Means Square Percentage Error vs OLS

Question

I am trying to run a simple regression $$Y = a+bX +e$$ however I want to optimize it on Mean Square Percentage Error and not Mean Square Error as in OLS. Like this: $$argmin: e/Y = (Y-a-bX)/Y$$ instead of $$argmin: e = Y-a-bX$$ The problem is this results in a biased estimator when you just run it through an optimizer. Can someone point me in the right direction please? I tried to work with some kind of weighted regression that but I can't seem to get an unbiased estimator. Much appreciated!