预测大学关闭和财务困境（英）

Investment Rating - The report does not explicitly provide an investment rating for the higher education sector Core Insights - The higher education sector is facing significant financial challenges, including declining enrollment and rising operational costs, which may lead to increased college closures in the future [7][13][19] - A comprehensive dataset from 2002 to 2023 was utilized to develop predictive models for financial distress in colleges, demonstrating that modern machine learning techniques significantly outperform traditional models in predicting closures [4][18] - The demographic cliff, characterized by a decline in the number of high school graduates, is expected to exacerbate enrollment challenges and increase the likelihood of college closures [9][18] Summary by Sections I. Introduction - The financial distress in higher education can have profound effects on local economies and communities, making the prediction of college closures increasingly important [7] - Enrollment in degree-granting institutions fell by 15% from 2010 to 2021, with a notable decline during the pandemic [8] II. Postsecondary Education Landscape and Fiscal Challenges - Financial distress has historically impacted American postsecondary education, with many institutions managing to survive despite challenges [20] - Factors associated with college closures include lower faculty salaries, smaller endowments, and higher instructional spending [22] III. Data Sources - The report utilizes data from the Integrated Postsecondary Education Data System (IPEDS) and the College Scorecard to analyze institutional characteristics and financial metrics [63][66] - Key variables include enrollment trends, revenue sources, and financial health indicators, which are critical for predicting financial distress [66][71] IV. Revenue and Expenditure Patterns - The American higher education system generates approximately $700 billion in expenditures and enrolls nearly 25 million students [29] - Public institutions rely heavily on state appropriations, while private institutions depend more on tuition and endowment income [42][51] - Operating costs have increased faster than inflation, driven by rising health insurance and administrative expenses [54] V. Predictive Models and Findings - The report highlights the effectiveness of machine learning models in predicting college closures, with a significant improvement in accuracy compared to traditional models [4][18] - Simulations indicate that future closures may rise due to demographic shifts and ongoing financial pressures [18]