## 1 Introduction  
The advent of digitalisation has ushered in an era with an enormous amount of available data [1]. In the context of the real estate market, multiple approaches, includingconventional statistical calculations, data mining strategies, and ML methods, have been applied in prior research to forecast property values [2]. However, predicting housing costs with precision is particularly challenging due to the strong correlation between real estate prices and factors such as geographical location, property size, and other attributes. Notably, the results of the same approach can vary significantly from one location to another [3]. These characteristics imply that simple methods may not effectively represent the ever-changing nature of data [4; 5]. Previous techniques have exhibited limitations in scalability and flexibility, which has hindered the ability to meet the needs of stakeholders seeking access to pertinent information in the housing industry [1; 6]. As rental value estimation undergoes rapid transformation, the application of advanced AI-based algorithms, such as ML models or deep learning (DL) methods, has introduced nuanced alternatives and offered promising solutions [7; 8]. However, leveraging the PyTorch framework has not yet been thoroughly investigated in the existing literature. Compared to the application of standalone algorithms, utilising a framework like PyTorch could offer superior accuracy and efficiency. This advantage can be attributed to factors such as the highly optimised libraries, predefined neural network layers, and its ability to sustain improved accuracy across various features, uncover complex patterns, and provide more precise, adaptable, and timely estimations [1]. The primary objectives, aims and contributions of this study are outlined below:  
* A variety of ML models were carefully selected for this regression task, including LR, random forest regression (RFR), gradient boosting regressors (GBRs), and XGB regressors. * Two neural networks, specifically a fully connected neural network (FNN) and a PNN, were evaluated to assess the effectiveness of DL methods in predicting rental values. * The PyTorch framework was applied to individual ML models, including those stacked with RFR, GBR, XGB, and LR, to enhance prediction accuracy. * A thorough assessment of each model was conducted using metrics such as RMSE and the R\({}^{2}\) score. * To verify the adequacy and reliability of the PyTorch framework, two different feature sets chosen by the CatBoost and PSO algorithms were used. * This research aimed to enhance the effectiveness of AI techniques within the housing industry, bridging the gap between computer science and property investment, and expanding the scope of current research on the German property market. This study employed AI-based algorithms to forecast rental prices in the German real estate market, with a focus on Munich. A variety of algorithms were employed as feasible solutions to provide more accurate and reliable predictions for market price evaluations, assisting agents, property owners, and renters in making objective assessments [9]. While this comprehensive investigation provided insights into the prediction capabilities of these approaches, the data source was gathered in 2020, underscoring the need for regular updates to maintain reliability over time. Some essential characteristics might be overlooked, or the attributes may not contribute meaningful predictive value during data pre-processing. Furthermore, each model was based on certain data assumptions, which could result in a misrepresentation of the effectiveness of AI-driven methods. The paper is organised as follows: Section 2 delves into existing research and the prospective impacts of this study, providing a comprehensive review o recent and relevant literature on forecasting home values. Section 3 presents an in-depth examination of the selected dataset, which informs the development of this AI technique.