Do these values of bias and variance make sense?

I have this code:

X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.3, random_state=1)
model = LinearRegression().fit(X_train, y_train)

from mlxtend.evaluate import bias_variance_decomp

print(y_train.min(), y_train.max(), y_test.min(), y_test.max())
#for your understanding of the data: 7283 517924 11510 450000

avg_expected_loss, avg_bias, avg_var = bias_variance_decomp(
        model, X_train, y_train.ravel(), X_test, y_test.ravel(), 
        loss='mse', random_seed=1)

print('Average expected loss: %.3f' % avg_expected_loss)
print('Average bias: %.3f' % avg_bias)
print('Average variance: %.3f' % avg_var)

The result is:

Average expected loss: 542162695.679
Average bias: 529311955.129
Average variance: 12850740.550

To me, these values seem to be too high. Am I missing something or this correct?