I don't think that part's necessarily true. If a school focuses on an area with high median salaries, the model will take that into account in the predicted salaries, so the school will have to have even higher actual salaries than typical for the field (and its input demographics, SAT scores, etc.) to get a positive value-add. See Caltech for an example of a STEM-focused school that does badly by this measure: from its SAT scores, demographics, and heavy concentration of STEM majors, the regression analysis predicts that it should produce graduates with a median salary of $82k. But the actual median is $74k, so its value-add is taken to be -$8k.
Some of the schools that do well are in areas with poor salaries, but score highly because they do better than you'd expect (or than the model would expect, anyway) for that area and student demographics. Otis College of Art and Design has a predicted salary of $29k from the regression analysis, but actual median is $42k, so implied value-add +$13k.