Housing Price Prediction by Divided Regression Analysis

Regression analysis is a statistical methodology to investigate the relationship between the dependent variable and the independent variables. In current era with the trend of big data, we might face some problems when performing statistical analysis for the massive volume of data. For example, the heavy burden of the computing load will cause the computation to be time consuming, the accuracy of the results might be affected in view of the vast volume of data. Hence, divided regression analysis is proposed to reduce the burden of the computing load. This approach performs subdivision of the dataset into several unique subsets, then the multiple linear regression is fitted into each subset. The results obtained from each subset are then combined to obtain a divided regression model which is treated as the original overall dataset. The dataset used in this paper is KC Housesales Data, obtained from the Kaggle website. The dataset contains statistics information about the housing price, for example, size of lot, size of living area and selling price of the house. The goal of this paper is to predict the selling price of a house from the given attributes. The dataset is partitioned into five subsets. Consequently, multiple linear regression is fitted for each subset. Then, some model adequacy checking will be applied on the models. The test in determining the existence of multicollinearity in the models is rather important as well because the collinearity among the independent variables will affect the overall results. Hence, the variance inflation factor (VIF) approach is used to determine the existence of multicollinearity. Finally, the divided regression model is obtained by combining results from all the subsets and the validity of divided regression model is verified.