Abstract
The real estate market requires effective and precise house price prediction, as conventional models often face difficulties in generalization, computational efficiency, and interpretability. The research problem is addressed by introducing the House Price Evaluation Model (HPEM), which utilizes a hybrid deep learning network for analyzing multi-source geographic data. The network integrates the attention mechanism with spatial feature extraction, and a bat optimization algorithm is used to improve explainability and accuracy. The gathered properties are processed using normalized techniques to convert unstructured data into structured data, which directly improves the overall prediction accuracy. The bat-optimized attention mechanism with spatial networks dynamically arranges high-impact features to effectively address unstable feature importances, computation inefficiency, and poor generalization issues. In addition, the echolocation-inspired approach explores optimal solutions by balancing exploration and exploitation, thereby minimizing the deviation between the outputs and reducing training time by 30% compared to existing methods. The efficiency of the system is then evaluated using the Housing Price Dataset information, where HPEM achieves 98.5% feature stability, 1.2 hours of human-in-loop updates, and a 4.2% mean absolute error (MAE) under distribution shifts. The effective exploration of dynamic features through bat optimization integration yields 15% closer convergences, enhancing regulatory compliance and accuracy. Therefore, the developed model is effectively utilized in real estate valuation schemes.