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Enhancing FTES Forecasting Through a Yield Projection Model at San Diego Community College District

This presentation by Sam Ballard and Daniel Miramontez at the 2009 RP/CISOA Conference explores the FTES Yield Projection Model utilized by San Diego Community College District to manage growth and improve enrollment forecasting. The model adjusts yields based on the number of sections offered and past performance to inform budget development and strategic decision-making. Key results from the past three years highlight the model's accuracy and areas for improvement. Discussion questions focus on model limitations and potential enhancements for more effective enrollment projections.

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Enhancing FTES Forecasting Through a Yield Projection Model at San Diego Community College District

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  1. Forecasting FTES Using a Yield Projection Model Sam Ballard, Research Analyst Daniel Miramontez, Research Analyst San Diego Community College District Presented at the 2009 RP/CISOA Conference Tahoe City, CA: April 27, 2009

  2. San Diego Community College District

  3. Enrollment • FTES for 2007-08 = 41,925 • College Total = 31,938 • Continuing Education Total = 9,987 • Number of sections offered at colleges in 2007-08 = 11,132 • Duplicated Headcount = 397,615 • 3.6% increase in Fall 2008 growth

  4. Office of Institutional Research & Planning Organizational Chart

  5. Office of Institutional Research & Planning Scope of Work Research and Information for: • Program and services • Program review reports (i.e., EOPS, TRIO, etc.) • External accrediting agencies • Accreditation self-study reports for WASC/ACCJC • Accountability • ARCC report • Planning and decision-making process • Productivity and projection reports (i.e. FTES)

  6. FTES Yield Projection Model • Yield Model • Adjusts to the number of sections being offered in the current term • Takes the previous yields multiplied by the current sections being offered

  7. Purpose • Primary function of the FTES Yield Projection Model • Manage growth and enrollment • establish growth targets • Budget development • budget guidelines • Who uses the information • Chancellor • College Presidents • Vice Chancellors • Instruction, Student Services and Business Services • FTES Yield Projection Model Pilot Testing • Last 3 years (06 07, 07 08, 08 09)

  8. Method • Start with FTES file from comparable term from previous year • Make exclusions • i.e. cancelled sections, non-residents • Total by different variables • i.e. accounting method, subject, course number • Calculate number of sections per course • i.e. 27 sections of PSYC 101

  9. Method Cont. • Calculate FTES for the total number of sections • 100.35 FTES for 27 sections • Calculate yield by dividing total FTES by the number of sections • i.e. 100.35/27 = 3.72 FTES per section

  10. Method Cont. • Now get file with current sections offered • Aggregate the number of sections offered • Current term is offering 20 sections of PSYC 101 • Match prior year’s yields to current term • Unique ID (PSYC101) • Multiply number of current sections by previous year’s yield • i.e. 20 sections * 3.72 yield = 74.4 FTES

  11. Method Cont. • Adjustments • Change in number of sections • CT – ((CT-PT)/PT) • Multiply adjusted sections by .99 • Increase Yields • Yield can be adjusted according to current trends • yield + 0.10

  12. Results • In the past three years we projected spring during fall • Spring 2006 to 2007 • The projection was off by 243 FTES • -1.81% error • Spring 2007 to 2008 • The projection was off by 654 FTES • -4.78% error

  13. Results Cont. • Spring 2008 to 2009 • The projection was off by 605 FTES • -4.44% error • Data as of 4/8/09

  14. Discussion • Limitations • Can only be calculated when the schedule is ready • New courses are given a marginal mean • Only used for three years • Possible Improvements • Add factors • unemployment rate • fill rates • physical improvements • % increase from term

  15. Worksheet Exercise!

  16. Discussion Questions • What do you see as other limitations of this model? • What are other ways to improve this model? • How does this model compare to other projection models?

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