Study on Factors Influencing the Prices of Houses in California Based on Factor Analysis Method

Abstract

This research investigates the factors influencing housing prices in California through a comprehensive analysis using the factor analysis method. The study explores various dimensions of the housing market, examining specific periods, environmental factors, transportation infrastructure, gated communities, city formation dynamics, and broader economic and market dynamics. The integration of machine learning techniques for housing price predictions is also explored. The dataset utilized is the "California Housing Prices" dataset obtained from Kaggle, comprising 10 types of metrics for each block group in California. Rigorous preprocessing was conducted, including handling missing values, removing outliers, and normalizing features. The factor analysis identified three significant factors explaining 76.588% of the variance in house prices. These factors represent geographical location, housing size, and population density. The weight analysis further reveals that factors associated with housing size and geographical location significantly impact housing price fluctuations. The findings offer valuable insights for homeowners, real estate professionals, and policymakers, enabling informed decision-making in the dynamic housing market.