HomeMy Public PortalAboutMarch 29, 2009 Letter and Report re Feasibility Study.pdf
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March 4, 2009
Mr. Chip Iglesias, Village Manager
Village of Key Biscayne Community Center
10 Village Green Way
Key Biscayne, FL 33149
Re: Clarifications to Feasibility Study
Dear Mr. Iglesias,
This submission is pursuant to the Council’s requests for additional
information to supplement and update the data provided by us in the
Municipal Charter High School Feasibility Study.
A summary document is attached that addresses each question raised by
the Council during the February 26th 2009 workshop. This is followed by
related supporting documents.
We would like to reiterate that the Municipal Charter School would be an
invaluable asset to the Village of Key Biscayne. The financial information
shows that the school’s operations are, basically, self-funded and fully
sustainable over the long term. While it is true that the Village will have to
provide the facilities funding, this amount also would be fully and quickly
amortized by virtue of the increased property values.
I would like to take this opportunity to re-emphasize the points I had
made at the workshop to illustrate why this project is vital to the best
interests of the Key Biscayne community.
I. It will allow the Village to leverage its modest investment to generate over
$6,000/student in revenue from the Miami-Dade school system.
II. It will allow the Village to control a vital part of its own destiny – the
education of its children and future citizens of the community, the nation
and the world. This is consistent with the history of the Village for self-
governance and its remarkable ability to build a prosperous and vibrant
community while overcoming objections and hurdles far greater than
those it faces to establish a charter school. Examples are the
establishment of the Village itself and, more recently, the development of
a superb Community Center.
III. It will result in significant increases in property values – by some
estimates homes on the Island could see values rise by as much as 10%
as a direct result of creating a world-class Charter school.
IV. It will directly benefit parents and children who live on the Island who
now have to send their children to schools outside the Island – often at
exorbitant cost.
V. It will directly benefit community members who have no children in school
by providing an added asset in the areas of adult education, distance
learning and other facilities currently unavailable on the Island.
VI. It will reduce traffic to and from the Island and result in a positive
contribution to the problem of greenhouse gases. The school will also
provide many other opportunities for the community to express its
support for sustainable design and the use of renewable energy.
VII. It will utilize the latent potential of the Village which has a population of
highly qualified retirees whose vast experience and wisdom can be tapped
toward the delivery of world-class education programs at the school
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VIII. It will increase revenues for the Village because students and parents will now create additional
demand for local goods and services with monies that were previously being spent off the Island.
IX. It will allow the Village of Key Biscayne --- already well-known as an Island Paradise, to further
enhance and update its image – this time for being an educational leader and for creating a world-
class school for a fraction of the funds that many other communities spend. Whereas Key Biscayne
is relatively well known today, the establishment of this Charter School will raise its profile so that
it becomes an International icon for excellence.
X. It will become a catalyst for reenergizing the community and building a new community spirit.
This will happen by having students participate actively in community service projects while
inviting parents and, indeed, the whole community to become actively involved in the important
task of educating children. In this manner, the community will build the school but at the same
time, the school will build the community.
With this submission, FNI’s work on this contract is now officially complete. Please don’t hesitate
to contact me if you have any questions. I would like to thank you, Jud and all the residents of
Key Biscayne for giving me the opportunity to participate in this exciting and inspiring project.
Sincerely,
Prakash Nair, REFP
President, FNI
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RESPONSES BY FNI/EDVISIONS TO THE FEB 26th 2009 WORKSHOP QUESTIONS BY THE KEY
BISCAYNE COUNCIL REGARDING THE FEASIBILITY OF ESTABLISHING A MUNICIPAL
CHARTER HIGH SCHOOL IN THE VILLAGE OF KEY BISCAYNE, FLORIDA
The following information highlights data that was used by Fielding Nair International and EdVisions to develop the
enrollment projections for the proposed Key Biscayne Municipal Charter High School. This document also provides
responses to the various budget-related and other issues raised by the Council during the Feb 26, 2009 workshop to
discuss the Feasibility Study submitted by FNI/Edvisions.
Key Biscayne Youth Population Estimates
THE DATA
Based on the 2000 Census, the Village of Key Biscayne had 2,697 residents in the 1 – 19 years age range.
This youth segment of the population grew by 55% between the 1990 Census and the 2000 Census which equates
to an average of 4.39% per year. The total population increased by 19% in the same period, from 8,854 to 10,507,
or about 1.9% per year.
Key Biscayne 2000 Census
Age Group Total Av. / Age % of Tot.
Under 5 years 766 192 7%
5 to 9 years 816 163 8%
10 to 14 years 660 132 6%
15 to 19 years 455 91 4%
Total 1 - 19 2,697 26%
Total population 10,507
The University of Florida reported the total population of the Village to be 11,160 at the end of 2004, producing an
average yearly growth rate of 1.65% between 2000 and 2004 (reference 2006 Key Biscayne Master Plan Evaluation
and Appraisal Report), or a 13% growth reduction over the 1990 to 2000 period, and suggesting this youth
population grew at a reduced rate of 3.82% per year, for a total of 3,133 as of the end of 2004.
There are no official population figures available for the 2005 – 2008 period. Although any projections can be
challenged, it would be fair to assume that the 2005 growth continued at the same rate as in the 2000 – 2004
period, decelerating in 2006 through 2008. Assuming a 33% annual decelerating between 2005 and 2008, it is
estimated that there are approximately 3,428 residents in Key Biscayne in the 1 – 19 age range.
Year Children ages 1 - 19 % Growth
2000 Census 2,697
2001 * 2,800 3.82%
2002 * 2,907 3.82%
2003 * 3,018 3.82%
2004 * 3,133 3.82%
2005 3,253 3.82%
2006 3,335 2.52%
2007 3,391 1.66%
2008 3,428 1.10%
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* Reference: University of Florida and 2007 Key Biscayne Master Plan Evaluation and Appraisal Report.
High School Age Students in Key Biscayne
In the absence of a Census, the most accurate way of determining the youth population of high school age would
be by accounting for the number of children graduating each year from public and private middle schools as it
would be expected that these children would continue on to public or private high schools and enroll for the 4 years
of high school. Based on available information, we estimate the number of Key Biscayne children of high school age
to be somewhere in the 808 to 936 range, or an average of 872.
The following 2 scenarios were considered in estimating Key Biscayne youth population of high school age.
Scenario 1.- Based on data provided by Miami-Dade County Public Schools (M-DCPS) the average middle school
grade has 117 Key Biscayne Children enrolled *. It would be expected that these children graduating from public
middle schools would enroll at either public or private schools, suggesting a population of 468 Key Biscayne
children in high school age graduating from the middle school grade at M-DCPS public schools.
There is anecdotal information based on the various meetings and workshops we have conducted that leads us to
conclude that a majority of Key Biscayne residents choose to send their children to private schools. However, even
if only half the number of middle school graduates from key Biscayne went to private schools, it would indicate that
at least an additional 117 children graduate from private schools each year. That would bring the total number of
Key Biscayne children graduating from public and private middle schools at 234 which, in turn suggests that the Key
Biscayne population of children of high school age totals about 936.
Scenario 2. In addition to the 117 children graduating each year from the middle public school system, a significant
number of private middle schools are attended by Key Biscayne Children in their middle years but no hard data is
available as to exactly how many children graduate from these private middle schools each year. However, a
conservative number of 85 children can be estimated based on feedback from parents of children and children
attending these schools. Estimate breakdown is as follows: Belen (6), Carrollton (10), Cushman (2), Gulliver (12);
International School (9), Miami Country Dade (4); Palmer Trinity (3), Ransom Everglades (12) and St. Agnes (27)
Based on this analysis, we can estimate a total of 808 Key Biscayne residents of high school age ((117+85) x 4).
Key Biscayne Charter High School Enrollment Projection
Year 1 Year 2 Year 3 Year 4 Year 5
Grade 2010 - 2011 2011 – 2012 2012 - 2013 2013 - 2014 2014 – 2015
9 75 75 100 100 100
10 50 75 75 100 100
11 50 75 75 100
12 50 75 75
Total 125 200 300 350 375
Student population estimate has been projected based on reported M-DCPS public schools population and
considering an average of 117 Key Biscayne children graduating from middle school each year from public middle
schools (100 of them from the KB K-8 Center)
Year 1 enrollment target could be met with just 62% of middle public school graduates enrolling in the new school.
It could also be expected that the 10th grade could enroll 40% of those public school students attending 9th grade at
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another public school. Enrollment in year 2 was maintained at a conservative number of 75 students in 9th grade,
with enrollment increasing to 100 per year for years 3 – 5 as school is in full operation and demonstrates its
qualities. School is expected to reach its 400 students capacity in year 6.
Enrollment of Key Biscayne residents in Miami-Dade County Public Schools
Grade Level 2006 – 2007 2007 – 2008 2008 - 2009
Average
per grade
EL/MID/HS
K 117 110 119
1 125 130 119
2 103 130 144
3 118 110 130
4 117 129 108 Elementary
5 112 123 144 127
6 101 115 121
7 120 111 122 Middle
8 88 122 108 117
9 51 74 79
10 44 51 71
11 37 47 57 High
12 22 28 47 64
Total 1155 1280 1369
% Growth 11% 7%
Growth trends
Middle School 2006 - 2007 2007 - 2008 2008 – 2009 2006 - 2008
309 348 351
% Growth 13% 1% 14%
High School 2006 - 2007 2007 - 2008 2008 – 2009 2006 - 2008
154 200 254
% Growth 30% 27% 65%
Observations on trends:
• A record high of 144 Key Biscayne students will graduate from elementary school this year, the great
majority of them (139) from the Key Biscayne K-8 Center. This number of Key Biscayne elementary
graduates represents a 17% increase over the previous year and a 29% growth over just 2 years ago.
• Enrollment in public high schools increased by 65% in the last 2 school years.
Although not accounted for in enrollment projections, some students from the private schools would be expected
to be interested in attending the Key Biscayne Charter High School in years 3 and beyond once it is determined to
be a well-established school.
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Total of Key Biscayne students attending MDCPS 2008-09 School Year by school and grade level
School Name Grade Level
Total for Grade
Level By Grade Total by Elem. Mid. HS
Average/
grade
HENRY S. WEST LABORATORY SCHL K 1
KEY BISCAYNE K-8 CENTER K 118 119
COCONUT GROVE ELEMENTARY 01 1
SUNSET ELEMENTARY 01 3
KEY BISCAYNE K-8 CENTER 01 115 119
ADA MERRITT K-8 CENTER 02 1
HENRY S. WEST LABORATORY SCHL 02 1
SUNSET ELEMENTARY 02 4
KEY BISCAYNE K-8 CENTER 02 138 144
ADA MERRITT K-8 CENTER 03 1
SUNSET ELEMENTARY 03 2
KEY BISCAYNE K-8 CENTER 03 127 130
SUNSET ELEMENTARY 04 7
KEY BISCAYNE K-8 CENTER 04 101 108
SUNSET ELEMENTARY 05 5
KEY BISCAYNE K-8 CENTER 05 139 144
Total Elementary Schools Elementary 764 127
YOUNG WOMEN'S PREPARATORY ACAD 06 2
GEORGE WASHINGTON CARVER 06 4
KEY BISCAYNE K-8 CENTER 06 115 121
SOUTH MIAMI MIDDLE SCHOOL 07 1
YOUNG WOMEN'S PREPARATORY ACAD 07 5
GEORGE WASHINGTON CARVER 07 7
KEY BISCAYNE K-8 CENTER 07 109 122
SOUTH MIAMI MIDDLE SCHOOL 08 1
YOUNG WOMEN'S PREPARATORY ACAD 08 3
GEORGE WASHINGTON CARVER 08 6
KEY BISCAYNE K-8 CENTER 08 98 108
Total Middle Schools Middle 351 117
MIAMI PALMETTO SENIOR HIGH 09 1
ACADEMY OF ARTS AND MINDS 09 2
DESIGN & ARCHITECTURE SENIOR 09 4
CORAL REEF SENIOR HIGH 09 4
MARITIME & SCIENCE TECHNOLOGY 09 8
INTERNATIONAL STUDIES CHARTER 09 9
CORAL GABLES SENIOR HIGH 09 51 79
CORAL REEF SENIOR HIGH 10 1
NEW WORLD SCHOOL OF THE ARTS 10 1
YOUNG WOMEN'S PREPARATORY ACAD 10 2
DESIGN & ARCHITECTURE SENIOR 10 3
ACADEMY OF ARTS AND MINDS 10 7
INTERNATIONAL STUDIES CHARTER 10 11
MARITIME & SCIENCE TECHNOLOGY 10 11
CORAL GABLES SENIOR HIGH 10 35 71
NEW WORLD SCHOOL OF THE ARTS 11 1
SCHOOL FOR ADVANCED STUDIES WC 11 3
YOUNG WOMEN'S PREPARATORY ACAD 11 3
DESIGN & ARCHITECTURE SENIOR 11 3
MARITIME & SCIENCE TECHNOLOGY 11 4
INTERNATIONAL STUDIES CHARTER 11 5
ACADEMY OF ARTS AND MINDS 11 5
CORAL GABLES SENIOR HIGH 11 33 57
CORAL REEF SENIOR HIGH 12 1
MIAMI SENIOR HIGH 12 1
SCHOOL FOR ADVANCED STUDIES WC 12 2
DESIGN & ARCHITECTURE SENIOR 12 2
MARITIME & SCIENCE TECHNOLOGY 12 6
INTERNATIONAL STUDIES CHARTER 12 7
ACADEMY OF ARTS AND MINDS 12 11
CORAL GABLES SENIOR HIGH 12 17 47
Total High Schools High Schools 254 64
Grand Total 1369
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Draft Budget
(For purposes of feasibility analysis only)
Year 1 Year 2 Year 3 Year 4 Year 5 5 Years
Students 125 200 300 350 375
Revenue $794,783 $1,303,444 $2,004,045 $2,396,504 $2,631,875 $9,130,652
Total Expenses (excl. facil.) $880,139 $1,368,613 $2,007,847 $2,298,085 $2,529,521 $9,084,205
P&L Operations -$85,356 -$65,169 -$3,802 $98,420 $102,354 $46,446
Grants $250,000 $75,000 $0 $300,000 $0 $625,000
P&L Operations & Grants $164,644 $9,831 -$3,802 $398,420 $102,354 $671,446
Other Potential revenue
PTSA $0 $0 $0 $0 $0 $0
Private Contributions $0 $0 $0 $0 $0 $0
After school programs $0 $0 $0 $0 $0 $0
Naming rights $0 $0 $0 $0 $0 $0
Other $0 $0 $0 $0 $0 $0
Total Other potential revenue $0 $0 $0 $0 $0 $0
Year 1 Year 2 Year 3 Year 4 Year 5 5 Years
Facility construction * $6,250,000
Start-up $400,000
Planning $300,000
Total loan / bond $6,950,000
Loan amortization $616,900 $616,900 $616,900 $616,900 $616,900 $3,084,500
Maintenance $62,500 $62,500 $62,500 $62,500 $62,500 $312,500
Total $679,400 $679,400 $679,400 $679,400 $679,400
P&L -$514,756 -$669,569 -$683,202 -$280,980 -$577,046 -$2,725,554
Draft Operational budget has been revised based on feedback from workshop.
Facilities costs are for the recommended 25,000 sq. ft. facility and its maintenance have been included as a place
holder as these numbers may vary substantially. Estimated construction cost of a new facility at $250 per square
foot would total approximately $6,250,000.
Total capitalization of $6,950,000 includes construction costs, school planning ($300,000) and start-up expenses
($400,000)
For reference purpose only, a very conservative 5% increment in real estate property values over the first 6
years of the project will produce an estimated $9,407,257 in additional revenues to the Village, with a Net
Present Value of $6,980,032 at 4% discount rate. (See addendum “Potential Impact on Village's real property
tax revenue with the implementation of a High School / Lifelong Learning Center”)
See detailed budget also attached.
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Notes:
1.- IB certification fee, teachers training for all teachers and exam fees have been included. Estimate is for 50% of
students to pursue the IB diploma.
2.- IB program coordinator / counselor has been added.
3.- Faculty has been updated to incorporate a more traditional curriculum. ESE teachers have been adjusted to
reflect 10% allocation of ESE students in revenue calculator.
Student to Teacher Rations
Maritime &
Science
Technology
Academy (MAST)
Design and
Architecture Senior
HS (DASH)
Coral Gables
Senior HS (CGSH)
Key Biscayne
Charter HS (KBCHS)
Teachers 36 36 145 19
ESE Teachers 1 1 20 2
Total Teachers 37 37 165 21
Assistant Teachers 0 0 18 7
Guidance Counsel 4 3 9 1.5
Total Faculty 41 40 192 29.5
Students 550 469 3525 375
Students to Teacher 15 13 21 18
Student to faculty 13 12 18 13
Guidance staff include 1 part time Counselor and 1 IB Coordinator / Counselor
4.- One administrator has been added for a total of 2.
5.- Books have been adjusted and included in capital equipment depreciation (3 years life span)
6.- Janitorial services have been included (9 months per school year).
7.- Funding for sports programs have not been included in the budget. Students interested in joining competitive
sport can participate in the Coral Gables Senior High sports programs. It would be expected that students would
continue to participate in the Village’s sport programs as they do today.
8.- Parents and private contributions have been removed.
9.- Potential sources of funding, such as after-school programs, naming rights, grants from private individuals or
foundations have not been included.
10.- Grants allocated to charter schools have been included below the operations P&L line.
11.- Start up expenses have been recalculated. It now includes books for 1st year of operation. Revised estimate is
just short of $400,000.
12.- Based on the assumptions made, and just as a place holder, capitalization of facilities, start-up ($400,000) and
planning expenses ($300,000) would total $6,950,000, representing an amortization of approximately $616,900 per
year in principal and interest on a 15 years term and a 4% interest rate loan / bond.
13.- Facilities maintenance has been estimated at 1% of building cost.
Conclusions on budget:
We feel confident that school operations (excluding facilities) can be self-funded while providing a high-quality
education worthy of Key Biscayne’s expectations. However, and as stated in page 9 of our Feasibility Report, “we
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assume the debt service coverage for construction will be picked up by the Village, using a similar model as
was used for the Community Center.”
Furthermore, we are also confident that property appreciation would produce incremental revenue to more
than offset loan amortization and maintenance of facilities.
In addition, we would expect parent and private contributions, fundraising and naming rights would produce
additional revenue to continue enhancing the quality of the school.
Per student funding to charter schools is expected to increase under the new administration, with some
special funding, such as construction and modernization, going to schools already in operation.
The attached spreadsheet can be used to calculate how any potential changes the Village decides to make
from this draft budget would affect the funding picture of the school.
Additional clarifications to questions brought up by Council and Community Members during workshop:
Information Technology: This function is expected to be performed by the students guided by the technology
teacher, as done in the EdVisions Schools.
Location: Certain locations, such as Calusa Park and Virginia Key, were not considered in the feasibility analysis as
these locations are not within the boundaries of Key Biscayne and the Village does not have control over their
usage and were not included in the Village’s list of sites to be evaluated. Furthermore, it is our understanding that
these locations have been pursued for many years in the past and discarded as not feasible.
Parking lot at the Village Hall was not considered as it was not presented as a potential site and is a Miami-Dade
owned property.
Faculty: Some comments were made about the faculty and staff allocated in the budget. The total number of
employees included in budget is 30 for 375 students instead of 15 as stated by a member of the community.
Schools at the Center of the Community: There is certainly a national trend to locate schools at the center of
communities. There is a significant amount of research that demonstrates how these schools produce benefits in
student learning, school effectiveness, family engagement and community vitality. We can affirm that the potential
benefits obtained by the community of Key Biscayne will significantly outweigh any drawbacks presented by
insufficient parking or other potential challenges of implementing the school within the Civic Center area.
As a reference, see report “Schools as Centers of Community: A Citizen's Guide For Planning and Design” sponsored
by The National Clearinghouse for Educational Facilities (funded by the US Department of Education),
KnowledgeWorks Foundation, The Council of Educational Facility Planners, Building Educational Success Together
(BEST) and the Coalition for Community Schools with the contribution of leading school and community planners,
including my partner Randy Fielding :
http://www.edfacilities.org/pubs/pubs_html.cfm?abstract=centers_of_community.
Playing fields: There seems to be a perception that a school within Key Biscayne needs to provide additional playing
fields, such as soccer, football, basketball, etc. However, the focus of the school should be on education while
making use of existing resources within the Island. The school is planned for the children residents of Key Biscayne,
and these children are already making use of available resources in the afternoon, evenings and weekends. Some
sports, such as beach volleyball or tennis could be promoted. Students seeking competitive sports could enroll in
the sports programs at Coral Gables Sr. High School.
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The school would be a school of choice and would not preempt any student, if so desired, from attending Coral
Gables Sr. High School or any other charter or magnet schools in the school district and participate in their sports
programs.
The school’s operating budget presents a $671,446 surplus which could be used for sports programs. In addition,
parents and private contributions could be used for funding other sports programs (golf, rowing, sailing, etc.) not
requiring playing fields and with plenty of opportunities to practice these sports.
Potential opportunities may exist for agreeing with Miami-Dade County to use fields at Crandon Park, tennis center
and possibly the Links golf course.
Impact on property values: As stated in our report, there is significant and overwhelming scientific evidence to
support the thesis that a quality school has a positive impact on property appreciation and we would expect that
Key Biscayne would also benefit from this appreciation. Copies of the following reports were handed out to Council
for reference and are attached to this document.
USA Today: Location, location, location Better schools mean higher property values Home buyers go shopping for
schools.
Clemson University: School Quality and Property Values.
Ohio State University: The Impact of School Quality on Real House Prices.
Federal Reserve Bank of Dallas: Neighborhood School Characteristics: What Signals Quality to Homebuyers?
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Potential Impact on Village's real property tax revenue with the implementation of a High School / Lifelong
Learning Center
Estimated Village's
Project
Approved
Construction
underway
1st year
operation
New revenue roll out Value Y 0 Y 1 Y 2 Y 3 Y 4 Y 5
Taxable real property $6,434,727,418 $6,479,127,037 $6,523,970,653 $6,569,262,704 $6,615,007,676 $6,661,210,098
Homestead Property Value (5) 31% $1,994,765,500 $1,994,765,500 $1,994,765,500 $1,994,765,500 $1,994,765,500 $1,994,765,500
Non-Homesteaded Value (5) 69% $4,439,961,918 $4,484,361,538 $4,529,205,153 $4,574,497,205 $4,620,242,177 $4,666,444,598
Total Taxable Value $6,434,727,418 $6,479,127,037 $6,523,970,653 $6,569,262,704 $6,615,007,676 $6,661,210,098
Property Appreciation Non-
Homesteaded . 0% 1% 1% 1% 1% 1%
Homesteaded tax Rev. (3) 3.2 $6,383,250 $6,383,250 $6,383,250 $6,383,250 $6,383,250 $6,383,250
Non-Homesteaded Tax Rev,
(3) 3.2 $14,207,878 $14,349,957 $14,493,456 $14,638,391 $14,784,775 $14,932,623
Total tax Revenue $20,591,128 $20,733,207 $20,876,706 $21,021,641 $21,168,025 $21,315,872
Incremental tax revenue $2,159,811 $0 $142,079 $285,578 $430,513 $576,897 $724,745
New revenue roll out Value Y 6 Y 7 Y 8 Y 9 Y 10 Y 11
Taxable real property $6,661,210,098 $6,661,210,098 $6,661,210,098 $6,661,210,098 $6,661,210,098 $6,661,210,098
Homestead Property Value (5) 31% $1,994,765,500 $1,994,765,500 $1,994,765,500 $1,994,765,500 $1,994,765,500 $1,994,765,500
Non-Homesteaded Value (5) 69% $4,666,444,598 $4,666,444,598 $4,666,444,598 $4,666,444,598 $4,666,444,598 $4,666,444,598
Total Taxable Value $6,661,210,098 $6,661,210,098 $6,661,210,098 $6,661,210,098 $6,661,210,098 $6,661,210,098
Property Appreciation Non-
Homesteaded . 0% 0% 0% 0% 0% 0%
Homesteaded tax Rev. (3) 3.2 $6,383,250 $6,383,250 $6,383,250 $6,383,250 $6,383,250 $6,383,250
Non-Homesteaded Tax Rev,
(3) 3.2 $14,932,623 $14,932,623 $14,932,623 $14,932,623 $14,932,623 $14,932,623
Total tax Revenue $21,315,872 $21,315,872 $21,315,872 $21,315,872 $21,315,872 $21,315,872
Incremental tax revenue Y6 -
11 $4,348,467 $724,745 $724,745 $724,745 $724,745 $724,745 $724,745
New revenue roll out Value Y 12 Y 13 Y 14 Y 15
Taxable real property $6,661,210,098 $6,661,210,098 $6,661,210,098 $6,661,210,098
Homestead Property Value (5) 31% $1,994,765,500 $1,994,765,500 $1,994,765,500 $1,994,765,500
Non-Homesteaded Value (5) 69% $4,666,444,598 $4,666,444,598 $4,666,444,598 $4,666,444,598
Total Taxable Value $6,661,210,098 $6,661,210,098 $6,661,210,098 $6,661,210,098
Property Appreciation Non-
Homesteaded . 0% 0% 0% 0%
Homesteaded tax Rev. (3) 3.2 $6,383,250 $6,383,250 $6,383,250 $6,383,250
Non-Homesteaded Tax Rev,
(3) 3.2 $14,932,623 $14,932,623 $14,932,623 $14,932,623
Total tax Revenue $21,315,872 $21,315,872 $21,315,872 $21,315,872
Incremental tax revenue Y12 -
15 $2,898,978 $724,745 $724,745 $724,745 $724,745
Total incremental revenue Years 1 - 15: $9,407,257
Net Present Value of Incremental. Revenue: $6,980,032
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Key Biscayne Charter High School – DRAFT Budget
KBHS enrollment targets:
Year 1 Year 2 Year 3 Year 4 Year 5
Grade 2009 - 2010 2010 - 2011 2011 - 2012 2012 - 2013 2013 - 2014
9 75 75 100 100 100
10 50 75 75 100 100
11 50 75 75 100
12 50 75 75
Total 125 200 300 350 375
Principal 1 1 1 1 1
Teachers 6 10 15 17 19
Teacher Assistants 2 3 4 4 4
ESE Teachers 1 1 2 2 2
ESE Assistants 1 1 2 2 2
IB Program coordinator 0 1 1 1 1
Total teachers 7 11 17 19 21
Total faculty 11 16 24 26 28
Students/Faculty ratio 11 13 13 13 13
Students / Cert. Teach 18 18 18 18 18
Year 1 Year 2 Year 3 Year 4 Year 5
FTE Revenue $794,783 $1,303,444 $2,004,045 $2,396,504 $2,631,875
Expenses Year 1 Year 2 Year 3 Year 4 Year 5
M-DCPS Admin. Fee $39,739 $65,172 $100,202 $119,825 $131,594
Teachers $282,000 $481,750 $740,691 $860,436 $985,705
ESE Teachers $52,000 $53,300 $109,265 $111,997 $114,797
ESE Teacher Assistants $25,000 $25,625 $52,531 $53,845 $55,191
Principal $95,000 $97,375 $99,809 $102,305 $104,862
Admin. Assistant (2) $60,000 $61,500 $63,038 $64,613 $66,229
Teacher Assistants $50,000 $76,875 $105,063 $107,689 $110,381
IB coordinator / Counselor $0 $51,250 $52,531 $53,845 $55,191
Fringe Benefits $141,000 $211,919 $305,732 $338,682 $373,089
Professional Develp. $4,800 $8,000 $12,000 $13,600 $15,200
Contract Serv. Counselor $12,500 $20,000 $30,000 $35,000 $37,500
Subst. Teachers $14,100 $24,088 $37,035 $43,022 $49,285
Capital / Tech /Books. Amort. $0 $48,510 $116,820 $170,445 $194,370
Class & Office Supplies $14,000 $22,000 $34,000 $38,000 $42,000
IB annual fee $9,100 $9,100 $9,100
IB Diploma test $23,438 $23,438
IB Teacher Cert. $12,000 $6,000 $2,400 $2,400
Janitorial Svcs $27,000 $27,675 $28,367 $29,076 $29,803
Payroll, Accounting $18,000 $18,450 $18,911 $19,384 $19,869
Phone / Comm. $5,000 $6,000 $7,500 $10,000 $11,000
Utilities $12,500 $20,000 $30,000 $35,000 $37,500
Insurance (prop. & Liab.) $10,000 $12,000 $14,000 $16,000 $18,000
Ind. Fin. Audit $5,000 $5,125 $5,253 $5,384 $5,519
Other $12,500 $20,000 $30,000 $35,000 $37,500
11
Total Expenses $880,139 $1,368,613 $2,007,847 $2,298,085 $2,529,521
P&L Operations -$85,356 -$65,169 -$3,802 $98,420 $102,354
Grants Year 1 Year 2 Year 3 Year 4 Year 5
Plan. & Prog. Design $25,000
1st year impl. Grant $225,000
2nd year impl. Grant $75,000
Construction Grant $300,000
Total Grants $250,000 $75,000 $0 $300,000 $0
P&L Operations and grants $164,644 $9,831 -$3,802 $398,420 $102,354
Accumulated P&L Operations and Grants $164,644 $174,475 $170,672 $569,092 $671,446
Other potential revenue (not included in P&L)
PTSA & KBEF $0 $0 $0 $0 $0
After School Programs $0 $0 $0 $0 $0
Capital equipment
Startup $0
Class equip. / technol. $0 $112,500 $150,000 $75,000 $37,500
Science labs $0 $10,000 $25,000 $25,000
Books $22,500 $30,000 $52,500 $30,000
Office Equipment $0 $2,000 $2,000 $10,000 $5,000
New Capital equipment $0 $147,000 $207,000 $162,500 $72,500
Value of Capital equipment $0 $147,000 $354,000 $516,500 $589,000
Accumulated - Deprec. $0 $48,510 $165,330 $335,775 $530,145
Budget assumptions:
Start up expenses:
Pre-opening operating
Start up coordinator 6 months $26,664.00 Feb - July
Principal - 6 months $57,950.00 Feb - July
Teachers - 1 month $33,956.67
Recruitment / Advertisement $15,000.00
Office supplies $5,000.00
Sub-Total operating $138,570.67
Capital equipment
Class equip. / technol. $187,500.00 $1,500.00
Science labs $15,000.00 $15,000.00
Office Equipment $20,000.00
Books $37,500.00 $300.00 Per set of grade books
Sub-Total Cap. Equip. $260,000.00
Total start up expenses $398,570.67
12
Total of Key Biscayne students attending MDCPS 2007-08 School Year by school and grade level
School Name
Grade
Level
Total for
Grade Level
COCONUT GROVE ELEMENTARY K 1
KEY BISCAYNE K-8 CENTER K 109
KEY BISCAYNE K-8 CENTER 1 126
SUNSET ELEMENTARY 1 3
HENRY S. WEST LABORATORY SCHL 1 1
KEY BISCAYNE K-8 CENTER 2 129
SUNSET ELEMENTARY 2 1
KEY BISCAYNE K-8 CENTER 3 102
SUNSET ELEMENTARY 3 8
KEY BISCAYNE K-8 CENTER 4 125
SUNSET ELEMENTARY 4 4
CORAL WAY K-8 CENTER 5 1
KEY BISCAYNE K-8 CENTER 5 117
SUNSET ELEMENTARY 5 5
KEY BISCAYNE K-8 CENTER 6 101
GEORGE WASHINGTON CARVER 6 8
YOUNG WOMEN'S PREPARATORY ACAD 6 6
KEY BISCAYNE K-8 CENTER 7 98
GEORGE WASHINGTON CARVER 7 7
PONCE DE LEON MIDDLE 7 1
SOUTH MIAMI MIDDLE SCHOOL 7 1
YOUNG WOMEN'S PREPARATORY ACAD 7 3
ALTERNATIVE OUTREACH PROGRAM 7 1
KEY BISCAYNE K-8 CENTER 8 107
GEORGE WASHINGTON CARVER 8 12
PONCE DE LEON MIDDLE 8 2
YOUNG WOMEN'S PREPARATORY ACAD 8 1
INTERNATIONAL STUDIES CHARTER 9 17
ACADEMY OF ARTS AND MINDS 9 9
YOUNG WOMEN'S PREPARATORY ACAD 9 5
CORAL GABLES SENIOR HIGH 9 28
DESIGN & ARCHITECTURE SENIOR 9 3
CORAL REEF SENIOR HIGH 9 1
MARITIME & SCIENCE TECHNOLOGY 9 8
NEW WORLD SCHOOL OF THE ARTS 9 1
ALTERNATIVE OUTREACH PROGRAM 9 1
MERRICK EDUCATIONAL CENTER 9 1
INTERNATIONAL STUDIES CHARTER 10 3
ACADEMY OF ARTS AND MINDS 10 3
YOUNG WOMEN'S PREPARATORY ACAD 10 4
CORAL GABLES SENIOR HIGH 10 32
DESIGN & ARCHITECTURE SENIOR 10 1
MARITIME & SCIENCE TECHNOLOGY 10 5
MIAMI LAKES EDUCATIONAL CENTER 10 1
MIAMI NORTHWESTERN SENIOR HIGH 10 1
NEW WORLD SCHOOL OF THE ARTS 10 1
INTERNATIONAL STUDIES CHARTER 11 8
LIFE SKILLS CENTER MIAMI-DADE 11 1
ACADEMY OF ARTS AND MINDS 11 11
SCHOOL FOR ADVANCED STUDIES WC 11 2
CORAL GABLES SENIOR HIGH 11 15
DESIGN & ARCHITECTURE SENIOR 11 2
CORAL REEF SENIOR HIGH 11 1
MARITIME & SCIENCE TECHNOLOGY 11 6
MIAMI SENIOR HIGH 11 1
INTERNATIONAL STUDIES CHARTER 12 6
ACADEMY OF ARTS AND MINDS 12 4
CORAL GABLES SENIOR HIGH 12 11
13
CORAL REEF SENIOR HIGH 12 2
MARITIME & SCIENCE TECHNOLOGY 12 4
INSTRUCTIONAL SYSTEMWIDE 12 1
Grand Total 1280
Total of Key Biscayne students attending MDCPS 2006-0 School Year by school and grade level
School Name
Grade
Level
Total for
Grade Level
MAYA ANGELOU ELEMENTARY K 1
KEY BISCAYNE K-8 CENTER K 115
ADA MERRITT K-8 CENTER K 1
KEY BISCAYNE K-8 CENTER 1 123
ADA MERRITT K-8 CENTER 1 1
SUNSET ELEMENTARY 1 1
COCONUT GROVE ELEMENTARY 2 1
KEY BISCAYNE K-8 CENTER 2 95
SUNSET ELEMENTARY 2 7
GEORGE WASHINGTON CARVER ELEM 3 1
KEY BISCAYNE K-8 CENTER 3 112
SUNSET ELEMENTARY 3 5
COMSTOCK ELEMENTARY 4 1
CORAL WAY K-8 CENTER 4 1
KEY BISCAYNE K-8 CENTER 4 109
NORTH BEACH ELEMENTARY 4 1
SUNSET ELEMENTARY 4 5
COCONUT GROVE ELEMENTARY 5 2
CORAL GABLES ELEMENTARY 5 1
KEY BISCAYNE K-8 CENTER 5 100
SUNSET ELEMENTARY 5 9
KEY BISCAYNE K-8 CENTER 6 92
GEORGE WASHINGTON CARVER 6 7
SOUTH MIAMI MIDDLE SCHOOL 6 1
YOUNG WOMEN'S PREPARATORY ACAD 6 1
KEY BISCAYNE K-8 CENTER 7 104
GEORGE WASHINGTON CARVER 7 13
PONCE DE LEON MIDDLE 7 2
ALTERNATIVE OUTREACH PROGRAM 7 1
KEY BISCAYNE K-8 CENTER 8 75
GEORGE WASHINGTON CARVER 8 9
CITRUS GROVE MIDDLE SCHOOL 8 1
PONCE DE LEON MIDDLE 8 1
SOUTH MIAMI MIDDLE SCHOOL 8 1
MERRICK EDUCATIONAL CENTER 8 1
INTERNATIONAL STUDIES CHARTER 9 6
ACADEMY OF ARTS AND MINDS 9 4
YOUNG WOMEN'S PREPARATORY ACAD 9 1
CORAL GABLES SENIOR HIGH 9 33
MARITIME & SCIENCE TECHNOLOGY 9 4
MIAMI LAKES EDUCATIONAL CENTER 9 1
MIAMI NORTHWESTERN SENIOR HIGH 9 1
NEW WORLD SCHOOL OF THE ARTS 9 1
INTERNATIONAL STUDIES CHARTER 10 9
ACADEMY OF ARTS AND MINDS 10 10
CORAL GABLES SENIOR HIGH 10 15
DESIGN & ARCHITECTURE SENIOR 10 2
CORAL REEF SENIOR HIGH 10 1
MARITIME & SCIENCE TECHNOLOGY 10 5
MIAMI SENIOR HIGH 10 1
ALTERNATIVE OUTREACH PROGRAM 10 1
INTERNATIONAL STUDIES CHARTER 11 6
ACADEMY OF ARTS AND MINDS 11 6
CORAL GABLES SENIOR HIGH 11 18
CORAL REEF SENIOR HIGH 11 2
MARITIME & SCIENCE TECHNOLOGY 11 4
MERRICK EDUCATIONAL CENTER 11 1
14
SCHOOL FOR ADVANCED STUDIES WC 12 1
CORAL GABLES SENIOR HIGH 12 18
MARITIME & SCIENCE TECHNOLOGY 12 2
NEW WORLD SCHOOL OF THE ARTS 12 1
Grand Total 1155
Location, location, location Better schools mean higher property values Home buyers go
shopping for schools
USA TODAY (pre-1997 Fulltext) - McLean, Va. Author: Del Jones Date: May 15, 1996 Section: MONEY
Abstract (Document Summary)
An exclusive USA TODAY study finds that in city after city, it's customary for a house to be worth at least
10% more than a comparable house across the street if that street is the boundary line between a highly
rated school district and a laggard. In some cases, houses in the best school districts cost almost twice as
much as those nearby.
Tremendous upheaval is the result. More people are house shopping almost exclusively for the right
schools, putting proximity to schools ahead of proximity to work -- even ahead of taxes. That leads to
overcrowding at schools with the best reputations, while the halls of others echo half-empty.
Throughout the country, homes take weeks or months longer to sell just for being on the wrong side of
the boundary. Home builders say they rarely bother breaking ground these days where schools aren't
good.
Childless house hunters are increasingly asking for houses in quality school districts because of greater
appreciation and pride in ownership. Only one-third of households have school-age children. Yet, 80% of
house hunters strongly consider schools vs. 50% 20 years ago, according to Weichert Realtors.
Location, location, location Better schools mean higher property values Home buyers go shopping for
schools
The three rules of real estate remain in stone: Location, location, location. But real estate agents,
appraisers, home builders and tax authorities overwhelmingly agree that proximity to high-quality
schools is now the No. 1 factor in determining what a home is worth in any given market.
For instance, two comparable houses in the same Dallas neighborhood were sold five months apart. One
went for $155,000, the other for $276,000. The difference? The more expensive house was in the
Highland Park public school district where college entrance test scores rank in the top 1% in the country.
An exclusive USA TODAY study finds that in city after city, it's customary for a house to be worth at least
10% more than a comparable house across the street if that street is the boundary line between a highly
rated school district and a laggard. In some cases, houses in the best school districts cost almost twice as
much as those nearby.
Tremendous upheaval is the result. More people are house shopping almost exclusively for the right
schools, putting proximity to schools ahead of proximity to work -- even ahead of taxes. That leads to
overcrowding at schools with the best reputations, while the halls of others echo half-empty.
Throughout the country, homes take weeks or months longer to sell just for being on the wrong side of
the boundary. Home builders say they rarely bother breaking ground these days where schools aren't
good.
The great migration puts school boards under constant fire to redraw school district boundaries. And,
that makes board members more vulnerable to coercion from real estate developers, builders and angry
homeowners with a stake in property.
School board meetings have become a ``community bloodletting played out in the public arena,'' says
E.E. ``Gene'' Davis, a former school superintendent in Alaska and Virginia, who now prepares boards for
the mire of controversy that awaits those attempting boundary changes.
Childless house hunters are increasingly asking for houses in quality school districts because of greater
appreciation and pride in ownership. Only one-third of households have school-age children. Yet, 80% of
house hunters strongly consider schools vs. 50% 20 years ago, according to Weichert Realtors.
Obviously, schools have long been important. What's changed is that, to many well-educated baby
boom parents, little else matters more than finding the best school to prepare their children for college.
Fifteen years ago, the average college graduate earned 50% more than those with high school
educations. Today they earn almost twice as much.
Baby boomers have lived through a decade of downsizing and want every possible advantage for their
children. Many parents are willing to live in high-priced cracker boxes with no closet space to be near
good schools, as they do in the 80-year-old neighborhood of Whitefish Bay on the east side of
Milwaukee.
Where parents used to rely on real estate agents and word-of-mouth to find good schools, now more
are demanding hard data: test scores, per-student spending, teacher-student ratios, the percentage of
high school graduates to go on to college. Moore Data Management Services, which sells such
information to real estate agencies, says demand is up more than 500% in five years.
More than 350 large companies, including Allstate Insurance, Honda of America and Johnson & Johnson
supply detailed comparative information about schools as part of their relocation packages, Moore says.
The information serves to narrow the choices for house hunters to a handful of schools in every city.
House hunting winds up tightly focused on neighborhoods surrounding the best public schools, driving
up prices in those areas and creating a growing chasm in home values.
It also creates tremendous pressure on schools to get test scores up. Employees of prize-winning
Stratfiel Elementary School in Fairfield, Conn., are being accused of changing test answers to enhance
the school's reputation. There's controversy in Milwaukee, where the school board is accused of
releasing good news about improvements, while suppressing bad news.
The most dedicated house hunters go beyond test data. They sit in on school classes and interview
principals and teachers. Police departments get calls from house hunters looking for the schools with
the fewest drug busts.
``I want to see the curriculum, the books and computers,'' says Tracy Migliozzi of Pittsburgh, who is
visiting schools while plotting a move to a better district. She has a 5-year-old daughter about to enter
kindergarten and an 8-year-old son who will be switching from parochial school.
Good-school premium
USA TODAY conducted a survey in April, enlisting the help of SchoolMatch, a Westerville, Ohio, company
that sells information about the USA's 16,665 public school systems to house hunters and real estate
agencies.
SchoolMatch suggested cities where a school district with high college entrance test scores and other
measures of quality bordered districts with worse performance. USA TODAY then asked real estate
agents in those cities to find houses that recently sold along the boundaries. They were told to find
houses that were in all other ways comparable, except that they were in different school districts.
In Milwaukee, Remax Lakeside Realtor David Delahunt found 16 houses that sold since November 1993
within an area of two square blocks. All had eight rooms, four bedrooms and between 11/2 and 21/2
bathrooms. The eight in Milwaukee School District, where performance on college entrance tests is in
the bottom 20% in the nation, sold for an average $170,625.
The eight in the Shorewood School District, where performance is in the best 1%, sold for an average
$240,563 -- 41% more.
``Visually, in most cases, you won't see a difference (between houses),'' says Steve Lauenstein, a
Milwaukee appraiser, ``which really proves that it has to do with schools.''
The value gap might be greater if Milwaukee quit requiring its 8,346 city employees to live in the
Milwaukee School District.
``There would be a huge sucking sound when all those people sold at depressed prices,'' Delahunt says.
``I have a brother-in-law who teaches in Milwaukee. His kids went to parochial school. He'd love to live
in the outlying area.''
Near Atlanta, 88 new ranch-style homes between 1,500 and 2,000 square feet sold since January in
Rockdale County for a median $115,000, or $64.16 per square foot, says Brian Stafford, co-owner of
Peachtree Appraisal. In neighboring Newton County, fewer than half that many homes sold for a median
$92,450, or $55.95 per square foot. Rockdale SAT scores average 60 points higher.
In San Diego, the Poway Unified district has a big word-of-mouth advantage over San Diego Unified
district among military transferees. In particular, they shun San Diego's Mira Mesa High School, says
Remax agent Ken Davis.
The districts are separated by a canyon, but Davis found two 2,000-square-foot houses on either side,
both built in 1993 with four bedrooms, 21/2 bathrooms, nice yards and a view of the hills. The one on
the Poway side sold in February for $227,000. The one on the San Diego side sold in December for
$197,000.
In Baltimore, a 67-year-old house on Pinehurst Road just sold for $209,900. But it sits within the
Baltimore City Public School System, where college entrance scores are in the bottom 20% nationwide.
A 65-year-old house, also on Pinehurst Road, sold for $280,000. Despite the proximity, it's in the
Baltimore County Public School district, where scores are in the top 21%.
``They are very similar, less than a block away,'' says Dawn Covahey, corporate sales manager with
Coldwell Banker Grempler.
Recent academic studies support USA TODAY's findings. Enrollment (kindergarten through 8th grade) in
Massachusetts public schools that rank in the state's top 10% by test scores swelled 14% between 1990
and 1994, says Wellesley College real estate economist Karl Case. Based on 1990 census data of children
then between the ages of 1 and 9, enrollment at those schools should have gone up just 4%. Meanwhile,
enrollment fell 2% at schools testing in the bottom 10%, when it should have risen 13%.
``It's people moving, there's no question,'' Case says.
In Ohio, students must pass a proficiency test to graduate high school. Cleveland State University
finance professor Michael Bond found that houses sold in 1994 fetched $471 more for every percentage
point increase in passing rates at the schools near them. More than 90% of students passed the test at
some Cleveland high schools, while barely 20% passed at others. The difference of 70 percentage points,
multiplied by $471, means as much as a $33,000 difference in home values.
Although test scores strongly mirror parents' income, property values are influenced by test scores even
in poorer districts, Bond says. ``We had to use fairly sophisticated techniques to weed out the influence
of income,'' he says. ``The evidence is overwhelming. Independent of income, better schools mean
higher property values.''
Home buyers, at least those with children, aren't being irrational. Each extra $20,000 paid for a house
and financed over 30 years at 8.35% interest adds $1,820 a year to a mortgage payment. Private school
can easily cost three times that much -- per child -- and is not tax deductible.
For childless households, the right financial decision is less clear. Kathleen Niesen would have had to pay
about $255,000 for a comparable house in the Shorewood district bordering Milwaukee. She paid
$226,000 in the Milwaukee school district and is happy with her decision.
Schools also drive rental rates. Milwaukee landlord Gerald Sobczak says he rents two- and three-
bedroom apartments in the Shorewood School District for $500 a month. Similar apartments two blocks
away in the Milwaukee district rent for $350 to $400. El Paso Realtor Ed Kot says he could get another
$150 a month for a house that he rents for $1,250 if it were a block over in the Polk Elementary district.
That rental premium materialized suddenly in 1994 when Polk Elementary won a Texas award for high
test scores that was well-publicized locally.
When El Paso was building Franklin High School three years ago, Kot says he warned house buyers in
writing that school boundaries were subject to change. ``I didn't want to get sued,'' he says.
The great migration
Parents say they have no regrets about paying more for less house as long as they wind up in the right
school district.
Schools were the top priority for Alice Li when her family moved to Wayne, N.J., from Pennsylvania 10
years ago. Her daughter Winnie ``was very little, but we knew the district had a talented and gifted
program.'' Winnie has scored perfectly three straight years on the national Latin exam, is editor of the
school newspaper, will graduate Wayne Valley High with a 4.0 grade point average and will attend
Harvard.
Victoria and Gerald Sobczak say their deaf son Chris wasn't getting adequate attention in the Milwaukee
district. Three years ago they moved a few blocks to a much smaller house in the Shorewood district
where the school provides an interpreter, who accompanies Chris to every class. ``We decided we
wanted more out of life than just a house,'' Victoria Sobczak says.
When Debbie and John Roesner moved from Chicago to Milwaukee, they could have spent $30,000 less
on a comparable house three blocks away. But they have two children now reaching school age and
willingly paid the difference.
Some parents admit that they just stumbled upon good schools. David and Pat Marin had no plans to
have children when they were house hunting in the San Francisco area in 1975. They found more house
for the money in the Los Altos High School district because it had a worse reputation than others
nearby.
Then, David Jr. came along, the high school improved markedly and became the public school of choice
among Japanese immigrants to the area. The baby grew up to score perfectly on his SATs. The house has
appreciated nearly 1,000%, more than area houses near other schools.
Meanwile, special property tax laws sharply limit tax increases on some California homes. That acts to
limit the supply of houses for sale in the face of higher demand for the school. The result: It's hard to
find a modest three-bedroom house for less than $500,000. ``We were really very, very lucky,'' Pat
Marin says.
2
Neighborhood School
Characteristics:
What Signals
Quality to
Homebuyers?
Kathy J. Hayes
Research Associate
Federal Reserve Bank of Dallas
and Professor of Economics
Southern Methodist University
Lori L. Taylor
Senior Economist and Policy Advisor
Federal Reserve Bank of Dallas
A nalysis suggests that
homebuyers and economists
share the same definition
of school quality.
Most people are familiar with the adage
that real estate values are determined by three
basic characteristics—location, location, location.
Economists consider this cliché only a modest
exaggeration because research suggests that
locational characteristics can explain much of
the variation in residential property values. Not
surprisingly, home prices tend to be lower
in communities with high property taxes and
higher in communities with low crime rates.
Home prices fall as the commute to the central
business district increases and rise as the
amount of air pollution decreases. Locations
near a city park command a premium, while
locations near the city dump sell at a discount.
Popular wisdom and economic research
suggest that the quality of the neighborhood
school should also be an important locational
characteristic. Many researchers have found that
property values are higher where school spend-
ing is higher (for example, Oates 1969; Sonstelie
and Portney 1980; and Bradbury, Case, and Mayer
1995). Other researchers have found a positive
relationship between housing values and the test
performance of students at the corresponding
school (for example, Jud and Watts 1981, Rosen
and Fullerton 1977, and Walden 1990). How-
ever, the economic literature on school quality
measurement argues that the appropriate meas-
ure of school quality is the school’s marginal
effect on students (see Hanushek 1986), and no
one has examined the relationship between
marginal school effects and housing values.1
Thus, we have an incongruity in the literature:
spending and test scores seem to influence prop-
erty values, but economists who study schools
would not generally consider these characteris-
tics measures of school quality. Meanwhile, the
literature has been silent on whether the econo-
mists’ notion of school quality is a locational
characteristic that matters to homebuyers.
In this article, we attempt to identify the
influence of neighborhood schools on the value
of residential homes. Using a hedonic model of
home purchases and historical data on homes in
the Dallas Independent School District (DISD),
we demonstrate that school quality can be an
important locational characteristic in determin-
ing housing values. We find evidence that prop-
erty values in DISD reflect student test scores
but not school expenditures. Interestingly, we
also find that the relationship between test scores
and property values arises from an underlying
relationship between property values and the
marginal effects of schools. Thus, our analysis
suggests that homebuyers and economists share
the same definition of school quality.
FEDERAL RESERVE BANK OF DALLAS 3 ECONOMIC REVIEW FOURTH QUARTER 1996
A simple model of housing values
A house is a collection of desirable charac-
teristics such as shelter, comfort, and location.
Therefore, the price that buyers are willing to
pay for a house should be related to the prices
they are willing to pay for its component charac-
teristics. By treating a house as the sum of its
parts, a hedonic housing model generates esti-
mates of the consumer’s willingness to pay for
each component characteristic.
Our hedonic model of housing prices in a
single labor market is adapted from Rosen (1974).
In this simplified model, consumers attempt to
maximize their own happiness, taking the hous-
ing stock as given. Consumers derive satisfac-
tion from consuming all sorts of housing
characteristics (Z = z1, z2,....zn ) and a composite
good (x). They earn an income (y) regardless of
their chosen residence and can only consume
combinations of Z and x that are affordable
given that income. There are many types of
consumers, and tastes for Z and x differ among
those consumers according to socioeconomic
characteristics (α) such as the person’s age or
educational attainment. In equilibrium, all con-
sumers with identical preferences and income
are able to achieve the same level of satisfaction.
After some manipulation, the individual
consumer’s decision-making can be described
with a willingness-to-pay relationship or, more
formally, a bid rent function:
(1)R = R (z1,z2....zn:y,α).
The value of the bid rent function represents the
amount the consumer is willing to pay to rent a
home with certain characteristics (Z ), given the
consumer’s income level and socioeconomic type.
Partial derivatives of the bid rent function with
respect to housing characteristics represent the
consumer’s willingness to pay for those charac-
teristics.
The price a potential buyer would be will-
ing to pay for a house represents the present
discounted value of the after-tax stream of bid
rents.2 If τR is the tax rate chosen by the jurisdic-
tion for real estate,3 θ represents the discounting
factor, and housing is an infinitely lived asset,
then the bid price of a house (P ) would be
or equivalently,
The variation in incomes and socioeconomic
characteristics generates a continuum of bid prices
over a variety of types of homes.
In equilibrium, the sale price of any par-
ticular house equals the highest bid offered by
potential consumers, regardless of their income
or socioeconomic type. The hedonic price func-
tion describes this equilibrium.4 The hedonic
price function that we estimate describes the
arm’s length sales price as a function of the
characteristics of the house and of its location.5
The locational characteristics include neighbor-
hood characteristics as well as local school char-
acteristics.
The data
Data for this analysis come from three
sources. Data on elementary school charac-
teristics have been provided by DISD. Data on
the characteristics of single-family homes in
DISD come from the SREA Market Data Center’s
annual publication of residential property
transactions. We restrict attention to the 288
DISD properties for which complete data are
available that sold in July 1987 and were located
in both the city and the county of Dallas.
Data on nonschool locational characteristics
come from the 1990 Census of Housing and
Population.
DISD has provided data on student body
characteristics, student achievement scores, and
per-pupil expenditures for ninety-six elementary
schools in its jurisdiction. From these data, we
construct four possible indicators of school quality
in 1987—current expenditures per pupil (SPEND),
average sixth-grade achievement in mathematics
on the Iowa Test of Basic Skills (MATH687 ), the
marginal effect of the school on sixth-grade
mathematics achievement (SCHL687 ), and the
expected achievement of the student body in
sixth-grade mathematics (PEER687 ). The first
two of these indicators are common measures
of school quality in the housing literature. The
second two indicators represent a decomposi-
tion of average mathematics achievement into
school effects and peer group effects (see the
appendix). SCHL687 measures the increase in
student achievement in mathematics that can be
attributed to the school. It corresponds to a
common measure of school quality in the eco-
nomics of education literature (see Hanushek
and Taylor 1990, Aitkin and Longford 1986, and
Boardman and Murnane 1979). PEER687 is in-
cluded as a possible indicator of school quality
because research has shown that a high-achiev-
ing peer group in the school can have a positive
effect on individual student performance (Sum-
mers and Wolfe 1977).
() ,2 P RPR=−τ
θ
()( , .... , ).3 12PRz z z yn=:
+R
α
θτ
4
The housing data used in this analysis in-
clude the log of the sale price of the property
(PRICE ), the year in which the home was built
(YRBUILT ), the number of square feet of living
area in the structure (SQFTLA ), and indicator
variables that take on the value of one if the
house has a swimming pool or a fireplace and
zero otherwise (POOL and FIREPL, respectively).
To capture potential nonlinearities in the rela-
tionship between the sale price and the age of
the property, we also include interaction terms
that take on the value of YRBUILT when the
residence has a pool (YR •POOL ) or fireplace
(YR •FIREPL ) and zero otherwise. We match the
potential school quality indicators with housing
characteristics using the SREA data on addresses
and a Realtor’s guide to DISD attendance zones
(Positive Parents of Dallas et al. 1987).
The address data also permit us to merge
in census tract characteristics from the 1990
Census of Housing and Population. The census
tract data support three nonschool locational
characteristics. These potential locational char-
acteristics are the demographic characteristics
of the neighborhood residents (NEIGHBORS ),6
the share of apartments in the neighborhood
housing stock (APARTMENTS ), and a proxy for
the accessibility of private schools (the share of
the elementary school population that is attend-
ing private school, denoted PRIVSCHL ).
Finally, we used the address data to con-
struct another nonschool locational characteris-
tic—the linear distance to the central business
district (DISTANCE )—and to divide the sample
into two parts according to whether or not the
property is located substantially north of down-
town Dallas.7
Table 1 presents descriptive statistics for
the data used in this analysis. As the table clearly
indicates, there are significant differences be-
tween northern and southern Dallas.8 On aver-
age, northern Dallas homes are more expensive,
bigger, and more likely to have a pool or fire-
place. Northern Dallas schools register higher
on all our potential indicators of school quality.
The average northern Dallas neighborhood has
a smaller share of apartments in the housing
stock and more access to private elementary
schools than the average southern Dallas neigh-
borhood. Meanwhile, the residents of southern
Dallas neighborhoods are more likely than the
residents of northern Dallas to be black or His-
Table 1
Descriptive Statistics: A Tale of Two Cities
Northern Dallas Southern Dallas
Standard Standard
Variable Mean deviation Mean deviation
PRICE $203,266 (204,301) $82,502 (55,926)
SQFTLA 2,192 (1,026) 1,471 (568)
YRBUILT 58.3 (13.2) 53.5 (18.7)
POOL .22 (.42) .04 (.19)
FIREPL .71 (.45) .42 (.50)
DISTANCE 2.46 (.65) 2.11 (.86)
APARTMENTS .18 (.20) .26 (.23)
PRIVSCHL .39 (.21) .10 (.08)
NEIGHBORS –1.47 (1.34) 1.59 (1.62)
MEDIAN INCOME $52,819 (26,841) $27,256 (7,735)
COLLEGE .72 (.15) .40 (.20)
BLUE-COLLAR .11 (.09) .31 (.13)
UNDER 12 .12 (.03) .18 (.05)
OVER 65 .19 (.06) .11 (.04)
HISPANIC .10 (.12) .32 (.25)
BLACK .03 (.05) .27 (.29)
SPEND $2,498 (381) $2,068 (232)
MATH687 76.97 (5.27) 69.56 (4.26)
SCHL687 29.55 (4.30) 26.86 (3.18)
PEER687 47.42 (3.21) 42.70 (3.07)
Number of observations 150 138
FEDERAL RESERVE BANK OF DALLAS 5 ECONOMIC REVIEW FOURTH QUARTER 1996
panic, young, hold a blue-collar job, have a
lower income, and to have not attended college.
The estimation and results
Because southern and northern Dallas dif-
fer so dramatically, we estimate the hedonic
price function separately for the two areas using
weighted least squares regression.9 Furthermore,
for comparison with the previous literature, we
examine three models of the hedonic price
function. In the first model, school quality is
measured by per-pupil spending. In the second
model, school quality is measured by both per-
pupil spending and test scores. In the third
model, which represents an unrestricted version
of the second model, test scores are decom-
posed into school effects and peer group effects.
We correct the standard errors from model 3 for
the problem of estimated regressors (SCHL687
and PEER687 ), using the technique suggested
by Murphy and Topel (1985).10 Table 2 presents
our estimation results.
Despite the dramatic differences between
northern and southern Dallas, Table 2 reveals
striking similarities in the consumer’s willingness
to pay for housing characteristics. In both parts
of the city, homebuyers pay a substantial pre-
mium for additional living space. Southern Dal-
las buyers tend to be slightly more sensitive to
the age of the property, but homebuyers in both
parts of the city have strong preferences for
newer homes. Fireplaces add value to older
homes, but the effect dissipates for newer
homes.11 After controlling for the age and size of
Table 2
Estimates of the Hedonic Price Function
Northern Dallas Southern Dallas
Variables Model 1 Model 2 Model 3 Model 1 Model 2 Model 3
INTERCEPT 3.465** 3.123** 3.174** 3.163** 2.865** 2.867**
(.334) (.380) (.391) (.341) (.592) (.596)
SQFTLA 5.0E–4** 5.0E–4** 5.0E–4** 5.5E–4** 5.4E–4** 5.4E–4**
(2.5E–5) (2.5E–5) (2.5E–5) (5.7E–5) (5.8E–5) (5.9E–5)
YRBUILT .007* .006* .007* .008** .008** .008**
(.004) (.004) (.004) (.002) (.002) (.002)
YR •POOL –.004 –.005 –.005 –.022** –.023** –.023**
(.003) (.003) (.003) (.011) (.011) (.011)
YR •FIREPL –.007* –.007* –.007* –.005 –.005 –.005
(.004) (.004) (.004) (.003) (.003) (.003)
POOL .272 .289 .301 1.211** 1.255** 1.258**
(.202) (.201) (.202) (.571) (.577) (.581)
FIREPL .448** .433** .441** .431** .419** .420**
(.205) (.203) (.204) (.204) (.205) (.206)
DISTANCE –.122** –.146** –.146** –.137** –.139** –.138**
(.039) (.041) (.041) (.034) (.034) (.036)
APARTMENTS .018 .007 .006 .074 .089 .088
(.092) (.092) (.092) (.121) (.123) (.124)
PRIVSCHL .450** .431** .435** 1.073** 1.078** 1.075**
(.142) (.141) (.141) (.515) (.516) (.520)
NEIGHBORS –.055** –.042* –.039* –.042 –.041 –.041
(.023) (.024) (.024) (.029) (.029) (.030)
SPEND 3.3E–5 –7.8E–6 1.7E–5 –8.6E–6 –4.1E–6 –2.4E–6
(7.0E–5) (7.3E–5) (8.3E–5) (1.2E–4) (1.2E–4) (1.3E–4)
MATH687 — .007* — — .004 —
— (.004) — — (.007) —
SCHL687 — — .009* — — .005
— — (.005) — — (.009)
PEER687 — — .004 — — .004
— — (.007) — — (.009)
Number of
observations 150 138
NOTE: Standard errors are in parentheses. The superscripts denote a coefficient that is significant at the 5-percent (**) or
10-percent (*) level.
6
the property and the presence of a fireplace,
pools have a negligible effect on home prices.12
Northern and southern Dallas homebuyers
are also similar in their willingness to pay for
most nonschool locational characteristics. In
both parts of the city, homebuyers are unwilling
to pay for a change in the concentration of
apartments (APARTMENTS ) but are willing to
pay for a shorter commute (DISTANCE ) and
greater access to private schools (PRIVSCHL ).
Furthermore, northern and southern Dallas home-
buyers pay similar premiums for a shorter
commute or greater access. Evaluated at the
mean, a 1-percent decrease in the distance to
the city center increases home prices by 0.36
percent in northern Dallas and 0.29 percent
in southern Dallas, while a 1-percent increase
in PRIVSCHL increases home prices by 0.17
percent in northern Dallas and 0.11 percent
in southern Dallas.13 Northern and southern
Dallas homebuyers differ substantially in their
willingness to pay for neighborhood demo-
graphics, however. Northern Dallas buyers
seem willing to pay a premium for a change in
resident characteristics, while southern Dallas
buyers do not.
Another significant difference between
northern and southern Dallas homebuyers ap-
pears in their willingness to pay for school qual-
ity. The data suggest that neither group considers
school spending an indicator of school quality
for which they are willing to pay. SPEND is
insignificant across all of the model specifica-
tions for both northern and southern Dallas.
However, the data indicate substantial differ-
ences in the willingness to pay for student
achievement on standardized tests. As model 2
illustrates, homebuyers in northern Dallas pay a
premium to live in the attendance zone of a
school where students score well on standard-
ized tests. Homebuyers in southern Dallas pay
no such premium.
Given the desegregation efforts during
the sample period, it is not particularly surpris-
ing that southern Dallas homebuyers were un-
willing to pay a premium for the neighborhood
schools.14 Busing students away from the neigh-
borhood school was much more common in
southern Dallas than in northern Dallas (Linden
1995). Therefore, while homebuyers might have
been able to rely on the attendance zone map in
northern Dallas, they had less reason to expect
that their choice of residence would guarantee a
specific school in southern Dallas. Given the
uncertainty about the stability of school atten-
dance zones, it is more surprising that northern
Dallas homebuyers were willing to pay a pre-
mium for school quality than that southern
Dallas homebuyers were unwilling to pay such
a premium.
One might suspect that northern Dallas
homebuyers are willing to pay for school zones
with good test scores because those scores indi-
cate characteristics of the students who live in
the area. If so, then the premium for test perfor-
mance would arise from the attractiveness of the
neighbors rather than the neighborhood school.
However, as model 3 illustrates, the test score
premium in northern Dallas arises from the mar-
ginal effects of the schools (SCHL687 ), not the
characteristics of the student body (PEER687 ).15
Evaluated at the mean, a 1-percent increase in
SCHL687 increases home prices by 0.26 percent.
Of the characteristics that we are able to ob-
serve, only the size and age of the property and
the distance from downtown have more influ-
ence than school effects on home prices in
northern Dallas.
Conclusions
Using a hedonic model of property values,
we examine the extent to which school quality
is a locational characteristic that influences
property values. We find that some home-
buyers are not only cognizant of differences in
school quality but also have revealed their
preferences for higher quality schools by pay-
ing a premium for their home. Our analysis
suggests that this premium for school quality
can be among the most important determinants
of housing prices.
Not all school characteristics appear to be
indicators of school quality, however. We find
no evidence that homebuyers are willing to pay
for changes in school expenditures or student
body characteristics. Instead, we find evidence
that the school characteristic for which home-
buyers pay a premium is the same characteristic
that economists associate with school quality,
namely, the marginal effect of the school on
student performance.
A number of policy implications can be
drawn from this research. The analysis suggests
that policies that impact school effects can have
a significant influence on residential property
values. It also casts considerable doubt on policy
analyses or policy initiatives that equate school
spending with school quality. Finally, the analy-
sis suggests that, at least as far as Dallas home-
buyers are concerned, researchers are on target
in trying to identify policy reforms that would
increase the marginal effectiveness of schools.
FEDERAL RESERVE BANK OF DALLAS 7 ECONOMIC REVIEW FOURTH QUARTER 1996
Notes
We would like to thank Rebecca Bergstrasser, Stephen
P. A. Brown, Thomas Fomby, Donna Ginther, Shawna
Grosskopf, Joe Hirschberg, and Jim Murdoch for
helpful comments and suggestions; Kelly A. George
for research assistance; and the Dallas Independent
School District for making its data available. Any
remaining errors are our own.
1 A few researchers, including Sonstelie and Portney
(1980), have examined the relationship between
property values and changes in test scores, but test
score changes are generally considered a poor proxy
for the marginal effects of schools.
2 This discussion ignores the differential tax treatment of
renters and owners.
3 If assessment errors are randomly distributed, then all
residences in a given government jurisdiction are
taxed at the same expected rate. Restricting analysis
to a single taxing jurisdiction eliminates the need to
measure the potential capitalization of tax rate differen-
tials and one can focus on estimating the hedonic
price function for housing characteristics (Z ).
4 For a further discussion of the hedonic price function,
see Bartik and Smith (1987).
5 An arm’s-length sales price can be considered an
equilibrium house price for that time and location.
6 NEIGHBORS is a principal components index of resi-
dent characteristics. The demographic characteristics
included in the index are median income of the census
tract and the shares of the population that are black,
Hispanic, over 65 years of age, under 12 years of age,
employed in a blue-collar occupation, and college
educated. The principal components index explains
65 percent of the variation in these variables. The
index is negatively correlated with median income
and the population shares of elderly and college
educated individuals and positively correlated with
the remaining demographic characteristics.
7 Residences north of a line along the southern border
of Highland Park Independent School District were
classified as being in northern Dallas. The remaining
residences were classified as being in southern Dallas.
8 The means are significantly different at the 5-percent
level for all of the characteristics.
9 The weight for northern Dallas is the reciprocal of the
product of the square root of (SQFTLA) and the square
root of (1 – PRIVSCHL); the weight for southern Dallas
is the reciprocal of the product of the square root of
(1/YRBUILT ) and the square root of (1 – PRIVSCHL).
Given these weights, the residuals are normally dis-
tributed and a Breusch–Pagan test can no longer
detect heteroskedasticity at the 5-percent level of
significance in either sample.
10 The Murphy–Topel error correction involves using the
variance–covariance matrix of the first-stage estimation
to inflate the standard errors that are used in hypothe-
sis testing in the second stage. Parameter estimates
are unaffected by the correction. Specifically, one tests
hypotheses using the variance–covariance matrix
∑^
corrected = ∑^
uncorrected + (Z ′Z )–1Z ′F *V^(θ^)F *′Z (Z ′Z )–1,
where Z is the matrix of second-stage regressors, F *
is a matrix of first-stage derivatives that is weighted by
the estimated coefficients on the generated regressors
from the second stage, and V^(θ^) is the variance–
covariance matrix from the first-stage regression.
Murphy and Topel demonstrate that the second term in
the above equation is a positive definite matrix.
11 It is unlikely that fireplaces, in and of themselves, have
such large effects on property values. Rather, fire-
places likely proxy for other desirable home character-
istics that we cannot observe in the data.
12 Pools appear to add value in southern Dallas, but the
effect may be spurious because only five southern
Dallas homes in our sample have pools.
13 These estimates come from model 3.
14 Of course, there are other possible explanations for not
finding a relationship between school quality measures
and property values in southern Dallas.
15 Omitting the potentially collinear NEIGHBORS from the
estimation does not alter this result.
8
References
Aitkin, M., and N. Longford (1986), “Statistical Modeling
Issues in School Effectiveness Studies,” Journal of the
Royal Statistical Society, A 149, pt. 1: 1–26.
Bartik, Timothy J., and V. Kerry Smith (1987), “Urban
Amenities and Public Policy,” in Handbook of Regional
and Urban Economics, ed. Edwin S. Mills (Amsterdam:
North Holland Press).
Boardman, Anthony E., and Richard J. Murnane (1979),
“Using Panel Data to Improve Estimates of the Determi-
nants of Educational Achievement,” Sociology of Educa-
tion 52 (April): 113–21.
Bradbury, Katherine L., Karl E. Case, and Christopher J.
Mayer (1995), “School Quality, Local Budgets, and Property
Values: A Re-Examination of Capitalization,” manuscript.
Hanushek, Eric A. (1986), “The Economics of Schooling:
Production and Efficiency in Public Schools,” Journal of
Economic Literature 24 (September): 1,141–76.
——— , and Lori L. Taylor (1990), “Alternative Assess-
ments of the Performance of Schools,” Journal of Human
Resources 25 (Spring):179–201.
Jud, G. Donald, and James M. Watts (1981), “Schools and
Housing Values,” Land Economics 57 (August): 459–70.
Linden, Glenn M. (1995), Desegregating Schools in Dallas:
Four Decades in the Federal Courts (Dallas: Three Forks
Press).
Murphy, Kevin M., and Robert H. Topel (1985), “Estimation
and Inference in Two-Step Econometric Models,” Journal
of Business and Economic Statistics 3 (October): 370–79.
Oates, Wallace E. (1969), “The Effects of Property Taxes
and Local Spending on Property Values: An Empirical
Study of Tax Capitalization and the Tiebout Hypothesis,”
Journal of Political Economy 77 (November/December):
957–71.
Positive Parents of Dallas, Dallas Chamber of Commerce,
and Dallas Independent School District (1987), All About
DISD.
Rosen, Harvey S., and David J. Fullerton (1977), “A Note
on Local Tax Rates, Public Benefit Levels, and Property
Values,” Journal of Political Economy 85 (April): 433–40.
Rosen, Sherwin (1974), “Hedonic Prices and Implicit Mar-
kets: Product Differentiation in Pure Competition,” Journal
of Political Economy 82 (January/February): 34–55.
Sonstelie, Jon C., and Paul R. Portney (1980), “Gross
Rents and Market Values: Testing the Implications of
Tiebout’s Hypothesis,” Journal of Urban Economics 7
(January): 102–18.
SREA Market Center Data Inc. (1987), North Texas
Annual 1987 (Atlanta: Damar Corp.).
Summers, Anita A., and Barbara L. Wolfe (1977), “Do
Schools Make a Difference?” American Economic Review
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Differential Impact of School Quality on Residential
Property Values,” Journal of Real Estate Research 5
(Summer): 221–30.
FEDERAL RESERVE BANK OF DALLAS 9 ECONOMIC REVIEW FOURTH QUARTER 1996
We decompose average test scores into
school effects and peer group effects, following the
methodology outlined in Hanushek and Taylor
(1990). Thus, we hypothesize that student achieve-
ment in period T is a function of the student’s
complete history of school (S ) and student and
family (F ) characteristics. However, because the
relationship is recursive, we can write
where AiT is the achievement of student i in period
T, the SikT are dummy variables that equal one if
the i th student attends school k in period T and
equal zero otherwise, and FiT represents student
and family characteristics in period T. In this
formulation, qkT represents the value added by
school k in period T and
represents the level of student achievement
that could be expected regardless of the school
attended. Thus, qkT is a measure of school effects,
and the average A^
iT for each school is a meas-
ure of peer group effects in that school.
Whenever student-level data are unavail-
able and the marginal effects of schools are inde-
pendent of the student and family characteristics,
equation A.1 can be estimated at the school level as
In this equation, AkT is average student achieve-
ment at school k in period T, FkT represents
average student and family characteristics at
school k in period T, γ + µkT = qkT + εkT , and εkT
represents the average estimation error for stu-
dents at school k in period T. At this level of
aggregation, γ + µkT is the best available proxy
for school effects, and PkT = λ~
AkT–1 + β~
T FkT is the
best available proxy for peer group effects. Be-
cause analysis at the school level incorporates
error into the estimates of school and peer group
effects, it is particularly important to treat these
Appendix
variables as estimated regressors in any subse-
quent analysis.
DISD provided data on student body charac-
teristics and student achievement scores for ninety-
six primary schools in its jurisdiction for the years
1986 and 1987. The student body characteristics
used in the analysis are the percentage of students
who were black or Hispanic (B&HISP ) and the
percentage of students who were not receiving free
or reduced-price lunches (the best available proxy
for socioeconomic status, SES ). The student
achievement data used in the analysis are average
scores on the Iowa Test of Basic Skills in mathe-
matics. We use sixth-grade scores from 1987
(MATH687 ) and fifth-grade scores from 1986
(MATH586 ) as the measures of student achieve-
ment. The variable XCOHORT (the percentage
increase in the number of students taking the
exam) controls for changes in cohort size between
1986 and 1987.
From these data and the estimated coeffi-
cients in Table A.1, we construct measures of
school and peer group effects for each of the
ninety-six schools in our study. Thus, for each
school, SCHL687k = 26.767 + µkT , and PEER687k
= 0.740 •MATH586k – 0.083 •XCOHORTk –
0.004 •B&HISPk + 0.004 •SESk .
Table A.1
Estimating School and Peer Group
Effects on Sixth-Grade Mathematics
Achievement
Parameter Standard
estimate error
INTERCEPT 26.767 6.301
MATH586 .740 .092
XCOHORT –.083 .017
B&HISP –.004 .002
SES .004 .021
Number of observations 96
R 2 .544
(.) ,A1 1
1
AA F qSiT iT T iT kT
k
ikT iT
=++ +−
=
∑λβ
(.)ˆA2 1AA FiT iT T iT
=+−λβ
(.)˜˜.A3 1AAFkT kT T kT kT
=+ + +−γλ β µ
Working Paper
WP 040203 April 2003
School Quality and Property Values
In Greenville, South Carolina
Kwame Owusu-Edusei
and
Molly Espey
Clemson University Public Service Activities
South Carolina Agriculture and Forestry Research
Department of Agricultural
and Applied Economics
Clemson University
Clemson, South Carolina 29634
Department of Agricultural
and Applied Economics
WP 040203 April 2003
School Quality and Property Values
In Greenville, South Carolina
Kwame Owusu-Edusei*,
Department of Ag & Applied Economics
Clemson University
Clemson, SC 29634-0355
Molly Espey
Department of Ag & Applied Economics
Clemson University
263 Barre Hall
Clemson University
Clemson, SC 29634-0355
mespey@clemson.edu
*Graduate Student and Associate Professor, respectively , Department of Agricultural
and Applied Economics, Clemson University, Clemson, SC.
Working Papers are not subject to review within the Department of Agricultural and
Applied Economics.
Copyright 2003 by Kwame Owusu-Edusei and Molly Espey. All rights reserved.
Readers may make verbatim copies of this document for non-commercial purposes by
any means, provided that this copyright notice appe ars on all such copies.
School Quality and Property Values in Greenville, South Carolina
Abstract
This study estimates the impact of school quality on property values within the city limits of
Greenville, South Carolina. This study differs from others in its use of a relative, rather that an
absolute measure of school quality. We apply a hedonic pricing model to estimate the impact of
K-12 rankings on the real constant-quality housing values. Based on 3,731 housing transactions
carried out from 1994 to 2000, our results suggest that those who choose to live within the city
limits of the study pay a premium to live in high quality school attendance areas. Therefore,
high-ranked schools have values capitalized into single-family house prices. Further, greater
distance to assigned K-12 schools has a negative impact on the value of the property.
Introduction
Just as there are various factors that compel people to relocate, the same or other factors
may account for where they eventually choose to go. The decision to move in or out of a
community is affected by the availability and quality of amenities in the area. The presence or
absence of certain neighborhood characteristics may encourage or discourage such movements.
Proximity to and quality of Public schools are examples of infrastructure amenities that may
influence locational decisions. Education is very important in the development of a society and
every parent wants to give the best they can offer to their kids. Therefore the ability of schools to
offer these services in a better and easier way is relevant to many households. This study
estimates how much people are willing to pay for better schools and reduced commuting time to
those schools through analysis of residential property values. The next section discusses previous
hedonic studies of school quality. Then the data and model used in this analysis are presented
followed by our empirical results.
Previous works
Some studies have looked at proximity to schools and how they affect the value of single-
family homes. Others have probed further and investigated whether quality of the school matters.
Clotfelter (1975) used data from published census tract statistics for the Standard Metropolitan
Statistical Area (SMSA) of Atlanta for the years 1960 and 1970. Clotfelter found that for an
average increase of 13.6 percent in the proportion of African Americans in schools, price of the
average house declined by 6 to 7 percent. He concluded that during that decade, housing values
fell where high schools experienced greater desegregation relative to areas where less
desegregation took place. Jud and Watts (1981) studied the effects of school quality and racial
composition on house values using data for single-family houses within the city of Charlotte,
North Carolina for 1977. Jud and Watts found that the quality (as measured by the average grade
point) of school is an important determinant of housing values. They found that a one grade point
increase in average student achievement test results in a 5.2 to 6.2 percent increase in the value
of an average house. They also concluded that the failure to account for school quality could
result in an overestimation of the negative effect of the racial component (percent black
population in the neighborhood).
Brasington and Haurin (1996) used data from Ohia (Ameristate, 1991) for single-family
detached dwellings to investigate the variations in real constant -quality house prices in a hedonic
price framework. Their sample size was 45,236 from 140 school districts, wit h over a third from
the central cities of six Metropolitan Statistical Areas (MSAs). They also found that school
quality (measured as the pass rate) is the most important cause of the real constant-quality house
price variation. Each percentage point increase in the pass rate increased house value by $400,
one -half percent of mean house value for that data set.
Hayes and Taylor (1996) used 1987 data on single-family homes in Dallas Independent
School District (DISD), using a variety of school quality measures. Indicators of school quality
were current expenditure per pupil (SPEND), average sixth-grade achievement in mathematics
(MATH687), the marginal effect of the school on sixth-grade mathematics achievement
(SCHL687), and the expected achievement of the student body in sixth-grade mathematics
(PEER687). Only SCHL687 was found to be significant, increasing home sale by 0.26 percent
for every 1 percent increase. They concluded that while homebuyers may not be responsive to
average measures of quality, they are responsive to quality measured as the marginal effect of the
school on the students’ performance.
Brasington (1999) also investigated a variety of measures of school quality in a study of
housing transactions from the six largest metropolitan areas in Ohio. Proficiency tests,
expenditure per pupil, and student-to-teacher ratio were found to have positive effects on the
value of houses. Average teacher salary and student attendance rates were sensitive to the
changes in statistical technique used. Value added (changes in student performance) was found
to have no significant effect on the price of properties, while the peer group effect had a positive
and significant effect on the value of an average house. Brasington concluded that parents do not
appear to choose schooling based on student improvements, but rather on the socio -demographic
characteristics.
Brasington (2000) investigated the role of private schools in the housing market and
estimated the demand and supply of public schools using the same data set for single-family
detached dwellings. A hedonic model was used to estimate an implicit price for the quality of
both private and public schools, which was then incorporated into a three-stage Least square
model to estimate school demand and supply. The own-price elasticity of demand was estimated
to be –0.19, tax elasticity of demand –0.49 and the income elasticity of demand 0.42. The price
elasticity of supply on the other hand was found to be 0.014. The cross-price elasticity between
public school and private schools was 0.11. Based on his findings, he concluded that the quantity
of public-school quality supplied is almost completely unresponsive to changes in the rate of
capitalization of public-school quality into house price in Ohio’s metropolit an areas. The cross-
price elasticity estimated led him to conclude that demand for public-school quality is not very
responsive to changes in the implicit price of private school quality.
Bogart and Cromwell (2000) studied the effect of school redistricting on house values in
Shaker Heights, Ohio in 1987. They showed that the disruption of neighborhood schools reduced
house values by about 9.9 percent, all else equal. They also found that providing transportation
services increased house values by about 2.6 percent.
The above-mentioned studies have used different types of measures of school quality.
There is no consensus on the best measure of school quality. This is primarily because these
measures tend to give different signals that are sometimes difficult to interpret. This study uses a
much more comprehensive relative measure that is based on all the different quality indicators
cited above.
Data
Sales data for single-family homes between 1994 and 2000 was obtained from the
Greenville County property office. The data contains price as well as housing characteristics
such as location (address), number of bedrooms, number of bathrooms, house square footage, lot
size for lots over one acre, whether or not the house has air conditioning and whether or not the
house has a garage. The database also includes a depreciation factor used to assess effective
house age, taking into account both actual age and the condition of the house. This variable has
a maximum value of 100 for a new house. Parks are categorized into four groups based on size
and the amenities available on them (see Espey and Owusu-Edusei 2001). GIS Data (shapefile)
on school and attendance areas within Greenville city was obtained from the School District of
Greenville County. School absolute ratings over a four -year period is obtained from the
Greenville District web site (http://www.myscschools.com/). The ratings are Unsatisfactory (U),
Below Average (B), Average (A), Good (G) and Excellent (E). Distance from the center of each
attendance area to assigned schools (elementary, middle and high) are computed. This gives the
average distance to the school.
Houses are mapped out on the Greenville city map using GIS software package. The
attendance area map is overlaid with the house maps to identify houses found within each
attendance area. Buffers are also created at 100 feet intervals around parks and a golf course.
Houses are assigned ranges based on the buffer they fall in. Map of Houses is also overlaid on
the Census block map, enabling assignment of neighborhood characteristics contained in the
census block data. The census block data includes number of housing units, median household
income, average household size and median household value. The number of housing units is
divided by the total census block area to obtain a measure of housing unit density within a block.
Model
A hedonic housing price technique is used to model the price of a house as a function of
the characteristics of a house as follows:
Pi = f(Si, Ni, Ei, Ri )
where Pi is the log of price of a given house, Si is a vector of structural characteristics including
condition (DEPR) with a higher value indicating better condition, the number of baths (BATH),
square footage of the house (SQFT), air conditioning (AC), lot size, and whether or not the house
has a garage (GARAGE). AC, GARAGE, and two lot size variables are 0-1 dummy variables
while the others are continuous variables. Ni is a vector of census block characteristics and Ei is
a vector of dummies for proximity to parks, a golf course and schools. Ri is a vector of dummies
for school rank categories. This study uses ordinary least squares estimation of a semi-log model,
the structural form found to produce the best results in previous hedonic studies. Definitions and
descr iptive statistics of variables in the regression models are reported in table 1. Definitions and
number of observations within all categories of open space proximities are also reported in tables
2 and 3.
Results and discussion
Table 4 shows regression results for four models using Ordinary Least Squares. Two
ranges are delineated for golf course, three for park type 1 and two for types 2, 3 and 4 each. All
housing and demographic characteristics had the expected signs. Annual dummies were included
to control for any year-specific differences in prices after deflating all into 1990 dollars using
monthly consumer price index. Prices are 3 percent higher between the months of April and
September.
Assigned elementary schools within 2640 feet (quarter of a mile) were 18 percent higher
than those beyond 10560 feet (two miles). Schools between 2640 and 5280 feet were 17 percent
higher than those beyond 10560 feet. Schools between 5280 and 10560 feet were 7 percent
higher in value. Assigned middle schools within 10560 feet sold for 16 percent higher than those
beyond 10560 feet range. Assigned high schools within 10560 feet sold for 12 percent higher
than those beyond 10560 feet range.
Unsatisfactory and Below Average ratings were left in the intercept. If the assigned
elementary school has an Average rating, there is no significant difference in the value of the
house. If it is Good, it sold for 12 percent higher and 10 percent higher if it is Excellent. For
middle schools, if the school is Average, it sold for 31 percent higher and 23 percent if it is
Above Average. House prices in attendance areas with high schools that are Average are not
significantly different from those Below Average. However those rated Above Average are 12
percent higher in value than those below average. Finally, if the house is within an attendance
area with all K-12 rated Average and Above, the value is 19 percent higher than the attendance
areas with Below Average schools.
Conclusion
This study has used another measure of school quality (school rankings), which is relevant in
making a choice between school attendance areas for those who choose to live within the city
limits of Greenville, South Carolina. It has been found that attendance areas with higher school
ratings have higher property values, all else constant. Also, distance to the assigned schools has a
negative impact on the value of the property. In general, golf course and parks have positive
impact on property values. Such information could be useful to developers deciding whether or
not to include schools, parks or golf courses in new subdivisions. It could also help city planners
and school districts determine potential tax revenue benefits that could accrue to the city if the
relative quality of schools were to increase. De mographic information obtained from census tract
data could help determine the relationship between demographic characteristics and the purchase
of housing near schools, golf courses and neighborhood parks.
References:
Bogart, William T. and Brian A. Cro mwell. “How Much is a Neighborhood School Worth?”
Journal of Urban Economics 47: 280-305.
Brasington, David M. 1999. “Which Measures of School Quality Does the Housing Market
Value?” Journal of Real Estate Research 18: 395-413.
Brasington, David M. 2000. “Demand and Supply of Public School Quality in Metropolitan
Areas: The Role of Private Schools.” Journal of Regional Science 40:583-605.
Clotfelter, Charles T. 1975. “The Effects of School Desegregation on Housing Prices.” Review
of Economics and Statistics 57 (Nov.): 446-51.
Espey, M. and Owusu-Edusei, K(2001). "Neighborhood Parks and Residential Property Values
in Greenville, South Carolina", Journal of Agricultural and Applied Economics, 33(3): 487-492.
Haurin, Donald R. and David Brasington. 1996. “Sc hool Quality and Real House Price:
Intra-and Interjurisdictional Effects,” Journal of Housing Economics 5: 351-368.
Hayes, Kathy J. and Lori L. Taylor. 1996. “Neighborhood School Characteristics: What Signals
Quality to Homebuyers?” Federal Reser ve Bank of Dallas Economic Review.
Jud, G. D. and Watts, J. M. 1981. “ Schools and Housing Values,” Land Economics 57: 459-70.
Table 1: Summary Statistics for Housing Characteristics (n = 3731)
Variable Mean Std. Dev. Minimum Maximum # of observations=1
for dummy variables
Quality
# of Bathrooms
Square footage
Air conditioning
Garage
1 to 4 acres
Over 4 acres
April – September sales
# Sold in 1994
1995
1996
1997
1998
1999
2000
80
1.68
1459.25
0.44
0.13
0.03
0.02
0.56
0.14
0.14
0.16
0.16
0.17
0.16
0.07
13.6
0.74
612.26
0.50
0.34
0.19
0.14
0.5
0.35
0.35
0.37
0.37
0.37
0.37
0.25
5
0.2
240
0
0
0
0
0
0
0
0
0
0
0
0
100
7
6276
1
1
1
1
1
1
1
1
1
1
1
1
2349
710
192
107
2986
522
522
597
597
634
597
261
Table 2: Proximity Measures by Type of Amenity
Open space type Proximity Number of houses in range
Golf course 1
Golf course 2
Park Type 1: Small basic
Park Type 2: Sm all attractive
Park Type 3: Medium attractive
Park Type 4: Medium basic
Schools
Elementary school
Middle school
High school
Abutting
300 – 1100 feet
Within 300 feet
300 – 500 feet
500 – 1500 feet
Within 600 feet
600 – 1500 feet
Within 200 fe et
200 – 1500 feet
Within 600 feet
600 – 1200 feet
Within half mile (2640 feet)
Half mile to one mile (2640 – 5280 feet)
One mile to two miles (5280 – 10560 feet)
Within two miles (10560 feet)
Within two miles (10560 feet)
16
78
31
100
481
132
287
5
13
81
441
1242
1227
889
3194
2316
Table 3: School Rank Categories
School
Rank
# of houses in rank
Elementary schools:
Middle schools:
High schools:
All schools
Below average
Average
Good
Excellent
Below average
Average
Above average
Below average
Average
Above average
Below average
Average and Above
average
97
1083
1367
1230
171
1958
1648
109
507
3161
268
3509
Table 4: Estimation results: dependent variable log of Price (n = 3731)
Variables Model 1 Model 2 Model 3 Model 4
Intercept
Quality
Quality squared
# of Bathrooms
Square footage
Square footage squared
Air conditioning
Garage
1 to 4 acres
Over 4 acres
April – September sales
Annual dummies
1995
1996
1997
1998
1999
2000
Abutting golf course
300 – 1100 f eet of golf course
Within 300 feet of type 1
300 – 500 feet from type 1
500 – 1500 feet from type 1
Within 600 feet from type 2
600 – 1500 feet from type 2
Within 200 feet from type 3
200 – 1500 feet from type 3
Within 600 feet from type 4
600 – 1200 f eet from type 4
Schools
Elementary: within 2640 feet
2640 – 5280 feet
5280 – 10560 feet
Middle: within 10560 feet
High: within 10560 feet
Rank categories:
Elementary Average
Good
Excellent
Middle Average
Above average
High Average
Above average
All Above average
Neighborhood Characteristics:
Housing unit density
Average household size
Median value
Adjusted R-square
2.28* (0.13)
0.06* (0.003)
-0.00033* (0.00002)
0.22* (0.013)
0.0009* (0.00004)
-1.14E-7* (8.95E-9)
0.036** (0.015)
0.054* (0.022)
0.079* (0.034)
0.1* (0.047)
0.03** (0.013)
0.003 (0.024)
0.06* (0.023)
0.1* (0.023)
0.15* (0.023)
0.22* (0.023)
0.22* (0.03)
0.23* (0.1)
0.13* (0.06)
-0.18** (0.08)
0.016 (0.04)
-0.04 (0.02)
0.13* (0.05)
0.07* (0.03)
0.2* (0.06)
0.01 (0.02)
-0.45* (0.18)
-0.23* (0.12)
0.18* (0.03)
0.17* (0.03)
0.07* (0.03)
0.01 (0.05)
0.12* (0.05)
0.1* (0.05)
-0.0002* (0.000012)
-0.11* (0.02)
2.5E-6* (2.88E -7)
0.70
2.27* (0.13)
0.05* (0.003)
-0.00033* (0.00002)
0.22* (0.013)
0.0009* (0.00004)
-1.14E-7* (8.95E-9)
0.036** (0.015)
0.054* (0.022)
0.09* (0.034)
0.13* (0.046)
0.03** (0.013)
0.003 (0.024)
0.06* (0.023)
0.1* (0.023)
0.15* (0.023)
0.21* (0.023)
0.21* (0.03)
0.25* (0.1)
0.15* (0.05)
-0.18** (0.08)
0.016 (0.04)
-0.04 (0.02)
0.13* (0.05)
0.07* (0.03)
0.2* (0.06)
0.01 (0.02)
-0.40* (0.18)
-0.22* (0.12)
0.16* (0.02)
0.31* (0.04)
0.23* (0.04)
-0.0002* (0.000013)
-0.1* (0.02)
2.5E-6* (2.66E -7)
0.71
2.23* (0.13)
0.06* (0.003)
-0.00033* (0.00002)
0.22* (0.013)
0.0009* (0.00004)
-1.14E-7* (8.95E-9)
0.036** (0.015)
0.054* (0.022)
0.079* (0.034)
0.1* (0.047)
0.03** (0.013)
0.003 (0.024)
0.06* (0.023)
0.1* (0.023)
0.15* (0.023)
0.21* (0.023)
0.21* (0.03)
0.25* (0.1)
0.15* (0.06)
-0.13** (0.08)
0.07 (0.04)
0.01 (0.02)
0.16* (0.05)
0.12* (0.03)
0.18* (0.05)
0.001 (0.02)
-0.46* (0.18)
-0.26* (0.11)
0.12* (0.01)
0.09 (0.05)
0.12* (0.05)
-0.0002* (0.000012)
-0.11* (0.02)
2.5E-6* (2.88E -7)
0.71
2.09* (0.13)
0.06* (0.003)
-0.00033* (0.00002)
0.22* (0.013)
0.0009* (0.00004)
-1.14E-7* (8.95E-9)
0.036** (0.015)
0.054* (0.022)
0.079* (0.034)
0.1* (0.047)
0.03** (0.013)
0.003 (0.024)
0.06* (0.023)
0.1* (0.023)
0.15* (0.023)
0.22* (0.023)
0.22* (0.03)
0.27* (0.1)
0.18* (0.05)
-0.14** (0.08)
0.042 (0.04)
-0.01 (0.02)
0.17* (0.05)
0.11* (0.03)
0.19* (0.05)
0.01 (0.02)
-0.37* (0.17)
-0.17* (0.11)
0.16* (0.03)
0.14* (0.03)
0.10* (0.03)
0.18* (0.02)
0.11* (0.01)
0.19* (0.03)
-0.0002* (0.000012)
-0.11* (0.02)
2.5E-6* (2.88E -7)
0.71
Standard errors are in parentheses. Significance levels *** = .1, ** = .05, * = .01.
SOUTH CAROLINA AGRICULTURE AND FORESTRY RESEARCH
CLEMSON UNIVERSITY, CLEMSON, SOUTH CAROLINA
JAMES R. FISCHER, DEAN/ DIRECTOR
South Carolina Agriculture and Forestry Research is a cooperative program
funded from federal and state funds.
Programs of SCAFR in cooperation with South Carolina State University and the
U.S. Department of Agriculture are offered to people of all ages, regardless of race, color,
sex, religion, national origin, disability, political beliefs, sexual orientation, or marital
or family status. SCAFR is a n equal opportunity employer.
1
The Impact of School Quality on Real House Prices:
Interjurisdictional Effects*
Donald R. Haurin
Departments of Economics and Finance
Ohio State University
1010 Derby Hall
154 N. Oval Mall
Columbus OH, 43210
David Brasington
Department of Economics
Ohio State University
431 Arps Hall
1945 N. High Street
Columbus OH, 43210
First submitted: May 30, 1996
Revised: September 13, 1996
*Acknowledgments: We thank the Center for Real Estate Education and Research at the Ohio
State University for providing funding. We also thank the referees for helpful suggestions, and
Hazel Morrow-Jones and Tom Biers for supplying the house price data. The following researchers
for the State of Ohio assisted in gathering community data: Jeffrey Knowles of the Office of
Criminal Justice Services, Julie Kasenbaum and Dave Miltko of Education Management
Information Services, and Francis Rogers of Education Policy Research.
2
Proposed Running Head: Impact of School Quality on House Prices
Send proofs to:
Donald R. Haurin
Ohio State University
1010 Derby Hall
154 N. Oval Mall
Columbus OH, 43210
Phone: 614-292-0482
FAX: 614-292-9530
ABSTRACT
This study focuses on explaining variations in real constant-quality house prices in
jurisdictions located in multiple MSAs. Using a hedonic house price framework, we test competing
theories of house price determination. Using two variants of the random coefficients model, we
find that public school quality has a very large impact on real constant-quality house prices. Our
results suggest that capitalization of school quality differences occurs on a per lot basis rather than
per square foot of land. Also important to the explanation of variations in house prices are
variables derived from urban theory, such as distance to the CBD, and from the amenity literature,
such as a community’s crime rate, arts, and recreational opportunities.
CLASSIFICATION NUMBERS: R21, I29, H22
3
LIST OF SYMBOLS
Greek alpha
Greek beta
Greek delta
Greek epsilon
Greek gamma
µGreek mu
Greek tau
Greek sigma
4
Many articles have focused on explaining cross-sectional or time-series variations in
metropolitan real constant-quality house prices. Such explanations are important for testing
theories of house price determination and for predicting future variations. Our study uses 134
jurisdictions in six metropolitan areas to test competing explanations of why real constant-quality
house prices vary, where we define a constant-quality house as a unit where structural and land
attributes, but not community attributes, are held constant. Included in the paper are tests of
hypotheses derived from spatial urban theory, local public economics, and the urban amenity
literature.
Our paper differs from most in that we include a wide array of jurisdictions in multiple MSAs
in the sample. Most studies of constant-quality house prices confine their analysis to consider
price variations in one class of urban areas such as MSAs, central cities, or the suburbs of a single
central city. By including suburban localities from multiple MSAs in the sample, we open the
analysis to consider intraurban house price variations explained by spatial urban economic
models, jurisdiction-specific amenities, and local public finance theory, as well as interurban
variations in metropolitan-wide variables such as the expected growth rate.
A particular focus of our study is analysis of the impact of variations in public school
outcomes on real constant-quality house prices. This focus results from the importance of school
quality to a household's locational choice (Graves and Linneman, 1979) and from the importance
of public schools in models of local public taxes and expenditures. We find that a measure of
student achievement is very important in explaining spatial variations in real constant-quality house
prices.
5
I. MODELS OF HOUSE PRICE DETERMINATION
A. Prior Studies
Theoretical models of house price determination can be separated, somewhat arbitrarily,
into those explaining intraurban variations in house prices and those explaining interurban
variations. The monocentric urban model suggests that land and housing rental rates are
functions of transport costs, household income, metro area population size, and agricultural rental
rates (Mills, 1967; Muth, 1969). Extensions to land and housing prices, rather than rents, by
Capozza and Helsey (1989, 1990) and Capozza and Schwann (1989) suggest land prices are
determined by transport costs, agricultural rents, income, population, the discount rate, and the
expected growth of income and population. Intraurban studies of house price differentials caused
by variations in amenities are numerous (Jackson, 1979; Li and Brown, 1980; McMillan, Reid, and
Gillen, 1980; Diamond, 1980; Michaels and Smith, 1990). Jud (1980), Pollakowski and Wachter
(1990), and Pogodzinski and Sass (1991) added zoning to the list of explanatory factors. Other
land use or supply restrictions were included by Hamilton (1978), Fischel (1980, 1981), and Rose
(1989), these studies reporting a mixed impact of land use restrictions on housing prices.
Theoretical models of interurban house price variation include those highlighting the impact
of variations in site-specific factors such as local climate (Haurin, 1980), air pollution and other
amenities (Rosen, 1979; Roback, 1982), or shocks to the urban labor market (Haurin and Haurin,
1988). Empirical studies with an interurban focus introduced factors such as crime, recreational
opportunities, and population demographics (Blomquist, Berger, and Hoehn, 1988; Beeson and
Eberts, 1989; Peek and Wilcox, 1991; Blomquist and Berger 1992; Potepan 1994).
A few studies adopted a supply and demand framework to explain house price variation
among metro areas. Ozanne and Thibodeau (1983) explained 58% of the variation in house
prices among 54 MSAs, but they found only three significant factors: percent nonelderly singles,
6
price of farm land, and number of municipalities in the MSA. Follow-up studies include Fortura and
Kushner (1986), who used Canadian data, and Manning (1986, 1989), who used 94 U.S. MSAs
and found better correspondence between theoretical predictions and the empirical results.
Manning explained 84% of the variation in house prices and found the most important explanatory
variables to be construction costs, farm land prices, climate, percentage of high income
households, and a measure of household operating and utility costs. However, crime, pollution,
total population, density, and population growth had no effect. Hendershott and Thibodeau (1990)
found variations in real income among 18 cities significantly affected real house prices, but spatial
variations in land supply restrictions and amenities had no impact. Studies that explain variations
in a single representative value of the house price index among multiple MSAs tend to ignore local
taxes and public goods because of the heterogeneity of taxes and public services within the MSA.
Many empirical studies have linked measures of local public goods and taxes to house
price variations. Interest increased after Oates’ 1969 2SLS test of the Tiebout hypothesis. Using
aggregate data, Oates found that increased school expenditures raise property values while
increased taxes lower values, this result replicated by Rosen and Fullerton (1977). Oates’
interpretation was critiqued by Linneman (1978), Hamilton (1976), Sonstelie and Portney (1980),
and Pollakowski (1982). Linneman and Hamilton argued that in equilibrium neither effect would be
present, thus neither regression coefficient would be significant. This point was countered by
Yinger (1982) who argued that supply restrictions allow local taxes and services to be capitalized
into house prices. Sonstelie and Portney used gross rent rather than market value to test the
Tiebout hypothesis. Pollakowski objected to Oates’ list of predetermined variables in his 2SLS
approach, noting they may be correlated with the error term in the property value equation. A
comprehensive critical review of the property tax literature and further empirical study is contained
Yinger, Bloom, Borsch-Supan, and Ladd (1988). They argue that the property value equation
should be specified in log rather than linear form and note that the effective property tax rate as
7
measured in most micro data studies is endogenous because it is computed as the ratio of the tax
on a property to its value. The tax on a property depends on assessor practices as well as the
stated tax rate; thus, they argue for using the community’s nominal tax rate and variables related to
assessor errors as instruments in a 2SLS econometric model. In a study of changes in property
values in response to changes in tax rates, Yinger, et al. find evidence for modest capitalization of
property taxes.
A recent flood of papers on MSA house price determination has been generated by an
interest in understanding observations of substantial intertemporal fluctuations in particular cities or
regions. This research has been aided by the development of better panel data sets (Thibodeau,
1989,1995; Nothaft, Gao, Wang, 1995; Pollakowski, 1995). Generally, the fundamental forces
hypothesized to affect MSA-wide house prices are found important in explaining long term
movements in house price. Poterba (1991) finds that shifts in income and construction costs are
important, but aggregate demographic effects and user cost variations have weak or no effect.
Significant effects of population demographics are found by Mankiw and Weil (1989) and Case and
Shiller (1990). In a panel data model, Kim (1993) finds that MSA house prices are explained by
construction costs, interest rates, metro population, income, income growth, and climate.
However, city-specific intertemporal residuals follow a cyclical pattern that is not explained by
actual or expected changes in market fundamentals. These short term fluctuations in house prices
may be driven by speculative bubbles forming and bursting (Abrahm and Hendershott, 1993,
1996). While these studies are informative and provide support for the hypotheses derived from
theoretical economic models of urban areas, they study only a single representative house price in
each MSA; in contrast, our approach combines intraurban and interurban models and focuses on
multiple jurisdictions within MSAs.
B. Current Study
We include factors influencing both intraurban and interurban variations in house prices.
8
Examples of factors believed to influence the price of housing within an MSA include distance from
the central business district, local amenities, jurisdiction-specific public services and property tax
rates. Examples of factors that primarily influence entire metropolitan areas and may differ among
MSAs include climate, overall accessibility, arts and recreational opportunities, and the expected
growth of the metro area.
Our primary community-level public sector explanatory variable is a measure of the
outcome of local public education. The literature regarding the appropriateness of our proficiency
test score is discussed later in the paper, but similar measures have been used in other hedonic
house price studies. Other community-level variables included in the explanation of variations in
constant-quality house prices are derived from urban spatial theory (e.g., distance from CBD), local
public economics (e.g., property tax rate), or urban-amenities theory (e.g., crime rate).
The standard urban economic monocentric model argues that within a metro area, the
principal variable causing variations in constant-quality house prices is land price. Spatial
variations in the rental rate or price per unit of land exist because of differences in transport costs
to the metro area’s central business district. A typical land rental (p) equation is
p(r) = P e where r is distance from the CBD, P is agricultural land rental, R is the distance to(R-r)
the metro area edge, and is a conversion parameter that depends on transport cost per mile and
community income. The land rental equation suggests that distance to the CBD should be1
included in the house price model. Agricultural land cost and transport cost per mile are very2
similar among our MSAs because the areas are in close proximity; thus, we do not include these
variables in our model. 3
We also test for the impact of MSA-level variables on real constant-quality house prices.
Examples include expected growth of the MSA and a measure of overall MSA accessibility.
Justifications for these variables are given in aggregative urban models such as Henderson (1985)
- accessibility, and Capozza and Helsey (1989, 1990) - growth. We include two MSA level amenity
9
measures, one measuring the opportunity to attend art events, the other measuring recreational
opportunities.
Our study uses the hedonic house price method to develop a measure of real constant-
quality house prices. Because our data are cross-sectional, alternative methods such as the
repeat-sales approach are not feasible. Our variant of the hedonic approach is to relate the natural
log of the real transaction prices for houses (ln V) to a set of structural and land characteristics
(X). To find the deflated house price, we divide the observed nominal price by the nonhousing4
price index for the MSA. We similarly deflate all other nominal variables in the study. 5
C. Model
Two forms of hedonic price equation are tested. In the first, the estimation interacts the
jurisdiction dummy variables with a property’s lot size (L ):i
ln V = X + (L J ) + (1)ij ij ij j j ij
where i is a transacted house, j is the school district, and is a random error. representsj
jurisdiction-specific shifts in the implicit price of a square foot of land, thus testing one form of the
capitalization hypothesis. The second form includes a series of dummy variables (J ) indicating thej
jurisdiction (school district) of an observation:
ln V = X + J ’ + (2)ij ij j j ij
The coefficients ’ represent the percentage deviations of an average house price in district j fromj
that of a constant-quality house. Only the regression intercept changes among districts, thus
testing another form of the capitalization hypothesis.
In the second step of the analysis, the coefficients and ’ are related to a vector ofjj
community and MSA level variables Z :j
= Z + µ (3)j j j
’ = Z ’ + µ’. (3')j j j
Eqns. (3) and (3') are the capitalization tests for the community and MSA variables. The functional
10
form of eqns. (2) and (3') tests for an impact through changes in the implicit price of land, thus the
impact differs among houses within a jurisdiction depending on a property’s lot size. In
contrast, the form of (1) and (3) tests for an equal percentage impact of variations in lot size on all
houses in a district.
II. DATA AND METHODS
A. Data
The primary source of data for this study is a file of 1991 housing transactions in the six
largest metro areas in Ohio (Amerestate, 1991). We limit the sample to single-family detached
houses and eliminate outliers. Eliminating any school district having less than 17 observed house6
sales trims the sample to 140 school districts. This process yields a sample of 45,236 transactions
with over a third being from the central cities of our six MSAs. Variable means and standard
deviations are reported in Table I for both the complete sample and a sample that excludes the
central city transactions. Detailed definitions of all variables are listed in the data appendix.
Explanatory variables in (3) are drawn from various sources including the Places Rated Almanac
(Savageau and Boyer 1993), the Ohio Department of Education, the School District Data Book
(MESA Group 1994), and the Office of Criminal Justice Services of the State of Ohio.
[INSERT TABLE I]
B. Method
The pair of eqns. (1) and (3) or the pair (2) and (3') are forms of random coefficient models;
estimation methods are discussed by Amemiya (1978). We follow the method of Garman and
Richards (1990) where equations (3) and (3') are substituted into (1) and (2) respectively. The
resulting single equation models are:
ln V = X + (L Z ) + µ + . (4)ij ij ij j j ij
11
and
ln V = X + Z ’ + µ’ + (4')ij ij j j ij
The form in (4) is the same as in Garman and Richards where a multistep GLS procedure
is used to correct for the heteroskedastic error. Assuming the errors in (1) and (3) are
uncorrelated, the variance of the error in (4) is ( L + ) where is the variance of µ and is22222
ij j
the variance of . These variances are estimated in auxiliary regressions of (1) and (3) where inijj
(3) is the vector of estimated coefficients of the interaction variables in (1). The final step is to
estimate (4) correcting for the estimated heteroskedasticity, yielding consistent and asymptotically
efficient estimates.
Eqn. (4') is a random effects model, this frequently used in the analysis of panel data and
requiring use of generalized least squares. It is appropriate in our case because we test for
jurisdiction-specific mean zero random errors in house prices (µ). j
7
C. Explanatory Variables
Measures of the house and lot characteristics include age, square footage of house and
garage, full and part baths, unenclosed and enclosed porches, deck, patio, pool, air conditioning,
fireplace, number of outbuildings, and quarter of sale within the survey year.
The highlighted local public service is a measure of public school outcomes, specifically,
the percentage of ninth grade students passing all four parts of a proficiency test administered in
all public schools in the state. This variable has a mean of 43.2 and ranges from 6 to 89. The test
is administered each year of high school; thus, aggregate passage rates rise as a cohort of
students moves through high school. State law requires that a student pass each of the four
components of the test to receive a high school diploma. The literature on measuring the8
outcomes of K to 12 education raises the question as to the best measure of school quality: test
scores, attendance rate, college continuation rate, dropout rate, or wages later in life. We argue
that our measure is easily observed (results are published in newspapers), varies greatly among
12
districts, and is directly relevant for parents’ judging the probability of a student receiving a high
school diploma; thus, it is an appropriate measure.
Prior studies have included measures of school quality or outcomes when explaining house
price variations. Li and Brown (1980) find that 4th graders’ test scores have a positive and
significant impact on house prices in Boston. Jud and Watts (1981) used test scores in a hedonic
house price study of a single jurisdiction and estimated the demand for school quality in a second
stage estimation. Dubin and Sung (1990) use a J test to contrast alternative combinations of
variables to use in their semilog hedonic house price equation and decide to exclude their two
measures of school quality because measures of race and socio-economic status dominate.
Pogodzinski and Sass (1991) find that scores on a statewide achievement test positively and
significantly affect house prices.
The other public sector variable in our model is the property tax rate in the community. Our
data set does not contain the tax on each property or the assessed value of the property; thus, we
cannot construct the effective tax rate for each property. Rather, we must use the nominal rate
reported for each jurisdiction. This measure is exogenous and Yinger, et al. (1988) proposed its
use as an instrumental variable for the effective tax rate. The omitted component of the property
tax rate is the property-specific assessor’s error (Goodman, 1983).
Other explanatory variables in the house value equation include three suggested by the
urban model: distance of a property to the CBD, an aggregate measure of accessibility in each
MSA, and expected MSA population growth. We expect increased accessibility or expected
growth, and decreased distance to the CBD to be positively related to constant-quality house
prices. We measure expected growth by the ratio of 1990 to 1980 MSA population, the
accessibility measure is from Places Rated Almanac (1993), and distance to CBD is approximated
by the distance of a jurisdiction’s centroid to the MSA’s CBD.
The amenity literature suggests that an increased crime rate in a jurisdiction reduces the
13
price per unit of housing. Our measure is the number of serious crimes per capita; the definition
includes murders, rapes, robbery, aggravated assault, motor vehicle theft, and arson. We also
include average income and MSA recreational and arts opportunities as measures of amenities
present in a jurisdiction. The final variables included in the estimation are the percentage of9
nonwhite households, this potentially capturing variations in house prices resulting from
discrimination, and the percentage of households residing in the jurisdiction for fewer than six
years, this being a measure of community stability.
III. RESULTS
Preliminary regressions of eqns. (1) and (2) reveals substantial variation in house prices
among jurisdictions. From (1) we find that constant-quality house prices range from 35% lower to
70% higher than the reference district, Columbus OH. Evaluated at the mean lot size, we find from
(2) constant-quality variation in the price of land of 30% lower to 85% higher than the reference
district. Both results suggest a much greater spread of constant-quality house prices at the
community level than at the MSA level. In the six MSAs in our study, the average MSA price
variation compared with the Columbus MSA ranged from 6.4% lower to 7.7% higher.Results10
of estimating eqns. (4) and (4') are contained in Table II. We report the results based on the
sample of jurisdictions excluding the six central cities, leaving 134 localities and 29,718
observations. Estimated coefficients are somewhat sensitive to whether central city properties are
included, suggesting possible specification problems when extending the model to cover both
central cities and suburbs.11
A. GLS Model with Lot Size Interaction Variables: Eqn (4)
The house and lot characteristics generally have the expected signs and are significant.
One exception is the number of outbuildings, this not significant. The interpretation of increased lot
14
size is complicated by the inclusion of the interaction terms, but the overall marginal impact is
positive. We find that increased square footage of the house or garage increase house price, but
at a decreasing rate. Increasing age reduces house value and the negative coefficient for AGE
SQUARED implies housing depreciates at an increasing rate.
In our estimation of (4) we include both the community variable-lot size interactions and the
level of the community variables (e.g., both TEST SCORE and LOT*TEST SCORE). If only the
coefficient of the interaction variable is significant, then the impact of the community level variables
is solely through changes in a property’s implicit price per square foot of land. Thus, large lots are
impacted at a higher rate. However, if the impact of high quality schools is to create a fixed
premium per property independent of lot size, then the coefficient of TEST SCORE will also be
significant. For example, a positive TEST SCORE coefficient combined with a negative coefficient
for the LOT-SCORE interaction implies that the capitalization of high quality schools into property
values is a greater percentage for small properties than large, this result consistent with a relatively
fixed school quality premium per property.
Table II’s results show that all community variables’ coefficients have the expected sign.
Moreover, all are significant except the MSA growth rate and the tax rate (both have t-values of
1.2). Generally, the lot size-community variable interactions have the opposite sign of the
community variables, the exceptions being the aggregate accessibility variable (not significant) and
average income and recreational opportunities (both significant). Thus, although the community
variables clearly affect the value of property in a jurisdiction, the change is not simply a constant
percentage increase or decrease in the price per unit of land.
The impact of our focal variable, school quality, on property value is shown through the
following example. Compare a house with sample mean value of $76,115 in a community with a
test score of 43 to an identical house and community except test score is one point higher. The
result is that house value rises by $380. If the community’s proficiency test pass rate is two
15
standard deviations above the sample mean (this value is still below the sample maximum), house
value rises to $89,930, an 18 percent increase.12
Another evaluation of the impact of variations in school quality is to compare two houses,
one with a larger lot than the other, but otherwise identical, in each of two communities. In the
community with a higher TEST SCORE the value of both houses is higher, but the changes
depend on the strength of the negative coefficient of the LOT*TEST SCORE variable compared
with the impact of the nonlinear estimation. The resolution of the offsetting effects is that within a13
standard deviation of the mean lot size, the impact of a higher test score on house value is nearly
independent of lot size. We find that superior school quality is capitalized into property values,14
but the capitalization is about the same for all properties in a school district.
B. Random Effects Model: Eqn. (4')
Generally, the results in the random effects model are similar to those in the interaction
variable model. Some differences are noted for the age of a house and the number of out
buildings. Overall explanatory power is the same. The coefficient of TEST SCORE implies that
every additional percent of students passing the test raises house value by $400, equaling one-half
percent of mean house value. 15
V. CONCLUSIONS
Our study focuses on explaining variations in constant-quality house prices among 134
communities in multiple MSAs. Explaining differences in house prices requires hypotheses to be
drawn from both intraurban and interurban economic models. We find that real constant-quality
house prices are explained by factors from both perspectives and they combine to explain 70% of
the observed price variation.
School quality is the most important cause of the variation in constant-quality house prices.
16
We find that each percentage point increase in the pass rate of ninth grade students on a
statewide proficiency exam increases house prices by one-half percent. Because pass rates vary
among sampled communities from 6 to 89 percent, constant-quality house prices vary greatly due
to this factor alone. The estimation results suggest that the capitalized premium for high quality
schools is relatively constant per lot rather than being constant per square foot of land.
Other factors important to the explanation of constant-quality house prices include distance
from the CBD, metro area accessibility, crime rate, percentage of nonwhite households, average
household income, and indexes of metro area arts and recreational opportunities. We found no
consistent impact of expected population growth, the nominal property tax rate, or community
stability.
Prior studies of a single house price representative of an entire MSA have been limited in
two ways. First, they exclude hypotheses about house price variations derived from spatial models
of urban areas and from local public economics. Second, they do not attempt to explain the
substantial house price variation among communities within a single MSA. Developing a model
that explains why house prices vary among communities within an MSA and among MSAs is the
greatest challenge, requiring hypotheses from both intraurban and interurban perspectives.
17
1. The negative exponential form is derived under the restriction that the price elasticity of demand
for housing is -1.0.
2. Some studies use population density instead of distance to the CBD. We select distance
because it is a more fundamental variable and density may be endogenous. If substituted for
distance, density works equally well in our estimation.
3. Construction costs are also relatively constant in the jurisdictions in the sample.
4. This semi-log form has been used in many studies with justifications listed in Thibodeau (1989).
We used a Davidson-MacKinnon test (1981) to determine whether a semi-log or a linear form of
hedonic equation is preferable; however, the test result is inconclusive. King (1977) objects to
inclusion of the property tax rate in a linear hedonic, but Sontelie and Portney (1980) note that this
objection is ameliorated if a semi-log form is used. We opt for the semi-log form based on the
literature and because the estimated coefficients better conform to theoretical expectations.
5. The deflators are from the American Chamber of Commerce cost of living index (ACCRA 1991,
1992).
6. We deleted observations with lot sizes greater than two acres and those with transaction prices
over $400,000 or below $10,000. Outliers in lot size, square feet of housing, and garage size were
also eliminated. Mean real house value is $76,115.
7. We use Limdep 7.0 (1995, Ch. 17.3) which uses a two step procedure.
8. Students completing high school, but failing to pass the exam receive a certificate of
attendance.
9. Climate is not included because the relatively close proximity of the six MSAs in the sample
yields little variation. Measures of zoning or land supply restrictions are not available.
10. The six MSAs in the sample are Akron, Cincinnati, Cleveland, Columbus, Dayton, and Toledo.
Footnotes
18
The smallest PMSA is Toledo with 1990 population of 0.61 million and the largest is Cleveland with
population 2.20 million.
11. We tested for systematic differences in house characteristic implicit prices between central city
properties and suburban properties by running separate hedonic regressions, then pooling the
data. A Chow test of the equality of the two sets of coefficients strongly rejects equality. Further,
coefficient equality is rejected even if a dummy variable for central city location is included in the
pooled regression.
12. A quadratic test score was tried in the estimation, but it proved to be insignificant with a
coefficient near zero.
13. That is, if the only impact of a higher test score was the linear term in the hedonic, house value
would rise more for the house with the larger lot because of the logarithmic form.
14. The point estimates suggest that the amount capitalized decreases slightly as lot size rises,
but not by a economically significant amount.
15. The results in the full sample are generally similar. Differences include the MSA growth rate
becoming significant and positive, as expected, and the tax rate unexpectedly becoming positive
and significant. The impact of TEST SCORE on house value is about the same, $400 per point.
19
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27
TABLE I
Descriptive Statistics
Values for the full sample of 45,236 are reported first, followed by values for the sample of 29, 718 transactions in
suburban jurisdictions.
Full Sample Suburban Sample
Variables Mean Standard Dev. Mean Standard Dev.
Age (10s) 4.21 2.48 3.44 2.25
Air Conditioning 0.36 0.48 0.44 0.49
Autumn Sale 0.28 0.45 0.28 0.45
Crime Rate 0.02 0.01 0.01 0.01
Deck 0.11 0.31 0.14 0.35
Distance to CBD 8.95 6.59 12.27 5.75
Enclosed Porches 0.15 0.38 0.14 0.36
Fireplace 0.39 0.49 0.47 0.50
Full Bath 1.31 0.50 1.40 0.54
Garage Size (1000s) 0.33 0.19 0.36 0.19
House Size (1000s) 1.46 0.51 1.55 0.54
Log House Price 11.04 0.61 11.24 0.51
Lot Size (10000s) 1.06 0.89 1.26 1.00
MSA Accessibility Index 47.88 6.03 48.72 5.74
MSA Arts Index 11.56 4.50 12.13 5.56
MSA Population Growth 1.73 4.93 1.39 4.70
MSA Recreation Index 20.91 4.59 21.03 4.80
Out Buildings 0.03 0.18 0.04 0.20
Part Bath 0.35 0.49 0.44 0.52
Patio 0.21 0.41 0.25 0.43
Nonwhite Households 0.18 0.17 0.08 0.11
Pool 0.01 0.11 0.02 0.13
Property Tax Rate 35.12 6.16 35.02 6.36
Real Income 32.64 14.67 39.33 13.77
Test Score 33.71 17.98 43.17 14.85
Turnover Rate 0.48 0.08 0.47 0.09
Summer Sale 0.31 0.46 0.31 0.46
Unenclosed Porches 0.76 0.73 0.67 0.70
Winter Sale 0.24 0.43 0.23 0.42
28
TABLE II
Estimation of Real House Prices a
Interacted Model Inte rcept Model
Variables Coefficient Standard Error Coefficient Standard Error b b
Constant 986.82** 5.7 948.77** 11.15
Age -3.96**0.28 -6.00** 0.29
Age-squared -0.07**0.03 0.06**0.02
Lot Size -2.60 4.29 10.12** 0.50
Lot Size Squared -0.10**0.01 -0.93** 0.07
House Size 43.22** 1.72 42.99** 1.66
House Size-squared -2.88**0.45 -2.95** 0.43
Garage Size 33.13** 2.36 34.86** 2.23
Garage Size-squared -23.99** 3.52 -23.53** 3.24
Deck 5.52**0.51 5.22**0.50
Unenclosed Porches 1.62**0.29 1.27**0.28
Enclosed Porches 1.29**0.52 1.62**0.51
Fireplace 8.14**0.41 8.12**0.41
Air Conditioning 6.81**0.41 6.69**0.40
Full Bath 6.31**0.46 6.88**0.45
Part Bath 5.67**0.40 5.73**0.39
Out Buildings 0.78 0.88 1.93* 0.81
Pool 3.32**1.30 4.30**1.19
Patio 2.36**0.49 2.48**0.46
Summer Sale 5.09**0.49 5.18**0.47
Autumn Sale 5.58**0.50 5.75**0.48
Winter Sale 5.22**0.51 5.28**0.50
Distance to CBD -1.32**0.07 -1.27** 0.15
Lot*Distance to CBD 1.14 0.60 ----
Test Score 0.68**0.04 0.52**0.09
Lot*Test Score -0.10**0.03 ----
Property Tax Rate -0.08 0.06 0.23 0.12
Lot*Property Tax Rate 0.14**0.05 ----
Real Income 0.21**0.04 0.31**0.08
Lot*Real Income 0.05* 0.02 ----
Crime Rate -292.73** 39.80 -272.38** 98.24
Lot*Crime Rate 73.95* 33.30 ----
Turnover Rate -9.45**4.46 17.22 9.08
Lot*Turnover Rate 8.46**3.38 ----
Variables Coefficient Standard Error Coefficient Standard Error b b
29
Nonwhite Households -49.19** 3.95 -32.69** 7.32
Lot*Nonwhite Household 22.92** 3.73 ----
MSA Population Growth Rate 0.12 0.10 0.30 0.19
Lot*MSA Population Growth Rate -0.03 0.08 ----
MSA Accessibility Index 0.46**0.08 0.78**0.16
Lot*MSA Accessibility Index 0.05 0.06 ----
MSA Arts Index 1.18**0.10 0.81**0.23
Lot*MSA Arts Index -0.35**0.08 ----
MSA Recreation Index 0.24**0.09 0.60**0.19
Lot*MSA Recreation Index 0.20**0.07 ----
Adjusted R 0.70 0.70 2
Dependent Variable is 1990 Log Real Transaction House Price. Sample size is 29, 718 transactions in suburban jurisdictions. *Significant at 5%a
level, **Significant at 1% level.
All coefficients and standard errors are x10 . b -2
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DATA APPENDIX
VARIABLE NAME DESCRIPTION and SOURCE (in parentheses)
House Characteristics
AGE Age of house in tens of years (2)
AIR CONDITIONING Air conditioning dummy (2)
DECK Deck dummy (2)
ENCLOSED PORCHES Number of enclosed porches (2)
FIREPLACE Fireplace dummy (2)
FULL BATH Number of full bathrooms (2)
GARAGE SIZE Garage size in thousands of square feet(2)
HOUSE SIZE House size in thousands of square feet (2)
LOG REAL TRANSACTION Log of transaction amount for house, deflated by MSA nonhousing price index (2)
HOUSE PRICE
LOT SIZE Lot size in tens of thousands of square feet (2)
OUT BUILDINGS Number of out buildings on property (2)
PART BATH Number of partial bathrooms (2)
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PATIO Patio dummy (2)
POOL Pool dummy (2)
UNENCLOSED PORCHES Number of unenclosed porches (2)
Community /MSA Characteristics
CRIME RATE Serious crimes including murder, forcible rape, robbery, aggravated assault, motor
vehicle theft, and arson, per 1,000 residents (5)
DISTANCE TO CBD A measured in mile of the distance from the centroid of a jurisdiction to the MSA’s
center
MSA ACCESSIBILITY INDEX A measure of the MSA ease of accessibility. The variable is a weighted average of
lower than average commuting time to work, mass transit availability, highway
accessibility, air and train accessibility, in thousands (1)
MSA ARTS INDEX A measure of the number of arts performances, museums, and library holdings in the
MSA (1)
MSA POPULATION GROWTH 1990 population of the MSA divided by 1980 population (1)
RATE
MSA RECREATION INDEX A measure of recreational opportunities in the MSA including theaters, sports, parks,
golf courses, zoo/aquarium, restaurants (1)
NONWHITE HOUSEHOLDS The percentage of nonwhite households/100 (4)
PROPERTY TAX RATE Nominal property tax millage rate (3)
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REAL INCOME Deflated average income, in thousands (4)
TEST SCORE Percentage of ninth grade students who passed all sections of the 1990 state
proficiency test. The test included sections on reading, writing, math, and
citizenship (3)
TURNOVER RATE Percentage of households who have lived in the district less than 6 years (4)
Sources:
(1) Places Rated Almanac .
(2) Amerestate housing tape.
(3) Ohio Department of Education, Division of Education Management Information Services.
(4) School District Data Book (MESA Group 1994).
(5) Office of Criminal Justice Services of State of Ohio.