Real Estate Property Management System with AI Chatbot and Google Calendar Integration

Abstract

The real estate sector continues to face challenges such as inefficient communication, lack of transparency, and manual scheduling of property visits. To address these limitations, this paper presents a smart and scalable web-based real estate property management system developed using the MERN stack. The proposed system provides a centralized platform that enables secure interaction among buyers, sellers, and administrators through role-based access control and JSON Web Token (JWT) authentication. An Artificial Intelligence (AI)-powered chatbot is integrated to enhance user experience by providing real-time assistance, answering property-related queries, and guiding users throughout the platform. Furthermore, the integration of the Google Calendar API enables seamless scheduling of property visits, significantly reducing manual coordination and improving operational efficiency. The system also incorporates a structured property lifecycle management model (pending ? verified ? suspended), ensuring data integrity, authenticity, and controlled visibility of listings. By combining modern web technologies with intelligent automation, the proposed solution improves usability, enhances transparency, and offers a scalable framework suitable for real-world real estate applications.

Introduction

The text describes a smart web-based real estate property management system developed using the MERN stack to address inefficiencies in traditional property transactions. Traditional methods—manual communication, physical visits, and third-party agents—are slow, lack transparency, and allow fraudulent listings, reducing trust among buyers and sellers.

Key features of the proposed system:

• Role-Based Access Control (RBAC): Three user roles—Admin, Seller, Buyer—ensure secure access and responsibilities; Admin verifies listings to prevent fraud.
• AI-Powered Chatbot: Provides real-time assistance, guiding users and answering queries.
• Google Calendar Integration: Enables easy scheduling of property visits, reducing manual coordination.
• Secure Architecture: RESTful design with JWT authentication and MongoDB ensures data security, scalability, and performance.
• Property Lifecycle Management: Tracks status from pending → verified → suspended for effective property management.

Results:

• Enhanced security and trust through verification and access control.
• Improved user interaction via AI chatbot and operational efficiency through calendar scheduling.
• System outperforms traditional platforms in usability, transparency, and performance.

Future enhancements:

• AI-based property recommendations, voice-enabled chatbot, real-time buyer-seller chat, cloud storage, mobile app, secure payments, advanced analytics, and ML-based market prediction.

In essence: The platform combines automation, intelligent AI features, secure access, and modern web technologies to provide a transparent, efficient, and user-friendly real estate management solution that enhances trust and operational efficiency.

Conclusion

The proposed real estate property management system provides a secure, efficient, and scalable solution using the MERN stack. By implementing role-based access control and JWT authentication, the system ensures safe and controlled access for all users. The integration of an AI chatbot enhances user interaction through real-time assistance, while Google Calendar simplifies property visit scheduling. Additionally, admin verification and lifecycle management improve transparency and ensure the authenticity of property listings. Overall, the system reduces manual effort, improves communication, and delivers a reliable and user-friendly platform suitable for real-world real estate applications.

References

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Copyright

Copyright © 2026 Jhansi ., Indhu , Deepthi , Bhavya , S N Murthy Nalluri. 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.