House prices are influenced by various factors that interact complexly, including economic conditions to physical characteristics of the houses. Researchers have extensively examined these factors in many locations, focusing on determining the key factors that influence the housing market. A total of 1517 data points was collected, consisting of primary survey data from various wards of Kolkata Municipal Corporation (KMC) and secondary data acquired from multiple housing listing websites. A comprehensive statistical analysis was performed on the refined dataset to highlight its central tendencies, variability, and distributional characteristics. The dataset's dimensionality was reduced by the application of principal component analysis (PCA), which converted the original variables into a new set of components. A hedonic regression model was constructed based on the PCA findings. This model allowed us to quantitatively assess the impact of various house attributes on their prices. The PCA generated four principal components, each contributing variably to the overall variance in the sample. The PCA indicates that factors such as the property's age and size, its proximity to important amenities and infrastructure, and transaction types associated with the property, greatly affect house values. This hedonic regression analysis shows the complex interplay between property characteristics, geographical considerations, and transaction types in determining house prices. The influence of each factor, whether advantageous or adverse, highlights differing buyer goals and market conditions.