I am reading about ridge regression in The Elements of Statistical Learning. In the ridge regression, we do not include intercept term $\beta_0$ in the penalty term. The book says, penalization of the intercept would make the procedure depend on the origin chosen for Y; that is adding a constant $c$ to each of the targets $y_i$ would not simply result in a shift of the predictions by the same amount $c$.
Could anyone explain why this is the case? I am having hard time grasping the issue conceptually and mathematically.