This study investigates the relationship between the visual and listing features of real estate listings and their corresponding Click-Through Rates (CTR). Several machine learning methods were used to identify the key attributes influencing CTR, including Decision Tree, Linear Regression, Random Forrest and XG-Boost. The models were trained and evaluated using a dataset comprising 3000 real estate listings. Visual features were extracted from listings’ cover images, while listing-related features – such as price, number of rooms, and district, were derived. Our findings indicate that visual image features and listing features have impact on CTR. In the study we also isolate the visual image and listing features and observed their individual contribution to CTR.