Aim: To enhance the accuracy in predicting the house prices using Novel Linear Regression and Decision Tree. Materials and Methods: This study contains 2 groups i.e Novel Linear Regression (LR) and Decision Tree. Each group consists of a sample size of 6 and G Power software is used to determine sample size with pretest power value 0.8 and alpha is 0.05 Results: The Novel Linear regression (LR) is 82% more accurate than the Decision Tree of 71.6% in classifying the House price prediction p = 0.620. Conclusion: The Novel Linear Regression(LR) model is significantly better than the Decision Tree in predicting the House price.