The objective of this project is to develop a secure, ML-powered rental platform that detects fraudulent property listings using image and text analytics. Built with the MERN stack, it allows brokers to upload property details, which are evaluated for authenticity. Fraud scores guide listing visibility, while JWT ensure secure access. A smart contract-like rental agreement system further enhances trust, transparency, and safety in the digital rental ecosystem.
The growing digitalization of property rental services has introduced new vulnerabilities, particularly the proliferation of fraudulent listings that deceive renters through manipulated images or misleading content. This project, titled “Rent-Hive – Smart Property Rental with Fraud Detection”, presents a secure and intelligent MERN stack-based platform that aims to enhance transparency and trust in the real estate rental process. The system enables user and broker registrations, allowing property owners to upload rental listings along with images and documents. Each submission is evaluated using a Python-based machine learning fraud detection module, which analyses the consistency of textual descriptions and detects fake or tampered images. A fraud score is generated and stored in MongoDB, influencing how listings are flagged or presented on the React-based renter portal, complete with filters and visual fraud warnings. Additionally, the system features a smart contract-like rental agreement generator, ensuring seamless and secure transactions. Rent-Hive thus integrates real-time analytics and modern web technologies to build a safer, more reliable rental ecosystem.
Keywords: Smart Contract Generator, Rental Platform, Real Estate Rental, MERN Stack
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SOFTWARE REQUIREMENTS:
ü Operating System : Windows 7/8/10
ü Programming Language : Java
ü IDE/Workbench : VS Code
ü Database : My SQL
ü Clint Side : React js
HARDWARE REQUIREMENTS:
ü Hard Disk - 160GB
ü Key Board - Standard Windows Keyboard
ü Mouse - Two or Three Button Mouse
ü Monitor - SVGA
ü RAM - 8GB