A Secure Three-Tier Web Framework for Verified Student Accommodation and Rental Management

Abstract

Finding convenient paying guest (PG) and student accommodation is a very big problem facing students who in migrating to new city seek higher education. Lack or availability of centralised, validated and student friendly accommodation portals can result into time consuming search, inaccurate information, and poor housing decision. The present paper proposes a specialized web-based accommodation portal, named as PGConnect, which will facilitate the process of searching and finding accommodation of the PG housing, with particular attention to the Delhi-NCR educational hub. The system is built on a friendly interface that is created using React, and the backend is based on the Laravel framework to handle business logic, authentication, and RESTful API security. A relational database is a PostgreSQL, which guarantees the data integrity and effective management of queries. The framework has a three-level architecture that helps increase scalability and maintainability. Experimental findings show that, PGConnect is very effective to decrease the time spent on the search and provide a high level of user satisfaction in comparison to manual approaches, which can be regarded as a valid and visible solution to the specific demands of the student population in metropolitan areas.

Introduction

The text discusses the growing challenges students face in finding affordable, secure, and convenient accommodation near educational institutions due to rapid urbanization and increasing student migration. Traditional methods such as brokers, word-of-mouth recommendations, and physical visits are often time-consuming, unreliable, and lacking transparency. Existing online rental platforms are mostly designed for general housing markets and fail to address student-specific needs like low budgets, room sharing, proximity to colleges, and short-term stays.

To solve these issues, the paper proposes PGConnect, a web-based student accommodation portal specifically designed for students seeking paying guest (PG) housing. The platform aims to provide a centralized system where students can search, compare, and filter accommodations based on factors such as rent, location, amenities, and availability. The system is built using React for the frontend, Laravel for backend operations and security, and PostgreSQL for reliable database management. It follows a three-tier architecture to ensure scalability, maintainability, and security.

The literature review highlights the evolution of online rental systems from simple directory listings to more advanced platforms with integrated services. Previous studies emphasize the importance of student-focused portals, scalable architectures, secure authentication, verification mechanisms, and location-based services. Research also shows that trust and usability are critical for successful adoption, while technologies such as GIS, recommendation systems, fuzzy logic, and reinforcement learning can improve personalized accommodation suggestions.

The paper identifies several research gaps in current rental platforms, including the absence of student-specific filtering, weak fraud prevention, lack of verified property listings, and insufficient real-time occupancy management for property owners. Many existing systems allow unverified listings, leading to “ghost listings” and reduced user trust.

Conclusion

The given paper introduced a secure 3-tier web platform, known as PGConnect that aims to ease the process of finding trusted student accommodations. The system has offered a centralized solution that enhances transparency and faith in the student rental market through verification of property listing to property administration prior to publication. The platform itself is developed on the basis of the three-layer architecture (React) (presentation layer), Laravel (application layer), and PostgreSQL (data layer), which increases scalability, modularity, and maintainability. Experimental testing indicates that the system can sustain an average of less than 250 ms of response time and offers a number of filtering parameters and user feedback confirms a 65 percent decrease in search time of accommodation than traditional systems. The next phase of work consists of the implementation of machine learning-driven recommendation systems, creation of a mobile application, and provision of real-time booking and integration of digital payments to improve the usability and scalability of the platform further.

References

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Copyright

Copyright © 2026 Vaduni Niranjan, Subhava Ojha, Shashwat Pandey, Satyam Kushwaha, Vishal Babu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.