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@article{209255,
        author = {Dr. MK Jayanthi Kannan and Aditya Anand and Jasmine Chauhan and Arihant Jain and Atreya Malviya and Krish Pandey and Ayush Shukla},
        title = {Kimat: AI-Driven Geospatial and Machine Learning-Based House Price Prediction System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {5},
        pages = {2243-2255},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=209255},
        abstract = {The real estate market is rapidly evolving with complex pricing dynamics, making manual property valuation subjective and prone to human error. The "Kimat" platform is designed as an AI-driven house price prediction system that transforms historical housing data into accurate real-time price predictions. By leveraging a focused regression workflow, the system analyzes structured property and locality attributes to output estimated prices accompanied by confidence-oriented explanations. Utilizing machine learning models such as Random Forest, which evaluate key property features and market trends, Kimat provides fast, reliable, and data-driven preliminary valuations. The platform compares Multiple Linear Regression against ensemble techniques using R2, RMSE, and MAE, explains the strongest price drivers, and exposes the selected model through a simple, pincode-aware prediction interface. This empowers homebuyers, real estate agents, and property investors by democratizing real estate valuation and saving time and appraisal costs through an intuitive web interface.},
        keywords = {House Price Prediction, PropTech, Machine Learning, Random Forest, Multiple Linear Regression, XGBoost, Real Estate Valuation, Predictive Analytics, Kimat.},
        month = {October},
        }