Our proposed decision tree using local support vector regression models (tSVR) is to handle the regression task of large datasets. The learning algorithm tSVR of regression models is done by two main steps. The first one is to construct a decision tree regressor for partitioning the full training dataset into k terminal-nodes (subsets), followed which the second one is to learn the SVR model from each terminal-node to predict the data locally in the parallel way on multi-core computers. The tSVR algorithm is faster than the standard SVR in training the non-linear regression model from large datasets while maintaining the high correctness in the prediction. The numerical test results on datasets from UCI repository showed that the proposed tSVR is efficient compared to the standard SVR.