Abstract
Higher education has been in a financially precarious position for many years – facing either a total transformation or elimination. Tuition increases and fewer college-age students from shifting demographics are primary reasons for the financial distress. Alternative financial stability models have assumed linear variable relationships and improperly calculate the probability of default. Stakeholders have historically relied upon models such as those developed by Edmit and the Department of Education which are inadequate at separating financially sound from unsound universities. We used an Automated Machine Learning approach utilizing multiple models to explain the relationship between metrics and the probability of default/closure allowing for more informed managerial decisions. This research, although applied to the homogeneous group of small liberal arts universities, can be applied to online and state universities and will allow the opportunity to take preventive steps to mitigate the likelihood of closing due to financial distress.
Published Version
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