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Influential Factors on California Regional Housing Price Analysed by Multiple Linear Regression
- 1 Victoria College, University of Toronto, Toronto, Ontario, Canada, M5S 1A1
* Author to whom correspondence should be addressed.
Abstract
While metropolises keep expanding with the increasing population in recent decades, the need for housing rises inevitably. This paper aims to find the most explanatory factors for the housing price in California; with 20433 entries of data found in Kaggle, the method of multiple linear regression (MLR) is applied to find the most influential factors. 500 entries in the dataset are chosen randomly and are divided into 2 datasets for training and testing purposes. Models have been developed in R by using the training dataset. After comparing the adjusted R square and variability of the models, the most convincible model will be selected to find out the result of this investigation on the test dataset. After model diagnostics, the result of this analysis is that the regional median income level has a strong positive correlation with the housing price, and it is the most influential factor. Other influential factors will be introduced in the conclusion.
Keywords
housing price, multiple linear regression, model construction
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Cite this article
Cao,Y. (2024). Influential Factors on California Regional Housing Price Analysed by Multiple Linear Regression. Advances in Economics, Management and Political Sciences,77,48-53.
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 Business and Policy Studies
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