A Model to Value Blockchain Assets
a Real World Example
When chatting with traditional finance practitioners about blockchain the conversation inevitably circles back to, “How do I value an investment in a blockchain?” Or in more popular terms, “How do I really know what the fair value of an ICO is?” The standard answer of, “We just don’t know how to value these things yet,” is lazy. We can do better and that is what this article sets out to do.
First, I must thank cburniske for the work he has previously shared here and has written a book on, which you can checkout here. While our models are different on the fringes, the guts are pretty similar and his work has been a large influence in mine. If you are familiar with his work and are curious as to the differences between the models I will detail them at the end of this article.
For the purposes of this article we will be evaluating the Omega One token sale which aggregates and routes cryptocurrency orders to the exchange with the best price while offering discounts to traders based on a number of factors.
In the model the Discounted Future Utility Value is the price we are most concerned with and what we will use to compare to the offering price of the token.
Finally, towards the end of this post there will be a link to the Google Sheet where you can go to adapt and improve the model to fit your requirements. It is read only so you will need to copy it and past it into a sheet of your own to make changes. A word of caution, however, it’s still fragile. You will likely need to do some formatting of your own to get it to work based on your new inputs. Consider yourself warned, but hopefully more educated.
Understanding How to Value a Blockchain Asset
Traditional businesses are generally valued on discounted cash flow or some other similar manner, but blockchain assets must be valued based on their overall utility value or based on the utility that they are providing to the user. Core to this valuation process is the following equation:
PQ=MV
P(price of service)*Q(quantity of service) = M(size of asset base)*V(velocity or # of times the tokens turn over)
Omega One’s blockchain is a membership token. Customers who hold tokens have the right to get their orders routed to the exchange with the best price and filled at a less expensive rate than the exchange itself would charge. The more tokens one holds the higher the level of service provided.
Where:
P(price) = Exchange & execution fees in percent terms
Q(quantity) = The amount of dollars that will pass through their routing service
M(size of asset base stored on blockchain) = (P*Q)/V
V(velocity) = The amount of time their tokens will change hands annually
Before we get started let’s go over a few housekeeping items.
- Inputs are in Blue or Yellow
- Blue inputs are objective
- Yellow inputs are subjective or assumptions
- The model can change drastically based on the yellow inputs
Now that we’ve got that out of the way I will outline the various calcs and inputs.
- Token Float
- Token Ownership, Lockups & Release Dates
- Inflation or Deflation
- General Market Factors
- Investment Objectives & Hold Time
- Adoption Curve of Product
- Long-Term Holder Decline Curve
- A Word on Velocity
1. Token Float
The first area we want to look at is the token float. We need to know the maximum supply of tokens that will ever reach the market.
2. Token Ownership, Lockups & Release Dates
In the next input area we will focus on who owns what tokens, how long they must hold those tokens and when they can release their tokens. With that we will look at the following along with their lock up and vesting periods:
- Founder’s Tokens
- Advisor Tokens
- Pre-Sale Tokens
- Crowd Sale Tokens
- Incentive and/or Node Tokens
3. Inflation or Deflation
Many token offerings have some plan for inflation or deflation. This is an important area understand because tokens released to or extracted from the market make a big difference in price down the road. More tokens coming onto the market means more downward pressure on price and more tokens being permanently removed from the market means upward pressure on price.
We calculate the inflation or deflation rate as well as the year it is anticipated to start.
- Burnt or Lost Tokens
- Year that Process Starts
- Inflation Rate
- Year that Process Starts
4. General Market Factors
These inputs help us define ways the market will view the product both from an adoption standpoint and a trading standpoint. This is where we define P, Q & V and once we have those defined we can calculate M. The areas we focus on here are:
- Total market size (in this case total USD of crypto traded annually)
- Annual growth of the market
- A Growth Slowdown Rate (at some point growth slows)
- Year Growth Rate Begins Decreasing
- P(price) (in this case the average execution cost as a % of dollars traded)
- Annual Price Decrease of Services Offered (generally competition will lead to a price decline)
- Q(quantity) (total marketshare Omega One is expected to capture)
- Market Share Ramp Up (the amount of time to reach 90% of expected marketshare)
- V(velocity) (the amount of times the total available tokens turn over per year). This is a calculation based on the average speculative velocity of utility tokens on the market and the velocity of longer term holders.
- Speculative Velocity (amount of times per year speculators turn over available tokens)
- Long-Term Holders (% of investors holding for the long-term, decreases over time, with a low threshold)
5. Investment Objectives & Hold Time
These are the inputs where we define the annual return we are happy receiving given the risk and the amount of years we plan to hold the asset.
- Discount Rate (desired annual return)
- Hold Time
6. Market Penetration Curve
As you are most likely aware the adoption of new technology does not happen in a linear fashion. So we have calculated an S curve to determine a more likely growth path for their overall market share.
Our S Curve Inputs are as follows:
- Saturation % (which is the same as Q)
- Start of fast growth
- Take Over Time (how long it takes to get to 90% adoption in years)
Long-Term Holders Decline Curve
This entire area is probably the most subjective part of the analysis. It is my belief that crypto investors at this stage of the hype cycle are generally not long-term holders. Even if they indicate that they are I theorize that the majority of early long-term investors trade out of their positions in the first two years. Based on that we have modeled this decline curve in that fashion with a low threshold of 5%.
A Word on Velocity
While other inputs we have entered play into the velocity calculations I wanted to take a moment to explain how and why I calculated them the way I did.
First, I took the average velocity of utility tokens on the market today to determine the speculative velocity. This is subjective, as an average does not necessarily correlate with a single token. Then I took the expected velocity of the long-term investors based on lockups and release dates and blended it in with the speculative velocity. I believe this gives us a more accurate picture of what the overall velocity will look like over time.
So What Does it All Mean?
Now that we have all our inputs figured out based on the white paper, speaking directly with the team and doing our market research we can see what we think the value of the Omega One blockchain is over our defined time frame.
Comparing Our Valuation with the Offering
At this point we need to compare this to the valuation that the team has put on their blockchain in order to see if we think the investment is under or over valued. We do that here.
Based on the model we believe the Discounted Future Utility Value to be $2.39 today or .008 ETH (with ETH trading at $299.71). The team has priced their Pre-Sale token at .0075 ETH and their Crowd Sale Token at .01 ETH. If they receive an equal allocation from both pre & crowd sale the average price of their token will be $2.62 (with ETH trading at $299.71).
If we liked the project and believed the team could execute on their vision we would feel comfortable paying .0075 ETH per token in the pre-sale, but we’d probably skip the crowd sale. However, don’t forget that this is based on my desired return of 40% annually over the next 10 years. If you were willing to accept 30% annually for the next 10 years you would probably view the proposed token price as a steal. Everyone has different investment objectives and risk tolerances so you will likely be different than me.
Some Items that Need to be Addressed
This is just the first crack at a model. There are items that need to be addressed. The most obvious are that the growth rate for the entire cryptocurrency trading market will not grow and slow in a linear fashion, but in the model that is how I have it at the moment.
Of course there are the things which I am sure I have left out or not considered. Please feel free to suggest them in the comments below.
The Google Sheets Link
What is Different from the cburniske model?
- Added an area to compare our valuation with the valuation that the founding team is proposing.
- PQ=MV calcs for the purpose of cross reference
- Utilized a power function to represent rapid, non-linear long-term investor drop off, based on my theory of long-term investor attrition at this stage of the cycle. This theory has not been tested.
- Blended and dynamic Velocity rate which changes over time based on long-term holder decrease rate and speculative holders turnover.
- Low threshold for long-term investors because while long-term holders will decline over time most likely will never go to zero. Therefore, we need to continue to account for their affect on overall velocity.
- Slowing market growth curve after a certain year to account for growth not going up forever. More on that in the next section.
- Annual Current Utility Value and Discounted Future Utility Value. This is so that I can see what a fair price would be for the token on a year by year basis both from an intrinsic and discounted future perspective. As actual uptake changes I can make adjustments to ensure that my valuation is still in line with my desired risk:reward scenario.
What am I Working to Add Which is not Included in the Current Model?
- Modeling in technology risk to account for delays in production.
- Please comment on what you would like to see. Anything you notice as missing, an inaccurate assumption or too over the top.
Thank you to the following people who contributed to the thought process or who permitted me to accost them with one or multiple renditions of this model.
Josh Rosenblatt and John Wagster of Frost Brown Todd, Kell Canty of Verady, Nick Potts & Amanda Way of ScriptDrop, John Lanahan of LaunchTN, Tyler Evans of BTC Media, Grant Blevins of FCA Ventures Partners, Taylor Weis of Council Capital, Evan Lengerich of Heritage Group, John Bass and Les Wilkinson of Hashed Health, Blake Pederson, Andrew Steinwold, Chris Kitze, Alex Treece.
