Predicting Enrollments – Principles and Practices
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Predicting Enrollments – Principles and Practices
Lou McClelland, Planning, Budget, and Analysis, CU-Boulder
Statewide Higher Education Budget Meeting, August 18, 2006
This document and accompanying Excel are posted at
http://www.colorado.edu/pba/records/enrlproj.htm
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See links under “Methods used”
University of Colorado at Boulder = CU-Boulder = Boulder = UCB. Background
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About 30,000 students in on-campus and continuing ed
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85% undergraduate
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69% Colorado residents
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Predominant unrestricted revenue sources: Out-of-state tuition, in-state tuition, COF,
fee for service.
Why predict?
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To manage
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Advance notice of need for more/fewer beds in dorms, instructors, etc.
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To meet formal constraints such as CCHE admission standard
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To meet goals
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To predict money
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Tuition
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COF, FTE
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Expenses – we do this very informally; not discussed here
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To answer questions from the press, administrators, others
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Reasons for you?
What do you want/need to predict?
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Headcounts or credit hour counts or some mix
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Boulder: Heads, period. Most students are full-time and credit loads are stable.
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Enrolled when?
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Boulder: Fall census, period. Because
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Fall drives spring, and almost 90% of new students enter in fall
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Freshmen 98%, UG transfers 65%, grad level 91%
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It’s earlier than end of term, and people want info earlier
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Our end of term counts are very similar to census
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How far out?
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Boulder: Coming fall, plus 5-10 years after
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Predicting Enrollments – Principles and Practices
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Enrolled how?
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Boulder: Degree-seeking students not enrolled solely through continuing ed
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Study abroad and faculty-staff waivers also excluded
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“Enrollment management population”
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“In Boulder to study, vs. studying while in Boulder”
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Restrict because others are not critical to management, constraints, goals, or
money
How chopped up
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Boulder:
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Grad vs. undergrad
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Resident vs. non-resident
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Why? We are 1/3 non-resident, and non-res tuition is 5 times resident
tuition, 3 times resident tuition plus COF
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New vs. continuing, with new freshmen and transfers separated
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We don’t project by college, or by full-part time, even though both affect tuition
charges. Why?
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Distributions over college and full-part are stable.
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The smaller the group projected, the less accurate the projections.
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You might need: Non-degree. Continuing ed. By college, by when entered, by
gender, by day/evening, by . . . . Depends on what outcomes are important to
you.
All these decisions determine what outcomes, what counts, you need
Boulder needs only 10 counts. Always in this order, with these totals/subtotals, with
Res, NR, All for each
Undergrads in colleges A&S, business, engineering, EV, journalism, music,
education
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Continuing and readmit
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New frosh
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New transfers
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All new UG (subtotal)
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All UG
(subtotal)
Graduate level in colleges GR, BG, Law
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Continuing and readmit
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New
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All grads (subtotal)
Total degree/certificate students with reportable hours -- excludes non-degree,
reciprocal, fac/staff, study abroad (grand total)
Write down what you want/need to predict
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Predicting Enrollments – Principles and Practices
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Doing the predictions – Basic steps
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Identify inputs, partition students by inputs
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What’s used in predicting new freshmen may be useless for predicting seniors,
grads, or repeating non-degree students
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Partitions may or may not be the same as the needed separate counts
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Develop a method for each partition
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Put effort on more important partitions
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Boulder: Continuing undergrads and new freshmen are by far the most important
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Project the same time point for each partition
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Add it all together, making sure there are no gaps and no overlaps or double-counts
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All this makes most sense by illustration!
Boulder partitions and inputs
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New freshmen and transfers
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HS grads, admissions activity
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Continuing undergrads
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Students available to continue, by class level. Freshmen stick around, seniors
don’t.
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New grads
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Continuing grads
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Students available to continue, by college. MBA, law, master’s, doctoral students
continue differently.
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All: Plus residency, history, knowledge of changes
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History = What it looked like last year, last term, some prior period
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What partitions and inputs will you need?
Illustration: Boulder cycle for an upcoming fall – say fall 03
 Census fall ’02 around Sept 15
 Oct 02, have all fall 02 census data.
 Formula prediction of fall 03 continuing undergrads.
 Biggest group!
 Base on current UG enrollment and fall 01 to fall 02 transition ratios by
residency and class level. Assume transitions from fall 02 to fall 03 will be the
same.
 Check error rates and trends. Committee hand adjusts if appropriate.
 Method: Ratios from individual-student data. See Excel for details.
 Formula prediction of fall 03 continuing grad level. Similar but much less
sophisticated, using aggregate data only. Method: Ratios from counts. See
Excel for details.
 Committee uses time trends, knowledge of current situation, knowledge of Univ.
and environment changes, etc. to estimate/guess fall 03 new transfers, new
grads
 Committee is Admissions, Registration, Aid, Bursar, Housing, Budget, PBA
 Estimate by on-the-spot Delphi
 Method: Extending time series. See Excel for details.
 Set starting guess or goal for freshmen
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Oct-August, predict fall census freshmen weekly. Transfers Jan-August.
 We admit freshmen starting in October. App deadline January, admit by April 1,
confirm by May 1.
 We have snapshot files for every week of the year for four years, with individual
student data. We create a new snapshot every week.
 Use the snapshot from this-time-last-year, plus last-year finals, to calculate lastyear probabilities of completion, admission given completion, and matriculation
given admission
 Based on the student’s then-current [same week last year] admission status,
academic credentials, housing contract status, and residency.
 Admission status = incomplete, complete no decision, refused, postponed
decision, admitted not confirmed, confirmed
 Apply the probabilities to this-year counts.
 Can adjust to accommodate
 Different expected N of total apps this year (used early in the season)
 Different admission probabilities from last year, for a status-credentialsresidency group.
 Expected increases or decreases in yield rates within groups
 Our rates vary between 6% and 60% over residency-credentials groups!
February – Adjust continuing undergrad projection based on spring census
Feb-May – Glance at new-grad activity. Revise estimates only if see earthquakes.
May-July, Project all grads and continuing UG based on registration for coming fall.
Start after all registration time assignments are past.
August/Sept – Prep for census. Census. Wrap up files, start new cycle.
Throughout the cycle, we issue 3 or 4 official “best guess” estimates – Oct or Nov,
sometimes Dec/Jan, Feb after spring census, May before budget retreat
Demonstration of pieces of the methods
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See demos in Excel http://www.colorado.edu/pba/records/enrlprojmeth.xls
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Includes tabs for
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Extending a time series
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Ratios from counts
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Ratios from individual student data
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Longer term projections
 Emphasis is on a tool to project several years given different assumptions
 Enrollment management population only
 Basis of projections
 Continuing undergrads: Based on same transition ratios used in fall-to-fall
estimates, by class level and residency. Can increase/decrease continuation
rates if desired
 All others: Type ‘em in. Can tie freshmen to HS grads if want, or not.
 Update annually about November. Reconcile to next-fall projections.
 Outcomes
 Total enrollment by grad/UG, new/continuing, frosh/transfers, by residency
 Many percentages and plots
 See small example with formulas, and the real thing, in the Excel.
Principles in developing methods
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Identify inputs, partition by inputs.
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Put your effort where it’s most important
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Determine your cycle and worry about time points.
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You must have data for multiple instances of the time point of interest.
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To use interim activity (apps, admits, registrations) you must have prior-cycle
data for interim points too. Frozen snapshot files are nice.
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Matching interim time points over years or terms can be tricky, with holidays,
some relative dates (first Monday in Sept), some absolute (May 1)
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Try it
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LOOK AT THE DATA, do reality checks
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Need calculation and display and error-tracking outputs
Principles – Overall
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Know what you want/need to predict, and why
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Show everything in a standard array.
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Make a formal “best guess,” label it so, and don’t change it very often. Don’t issue
new projections every week or month.
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In setting best guesses, consider the costs of estimating too high, and of
estimating too low. We set best guesses so that equal chance actuals will be
higher or lower.
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Track what happened. Calculate error rates. If you’re just starting, apply your
methods on already-past cycles and calc error rates on those. Look at plots of
actual vs. projected.
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Understand the processes that generate your inputs, and changes in them.
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Example: Application deadline changed by a month
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Example: Last year Orientation phoned no-shows, this year they didn’t. Affects
cancellations.
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Know plans, changes that might change the relationship between inputs and
outcomes
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Example: A bunch of admits got $5000 scholarship offers this year; none did last
year. Yield went up.
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Example: Probation policy changes
Educate your users about error and assumptions
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Expose the error rates. With bands, labels, and admonitions, caution that
projections are just estimates or guesses, have assumptions [list them], have
error.
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Round. Your estimates aren’t precise; don’t present them that way.
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Recognize (and publicize) that in the middle of a cycle there’s no way to
differentiate changes in overall numbers from changes in speed
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It’s Jan 1 and you have 20% fewer apps or registrations than last year. Is this
because 20% fewer will apply/register ever, or because students are slower
this year? You can’t tell.
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Realize and publicize that every projection involves many assumptions about
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Student/applicant behavior
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The college or university’s behavior
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The state’s behavior
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