I’m spending some time this weekend looking through the 2009 3-year default rates in preparation for the release of the 2010 3-yr rates (likely this September).  For years, the DOE has “officially” reported only on the 2-yr rate, even though study after study has shown that the 3 year mark better captures eventual long term default rates (and many researchers have recommended up to a 5 yr rate, noting that default rates don’t actually level off until about that point).  Starting with the 2005 cohort (released in 2008) though, the federal government began to collect and distribute 3-yr rates on a trial basis.  In 2012 they for the first time released “official” 3-yr rates for the 2009 cohort.  For whatever reason, those 3 yr rates haven’t yet made it to the site intended for “consumers”, only to the more data-heavy, Window-95-ish, “default management and prevention” data page.

The difference between these two rates is significant, and my guess is that it may also vary further by sector and certainly by institution.  What we know for sure is that for the 2009 cohort, the 2 year default rate overall was 9.1% while the 3 year rate of default was 13.4%.  For those keeping score at home, that means default rates increase by almost 50% between the 2nd and 3rd year.  Yet, for some reason that escapes me, nearly all of the public coverage on this issue (and certainly all of the better data visualizations I have seen) continue to use the 2 year default rate as the frame of reference.  Things are going to get serious around these 3-year rates soon- starting with the 2011 cohort rates (which will likely be released in early fall of next year) institutions with greater than 30% of students in default will face sanctions (that’s a shift from the current 25% at 2-year threshold, and given what we know about how rates change from year 2 to year 3, should be stricter).

So, with that in mind…

Here is a first pass at some interactive data visualizations of the 2009 3 year student loan default rates by institution, mapped by zip code, colored (and filterable) by institutional sector (public/private/proprietary), and additionally filterable by institutional default rate and institutional type (Associate’s, Bachelor’s, MA/PhD, etc).  The size of each circle represents the actual number of students in default by institution, not the default rate (but remember, that’s a filter); while that tracks institution size to some degree, you may be surprised at how much variation there is across similarly-sized institutions.  You can mouse over (or tap on iPad) to get detail on the institution represented by a particular circle.  To see which schools are at risk of sanction under the new policy, just limit the default rate to >30%.  To get the full screen experience, just click here.

Let me know your thoughts- what variables are missing here?  What are other visualizations that might be helpful?  I’m thinking about a look at institutions/sectors where the jump from 2 to 3 year default rates is particularly large, and some views that take into account changes in default rates since the release of the 3 year data in 2005.

Since the initial posting, I also threw this next viz together- a scatter of # of students of default (logarithmic) by default rate, with color corresponding to sector and shape corresponding to institution type.  As with the last viz, you can filter by institutional sector and type.  Again, here’s a link that will take you to a full screen version

This gives a better sense of the spread within sector than the previous version.  Note that while all sectors have a spread across # of students in default (reflecting the diversity of each sector), the actual spread in default rate is particularly wide for proprietaries (for-profits).  Private not-for-profits tend to cluster furthest to the left, then publics, then for-profits, but remember that this is largely a reflection of the student populations they serve- similarly, if you control for type of institution (2 yr vs 4 yr vs MA/PhD) publics and privates start to look more similar along the rate axis (publics are almost always larger as a group), reflecting the concentration of 2yr/Associate’s institutions in the public sector.

To go back to the geographic theme, here’s a state-level view where, as with the national map above, circle size corresponds to the actual # of defaulters at a given institution, but here the circles are colored by a scheme based on the default rate.  Relatively lower rates are greener and relatively higher rates are redder (so a large school might, not surprisingly, have a larger number of defaulters, but that school might still have a proportionately small default rate and thus would show up as green).  This may be unintentionally confusing in Wisconsin where, between UW and the Packers, red and green arguably both have positive connotations, but I think you’ll get the idea.  As with the previous vizzes (vises?  vizes? vizs?) a full-screen version can be accessed here:

Update:

One suggestion (thanks to dfcochrane for both the suggestion and prediction of the color scale problem!) was to simply scale-up the Wisconsin version (which colors default rate on a green to red scale) to the national level.  Straightforward… except for those pesky little institutions with a single borrower in repayment who also happened to be in default, making the overall default rate a whopping 100% (A+?).  That skewed the scale, making nearly everything that you could see without zooming somewhere between “green” and “forest green”.  Solution?  I shifted the center of the color scale to 13.4%, the average institutional default rate, so now every green circle is a school with a rate somewhere below 13.4% (below average) and every red circle is a school with a rate above 13.4% (above average).  Remember that you can use the default rate slider to limit further, such as, for example, only looking at schools with rates at or above 30% (and thus at risk for federal sanctions).   Full screen view here:

Another interesting request (thanks, n_hillman, for the idea and link to the source data) was to color/filter by accrediting agency.  There are, of course, SO MANY accrediting agencies if you consider all of their sub-regional and specialty variations  that you would definitely need a Crayola Big Box range of colors, and there would be a lot of “Is this ‘Screamin’ Green’ or ‘Granny Smith Apple?'”-type questions.  Given that I’ve decided to limit to institutional accreditators, and then I’ve further limited that to those institutions covered by the “Big 8” Regional Accreditors.  That said, I think it might actually be interesting to look for patterns within some of the lesser known accreditors.  Full-screen view here.

Side note- using a “color-blind” palette below after hearing from a colleague that a non-negligible number of readers (disproportionately men) have trouble making distinctions on most color-coded charts of this style.  If you normally have trouble with red/green, would love feedback.

Other ideas (for these or other datasets that might benefit from a little visualization)?  Edits? Recipes?  Let me know!