Modelling House Price Using Ridge Regression and Lasso Regression

  • Authors

    • Seng Jia Xin
    • Kamil Khalid
    2018-11-30
    https://doi.org/10.14419/ijet.v7i4.30.22378
  • Adjusted R-squared, Lasso Regression, Ridge Regression, Root mean square error (RMSE)

  • 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.

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  • How to Cite

    Xin, S. J., & Khalid, K. (2018). Modelling House Price Using Ridge Regression and Lasso Regression. International Journal of Engineering & Technology, 7(4.30), 498-501. https://doi.org/10.14419/ijet.v7i4.30.22378

    Received date: 2018-11-29

    Accepted date: 2018-11-29

    Published date: 2018-11-30