Paths to unlocking capital efficiency – A study on NFT mortgage and borrowing protocols

Paths to unlocking capital efficiency - A study on NFT mortgage and borrowing protocols

How to use NFT as collateral for efficient financing? This article introduces the advantages and disadvantages of various methods from two different levels of pricing mechanism (time-weighted average price, user valuation, liquidity pool valuation) and counterparty model (peer-to-peer, peer-to-peer pool). A primer on the pricing of high-volatility, low-liquidity assets for readers.

Over the past year, we’ve all witnessed the dramatic development of the NFT space, but we’re also aware of a fact that can’t be ignored: the more NFTs we have in our portfolio, the worse the liquidity. As the rapidly developing NFT technology brings more and more novel applications, the need to financialize NFTs to improve capital efficiency is growing.

NFTs are illiquid assets much like real estate. In the traditional field, real estate is usually used as collateral for loans, and users can mortgage their assets to obtain loans. We can think of NFT-backed loans as home mortgages, where users are able to lend or borrow funds by using these illiquid assets as collateral for the loans they make. The intermediaries that facilitate this process are called NFT mortgage and lending protocols. In this report, we will focus on the research surrounding such agreements, including pricing mechanisms and different types of approaches based on transacting parties.

An NFT that meets the collateral requirements needs to form enough consensus around its value to the point where the mainstream believes that its value will not fade anytime soon. This requires high transaction volume and a good reputation of the creator, both of which are indispensable. Some of the most recognized NFT collaterals include CryptoPunk, BAYC, MAYC, Azuki, and Doodles, which are also the so-called “blue-chip” NFT series. If we compare these “blue-chip” NFTs to properties in home loans, they are undoubtedly first-tier cities, while the “blue-chip” NFTs with the rarest features are luxury residential areas in first-tier cities.

However, NFTs are highly volatile assets, and the value of even “blue chip” collections can fluctuate wildly. Before the Otherdeed mint, BAYC’s floor price had hit a new all-time high in ETH terms, and has since fallen more than 50%. A long-standing challenge in the design of NFT collateral lending protocols is: how to unbiasedly determine the value of the underlying asset NFT collateral? Existing players have taken some different solutions:

Paths to unlocking capital efficiency - A study on NFT mortgage and borrowing protocols

https://www.coingecko.com/en/nft/bored-ape-yacht-club

Time Weighted Average Prices (TWAPs)

An oracle like Chainlink can capture and publish time-weighted average prices for sale prices and floor prices, creating such a hybrid price to value NFTs. Such a model can reduce the impact of abnormal events on prices by averaging multiple prices over a predetermined time period, thereby increasing the difficulty of potentially malicious price manipulation.

However, the use of TWAPs in the valuation of NFTs has some major drawbacks: TWAPs can only be applied to NFT products with active markets and large transaction volumes, and only such NFTs are less vulnerable to attacks against price oracles. The TWAPs approach is also less capital efficient, as the protocol tends to set a smaller asset collateralization ratio to avoid the impact of extreme market conditions.

Examples: BendDAO, JPEG’d, Drops DAO, Pine Protocol, DeFrag

User Valuation Method

In the user valuation method, the pricing of NFT is based on the price prediction given by the user. This way of letting users make valuations can be applied to a wider collection of NFTs, as it doesn’t require very strict qualifications on the quality of NFTs like TWAPs. By providing certain incentives to individuals or curatorial committees, a relatively fair price discovery for NFTs can be achieved. However, this valuation method needs to reward the valuer, its valuation cost is significantly higher than other methods, the process is less efficient, and the results may be inaccurate.

Examples: Taker Protocol, Upshot V1

Liquidity Pool Valuation Method

One of the most important problems with the user valuation method is that it cannot provide real-time prices for NFTs. In the liquidity pool valuation method, this problem does not exist. In this approach, every NFT invested in the protocol is actively traded by active lenders in the pool, resulting in a constant spot pricing on the NFT equal to the total ETH in the pool. Once the NFT is locked in a pool by the borrower, traders can start depositing ETH into the pool to bring the NFT to what they think it is worth. If the NFT is overvalued in the event of a public auction, traders may lose their ETH; in the event of an NFT being undervalued, traders will put ETH in the pool to fill the pool until they think the true market value of the NFT is reached, in an effort to make a profit on the sale. By encouraging traders to speculate in the NFT pool, the valuation of NFTs will become more accurate in such a dynamic way.

Example: Abacus

While some of the examples above fall outside the scope of NFT lending protocols, these pricing mechanisms play a critical role in determining loan amount caps and determining whether to liquidate collateral. Once the value of the NFT is determined, these agreements can be divided into two models depending on the type of counterparty:

peer-to-peer lending

This approach is theoretically applicable to all NFTs and makes it easier to come to a consensus on the value of NFTs. Think of it as an open market, with lending protocols as an accelerator that facilitates transaction formation. On one side, NFT holders can create loans with the terms they want, and on the other side, funding providers can browse the platform to decide who they want to lend their money to. Once the lender of funds (aka funding provider) accepts the loan proposal, the lending protocol will create a smart contract and the NFT used for collateral will be sent to an escrow account guarded by the protocol. At the same time, the protocol will transfer the loan to the borrower along with the NFT exchange note (used to redeem the NFT).

When the borrowers and lenders agree on the terms and details of the loan term, asset-to-backup ratio (LTV), annualized rate of return, etc., the systemic risk can be mitigated because defaults only occur between the borrowers and lenders of a single order. However, with such customizability comes poor liquidity and scalability, as borrowers and lenders need to wait for matching to reach a common agreement.

Examples: NFTFi, Arcade, MetaStreet

peer-to-peer lending

This is a more “market-oriented” approach than a “bid-ask” loan transaction that may never be reached. In this way, the liquidity funds provided by the lenders will be pooled together to form a fund pool, and the interest repaid by the borrowers will be shared together. The calculation method of the specific interest depends on the situation of the supply and demand sides. In the event that the borrower cannot repay the loan, or there is a liquidation problem caused by the NFT falling in price, the protocol will automatically auction the NFT and return the proceeds to the lender.

Through peer-to-peer pool lending methods, the total amount that can be loaned can be significantly increased. Borrowers can immediately access funds by staking NFTs without waiting for lenders to confirm the terms of the agreement. However, this also means that the terms of the loan agreement need to be automatically generated by generating a reliable price feedback through the oracle. Therefore, this method can only be applied to mainstream NFT products, and long-tail NFT assets are easily affected by price manipulation.

Examples: JPEG’d, DeFrag, BendDao, MetaLend, Pine, Drops DAO

For ease of comparison, I have included the following table, including some important metrics when evaluating NFT lending protocols. Some protocols decide to put a cap on the collateralized funding ratio (LTV) to limit the possibility of default. And for NFTs that are more liquid and in demand, the ratio is usually higher. Protocols vary widely in the scope of NFTs covered, but peer-to-peer protocols outperform most peer-to-peer protocols. Note that most protocols are constantly increasing the range of NFTs they support while adjusting their pricing mechanisms and LTV ratios.

Paths to unlocking capital efficiency - A study on NFT mortgage and borrowing protocols

Although there is a lot of controversy surrounding NFT mortgage lending protocols, we expect more NFT lending and financialized products to enter this space, providing NFT collectors with a way to unlock greater value from digital collectibles. Going a step further, if a sustainable amount of NFTs is one day locked up in lending protocols, these protocols could turn into having some degree of pricing power over NFTs. There is a lot of untapped potential waiting for us, and I have no doubt that financialization will be one of the most powerful narratives for NFTs this year.

Posted by:CoinYuppie,Reprinted with attribution to:https://coinyuppie.com/paths-to-unlocking-capital-efficiency-a-study-on-nft-mortgage-and-borrowing-protocols/
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.

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