NFT Lending

A direct, over-the-counter model where lenders and borrowers negotiate terms for specific NFT assets. This is the foundational model, offering maximum flexibility.

• Key Feature: Terms like loan-to-value (LTV) ratio, duration, and interest rate are set per deal.
• Example: A borrower lists their CryptoPunk for a 90-day loan; a lender offers ETH at a 40% LTV and 15% APR.
• Platforms: Early platforms like NFTfi and Arcade operate on this model.

A pooled liquidity model where lenders deposit funds into a shared pool to earn yield, and borrowers draw loans directly from this pool using approved NFT collateral.

• Key Feature: Enables instant, algorithmic loans without waiting for a counterparty.
• Mechanism: The protocol uses an oracle to value the NFT and automatically determines the maximum loan amount based on a pre-set LTV ratio.
• Example: BendDAO and JPEG'd pioneered this model for blue-chip NFT collections.

The process of determining the value of an NFT to calculate the loan amount. This is the critical risk-management component.

• Methods:
  • Oracle Prices: Using floor prices from marketplaces like OpenSea.
  • Appraisal Committees: Human committees assess rare, non-floor assets.
  • Trait-based Pricing: Algorithms value NFTs based on specific attributes.
• Risk: Over-reliance on volatile floor prices can lead to liquidation cascades if the market drops suddenly.

The process by which a loan's NFT collateral is automatically seized and sold if its value falls below a predefined threshold, protecting the lender.

• Health Factor: A numerical representation of a loan's safety (e.g., Collateral Value / Loan Debt). If this falls below 1, the loan is underwater.
• Liquidation Process: Typically involves a liquidation auction where liquidators can buy the NFT at a discount to repay the loan and keep the remainder.
• Example: A Bored Ape used as collateral may be liquidated if the floor price drops 30%, triggering a 48-hour Dutch auction.

The specific financial agreements governing an NFT-backed loan, defining risk, duration, and cost.

• Fixed-term vs. Open-term: Fixed-term loans expire on a set date; open-term (evergreen) loans continue until repaid or liquidated.
• Interest Rates: Can be fixed (agreed upfront) or variable (adjusted based on pool utilization).
• Loan-to-Value (LTV) Ratio: The primary risk metric (e.g., a 30% LTV means a $100k NFT secures a $30k loan). Lower LTV ratios are safer.

The practical applications and actors within the NFT lending ecosystem.

• For Borrowers:
  • Liquidity Access: Use NFT value for other investments without selling.
  • Leverage: Borrow against one NFT to buy another.
• For Lenders:
  • Yield Generation: Earn interest on capital by funding loans.
  • Liquidation Arbitrage: Profit by purchasing undervalued NFTs during liquidations.
• For Protocols: Generate fee revenue from origination and transactions.

The process of determining the value of an NFT used as loan collateral, a critical risk factor for lenders. Methods vary by model.

• In P2P, valuation is often manual, based on the lender's assessment of floor price, traits, and recent sales.
• In P2P Pool, valuation is automated using price oracles (e.g., Chainlink) or the protocol's own pricing model, which may reference floor price aggregates.
• Inaccurate valuation can lead to undercollateralized loans or inefficient liquidations.

The process by which collateral is seized and sold to repay a loan when its value falls below a predefined threshold (the liquidation LTV).

• Trigger: Occurs when the health factor (collateral value / debt value) falls below 1.
• P2P Models: Often use a grace period for the borrower to top up collateral before the lender can claim the NFT.
• P2P Pool Models: Typically employ automated Dutch auctions, starting at a high price that decreases until a buyer is found, with proceeds repaying the debt.

A core risk metric representing the loan amount as a percentage of the collateral's appraised value.

• Calculation: LTV = (Loan Amount / Collateral Value) * 100.
• Purpose: Determines the level of overcollateralization. A lower LTV (e.g., 30%) provides a larger safety cushion against price volatility.
• Liquidation LTV: The threshold (e.g., 80%) at which the loan becomes eligible for liquidation. The health factor is inversely related to the LTV.

Uncollateralized loans that must be borrowed and repaid within a single blockchain transaction. Primarily used for arbitrage, collateral swapping, or self-liquidation in DeFi strategies involving NFTs.

• Key Feature: No upfront collateral required.
• Mechanism: The entire transaction is atomic; it reverts if the loan isn't repaid, eliminating default risk for the lender.
• Use Case: Using a flash loan to buy an NFT, use it as collateral for a larger loan on another platform, and repay all within one block.

Critical infrastructure that provides real-time price feeds for NFT collateral. They determine loan-to-value ratios, liquidation thresholds, and pool health.

• Methods: Floor price oracles track collection minimums. Trait-based oracles use rarity and sales history for individual appraisal.
• Example: Upshot and Abacus provide on-chain verifiable appraisal data for lending protocols.
• Risk: Oracle manipulation or stale data can lead to inaccurate collateral valuation and systemic risk.

Automated processes that seize and sell collateralized NFTs when a loan becomes undercollateralized. This protects lenders from default risk.

• Trigger: Occurs when the loan-to-value (LTV) ratio exceeds a protocol's maximum, often due to falling NFT prices.
• Process: Liquidators repay the outstanding debt in exchange for the NFT, often at a discount.
• Challenge: Illiquid collateral can lead to bad debt if the NFT cannot be sold quickly enough during a market downturn.

Allows NFT holders to access liquidity without selling their assets. Users deposit NFTs into a smart contract as collateral to borrow stablecoins or other cryptocurrencies. This is crucial for high-value, illiquid assets like generative art or PFP collections, enabling owners to pay expenses, fund new investments, or participate in DeFi while retaining ownership.

The dominant model where lenders deposit funds into a liquidity pool to earn yield. Borrowers take loans against their NFTs, with the pool collectively underwriting the risk. Key mechanisms include:

• Loan-to-Value (LTV) Ratios: Determined by NFT floor price or appraisals.
• Interest Rates: Set algorithmically based on pool utilization and collateral risk.
• Liquidation: Triggered if collateral value falls below a maintenance threshold, protecting lenders.

The core challenge is accurately pricing unique, illiquid collateral. Protocols use various methods:

• Oracle Prices: Rely on floor price feeds from marketplaces like Blur or OpenSea.
• Appraisal Models: Use trait rarity, collection history, and sales data for blue-chip NFTs.
• Peer-to-Peer Negotiation: In some protocols, lenders manually assess and offer terms for specific NFTs, allowing for bespoke loan terms.

A critical risk management feature that protects lenders. If an NFT's value drops and the borrower doesn't add collateral or repay, the loan enters liquidation. Common methods include:

• Dutch Auctions: The NFT is auctioned starting at the debt value, with the price decreasing until a buyer is found.
• Fixed-Price Sales: The NFT is listed at a discount to the market for a quick sale.
• Keepers: Network participants are incentivized to trigger liquidations and execute these sales.

Provides a yield-bearing avenue for capital by funding NFT-backed loans. Lenders deposit assets (e.g., ETH, USDC) into a protocol's pool to earn interest paid by borrowers. Returns are typically higher than traditional DeFi lending due to the perceived higher risk of NFT collateral. This creates a new fixed-income market within the digital asset ecosystem.

Enables sophisticated on-chain strategies. A borrower can use borrowed funds to purchase more NFTs, which can then be used as collateral for additional loans, creating leverage. This is common in NFT flipping and collection-building strategies. However, it significantly amplifies risk, as a market downturn can lead to cascading liquidations across multiple positions.

The primary risk for borrowers is the forced liquidation of their NFT collateral if its value falls below the required loan-to-value (LTV) ratio. This process is often automated by oracles or peer-to-pool mechanisms. Key factors include:

• Oracle reliability: Inaccurate price feeds can trigger unnecessary liquidations.
• Market illiquidity: A "floor price" NFT may be liquidated at a value far below its perceived worth if no buyers exist.
• Volatility: Rapid price swings in NFT collections can lead to sudden, unexpected liquidation events.

Users are exposed to the technical security of the lending protocol itself. This includes:

• Smart contract vulnerabilities: Bugs or exploits in the protocol's code can lead to loss of funds or NFTs.
• Admin key risk: Protocols with upgradable contracts or multi-signature wallets controlled by a team introduce centralization and trust assumptions.
• Integration risk: Vulnerabilities in integrated price oracles, NFT marketplaces, or cross-chain bridges can compromise the entire system.

Lenders face the risk of borrower default and the subsequent process of claiming the underlying NFT. Critical considerations are:

• Wrapped collateral: Many protocols use a wrapped NFT (wNFT) or vault model, where the NFT is custodied by a smart contract, not the lender.
• Claim process complexity: Successfully claiming a defaulted NFT may require manual steps, gas fees, and interaction with secondary markets.
• Bad debt: If the liquidated NFT's value does not cover the loan principal and accrued interest, the lender incurs a loss, which may be socialized in pool-based models.

NFT valuation is inherently subjective, making price oracle design a critical attack vector. Risks include:

• Floor price reliance: Basing loans on the cheapest NFT in a collection (floor price) is vulnerable to wash trading and sudden market manipulation.
• Rarity manipulation: Loans based on trait-based pricing models can be gamed if the oracle's rarity calculation is flawed or outdated.
• Data latency: Slow price updates during volatile markets can create arbitrage opportunities at the expense of lenders or the protocol.

Both lenders and borrowers face challenges related to market depth and the ability to enter/exit positions.

• Lender liquidity: In peer-to-pool models, lenders may not be able to withdraw their funds immediately if the pool's liquidity is tied up in active loans.
• Borrower refinancing: At loan maturity, a borrower may be unable to repay and could be forced to liquidate if they cannot secure a new loan (rollover risk).
• Protocol insolvency: A cascade of bad debt or a major hack can render a lending protocol insolvent, potentially locking all user funds.