Estimating a rbf kernel SVM, followed by Stochastic Gradient Descent

I wanna estimate a rbf SVM to predict property prices. My data set has 11 features and roughly 57,000 rows. When I set C=10, R^2 is about 0.88 while MSE and RMSE are 0.1191 and 0.3451. The results are pretty good.

Afterward, I estimate a SGD, using linear_model.SGDRegressor and loss='squared_epsilon_insensitive'. When I use adaptive learning rate, R^2 is reduced to 0.75 while MSE and RMSE are 0.2441 and 0.4940, respectively. When I use optimal learning rate, the results are even worse.

Did I do something wrong?