Making sense of Pricing Results
Our protocol combines the power of collective intelligence and cryptoeconomics to generate accurate price estimates. What exactly does that mean though?
In this article, we’ll dive into the factors that our algorithm considers to bring you price estimations and insights into how you can interpret the data.
How do we do it?
Collective intelligence is the process of collaboration by a group of individuals to solve a difficult problem together. At Lithium, we leverage on thousands of experts to price illiquid assets such as rare NFTs.
During this process, experts from the community would come to Lithium to share their view on how they believe the market would value an asset. These experts would also stake economic tokens (such as LITH, USDC, ETH, or MATIC) along with Reputation tokens to signal their confidence in the accuracy of the price estimate they submit. Our staking and reward mechanisms are designed to incentivize genuine and accurate price estimates from participants, and deter bad actors from our platform at the same time.
Reputation Token
Tokens held by Price Experts to signal their reputational capital. Reputation Tokens are not transferable and can only be earned. They are used as a weighting factor for Lithium’s pricing mechanism through Reputation Staking.
Our Pricing Results — the Collective Intelligence Advantage
Collectively, we believe experts from the community offer better and more pricing insight compared with pricing estimated by any single analyst or tool. Here are the key advantages:
Large sample size
Some rare NFTs are never or infrequently traded. Limited price data in small sample size imposes real challenges in valuing rare NFTs. They are statistically more susceptible to error and negative feedback loop, since one bad data point would carry a relatively large impact in pricing data.
At Lithium, thousands of experts contribute price estimates in each valuation. During the first week of our testnet, for example, 10,334 experts participated in three NFT appraisals.
Behavioral Insights / Endowment Effect
We source price estimates from experts in two-way bid and ask format. This enables us to address the situation where people assign a different value to buying and selling (known as Endowment Effect). It also allows us to weight price estimates with Reputation, perform Reputation Clearing, and perhaps most interestingly, extract additional behavioral insight.
Consensus Estimate produced by Lithium is based on a Reputation Clearing Mechanism. It weights pricing estimates with Reputation and takes these estimates as if they are orders to buy and sell the item (denominated in units of Reputation). After that, we match these buyers and sellers to clear as many orders (denominated in units of Reputation) as possible to arrive at the Consensus Estimate, the clearing price at which the last willing buyer and seller are matched.
From the above valuation of Doodles #1205 on our testnet, we can see that Consensus Estimate of the NFT is approximately 20 ETH or 1.54x of the floor price at the time.
We can also see that a larger number of Price Experts believe the market would be prepared to buy the NFT at a price below the Consensus Estimate, at around 13 ETH or 1.0x of the floor price at the time. This could be considered as Fire Sale Value if an owner might require to sell quickly for immediate liquidity.
Given NFTs are unique in their nature, their prices are not determined by the average bid, but the highest bid. The output also suggests that it might be possible for the owner to stretch the price to 28–30 ETH range (2.15x to 2.30x floor price) if time and effort is invested to find the perfect buyer who values the item the most.
Outliers and Bad Actors Handling
Bad actors are not always avoidable. In the Doodles #1205 example, a small number of users did submit price estimates as high as 400,000 ETH in an attempt to skew the output.
A simple average of all submissions would predict the NFT to be worth between 100 to 330 ETH because of these bad actors. Our reputation weighted clearing approach eliminates this problem and provides an anchor (the clearing price or the Consensus Estimate), to help objectively identify outliers.
What would you do with these price estimates?
We walked you through how Lithium generates price estimates by leveraging on collective intelligence and how results and data could be interpreted.
Now, let us know what you would do with this new information. We’d always love to hear more feedback from the community to improve the product for you.
About Lithium Finance
Lithium Finance is the first decentralized asset pricing mechanism powered by collective intelligence. We bring market participants together to estimate pricing for illiquid assets on demand.
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