economy such as India could very well utilise elsewhere. Yet, the large number of cars in the country that are standing still at any point in time show that despite being an overall inefficient use of capital, a large number of people value the inbuilt option value.

Back in the time when Nehru had his way, he had solved the problem in his own unique way – by limiting the number of cars that could be manufactured and sold in the country, which automatically put a limit on the number of parked cars. In this technologically advanced day and age, however, we don’t need such drastic measures.

All we need is a restructuring of economic incentives such that the option value of a parked car goes down. And what better incentive than to provide the option to summon a car on demand? While this summoned car might have a higher marginal cost per trip than an owned car, taken in aggregate it leads to a significantly lower cost.

Thus, the Nehruvian answer to the inefficient capital wasted in parked cars would be to encourage services that allow you to summon a car on demand. In other words, services such as Uber and Ola fulfil a Nehruvian objective by freeing up capital that was being earlier wasted in parked cars. There is data to show that such services have resulted in a decline in growth of car ownership.

Given that Uber and Ola follow the Nehruvian ideal of reducing wasteful capital, it is baffling that the government in Karnataka, which belongs to the Congress party which is based on Nehruvian ideals, or the government in Delhi, headed by the Nehruvian Arvind Kejriwal, were to campaign to clamp down on such Nehruvian services.

There might be some tremors under Shanti Van.

Uber’s anchoring problem

The Karnataka transport department has come out with a proposal to regulate cab aggregators such as Uber and Ola. The proposal is hare-brained on most  counts, such as limiting drivers’ working hours, limiting the number of aggregators a driver can attach himself to and having a “digital meter”. The most bizarre regulation, however, states that the regulator will decide the fares and that dynamic pricing will not be permitted.

While these regulations have been proposed “in the interest of the customer” it is unlikely to fly as it will not bring much joy to the customers – apart from increasing the number of auto rickshaws and taxis in the city through the back door. I’m confident the aggregators will find a way to flout these regulations until a time they become more sensible.

Dynamic pricing is an integral aspect of the value that cab aggregators such as Uber or Ola add. By adjusting prices in a dynamic fashion, these aggregators push information to drivers and passengers regarding demand and supply. Passengers can use the surge price, for example, to know what the demand-supply pattern is (I’ve used Uber surge as a proxy to determine what is a fair price to pay for an auto rickshaw, for example).

Drivers get information on the surge pricing pattern, and are encouraged to move to areas of high demand, which will help clear markets more efficiently. Thus, surge pricing is not only a method to match demand and supply, but is also an important measure of information to a cab aggregator’s operations. Doing away with dynamic pricing will thus stem this flow of information, thus reducing the value that these aggregators can add. Hopefully the transport department will see greater sense and permit dynamic pricing (Disclosure: One of my lines of business is in helping companies implement dynamic pricing, so I have a vested interest here. I haven’t advised any cab aggregators though).

That said, Uber has a massive anchoring problem, because dynamic pricing works only in one way. Anchoring is a concept from behavioural economics where people’s expectations of something are defined by something similar they have seen (there is an excellent NED Talk on this topic (by Prithwiraj Mukherjee of IIMB) which I hope to upload in its entirety soon). There are certain associations that are wired in our heads thanks to past information, and these associations bias our view of the world.

A paper by economists at NorthEastern University on Uber’s surge pricing showed that demand for rides is highly elastic to price (a small increase in price leads to a large drop in demand), while the supply of rides (on behalf of drivers) is less elastic, which makes determination of the surge price hard. Based on anecdotal information (friends, family and self), elasticity of demand for Uber in India is likely to be much higher.

Uber’s anchoring problem stems from the fact that the “base prices” (prices when there is no surge) is anchored in people’s minds. Uber’s big break in India happened in late 2014 when they increased their discounts to a level where travelling by Uber became comparable in terms of cost to travelling by auto rickshaw (the then prevalent anchor for local for-hire public transport).

Over the last year, Uber’s base price (which is cheaper than an auto rickshaw fare for rides of a certain length) have become the new anchor in the minds of people, especially Uber regulars. Thus, whenever there is a demand-supply mismatch and there is a surge, comparison to the anchor price means that demand is likely to drop even if the new price is by itself fairly competitive (compared to other options at that point in time).

The way Uber has implemented its dynamic pricing is that it has set the “base price” at one end of the distribution, and moves price in only one direction (upwards). While there are several good reasons for doing this, the problem is that the resultant anchoring can lead to much higher elasticity than desired. Also, Uber’s pricing model (more on this in a book on Liquidity that I’m writing) relies upon a certain minimum proportion of rides taking place at a surge (the “base price” is to ensure minimum utilisation during off-peak hours), and anchoring-driven elasticity can’t do this model too much good.

A possible solution to this would be to keep the base fare marginally higher, and adjust prices both ways – this will mean that during off-peak hours a discount might be offered to maintain liquidity. The problem with this might be that the new higher base fare might be anchored in people’s minds, leading to diminished demand in off-peak hours (when a discount is offered). Another problem might be that drivers might be highly elastic to drop in fares killing the discounted market. Still, it is an idea worth exploring – in my opinion there’s a sweet spot in terms of the maximum possible discount (maybe as low as 10%, but I think it’s strictly greater than zero)  where the elasticities of drivers and passengers are balanced out, maximising overall revenues for the firm.

We are in for interesting days, as long as stupid regulation doesn’t get in the way, that is.

Market depth, pricing and subsidies

A few days back I had written about how startups should determine how much to subsidise their customers during the growth phase – subsidise to the extent of the long-term price. If you subsidise too much initially, elasticity might hit you when you eventually have to raise prices, and that can set you back.

The problem is in determining what this long-term sustainable price will be. In “one-sided markets” where the company manufactures or assembles stuff and sells it on, it is relatively easy, since the costs are well known. The problem lies in two-sided markets, where the long-term sustainable price is a function of the long-term sustainable volume.

A “bug” of any market is transaction costs, and this is especially the case in a two-sided market. If you are a taxi driver on Ola or Uber platform, the time you need to wait for the next ride or distance you travel to pick up your next customer are transaction costs. And the more “liquid” the market (more customers and more drivers), the lesser these transaction costs, and the more the money you make.

In other words, the denser a market, the lower the price required to match demand and supply, with the savings coming out of savings in transaction costs.

So if you are a two-sided market, the long-term sustainable price on your platform is a function of how big your market will be, and so in order to determine how much to subsidise (which is a function of long-term sustainable price), you need to be able to forecast how big the market will be. And subsidise accordingly.

It is well possible that overly optimistic founders might be too bullish about the eventual size of their platform, and this can lead to subsidising to an extent greater than the extent dictated by the long term market size. And some data points from the Indian “marketplace industry” show that this has possibly happened in India.

Having remained credit card only for a long time now, Uber has started accepting cash payments – in order to attract customers who are not comfortable transacting money online. This belated opening shows that Uber perhaps didn’t hit the numbers they had hoped to, using their traditional credit card / wallet model.

Uber has problems on the driver side, too, with an increasing number of its drivers turning out to be rather rude (this is anecdata from several sources, I must confess), refusing rides, fighting with passengers, etc. Competitor Ola has started buying cars and loaning them to drivers, perhaps indicating that the driver side of the market hasn’t grown to their expectations. They are all indicative of overestimation of market size, and an attempt to somehow hit that size rather than operating at the lower equilibrium.

So an additional risk in running marketplaces is that if you overestimate market size, you might end up overdoing the subsidies that you provide to build up the market. And at some point in time you have to roll back those subsidies, which might lead to shrinkage of the market and a possible death spiral.

Now apply this model to your favourite marketplace, and tell me what you think of them.

Barriers to entry in cab aggregation

The news that Reliance might be getting into the cab aggregation game got me thinking about the barriers to entry in this business. Considering that it is fundamentally an unregulated industry, or rather an industry where players actively flout regulations, the regulatory barrier is not there.

Consequently, anyone who is able and willing to make the investment and set up the infrastructure will be able to enter the industry. The more important barrier to entry, however, is scale.

Recently I was talking to an Uber driver who had recently switched from TaxiForSure. The latter, he said had lost “liquidity” over the last couple of months (after the Ola takeover), with customers and drivers deserting the service successively in a vicious cycle. Given that cab aggregation is a two-sided market, with prominent cross-sided network effects (number of customers depends on number of cabs and vice versa), it is not possible to do business if you are small, and it takes scale.

For this reason, for a new player to enter the cab aggregation business, it takes significant investments. The cost of acquisition for drivers and passengers is still quite high, and this has to be borne by the new player. Given that a significant number of drivers have to be initially attracted, it takes deep pockets to be able to come in.

Industry players were probably banking on the fact that with the industry already seeing consolidation (when Ola bought TaxiForSure), Venture Capitalists might stop funding newer businesses in this segment, and for that reason Uber and Ola might have a free rein. Ola had even stopped subsidising passengers in the meantime, reasoning (correctly for the time) that with their only competition being Uber they might charge market rates.

From this perspective it is significant that the new player who is entering is an industrial powerhouse with both deep pockets and with a reputation of getting their way around in terms of regulation. The first ensures that they can make the requisite investment (without resorting to VC money) and the second gives the hope that the industry might get around the regulatory troubles it’s been facing so far.

I once again go back to this excellent blog post by Deepak Shenoy on the cab aggregation industry. He had mentioned that what Uber and Ola are doing is to lay down the groundwork for a new sector and more efficient urban transport services. That they may not survive but the ecosystem they create will continue to thrive and add value to urban transport. Reliance’s entry into this sector is a step in making this sector more sustainable.

Will I switch once they launch? Depends upon the quality of service. Currently I’m loyal to Uber primarily because of that factor, but if their service drops and Reliance can offer better service I will have no hesitation in switching.

The ET article linked above talks about drivers cribbing about falling incentives by Uber and Ola. It will be interesting to see how the market plays out once the market stabilises and incentives hit long-run market rates (at which aggregators need to make a profit). A number of drivers have invested in cabs now looking at the short-term profits at hand, but these will surely drop with incentives as the industry stabilises.

Reliance’s entry into cab aggregation is also ominous to other “new” sectors that have shown a semblance of settling down after exuberant VC activity – in the hope that VCs will stop funding that sector and hence competition won’t grow. After the entry into cab aggregation, I won’t be surprised if Reliance Retail were to move into online retail and do a good job of it. The likes of Flipkart beware.

Cabs to airports

Early yesterday morning I had a minor scare when Mega Cabs stood me up. I had a flight to catch at 7 am to Mumbai, and had booked a Mega Cab for 5am. This was after consulting a few friends who are frequent travellers on Monday mornings, who advised that finding an Uber or Ola at 5am is not particularly straightforward. I must mention that I haven’t done business trips for a while, which means I haven’t had to catch 7am flights, so the last time I took one such flight was before Ola/Uber became big in Bangalore (October 2014). And I’ve always preferred Mega to Meru since their cabs are relatively better maintained and more prompt.
And then Mega stood me up. The assigned driver Nagesh N never called me, and when I called him, didn’t pick up. I didn’t panic, since I knew I could get a cab on Uber or Ola, except that neither had any cabs available. I called Mega customer care, who promised an alternate cab at 5:15 (still leaving enough time to get to the airport and catch my flight). But then I received an SMS saying that I’ll get a cab at 6:15. Rather than arguing with Mega, I tried Uber once again, and this time I was in luck, finding a cab that would take me to the airport at a surge of 1.8X (80% more than the “normal” fare).
So on the way to the airport I got talking to Kumar, my Uber driver, about the economics of cab rides in Bangalore, and airport trips. As I had mentioned in my earlier post on Uber’s new pricing model, the reduction in per kilometer fare and increase in per rupee fare has meant that an airport run is normally not remunerative for an Uber driver. Add to this the fact that Uber’s bonus payments to drivers are on a “per trip” basis rather than a percentage or distance basis, that a driver reaching the airport at around 6am has to wait for at least a couple of hours to get a passenger to ride back to the city, and that Uber’s new bonus structures that began today not paying much incentives for trips before 7 am (this was told to me by Kumar), drivers have responded by simply not switching on their Uber systems at 5 in the morning, when the likelihood that any trip is an airport run approaches 1.
This is clearly inefficient, and  consequence of bad pricing on behalf of Uber. On the one hand, drivers are denied opportunities to carry customers over long distances, which is an airport run. On the other, customers are inconvenienced thanks to the lack of cabs, and have to rely on the otherwise rather unreliable and mostly unused Meru or Mega cabs, whose cars are of poor quality and drivers unresponsive. A lose-lose situation. All thanks to bad regulation (read my post in Pragati on how Uber is like a parallel regulator).
The solution is rather simple – an airport surcharge. Any trips to or from the airport on Uber can be slapped a further surcharge (of Rs. 200, perhaps). Such a surcharge will make the ride remunerative for drivers, while at the same time still keeping Uber much cheaper than the likes of Meru or Mega. In fact, this morning’s trip, after the 1.8X surge, cost me Rs. 780, which is cheaper than what it would have cost me if Nagesh N of Mega Cabs had not ditched me, and I could pay in a “cashless” manner, directly from my Paytm account. It’s a surprise that Uber hasn’t yet figured this out, given all their “data science” prowess!
A friend who I met on the flight told me that in his town (Whitefield) it’s not hard to find an Uber/Ola cab at 5am on Mondays, except that the drivers cancel rides once they figure out it’s for an airport drop. Again pointing to the fact that incentives are not aligned for maximum throughput

Uber’s new pricing structure

So Uber has changed its pricing structure in Bangalore. Earlier they nominally charged Rs. 50 fixed, Rs. 15 per kilometer and Rs. 1 per minute, and then slapped a 35% discount on the whole amount. From today onwards the new fare structure is Rs. 30 fixed, Rs. 8 per kilometer and Rs. 1 per minute, without any further discounts. They’re marketing it using the Rs. 8 per kilometre number.

I took a ride this afternoon under the new fare structure, and the bill was Rs. 152, about the same as it would have been under the old fare structure. In that sense, I guess this was an “average ride”, in terms of the distance by time covered. This was the kind of ride where their assumption on distance travelled per unit time (in coming up with their new formula) was exactly obeyed!

So how do we compare the old and new formulae? We can start by applying the discount on the nominal numbers of the old formula. That gives us a fixed cost of Rs. 32.5, a per kilometer cost of Rs. 9.75 and a per minute cost per 65 paise. We can neglect the difference in fixed cost. Comparing this to the new cost structure, we find that the passenger now gets charged a lesser amount per kilometre, but a higher amount per minute.

In face, taking the “slope” between the old and new rates, the per kilometre cost has come down by Rs. 1.75 while the per minute cost has risen by 35 paise. Taking slope, this implies that Uber has assumed a pace of a kilometre per five minutes, or twelve kilometre per hour.

So if your journey is going to go slower than twelve kilometres per hour, on average, you will end up paying more than you used to earlier. If your journey is faster than twelve kilometres per hour, then you pay less than you did under the previous regime.

A few implications of the new fare structure are:

1. Peak hour journeys are going to cost more, for they are definitely going to go slower than twelve kilometres an hour
2. Your trips back from the pub should now be cheaper, for late nights when the roads are empty you’ll travel significantly faster than twelve kilometres an hour
3. What does this imply for the surge pricing in the above two cases? I think the odds of a surge during peak office hours will come down (since the “base price” of such a trip goes up, which will push down demand), and  the odds of a surge late on a Friday or Saturday night might go up (since base fare has been pushed down for that).
4. The Rs. 30 fixed cost implies that if a driver travels at 12 km/hr when looking for a new ride, the gap between rides for a driver is 11.5 minutes (if the driver spends X minutes, he will travel X * 12/ 60 kilometres in that time. The compensation for this combination is X + X*12/60 * 8, which we can equate to 30. This gives us X = 11.54).
5. Trips to/from the airport will now be cheaper, for you can travel much faster than 12 km/hr on that route. So Uber will become even more competitive for airport runs. Again this might increase probability of a surge at peak flight times.

I continue to maintain that Uber has the most rational price structure among all on-demand taxi companies, since the fare structure fairly accurately mirrors drivers’ opportunity costs. Ola doesn’t charge for the easily measurable time, and instead charges for “waiting time”, which is not well defined. Ola also has a very high minimum fare (Rs. 150). I wonder how they’ll play it if their planned acquisition of TaxiForSure goes through, since TaxiForSure was playing on the short trip model (with minimum fares going as low as Rs. 49). Given the driver approval before a ride, though, I doubt if anyone actually manages to get a Rs. 49 ride from TaxiForSure.

Times continue to be interesting in the on-demand taxi market. We need to see how Ola responds to this pricing challenge by Uber!