Growth allocation model (GAM) residential calibration 2005-2035 model

              UNIFIED PLANNING WORK PROGRAM
FY 2009
TASK 1.01, SUBTASK 1
Growth Allocation Model (GAM)
Residential Calibration
2005-2035 Model
ASSOCIATION OF CENTRAL OKLAHOMA GOVERNMENTS
September 2010
PRELIMINARY
Not For Publication
This report is the product of a project (study) financed in part by the Federal Transit Administration and the Federal Highway Administration of the U.S. Department of Transportation.
The contents of this report reflect the views of the Association of Central Oklahoma Governments (ACOG), the Metropolitan Planning Organization for the Oklahoma City Area Regional Transportation Study (OCARTS) Transportation Management Area. ACOG is responsible for the facts and the accuracy of the data presented herein. The contents do not necessarily reflect official views or policy of the U.S. Department of Transportation. This report does not constitute a standard, specification, or regulation. ASSOCIATION OF CENTRAL OKLAHOMA GOVERNMENTS
21 East Main Street, Suite 100
Oklahoma City, OK 73104-2405
Telephone: (405) 234-ACOG (2264)
Fax: (405) 234-2200
www.acogok.org
ACOG Growth Allocation Model Residential Calibration
ASSOCIATION OF CENTRAL OKLAHOMA GOVERNMENTS
John G. Johnson ......................................................................... Executive Director
TRANSPORTATION PLANNING & DATA SERVICES
Douglas W. Rex ............................................................................ Division Director
Holly Massie ...................................................................... Special Programs Officer
John Sharp ....................................................... Principal Author - Program Coordinator
Andrea Weckmueller-Behringer .................................................... Program Coordinator
Pong Wu .................................................................................... Associate Planner
Darla Hugaboom .......................................................................... Associate Planner
Kara Chiodo ................................................................................ Assistant Planner
Meredith Williams ......................................................................... Assistant Planner
Ryan Billings ................................................................................ Assistant Planner
Daniel Fazekas ............................................................................. Assistant Planner
Beverly Garner .................................................................... Administrative Assistant
Ellen Owens ......................................................................... Department Secretary
ACOG Growth Allocation Model Residential Calibration
ACOG Growth Allocation Model Residential Calibration
TABLE OF CONTENTS
INTRODUCTION .......................................................................... 1
BACKGROUND ............................................................................ 3
2000-2030 GROWTH ALLOCATION MODEL REVIEW .................................................... 3
GEOGRAPHY ................................................................................................ 3
CALIBRATION PROCESS ................................................................. 5
SUM OF DWELLING UNITS BY TRAFFIC ANALYSIS ZONE ............................................... 9
2005 RATES AND WEIGHTS ANALYSIS ................................................................... 10
INCOME ..................................................................................................... 15
DENSITY .................................................................................................... 18
SCHOOL .................................................................................................... 21
TREND ...................................................................................................... 22
OTHER VARIABLES REVIEWED ........................................................................... 24
WEIGHTS ................................................................................................... 24
CONCLUSION ............................................................................ 25
ACOG Growth Allocation Model Residential Calibration i
ACOG Growth Allocation Model Residential Calibration ii
TABLES
Table 1: Percent Change of the 2000-2030 GAM Population and the 2000-2004 Building Permits ................................................................................................ 5
Table 2: TAZs with 300 or more units added ..............................................................13
Table 3: 2005 BPs by Ten Income Categories..............................................................17
Table 4: Building Permits Per Square Mile .................................................................17
Table 5: 2005 BPs by Density Category .....................................................................20
Table 6: Density of BPs Per Square Mile ...................................................................20
Table 7: Edmond School Rates ...............................................................................21
Table 8: 2005 BPs by Trend ..................................................................................22
FIGURES
Figure 1: 2005 OCARTS Area Entities ...................................................................... 2
Figure 2: 2005 OCARTS Percent Growth for BPs vs. GAM - North vs. South .......................... 6
Figure 3: 2005 OCARTS Percent Growth for BPs vs. GAM - East vs. West ............................ 7
Figure 4: 2005 OCARTS Percent Growth for BPs vs. GAM – Four Quadrants .......................... 8
Figure 5: 2000-2005 Growth by TAZ ....................................................................... 9
Figure 6: Number of BPs by 2005 TAZ ................................................................... 11
Figure 7: 19 TAZs with BPs Greater than 300 .......................................................... 12
Figure 8: The TAZ with the highest number of BPs was TAZ 735 in Oklahoma City with 718 BPs ................................................................................................ 13
Figure 9: TAZ with Second Highest 2000-2004 Permits ............................................... 14
Figure 10: Income Rate by TAZ – 2005 dollars .......................................................... 16
Figure 11: 2005 TAZs by Density Category .............................................................. 19
Figure 12: Edmond School Rates by School ............................................................. 21
Figure 13: TAZs by Trend Rate ........................................................................... 23
INTRODUCTION
The Association of Central Oklahoma Governments (ACOG), in its capacity as the Metropolitan Planning Organization (MPO) for the Oklahoma City Area Regional Transportation Study (OCARTS) area, used the 2000 Census data and building permit change data to develop the 2005 base, and adopted the 2035 population forecast for use in the 2035 OCARTS Plan. The 2035 population distribution forecast was determined by the Growth Allocation Model (GAM) for all cities and unincorporated county portions within the OCARTS area (Figure 1).
The GAM is a land use allocation model used to make predictions about future development in the OCARTS area based on historical trends and assumptions. The model is designed to take regional estimates of population and growth for a forecast year (2035), and distribute the marginal growth expected to occur between the base year (2005) and the forecast year to various subareas based on the availability of land. The model also takes into account various growth factors that are used to quantify the growth potential, or attractiveness, for different parts of the region.
The GAM calibration determined the significant variables influencing the attractiveness of geographic areas within the OCARTS region. Specifically, the calibration correlated residential dwelling unit growth recorded between the 1990 and 2000 Census, to independent variables referred to as growth factors. The growth factors used in the 2035 GAM were existing residential densities, household income, school district, and the past residential growth trend in a region.
Once the calibration process had concluded, the GAM was run to distribute 2035 regional population projections to the most attractive areas. Next, the GAM applied 2035 growth assumptions to quantify the type and amount of residential dwelling unit growth for each area. The resulting 2035 OCARTS population and dwelling unit forecasts described the population and residential growth expected to occur between the years 2005 and 2035. Most growth was in the form of new homes or dwelling units. However, a small share of the new residential growth was in dormitories and other group residential settings, called group quarters.
Additional reports that discuss the GAM population modeling and results include: FY 2009 UPWP Report – Task 1.01(2a), Year 2005 Population and Dwelling Units and FY 2009 UPWP Report – Task 1.01 (2b), Year 2035 Population Controls.
ACOG Growth Allocation Model Residential Calibration 1
Figure 1: 2005 OCARTS Area Entities
ACOG Growth Allocation Model Residential Calibration 2
BACKGROUND
2000-2030 GROWTH ALLOCATION MODEL REVIEW
After the development of the 2030 OCARTS Plan, staff reviewed the GAM to make recommendations for refinements to the model (see 2004-1.01(1c) Growth Allocation Model (GAM) Calibration.MB.doc).
Recommendations of the Calibration report:
1.
The GAM be run at the traffic analysis zone level geography to decrease the zonal variations produced by the current GAM data zone geography.
2.
The number of rating categories be increased from five to a larger number.
3.
More time be taken for the calibration process and analysis of the calibration results. In particular, more analysis to adjust weights and rates is needed.
4.
Weights be computed for the growth factors at the traffic analysis zone, city, and/or subarea geographies to provide adequate zonal variation.
5.
Consideration should be given to incorporating rural or urban characteristics when determining the calibration and weighting parameters.
6.
Staff further investigate mathematical or statistical methods to determine the rating schemes, e.g., Jenks Natural Breaks.
7.
Consideration be given to rating individual schools instead of school districts. The correlation of growth to each school’s attendance area (e.g., elementary, middle, and high school), test scores, and education level may prove useful. This would introduce zonal variations to school ratings since current school districts are so large.
8.
The median age of structure be considered for inclusion as a growth factor. Inconsistencies between the 1990 and 2000 Census sample data and the time required for further analysis precluded its use for the 2030 Plan.
9.
Staff review alternative growth allocation equations and computer programs. The current GAM structure has become out of compliance with current GIS software programming languages.
10.
Consideration be given to additional growth factors for inclusion such as residential land costs, availability of urban services, availability of processed water, amount of area in the floodplain, and severity of wastewater requirements.
GEOGRAPHY
The data in the OCARTS area was collected for different levels of geography. The OCARTS study area, county, or county part, 48 entities and traffic analysis zones (TAZs). The TAZs were developed by using census block geography as building blocks. The GAM model allocates dwelling unit and population data to the Place-TAZ level based on a TAZ’s attractiveness and having land available for development.
ACOG Growth Allocation Model Residential Calibration 3
ACOG Growth Allocation Model Residential Calibration 4
CALIBRATION PROCESS
Where possible, staff incorporated some of the recommendations of the 2030 Calibration Report. The first of those recommendations was to study the growth rate of the residential permits from the 2030 Plan (2000-2030) versus the 2000-2004 period, which were used to develop the 2005 base for the 2035 Plan.
Table 1: Percent Change of the 2000-2030 GAM Population and the 2000-2004 Building Permits
(1). Straight line estimate for 2005 = (2000-2030)/6
2030 GAM
Building Permits(4)
2000
2030
2005(1)
Estimate
2005
% Change
BPs
2000-2004
2005
% Change
Total Population
990,595
1,335,036
68,888
6.95%
84,416
8.52%
SF
828,768
1,132,504
60,747
7.33%
76,600
9.24%
MF
133,772
168,546
6,955
5.20%
6,631
4.96%
GQ
28,055
33,986
1,186
4.23%
1,186
4.23%
Total DU
427,067
575,735
29,734
6.96%
38,886
9.11%
SF
341,041
467,776
25,347
7.43%
33,857
9.93%
MF
86,026
107,959
4,387
5.10%
5,030
5.85%
Total Occ DU
390,444
524,782
26,868
6.88%
35,785
9.17%
SF
317,514
432,793
23,056
7.26%
31,521(2)
9.93%
MF
72,930
91,989
3,812
5.23%
4,264(3)
5.85%
(2). SF BPs build out rate = 0.98 * 32,164 = 31,521
(3). MF BPs build out rate = 0.98 * 4,351 = 4,264
(4). Calculations are based on Region wide ratios using the formula.
BPs_Population = 0.98 * BPs * Persons Per DU * Occupancy Rate
For the purpose of this analysis, the 2000-2004 new building permits (BPs) were summed by the 2000-2030 TAZ structure of 878 zones. The permit numbers in Table 1 shows that the 2000-2004 BPs grew at a slightly faster rate (9%) than the straight line growth from the 2030 Growth allocation Model (7%). Should this new trend continue to 2030, the population of the study area will be 1,400,000 compared to the 2030 GAM estimate of 1,335,000. The 2000-2004 BPs annual rate of growth is 1.4% versus the 2030 GAM annual rate of 1.2%.
The 2000-2004 single family BPs increased 2.6% over the 2030 GAM single family occupied dwelling units while the multi family BPs only increased 0.6% over the 2030 GAM multi family occupied dwelling units (DUs). The multi family BPs were only 12% of the total BPs while in 2000 the MF occupied DUs were 18% of the total.
One thing to keep in mind, the 2005 figures were derived from the building permits from 2000-2004. This is only a short term trend of 5 years, while the 2030 GAM included growth over 30 years. ACOG Growth Allocation Model Residential Calibration 5
Figure 2: 2005 OCARTS Percent Growth for BPs vs. GAM - North vs. South
(In many of these maps, BPU reflects 2000-2004 building permits)
The dots on the above map depict residential building permits between 2000-2004. This allowed for a percent growth comparison between dwellings added over the five year period and the first five years of the 2030 GAM. In this case, the percent growth from 2000-2005 and the GAM growth prediction have a 6% difference in the north and south halves of the OCARTS area.
ACOG Growth Allocation Model Residential Calibration 6
Figure 3: 2005 OCARTS Percent Growth for BPs vs. GAM - East vs. West
The difference between the 2000-2004 BPs data and the 2005 estimate in the east and west halves is only 4%.
ACOG Growth Allocation Model Residential Calibration 7
Figure 4: 2005 OCARTS Percent Growth for BPs vs. GAM – Four Quadrants
When dividing the permit growth into the OCARTS area four quadrants, there were just small differences between the two time periods. The biggest difference between the BPs trend and the GAM estimate is in the SW quadrant at 6%, followed by the NE quadrant at 4% and the NW quadrant at 2% and the SE quadrant with no difference.
ACOG Growth Allocation Model Residential Calibration 8
SUM OF DWELLING UNITS BY TRAFFIC ANALYSIS ZONE
The building permits that were added between 2000-2004 totaled 36,516 units and were converted to occupied dwelling units by location and added to the year 2000 TAZ structure to develop a preliminary 2005 occupied dwelling unit count. This preliminary 2005 occupied dwelling unit count was then compared to the 2030 GAM estimated occupied dwelling units for each TAZ. The 2005 number exceeded the 2030 GAM estimated occupied dwelling units in only 65 (7%) of the TAZs. These are shown in red. However, the BPs exceed the 2005 GAM estimated occupied dwelling units in 345 (39%) of the TAZs and these are shown in green. The coefficient of determination of the BPs to the 2030 GAM occupied dwelling units was R2 = 0.40. The average difference was 18 with a Standard deviation of 80.
Figure 5: 2000-2005 Growth by TAZ
2005 RATES AND WEIGHTS ANALYSIS
The purpose of this step was to analyze the growth in building permits from 2000 to 2005 and to determine the effect that the variables: school, population density, income and historical trend have on that change. Also, the average age of population, average age of structure, median household income, median cost of structures, average travel times and government policies were included to determine their impact on growth.
In order to eliminate the bias injected into the study by the difference in area between TAZs, the basic analysis tool was determined to be total dwelling unit change from 2000 to 2005 by square mile. At the beginning of this step, 2,042 TAZs were used, this was later increased to 2,450. This procedure was used to check each variable against building permit growth by TAZ to determine the impact of the variable on growth.
ACOG Growth Allocation Model Residential Calibration 10
ACOG Growth Allocation Model Residential Calibration 11
Figure 6: Number of BPs by 2005 TAZ
In Figure 6, the TAZs with gray shading depict areas that had no new building permits recorded between 2000-2004. In the 2,042 TAZs, only 1,164 (57%) had new BPs that were located within their boundaries. There were 19 TAZs that had 300 or more BPs recorded.
Figure 7: 19 TAZs with BPs Greater than 300
The TAZs with large additions of BPs between 2000-2004 were scattered throughout the OCARTS area. None of these TAZs were located in the inner core of Oklahoma City. ACOG Growth Allocation Model Residential Calibration 12
Table 2: TAZs with 300 or more units added
Figure 8: The TAZ with the highest number of BPs was TAZ 735 in Oklahoma City with 718 BPs
ACOG Growth Allocation Model Residential Calibration 13
The TAZ with the second highest building permit total was TAZ 887, which is in Oklahoma City and Canadian county. This TAZ added 518 BPs between 2000-2004.
Figure 9: TAZ with Second Highest 2000-2004 Permits
These two TAZs included mobile home parks that often have homes move in and out on a regular basis. Therefore, staff researched these and other TAZs that might have inflated numbers due to mobile home parks.
ACOG Growth Allocation Model Residential Calibration 14
INCOME
The first variable that was tested for the rates and weights was income. The building permits were summed for ten years of the 2030 Plan and the first 5 years of the 2035 Plan. These permits were then matched to five income categories and summed. The categories were based on the 2000 dollars. The permits were also summed per square mile and a rate per square mile was calculated to take out the bias of the varying sizes of TAZs.
As can be seen in the table, there is a significant difference in the ratings between the first ten years of the old plan versus the first five years of the new plan. There is even a significant difference between rate by number of units and rate by square mile within each study period. This difference will be tested in the 2005-2035 GAM runs to see which ratings category gives the best response.
1990-2000 Dwelling Unit change by Income Group
2000-2004 Building Permits
Year 2000 dollars
Year 2000 dollars
1990-2000
Rate
DU Per
Rate
2000-2004
Rate
BP Units
Rate
Demolitions
Income
Total DU
#Units
Sq. Mile
Sq. Mile
BP Units
#Units
Sq. Mile
Sq. Mile
<25000
-2620
1
-39
1
2393
3
16.67
2
907
25000-50000
12127
4
14
2
16307
5
13.55
1
971
50000-75000
19688
5
29
3
14702
4
22.74
3
188
75000-100000
6286
3
118
5
2341
2
30.57
4
51
> 100000
349
2
33
4
772
1
32.79
5
26
ACOG Growth Allocation Model Residential Calibration 15
Figure 10: Income Rate by TAZ – 2005 dollars
The 2005 median family income by TAZ was calculated by using the 2000 median income multiplied by an inflation factor of 1.13 taken from the Consumer Price Index (CPI). Rating categories were increased from five in the 2000 study to 10 in this study, the income levels are divided into 10 categories shown in the table below and a traffic analysis zone was placed in one of these categories based on its median income.
ACOG Growth Allocation Model Residential Calibration 16
Table 3: 2005 BPs by Ten Income Categories
Income Category
#
2000-2004
BPs
Rate
# Units
Acres
Sq. Mile
BPs Per
Sq. Mile
Rate
Sq. Mile
Demolitions
< 20000
245
1185
5
37181
58.10
20.40
7
298
20000-40000
612
5276
8
309637
483.81
10.91
4
1277
40000-60000
721
14930
10
652929
1020.20
14.63
5
317
60000-80000
324
11530
9
252617
394.71
29.21
8
164
80000-100000
84
1747
7
61470
96.05
18.19
6
55
100000-120000
29
1226
6
17206
26.88
45.60
9
14
120000-140000
6
514
4
3961
6.19
83.05
10
6
140000-160000
2
9
2
1285
2.01
4.48
2
0
160000-180000
0
0
1
0
0.00
0.00
1
0
> 180000
12
98
3
3445
9.76
10.04
3
12
The columns in gray are the rating (Rank) by number of units and by square mile. As can be seen in the income category $40,000-60,000 there is a significant difference in the rate by number of units with 10 and the rate by square miles with 5. For this run the units per square mile were used as its relation to BPs growth resulted in R = 0.19 as compared to rate by number of units R = 0.04.
Table 4: Building Permits Per Square Mile
BPU_SqMile0.0010.0020.0030.0040.0050.0060.0070.0080.0090.00< 20000 20000-4000040000-6000060000-8000080000-100000100000-120000120000-140000140000-160000160000-180000> 180000IncomeUnits per SqMileBPU_SqMile
ACOG Growth Allocation Model Residential Calibration 17
DENSITY
Density is a calculation of the total single family dwelling units + total multi-family dwelling units divided by the total single family acres + total multi-family acres of land use. The density rate for 2005 was first compared against the 2000 density rates to check for differences.
1990 - 2000 Dwelling Unit change by Density Category
2000-2004 Building Permits
1990-2000
Rate
DU Per
Rate
2000-2004
Rate
BP Units
Rate
Demolitions
Density
Total
DU
# Units
Sq. Mile
Sq. Mile
BP Units
# Units
Sq. Mile
Sq. Mile
< 1
16064
5
75
2
11238
4
7
1
236
1-1.99
3761
2
191
4
3453
1
33
2
118
2-2.99
7997
4
590
5
4768
3
67
5
75
3-3.99
3377
3
181
3
4595
2
66
4
162
>=4
707
1
11
1
12461
5
54
3
1553
Five density categories were used in the 2000-2030 GAM study, the 2000-2004 BPs were allocated to the 878 TAZs used in the 2000 study. Unlike income there is not a significant difference between the 1990-2000 dwelling units change and the 2000-2004 BPs growth in rates by square mile. The preferred density rate category in 2000 and 2005 is still the 2-2.99, however the 2005 rate is leaning more toward the 3–3.99 category.
Like income, there is a significant difference between rate by number of units and rate by square mile within each study period. This difference will be tested in the 2005-2035 GAM runs to see which ratings category gives the best response. Approximately 72% of the demolitions occur in the in the >=4 density group.
ACOG Growth Allocation Model Residential Calibration 18
ACOG Growth Allocation Model Residential Calibration 19
Figure 11: 2005 TAZs by Density Category
The rating categories were increased from five in the 2000 study to 10 in this study, the density levels were divided into 10 categories shown in the table below and each TAZ was allocated to a category based on its density.
Table 5: 2005 BPs by Density Category
Density
#
2000-2004
BPs
Rate
# Units
Acres
Sq. Mile
BPs Per
Sq. Mile
Rate
Sq. Mile
Demolitions
< 1
647
11238
10
1025175
1601.84
7
1
236
1-1.99
115
3453
6
67517
105.50
33
3
118
2-2.99
85
4768
8
45885
71.70
67
10
75
3-3.99
135
4595
7
44378
69.34
66
8
162
4-4.99
133
5940
9
56470
88.23
67
9
237
5-5.99
164
3278
5
33962
53.07
62
7
406
6-6.99
133
1118
4
25225
39.41
28
2
323
7-7.99
98
963
3
13935
21.77
44
6
287
8-8.99
47
369
1
5919
9.25
40
4
110
>=9
151
793
2
12829
20.05
40
5
190
Table 5 shows the number of BPs (BPUs) by density category and by square mile. The columns in gray are the rating (Rank) by number of units and by square mile. Note that the density category < 1 has more than twice the number of BPs as any other category and rates a 10, however when viewed from a number of units per square mile perspective it has far lower BPs per square mile than any of the other groups and rates a 1.
For this run the units per square mile was used as its relation to BP growth was R = 0.27 as compared to rate by number of units, which was R = 0.04.
Table 6: Density of BPs Per Square Mile Density BPUs Per sq mile01020304050607080Density< 11-1.992-2.993-3.994-4.995-5.996-6.997-7.998-8.99>=9DensityBPUs per sq mileBP Per
ACOG Growth Allocation Model Residential Calibration 20
SCHOOL
Unlike previous studies, the school ratings were developed for each elementary school within a school district, rather than the whole district. This will allow the model to become more sensitive at lower levels of geography and will more closely tie in with the TAZs. The total building permits and square miles were summed by each elementary school membership area to determine the amount of growth per square mile. Each elementary school within each school district was then rated on a scale of 1 to 10 with 10 having the highest growth in BPs and 1 being the lowest. The TAZ’s within the elementary school membership area were then given that rating. For example the Edmond school rates are listed below.
Figure 12: Edmond School Rates by School
Table 7: Edmond School Rates
Elementary Schools
Building
Permits
Square
Miles
BPs per
Sq. Mile
Rate
Charles Haskel
132
0.50
264
10
John Ross
998
7.15
140
9
Sunset
179
3.28
55
8
Ida Freeman
101
2.04
50
7
Northern Hills
278
8.98
31
6
Washington Irving
18
0.82
22
5
Will Rogers
308
13.70
22
5
Orvis Risner
37
2.08
18
4
Cross Timbers
336
22.94
15
3
Chisholm
61
14.43
4
2
Prairie Valley
8
2.03
4
1
West Field
0
1.04
0
1
The correlation coefficient for school rate and total dwelling unit change is R=0.25.
ACOG Growth Allocation Model Residential Calibration 21
TREND
Trend is based on the growth in building permits from 2000 to 2004 for each TAZ. The trend rate was then applied to each TAZ based on its BPs per square mile growth. Approximately 87% of the demolitions occurred in the 0-40 building permit trend group. The correlation coefficient for the trend rate was calculated at R = 0.79.
Table 8: 2005 BPs by Trend
BPs
#
2000-2004
BPs
Rate
# Units
Acres
Sq. Mile
BPs Per
Sq. Mile
Rate
Sq. Mile
Demolitions
0<40
2983
14299
5
514347
803.67
17.79
1
546
40<80
107
5895
8
32045
50.07
117.73
2
53
80<120
35
3481
10
12765
19.95
174.53
3
9
120<160
20
2705
9
5814
9.08
297.76
5
11
160<200
15
2612
7
5435
8.49
307.58
6
3
200<240
8
1751
6
2720
4.25
412.00
7
1
240<280
3
759
4
864
1.35
562.22
8
0
280<-320
6
1809
2
1400
2.19
826.97
9
2
320<-360
2
700
1
295
0.46
1518.64
10
0
> 360
7
2644
3
2005
9.76
270.90
4
2
ACOG Growth Allocation Model Residential Calibration 22
ACOG Growth Allocation Model Residential Calibration 23
Figure 13: TAZs by Trend Rate
OTHER VARIABLES REVIEWED
Other variables reviewed with respect to total dwelling unit change from 1990 to 2000 were:
Median age of structure(R=0.48)
Median age of population(R=0.04)
Mean travel time to work(R=0.05).
From these three variables only median age of structure had a high enough correlation with total dwelling unit changes to be considered for use in predicting total dwelling unit distribution within the OCARTS area. Further analysis revealed that median age of structure might also be used as a guide in calculating lost dwelling units (demolitions).
When the median age of structures in a TAZ is greater than 40 years, 4.5% of the total dwelling units within that TAZ are lost. Also, 1.9% of total dwelling units in a TAZ will be lost when the median age of structures is from 30 to 40 years old.
WEIGHTS
Weights were in a range from 1 to 3 and were used to weight the rate based on its importance in determining where future dwelling units will be built. Since the correlation coefficient measures the relationship between the dependent variable (total dwelling units) and the independent variables (Density, School, Income, and Trend), it was used to determine the weight assigned to each rate.
Variable Weights
Variable
R Value
% of Total
Weight
Density
0.27
18.0%
2
School
0.25
16.7%
2
Income
0.19
12.7%
1
Trend
0.79
52.7%
3
Total
1.5
100.0%
ACOG Growth Allocation Model Residential Calibration 24
CONCLUSION
Staff took many of the recommendations from the Residential Calibration of the 2000-2030 Model report. The recommendation of going from 5 ratings categories to 10 allowed for a greater variety of TAZ scores within the OCARTS area. Though Age of Structure was not used as variable, it was used as an indicator of residential demolitions. Individual schools were given a rating, rather than school districts. This would allow for more variation in TAZs.
ACOG Growth Allocation Model Residential Calibration 25