The higher education landscape in the United States is facing unprecedented financial challenges. At least 74 public or nonprofit colleges have closed, merged, or announced closures or mergers since March 2020. As institutions grapple with declining enrollment, rising operational costs, and shifting demographic trends, the risk of college closures has become a pressing concern. The study Predicting College Closures and Financial Distress was released in December 2024 and written by three scholars from the Federal Reserve Bank of Philadelphia Consumer Finance Institute. It provides a rigorous analysis of the factors leading to institutional closures and presents predictive models that outperform existing federal monitoring mechanisms.
By assembling a comprehensive dataset covering U.S. colleges and universities from 2002 to 2023, this study identifies patterns in institutional closures and demonstrates how machine learning can enhance the accuracy of predicting financial distress. Below, we explore the study’s key findings, methodologies, and implications for the future of higher education.
Understanding the Rise in College Closures
The closure of a college or university has wide-ranging consequences. Beyond the immediate impact on students and faculty, institutional shutdowns can destabilize local economies, eliminate job opportunities, and reduce educational access in affected regions. The study highlights several alarming trends:
From 1996 to 2023, 1,671 colleges closed, with the highest number of closures occurring between 2016 and 2018.
The vast majority of closures occurred in the for-profit sector, particularly among two-year institutions, which tend to be more financially fragile and tuition-dependent.
Public institutions rarely close, owing to government subsidies and political pressure to keep them open. Instead, struggling public colleges often pursue mergers or consolidations as a survival strategy.
The median closed institution had 1,389 full-time equivalent (FTE) students, indicating that smaller colleges face the highest risk.
These trends suggest that the financial instability of U.S. higher education is not a short-term issue but rather a structural challenge that will persist as demographic and economic conditions evolve.
Key Drivers of Financial Distress in Higher Education
The study identifies several primary factors that contribute to financial distress and eventual closure:
Enrollment Declines
One of the strongest predictors of financial distress is declining student enrollment. Between 2010 and 2021, U.S. college enrollment dropped by 15%, and while there was a modest rebound in 2023, the long-term trend remains concerning. The study emphasizes the impending demographic cliff, a projected decline in the number of high school graduates, which will further erode enrollment numbers in the coming years.
Revenue and Expense Patterns
Many institutions rely heavily on tuition revenue, making them vulnerable to enrollment fluctuations. Colleges with limited alternative revenue streams—such as endowments, research funding, or auxiliary services—struggle to maintain financial stability. Additionally, operating expenses, including rising faculty salaries, benefits, and administrative costs, continue to climb, putting further pressure on institutions.
Liquidity and Debt Levels
Institutions with persistent operating deficits, high debt-to-asset ratios, and low cash reserves face significantly higher risks of closure. Colleges with limited liquidity may be unable to withstand economic downturns or unexpected financial shocks, leading to sudden shutdowns.
Ineffectiveness of Federal Accountability Metrics
The study critiques existing federal oversight mechanisms, such as the Financial Responsibility Composite (FRC) score and Heightened Cash Monitoring (HCM) levels, for failing to accurately predict closures. These metrics often rely on outdated financial reports and are not designed to capture the complexity of institutional financial health.
Enhancing Predictive Accuracy with Machine Learning
To improve upon traditional financial risk assessment models, the study employs machine learning techniques to analyze a more extensive range of financial and operational indicators. The researchers tested several predictive models, including: Traditional linear probability models (OLS) using limited financial data; OLS models with expanded financial indicators; Machine learning models (XGBoost) that integrate diverse data points, including institutional finances, enrollment trends, and local economic conditions.
Key Findings from the Predictive Models
The machine learning model achieved an 84% accuracy rate in predicting closures, compared to just 47% accuracy for federal accountability metrics.
Traditional models struggled with missing data, whereas machine learning algorithms could accurately assess closure risk even for institutions with incomplete records.
The most effective predictive models combined multiple financial indicators, enrollment data, and economic trends to assess institutional health.
These findings suggest that higher education regulators and policymakers should adopt more sophisticated predictive models to identify at-risk institutions earlier and intervene before closures become inevitable.
Simulating the Impact of Enrollment Declines
The study also explores the potential effects of future enrollment declines on college closures. Using predictive simulations, the researchers estimate that:
If current enrollment trends continue, an additional 1% of institutions will close each year.
A severe 15% drop in enrollment—as projected in some demographic models—could lead to 80 additional closures annually, a 142% increase over current rates.
The hardest-hit institutions will be small, tuition-dependent private colleges, particularly in the Northeast and Midwest.
These simulations highlight the urgency of proactive financial planning among higher education leaders.
Why Small, Tuition-Dependent Private Colleges in the Northeast and Midwest Are at High Risk of Closure?
The study finds that small private colleges—especially those heavily reliant on tuition—are particularly vulnerable to financial distress. This is largely due to demographic and economic shifts that disproportionately affect these institutions. As indicated, the most at-risk colleges are concentrated in the Northeast and Midwest, regions experiencing population decline and shrinking pools of high school graduates.
Key Factors Driving Risk in Small Private Colleges
Declining High School Graduates in the Northeast and Midwest. These regions are seeing a steep drop in the number of high school graduates, a key pipeline for college enrollment. The demographic cliff—particularly pronounced in states like Pennsylvania, Michigan, and Ohio—reduces the number of prospective students, making it harder for tuition-dependent schools to maintain enrollment.
Tuition Dependency and Limited Endowments. Many small private colleges lack significant endowments or alternative revenue streams (such as research grants or corporate partnerships). Without a financial cushion, declining enrollment quickly translates into budget deficits and financial insolvency.
Increased Competition and Changing Student Preferences. Larger public universities and well-funded private institutions offer competitive tuition discounts, better financial aid, and broader academic programs, drawing students away from smaller colleges. The growth of online learning and hybrid education models has also reduced the appeal of small, location-bound campuses.
Higher Tuition Discounting. Many small colleges are caught in a vicious cycle of increasing tuition discount rates to attract students, which reduces actual revenue. The study highlights that tuition discounting has surpassed 50% at many private nonprofit colleges, severely impacting their financial sustainability.
Strategic Recommendations for Institutions and Policymakers
The findings of this study underscore the need for a comprehensive approach to financial resilience in higher education. Institutions should consider the following strategies:
Diversify Revenue Streams. Colleges must reduce their reliance on tuition by expanding alternative funding sources, such as: 1) Endowment growth and philanthropic giving; 2) Online education and professional certificate programs; 3) Corporate partnerships and workforce training programs.
Improve Financial Transparency. Higher education institutions should adopt real-time financial monitoring systems to track key performance indicators and identify early warning signs of distress.
Invest in Enrollment Management. With demographic shifts on the horizon, institutions should develop targeted recruitment strategies, focusing on: 1) Attracting international and non-traditional students; 2) Strengthening community college transfer pathways; 3) Expanding flexible learning options, such as hybrid and online courses.
Consider Mergers and Strategic Partnerships. For colleges at risk of closure, mergers and alliances may provide a sustainable alternative. Institutions should explore: 1) Collaborations with nearby colleges to share administrative resources; 2) Joint academic programs and cross-institutional faculty appointments; 3) Consolidations that preserve institutional missions while improving financial viability.
The Future of College Closures: A Call to Action
The findings of this study serve as a crucial warning for the future of higher education. College closures are no longer isolated events but part of a larger structural shift driven by financial, demographic, and policy factors. However, with the right tools and strategies, many at-risk institutions can adapt and thrive in a changing environment.
The application of machine learning in financial risk assessment offers a powerful new avenue for policymakers, regulators, and higher education leaders to identify vulnerabilities early and take proactive measures. By embracing data-driven decision-making, institutions can safeguard their financial future, ensuring continued access to quality higher education for generations to come.
As higher education continues to evolve, institutions must remain vigilant and innovate in their financial and enrollment strategies. The future is uncertain, but those who adapt quickly and strategically will be best positioned to survive and succeed.
