Author: Sally, IOSG Ventures

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How NFTs are priced has been an interesting topic. Because pricing is an unavoidable intermediate operation, which contains two parts that can be calculated and cannot be calculated, it needs to be solved in any NFT Fi application scenario. In order to widely apply NFT to DeFi and fully activate the liquidity of NFT, we first need to make a valuation judgment that is as real and widely accepted as possible for the asset value of NFT.

However, due to the following three aspects, NFT is difficult to be evaluated and calculated simply like traditional financial assets:

1. NFT's non-homogeneous properties lead to its endogenoussubjectivity and illiquidity

2. NFT'sRarity is relatively vague, and rarity and price level are not exactly positively correlated

3. NFTPrices fluctuate wildly(Due to team and policy issues, violent pulls and plunges often occur)

However, if the pricing mechanism cannot be well resolved, then behaviors such as NFT loan transactions will often be difficult to win the trust of the market due to high risks, which will lead to two problems:

• Lack of sufficient liquidity to support the depth of the trading pool

• It is difficult to construct diversified financial derivatives in the form of NFT

In order 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 category: can be subdivided into(a) subject evaluation pricing and (b) liquidity pool game pricing

• Oracle pricing category: can be subdivided intofirst level title

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a. Crowds Subject Evaluation Pricing

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Taker V1

Taker is a liquidity protocol for NFT assets, mainly through the form of DAO to provide liquidity for NFT loans, supporting various forms of assets including NFT, securities, synthetic assets, etc. After holding TKR tokens, you can obtain DAO membership and participate in the design of decision-making lending rates and fair pricing. At the same time, holding TKR can also obtain additional income through pledge.

There are multiple curator DAOs (sub-DAOs) inside Taker DAO,text

Under the Taker group pricing model, the complete lending process is as follows:

1. Taker community members (lenders) deposit funds into the DAO.

2. The DAO mints DAO Tokens (TKR) to represent members' stakes

3. Curator (promoter) subjectively priced the NFT collections in the initiated DAO

4. Borrowers use NFT as collateral to make loans based on Curator's evaluation price

5. Borrower repays loan with interest

6. DAO members receive rewards (according to interest yield)

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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 its own price expectations, so as to bind the valuation of NFT with the asset price in the liquidity pool. This is also a pricing method that largely relies on LP's subjective decision-making. The advantage is that it can realize the real-time valuation of NFT, link the transaction value with the real value, and release greater liquidity. However, it also has a complex pricing mechanism, which is not suitable for rapid pricing of low-value long-tail NFTs.

Abacus

Abacus is a simple and clear NFT valuation system, which mainly uses 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 discussed here. Converting the value in the liquidity pool into ETH is equivalent to the value of the NFT in the pool. Under this mechanism,It is equivalent to formulating a set of ETH/NFT trading pairs, which can reflect NFT prices in real time like Opensea.

Based on its liquidity pool pricing method, the complete lending process is as follows:

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  1. Open the pool:

  NFT is non-custodial, and the owner needs to sign on the pool as a certificate (proof of life)

  
  

• Owners will receive an NFT (ERC721) token to represent their property and earn transaction fees

2. Traders lock ETH in the pool

• Determine the amount of locked ETH: If the fund pool is 2eth, but the trader thinks that 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.

• Determine the lock-up time: If the trader thinks that the price will only stay at the current price for a short time, they can only lock for 2 weeks. On the contrary, if traders have confidence in the reserve price, they can lock it for a longer period of time, thereby obtaining more rewards.

3. NFT owners open release

4. NFT owner sends loan request

5. Ownership is transferred to the lending platform

6. The lending platform checks the spot size and locking time

7. The lending platform issues loans

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a.TWAP

The typical way for the oracle to evaluate the pricing of NFT is to calculate the weighted average of the NFT sales price and reserve price based on a simple traditional algorithmic trading strategy, so as to obtain 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 start and end cumulative prices P1 and P2, and the difference between the start time T1 and end time T2, and combine the two to calculate the TWAP within 1 hour. The most well-known TWAP oracles include Chainlink and Uniswap’s V2.

Actually TWAP pricing isEasiest to implement and most efficientAs long as the price data of the NFT trading platform is integrated, crawled and cleaned, and multiple prices are selected to take the average value within the set time series, the possibility of malicious manipulation can be reduced, and an acceptable and relatively accurate NFT price.

However, TWAP is not a perfect solution, because in extreme market environments, when the price fluctuates sharply, the TWAP oracle machine is easily affected and becomes inaccurate. Therefore TWAPIt is considered to be only suitable for pricing blue-chip NFTs with high market activity, good liquidity and relatively stable prices.

BendDAO

BendDAO is a lending protocol that solves NFT liquidity problems. Borrowers can lend ETH to cash out by mortgaging NFT. 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 a typical TWAP pricing method. ThatBy cooperating with chainlink, calling multi-nodes to collect the floor price of the mortgaged NFT on Opensea and LooksRare two trading platforms, calling the contract interface to feed the floor price to the chain and then calculating the corresponding TWAPpicture

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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, itsValuable information such as main attribute classifications, rare features, and historical sales data can be used as model indicators through metadata decomposition.The agreement can then carry out modeling processing based on this series of indicators and data sets, so as to give a relatively reliable and accurate pricing or pricing range.

This type of valuation method has extremely high technical barriers and is relatively friendly to long-tail NFT assets.It 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 be sure whether the training and fitting results are valid. And once the attribute characteristics of NFT change, the model is likely to fail, so it needs to be iterated continuously.

Banksea

The oracle protocol represented by Banksea mainly uses AI models to train NFT data sets, so as to generate accurate and efficient forecast prices for different NFT assets. Its whole is mainly composed of two modules of mining b layer and NFT layer.

On the acquisition 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 it plays a role in risk control and stability maintenance when the market fluctuates violently or encounters an oracle attack.

On the NFT layer, Banksea will conduct AI model training based on time series by extracting the multi-dimensional features of NFT, and regularly generateStandard Valuations and Valuation Rangestwo results. In addition, it will fit and regression-verify the valuation calculated by the AI ​​model with the real-time transaction price, so as to optimize the final result and narrow the error range.

Banksea's off-chain pricing process 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: AI node cluster performs model training and deployment based on the input data set, calculates the predicted price and risk score, and returns the result to the Banksea smart contract

4. Data submission: After the outliers are removed by the smart contract on the chain, the data within a reasonable range is extracted and submitted to the third-party program

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SummaryOne More Thing

Finally, we can summarize the four specific solutions in the two major paradigms of NFT pricing currently on the market through the following dimensions:

It can be seen that no matter which valuation method is used at present, 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 can be pruned more accurately and quickly.

NFT pricing is like a game of Go. It is a seemingly simple and complex game. It is a problem consisting of a series of decisions. We can use peer intuition to judge the range, and we can also use oracle algorithms to predict the future.

And if you have to ask what is a good pricing paradigm? I think the key to the problem may be as Wu Qingyuan said,Original link

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