Thread by @lingchenjaneliu

1/ Financialized NFTs is an evolving space with huge potential. Yet, low liquidity and inadequate price discovery are key limitations to be tackled.

Here I explain its importance, bottlenecks, market landscape, and potential opportunities.
fundamentallabs.substack.com/p/financialize…
A thread🧵👀

2/ The importance of NFT financialization

Securitization has been a long-time practice in the field of finance.

Financialization transforms illiquid assets into tradable securities, which helps to channel cash flow to borrowers and fund significant economic development.

3/ Where is the market today?

@NFTfi issued $36 million worth of loans in May, while NFT transaction volume exceeded $3 billion.

The penetration rate of NFT credit market is slightly north of 1%

vs. 10% in traditional art lending and >50% in mortgage markets.

4/ NFTs suffer from low liquidity:
‣ Unique characteristics -> limited market
‣ High price -> low affordability
‣ Lack of utility -> speculation deters investors

Which leads to:
‣ High slippage
‣ Capital inefficiency
‣ Poor price discovery and valuation

5/ Current market landscape for NFT financialization

Categorized into...
‣ Marketplace and Aggregator -> facilitate transactions
‣ Lending and Rental -> improve capital efficiency and
explore yield-generating possibilities
‣ Liquidity and Valuation -> infra for trading

6/ Protocol deep-dive

LIQUIDITY POOLS
Liquidity is a key trading infra.
Abundant liquidity ->
+asset utilization rate
+user participation.

@NFTX_ regards the NFTs within the same vault fungible. The mechanism is suitable for floor items within the same collections.

7/ Deeper pool can maintain a stable price, and create a smooth trading experience.

Here is when @FloorDAO comes in...

With bond & rebase mechanisms by @OlympusDAO, @FloorDAO accumulates liquidity for NFTs, which are deployed in @NFTX_ to generate yield.

8/ FRACTIONALIZATION

@fractional_art operates the same way as @NFTX_/@NiftyMuseum.

The similarity comes from transforming ERC721 tokens into ERC20 tokens and the difference lies in the ability of yield generation.

9/ Why is fractionalization not a solution?

Fractionalization just transfers existing liquidity issues from NFTs to fractional NFTs, meaning: instead of having one illiquid NFT, you now have many illiquid ERC-20 tokens.

Good for community building, not so good for trading.

10/ LENDING

There are three mechanisms to facilitate NFT-as-collateral loans:
‣ Peer-to-peer lending
‣ Peer-to-pool lending
‣ Over-the-counter (OTC) lending

11/ Peer-to-peer lending

@NFTfi
The loaning process is similar to a loan marketplace, where the NFT owner selects the offer from several liquidity providers based on their terms.

12/ Peer-to-pool lending

The segment of NFT lending protocols utilizing the peer-to-pool mechanism has many strong players, including @BendDAO, @dropsnft, @XCarnival_Lab, @PineLoans, @GradientFinance, @JPEGd_69 and @PilgrimProtocol amongst others.

13/ COMPARISON: NFT LENDING

P2P
+long waits; suits long-tailed NFTs
-not scalable

P2Pool
+Instant loan
-blue chips only

OTC
+institutional scale
-KYC required; blue chips only

All with low LTV->low capital efficiency

14/ RENTING

NFT rental market is still young. As of June there are <100 NFTs for rent on @renftlabs, indicating room to grow.

Lack of PFP utility + guides providing in-game asset rental service = low rental demand

However, we expect new use-case NFTs to boost the growth.

15/ VALUATION AND APPRAISAL

Valuation and appraisal is a fundamental infrastructural component of any trading market and can be used across many NFT financialization use-cases.

Pricing mechanisms can be categorized into:
‣ oracle-based
‣ optimistic proof of stake pricing.

16/ @abacus_wtf uses optimistic proof-of-stake to create a liquid backed valuation system.

The value of the NFT equals the total value locked in the pool.

The valuation mechanism of Abacus is one of a kind and its effectiveness remains to be proven over time.

17/ There are several protocols experimenting on leveraging machine learning technology to predict NFT prices, such as @NFTbank_ai, @banksea_finance, and @UpshotHQ.

Data fed for training models can be categorized into three types:
‣ pricing data
‣ NFT metadata
‣ market data

18/ Determining valuation: Pricing oracles

In this method pricing is determined through on-chain data that is fed to the protocol using oracles. While this improves trustlessness, unexpected short-term upsides cannot be captured as this method is backwards-looking.

19/ Determining valuation: Machine learning based pricing oracle

This method also relies on pricing oracles for on-chain data, but also off-chain data and uses pattern recognition to provide more context to the data. This method needs more data to properly evaluate a collection.

20/ Determining valuation: Optimistic Proof of Stake pricing

Spot traders form a network of real-time human appraisers, whereas the value of the trading pool reflects the value of the NFT. While unique, this approach faces scalability challenges that are yet to be overcome.

21/ The development and experimentation of NFT financialization just started.

Despite all the progress in various verticals, we see some common challenges:
‣ Foundations waiting to be established
‣ Limited utility layers

22/ Here are some of my thoughts on where we can find opportunities in building this space.

‣ Segmenting NFTs into different categories with
different tranches of risk and return profiles
‣ Building risk profiles of borrowers
‣ Extending utility layers

23/ If you're working on building this space, have feedback on this piece, please reach out!

Special thanks to @0xPhillan for his support on this piece.