Stafford County Public Schools 2014-15 Update Student Membership Forecast OPERATIONS RESEARCH AND...

Stafford County Public Schools 2014-15 Update

Student Membership Forecast

O P E R AT I O N S R E S EA R C H A N D E D U C AT I O N L A B O R ATO RYI 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

A U G U S T 1 4 , 2 0 1 4

Integrated Planning for School And Community

2014-15 Update and Forecast

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

• 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

OPERATIONS RESEARCH AND EDUCATION LABORATORY

I N ST I T U T E FO R T R A N S P O RTAT I O N R ES EA R C H A N D E D U C AT I O N

Today’s Presentation

• Perspective• Land Use Update• Forecast Models

– Cohort Ratio Model– A P U Models

• Out of Capacity Tables

Perspectives

Population• 2000 92,446• 2010 128,961• 2013 136,788

2000 to 2005Very high growth rate

Source: U S Census

Predicting Growth In Stafford County

• Predictions in 2009 by Virginia Employment Commission:135806 2010176710 2020218722 2030

• US Census Data128961 2010136788 2013


2000 2005 2010 2015 202080000

100000

120000

140000

160000

180000

U S Census Data

VEC Projected Pop2020 176710


2000 2005 2010 2015 202080000

100000

120000

140000

160000

180000VEC Projected Pop2020 176710

162,000 ?


2000 2005 2010 2015 202080000

100000

120000

140000

160000

180000VEC Projected Pop2020 176710

155,000 ?

Housing Units

~1000 housing units

added annually

Source: U S Census

Population – Housing Units – Membership

YearCounty

Population# Housing

UnitsMembership

SCPSRatio:

M/# HURatio: 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.

YearCounty

Population# Housing

Units M SCPSRatio:

M/# HURatio: 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

YearCounty

Population# Housing

Units M SCPSRatio:

M/# HURatio: 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.



YearCounty

Population# Housing

Units M SCPSRatio:

M/# HURatio: 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 11012001 14682002 16922003 13952004 1982

2005 16312006 8602007 7582008 4162009 5242010 5462011 4662012 6402013 10042014

2000 to 2010 Census Data

Population by Age2000 2010 %▲

• 0 to 4 years old 7172 8719 22%• 5 to 17 years old 21997 28478 29%• 18 to 64 years old 57803 83300 42%• 65 years or older 5474 9464 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 StudyData from• Stafford County Public

Schools• Stafford County

Planning• Stafford County GIS• Interviews with SCP

and SCPS

Data• Student File: May 2014

data geocoded to identify where each student resides

• GIS Files from SC GIS: parcel data, structure data, subdivision data

• 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.

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.

Section 6

Section 6

APU 124

Section 8

Sections 5&7

Land Use Data

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

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.

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

Results of the Land Use StudyResidential Growth

• Largely SFD• New Dwelling Units #

735 impacting 2014-15869 impacting 2015-161066 impacting 2016-17852 impacting 2017-18828 impacting 2018-19

Student Growth

• Number of students generated by residential growth* 2014-15 426 2015-16 512 2016-17 613 2017-18 506 2018-19 464

# 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 StudyResidential Growth

• Largely SFD• New Dwelling Units

735 impacting 2014-15869 impacting 2015-161066 impacting 2016-17852 impacting 2017-18828 impacting 2018-19

Student Growth

• Number of students generated by residential growth

2014-15 426 2015-16 512 2016-17 613 2017-18 506 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 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)


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


Historical Data

More seniors are graduating

than kindergarteners

entering.

New Births

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 land-use 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 ForecastExampleAPU 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 2014-15 2015-16 2016-17 2017-18 2018-19# Dwellings 50 60 60 60 60

APU Forecast

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.

OREd K G1 G2 G3 G4 G5 G6 G7 G8 G9 G10 G11 G122013-14 24 30 27 23 28 33 22 41 50 31 35 35 272014-15 29 29 34 32 27 32 38 26 46 57 34 38 382015-16 34 35 35 40 37 33 38 44 32 52 64 40 44

Year 2014-15 2015-16 2016-17 2017-18 2018-19

# Dwellings 50 60 60 60 60

APU Forecast Results

2008-09 2013-14 2018-190 2 4 6 8 1 0 1 2

24500

25000

25500

26000

26500

27000

27500

28000

28500

29000

29500

Forecast Comparison

2008-09 2013-14 2018-19

0 2 4 6 8 1 0 1 2

24500

25000

25500

26000

26500

27000

27500

28000

28500

29000

29500

Impact of adding students from new

development into the system

Cohort Survival ModelUnadjusted

APU Model

Cone of Uncertainty

2008-09 2013-14 2018-190 2 4 6 8 1 0 1 2

24500

25000

25500

26000

26500

27000

27500

28000

28500

29000

29500


APU Model

Data Student Numbers

What are the advantages/disadvantages of these different forecast models?

• CSR Forecast• APU Forecast

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: year-

by-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 Comparison

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 fast-growing 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 Limitations

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)

Forecast Results

2003-04 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11 2011-12 2012-13 2013-1423000

23500

24000

24500

25000

25500

26000

26500

27000

27500 Trend? Trend?

Forecast Results

2003-04 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11 2011-12 2012-13 2013-1423000

23500

24000

24500

25000

25500

26000

26500

27000

27500

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!

Informed CSR Forecast

2008-09 2013-14 2018-19


APU Model

0 2 4 6 8 1 0 1 2

24500

25000

25500

26000

26500

27000

27500

28000

28500

29000

29500

Informed CSR Forecast

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 2023-240

2000

4000

6000

8000

10000

12000

14000

11206 11332 11357 11429 11501 1160011844 11863

12269 12367 12379 12441 12487 12514 12618 12776

6254 6309 6323 6308 6294 6354 6458 6573 6554 6675 67877120 7313 7398 7441 7424

8671 8790 8859 88419130 9055 9074 9099 9307

9609 9725 97229972

10207 1038310879

K to 5 6 to 8 9 to12

Elementary

Middle

High

31,7092023-24

Projected Growth RatesThe 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 SGFSC SGF

Color-coded forecast at the school-level

Out – Of – Capacity Tables OREdSGF

Design Capacities

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.

Out – Of – Capacity Tables S CSGF

Design Capacities

Projected Growth RatesUsing 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 – CapacityTables

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.

?QUESTIONS

Mike MillerRob Kimball

Stafford County Public Schools 2014-15 Update Student Membership Forecast OPERATIONS RESEARCH AND...

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