t3.4 predictive modeling to plan enrollment, financial & facility resource needs________

T3.4 Predictive Modeling to Plan Enrollment, Financial & Facility Resource Needs________

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SACRAO 2014T3.4 Predictive Modeling to Plan

Enrollment, Financial & Facility Resource Needs

Rodney Miller, Dean of RecordsCovenant College, Lookout Mountain, GA

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Some of the common questions I have been asked by fellow administrators:

• How many new students do we expect to enroll?

• How many total students do we expect to enroll?

• How many beds do we need?• How many beds will be have available?• When do we need to have a new residence

hall completed?

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Some of the common questions I have been asked by faculty departments:

• How many seats and sections of _________ do we need next year?Core requirements - Typical Freshmen Courses:

Old Testament, English Composition, fine arts, humanities, social science, physical education

Major Courses: based on # of new English majors, how many introductory English courses.

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Some of the common questions I have been asked by faculty departments:

• How many seats and sections of _________ do we need next year?Core requirements - Typical Freshmen Courses:

Old Testament, English Composition, fine arts, humanities, social science, physical education

Major Courses: based on # of new English majors, how many introductory English courses.

(Forecast how many students, and what kind?)

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Concerns of making Predictions:

• I don’t have the training!– (I am not a statistician)

• I don’t have the data or the tools!– (I cannot learn a little Excel)

• I am afraid I won’t be accurate!– (I cannot look like a fool to my boss)

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Concerns of making Predictions:• I don’t have the training!

– (I am not a statistician)

• I don’t have the data or the tools!– (Can you learn a little Excel)

• I am afraid I won’t be accurate!– (I cannot look like a fool to my boss)

• Think outside the box - Be BOLD, Take RISKS (What if you’re close?)

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Forecasting Defined

Forecasting is…A prediction of what will happen in the

future given some assumed set of circumstances.

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Forecasting Defined

Forecasting is…A prediction of what will happen in the

future given some assumed set of circumstances.

Forecasts are developed…By combining quantitative methods

with expert knowledge and managerial insight.

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Updated 09/25/2013Projecti

onProjecti

onF

200F 2009 F 2010 F 2011 F 2012 F 2013 F 2014 F 2015

Fall New Stu Original Goal 330 300 325 305

Fall New Stu Project Calc 320 300 318 326 305 325 325 325

Spring New Stu Proj Calc 20 25 17 20 15 25 15 15 Actual Fall New Enroll 310 312 318 328 305 326 Actual Spr New Enroll 19 24 17 10 18 12 Projected Class Head Count

Freshmen 297 290 292 306 309 313 312 319Sophomores 296 273 264 263 281 260 262 286

Juniors 207 197 190 237 231 241 246 225Seniors 167 216 215 201 204 210 234 232 Totals 967 976 961 1007 1024 1024 1054 1062

Increase from Last Year -20 9 -15 46 18 -1 30 8

Projected Fall Budget FTE 1016 1016 1045 1053

Enrollment Projections

Yellow is Actual Enrollment

The Role of Forecasting

• Short Term: Scheduling of existing resources

Acquiring additional resources

• Long Term: Strategic planning for future

resource needs and options

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The Role of Forecasting

• Short Term: Scheduling of existing resources

Acquiring additional resources

• Long Term: Strategic planning for future resource needs and options

You must know your institution, because:

Forecasting is a Process!It Is Both An

Art and a Science

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Covenant started in 1955 in Pasadena, CA for one year, before moving to St. Louis, MO. Started Covenant Theological Seminary,

outgrew location.In 1964, it moved to Lookout Mtn, GA into the

old Castle in the Clouds Hotel (building in 1928)

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• Affiliated and operated by the Presbyterian Church in America (PCA)

• Our goal is to equip our students as biblically grounded men and women to live out extraordinary callings in ordinary places.

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Covenant is a Christ-centered institution of higher education, emphasizing the liberal arts.

A little about Covenant College - Fall 2013:

• Number of Students: 1045 UG; 69 GR – (1114 Total)

• UG Student-to-Faculty Ratio: 14 to 1• UG Average Class Size: Approx. 23• UG Faculty: 69 full time; 90% of faculty

hold doctorate or terminal degree (10% taught of classes taught by adjuncts)

• UG Participation in Varsity Sports : 34%• 1st year full membership – NCAA DIII

• UG Most Popular Majors:• Art, Education, English, History, Sociology

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• In my 26 years at Covenant, we have:– Grown from 488 to 1132 students,– Added majors and faculty, and doubled

the number of buildings,– Implemented 3 different administrative

software systems, and – Experienced challenges trying to predict

our future needs

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Challenges of Achieving an Accurate Forecast

• Lack of data, too much data or poor quality data

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Challenges of Achieving an Accurate Forecast

• Lack of data, too much data or poor quality data

• Lack of understanding of customer needs, market characteristics and economic conditions

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Challenges of Achieving an Accurate Forecast

• Lack of data, too much data or poor quality data

• Lack of understanding of customer needs, market characteristics and economic conditions

• Lack of resources (a skilled analyst)

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Challenges of Achieving an Accurate Forecast

• Lack of data, too much data or poor quality data

• Lack of understanding of customer needs, market characteristics and economic conditions

• Lack of resources (a skilled analyst)

• The future cannot be predicted with certainty, especially when working with 18-21 year olds!

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Forecasts = Pattern +/- Randomness

Patterns are quantitative methods of determining correlative actions based on statistical analysis of historical data.

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Forecasts = Pattern +/- Randomness

Patterns are quantitative methods of determining correlative actions based on statistical analysis of historical data.

Randomness is a qualitative judgment that relies on intuition, expert opinion, market knowledge, and knowledge of your students and constituents.

God’s providence; others call “luck”

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How do develop a forecast?

If you have research staff in your office, GREAT, use them!

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How do develop a forecast?

If you have research staff in your office, GREAT, use them!

If not, establish your own simple research, looking for patterns – assumptions. But, you don’t have to be a statistician!

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How do develop a forecast?

If you have research staff in your office, GREAT, use them!

If not, establish your own simple research, looking for patterns – assumptions. But, you don’t have to be a statistician!

Become comfortable with the margin of error based on our assumptions (again, given 18-21 year olds).

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Convert Observed Patterns into Quantitative Assumptions for Predicting Total Enrollment

What are the key variables for predictingTotal Enrollment?

(Audience Participation!!!!!)

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Convert Observed Patterns into Quantitative Assumptions for Predicting Total Enrollment

What are the key variables for predictingTotal Enrollment?

• New Student Enrollment Goals (or Limit)– % FR, SO, JR, or SR

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Convert Observed Patterns into Quantitative Assumptions for Predicting Total Enrollment

What are the key variables for predictingTotal Enrollment?

• New Student Enrollment Goals (or Limit)– % FR, SO, JR, or SR

• Fall-to-Fall Retention Rates – Class Level– % FR stay FR -- % FR to SO– % SO stay SO -- % SO to JR– % JR stay JR -- % JR to SR– % SR stay SR

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Convert Observed Patterns into Quantitative Assumptions for Predicting Total Enrollment

What are the key variables for predictingTotal Enrollment?

• New Student Enrollment Goals (or Limit)– % FR, SO, JR, or SR

• Fall-to-Fall Retention Rates – Class Level– % FR stay FR -- % FR to SO– % SO stay SO -- % SO to JR– % JR stay JR -- % JR to SR– % SR stay SR ? Do we use Cohort only or all students? (Audience?)

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What are the key variables for predicting

New Student Enrollment?The FUNNEL - numbers•Institutional Fit•High School GPA of College Prep Curriculum•Test Scores – SAT/ACT, specially Critical Reading/English in a liberal arts school•Financial Need•Class Level Percentages Rates (e.g. 15th of 100) •Application Date, or Deposit Date

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What are the key variables for predicting

New Student Enrollment?The FUNNEL - numbers•Institutional Fit•High School GPA of College Prep Curriculum•Test Scores – SAT/ACT, specially Critical Reading/English in a liberal arts school•Financial Need•Class Level Percentages Rates (e.g. 15th of 100)•Application Date, or Deposit Date

•Covenant worked with a consultant, and we don’t fit their usual models given our market niche.

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What are the key variables for predicting

Course Needs?• History of course offerings:

Course sequencing within the major8-semester planning tools

• Student body characteristics: # in majorclass level

• Room availability

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What are the key variables for predicting Residence Hall Needs?

• Availability of Housing:Percentage of capacity utilizedNeed to take units off-line for maintenance

• Philosophy of Residence Life: Class level inclusion vs. dedicated halls

(e.g. freshman halls, honors, athletics)

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What are the key variables for predicting SACRAO LAC Room & Food

Guarantees?

• How many rooms to guarantee with hotels

• How many meals to guarantee for :– Sunday Partner Reception– Tuesday Big Event– Wednesday Breakfast

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Start a total enrollment model with an algebraic formula.

• Organize your data. (continually changing that)– Who liked High School Algebra? (LB?)

• Create a spreadsheet with your data.– Know that you will have to change it as

you continue to use it for:• More efficient display• Maintenance of historical information

• To predict the Fall 2014 Total Enrollment from August – December 2013, the first formula:

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Start a total enrollment model with an algebraic formula.

• Organize your data. (continually changing that)– Who liked High School Algebra? (LB?)

• Create a spreadsheet with your data.• To predict the Fall 2014 Total Enrollment

from August – December 2013, the first formula:

• Projected Fall 2014 Freshman =% of New Fall 2014 Student Projection +% of Fall 2013 Actual Freshman Students +% of New Spring 2014 Actual Students

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Updated 09/25/2013 Yellow is Actual Enrollment Projection Projection

  F 2009 F 2010 F 2011 F 2012 F 2013 F 2014 F 2015

Fall New Stu Original Goal       330 300 325 305Fall New Stu Project Calc 300 318 326 305 325 325 325Spring New Stu Proj Calc 25 17 20 15 25 15 15

Actual Fall New Enroll 312 318 328 305 326    Actual Spr New Enroll 24 17 10 18 12                 Projected Class Head Count              

Freshmen 290 292 306 309 313 312 319Sophomores 273 264 263 281 260 262 286

Juniors 197 190 237 231 241 246 225Seniors 216 215 201 204 210 234 232 Totals 976 961 1007 1024 1024 1054 1062

Increase from Last Year 9 -15 46 18 -1 30 8              

Actual Fall Head Count F09 F10 F11 F12 F13 F14 F15Freshmen 303 292 302 284 285   

Sophomores 232 264 260 277 276   Juniors 211 190 226 221 241   Seniors 229 215 190 216 238    Totals 975 961 978 998 1040 0 0

Increase HC from Last Year -8 -14 17 20 42 14 0

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Proj Budget Retention % by Class (not-Cohort) F09 % F10 % F11 % F12 % F13 % F14 % F15 %

FR to FR 0.051 0.040 0.052 0.122 0.071 0.082 0.092FR to SO return 0.660 0.719 0.718 0.786 0.741 0.748 0.758SO to SO return 0.051 0.069 0.060 0.047 0.059 0.055 0.054SO to JR return 0.777 0.760 0.792 0.814 0.789 0.798 0.800JR to JR return 0.021 0.019 0.067 0.048 0.045 0.053 0.049JR to SR return 0.835 0.817 0.933 0.826 0.859 0.873 0.852SR to SR return 0.099 0.085 0.112 0.089 0.095 0.099 0.094

Fall New% by Class (running three year average)              

FR 0.905 0.865 0.863 0.875 0.877 0.870 0.871SO 0.074 0.085 0.103 0.095 0.089 0.093 0.095JR 0.016 0.041 0.034 0.020 0.028 0.031 0.028SR 0.003 0.009 0.000 0.010 0.006 0.006 0.005

Spring New % by Class              FR 0.54 0.58 0.53 0.53 0.50 0.50 0.50SO 0.24 0.24 0.24 0.24 0.28 0.28 0.28JR 0.22 0.18 0.23 0.23 0.22 0.22 0.22

Start a total enrollment model with an algebraic formula.

• Organize your data. (continually changing that)– Who liked High School Algebra? (LB?)

• Create a spreadsheet with your data.• To predict the Fall 2014 Total Enrollment

from August – December 2013, the first formula:

• Projected Fall 2014 Freshman =% of New Fall 2014 Student Projection +% of Fall 2013 Actual Freshman Students +% of New Spring 2014 Student Projection

# FR = (L6*L63)+(K25*L55)+(K9*L68)

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Next Step in the Forecast

• Projected Fall 2014 Sophomores =% of New Fall 2014 Student Projection +% of Fall 2013 Actual Freshman Students +% of Fall 2013 Actual Sophomore Students +% of New Spring 2014 Student Projection

# SO = (L6*L64)+(K25*L56)+(K26*L57)+(K9*L69)

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Next Step in the Forecast

• Projected Fall 2014 Sophomores =% of New Fall 2014 Student Projection +% of Fall 2013 Actual Freshman Students +% of Fall 2013 Actual Sophomore Students +% of New Spring 2014 Student Projection

• # SO = (L6*L64)+(K25*L56)+(K26*L57)+(K9*L69)

• Projected Fall 2014 Juniors =% of New Fall 2014 Student Projection +% of Fall 2013 Actual Sophomore Students +% of Fall 2013 Actual Junior Students +% of New Spring 2014 Student Projection

•# JR = (L6*L65)+(K26*L58)+(K27*L59)+(K9*L70)

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Last Step in the Forecast

• Projected Fall 2014 Seniors =% of Fall 2013 Actual Junior Students +% of Fall 2013 Actual Senior Students +% of Fall 2014 Student Projections

• Projected Fall 2014 Total Enrollment =# FR = (L6*L63)+(K25*L55)+(K9*L68)# SO = (L6*L64)+(K25*L56)+(K26*L57)+(K9*L69)# JR = (L6*L65)+(K26*L58)+(K27*L59)+(K9*L70)# SR = (L4*L59)+(K20*L53)+(K21*L54)

Formulas will change depending on when you are making the calculations.

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Let’s look at some spreadsheets!

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Look for Patterns that will inform other decisions:

• Actual Fall New Enrollment : What happened 2008?

• Projected Enrollment change from last year.

• Compare Actual Freshman Retention and Graduation Rate.

• Projecting Core Course Needs

• Projecting Expenses and Housing Needs

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Summary Thoughts to Hold On To:

•Projections are only as good as your data, and your assumptions.

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Summary Thoughts to Hold On To:

•Projections are only as good as your data, and your assumptions.•There is still “randomness” to be expected.

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Summary Thoughts to Hold On To:

•Projections are only as good as your data, and your assumptions.•There is still “randomness” to be expected.

•When I am relatively close and have helped provide direction for the college, I thank the Lord and accept the praise I receive!

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Summary Thoughts to Hold On To:

•Projections are only as good as your data, and your assumptions.•There is still “randomness” to be expected.

•When I am relatively close and have helped provide direction for the college, I thank the Lord and accept the praise I receive!

•When I am way off and am acknowledged for my good efforts, I remember we are dealing with 18-22 year olds in a period of economic uncertainty and thank the Lord for job security as a Registrar. (unless you can help me come up with a better excuse )

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T3.4 Predictive Modeling to Plan Enrollment, Financial & Facility Resource Needs

I welcome comments on how you have utilized predictive modeling at your institution.

Email if you have any questions or if you did not receive a handout. You can access this handout on the Covenant website at:

Rodney Miller miller@covenant.eduDean of Records Office 706-419-1134Covenant College

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T3.4 Predictive Modeling to Plan Enrollment, Financial & Facility Resource Needs________

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