AI-Driven Transformation of the Real Estate Industry: A Framework for Property Valuation, Market Forecasting and Intelligent Decision-Making | IJCT Volume 13 – Issue 6 | IJCT-V13I6P78
IJCT
International Journal of Computer Techniques
ISSN 2394-2231 · Peer-Reviewed · Open Access
📚 Volume 13, Issue 6
📅 November 11, 2026
📄 Pages 670–675
🔖 ID: IJCT-V13I6P78
AI-Driven Transformation of the Real Estate Industry: A Framework for Property Valuation, Market Forecasting and Intelligent Decision-Making
Author(s)
Ganjikuntla Gopinath, Dr. Battapotula Venkataratnam
Abstract
As machine learning (ML), geospatial analytics, and generative AI take the place of manual evaluation and intuition-driven investment, the real estate industry is undergoing a fundamental transformation. Current research limits practical adoption by treating forecasting, valuation, and decision assistance as distinct activities. A five-layer AI system that combines multi-source data intake, feature engineering using spatial and visual signals, an ensemble valuation engine, a temporal market-forecasting module, and an explainable decision-support layer is proposed in this study. We provide a replicable evaluation procedure, formalize each component, and address governance concerns such as privacy, bias, and regulatory compliance. The framework provides researchers and practitioners with a standard architecture for developing transparent, scalable, and auditable AI systems for real estate markets.
Keywords
Real Estate, Artificial Intelligence, Automated Valuation Model, Market Forecasting
Conclusion
In order to enable responsible deployment, this article proposed a unified framework that links explainable decision assistance, market forecasts, and AI-based valuation
References
[1] M. Baldominos et al., “Identifying real estate opportunities using machine learning,” Applied Sciences, vol. 8, no. 11, 2018.
[2] E. Zurada, A. Levitan, and J. Guan, “A comparison of regression and artificial intelligence methods in a mass appraisal context,” J. Real Estate Res., vol. 33, no. 3, pp. 349–387, 2011.
[3] S. Rosen, “Hedonic prices and implicit markets,” J. Political Economy, vol. 82, no. 1, pp. 34–55, 1974.
[4] R. K. Pace, R. Barry, and C. F. Sirmans, “Spatial statistics and real estate,” J. Real Estate Finance Econ., vol. 17, no. 1, pp. 5–13, 1998.
[5] L. Breiman, “Random forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, 2001.
[6] T. Chen and C. Guestrin, “XGBoost: A scalable tree boosting system,” in Proc. ACM SIGKDD, 2016, pp. 785–794.
[7] G. Ke et al., “LightGBM: A highly efficient gradient boosting decision tree,” in Proc. NeurIPS, 2017.
[8] N. Kok, E.-L. Koponen, and C. A. Martínez-Barbosa, “Big data in real estate? From manual appraisal to automated valuation,” J. Portfolio Management, vol. 43, no. 6, pp. 202–211, 2017.
[9] S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Computation, vol. 9, no. 8, pp. 1735–1780, 1997.
[10] A. Vaswani et al., “Attention is all you need,” in Proc. NeurIPS, 2017.
[11] T. N. Kipf and M. Welling, “Semi-supervised classification with graph convolutional networks,” in Proc. ICLR, 2017.
[12] S. M. Lundberg and S.-I. Lee, “A unified approach to interpreting model predictions,” in Proc. NeurIPS, 2017.
[13] C. Huang, B. Liang, Z. Li, J. Wang, and F. Chen, “Multimodal machine learning in real estate appraisal: Models, data, and trends,” International Journal of Data Science and Analytics, vol. 22, Art. no. 216, 2026, doi: 10.1007/s41060-026-01204-8.
[14] V. Cohen, A. Kiaulakis, T. Vilutienė, and M. Christofi, “A literature review of artificial intelligence applications in real estate: State-of-the-art and future research directions,” Technology in Society, vol. 88, Art. no. 103494, 2026, doi: 10.1016/j.techsoc.2026.103494.
[15] L. Deng, “Real estate valuation with multi-source image fusion and enhanced machine learning pipeline,” PLOS ONE, vol. 20, no. 5, Art. no. e0321951, 2025, doi: 10.1371/journal.pone.0321951.
[16] A. Nouriani and L. Lemke, “Vision-based housing price estimation using interior, exterior & satellite images,” Intelligent Systems with Applications, vol. 14, Art. no. 200081, 2022, doi: 10.1016/j.iswa.2022.200081.
[16] N. Karanikolas, E. Kyriakidou, and E. Athanasouli, “Artificial intelligence and real estate valuation: The design and implementation of a multimodal model,” Information, vol. 16, no. 12, Art. no. 1049, 2025, doi: 10.3390/info16121049.
[17] A. Patial, A. Thakur, R. S. Rana, A. Sharma, S. Shubham, and A. Pal, “Deep multimodal learning for real estate price estimation from satellite and street view data,” in Proc. Int. Conf. Computational Intelligence, 2026, pp. 220–229.
[18] H. Sharma, H. Harsora, and B. Ogunleye, “An optimal house price prediction algorithm: XGBoost,” Analytics, vol. 3, no. 1, pp. 30–45, 2024, doi: 10.3390/analytics3010003.
[19] K. V. Mathotaarachchi, R. Hasan, and S. Mahmood, “Advanced machine learning techniques for predictive modeling of property prices,” Information, vol. 15, no. 6, Art. no. 295, 2024, doi: 10.3390/info15060295.
[20] S. Canaz Sevgen and Y. Tanrivermiş, “Comparison of machine learning algorithms for mass appraisal of real estate data,” Real Estate Management and Valuation, vol. 32, no. 2, pp. 100–111, 2024, doi: 10.2478/remav-2024-0019.
[2] E. Zurada, A. Levitan, and J. Guan, “A comparison of regression and artificial intelligence methods in a mass appraisal context,” J. Real Estate Res., vol. 33, no. 3, pp. 349–387, 2011.
[3] S. Rosen, “Hedonic prices and implicit markets,” J. Political Economy, vol. 82, no. 1, pp. 34–55, 1974.
[4] R. K. Pace, R. Barry, and C. F. Sirmans, “Spatial statistics and real estate,” J. Real Estate Finance Econ., vol. 17, no. 1, pp. 5–13, 1998.
[5] L. Breiman, “Random forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, 2001.
[6] T. Chen and C. Guestrin, “XGBoost: A scalable tree boosting system,” in Proc. ACM SIGKDD, 2016, pp. 785–794.
[7] G. Ke et al., “LightGBM: A highly efficient gradient boosting decision tree,” in Proc. NeurIPS, 2017.
[8] N. Kok, E.-L. Koponen, and C. A. Martínez-Barbosa, “Big data in real estate? From manual appraisal to automated valuation,” J. Portfolio Management, vol. 43, no. 6, pp. 202–211, 2017.
[9] S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Computation, vol. 9, no. 8, pp. 1735–1780, 1997.
[10] A. Vaswani et al., “Attention is all you need,” in Proc. NeurIPS, 2017.
[11] T. N. Kipf and M. Welling, “Semi-supervised classification with graph convolutional networks,” in Proc. ICLR, 2017.
[12] S. M. Lundberg and S.-I. Lee, “A unified approach to interpreting model predictions,” in Proc. NeurIPS, 2017.
[13] C. Huang, B. Liang, Z. Li, J. Wang, and F. Chen, “Multimodal machine learning in real estate appraisal: Models, data, and trends,” International Journal of Data Science and Analytics, vol. 22, Art. no. 216, 2026, doi: 10.1007/s41060-026-01204-8.
[14] V. Cohen, A. Kiaulakis, T. Vilutienė, and M. Christofi, “A literature review of artificial intelligence applications in real estate: State-of-the-art and future research directions,” Technology in Society, vol. 88, Art. no. 103494, 2026, doi: 10.1016/j.techsoc.2026.103494.
[15] L. Deng, “Real estate valuation with multi-source image fusion and enhanced machine learning pipeline,” PLOS ONE, vol. 20, no. 5, Art. no. e0321951, 2025, doi: 10.1371/journal.pone.0321951.
[16] A. Nouriani and L. Lemke, “Vision-based housing price estimation using interior, exterior & satellite images,” Intelligent Systems with Applications, vol. 14, Art. no. 200081, 2022, doi: 10.1016/j.iswa.2022.200081.
[16] N. Karanikolas, E. Kyriakidou, and E. Athanasouli, “Artificial intelligence and real estate valuation: The design and implementation of a multimodal model,” Information, vol. 16, no. 12, Art. no. 1049, 2025, doi: 10.3390/info16121049.
[17] A. Patial, A. Thakur, R. S. Rana, A. Sharma, S. Shubham, and A. Pal, “Deep multimodal learning for real estate price estimation from satellite and street view data,” in Proc. Int. Conf. Computational Intelligence, 2026, pp. 220–229.
[18] H. Sharma, H. Harsora, and B. Ogunleye, “An optimal house price prediction algorithm: XGBoost,” Analytics, vol. 3, no. 1, pp. 30–45, 2024, doi: 10.3390/analytics3010003.
[19] K. V. Mathotaarachchi, R. Hasan, and S. Mahmood, “Advanced machine learning techniques for predictive modeling of property prices,” Information, vol. 15, no. 6, Art. no. 295, 2024, doi: 10.3390/info15060295.
[20] S. Canaz Sevgen and Y. Tanrivermiş, “Comparison of machine learning algorithms for mass appraisal of real estate data,” Real Estate Management and Valuation, vol. 32, no. 2, pp. 100–111, 2024, doi: 10.2478/remav-2024-0019.
📋 How to Cite This Paper
Ganjikuntla Gopinath, Dr. Battapotula Venkataratnam (2026). AI-Driven Transformation of the Real Estate Industry: A Framework for Property Valuation, Market Forecasting and Intelligent Decision-Making. International Journal of Computer Techniques, 13(6), 670–675. ISSN: 2394-2231. DOI: https://doi.org/10.5281/zenodo.24129809
