Baltimore County Public Schools Pupil Yield Factor Study
The map below shows the Single-Family Attached (rented) pupil yields.
This map reflects that the 12th District yields the most students from rented single-family attached housing
developments; with a yield of 1.02 students per household. This is a very high yield for a rented singlefamily attached housing classification. One contributing factor that could explain this is the small sample
size of housing units in this area, which can typically skew the results. Election Districts 2, 4 & 11 still yield
a considerable number of students from rented single-family attached housing. The table on the map
shows that these areas yield from 0.32 students to 0.61. The table also reveals that the green areas of the
district have no housing that falls under this classification; therefore, no yield factors were generated for
these areas.
Baltimore County Public Schools Pupil Yield Factor Study
October, 2004
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Baltimore County Public Schools Pupil Yield Factor Study
The map below shows the Single-Family Attached (owned) pupil yields.
This map shows that the 12th and 15th District yields the highest numbers (0.65 and .56) of students per
households in the owned single-family attached housing category. These areas have a lot of future planned
housing so they will yield a significant number of students in the future. Districts 13 and 14 yield 0.38 to
0.46 students per household, while Districts 1, 2, 4, 9, and 11 yield 0.23 to 0.37 students per household.
The remainder of the district either has no housing developments in this category or yields a small number
of students.
Baltimore County Public Schools Pupil Yield Factor Study
October, 2004
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Baltimore County Public Schools Pupil Yield Factor Study
The map below shows the Single-Family Detached pupil yields.
The single-family detached housing category is probably the most important housing classification to
study, primarily because most of the historical and planned housing in the county falls under this category.
The map reveals that the northern portion of the district is yielding the highest number of students per
household. Election District 6 yields 0.78 students per household, while its neighbors yield 0.67 in District
7 and 0.53 in District 5. The other yellow and orange Election Districts need to be considered when
analyzing student yields because they yield a significant number of students. When comparing these yield
rates to the national average of 0.51 students per single-family (National Multi-Housing Council), it is
evident that the rates are higher than those occurring in other areas of the country.
Baltimore County Public Schools Pupil Yield Factor Study
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Baltimore County Public Schools Pupil Yield Factor Study
BALTIMORE COUNTY PUBLIC SCHOOLS 2004 PUPIL YIELD FACTOR METHODOLOGY FOR
EXISTING DEVELOPMENTS
Up to now only post-1980 developments have been analyzed due to the availability of accurate data.
However, it is important that all developments are accounted for regardless of their age. Baltimore County
has a fairly old housing stock with significant numbers of developments that were built before 1980. It is
important to understand how older developments throughout the district relate to the number of students
that are coming out of these neighborhoods. In order to better understand how all housing relates to the
students, comparisons were made on 2000 census housing, age, and value versus student population totals.
The Census data includes housing data up to the year 2000.
Since detailed data was not collected before 1980, the existing development analysis relies highly on the
2000 Census household figures. The Census Bureau tracks age of housing, value of housing and the
number of household units by block group. A block group is made up of multiple city blocks, and the
sizes of block groups are dependent on the density of the population in any given area. For instance, a
block group in a densely populated area will have a much smaller area than a block group in a rural area.
Multiple attributes were totaled by Census block group and then aggregated into Election Districts.
Election Districts were used as the reporting geography for this report because this is the same method that
was used in the new development analysis. Factors that were accounted for when determining student
yields are below:
x
x
x
x
x
Total number of students by grade
Total number of household units (pre-1980)
Median age of households
Median value of households (owned property)
Median rent of households (rented property)
Using the data listed above, yield factors by Election District were generated which gives a generalized view
of how older developments are yielding students in relation to their age and housing value.
2004 PUPIL YIELD FACTOR STUDY FINDINGS FROM EXISTING DEVELOPMENTS
The table below reveals the yield factors for every Election District, along with the supporting data that was
used to calculate the factors. In addition, median housing value and age are displayed so that further
analysis could be performed.
Election
Pre-1980
Median Year
Students Yield Factor
District Household Units
Built (Own)
01
29018
10147
0.35
1960
02
33758
15254
0.45
1971
03
20868
5121
0.25
1969
04
19617
8271
0.42
1981
05
1520
620
0.41
1972
06
1640
991
0.60
1980
07
2824
1377
0.49
1978
08
27248
7145
0.26
1976
09
40088
10336
0.26
1955
10
3726
1498
0.40
1975
11
26465
8906
0.34
1981
12
21175
8943
0.42
1952
13
13916
5783
0.42
1954
14
17689
6454
0.36
1961
15
40325
16754
0.42
1957
Source: BMC Oct. 2004
Baltimore County Public Schools Pupil Yield Factor Study
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Median Year
Built (Rent)
1971
1976
1977
1974
1955
1959
1949
1976
1967
1954
1984
1954
1959
1974
1966
Median Value of
Housing (Own)
$123,700
$121,200
$140,100
$142,300
$191,600
$196,100
$209,500
$199,400
$133,800
$294,700
$148,200
$83,100
$106,900
$113,300
$104,300
Median Rent of
Housing (Rental)
$589
$625
$729
$597
$594
$584
$522
$670
$603
$641
$739
$462
$488
$558
$451
Baltimore County Public Schools Pupil Yield Factor Study
To help visualize the numbers from the table above, maps are provided for each attribute of Election
Districts. This helps to illustrate visual patterns and consistencies that might not be noticeable when
looking at raw numbers.
The map above shows that the northwestern portion of the district is yielding the highest number of
students from existing development. This indicates that the ratio of existing housing units to number of
students is the highest, with .60 students per household. The Median Year Built (Own) map reflects that
the northwestern portion has some of the most recent development activity in the county. This can be a
possible indicator that the more recent development yields more students. However, Election District 11
falls in the same category as District #6 as far as the year built for households. Election District #11 is
yielding far fewer students, which reveals that the age of housing alone can not be used to determine trends
in student yields. Perhaps the type of housing in District 11 is impacting the numbers of students that live
in the area. The rental cost of housing in this area is among the highest in the district, which could
indicate that there is more demand for multi-family developments which tend to yield fewer students. The
maps above show that the northern area has some of the newest and most expensive homes. Also this area
seems to have the fewest rental properties, which could explain the higher student yields in the area.
Election Districts 3, 8, and 9 are yielding the fewest numbers of students from existing development, but
the age or value of housing from the Census data does not help to explain this phenomenon.
Baltimore County Public Schools Pupil Yield Factor Study
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Baltimore County Public Schools Pupil Yield Factor Study
There is a possibility that the Census data is not detailed enough to be able to analyze spatial relationships
between student yields and the age and value of housing. As Baltimore County’s technology progresses and
their data collection methods improve, there will be more opportunities in the near future to analyze these
relationships.
The purpose of the map below is to help analyze the number of households and the number of students
from each Election District. The number of students and the number of households were the two factors
used to calculate student yields.
The map to the right shows
how the number of students
and households relate to the
student yields that were
derived from the Census data.
The northern area has the
least number of students and
households, but the ratio
between both is the highest
which reflects a higher
student yield.
From a
district-wide view, the eastern
portion of the district
(District 11) and the western
portion (District 4) have the
highest numbers of students.
These areas also tend to have
some of the largest numbers
of households, but the
student yields vary for each
area. Keep in mind that these
yields only reflect existing
development, so it is possible
that recent development
(post-2000) will cause the
overall yields for these areas
to increase.
It would be
beneficial to merge all of the
new and existing data
together to analyze it as a
whole, but the sources and
methods differ too much to
be able to do so.
As mentioned earlier in the
report, the new development
analysis is more effective in
determining the impacts of
new
subdivisions
in
Baltimore County. The existing development analysis is useful and can help to anticipate how student
yields may be affected as housing matures, and gives an overall depiction of how the age and value of
housing impacts student yields.
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Baltimore County Public Schools Pupil Yield Factor Study
The methods that were used to develop yield factors for Baltimore County Public Schools are common to
the techniques that other school districts are using throughout the country. As previously stated,
advancement in technology and data collection/storage have enabled planners and school administrators to
depict student patterns in a more accurate and efficient manner. Combining Baltimore County’s detailed
building permit tracking systems and Geographic Information Systems resulted in the most accurate
student yields possible for Baltimore County Public Schools.
NATIONAL YIELD FACTORS
The National Multi-Housing Council, [NMHC] conducted a July, 2002 study on the impact apartments
have on school enrollment, and reported the following findings:
x Rental apartments house fewer school age children [.31 per household] than single family
residences [.53 per household]
x Newer apartments house even fewer students, as do high rise and upscale apartments.
x There are 106.4 million households in the U.S. and 51.1 million school age children for an average
of .48 children per household. They report that this figure is misleading since the majority of
households [71%] actually have no children. A comparison of owner households [single family,
condo, and co-op owners] and renter households [single family renters, renters in 2-4 unit
buildings, and apartment renters] shows that 70% of the nation’s school children live in owner
occupied housing.
x They report that a more appropriate comparison is between single family owners and apartment
renters [in properties of five or more units], and that 30% of owners have children, while just 20%
of apartment renters have children. House owners are not only more likely to have children, but
they have more children [.51 children/household] as compared to .31 children/household for
apartment renters. The differences were even greater for residences built since 1990. Newer singlefamily homes average .64 children per household, while apartment renters average .29 children per
household. [For garden apartments and high rise apartments, the post-1990 yield is even lower [.30
and .19, respectively].
The above-mentioned study should be considered as a point of reference. The classifications of “single
family” and “multi-family” are not identical to the classifications used in the Baltimore County Yield
Factor study.
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Baltimore County Public Schools Pupil Yield Factor Study
COMPARISON WITH METHODOLOGY USED BY OTHER DISTRICTS:
For comparison purposes, a few other school districts are mentioned and their methodology for tracking
student yields is discussed. These school districts are located in various locations throughout the country
and have proven to have success when analyzing student populations in various housing developments.
GIS was also used in the majority of these examples, and has proven itself to be a benchmark for analyzing
spatial data.
WHITE BEAR LAKE AREA SCHOOL DISTRICT
WHITE BEAR LAKE, MN
In 2001 the Hugo Facilities Committee was formed to determine the potential impact of new and planned
housing in the White Bear Lake Public Schools within the Hugo area. This committee, comprised of
housing developers, school administrators, City of Hugo administrators, community members and parents,
and teachers performed the following to help determine the magnitude of the problem:
o
o
o
o
Analyzed the current use of Hugo Elementary School
Studied elementary demographics of the area.
Collected data from current and planned development to determine future potential.
Developed enrollment projections for the area based on historical and future housing.
After all data was reviewed and analyzed, recommendations were formulated to help accommodate future
students.
In 2003, DeJONG, Inc. was hired to update the 2001 Facility Study and validate the recommendations that
were made to the Board. The first step in this process was to find an updated number of students who live
in subdivisions in the Hugo area, and determine the change in students per household from the past two
years. Students were successfully mapped out and integrated into a GIS so that detailed analysis could be
performed. In addition to this, parcel maps from the MetroGIS were incorporated to determine
subdivisions and their relation to the location of students. Updated numbers of total students in these
subdivisions could then be tabulated based on the GIS data. The City of Hugo was contacted to confirm
total households and completeness of each subdivision.
As of June 2003, all of the listed subdivisions had been fully developed with the exception of Sweet Grass
Meadows and Creekview Preserve. These two subdivisions have one phase left until completion. The table
below indicates an updated number of students per subdivision. The reported number of students for each
subdivision refers to student counts from the 2001 Hugo Facilities Study, and the current number of
students was taken from GIS analysis. In order to maintain consistency with previous reporting, all K-5
public and non-public students were counted. In addition to this, an updated yield factor of students per
home and previous numbers of students and yields are listed for comparison.
Baltimore County Public Schools Pupil Yield Factor Study
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Baltimore County Public Schools Pupil Yield Factor Study
Subdivision Name
Birch Tree Ponds
Oneka Estates
Rice Lake Meadows
Approx. age at time reported
Number of households
Reported number of students
Total Yield (June 2001)
11 Years
104
71
0.683
6 Years
54
28
0.519
9 Years
125
54
0.432
Approx. age at time estimated
Number of households
Current number of students
Total Yield (June 2003)
13 Years
104
67
0.644
8 Years
56
28
0.500
11 Years
127
63
0.496
Sweet Grass Meadows
Creekview Preserve
Country Ponds
Approx. age at time reported
Number of households
Reported number of students
Total Yield (June 2001)
3 Years
139
24
0.173
3 Years
214
42
0.196
7 Years
57
22
0.386
Approx. age at time estimated
Number of households
Current number of students
Total Yield (June 2003)
5 Years
141
23
0.163
5 Years
222
89
0.401
9 Years
57
32
0.561
Subdivision Name
Source: Hugo Facilities Committee Report June 11, 2001; DeJONG
The table above reveals that as these subdivisions mature, the average number of students per household
for the Hugo area is rising.
The following table shows the change in numbers of households, students, and yield factors per
subdivision from June 2001 to June 2003.
Birch Tree Ponds
Oneka Estates
Rice Lake Meadows
Sweet Grass Meadows
Creekview Preserve
Country Ponds
Changes in….
Number of Households Number of Students
0
-4
2
0
2
9
2
-1
8
47
0
10
Source: DeJONG
Baltimore County Public Schools Pupil Yield Factor Study
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Yields Per Household
-3.8%
-1.9%
6.4%
-1.0%
20.5%
17.5%
Baltimore County Public Schools Pupil Yield Factor Study
The following map shows the White Bear Lake Area School District with sections overlaid on it, which
gives a visual representation of locations where planned development will occur within the School District.
Baltimore County Public Schools Pupil Yield Factor Study
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