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@article{196485,
        author = {Apurva Mane and Purva Patil and Nikita Patkar and Sonal Kadam},
        title = {RentIt: A Cloud-Based Property Rental System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {4501-4511},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=196485},
        abstract = {The traditional property rental process is largely broker-driven, manual, and fragmented, resulting in high costs, low transparency, and inefficient communication between tenants and property owners. To address these challenges, this paper presents RentIt, a cloud-based intelligent property rental system designed to automate and modernize rental management. The proposed platform integrates role-based authentication, AI- based document verification using Optical Character Recognition (OCR), Machine Learning-driven rental price prediction using Random Forest regression, secure online payment gateways, and real-time communication modules. Additionally, the system enhances user experience through interactive 3D property visualization and geospatial mapping integration. Built on a scalable cloud-native architecture using modern web technologies and Firebase services, RentIt ensures data security, real-time synchronization, and seamless deployment. The system eliminates intermediaries, reduces rental costs, improves transparency, and provides a reliable digital ecosystem for tenants and property owners. Experimental evaluation of the ML model demonstrates effective rental price estimation using MAE, RMSE, and R2 metrics. The proposed solution contributes toward the digital transformation of the real estate rental sector.},
        keywords = {Cloud Computing, Property Rental System, Artificial Intelligence, Machine Learning, 3D Visualization, SaaS Architecture},
        month = {April},
        }