RWA Estimator

Demo

About

The problem it solves Many existing RWA (Real-World Asset) tokenization projects rely on **centralized and opaque valuation mechanisms**. This lack of transparency creates risks for investors and makes it difficult to attract additional liquidity to RWA-backed tokens. This project addresses the problem by introducing a **decentralized and fully on-chain valuation protocol** that combines two key sources of pricing data: - **External API-based prices**, fetched securely via [Chainlink Functions](https://docs.chain.link/chainlink-functions) - **Independent appraiser submissions**, aggregated through an on-chain weighted averaging mechanism By combining these sources, the system enables more reliable and tamper-resistant asset valuations. To further encourage participation and maintain trust in the system, the protocol introduces a **Rent Payment model**: - Asset owners make periodic payments - These payments are distributed between: - **Appraisers**, based on the accuracy of their price submissions - **Investors**, proportionally to their token holdings The protocol also includes **security mechanisms** such as: - **Safety payments** (collateral from the asset owner to protect participants) - **On-chain liquidation**, allowing any user to trigger a liquidation if the asset owner fails to meet payment deadlines Together, these mechanisms make RWA investing **more transparent, safer, and incentive-aligned**, helping attract both appraisers and liquidity providers to the ecosystem.

How it's made

Challenges we ran into While building this project, I encountered several technical and conceptual challenges: ### 1. Debugging Chainlink Functions and Automations Getting Chainlink Functions and Chainlink Automations to work properly was a significant challenge. Transactions would often revert with unclear reasons, making debugging difficult. What helped was diving into the detailed Chainlink developer documentation and the examples provided during the Tokenized RWA Bootcamp, which clarified many edge cases and contract setup requirements. ### 2. Designing Incentives and Mitigating Malicious Behavior It was difficult to create an incentive model that fairly rewards all participants while minimizing risks from bad actors. - For **appraisers**, the risk of manipulation is addressed through a weighted price aggregation mechanism — with room for further improvements in accuracy and resistance to collusion. - For **asset owners**, risks are mitigated by requiring safety margin collateral and introducing an onchain **liquidation mechanism** that any user can trigger upon missed payments. ### 3. Building a Functional Frontend With limited prior experience in React, building a working frontend was challenging. After some initial friction, I was able to understand the basic component model and build a UI that’s good enough for demo purposes within the hackathon timeframe. ### 4. Testing Complex Contract Logic The protocol includes complex interactions between contracts that could easily break in edge cases. Writing thorough smart contract tests using **Foundry (forge)** helped identify and fix bugs early. For frontend testing and realistic simulation, using **Anvil forked chain** with time and balance manipulation turned out to be incredibly helpful.