Building Trust into Real Estate: Inside a Verification-First Property Marketplace

Buying or selling property is one of the most consequential financial decisions most people ever make — and yet, in many markets, it remains plagued by opaque pricing, unreliable middlemen, and paperwork nightmares. This project sets out to fix that by building a real estate marketplace where trust isn't assumed, it's engineered — through structured verification, agent accountability, and data-driven pricing.

A Platform With Three Kinds of Users

At its core, the platform serves three types of participants: buyers, sellers, and agents. Rather than treating agents as optional add-ons, the system assigns a platform agent to every property based on city, making agent oversight a built-in part of every transaction rather than an afterthought.

Authentication is handled through a clean JWT-based login system, with role-based access control ensuring agents unlock a different set of powers than everyday buyers and sellers — from verifying listings to approving final payments.

Listings You Can Actually Trust

When a seller lists a property, it doesn't go live as "verified" by default. Every listing — whether it's a flat, house, plot, or commercial space — enters a verification pipeline. An assigned agent reviews the details and either approves it or requests changes with specific remarks. Sellers get a fixed window to respond, and any edit to a listing automatically resets its verification status, ensuring nothing slips through unreviewed after a change.

Buyers browsing the marketplace can filter listings by city, price range, purpose (buy or rent), and property type — and every price shown already factors in the agent's fee, so there are no surprise costs later.

Negotiation, Not Just Transactions

Real estate deals rarely happen at the asking price, and this platform embraces that reality with a full bidding and negotiation system. Buyers submit interest with a bid amount and message; sellers can accept, counter, or reject. Buyers can then accept or reject a counteroffer, or withdraw entirely. The moment an agreement is reached, the system automatically:

Creates a formal Deal record
Marks the property unavailable
Rejects all other competing bids on that property

This atomic handoff from "negotiation" to "deal" removes ambiguity about which offer actually won.

Paperwork, Without the Headache

Once a deal is struck, buyers and sellers upload required documents — PDFs, images, contracts — straight to secure cloud storage. Agents review each document and mark it verified or rejected, and if something's rejected, the platform automatically sets a reupload deadline so the deal doesn't stall indefinitely. Only when every document clears review does the deal move forward to payment, followed by agent-confirmed completion.

Where Machine Learning Comes In

Perhaps the most distinctive feature is the platform's built-in price estimation engine. Powered by a trained ML model, it predicts a fair market price for any property based on city, size, bedrooms, bathrooms, furnishing status, and purpose — complete with a confidence interval and model accuracy metrics.

This isn't just a novelty feature tucked into a search page. It's woven directly into the bidding flow: if a buyer's bid deviates more than 30% from the model's estimated fair price, the system flags it immediately, giving both sides a data-backed reality check before money and paperwork are exchanged.

The Bigger Picture

Taken together, this project reimagines a property marketplace not as a simple listings board, but as a guided transaction pipeline — one where every property is vetted, every deal is mediated, every document is verified, and every price is measured against real market data. It's an attempt to bring the structure of an escrow-backed, agent-mediated real estate transaction into a modern, API-driven platform.