## 7 Conclusions  
To conclude, this research presents a new strategy for rental price prediction in the German real estate market by employing AI-based approaches. The study shows how merging conventional models with advanced neural network frameworks can improve prediction accuracy through the integration of PyTorch with different ML approaches. By leveraging both neural networks and ensemble learning, this approach outperforms earlier methods that struggled with the complexity of large datasets. The principal benefit of the suggested approach lies in its ability to address nonlinearity and high-dimensionality in rental pricing data using sophisticated techniques like XGBoost and PyTorch-based neural networks. Additionally, the stacking model technique effectively combines predictions from multiple models when working with heterogeneous feature sets, resulting in more reliable and accurate results. Despite its advantages, the proposed approach has some limitations. Key features that could have enhanced prediction accuracy were excluded during feature selection. Time and computational resource constraints also limited the extent of hyperparameter tuning, potentially impacting model performance. While R2 and RMSE are vital evaluation metrics, these may not fully capture the complex behaviour of advanced models. Furthermore, the complexity of stacked models and neural networks can present challenges for individuals without advanced ML expertise. Future research should concentrate on extending statistical validation across more datasets and real-world scenarios, as well as enhancing the transparency and interpretability of complex models. This study offers several opportunities for further exploration by readers and future scholars. Utilising NLP techniques could allow prediction models to incorporate additional data sources, such as textual information from real estate listings, potentially boostingprediction accuracy. Expanding this study to other regions, such as different German cities, China, and the UK, could help evaluate how well the recommended strategy generalizes to other real estate markets. Additionally, more advanced hyperparameter optimisation methods, like automatic machine learning (AutoML) or Bayesian optimisation, could further refine model performance. Future work can examine other prevalent frameworks like TensorFlow, Keras, and MXNet to assess whether PyTorch offers unique benefits in rental price prediction. A comparative evaluation of these frameworks could highlight their respective advantages and help determine the most effective approach for various real-world applications. Comparative studies on training times and processing speeds across frameworks would also provide valuable insights, particularly for applications requiring rapid predictions. Developing hybrid models that integrate traditional ensemble methods with DL approaches, such as transformer-based or recurrent neural networks (RNNs) is another promising direction. In addition, enhancing model interpretability using techniques like Local Interpretable Model-Agnostic Explanations (LIMEs) would be beneficial. LIME can help users understand the decision-making process of complex models and can be used to a range of inputs, including text, images, diagrams, and tabular data. Improving interpretability is vital for practical applications, particularly in fields where transparency is critical. Extending statistical validation to more datasets and diverse market situations is another crucial subject for future research. While this study shown statistically substantial improvements in PNN models, further validation could reinforce the robustness and reliability of the suggested models, ensuring that they are applicable to a wider range of datasets and market scenarios.