Predicting Housing Prices Using Advanced Analytics: A Study on Key Property Features and Market Dynamics

Abstract

Effective forecasting of housing prices becomes essential for buyers, sellers, developers, and politicians to make informed real estate decisions. This study seeks to create a forecast model for real estate prices by examining essential property characteristics including living area, property quality, furnishing status, geographic location, and coastal proximity. The study employs a comprehensive dataset and utilises statistical techniques, such as correlation analysis, multiple linear regression, and ANOVA, to ascertain the primary factors affecting property valuation. The results indicate that property quality, living space, and cost per square foot are the strongest predictors of housing prices. At the same time, location-based factors, including coastal proximity, significantly influence decision-making. Interestingly, total land area exhibits a weaker correlation with price, suggesting buyers prioritize functional living space over expansive lot sizes. Furnished properties also command a significant premium over unfurnished ones, emphasizing the role of interior readiness in property valuation. These results integrate conventional statistical methods with data-driven insights, therefore contributing to the developing field of real estate analytics. With evidence-based recommendations for maximising pricing methods and market interventions, the study has realistic consequence for developers, real estate investors, and legislators. Future research should explore the impact of macroeconomic variables and machine learning-based approaches to enhance predictive accuracy further.