An Approach for House Price Prediction Using Bayesian Regression

This study investigates the utilization of Bayesian regression for modeling house prices, acknowledging its importance within the context of expanding urban environments and the subsequent increase in residential property transactions. By incorporating prior knowledge and regularization techniques, Bayesian models provide robust frameworks for predicting house prices in situations with limited or noisy data. The research examines the benefits of Bayesian models, particularly in the precise quantification of uncertainty and the facilitation of well-informed decision-making, which are essential in real estate appraisal. Following the procedural steps inherent in the Bayesian approach, the study outlines the background, literature review, data collection, methodology, parameter estimation, and model assessment. Through thorough analysis and discourse, this investigation seeks to broaden the applicability and generalization of Bayesian methodologies in the realm of house price prediction, highlighting their potential advantages over traditional models such as multivariable regression.