House Price Estimation Using Linear Regression: A Machine Learning Perspective

Abstract

House price prediction plays a crucial role in the real estate industry, helping buyers, sellers, and investors make well-informed decisions. Accurate estimation of property values enables stakeholders to assess market trends, plan investments, and minimize financial risks. This study focuses on the application of linear regression, a fundamental and widely used machine learning algorithm, to predict house prices based on multiple influencing factors. These factors include location, property size, number of bedrooms, amenities, and other relevant characteristics that significantly impact pricing. Linear regression models the connection between input variables (features) and an output variable (such as house price) using a linear equation. By examining past housing data, it uncovers patterns and relationships among variables, enabling it to predict outcomes for new, unseen cases. Its straightforward nature and ease of interpretation make it a popular option, particularly for initial analysis and baseline modeling in real estate applications. To assess how well the model performs and how reliable it is, metrics like Mean Squared Error (MSE) and the coefficient of determination (R²) are used. These measures help evaluate prediction accuracy and indicate how effectively the model explains variations in house prices. The results of this study indicate that linear regression can deliver reasonably accurate predictions when the data is well-structured and relevant features are selected. Overall, this approach demonstrates that linear regression serves as a simple, efficient, and practical tool for house price estimation, offering valuable support for real estate market analysis and decision-making processes.