A Multivariate Regression Analysis of Factors Influencing California Housing Prices
Abstract: This study investigates the factors influencing home price volatility in California, with a focus on home age, size, bedrooms, population, family structure, and location. Analyzing diverse California housing data using multiple linear regression and multicollinearity analysis, key findings emerge: Firstly, home age significantly impacts prices, with newer homes typically commanding higher prices due to buyer preferences for newer properties and lower maintenance costs. Secondly, geography plays a vital role, with proximity to the bay and ocean correlating with higher house prices, reflecting regional price disparities. Additionally, factors like the total number of rooms, bedrooms, and family structure also influence home prices, reflecting the property's size and layout. These insights benefit homebuyers, investors, and market participants seeking to understand trends and risks in California's real estate market. Future research should expand datasets and consider more factors, particularly delving into the effects of different geographical locations and home age on house prices.
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