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T3.4 Predictive Modeling to Plan Enrollment, Financial & Facility Resource Needs________ Use Mobile Guidebook to Evaluate this Session. Please Silence.

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Presentation on theme: "T3.4 Predictive Modeling to Plan Enrollment, Financial & Facility Resource Needs________ Use Mobile Guidebook to Evaluate this Session. Please Silence."— Presentation transcript:

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2 T3.4 Predictive Modeling to Plan Enrollment, Financial & Facility Resource Needs________ Use Mobile Guidebook to Evaluate this Session. Please Silence mobile devices. 1

3 SACRAO 2014 T3.4 Predictive Modeling to Plan Enrollment, Financial & Facility Resource Needs Rodney Miller, Dean of Records Covenant College, Lookout Mountain, GA 2

4 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? 3

5 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. 4

6 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?) 5

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8 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) 7

9 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?) 8

10 Forecasting Defined Forecasting is… A prediction of what will happen in the future given some assumed set of circumstances. 9

11 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. 10

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13 The Role of Forecasting Short Term: Scheduling of existing resources Acquiring additional resources Long Term: Strategic planning for future resource needs and options 12

14 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 13

15 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) 14

16 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. 15 Covenant is a Christ-centered institution of higher education, emphasizing the liberal arts.

17 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% 1 st year full membership – NCAA DIII UG Most Popular Majors: Art, Education, English, History, Sociology 16

18 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 17

19 Challenges of Achieving an Accurate Forecast Lack of data, too much data or poor quality data 18

20 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 19

21 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) 20

22 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! 21

23 Forecasts = Pattern +/- Randomness Patterns are quantitative methods of determining correlative actions based on statistical analysis of historical data. 22

24 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” 23

25 How do develop a forecast? If you have research staff in your office, GREAT, use them! 24

26 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! 25

27 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). 26

28 Convert Observed Patterns into Quantitative Assumptions for Predicting Total Enrollment What are the key variables for predicting Total Enrollment? (Audience Participation!!!!!) 27

29 Convert Observed Patterns into Quantitative Assumptions for Predicting Total Enrollment What are the key variables for predicting Total Enrollment? New Student Enrollment Goals (or Limit) – % FR, SO, JR, or SR 28

30 Convert Observed Patterns into Quantitative Assumptions for Predicting Total Enrollment What are the key variables for predicting Total 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 29

31 Convert Observed Patterns into Quantitative Assumptions for Predicting Total Enrollment What are the key variables for predicting Total 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?) 30

32 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. 15 th of 100) Application Date, or Deposit Date 31

33 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. 15 th of 100) Application Date, or Deposit Date Covenant worked with a consultant, and we don’t fit their usual models given our market niche. 32

34 What are the key variables for predicting Course Needs? History of course offerings: Course sequencing within the major 8-semester planning tools Student body characteristics: # in major class level Room availability 33

35 What are the key variables for predicting Residence Hall Needs? Availability of Housing: Percentage of capacity utilized Need to take units off-line for maintenance Philosophy of Residence Life: Class level inclusion vs. dedicated halls (e.g. freshman halls, honors, athletics) 34

36 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 35

37 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: 36

38 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 37

39 38 Updated 09/25/2013 Yellow is Actual Enrollment Projection F 2009F 2010F 2011F 2012F 2013F 2014F 2015 Fall New Stu Original Goal 330300325305 Fall New Stu Project Calc300318326305325 Spring New Stu Proj Calc251720152515 Actual Fall New Enroll312318328305326 Actual Spr New Enroll2417101812 Projected Class Head Count Freshmen290292306309313312319 Sophomores273264263281260262286 Juniors197190237231241246225 Seniors216215201204210234232 Totals97696110071024 10541062 Increase from Last Year 9-154618308 Actual Fall Head CountF09F10F11F12F13F14F15 Freshmen303292302284285 Sophomores232264260277276 Juniors211190226221241 Seniors229215190216238 Totals975961978998104000 Increase HC from Last Year -8-14172042140

40 39 Proj Budget Retention % by Class (not-Cohort)F09 %F10 %F11 %F12 %F13 %F14 %F15 % FR to FR0.0510.0400.0520.1220.0710.0820.092 FR to SO return0.6600.7190.7180.7860.7410.7480.758 SO to SO return0.0510.0690.0600.0470.0590.0550.054 SO to JR return0.7770.7600.7920.8140.7890.7980.800 JR to JR return0.0210.0190.0670.0480.0450.0530.049 JR to SR return0.8350.8170.9330.8260.8590.8730.852 SR to SR return0.0990.0850.1120.0890.0950.0990.094 Fall New% by Class (running three year average) FR0.9050.8650.8630.8750.8770.8700.871 SO0.0740.0850.1030.0950.0890.0930.095 JR0.0160.0410.0340.0200.0280.0310.028 SR0.0030.0090.0000.0100.006 0.005 Spring New % by Class FR0.540.580.53 0.50 SO0.24 0.28 JR0.220.180.23 0.22

41 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) 40

42 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) 41

43 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) 42

44 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. 43

45 Let’s look at some spreadsheets! 44

46 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 45

47 Summary Thoughts to Hold On To: Projections are only as good as your data, and your assumptions. 46

48 Summary Thoughts to Hold On To: Projections are only as good as your data, and your assumptions. There is still “randomness” to be expected. 47

49 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! 48

50 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 ) 49

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53 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 Millermiller@covenant.edu Dean of RecordsOffice 706-419-1134 Covenant College 52

54 T3.4 Predictive Modeling to Plan Enrollment, Financial & Facility Resource Needs________ Use Mobile Guidebook to Evaluate this Session 53


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