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Predicting Housing Price Using Regression Analysis: A Study Based on California Housing Price
- 1 Shanghai International High School of BANZ, Shanghai, 200000, China
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Abstract
The purpose of the essay is to examine the factors influencing housing prices in California, which can help people to obtain a perspective to observe the US economy. This paper uses the California House Price dataset to analyze the factors influencing house prices of blocks of California by performing correlation analysis and multiple linear regression. From the analyses, it is discovered that median income has the strongest correlation with median house price of blocks in California. A model predicting California house prices is also shown. It is observed that median income has the most significant correlation with median housing price in California from the correlation analysis. Additionally, the linear regression model is obtained to estimate the housing prices, with a good fit of the R2 value at 0.637. Though the issue of multicollinearity occurs, this model still provides an insight into explaining housing prices in California. For a more advanced housing price predicting model, this variable and other factors of blocks in California may be included.
Keywords
Housing price, linear regression model, influencing factors
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Cite this article
Gu,Y. (2025). Predicting Housing Price Using Regression Analysis: A Study Based on California Housing Price. Theoretical and Natural Science,105,128-134.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 3rd International Conference on Mathematical Physics and Computational Simulation
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