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@article{205336,
        author = {Mrs. J. Princess Bala and S. Vanaja},
        title = {Multimodal Machine Learning for House Price Prediction with Geo-Spatial Embedding},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {6070-6073},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=205336},
        abstract = {The multimodal machine learning house price prediction with geo-spatial embedding techniques to model the spatial relationships between properties and nearby amenities such as schools, hospitals, transportation hubs, and commercial areas. These embedding’s capture the influence of neighbourhood characteristics on property prices. Additionally, the model incorporates multiple data modalities, including numerical features, geographic coordinates, and optionally image or textual data, enabling a more comprehensive understanding of real estate dynamics.  A deep learning architecture, such as a combination of neural networks and embedding layers, is employed to learn complex nonlinear relationships between features. The model is trained and evaluated using real-world datasets, demonstrating improved performance compared to traditional machine learning approaches like linear regression and random forest.  The results show that integrating multimodal data and geo-spatial embeddings significantly enhances prediction accuracy, making the system suitable for real-world applications in real estate analytics, smart city planning, and investment decision-making. This approach highlights the importance of combining spatial intelligence with deep learning to build robust and scalable property valuation models.},
        keywords = {Multimodal Learning, Deep Learning, House Price Prediction, Geo-Spatial Embedding, Neural Networks, Real Estate Analytics.},
        month = {June},
        }