Abstract

Accurate enrollment forecasting has become a critical function in higher education, supporting data-driven decision-making across admissions, academic planning, and resource allocation. This paper presents a structured analytical framework for developing forecasting models that leverage historical enrollment data alongside transfer records. These models enable institutions to predict student enrollment patterns with greater precision, enhancing their ability to plan proactively and allocate resources effectively. The study explores the foundational role of administrative data, highlighting its granularity, collection frequency, and the importance of preprocessing steps such as data cleaning, normalization, and privacy compliance. Various modeling techniques are evaluated, including statistical approaches—valued for their transparency—and machine learning algorithms, recognized for their flexibility and predictive strength. Practical applications are discussed, demonstrating how these models inform strategic enrollment management, academic staffing, and institutional policy decisions. Finally, the paper reflects on methodological considerations and outlines future directions in enrollment analytics, emphasizing the need for continued integration of evolving data sources and institutional capabilities.