The study explores the application of Partial Dependence Plots (PDP) in the analysis of real estate features. The study centers on a selected real estate market in Szczecin, Poland, aiming to highlight the efficacy of PDP in understanding and interpreting the complex relationships between various features and property prices. The primary objective is to showcase the potential of PDP in capturing the nuanced interactions between real estate attributes and their impact on market prices. The CatBoost model, known for its robust handling of categorical features and strong predictive capabilities, is employed as the machine learning algorithm for this analysis. The performance of this model will be compared against a traditional multiple linear regression model, providing insights into the advantages of leveraging advanced machine learning techniques in real estate analysis. Results obtained from the analysis will be presented and discussed, shedding light on the interpretability and accuracy of the CatBoost model compared to the traditional linear regression approach. The presentation will conclude with implications for real estate practitioners and researchers, emphasizing the potential for PDP to enhance the transparency and understanding of complex models in the real estate domain. This research contributes to the growing body of knowledge on the application of advanced machine learning techniques in real estate analysis.