How to price NFTs has always been an interesting topic. Due to the characteristics of low liquidity, difficult to judge scarcity, and large price fluctuations, NFTs are difficult to be easily evaluated and calculated like traditional financial assets.How NFTs are priced has always been an interesting topic. Because pricing is an unavoidable intermediate operation, it contains two parts, computable and non-computable, which need to be solved in any NFT Fi application scenario. In order to widely apply NFT to DeFi and fully activate the liquidity of NFT, first of all, we need to make the most realistic and widely accepted valuation judgment on the value of NFT as an asset.
However, due to the following three aspects, it is difficult for NFT to be easily evaluated and calculated like traditional financial assets:
1. Endogenous subjectivity and illiquidity caused by the non-homogeneous nature of NFTs
2. The rarity of NFT is relatively ambiguous, and the rarity and price level are not fully positively correlated
3. The price of NFT fluctuates wildly (influenced by team and policy issues, there are frequent pull-ups and crashes)
However, if the pricing mechanism cannot be well resolved, behaviors such as NFT loan transactions are often difficult to gain market trust due to high risks, which leads to two problems:
- Lack of sufficient liquidity to support the depth of the trading pool
- Difficult to build diversified financial derivatives in the form of NFT
To solve this problem, more and more NFT pricing platforms and emerging methods are emerging in the market. Here we can simply divide these pricing solutions into two categories:
- Peer pricing: can be further subdivided into (a) subject evaluation pricing and (b) liquidity pool game pricing
- Oracle pricing category: can be further subdivided into (a) TWAP pricing and (b) off-chain computing pricing
a. Crowds Entity Assessment Pricing
The subject evaluation pricing is a form of pricing that is highly subjective at present. The liquidity lending protocol represented by Taker V1 can whitelist and price NFTs in the form of subject evaluation and voting decisions by combining the interests of the DAO with the interests of the lenders, so that to a certain extent, the lenders can be effectively Risk exposure is reduced. At the same time, this method has no restrictions on the quality requirements of NFT assets, and can be widely used in the price discovery of long-tail and emerging NFT collections. However, this method relies heavily on the judgment ability of the curator, and it cannot provide real-time updated NFT prices, and the overall efficiency is low.
Taker is a liquidity protocol for NFT assets. It mainly provides liquidity for NFT lending in the form of DAO, and supports various forms of assets including NFT, securities, synthetic assets, etc. After holding TKR tokens, you can obtain DAO membership and participate in decisions such as lending rates and fair pricing. At the same time, holding TKR can also obtain additional income through staking.
There are multiple curator DAOs (sub-DAOs) inside Taker DAO, and each sub-DAO can control its own whitelist and the floor price of any NFT on the whitelist to prevent borrowers from defaulting. In addition, members of the sub-DAO vote collectively to invest funds from their own treasuries on specific types of NFT assets. For example, some sub-DAOs can only focus on Metaverse land assets, and some sub-DAOs can only focus on pfp art assets.
Under the Taker group pricing model, the complete lending process is as follows:
1. Taker community members (lenders) deposit funds to the DAO.
2. The DAO mints DAO Tokens (TKR) to represent members’ shares
3. Curator (initiator) subjectively price the NFT collections in the DAO initiated
4. The borrower uses NFT as collateral to make a loan based on Curator’s appraisal price
5. The borrower repays the loan with interest
6. DAO members get rewards (based on interest rate)
7. With the development and growth of DAO, DAO members get more and more benefits
b. Spot liquidity pool game
The liquidity pool game pricing mechanism is similar to defi, mainly through the optimistic pledge certificate mechanism, that is, the liquidity provider pledges according to their own price expectations, thereby binding the valuation of NFT to the asset price in the liquidity pool. This is also a pricing method that relies heavily on LP’s subjective decision-making. The advantage is that it can realize real-time valuation of NFT, link transaction value with real value, and release greater liquidity. However, it also has a complex pricing mechanism and is not suitable for a large number of fast pricing of low-value long-tail NFTs.
Abacus is a simple and clear NFT valuation system that mainly utilizes optimistic PoS to create a liquidity pool-based NFT valuation method. Abacus has two valuation methods, one is the group pricing we mentioned above, and the other is the liquidity pricing we discuss here. Converting the value in the liquidity pool into ETH is equivalent to the value of the pooled NFT. Under this mechanism, it is equivalent to formulating a set of ETH/NFT trading pairs, which can reflect the NFT price in real time like Opensea.
Based on its liquidity pool pricing methodology, the complete lending process is as follows:
1. Turn on the pool:
a. NFT is non-custodial, the owner needs to sign on the pool as a proof of life
b. The owner will receive an NFT (ERC721) token to represent their property and earn transaction fees
2. Traders lock ETH in the pool
a. Determine the amount of ETH locked: If the fund pool is 2eth, but the trader thinks the NFT is worth 2.5eth, the trader can put 0.5eth into the fund pool, and now the fund pool is worth 2.5eth.
b. Determine the lock-up time: If the trader believes that the price will only stay at the current price for a short period of time, it can be locked for only 2 weeks. Conversely, if the trader is confident in the floor price, they can lock in for a longer period of time and thus earn more rewards.
3. The NFT owner opens the release
4. NFT owner sends loan request
5. Ownership is transferred to the lending platform
6. Lending platform checks spot size and lock-up time
7. Lending platform to issue loans
8. Once the borrower fails to execute the repayment, the NFT will be auctioned immediately
Oracle Oracle Pricing
The typical way for oracles to evaluate NFT pricing is to perform a weighted average calculation of the NFT sales price and reserve price based on a simple traditional algorithmic trading strategy, thereby comprehensively obtaining a TWAP (Time Weighted Average Price). To give a simple example, if we want to calculate the TWAP price with a time interval of 1 hour, we can take the difference between the accumulated prices P1 and P2 at the start and end, and the difference between the start time T1 and the end time T2. Divide the TWAP to calculate the TWAP within the 1 hour period. The most well-known TWAP oracles include Chainlink and Uniswap’s V2.
In fact, TWAP pricing is the easiest and most efficient way to implement. As long as the price data of the NFT trading platform is integrated, crawled and cleaned, multiple prices are selected and averaged in the set time series to reduce malicious manipulation. Possibly, provide an acceptable, relatively accurate NFT price.
However, TWAP is not a perfect solution, because in extreme market conditions, when prices fluctuate wildly, TWAP oracles are easily affected and become inaccurate. Therefore, TWAP is considered to be only suitable for pricing blue-chip NFTs with high market activity, good liquidity and relatively stable prices.
BendDAO is a lending protocol that solves the liquidity problem of NFTs. Borrowers can borrow ETH to cash out by mortgaged NFTs. Currently, BendDAO can support the lending of 9 blue-chip NFTs including BAYC, Cryptopunks, Azuki, MAYC, CloneX, World Of Women, Coolcats, CyberKongz and Doodles.
The NFT pricing method adopted by BendDAO is typical TWAP pricing. By cooperating with chainlink, it calls multi-nodes to collect the floor price of the mortgaged NFT on the two trading platforms of Opensea and LooksRare, and calls the contract interface to feed the floor price to the chain to calculate the corresponding TWAP, thereby filtering out the price fluctuation of the trading platform. Impact. It can be seen from the figure below that for the collection of cryptopunk, the floor price provided by the oracle machine is consistent with the average transaction price and TWAP.
Similar to BendDAO, protocols that use TWAP oracles for pricing include JPEG’d, DeFrag, DropsDAO, Pine, etc.
b. Off-chain computation
Off-chain computing based on AI and machine learning has gradually become an emerging NFT oracle pricing method.Due to the non-homogeneity of NFT, its main attribute classification, rare features, historical sales data and other valuable information can be used as model indicators through metadata decomposition. The protocol can then be modeled based on this series of indicators and data sets, thereby giving a relatively reliable and accurate pricing or pricing range.
This type of valuation method has extremely high technical barriers, is relatively friendly to long-tail NFT assets, and can be considered as the solution with the greatest possibility of large-scale application. But the problem is that this method requires high computing power and metadata. Since the algorithm is not disclosed, we cannot determine whether the training and fitting results are effective. And once the attribute characteristics of NFT change, the model is likely to fail, so it needs to be iterated continuously.
The oracle protocol represented by Banksea mainly uses AI models to train NFT data sets, thereby generating accurate and efficient forecast prices for different NFT assets. The whole is mainly composed of two modules of mining b layer and NFT layer.
On the collection layer, Banksea will collect NFT transactions and listing records on the chain to calculate three types of prices in real time: market floor price, AI floor price and 24-hour average price. The AI floor price represents the lowest price among all AI valuations, and plays a role in risk control and stability maintenance when the market experiences drastic fluctuations or is attacked by oracle machines.
On the NFT layer, Banksea will perform AI model training based on time series by extracting the multi-dimensional features of NFT, and regularly generate two results of standard valuation and valuation range. In addition, it will fit and regress the valuation calculated by the AI model with the real-time transaction price, thereby optimizing the final result and narrowing the margin of error.
The off-chain pricing process of Banksea based on the AI model can be seen as follows:
1. External API query: monitor and capture comprehensive NFT data, sources include trading platforms, social platforms, public chains and mortgage platforms, etc.
2. Data aggregation: Clean the collected NFT data, extract feature attributes, and input them to AI nodes
3. AI modeling: The AI node cluster conducts model training and deployment based on the input data set, calculates the predicted price and risk score, and returns the results to the Banksea smart contract
4. Data submission: After the smart contract on the chain removes outliers, the data within a reasonable range is extracted and submitted to a third-party program
In addition to Banksea, Upshot and NFTBank also provide oracle solutions for accurate pricing of NFTs based on more subdivided machine learning (ML) methods in AI. In addition, community tools such as Defi Kingdom and Axie Infinity integrate AI off-chain algorithms for pricing.
SummaryOne More Thing
Finally, we can summarize the four specific solutions in the two major paradigms of NFT pricing on the market through the following dimensions:
It can be seen that no matter what kind of valuation method is currently used, there are certain advantages and disadvantages. We look forward to discovering and improving more emerging NFT pricing methods in the near future.Especially for the oracle pricing method of off-chain computing, we believe that with the advancement of technology and the participation of more high-quality project parties, more AI algorithm technologies such as deep neural network (DNN) can be put into the fitting evaluation function. , so that the pricing decision tree gets more accurate and fast pruning.
NFT pricing is like a game of Go, a complex game that looks simple, and a problem composed of a series of decisions. We can use peer intuition to judge the range, and we can use oracle algorithms to predict the future.
And if you have to ask what is a good pricing paradigm? I think the crux of the problem, as Wu Qingyuan said, is not how many times and how far to count, but how wide, how fast and how accurate it is.
Posted by:CoinYuppie，Reprinted with attribution to:https://coinyuppie.com/this-article-sorts-out-2-major-nft-pricing-paradigms-and-4-solutions-on-the-market/
Coinyuppie is an open information publishing platform, all information provided is not related to the views and positions of coinyuppie, and does not constitute any investment and financial advice. Users are expected to carefully screen and prevent risks.