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@article{178747,
        author = {Rupesh Manohar Sankale and K. N. Hande},
        title = {Real Estate Price Estimation Using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {5911-5915},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=178747},
        abstract = {The real estate market is highly dynamic and sensitive to price fluctuations, influenced by multiple economic stakeholders such as governments, agents, and buyers. Accurately predicting property sale prices remains a key challenge due to the market's complexity and volatility. This project proposes a property price prediction model using Decision Tree Regression, incorporating socioeconomic indicators like GDP, CPI, PPI, and HPI. By leveraging machine learning techniques alongside target encoding, the model aims to predict whether a property's final sale price will exceed or fall below its listed price. Geographic factors, historical pricing trends, and projected market shifts are used to enhance forecasting accuracy. Evaluation metrics such as Root Mean Square Error (RMSE) validate model performance. The ultimate goal is to assist individuals in estimating property values reliably, potentially reducing dependence on intermediaries.},
        keywords = {},
        month = {May},
        }