Which Colleges Will Close Next?
Over 200 U.S. colleges have closed since 2015. The next decade will be worse — and the closures are already visible in birth certificates from 2005.
We analyzed real College Scorecard and IPEDS data across thousands of degree-granting institutions with three tiers of closure prediction models. A simple 3-variable stress score catches most historical closures. Gradient boosting adds modest value. For monitoring decisions, the simple rule is the one you'd deploy.
Same data. Three models. The simplest one is the most defensible.
Six Questions, Three Models
Read the Full Study →
Six investigations covering financial stress, geographic demographics, survival thresholds, the online pivot, whether gradient boosting beats a simple rule, and how well our initial synthetic assumptions matched reality. Plus the fidelity ladder that makes the ADM thesis concrete.
Explore the Data
Look up any institution, step through the fidelity ladder, or calculate your degree's ROI.
Institution Risk Screener
Type any college name. See its stress score, enrollment trend, demographic headwind, and closure probability across all three model tiers.
Fidelity Reveal
Same question at three fidelity levels. Watch the answer change as we add demographics, competition, and gradient boosting — and see where the extra complexity stops helping.
Degree Value Calculator
Pick a school type and major. See the payback period, lifetime earnings premium, and AI automation exposure. Earnings calibrated to published College Scorecard statistics.
Three Models, One Lesson
Each tier adds complexity. The question is whether that complexity adds value — or just noise.
Tier 1: Screening Rule
Three financial ratios: enrollment trend, tuition dependency, cash reserves. Catches the majority of historical closures. Fast, interpretable, deployable today. For monitoring, this is the one to use.
Tier 2: Multi-Variable Model
Adds demographics, geography, completion rates, default rates. The geographic signal — regional birth rate decline — explains more variance than school performance. Valuable for targeting interventions.
Tier 3: Gradient Boosting
Gradient boosting on 17 features. Real but modest improvement over the rule. The interaction between online share and sector is a genuine insight the simple rule misses. Worth it for deep-dive triage, not for screening.
For a state higher ed board monitoring its institutions, the 3-variable stress score is the most defensible model. It's interpretable, it doesn't overfit, and it uses publicly available data. The gradient boosting model does better on paper, but with a small closure training set, the confidence intervals are wide. More complexity didn't clearly earn its cost here.
Six Questions
Which Colleges Are Most at Risk?
A 3-variable financial stress score catches the majority of historical closures. Enrollment decline + tuition dependency + low reserves = the danger trifecta.
How Much Does Geography Matter?
Regional birth rate decline predicts enrollment loss better than school performance. The closures are already written in birth certificates from 2005.
When Does Decline Become Fatal?
Small enrollment + consecutive decline years = a clear tipping point below which recovery is extremely rare. The threshold is concrete and actionable.
Did Online Colleges Win or Lose?
For-profit online = collapse. Non-profit online = growth. Same variable, opposite direction by sector. A genuine interaction the simple rule misses.
Can Gradient Boosting Beat the Stress Score?
Gradient boosting adds real but modest recall over the stress score. With a small closure training set, confidence intervals are wide. The stress score is the one you'd deploy.
How Well Did Our Assumptions Match Reality?
We built this study first on synthetic data, then re-ran it on real College Scorecard data. Here's what our NCES-calibrated assumptions got right — and what they got wrong.
What We Don't Know
Small closure sample. Models trained on ~200 closures over a decade. This is a small-sample problem. Confidence intervals on recall are wide, and the gradient boosting model's advantage over the simple rule may not be statistically significant.
Financial data limitations. Two key stress score inputs — tuition dependency and days of cash on hand — are estimated from IPEDS Finance surveys where institution-level data wasn't available. Where data was missing, we used sector-level averages. This is declared honestly as a fidelity choice throughout the analysis.
Enrollment trends limited to recent years. Historical enrollment data covers a 5-year window. Institutions with longer decline trajectories may look stable in this window.