[jldugger]

> Here's another way to see my concern. Suppose you had a classifier that achieves 1.0 R^2; then since it perfectly predicts each school's expected value, it'll assign each school a score of 0. I'm pretty suspicious of an approach where the results get worse with better predictive power.

This depends on your view of the importance of undergraduate education, and what worse is. From my point of view, undergraduate education is an institutional obligation used to fund or justify faculty's personal objectives: research.

The reason that the model counts location is simple: universities tend to place candidates locally. I'm pretty sure the recruiters attending fairs at Stanford and Berkeley have higher starting wages than the ones at University of Kansas, and that a lot of that difference is simply regional cost of living. If you don't factor that in, you risk a bad school in an expensive place ranking higher than a good school in a cheap place.