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Enrollment Projections and the Budget Process: A Technique for Smart Planning

Enrollment Projections and the Budget Process: A Technique for Smart Planning. SCUP-39 Annual Conference Toronto, Canada July 20, 2004. Carol Rylee, Director Budget Office University of Delaware Phone: 302-831-1234 Fax: 302-831-8530 Email: rylee@udel.edu.

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Enrollment Projections and the Budget Process: A Technique for Smart Planning

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  1. Enrollment Projections and the Budget Process: A Technique for Smart Planning SCUP-39 Annual Conference Toronto, Canada July 20, 2004 Carol Rylee, Director Budget Office University of Delaware Phone: 302-831-1234 Fax: 302-831-8530 Email: rylee@udel.edu Dale Trusheim, Associate Director Office of Institutional Research and Planning University of Delaware Phone: 302-831-2021 Fax: 302-831-8530 Email: trusheim@udel.edu

  2. Summary of Presentation • Enrollment Projection Methods • UD Enrollment Model • Brief Demo of Excel Enrollment Proj Model • IR Enrollment Model informs Budget Office Tuition Model • Parameters & Relevance of Tuition Model • Brief Demo of Tuition Model • Questions

  3. Enrollment Projection Methods • Ad Hoc • Cohort Survival • Moving averages • Exponential smoothing • Time series (Box-Jenkins) • Linear regression and auto regression • SPSS Trends

  4. UD Method • Enrollment in a given semester is simply a function of the behaviors of two groups of students: 1. New Students (Inputs) 2. Continuers

  5. UD Method NewStudents Continuing Students + Freshmen Total number enrolled from prior semester minus (number of graduates + number of withdrawals) Transfers Readmitted

  6. UD Method Historical Data Predicted Data FALL 2002 FALL 2003 FALL 2004 History SPRING 2003 SPRING 2004

  7. UD Method • Primary task is to develop a model to depict student flow and to generate a prediction for future enrollment . . . . . . MS EXCEL

  8. Excel Model — 3 Components • Input headcount enrollment for three groups of students (historical average or one-time) • New Freshmen • Transfers • Readmits • Predict headcount enrollment for • Continuing students • Estimate percentage of full-time and part-time students

  9. Enrollment Projection Accuracy

  10. EnrollmentProjection Model

  11. Tuition Model • Budget Office Model uses separate Excel model to projection tuition income • Request Enrollment Model 3-4 times per year • Use numbers from enrollment model in spreadsheet model of tuition income

  12. Tuition Model Parameters • FT/PT mix • Resident/NonResident Mix • Fall to Spring Attrition • Upcharge over 17 credits • Grad vs. Undergrad

  13. Uses of Model • Forecast Full-time Undergrad • Construct what-if scenarios in regard to enrollment • Easily calculate each x% or $x change in tuition • Identify source of variances of actual from budgeted.

  14. Relevance of models • Accurate budgeting of tuition • Don’t want to underbudget – campus confidence in numbers Is important • Don’t want to overbudget – leads to shortfall in income at year-end • Assessment of accuracy of model • Can quickly identify source of variances • Allows fine-tuning of models

  15. Relevance of Models • Allows viewing of all factors related to enrollment and tuition income in succinct fashion • Facilitates comparision of tuition budgets from year to year

  16. Other than Full-time Tuition • Part-time, Graduate and Special Sessions • Model is based on numbers of credit hours by residency. • Model is used in years when major changes are anticipated in mix and/or credit hours • Otherwise, use projection of prior year actual increased by estimated tuition increase

  17. TuitionProjection Model

  18. Questions & Info • Questions? • Copy of Powerpoint and Excel Models are available at: http://www.udel.edu/ir/reports/presentations.html

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