I’m going to answer it anyway!
As it happens, I have often dealt professionally with data that is highly flawed in various ways, but you still have to try to extract some sort of information or insight from it.
Although a lot of the cited flaws in their methodology are obviously correct, it is an interesting question how badly that actually distorts their reported results.
Like, individually, we may not want to rely on any one response sheet. However, when you aggregate a bunch, the average result may not be that bad. Or it might still be bad.
Big picture, this is a question about whether the main sources of error are biased, or just adding what is sometimes called noise. If it is just noise, then aggregation of enough responses can work to extract the signal. If it is biased, though, then more responses may not do anything to correct that bias.
There are sophisticated things you can do to test for all this given a data set like this, and I highly doubt US News is bothering. My own guess, though, is that there is probably not TOO much bias.
And if there is bias, it is probably exactly the sort of bias the “prestige” folks would want. Like, there may be a little too much inertia in favor of colleges with a previously-established high reputation. There may be some bias in favor of colleges with more publicity. And so on.
And while that sort of thing might in fact be considered bias when thinking about something like actual academic quality, it would actually be arguably helpful when using this as a proxy for “prestige”.