Abstract
House price prediction is important for the government, finance company, real estate sector and also the house owner. The data of the house price at Ames, Iowa in United State which from the year 2006 to 2010 is used for multivariate analysis. However, multicollinearity is commonly occurred in the multivariate analysis and gives a serious effect to the model. Therefore, in this study investigates the performance of the Ridge regression model and Lasso regression model as both regressions can deal with multicollinearity. Ridge regression model and Lasso regression model are constructed and compared. The root mean square error (RMSE) and adjusted R-squared are used to evaluate the performance of the models. This comparative study found that the Lasso regression model is performing better compared to the Ridge regression model. Based on this analysis, the selected variables includes the aspect of house size, age of house, condition of house and also the location of the house.
Highlights
In the year of 1990s, the house price in United State has increased sharply and from the year onward the price has been increasing for seven percent at the nation level annually [1]
It is significant to study the growth of the house price as the pattern and the fluctuation of the data of house price which will provide the important information to the people who related [2]
The performance of the Ridge regression and Lasso regression are evaluated based on the root mean square error and adjusted R-squared
Summary
In the year of 1990s, the house price in United State has increased sharply and from the year onward the price has been increasing for seven percent at the nation level annually [1]. It is significant to study the growth of the house price as the pattern and the fluctuation of the data of house price which will provide the important information to the people who related [2]. With the house price model, it will contribute important information to the real estate market which will indirectly influence the economic growth of the country [4]. Ridge regression model and Lasso regression model are constructed to predict the house price in the United State. This will provide the important information to government, financial firm such as bank, or even a houseowner. There are quite considerable studies were carried out to study the application of statistics [11, 12, 13]
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