# Applying valuation methods to the FLOW token

**Published by:** [vlang](https://paragraph.com/@vlang/)
**Published on:** 2022-05-27
**URL:** https://paragraph.com/@vlang/applying-valuation-methods-to-the-flow-token

## Content

//initially published on Medium on Jul 9, 2021// In this article, I try to use existing valuation models on the FLOW token. FLOW is the native token for the Flow blockchain, which is built for NFTs, interactive and collectible crypto experiences. Flow is also the team behind one of the first NFT projects — CryptoKitties, and they’ve built the Flow blockchain to enable more consumer applications on blockchain. The most well-known project on Flow is NBA topshot, where users can purchase and collect packs with moments of NBA games. According to the FLOW token economics, the token could serve as 1. payment for computation and validation services (transaction fees), 2. medium of exchange, 3. deposit for data storage, 4. collateral for secondary tokens and 5. participation in governance. In this model, I value FLOW as a currency for the first three use cases mentioned above. However, there is a gap between the intrinsic value and the price of a token so I tried to incorporate** investor value** in the final token price. The framework would be using the equation of exchange to estimate the currency value of FLOW, then the nth order investor model to anticipate investor value. For velocity, I used the Baumol-Tobin model for money demand to estimate the holding period of FLOW as a medium of exchange. In the last section, more details and rationale behind the assumptions made are elaborated. Most of the existing theories and models have been used to value hypothetical tokens, here I included the characteristics of FLOW tokens in these valuation methods. There are a lot of assumptions in this model, and they would vary depending on your view of the crypto economy, how successful the Flow blockchain would be, and how the use cases of FLOW token would evolve over time. Although the final product of the model wouldn’t be able to predict the price of the token, it gives us information about where the majority of value comes from. This process also demonstrates some difficulties one would encounter when applying valuation models to existing tokens. Feel free to download this model and make different assumptions to see how they affect the price. Equation of Exchange In monetary economics, the equation of exchange defines nominal expenditure of an economy (GDP) over a certain period of time as money supply times velocity, which is the frequency of a unit of money being spent. Both Chris Burniske and Vitalik Buterin applied this equation to cryptoasset valuations, here I’m also using this equation as the framework to value the medium of exchange function of FLOW. In traditional macroeconomics, M stands for money supply, V for velocity, P for price level and Q for real expenditures. The same concept applies to FLOW, where M is the token supply, V is the velocity of tokens, P is the price of utility in the Flow network and Q is the expenditures on Flow. The P*Q part of the equation could also be interpreted as the GDP of the Flow economy. One thing to mention is that every parameter in this equation is priced in USD, so that we could solve for the USD price of FLOW by simply dividing the token supply base (M) by the amount of tokens. More details of the inputs in this equation are provided in the following sections. M*V=P*Q M — token supply base, how staking rewards and locked-up investments affect the token supply V — derived from the Baumol-Tobin model, how transaction costs and forgone return on store of value would affect the holding period of Flow P — Flow use cases, how much utility costs, how much demand for the network would be converted to demand for the token Q — market/industry fundamentals, how much demand there will be for the Flow network Q — Market/Industry Fundamentals First, we are going to estimate how much demand there will be for the Flow network by looking at the markets and Flow’s position in them. Three measures of the Flow economy are needed, number of transactions for transaction fees, users for new storage fees and transaction volume for the amount settled in FLOW. For markets, I separated the NFT and DeFi market since they are not comparable in terms of transaction volume. Then market growth and market share of Flow would be estimated for each market, and the product of market growth and market share growth would be the growth of the Flow economy. I assumed the three measures grow at the same rate the Flow economy grows in NFT and DeFi markets. This wouldn’t be an accurate estimation, since a user could transact more times and volume as more projects are built on Flow. However, since storage fees and transaction fees account for a small fraction of the total GDP of the economy (results are seen later), this imprecision should be acceptable. For the NFT market, I estimated a high growth of 120% this year and it steadily declines to 10% in 10 years as the industry matures. For market share captured by Flow, I used an S-curve with a 35% market share at peak. For the DeFi market, 500% growth this year and declines to 10% as well, and a 10% market share at peak. A much lower estimated market share for DeFi since Flow is built for NFTs and I believe the success of NBA topshot would attract more NFT-related projects rather than DeFi to be built on Flow. More rationale is provided in the last section. I used data of projects from dappradar for the first half of year 1 (Nov 16, 2020- May 15, 2021) as a benchmark for future estimations of the three measures, and categorize NBA topshot, VIV3 and MotoGP Ignition as NFT and Bloctoswap as DeFi. Number of transactions and transaction volume are available data, but users require an estimation. I estimated 800,000 users in NFT and 10,000 users in DeFi on Flow for the first half of year 1. To summarize, the following graphs depict my assumptions of number of transactions, transaction volume and users for Flow in the NFT and DeFi market. \*year ends on Nov 15 to follow the token supply schedule P — Use cases of the FLOW token The main use cases of FLOW tokens are for staking, medium of exchange, utility for the Flow network and voting for governance. I included both medium of exchange and payment for utility as the total GDP (or P*Q) of the Flow economy, since they offer FLOW functions similar to a currency and can be solved by the equation. Medium of exchange accounts for transaction volume of projects settled in FLOW. For example, VIV3 is a NFT marketplace where all the artworks are priced and transacted in FLOW, so the token would be used as a medium of exchange on this marketplace, and users would have to own FLOW in order to purchase artwork on VIV3. On the other hand, NBA topshot accepts payments with credit card and other cryptocurrencies then settles in USDC, so FLOW wouldn’t be essential to users. Note that if the project exchanges currencies for users and settles transactions in FLOW, the token still accounts for a medium of exchange function since it increases the demand for the token likewise. For demand from medium of exchange, I used an S-curve to estimate the percentage of transaction volume settled in FLOW. In the early days, many projects may accept multiple cryptocurrencies or even fiat for payment, but as the network and industry mature more of these transactions will be settled in the native currency. However, this percentage will largely depend on whether these NFT projects would accept multiple payment methods like NBA topshot or accept only FLOW like VIV3. \*year ends on Nov 15 to follow the token supply schedule The two payments for utility on Flow are transaction fees and storage fees. Note that these utility fees could be paid by projects on behalf of their users, but who the actual payer is should be irrelevant to the demand or the value of the tokens. Transaction fees are paid to validators/stakers as rewards for contributing to the network. According to the team, transaction fees would start low at 0.001 FLOW in the early days, and emphasizes the importance of transaction and storage fees being** responsive to market demand**. Therefore, I estimated both transaction and storage fees in USD with their growth aligned with the growth of the Flow economy (the growth we discussed in the previous section). I used a $10 FLOW token as the reference price for estimating the current utility prices in USD. Using USD as the currency for these fees enables us to estimate the P*Q part of the equation without using the prices of FLOW tokens every year as an input in the model. Storage fees would include 0.001 FLOW as a new user deposit for 10kb storage, additional fees are required for extra storage on the chain. Additional storage fees are not elaborated in the token economics paper, so I assume a 0.001 FLOW fee for a certain amount of storage. Demand for additional storage is also estimated from an S-curve of percentage of total users, assuming users require more storage as each owns more assets. \*year ends on Nov 15 to follow the token supply schedule The assumptions on transaction fees and storage fees could seem unfounded, but estimating utility prices based on demand is the most reasonable method we have. In addition, from the graph below we could infer how minor the estimations for utility fees are. This graph shows the breakdown of total GDP (P * Q), suggesting transaction volume settled in FLOW accounts for the majority payment amount. We could therefore conclude that the demand for FLOW tokens is highly dependent on whether projects settle in FLOW for payments. Users are required to pay for utilities with FLOW tokens, but because Flow is built for consumer applications, the utility costs are low. Therefore, the value of FLOW tokens comes more from its function as a medium of exchange for different projects, and less from paying for the utility on Flow blockchain. \*year ends on Nov 15 to follow the token supply schedule Another thing to consider is that these storage fees would be held out of circulation, and this would serve as a constraint on token supply. To avoid using FLOW token price as an input in the model, I wouldn’t be able to estimate the total storage fees held out of circulation (in FLOW) and deduct them from the amount of circulating tokens. This would result in an underestimation of token value, but accumulated storage fees held out of circulation accounts for a little fraction of circulating tokens so the underestimation should be negligible. M — Token supply and distribution The Flow team disclosed an indication of the circulating supply schedule as tokens unlock and new tokens are distributed to stakers/validators. All the investments are locked-up until the end of year 1 (Nov 15, 2021), so until then all the tokens in circulation are staking rewards. Inflation in the Flow economy is a combination of staking rewards and new issuance of tokens, but they only issue new tokens as necessary to make up the difference between transaction fees and guaranteed payment, excess amounts are held in escrow to offset future inflation. From the schedule, we know that the team plans to cap inflation at about 3% starting from the third year. Therefore I extended this schedule to fit the 10-year timeframe of this model by setting a 3% inflation to the total supply fully distributed to stakers, as shown in the table below. \*amount in thousands, month 1 begins Nov 16, 2020 Something important to define here is the difference between circulating tokens, tokens outstanding and total supply. This concept is similar to how floating shares, outstanding shares and authorized shares are different. Circulating tokens are staking tokens available in the market, including staking rewards, unlocked investments and staked tokens that are not locked-up. These are the tokens that users can conduct transactions with, meaning the medium of exchange/currency function of FLOW is only available to the ones in circulation. Tokens outstanding are defined as total supply less dapper labs ownership, collateral reserve and foundation reserve (the green portion of the table) that would not enter the supply directly. These are the tokens that all the values of Flow network are distributed to. Even though the ones locked-up do not contribute medium of exchange value, they could be sold in the market at the same price when the lockup period is over so they share the entire value of FLOW tokens. Finally, total supply represents all tokens in existence. V — Baumol-Tobin Model Velocity is probably the most important but controversial input in the MV=PQ equation, in the VOLT model by Alex Evans he introduced using the Baumol-Tobin model to estimate the velocity of tokens. This model shows the tradeoff between having liquidity by holding a medium of exchange and earning return by holding a** store of value**. Here the return does not mean an economic return, but more of a nominal return that preserves one’s purchasing power. People should always keep their fortune in store of value assets, and only keep a minimal amount of medium of exchange as “working capital” for expenditures. In the crypto economy, I assume people will view Bitcoin and stablecoins as store of value as they view gold and cash in the traditional financial industry, stablecoins would be the major medium of exchange, FLOW and other utility tokens would be the medium of exchange for their native economy/network (similar to how different countries have their own currency). This model minimizes the money management costs between store of value and medium of exchange, including the transaction costs and forgone return(opportunity cost). Below shows inputs for this model, and in the following context are details about the selection of these inputs and the assumptions made. Inputs for this model Opportunity cost: the forgone return of holding FLOW instead of a store of value Transaction cost: all costs involved in exchanging store of value to medium of exchange Total spending: annual spending or demand for medium of exchange to cover expenditures The below graph demonstrates how users in this model would spend their tokens. Users exchange a fixed amount of their store of value assets to FLOW each time and spend them at an even rate. This fixed amount is the optimal cash balance a user should exchange each time to minimize money management costs. The optimal cash balance times transfers per year (4 in this graph) would be the annual spending of a user. In the VOLT model, expected return on store of value was used as the forgone return/opportunity cost of holding a medium of exchange. However, the fact that you could stake these FLOW tokens and earn rewards should reduce the opportunity cost of holding a medium of exchange. So in this model I used expected return on store of value less expected return on FLOW as the opportunity cost. Expected return on store of value is a difficult estimation since it represents the average expectation of people in the cryptoeconomy. I assumed a 10% expected return from store of value assets, with a combination of Bitcoin and stablecoins. For FLOW, I used the inflation of the token supply as the expected return. This would not be accurate because if token holders consist of investors and speculators, the price would diverge from its currency value, increasing the return of holding FLOW. Other than that, the growth of the currency value may be much higher than the inflation rate. This transaction cost should include all costs associated with exchanging a store of value asset to FLOW (medium of exchange), including network fees, exchange fees, spreads and other costs resulting from inconvenience. This represents the** friction** in the economy, the lower the friction is the less FLOW people hold, because they can always convert their store of value assets to FLOW when they need it. The downstream effect on velocity is that the less FLOW people hold, the higher the velocity would be, and the lower the token price would be. For simplicity, I used Bitcoin transaction fees as the transaction cost for this model, since it’s difficult to measure all the associated costs. Transaction fees on-chain are highly correlated to network demand, in high-demand times BTC transaction fees could be as high as $40 but in low-demand times it could be under $10. For this model I applied a $10 cost for each transaction, considering increasing demand could increase transaction fees, it increases to about $15 in 10 years. There could be a debate on this transaction cost estimation, and I’d like to hear any suggestions. The total spending in this model represents annual expenditure of FLOW per user. For this input, I simply used the total payment amount of the network divided by total users. The product of this model would be the average velocity of all tokens as medium of exchange, however there are other tokens outstanding that are staked or locked up as investment. Tokens staked or locked up have a velocity of 0, decreasing the velocity of the entire tokens outstanding pool. Introducing the mechanism of staking serves as an incentive for users to hold tokens, which decreases the velocity of tokens. Data from flowscan and our token supply schedule can give us information on what percentage of total token supply are staked (ranging from 43%-98%), but not the percentage of circulating tokens staked. I tried to assume that all the reserves and locked-up investments are staked, but according to the calculation that assumption is invalid. So I estimated 90% of tokens in circulation staked in 2022 and the percentage declines as more demand increases and more use cases become available. \*year ends on Nov 15 to follow the token supply schedule, week 1 begins Dec 16, 2020 Nth order investor There is always a gap between valuing and pricing. There are many determinants of price that are not taken into consideration when we do valuations, so this nth order investor model tries to simulate how “groupthink” affects prices. In How to Value a Crypto Asset by Brett Winton, he introduced the nth order investor method to estimate the investor value of a token. Different from users that hold tokens to transact or pay for storage, investors would hold tokens to anticipate future appreciation. This method assumes investors have a fixed hurdle rate and invest horizon, therefore the 1st investor would expect the current price to be the present value of the utility value at the end of his/her investment horizon. Other investors could do the same by discounting the price other investors anticipate, but the price of the token should be the maximum amount any investor would pay for. In Improving the equation of exchange, they improved the model by setting various investment horizons, and I used their version of nth order investor model for FLOW. In the table below, the 0th investor value is the medium of exchange/currency value per token we derived from the equation of exchange. Each nth order investor would discount the (n-1)th order investor price one year from now to know his/her investor price. So the 3rd order investor in 2022 could be anticipating the currency value in 3 years, the 1st order investor price in 2 years, or the 2nd order investor price in a year. The final fair value of the token would be the maximum amount among all the investor value orders. \*year ends on Nov 15 to follow the token supply schedule One could argue that if we extend the forecast horizon to over 10 years, these investor values would change. But it also makes sense that as the token and its economy matures, most token holders would be actual users instead of investors or speculators, therefore currency value and fair value of token would converge as shown in the bottom graph. Both values decrease in 2030 in this graph because by then inflation exceeds the growth of total token value, just as prices decrease when supply grows faster than demand. I wouldn’t say this would certainly happen, but under the assumptions made in this model where inflation stays at 3% while the growth of Flow economy continues to decrease, the value of token would eventually decrease. \*year ends on Nov 15 to follow the token supply schedule For discounting, I applied a 40% discount rate for token investors. There is room for discussion on this selection just as for a VC discount rate for early-stage companies. Discount rates decrease throughout the years, assuming as blockchain industry matures and crypto becomes mainstream we would see diminishing returns in investing in crypto and therefore investors would require lower returns and less risk premium. In conclusion, the currency value derived from MV=PQ equation would be the value floor of the token given its means of exchange function, and the nth order investor model helps us estimate the investor value by speculating on the currency value an investor would anticipate. Discussion The value of a governance token An important use case of the FLOW token is participation in governance. As the network expands, the value of voting rights or determining crucial matters of the Flow blockchain would definitely increase. However, this model does not include any value from its use case in governance, because there aren’t any valuation methods in merely valuing voting rights. There’s definitely room to discuss and I’d love to hear suggestions. Staking I struggled whether staking rewards are similar to dividends and could be valued using a DCF model like in traditional finance. I did not include staking rewards because they are a form of inflation like how stock dividends don’t create additional value to stockholders. One could argue that not all token holders are stakers so the token value to stakers should be higher, and value to “pure users” (who don’t stake) should be lower. However, I believe in equilibrium all token holders would be both users and stakers since not staking would decrease the real value of your tokens. Applying this notion to the Baumol-Tobin model would mean that all users would stake their tokens when they hold them as working capital for future needs. In equilibrium, the percentage of tokens staked would not be a distribution between people who stake and don’t stake, but between times when tokens are staked and times when tokens are traded during transactions. In conclusion, the staking mechanism contributes value to the token by decreasing velocity and accepting FLOW as rewards for validating transactions. S-curve details I applied S-curves to the percentage of market share of Flow, transaction volume settled in FLOW, demand for additional storage and tokens staked. These numbers depend on both internal and external factors such as the development of the Flow network, the adoption of FLOW tokens and the overall market and cryptoeconomy. For all the S-curves in this model, I assumed 2023 as the start of high-growth phase and the takeover time to be 7 years. As the Flow network grows, its market share, transaction volume settled in FLOW and demand for additional storage increases while percentage of tokens staked decreases. Inputs for the S-curve Peak value: the highest value achieved Start of high-growth phase: when it reaches 10% of peak value and begins high growth phase Takeover time: amount of time it takes from 10% to 90% of the peak value NFT and DeFi market More details and rationale behind estimates of the NFT and DeFi market and the position of Flow in the industries are discussed here. In the past two years, the growth of the NFT market is 246% and 139% respectively. NFTs are still in the early stage with very high growth, but like all industries the growth slows down and remains stable in mature stages. I applied a 120% growth rate for 2021 and assumed it declines to a modest 10% at the end of the forecast period, this would imply 54% CAGR over the next ten years. data source: statista If you look at the top NFT projects other than NBA topshot, most of them are built on Ethereum. On the positive side, Flow is built for consumer applications with low transaction fees and its success in monetizing existing IPs with NBA topshot could bring huge partnerships in the future. (Flow has already announced partnerships with UFC and Dr.Seuss to release collectibles.) On the negative side, solutions for lowering gas fees on Ethereum and the development of other blockchains could limit the growth of Flow. Considering both sides, I applied a 35% market share at peak for Flow. If we measure the DeFi market size from total value locked, it has 22x in 2020 and has more than doubled in the first half of 2021. I applied a 500% growth rate for 2021 and assumed it declines to a modest 10% at the end of the forecast period as well. For the market share captured by Flow, I also applied an S-curve but with a much lower share (10%) at peak. First because currently the DeFi market is dominated by Ethereum-based projects. Second, Flow is built for NFTs and has proven success in NBA topshot so would probably attract much more NFT than DeFi projects to build on this blockchain. data source: DeFi Pulse Including token price or return in the model An ideal valuation model should not include inputs like FLOW token price or expected return on FLOW, which are products that this model is trying to estimate. I tried to avoid circular references like this but still couldn’t exclude all of them. In the P section, I included the current token price as a reference of current utility costs in USD. And in estimating velocity, I also included expected return on FLOW to know the net forgone return of holding a medium of exchange instead of store of value assets. I’d love to hear if there are better methods on avoiding circular references in token valuation. How this framework can be applied to other utility tokens This valuation framework allows us to understand where the value of the token comes from by breaking down the functions of the token and estimate the demand for each of the use cases, which could be applied to other utility tokens as well. As model statistician George Box said, “All models are wrong, but some are useful”. Although there are a lot of assumptions and extrapolations involved in this model, it certainly offers a more methodical way to value a token.

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