20020672_Memorandum of Understanding_20120911

fib" 55 ► Gil
Draft Memorandum .
To File Date September 11 2012
From Michael Baker Jr Inc Subject Union County Growth Factors
Introduction
This memorandum describes the historic growth trends in the Charlotte region reviews research related to
growth dynamics reviews forecasts of growth in the Charlotte region and quantifies some of the major
growth indicators in the Charlotte metropolitan region (study area) A major focus of this memo is on the
historic growth of Union County and the potential for growth in Union County over the next 20 years A
major question raised during litigation over the Monroe Connector EIS was whether substantial growth
would occur in Union County without the construction of the Connector The question of how much
influence transportation projects have on growth trends at a county level is difficult to isolate in terms of
the cause and effect relationship between growth and infrastructure development This memo will focus
on growth factors identified by others that correlate with growth independent of transportation
infrastructure improvements Specifically the memo examines various attributes that are correlated with
growth such as population density median household income housing affordability and school quality
Other factors such as commuting times are also examined
The first step to analyzing regional growth dynamics is to establish a set of counties to which one can
compare growth patterns Many definitions of the metropolitan region exist but the most common and
applicable to this situation are the following
• The Census Bureau defines the Charlotte Gastonia Rock Hill NC SC Metropolitan Statistical
Area (MSA) to include Mecklenburg Union Gaston Cabarrus and Anson Counties in North
Carolina and York County in South Carolina
• The Census Bureau defines the Charlotte Gastonia Salisbury NC SC Combined Metropolitan
Statistical Area(CMSA)to include all of the above counties plus Iredell Lincoln Rowan Stanly
and Cleveland Counties in North Carolina and Chester and Lancaster Counties in South Carolina
• The Charlotte Regional Partnership a regional economic development advocacy organization
defines the metropolitan area as including all of the above CMSA counties plus Catawba and
Alexander Counties in North Carolina and Chesterfield County in South Carolina
• The Mecklenburg Union Metropolitan Planning Organization (MPO) develops a regional travel
demand model for the metropolitan area that includes socioeconomic forecasts of population and
employment at the Traffic Analysis Zone (TAZ) level The socioeconomic forecasts for the
metropolitan area cover all of Mecklenburg Union Gaston Cabarrus Lincoln Rowan and
Stanly Counties plus portions of Iredell and Cleveland Counties in North Carolina and all of York
County and portions of Lancaster County in South Carolina
1 DRAFT 8/30/2012

1
Table 1 summarizes the population and growth in the CMSA counties in the region from 1990 to 2010 It
also shows the MUMPO forecast coverage for each The MSA definition limits the comparable counties
to too few and excludes counties such as Iredell and Lincoln which have captured more than marginal
shares of regional growth in the last two decades The CMSA definition includes a number of counties
i that have captured relatively small percentages of regional growth and currently have limited relationship
l to the overall regional growth dynamics Some of these counties however are expected to see substantial
increases in population in the future and therefore they will be included in the analysis Of important note
in Table 1 is the percent of CMSA population growth from 1990 to 2010 These percentages show how
much of the overall growth of the region each county has captured
Table 1 Population and MUMPO Forecast Status for CMSA Counties
County State MUMPO Population
Forecasts 1990 2000 2010 1990 to °/G or wth A of CMSA
Coverage 4 ,A,` '>�� > 1 ?2010 ,� 1990 20104 pop�ylatio�Growth
4•,4, Growth 1t 1990 2010 .,
MSA Counties
Mecklenburg NCM Whole p511433 ,994543 919 628; t,<,,, 408 195 v4., ...79 55 �a°'�y 'Tr45 3/
Union NC Whole 84 211 123 677 201 292 117 081 139 0/ 13 0/
Gaston r .NC 1"Whole< f '174 77669f,14190 365' ,,206 086 tag31 317. 17 7/ '"` ts "3 5/'
Cabarrus NC Whole 98 935 131 063 178 011 79 076 79 9/ 8 8/
York, SC „Whole� 131 497 ,t164 614 226 073 94 576 71 9/ , 10 5/
A.
Anson NC None 23 474 25 275 26 948 3 474 12 9/ 0 4/
x" y * t,� s ", ' Z $• le , g CMSA Count es y -- ,7 f
Iredell NC Partial 93 205 122 660 159 437 66 232 71 6/ 7 4/
Lincoln "ANC w,,Whole ' 50 319 63 780w 78 265 its 27 946 55 5/ t 4* 3 1/
Rowan NC Whole 110 605 130 340 138 423 27 818 25 2/ 3 1/
Stanly 4' ANC, Whcole x511765 i 58 100 60 585 48 8201 ""17 0/'; ' 1 0/
Chester SC None 32 170 34 068 33 140 970 3 0/ 0 1/
Lancaster SC Partial .1 /54 516 61 351 t 76 652 s 22136 < 740 6/ x 2 5/
Cleveland NC Partial 84 958 96 287 98 078 13 120 15 8/ 1 5/
..,, "k,„?,,,,, ' , ' Total 41 501 857 1 897 034 2 402 618 P& 900 761 , 600% ,4,® AK\<, /
Source US Census 1990 2000 and 2010 MUMPO Socioeconomic Forecasts
2 DRAFT 8/30/2012

................. . ,
Rowan
• County
•a
•
Gaston County
Stanly County
cur
itrk C.0 1i Unran C1r#rtfy
LEGEND k z Anson County
Percent Populaton Change T?
(2000 2010) `
<=10 0/
100 199/ 1 1 --
20 0 29 9/ Chester County
300 399
>-40 09' t 25 50
Miles
5 US C 200042410 -
As seen in Table 1 and Figure 1 Union County has experienced the highest population growth rate in the
study area Specifically the county witnessed a 46 9 percent population increase (39 466) from 1990 to
2000 and a 62 8 percent increase (77 615) from 2000 to 2010 Meanwhile the CMSA experienced 26 3
percent growth and 26 7 percent growth respectively over the same time In 2010 Union County
accounted for 8 4 percent of the study area s total population up 2 8% percent since 1990 The rate of
population growth in Union County has been quite high for many years From 1990 to 2000 the average
annualized growth rate was 3 9 percent That average annualized growth rate rose significantly to 5 7
percent from 2000 to 2005 and then fell back to 4 3 percent from 2005 to 2010 In each time period
however Union County has been the fastest growing county in the region
This high growth rate does not mean however that Union County has captured most of the regional
growth As Table 1 shows Mecklenburg County has captured 45 3 percent of the regional population
growth over the last 20 years Its growth rate has been lower however as it was growing from a much
larger population base Union County captured the second largest share of regional population growth
with 13 percent while York captured 10 5 percent and Cabarrus 7 4 percent No other county captured
more than 5 percent of the regional population growth over the last 20 years Some counties such as
Lancaster County experienced significant growth in percentage terms but only captured small
percentages of the region s overall growth
3 DRAFT 8/30/2012

Historic data therefore suggests that Mecklenburg Union York Cabarrus and Iredell Counties would
capture most of the regional growth over the next 20 to 30 years Nevertheless dynamics that have
encouraged this pattern of growth may or may not continue to exist Therefore understanding some of the
dynamics underlying why those counties have captured a substantial share of regional growth and
whether they may continue to capture a substantial share of regional growth is critical to understanding
which counties are poised to grow in the future
Hammer Study and Regional Forecasts
In the 2003 Demographic and Economic Forecasts for the Charlotte Region prepared by Dr Thomas
Hammer historic growth in 227 counties within 29 separate metropolitan areas was modeled to identify
the predictive factors that drive regional growth and the distribution of that growth across the regional
jurisdictions Trends are a significant driver of county level shares of growth but the model developed
by Dr Hammer isolates the factors that differentiate growth dynamics at the county level which requires
greater complexity than examining trends in isolation In other words Dr Hammer s model attempted to
isolate the factors that most strongly affected whether a county saw higher or lower growth than a trend
line projection would forecast Importantly the Hammer report notes the following
People trying to imagine what the world will be like decades in the future — can easily be
drawn into focusing upon what should occur rather than what is most likely to occur Urban
planners and others with a professional or personal stake in shaping the future are particularly
susceptible (The strong preference of many planners for bottom up forecasting comes from the
flattering notion that they through the design of land use controls and mass transit facilities will
be telling future development where to go) Forecasts can verge into being prescriptive rather
than predictive and while prescriptive forecasts have their value the present investigator is not
in that business So the approach described here mandates the use of allocation relationships
established through formal analysis of empirical data Statistical calibration confers advantages
of realism as well as objectivity because the interactions of urban activities over space are so
complex and multifaceted that it is very hard to specify the existence much less the magnitude of
relationships without recourse to historical evidence (p 4)
Dr Hammer s initial step was to develop a total regional population and employment forecast for the
region This step used an input output economic model to estimate the overall employment and
population based on national economic trends local industrial sector analysis and local and national
demographic trends These regional level forecasts were driven by large scale economic trends and
demand side influences as opposed to supply side influences such as the availability of land
transportation infrastructure or utilities Table 2 outlines the regional forecast of population resulting from
Dr Hammer s analysis of economic and demographic trends While the growth forecast seems very high
at first glance compared to other large growing regions in the south the growth forecast is quite
reasonable Dr Hammer notes
Given the present forecast for the Charlotte region and its performance since 1990 the region s
highest 30 year percent change in population will be an 83%gain for the period from 1990 to
2020 The 30 year percent changes for the region will then trend downward to 73%for the 2005
2035 interval Thus Charlotte will not come within thirty percentage points of the increases
posted by the three monsters of the south [Dallas Houston and Atlanta] In fact the Charlotte
region s peak gain of 83% during 1990 2020 will only be midway between the national growth
4 DRAFT 8/30/2012

rate of 33%for that period and Atlanta s 30 year record of 134%for 1970 2000 So the future
expansion of the Charlotte region will be robust but by no means unprecedented (p 27)
Table 2 Forecasts of Regional Population Hammer Report
Year Population 5 Year Annualized
Change Growth Rate
2000 149'8 C903
2005 2 179 103 192 200 1 86%
2010 2 385 288 1206 185 �1 82%
2015 2 624 430 239 142 1 93%
2020 2 889 969 265 539 1 95%
2025 3 175 350 285 381 1 90%
2030 ,A 3 474 012` N 298 662 1 81%
2035 3 779 397 305 385 1 70%
Next the overall regional forecast was apportioned among the various jurisdictions using an allocation
model that distributed the forecasted regional growth to individual counties The model used past trends
from 227 counties across 29 metropolitan regions across the eastern United States to guide the allocation
process The variables used to allocate growth were understandably limited by data availability across
such a large number of jurisdictions As such the allocation model focused mostly on demand side
variables such as past economic and demographic trends existing economic and demographic conditions
the influence of income on growth patterns and the physical proximity of places Two major supply side
factors were considered
1 The availability of land estimated on the basis of development magnitudes and based in part on
population density
2 The effect that land use regulations and infrastructure policies have had on past growth would
influence the model to the extent that those policies affected historic growth trends
While physical proximity in straight line distance is one factor that Dr Hammer identified in the
analysis it was indexed by the more significant factor of available land in order to provide a predictive
function for growth allocation The other significant factor in his allocation model is household income
These two factors are examined in light of existing data in the next section
Dr Hammer s final estimates for each county are summarized below in Table 3 The values include a
low middle and high estimate for each jurisdiction They do not constitute the final estimate of
population for each county in the region as the forecasts were adjusted during a regional reconciliation
process Nevertheless the final adopted forecasts were within the ranges provided by Dr Hammer
5 DRAFT 8/30/2012

Table 3 Population Forecast Ranges Hammer Report
2030 Population
Counter Lower Most Upper
Likely Limit
Anson County 36 967 40 847 43 175
Cabarrus County. 247 142 283 115 304`699
Cleveland County 125 373 134 563 140 077
'Gaston County g 235 2281"4" ?49 261 295 071
Iredell County 227 287 259 906 279 477
'Lincoln County 1413 206 128857 138 247
Mecklenburg 1 051 400 1 157 311 1 220 858
County
Rowan County 183 747 200 639 X210 774
Stanly County 80 171 87 366 91 682
Union County >268 543 312 147 338 309-4
Cherokee County 83 228 93 168 99 132
Chester County,. x52278 58 306 61 923
Lancaster County 91 781 101 680 107 619
Union County SC 38 480 41 466-'1' 43 258
York County 272 096 305 228 334 080
The county level forecasts from the adopted MUMPO 2004 estimates place Union County s 2030
population at 337 317 As previously noted this county level control total forecast was developed using
an economically driven modeling approach that excluded major transportation infrastructure
improvements from its consideration Growth in Union County has followed the forecasted growth rather
closely As detailed below in Table 4 the population of Union County from the 2010 Census is very close
to the population forecast in the MPO projections The MPO forecast a population of 200 290 while the
2010 Census count was 201 292 Furthermore the growth rates projected by the MPO forecasts are
modest compared to historic growth in the county To reach the 337 317 population estimate by 2030
growth in Union County would have to slow to an average annualized growth rate of 2 6% based on the
2010 Census count Figure 21 shows the differences in average annual growth rates across the four
different time periods (1990 to 2000 2000 to 2005 2005 to 2010 and 2010 to projected 2030) The
difference between 2000 2005 2005 2010 and 2010 2030 average annual growth rates reflects a typical
s curve of decreasing growth rates over time as a population base expands
Figure 2 compares growth rates to a 7 county region as the final adopted forecasts for whole counties are only
available for Cabarrus Gaston Lincoln Mecklenburg Rowan Union and York Counties
6 DRAFT 8/30/2012

Figure 2 Average Annualized Growth Rates Comparison
65'
• 5°l
Jai
7 County Region
3/
Union County
Q 'a . r .
Mecklenburg County
C.? 2/ .,
:,: ra Ivy gm
1/
0/a
1990 2000 2000 2005 2005 2010 2010 2030
Note The adopted MUMPO forecasts for whole counties are only available for Cabarrus Gaston Lincoln Mecklenburg
Rowan Union and York Counties
Sources US Census 2000 and 2010 MUMPO 2030 Socioeconomic Forecasts
Forecasts from other sources show a wide range of future growth trends for Union County Two of the
most commonly cited privately developed forecasts are from Woods & Poole and Global Insights Both
firms use cohort component projections a demographic projection method that focuses on fertility
mortality and net migration to estimate total population by year The Global Insight model incorporates
the predictions of a regional macroeconomic model thereby incorporating some economically driven
assumptions of jobs growth into the forecasting process The North Carolina State Data Center also
generates population projections using a time series trends forecasting process that uses exponential
smoothing and autoregressive integrated moving average (ARIMA) models to fit future growth to past
trends Table 4 summarizes five different forecasts of population to 2030 from four different sources
1 MPO Forecasts(developed in 2004)
2 Global Insights Forecasts(developed in 2009)
3 Woods&Poole Forecasts(developed in 2009)
4 NC State Data Center Forecasts(developed in 2009)
5 NC State Data Center Forecasts (developed in 2011)
As all of the forecasts operate from either demographic trend projection or economic modeling
projections they do not incorporate expectations of transportation infrastructure development except to
the extent that past infrastructure development has affected past trends Once key to understanding the
differences in these forecasts is to compare the actual change in each five year increment The
demographically driven forecast approaches used by Woods & Poole and the NC State Data Center
forecasts very similar changes in each five year increment of their forecasts whereas the Global Insights
7 DRAFT 8/30/2012

and MPO forecasts which are more economically driven models show significant differences in each
five year increment of changes
As to the actual forecast of future population in Union County the highest forecast is from the NC Data
Center in 2009 which forecasted a 2030 population of 400 683 The NC Data Center s forecast from
2011 however predicts a 2030 population of 271 289 the lowest of all the forecasts The Global
Insights forecast from 2009 predicts a 2030 population of 393 407 while Woods & Poole from 2009
predicts a 2030 population of 283 433 The MPO forecasts the oldest of all those developed fall
generally in the middle of all these forecasts predicting a 2030 population of 337 317 for Union County
Most interesting is how closely the MPO forecasts predicted the 2010 populations of Mecklenburg and
Union Counties In the case of Mecklenburg the MPO forecast for 2010 population is only 1 3 percent
higher than the actual 2010 Census count In the case of Union the forecasted population in 2010 is only
0 5 percent lower than the actual 2010 Census count
Table 4 Comparison of Population Projections
Global Insights(2009)
Annualized Annualized Annualized
'Mecklenburg* Change /Change t;Change Region* Change
g / Change /Change
2005 806 834 161 765 1 314 553
2010 956'823 X149 989 3 47/ 219 690 57 925 6 31/ 1 570 976 256 24 3 �3 63%1
2015 1 065 308 108 485 2 17/ 263 298 43 608 3 69/ 1 749 656 178 680 2 18/
2020 °1 171 442 106 134 1 92/ 303 978 40 680 2 92/ 1 920 865 171 209 -I 188/
2025 1 275 768 104 326 1 72/ 349 186 45 208 2 81/ 2 097 412 176 547 1 77/
2030 1'1'382 406 10663'6' 1 62/ 1%3'407 44 221 2 41/ 2 280 808 p183 396 1 69/
Woods&Poole(2009)
Annualized Annualized 4 'p Annualized
Mecklenburg Change Union Change Region* Change
*4 /Change /Change 4. /Change
2005 802 400 160 876 1 307 329
2010 -; , 916 747 114 347 2 70/ 197 554 36 678 4 19/ 1 497 063 189 734
704,x 2 75/
2015 1 000 055 83 308 1 75/ 218 988 21 434 2 08/ 1 630 535 133 472 1 72/
2020 1 084 264 84 209 1 63/ 240 490 6 21 502 ' &1 89/ 1 765 570 135 035 % 1 60/
2025 1 168 900 84 636 1 51/ 261 995 21 505 1 73/ 1 901 371 135 801 1 49/
2030 1 253 544 84 644 I 1 4411/ /283133 21438 4 1"/59/ 2 037 236 / 135 865 1 39/
MPO Forecasts(2004)
Annualized Annualized Annualized
Mecklenburg w Change / Change Union Change /Change Region* Change /Change
2005 837 844 168 728 1 369 427
.
2010 vey9311591 93 747 4 2 14/ 200 290 31 562 3 49/ 1 539 304''a 169 877 ° 2 37/
2015 1 024 722 93 131 1 92/ 231 986 31 696 2 98/ 1 718 936 179 632 2 23/
2020/, Z 1 110 893 xe 86 171 - 1 63/ 266 617 34 631 c 82/ 1891 585 172 649 1 93/
2025 1 196 462 85 569 1 50/ 301 053 34 436 2 46/ 2 063 312 171 727 1 75/
2030 1)76-77 24 74 262 1 21/ X337 317 36''264 2 30/ 2 220 724 157 412 1 48/ .
8 DRAFT 8/30/2012

NC State Data Center(2009)
Annualized Annualized . Annualized
Mecklenburg Change Union Change Region* Change
g, ,>/Change /Change ,.. ,,,,,,, /Change
2005 796 529 159 726 1 298 879
onr4,-4 010 v 4
2 ,4911 252 114 723 12 73/ 210 069 50 343 5 63/x; 1 518 920N 220 041 c 3 18/
2015 996 414 85 162 1 80/ 257 378 47 309 4 15/ 1 706 871 187 951 2 36/
2020 1 081 577 >85 163 1 65 X304 688 47 310 3 43/ 1 894 854 ' 187 9831* 2 11/
2025 1 166 740 85 163 1 53/ 351 996 47 308 2 93/ 2 082 842 187 988 1 91/
203b #"4 1539 8 `�4*86 458 1 449' 400 68341 , X48 687 "2'62/ 2 274 700 191 858 , 1 78/
1 NC State Data Center(2012)
Annualized / Annualized Annualized
Mecklenburg Change Union Change Region* Change
4 O /Change /Change a / Change
2005 802 998 160 260 1 305 092
X2010 923 144 120 146 2 83/ X2022 200 41940 p 4 76/ 1 510 094 205 002 2 96/
2015 1 009 658 86 514 1 81/ 219 522 17 322 1 66/ 1 634 793 124 699 1 60/
2020 1 095 857 86 199 1 65/ 236c778 3'17 256 1 52/ 1 758 306' *123 513 147/
2025 1 182 056 86 199 1 53/ 254 034 17 256 1 42/ 1 881 818 123 512 1 37/
2030 1 268 257 ,� 86 201 1 42 / 271 289 17 255 1 32/ 2 005 336 123 518 rW128/
Pat x 3a
*The Regional forecasts he are for a four county region of Cabarrus Gaston Mecklenburg and Union Counties This is due to data limitations from the
various sources g j � `�
Growth Factors
Dr Hammer s empirical study indicates that land availability is the major factor driving higher than trend
line growth The data used to capture land availability in his analysis was population and employment
density Therefore a comparison of population density provides a rough estimate of the land availability
in each jurisdiction as those counties with higher population densities would naturally have lower land
availability due simply to the fact that more land was already developed
In 2000 Union County had a population density of 196 0 persons per square mile ranking it tenth out of
13 counties in the CMSA In 2010 Union County s population density was 319 persons per square mile
fifth highest of the 13 counties and only four percent lower than the fourth highest county York (see
Figure 3) For comparison the most densely populated county in the region Mecklenburg County had
population densities of 1 327 6 and 1 755 6 per square mile in 2000 and 2010 respectively The vast
difference in population densities between Mecklenburg County and its surrounding counties indicates
that there is substantial land available for development in the less developed surrounding counties
Furthermore the lower population density of Union County relative to Cabarrus and Gaston Counties
indicates more land is likely available in Union County versus those two counties Based on Dr
Hammer s criteria one would expect growth to be higher in Union County than in Cabarrus or Gaston
over the next 20 years Figure 3 compares the population density for the 12 suburban counties in the
CMSA Mecklenburg is excluded from this figure to make comparison between the suburban counties
clearer
9 DRAFT 8/30/2012

Figure 3 Population Density in the CMSA
700 2000 and 2010 (Excluding Mecklenburg)
wm600
L
• 6 500
• 400
to a
o.c300
�,• 200 ml
rl
100
c�a �ti� ��a o cc� �w� cy �� c,, 4. 4.
0 co co coJ co° c o° cp° coJ� cow 'cr w� 4c,
��,00 P�yoo C.���i{J4�vJQ�•b �y�oc ��ae `o\� o���ey�cco `'�i`o -���`o
L
Population per Square Mate 2 2000 2010
Note Mecklenburg County Densities 1 327 6(2000)and 1 755 6(2010)per square mile
Source US Census 2000 and 2010
According to the empirical study by Dr Hammer income differences also play a key role in attracting
growth to certain counties In particular areas with higher median household income typically see higher
than trend line growth Union County currently has the highest median household income in the region
(Figure 4) In 2000 the county s median household income ($50 354) was comparable to that of
Mecklenburg County ($50 311) Since then however Union County has seen a 25 9 percent increase in
median household income while Mecklenburg County has seen a much more modest (7 5 percent)
increase Again based on Dr Hammer s criteria one would expect Union County to grow faster than
trend line growth would suggest
10 DRAFT 8/30/2012

Figure 4 Median Household Income in the CMSA
2000 and 2010
70 000
60 000
E 50 000
tfItt
40 000 ,, „,, 77,7( '"%41 „„m
30 000
20 000 ' :$,
10 000 v : �a
0 I
�� Ct •1/4.4. i� hL 4- 4
co co co co co co co
.°� c,°r cJ�' ca °� a��0 4°mac ��toO ,�r�� a� L°
J� Pc at �ee\a a4 xc �o• � ha °� ���� ay�t y°pc'
mac Lr t•°
Median Household Income 8 2000 2010
Source American Community Survey 2008 2010 3 Year Estimates Table$2503(Financial Characteristics)
Other factors that often drive growth are housing affordability school quality and commuting times Dr
Hammer s report did not address these factors in his analysis but they are commonly cited reasons for
household location decisions2 According to the American Community Survey Union County has the
highest median housing costs ($1 146 per month) Furthermore as shown in Table 5 it also has the
highest median home values in the CMSA When assessing the relative ratios of housing costs to income
in each county however Union County is actually more affordable than Mecklenburg County and is on
target with the regional median For example a median household in Union County spends 21 7 percent
of its income on housing costs Meanwhile a median household in Mecklenburg County spends 23 8
percent of its income on housing costs Union County however becomes substantially less affordable'"
when one substitutes the county s median household income with the region s median household income
When doing so Union County s housing stock remains the least affordable in the region typically
requiring 28 7 percent of household income
2 National Association of Realtors Profile of Home Buyers and Sellers 2011
11 DRAFT 8/30/2012

Table 5 Selected Housing Characteristics for the CMSA
TT y a a C C C N 'O C C C C
U L. U Z.
i > C > p H 0N C
.?
O• O O O C y a.
U U y C U C 0 7 U C d C V C U N
O• o U U O U 2 5 W U C U O r0 O
QU co al c �'' O U —1 U >
V Owner w 83 3 ,e65 3 74 1 >,66 2 68 1 74 1 '7'414, 61 91 692 ,„69e7,,,, §76 4 z 73 1 72 1
occupied
V Renter
occupied 16 7 34 7 25 9 33 8 31 9 25 9 25 1 38 1 30 3 30 3 23 6 26 9 27 9
Median Home ',V,4 4 , �,. '','T ^ ,,, amp
Value Cs) � 2( 8 4 1� 104,800 a 124,500 168,200 156700 190,900 a�124000 1287007 85,800 1 164700
V Single Family
Detached 84 9 68 2 76 6 67 5 75 0 73 0 67 9 60 3 74 9 67 5 68 5 75 0 68 1
Housing
Median Number s y ,
of Rooms per 64 53 57 53 t/53 4 57 56 561 '�55 54 55 x56,, 4 57
Unit 3 3 ',
Percentage of Units by Number of Bedrooms
Nobedroom 055 05 08 08 13 � 06 06 12 09 15 X01 12 5 07
4
1bedroom 26 50 45 48 57 37 25 109 52 38 45 30 57
2 bedrooms 14 4 30 4 24 4 , 31 8 30 9 24 4 27 5% 25 1 27 5 g.31 7 32 6 27 44 24 5
3 bedrooms 49 7 52 3 47 1 52 4 47 3 50 3 53 0 39 1 54 4 48 1 48 1 52 9 48 6
4 bedrooms 226 105 "=1778 87 123 166 129 191 95 119 1124127 .c,161
Sormore 100 14 55 15 25 43 35 45 27 30 35 27 43
bedrooms
The fact that Union County has higher than average housing costs is not necessarily a deterrent to growth
The higher cost for housing in Union County is also reflective of the larger size of housing in the County
Table 5 listed selected housing characteristics from the eight county region Union County has the highest
percentage of owner occupied housing the highest percentage of single family detached housing and the
highest median number of rooms per unit (a full 12 percent higher than the next highest county)
Furthermore nearly one third of housing units in Union County have four or more bedrooms much
higher than typical for the CMSA All of these housing characteristics suggest that the higher housing
costs reflect the fact that housing in Union County is larger newer and likely built to serve the higher
income households moving to the county Overall then the housing stock itself would be positive
indicator of future growth
The quality of a school district is also an important factor driving household location decisions Jack
Dougherty3 succinctly describes how public school quality helps to drive suburban growth
shopping for schools clearly became an important family strategy for upward mobility as
higher salary positions increasingly depended on educational credentials which in turn relied on
the status of one s public school system During the course of the twentieth century suburban
families became more conscious of this equation buying a home in the right neighborhood in
3 Dougherty Jack Shopping for Schools How Public Education and Private Housing Shaped Suburban Connecticut Journal
of Urban History 28 no 2(March 2012) 205 224
12 DRAFT 8/30/2012

order to send their children to a good public school would increase them odds of being
accepted to a top ranked college and help them to land the perfect job
Other researchers have shown the strong correlation between school district quality and the value of
housing which shows the high demand for housing in good school districts Theodore Crone notes
home buyers seem to evaluate the quality of public education at the district level 4 Finally other
researchers have noted that [i]n towns where it is easy to build more housing better quality schools do
not lead to higher property values Instead they lead to more real estate development 5
Since most school districts in North Carolina and South Carolina conform to county boundaries
households therefore are likely to consider location decisions by county when shopping for schools
Comparisons with York County schools are slightly more complicated as York County is divided into
four separate school districts Two major sources of data provide insight into the perceptions of quality of
schools in the area average SAT scores and the percentage of students graduating in four years SAT
comparisons among the sixteen school districts show that Union County has the second highest average
SAT composite score and is the highest among the districts that cover whole counties Similarly Union
County had the second highest rate of students taking the SATs but first among districts that cover whole
counties Four year graduation rates show the same dynamics with Union County second overall and first
among countywide school districts These measures indicate that the Union County School District would
be a highly desirable school district in which to locate for households concerned with public school
quality Therefore demand for housing in Union County will be higher particularly among families with
school age children or families that anticipate having children in the near future
4 Crone Theodore M Capitalization of the Quality of Local Public Schools What Do Home Buyers Value9 Working Paper
No 06 15 Federal Reserve Bank of Philadelphia August 2006
5 Sinai Todd Feedback between Real Estate and Urban Economics Journal of Regional Science 50 423 448 February 2010
13 DRAFT 8/30/2012

Table 6 Average SAT Scores for Major School Districts in the CMSA
'C".'Chfl��y � Rea ding, {I 'It�i'1 �°
',T1-.,q"-.- Te-A'iii ( lW VVI EQL
Score, Sere Stir
Anson County Schools 159 53 7 436 427 407 863 1019 1502
1270
Cabarrus County Schools 1169 65 3 522 497 483
Cleveland County Schools 589 58 6 500 470 451 970 1421
Gaston County Schools 1136 58 3 495 480 455 975 1430
Iredell Statesville Schools 847 60 4 524 502 480 1026 1506
Lincoln County Schools 449 58 7 513 478 456 991 1447
Charlotte Mecklenburg 5240 68 5 507 495 480 1002 1482
Schools
Rowan Salisbury Schools 676 51 9 495 474 453 969 1422
Stanly County Schools 339 57 495 465 442 960 1402
Union County Public Schools 1635 68 7 524 503 491 1027 1518
Chester SC 93 27 491 451 453 942 1395
Lancaster SC 399 54 454 440 423 894 1317
York 1 137 42 478 457 432 935 1367
York 2 Clover 243 59 493 486 460 979 1439
York 3 Rock Hill 645 54 482 470 455 952 1407
York 4 Fort Mill 477 72 535 529 505 1064 1569
Sources North Carolina State Board of Education Accountability Services Division SAT Report 2011
South Carolina Department of Education Public School District Distribution Mean SAT Scores for 2011
14 DRAFT 8/30/2012

Table 7 Four Year Graduation Rate for Major School Districts in the CMSA
6rast i
Anson County Schools 75 9
Cabarrus County Schools 84 1
Cleveland County Schools 73 2
Gaston County Schools 75 4
Iredell Statesville Schools 85 1
Lincoln County Schools 81 6
Charlotte Mecklenburg Schools 73 5
Rowan Salisbury Schools 76 9
Stanly County Schools 77 9
Union County Public Schools 89 1
Chester SC 73 1
Lancaster SC 73 7
York 1 78 3
York 2 Clover 77 3
York 3 Rock Hill 73 5
York 4 Fort Mill 91 2
Sources North Carolina State Board of Education
Accountability Services Division 4 Year Cohort Graduation
Rates
South Carolina Department of Education
Annual School District Report Cards
As the realtor survey shows access to fobs is an important factor to household location decisions The
Census Bureau tracks travel time to work and comparisons among counties in the region are revealing In
2010 the average commuting time for Union County residents (27 8 minutes) is about eleven percent
higher than the regional (MSA) average of 25 1 minutes (see Table 5) Relative to other jurisdictions
Union County had the highest commute times in the region in 2000 and is a close third to Chester and
Lancaster Counties in 2010 Compared to 2000 commute times for Union County residents and across
the region are down slightly except for Chester and Lancaster Counties in South Carolina In 2000
Union County commute times were on average 29 minutes dust more than eleven percent higher than the
regional average Thus over the last ten years Union County has grown faster than any other county
despite having some of the longest commute times in the region Average commute times for Union
County residents have not risen dramatically during the past decade despite the significant growth
While it may seem counter intuitive that households would choose to live where commute times are
longer research suggests that within a reasonable range of commute time households will choose
locations based more on other preferences such as school quality neighborhood quality or other factors
In their summary of research on the impacts of transportation on land use the National Research Council6
noted the following
6 National Research Council Expanding Metropolitan Highways Implications for Air Quality and Energy Use
Special Report 245 Washington DC The National Academies Press 1995 p 189
15 DRAFT 8/30/2012

Research on commuting patterns within the current distribution pattern of jobs and residences in
the Los Angeles metropolitan area however indicates that commuting trips are two thirds
greater than would be required if workers were located in neighborhoods that minimized their
commutes (Small and Song 1992) This indicates that a key assumption of location theory does
not hold in practice The excess commuting that occurs may be explained by preferences for
neighborhoods with low crime rates or amenities such as schools the difficulty of minimizing
commutes for both workers in dual worker households and other influences such as racial
discrimination (Giuliano and Small 1993 Mills 1994)
Table 8 Average Commute Times for the Eight County Region
Safi 29061£. .
Mean Travel Difference from Mean Travel Difference from
Time to Work Regional Average Time to Work Regional Average
Anson County 27 5
Cabarrus County 26 0 3 6/ 27 0 3 4/
Cleveland County 23 5
Gaston County 25 0 0 4/ 24 6 5 7/
Iredell County 24 2 3 6/ 24 5 6 1/
Lincoln County 27 1 3 8/
Mecklenburg County 24 7 1 6/ 26 0 0 4/
Rowan County 23 2 7 6/ 23 3 10 7/
Stanly County 25 3
Union County 27 8 10 8/ 29 0 11 1/
Chester County 28 1 11 9/ 27 8 6 5/
Lancaster County 27 9 11 1/ 27 0 3 4/
York County 24 0 4 4/ 27 2 4 2/
Charlotte MSA 25 1 26 1
Notes 2010 Travel Time data not available for Anson Cleveland and Lincoln Counties
Sources 2000 Census Summary File 3 American Community Survey 2008 2010 3 Year Estimates Table S0802
Conclusions
The prior empirical analysis and associated research suggest that income and land availability serve as the
prominent growth factors that would tend to attract a greater share of regional growth within a
metropolitan region This memo assessing regional characteristics in 2000 and 2010 confirms that based
on factors like median income housing stock school quality and population density one would expect
Union County to garner a growing share of regional growth based strictly on these dynamics Most
notably the county has the highest median household income level in the metropolitan area Union
County also continues to have low population density particularly when compared to that of the much
more built out regional core in Mecklenburg County Further while the County s median housing value
is the highest in the region this is mostly reflective of the larger size of housing in Union County In
addition Union County offers a high quality school district that would be highly desirable to families
with children Finally while commute times for Union County residents are higher than the regional
average this has been true for more than a decade and the county has still managed to maintain a high
rate of growth despite this factor
16 DRAFT 8/30/2012

An examination of the growth trends in the region including the factors used in the MPO regional
forecasting process suggest that transportation infrastructure improvements are not the main driver of the
rapid growth in Union County The fact that growth in Union County has tracked closely to the projected
growth in the MPO regional forecasts in spite of a national recession slow down in the housing market
and the high commute times relative to other jurisdictions support the conclusion that the primary drivers
of growth in Union County do not depend solely on new transportation capacity but rest more on the
major attractors of growth noted in this memo
New transportation capacity can certainly affect the location and pace of development The National
Research Council also noted in their analysis of transportation impacts to land use that [i]n less
developed portions of growing metropolitan areas—where developable land is available and most growth
is occurring—major highway capacity additions such as a freeway bypass or a major interchange
reconstruction are likely to attract further development ' Nevertheless they also note [t]ransportation
investments may affect growth when other conditions hold but it is important to distinguish between net
new growth and redistributed growth A highway project providing access to undeveloped land may
encourage a shift of future development from a developed to an outlying area but this may merely be a
redistribution of growth that would have occurred elsewhere in the region 8
It is possible that population and employment growth in Union County will be higher if the Monroe
Connector is built The Quantitative ICE report documents additional households and additional
commercial and industrial development associated with a Build Scenario The contention that the growth
that has been forecasted by the MPO is substantially higher than what would occur if the Monroe
Connector were not built does not appear to be likely The growth forecasted by the MPO was based on
an economically driven modeling approach that did not consider transportation infrastructure
improvements in its top down allocation and forecasting process Instead the forecasts were based on
projections of economic conditions and job growth over the next 20 30 years As with any forecast the
actual economic performance may vary dramatically from the forecasted conditions in any given year as
the economy moves through cycles of boom and bust Nevertheless the actual growth in Mecklenburg
and Union Counties has tracked closely to the MPO forecast for overall growth despite the most recent
economic recession Furthermore the variables that strongly correlate with growth identified in Dr
Hammer s analysis and other factors cited by other sources as drivers of household location decisions all
point toward Union County being a highly desirable place to live
r
Ibid p 7 8
8 Ibid p 181
17 DRAFT 8/30/2012

Works Cited
Crone Theodore M Capitalization of the Quality of Local Public Schools What Do Home Buyers
Value? Working Paper No 06 15 Federal Reserve Bank of Philadelphia August 2006
Dougherty Jack Shopping for Schools How Public Education and Private Housing Shaped Suburban
Connecticut Journal of Urban History 28 no 2 (March 2012) 205 224
Hammer Thomas R Demographic and Economic Forecasts for the Charlotte Region Prepared for
Charlotte Department of Transportation December 8 2003
National Association of Realtors Profile of Home Buyers and Sellers 2011
National Research Council Expanding Metropolitan Highways Implications for Air Quality and Energy
Use Special Report 245 Washington DC The National Academies Press 1995
Sinai Todd Feedback between Real Estate and Urban Economics Journal of Regional Science 50
423 448 February 2010
18 DRAFT 8/30/2012

a_ oC1a
NORTH CAROLINA
Turnpuke Authority
Monroe Bypass
STIP Nos R-3329 / R-2559
AGENDA
September 12 2012
NCDOT Century Center
Meeting Purpose Discuss ongoing activities and obtain agency input on topics to be addressed
and methodologies to be used in NEPA document
1 Current Activities
2 Outreach Activities
3 Union County Growth Tech Memo
4 Review Purpose and Need
5 Future Activities
6 Projected Schedule
R®J EGTED SCHE LE �� �. � - ��4 - ..
2012 2013
May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr
1111111i1111111Assess existing documents
Public Meetings •
Agency Coordination Meetings • • • • • •
Data collection and analysis
Prepare NEPA Document
Public Hearing(s) •
Record of Decision (ROD) •
Permits •
Re initiate R/W and Construction •
Monroe Bypass Meeting

Potential Monroe Bypass Agency Meeting Topics
October 2012
• Update on additional documentation and analyses underway
o Indirect and cumulative effects
o Upgrade existing US 74
o Traffic forecasts
o Alternatives development and analysis
o Natural and jurisdictional resources
o Noise
November 2012
• Overview of NEPA document
January 2013
• Public hearing summary
• Agency and public comments on NEPA document
Monroe Bypass Meeting