TrustRent

Our Journey: From Frustration to Solution

What Inspired Us

As engineering students managing PG accommodations and apartment hunts during placement season, we've all faced the same nightmare: scrolling through hundreds of rental listings, only to discover they're fake, the prices are scams, or the "owner" doesn't actually own the property.

I personally lost ₹15,000 to a fake landlord in Delhi. My teammate's sister was locked out of a room after paying 6 months advance to someone who had no legal claim to the property.

The core insight: The Indian rental system has a massive trust gap. Renters manually verify properties like amateur detectives, with zero institutional support or legal recourse.

What We Built

TrustRent is an automated rental verification platform that:

• Verifies property ownership against municipal records
• Builds community trust scores for landlords
• Detects scams using smart algorithms
• Generates legal rental agreements
• Provides dispute resolution

Our MVP is a web application where renters upload property details and receive instant verification status, community reviews, and scam alerts—all in under 2 minutes.

How We Built It

• Days 1–2: User research (20+ renter interviews, 10 landlord interviews)
• Days 3–5: Compiled municipal property database (500+ verified properties, Delhi test market)
• Days 6–10: Built Next.js frontend + Node.js backend + Supabase database
• Days 11–14: Deployed MVP on Vercel, tested with 50 IITM students
• Tech Stack: React, Node.js, PostgreSQL (Supabase), Google OAuth, Vercel

Key Technical Challenges We Faced

Challenge 1: Municipal Data Access

• Problem: Municipal property tax records aren't easily accessible via public APIs
• Solution: Built relationships with Delhi Municipal Corporation; negotiated partial API access. For MVP, created hybrid model: official verification for major cities + community-flagged data for others
• Learning: Government data partnerships require patience and persistence

Challenge 2: Duplicate Listing Detection

• Problem: Same property listed 10x with different prices/photos across OLX, 99acres, Facebook
• Solution: Implemented image similarity matching (reverse Google Images API + OpenCV) + address fuzzy matching algorithm
• Learning: Data deduplication at scale requires multi-modal matching, not just text

Challenge 3: Legal Compliance & Aadhaar Privacy

• Problem: Using Aadhaar for verification raises UIDAI compliance and privacy concerns
• Solution: Encrypted hashing, opt-in consent, zero data resale, compliance with UIDAI guidelines
• Learning: Privacy-first design isn't optional—it's the foundation of user trust

What We Learned

1. User research changes everything. Our initial idea (full legal platform) shifted to simple verification because users cared most about scam prevention, not document generation.
2. Data is the moat. The best verification logic is worthless without clean, verified property data. Municipal partnerships matter.
3. Incentives drive adoption. Landlords won't verify unless they see tangible benefit (more inquiries, community credibility). Renters evangelize when they feel safe.

What's Next

• Launch in 3 cities (Delhi, Mumbai, Bangalore) with 10,000+ verified properties
• Integrate with rental platforms (OLX partnership)
• Build landlord verification workflow
• Add AI-powered dispute resolution (auto-escalate to authorities if needed)

Built With

• express-database:-postgresql-(supabase)-authentication:-google-oauth-apis:-openstreetmap
• frontend:-next.js
• municipal-property-tax-records-deployment:-vercel-(frontend)
• railway
• react
• reverse-image-search
• tailwind-css-backend:-node.js