Development of a Shared Flat Matching Platform with Personality-Based Algorithm

Urban rental markets have experienced rising housing costs, prompting young professionals to seek shared flats. However, inaccurate or incomplete information about potential roommates and flats can lead to poor matches, wasted time, and suboptimal rental decisions. There is a need for an intelligent system that facilitates accurate, efficient, and compatible roommate matching based on diverse personal and property data to improve decision quality.

A technology-driven real estate platform specializing in shared accommodation listings and roommate matching services for urban professionals.

• Implement a web application that integrates a matching algorithm to connect flat seekers and offers based on comprehensive profile data.
• Reduce decision-making time by providing users with prioritized roommate and flat recommendations.
• Enhance matching accuracy by analyzing personality traits, habits, preferences, and property details.
• Enable users to view all relevant matches and communicate directly within the platform to facilitate rental negotiations.
• Leverage modern technology to ensure a scalable, secure, and user-friendly platform.

• User profiles with personal details, preferences, and personality questionnaire.
• Input forms capturing location, flat attributes (size, cost, rental start), and roommate preferences (gender, habits, interests).
• Matching algorithm that evaluates and scores compatibility based on personality traits, habits, preferences, and property features.
• Dual-tab interface showing 'Recommended' matches with match percentage and 'All matches' within selected location.
• Messaging feature for direct communication between potential roommates for further discussions.

• Image and media management via cloud storage (e.g., Cloudinary)
• Internal messaging and notification system
• Optional integration with external property listing services or APIs for updated flat data

• System should support at least 10,000 concurrent users with minimal latency
• Data privacy and security measures compliant with relevant standards
• High availability and fault tolerance to ensure uninterrupted service
• Responsive UI compatible across multiple devices and browsers

The implementation of this matching platform is expected to significantly improve the accuracy and efficiency of shared flat decisions, reducing the time to find suitable roommates and flats by up to 50%. It will enhance user satisfaction and trust in the service, leading to increased platform engagement and higher user retention rates. Ultimately, the system aims to facilitate more optimal housing matches, contributing to a better rental experience for young professionals in high-cost urban markets.