How to Value a Crypto-Asset — A Model
Over the past two years blockchain-based tokenized networks — entities that we believe comprise a new asset-class — have accrued billions of dollars in value and raised more than a billion dollars in capital. Since these networks are fundamentally different from more well-established assets, traditional valuation frameworks do not apply. What do you pay for an asset that offers no claim on an underlying business, generates no operating cashflow, and promises to pay nothing out to its owners?
For many skeptics the answer has been: 0. They claim these assets are no more than Ponzi schemes, which will retain value only so long as the marginal sucker is willing to buy them at a higher price.
For many others, the answer has been quite a bit more than 0: there are more than 50 protocols valued at more than $100 million and 9 valued at more than $1 billion, and Bitcoin, the largest is valued at roughly $60 billion.
So which is it? Are they worth 0 or billions? How can we ascribe a fundamental value to these assets?
Here I present a simple generic model for valuing a crypto asset, adaptable to any particular end-use. It is available here in this github repository. Feel free to download the spreadsheet to adapt for your own work. We only ask that you attribute credit to ARK Invest and share your insights back with us.
To make the example more tangible I have a chosen a plausible end-use application — a decentralized social media/content platform. Imagine if Twitter or Facebook were instead supplanted by a blockchain protocol, this is how it might be valued. Model outputs appear in rows 4 through 31, and inputs to change the model begin at row 34. A line-by-line annotation of the model follows. (So download the model to follow along.)
These are values that are unique to the particular end-market that the crypto-asset is trying to address. In this case we assume that a social media or content platform will accrue transaction volumes at a consistent dollar rate per user hour. This transaction volume would likely be composed of a user directly paying for content via micro-transaction and/or advertisers paying for content on the user’s behalf.
By multiplying social media users by hour-per-user by spend-per-hour we yield total spend on all of social media. This (row 9) represents the total addressable market for the protocol in question. One could input any addressable market into this line to adapt to a different protocol valuation.
We model adoption assuming that the protocol will follow a diffusion curve. The inputs that control the shape of that adoption include the launch year of the protocol (row 40), the year at which it crosses the early-adopter threshold (row 41), the time it takes to become fully adopted by its mainstream users (row 42), and the total share of the market that the decentralized social network will ultimately address (row 43).
This adoption curve is multiplied by total addressable social media hours and total addressable transactions to generate aggregate network engagement (row 13) and the annual total value of transactions that we would expect to see inscribed in the blockchain (row 14).
Most crypto-assets have a predetermined token supply schedule. Row 40 determines the first year that tokens become available (typically via an ICO), row 41 details the total number of tokens outstanding (including any that remain in possession of the developer team or network foundation) and row 42 the annual inflation (or deflation) rate for the tokens.
Combined these values cover the fundamental mechanics of the underlying protocol. Per the model, by 2025 our network will have a little fewer than 110 million tokens outstanding, and those tokens will facilitate roughly $9.2 billion in annual transactions. Now how do we figure out what the network, and hence each individual token, is worth?
At the simplest level, the network’s value is determined by the value of tokens that get held aside in user wallets to facilitate the network’s transaction flow. We call this Network Utility Value (row 18), and it’s determined by total transactions divided by User Velocity (row 50). Put tangibly, each end-user on this network spends roughly $2.50 per month (or $30 per year) purchasing content. If each end-user keeps $5 in an in-network wallet to facilitate that spend, then the User Velocity of the network comes to 6x because that $5 gets held aside to facilitate that user’s $30 in spend. Absent a shorting mechanism a network’s value should never fall below its Utility Value. To derive the Utility Price, simply divide Network Utility Value by the total number of tokens outstanding. One could consider the Utility Price the valuation floor.
But how do we define the valuation-ceiling?
Tokens will also be held aside by Investors that don’t use them to transact on-network but instead anticipate future price appreciation. For many of the cryptoassets today, which lack working products, every holder is an investor, and the entire value of the network is determined by the return-expectations of these investors. So how we define these return-expectations?
To put money into an investment asset an investor requires a rate of return realized over a certain timeframe. In effect the asset must reward the investor for not spending the money today as well as compensate for risk that the price may go the wrong way. These factors determine the investor’s discount rate (row 53). An investor will also expect to liquidate the investment after a certain amount of time has elapsed (row 54). These factors combine to determine how much the investor will pay for a token in the network.
Put tangibly an investor (row 21) may look forward to 2022 and, anticipating a 5-year-forward Utility Value of ~$3.2, buy at ~$2.3 today. By doing so this investor anticipates that his money will compound by 7% (the discount rate) over 5 years (the time horizon). So that is a second way to define the value of the network: the price that a first-order investor, defined as an investor who solely anticipates future utility, would demand before buying a token.
Of course these first-order investors do not exist in a vacuum. They will be aware that other investors are using the same valuation methodologies and, rather than anticipating future utility, investors may instead anticipate the price that a future first-order investor will pay to own a token in the network. As defined in the model in row 22, this investor looks over the same realization time horizon and has the same return-expectations, but anticipates that he will ultimately sell the token to a first-order investor rather than to a network user. Another layer of investors, in row 23, anticipates the value that these second order investors will pay and so on through 4th, 5th and 6th order investors.
Ultimately, the clearing price of the token, as defined on row 28, should be the maximum amount that any of the potential holders would pay. On ICO, as modeled, this particular token would price at a bit more than $75, upon network launch $83, when it begins entering the mainstream $128, and $297 as it enters maturity. Price expectations can be seen below.
Over time price-setting migrates from higher order speculators all the way down to the network users themselves. Consistent with this, as depicted below, the share of tokens held in user-wallets for on-platform-use increases until the entire network is held aside to facilitate its utility-function. As these users become a larger share of the token-base transactional velocity should accelerate.
Ultimately, of course, this model is a simple one, intended to illustrate some of the key drivers of token valuation. In future posts I will expand upon some of the nuances of the model’s inputs and their implications for the assets in the space.
What this model does not capture, however, is almost certainly the most important feature of any prospective network: the vision and talent of the underlying developer team.
Please feel free to adapt this model (by the terms of the license) and share your learnings regarding the same. There is additional research and information available on our website, and we produce a weekly newsletter, ARK Disrupt, that covers all disruptive technologies, including blockchain. We encourage you to sign up, reach out and participate.
 Note that many of the inputs to this model were chosen to illustrate some of the characteristics of crypto-asset valuation and may significantly vary from the inputs embedded within the current market-pricing of these assets. This was not an attempt to value any particular existing asset, but instead an attempt to illustrate valuation principles. The model is licensed under a Creative Commons Attribution 4.0 International License.
 This value excludes any trading-related volume.
 Alternatively, advertisers may be spending $2.50 per month on-network on each user’s behalf.
 For many networks users investors will become users and users will be investors; throughout this explainer, for semantic simplicity, we will refer to them as wholly separate entities.
 Practically speaking, the more removed an investor is from the network’s realized utility value the higher the discount rate that the investor will require. For simplicity’s sake we assume that all investors operate with the same time horizon and discount rate.