Stafford County Public
Schools
2014-15 Update
Student Membership
Forecast
O P E R AT I O N S R E S E A R C H A N D E D U C AT I O N L A B O R ATO R Y
I N S T I T U T E F O R T R A N S P O R TAT I O N R E S E A R C H A N D E D U C AT I O N
C E N T E N N I A L C A M P U S @ N O R T H C A R O L I N A S TAT E U N I V E R S I T Y
AUGUST 14, 2014
2014-15 Update and Forecast
Integrated Planning for
School And Community
Data-driven and policy-based model for
forecasting school membership and
determining the optimal locations for
new schools and attendance zones.

Land Use Studies

Membership Forecasting

Out-of-Capacity Analysis

School Site Optimization

Attendance Boundary Optimization
 Part of the Institute for Transportation Research and
Education (ITRE) at the NC State University, Centennial
Campus
 Specializing in the applications of decision science for
school districts dealing with the politically sensitive and
complex issues of student reassignment and new school
planning
 Over 20 years of experience working with school districts
in NC, SC, and VA
 Providing school planning solutions that are driven by
data and supported by policy
O P ER ATI ONS R ES EARCH A N D ED U CAT I O N L A B O R ATORY
I N S T I T U T E F O R T R A N S P O R TAT I O N R E S E A R C H A N D E D U C AT I O N
• Alamance-Burlington School System – 02, 03, 06, 07, 08, 09, 10,
11, 12, 13
• Asheboro City Schools – 04, 05, 06, 07
• Berkeley County Schools (SC) – 09, 10, 11, 12
• Bladen County Schools – 04
• Buncombe County Schools – 98, 99
• Brunswick County Schools – 03, 04
• Cabarrus County Schools – 12
• Carteret County Schools – 09
• Chapel Hill-Carrboro Schools – 95, 96, 97, 98, 99, 00, 01, 02, 05,
06, 07, 12
• Chatham County Schools – 03, 05, 06, 07, 08, 09, 10, 11, 12, 13
• Craven County Schools – 96, 97, 98, 99, 00, 01, 02, 04, 05, 06, 07,
08, 12
• Cumberland County Schools – 08, 09
• Cleveland County Schools – 08
• Currituck County Schools – 09
• Duplin County Schools – 09
• Durham Public Schools – 08, 09, 10, 11, 12
• Edgecombe County Public Schools – 09
• Elizabeth City-Pasquotank County Schools – 07
• Franklin County Schools – 08, 11, 12
• Iredell-Statesville Schools – 98, 99, 00, 01, 02, 03, 04
• Johnston County Schools – 94, 95, 96, 97, 98, 99, 00, 01, 02, 03,
04, 05, 06, 07, 08, 09, 10, 11, 12, 13
• Jones County Schools – 09
• Gaston County Schools – 98, 99, 00, 01, 02, 03, 04
• Granville County Schools – 02, 03, 04, 05, 06, 07, 08, 09, 10
• Guilford County Schools – 94, 95, 96, 97, 98, 09, 10, 11, 13, 14
• Harnett County Schools – 98, 99, 00, 01, 02, 03, 06, 07, 08, 09, 10,
11, 12, 13
• Haywood County Schools – 99
• Hoke County Schools – 99, 08, 09, 11, 12
• Lee County Schools – 08, 09
• Lenoir County School – 09
• Moore County Schools – 04, 06, 07, 08, 12, 13
• Mooresville Graded Schools – 99, 00, 01, 04
• Nash-Rocky Mount Schools – 04, 05, 06, 07, 08, 09, 10, 11, 12
• New Hanover County Schools – 95, 96, 97, 98, 99, 00
• Onslow County Schools – 03, 04, 05, 06, 07, 08, 09, 10, 11, 12, 13
• Orange County Schools – 95, 09, 10, 11, 13
• Pamlico County Schools – 09
• Pender County Schools – 13
• Randolph County Schools – 05, 06, 07, 08, 09
• Richmond County Schools – 00, 08
• Robeson County Schools – 08
• Rock Hill Schools (SC) – 02, 03, 04, 05, 06, 07, 08, 09, 10, 11, 12, 13
• Rowan County Schools – 09
• Pitt County Schools – 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 00, 01,
02, 03, 04, 05, 06, 07, 08, 09, 10, 11, 12, 13
• Stafford County Public Schools (VA) – 12
• Stanly County Schools – 12
• Stokes County Schools – 05, 06, 08
• Tupelo Public Schools (MS) – 07
• Union County Schools – 99, 00, 01, 02, 03, 04, 05, 06, 07
• Vance County Schools – 09
• Wayne County Schools – 95
• Wake County Public School System – 97, 04, 05, 06, 07, 08, 09, 10,
11, 12, 13, 14
Today’s Presentation
• Perspective
• Land Use Update
• Forecast Models
– Cohort Ratio Model
– A P U Models
• Out of Capacity Tables
2000 to 2005
Very high growth rate
Perspectives
Population
• 2000
92,446
• 2010
128,961
• 2013
136,788
Source: U S Census
Predicting Growth In Stafford County
• Predictions in 2009 by Virginia Employment
Commission:
135806
2010
176710
2020
218722
2030
• US Census Data
128961
2010
136788
2013
Predicting Growth In Stafford County
• Predictions in 2009 by Virginia Employment
Commission:
135806
2010
176710
2020
218722
2030
• US Census Data
128961
2010
136788
2013
Predicting Growth In Stafford County
180000
VEC Projected Pop
2020 176710
160000
140000
120000
U S Census Data
100000
80000
2000
2005
2010
2015
2020
Predicting Growth In Stafford County
180000
VEC Projected Pop
2020 176710
160000
162,000 ?
140000
120000
100000
80000
2000
2005
2010
2015
2020
Predicting Growth In Stafford County
180000
VEC Projected Pop
2020 176710
160000
155,000 ?
140000
120000
100000
80000
2000
2005
2010
2015
2020
Housing Units
~1000
housing
units added
annually
Source: U S Census
Population –
Housing Units –
Membership
County # Housing Membership Ratio:
Year Population
Units
SCPS
M/# HU
Ratio:
M/Pop
2000 92446
31405
20000
0.64
0.22
2010 128961
41769
26500
0.63
0.21
2013 136788
44124
27000
0.61
0.20
OREd found the county’s student generation factor (SGF – a ratio of
students to existing housing units including single family & multi-family) to
be 0.61 in 2012 and 0.64 in 2014.
Population – Housing Units Membership
County # Housing
Year Population
Units
M SCPS
Ratio:
M/# HU
Ratio:
M/Pop
2000 92446
31405
20000
0.64
0.22
2010 128961
41769
26500
0.63
0.21
2013 136788
44124
27000
0.61
0.20
2020 170000* 56700*
34000
0.60
0.20
Reaching the projected population of SC (VEC) by 2020 would
require a rate of growth would require 1800 housing starts
annually from 2013 through 2020.
Population – Housing Units Membership
County # Housing
Year Population
Units
M SCPS
Ratio:
M/# HU
Ratio:
M/Pop
2000 92446
31405
20000
0.64
0.22
2010 128961
41769
26500
0.63
0.21
2013 136788
44124
27000
0.61
0.20
2020 170000* 56700*
34000
0.60
0.20
A population of 170,000 persons in SC would suggest that SCPS
would have 34,000 students enrolled in 2020.
Population – Housing Units Membership
County # Housing
Year Population
Units
M SCPS
Ratio:
M/# HU
Ratio:
M/Pop
2000 92446
31405
20000
0.64
0.22
2010 128961
41769
26500
0.63
0.21
2013 136788
44124
27000
0.61
0.20
2020 160000* 53300*
32000
0.60
0.20
Adjusting the projected population of SC to 160,000 in 2020
would still require a rate of growth would require 1300 housing
starts annually from 2013 through 2020.
# Building Permits
Stafford County
Year
# Building Permits
2000
2001
2002
2003
2004
1101
1468
1692
1395
1982
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
1631
860
758
416
524
546
466
640
1004
2000 to 2010 Census Data
Population by Age
•
•
•
•
0 to 4 years old
5 to 17 years old
18 to 64 years old
65 years or older
2000
7172
21997
57803
5474
2010
8719
28478
83300
9464
%▲
22%
29%
42%
73%
Conclusions
• Projecting population or membership is
difficult during volatile periods
• 2014 – 2018 is likely to be a volatile period
• Growth in Stafford County is not likely to mirror
the 2000 – 2005 growth rate
• Demographics (in Stafford County and in the
US) are changing and these changes will impact
the number of school-aged children – the
number of school-aged children per household
Land Use
Study
July, 2014
Land Use Study
Data from
Data
• Stafford County Public • Student File: May 2014
Schools
data geocoded to identify
where each student
• Stafford County
resides
Planning
• GIS Files from SC GIS:
• Stafford County GIS
parcel
data,
structure
• Interviews with SCP
data, subdivision data
and SCPS
• Subdivision Data from SCP
Land Use Study – “Active Subdivisions”
Brentsmill is an active subdivision, as defined by SC Planning, located in
APU 304. (OREd divided the county into 221 planning units that are, for the most
part, homogeneous in terms of the type of residential development.)
From SCP, there were 188 approved lots in Brentsmill on which 185
single-family dwellings have been built. (July 2014/SCP)
Land Use Data
GIS data (from March, 2014) shows Brentsmill Subdivision; the parcels and
the structures (purple having been constructed within the last 18 months).
GIS data shows 119 K-12 students living in the 181 structures producing a
student generation factor (SGF) of 0.66.
Further analysis indicates that dwelling units have been constructed on about
50 lots since 2/11/13. That indicates that this subdivision will have a potential
impact on 2014-15 numbers even though there are now only a few vacant lots left.
OREd calculations indicate that about 10 new K-12 students will enter SCPS
from this subdivision in 2014-15.
Leeland Station (sections 1-7) is in APU 124 with section 8 in APU 113.
The subdivision is approved for a total of 772 residential lots of which 448 have
single family dwelling units built on them as of July of 2014. There are 324 lots
that have either not been developed or not been built upon. GIS data shows
399 students producing a SGF of 0.891.
Land Use Data
Section 6
Section 6
Section 8
Sections 5&7
APU 124
SCP data in July of 2014 showed 537
approved lots in LS (west of Leeland
Rd), 203 approved lots in sections 5&7
(east of Leeland Rd), and 32 approved
lots in section 8.
SCP data showed 389 of 537 approved lots west
of Leeland Rd, and 70 of 203 approved lots in
Sect 5&7 developed (Single Family Dwellings).
Note that many dwellings were built on lots
within the past 18 months (purple)
Sections 5 & 7
SCP data showed 389 of 537 approved lots
west of Leeland Rd, and 70 of 203 approved
lots in Sect 5&7 developed (Single Family
Dwellings). Note that many lots were built
within the past 18 months (purple)
Sections 5&7
The forecast model uses 50 lots
impacting 2014-15 producing ~50 new
students. The remaining ~280 lots (not
Section 8) spread out from 15-16 to 18-19
producing about 60 new K-12 students
each year. (Pace / Build-out)
The 32 lots in Section 8 appear in 17-18
through 19-20.
Results of the Land Use Study
Residential Growth
Student Growth
• Largely SFD
• Number of students generated
by residential growth*
• New Dwelling Units #
2014-15
426
735 impacting 2014-15
2015-16
512
869 impacting 2015-16
2016-17
613
1066 impacting 2016-17
2017-18
506
852 impacting 2017-18
2018-19
464
828 impacting 2018-19
# The number of new dwelling units represents the result after dialogue with SCPS and SCP/GIS
and OREd; qualifying subdivisions, pace of development, and type of development.
* New residential growth does not always mean “new students”. Students occupying new
dwelling units may come from in-migration or from other dwelling units in Stafford County.
These calculations come from the product of the # of dwelling units and the SGF.
Results of the Land Use Study
Residential Growth
Student Growth
• Largely SFD
• Number of students generated
by residential growth
• New Dwelling Units
735 impacting 2014-15 2014-15
426
869 impacting 2015-16 2015-16
512
1066 impacting 2016-17 2016-17
613
852 impacting 2017-18 2017-18
506
828 impacting 2018-19 2018-19
464
Information gathered and analyzed in 2014 cannot accurately portray the potential for new
development past the next few years. New developments are being considered by the County
now that will impact numbers past 2018. Other developments will occur that OREd nor the
County know anything about. Hence, land use data should only be considered relevant over
the next few years.
Data  Student Numbers
Membership Forecast Models
• CSR (Cohort Survival Ratio) Forecast
• APU Forecast
Cohort Survival Ratio
System-Wide Forecast
Cohort Survival Ratios
are used to predict how
cohorts of students will
advance through the K-12
system by grade.
CSR values greater than 1 suggest
in-migration into the district.
Cohort Survival Ratio (CSR): Comparison of student counts
by consecutive grade for consecutive years.
Cohort Survival Ratio
System-Wide Forecast
Cohort Survival Ratios
are widely used as an
acceptable model for
system-wide forecasts.
Example:
The month-1 ADM for Grade 8 in 2012-13 was 2113. The
month-1 ADM for Grade 9 in 2013-14 was 2254;
2254/2113 = 1.067, the CSR circled above.
This ratio is used to predict the number of 9th graders in
2014-15: (# 8th graders in 2013-14 x 1.067)
Cohort Survival Ratio
System-Wide Forecast
Each CSR contains
historical in-migration as
a portion of each ratio.
1.067 =
# of 8th graders last year + # of 9th graders who are new to
the system ― # of 8th graders who moved out of the system
# of 8th graders last year
New
Births
Historical Data
Cohort Survival Ratio
System-Wide Forecast
Student
Membership
(11-15-13)
Forecast based on unadjusted Cohort Survival Ratios:
Without any adjustments, the CSR forecast is fairly flat:
0.30% annual growth.
The COHORT model suggests 27060 students in 2014-15 and
27297 students in 2018-19.
COHORT MODEL – forecast using
cohort survival ratios based on
historical data
Area Planning Unit (APU) MODEL –
forecast using smaller areas of the
County that are impacted by landuse data. Grade-by-grade cohorts
are moved forward year-by-year
using cohort survival ratios.
Area Planning Unit (APU)
Forecast
• Geocoded student data is translated spatially to APU
Cohorts: students grouped by grade and by APU
• APU Cohorts are moved by grade from year to year using
historically-based optimal cohort survival ratios
• Students from new development are added to APU
Cohorts by grade annually using the SGF for that APU
and the number of new dwelling units projected for that
APU each year.
Geocoded student data was obtained in the spring of 2014 meaning the number of K-12
students at that point in the 2013-14 school year was different from the ADM data collected
for month-1. In addition, the district grants a significant number of transfers meaning that all
students don’t attend the school to which they would be assigned by attendance zone.
APU Forecast
Example
APU 124 includes most of Leeland Station, an active
subdivision with several phases remaining. There were 55
construction starts in 2011-12, 36 in 2012-13 and 32 from
9/13 through 5/14 (from SCPS Construction Start worksheet)
That leaves about 240 lots on which dwellings may be built.
OREd, in conjunction with SCPS and SC Planning and GIS,
agreed on the “pace of development” as shown below.
Year
# Dwellings
2014-15
2015-16
2016-17
2017-18
2018-19
50
60
60
60
60
APU Forecast
Year
# Dwellings
2014-15
2015-16
2016-17
2017-18
2018-19
50
60
60
60
60
These new dwellings are translated into new students using
the appropriate SGF. The growth in each cohort is largely a
factor of these new lots producing “new” students.
K
OREd
2013-14 24
2014-15 29
2015-16 34
G1
30
29
35
G2
27
34
35
G3
23
32
40
G4
28
27
37
G5
33
32
33
G6
22
38
38
G7
41
26
44
G8
50
46
32
G9
31
57
52
G10
35
34
64
G11
35
38
40
G12
27
38
44
APU Forecast Results
29500
29000
28500
28000
27500
27000
26500
26000
25500
0
2
2008-09
4
6
2013-14
8
10
12
2018-19
Forecast Comparison
29500
APU Model
29000
Impact of adding
students from new
development into the
system
28500
28000
27500
27000
Cohort Survival Model
Unadjusted
26500
26000
25500
0
2
2008-09
4
6
2013-14
8
10
12
2018-19
Cone of Uncertainty
29500
29000
APU Model
28500
28000
27500
27000
Cohort Survival Model
Unadjusted
26500
26000
25500
0
2
2008-09
4
6
2013-14
8
10
12
2018-19
Data  Student Numbers
What are the advantages/disadvantages of
these different forecast models?
• CSR Forecast
• APU Forecast
Forecast Comparison
Cohort Survival Ratio Forecast
• During stable times, the Cohort Survival Ratios
provide a dependable system-wide forecast.
• Historical net-migration provides a reasonable
expectation for a forecast.
• System-wide forecasts are affected less by anomalies
found in APUs.
• Student numbers by grade and by year don’t provide
information on which to make good decisions
regarding shifting attendance lines.
• A CSR may not include the total impact of new
development
Forecast Comparison
APU Forecast
• Smaller areas (individual APUs) are volatile: yearby-year cohorts may increase and decrease
substantially without explainable cause.
• By combining student numbers with planning data
on smaller segments of the district, the forecast
can identify areas of significant growth/decline.
• APU forecast enable planners to shift attendance
lines based on reliable information and then see
what the forecast predicts because of those shifts.
Forecast Limitations
The predicted growth in the very large subdivisions now
underway begins to dwarf all other planned/forecasted
growth in the system in the 2019-22 time period.
This makes it difficult to add enough students in fastgrowing APUs simply because there aren’t enough
additional students forecasted for the entire system.
There will be new subdivisions begun in this same
window (2015 through 2022) that will alter growth
patterns and projections.
Forecast Results
During unstable times (times of significant growth or
decline – when trends are broken), the APU forecast
should guide adjustments to the Cohort Forecast.
(using planning data at the subdivision-level)
27500
27000
26500
26000
25500
25000
24500
24000
23500
23000
2003-04
2004-05
2005-06
2006-07
2007-08
2008-09
2009-10
2010-11
2011-12
2012-13
2013-14
Forecast Results
The recent economic rebound in Stafford County
bucks the trend of the past 4 years. However, there
are indications that this rebound may be short-lived;
or, at the least, be in the midst of a hiccup!
27500
27000
26500
26000
25500
25000
24500
24000
23500
23000
2003-04
2004-05
2005-06
2006-07
2007-08
2008-09
2009-10
2010-11
2011-12
2012-13
2013-14
Informed CSR Forecast
29500
29000
APU Model
28500
28000
27500
27000
Cohort Survival Model
Unadjusted
26500
26000
25500
0
2
2008-09
4
6
2013-14
8
10
12
2018-19
Informed CSR Forecast
14000
Elementary
12000
11206
12269
11332
11357
11429
10000
8671
8790
8859
9130
11600
9055
9074
12379
12441
12487
12514
12618
10207
10383
7398
7441
10879
11863
9099
9307
12776
9609
9725
9722
9972
High
8000
6000
6254
8841
11501
11844
12367
6309
6323
6308
6294
6354
6458
6573
6554
6675
6787
7120
7313
7424
Middle
4000
2000
K to 5
6 to 8
9 to12
0
2008-09 2009-10 2010-11 2011-12 2012-13 2013-14 2014-15 2015-16 2016-17 2017-18 2018-19 2019-20 2020-21 2021-22 2022-23 2023-24
Projected Growth Rates
The informed COHORT model projects
a system wide growth of 1.4% over
the next 10 years.
From 2013-14 to 2018-19, the growth
by level is
779
Elementary
433
Middle
670
High
1882
K-12
Out – of – Capacity Tables
OREd SGF
SC SGF
Color-coded forecast at the school-level
Design Capacities
Out – Of – Capacity Tables
OREd was asked to create a second model
based on what the County uses for a Student
Generation Factor when considering the impact
of new development. When the County’s SGF
(generally a higher number than the OREd SGF)
is used for new development, more students
are added to the system because of new
development.
Design Capacities
Out – Of – Capacity Tables
Projected Growth Rates
Using the OREd SGF in
the APU model, the
COHORT model projects a
system wide growth of
1.4% over the next 10
years. From 2013-14 to
2018-19, the growth by
level is
779
Elementary
433
Middle
670
High
1882
K-12
Using the SC SGF in
the APU model, the
COHORT model projects
a system wide growth of
3.22% over the next 10
years. From 2013-14 to
2018-19, the growth by
level is
1195
Elementary
842
Middle
1129
High
3166
K-12
Out – Of – Capacity
Tables
Provide an indication of where “pressure points” are regarding capacity.
Re-alignments to existing attendance zones, adjusted for significant
growth by locality, will alter this projection.
Changes to out-of-district ratios will alter this projection.