Georgia State University Georgia State University
ScholarWorks @ Georgia State University
ScholarWorks @ Georgia State University
CSLF Working Papers Center for State and Local Finance
11-5-2015
Forecasting School Districts’ Revenues and Expenditures
Forecasting School Districts’ Revenues and Expenditures
Nick Warner
Georgia State University
Follow this and additional works at: https://scholarworks.gsu.edu/ays_cslf_workingpapers
Recommended Citation Recommended Citation
Warner, Nick, "Forecasting School Districts’ Revenues and Expenditures" (2015). CSLF Working Papers. 28.
Forecasting School
Districts’ Revenues
and Expenditures
Nick
Warner
What is forecasting?
•
Predicting
the
future
as
accurately
as
possible,
given
all
of
the
information
available,
including
historical
data
and
knowledge
of
any
future
events
that
might
impact
the
forecasts
•
Many
methods
‐
Forecasters
use
the
one
that
Why is forecasting challenging?
•
A
forecast
must
be
based
on
what
we
know
at
the
time,
and
things
could
change.
•
Some
data
is
predictably
variable
or
volatile,
so
any
forecast
includes
a
range
of
possible
Why would a school district
use forecasting?
Why would a school district use
forecasting?
•
Inform
long
‐
and
short
‐
term
budget,
capital,
and
staffing
plans
–
Are
expected
revenues
sufficient
to
cover
expected
expenditures
under
current
conditions?
–
Are
expected
revenues
sufficient
to
implement
new
improvement
initiatives?
–
Is
the
district
positioned
to
handle
changes
in
student
population?
How can we forecast data
How would a school district forecast?
•
Do
it
yourself,
based
on
known
data
sources
and
info
about
your
district
–
Methods
range
from
very
basic
to
extremely
advanced,
but
all
will
provide
very
useful
insights
and
help
inform
decisions
and
planning.
•
Get
help
from
experts
Warning:
A
forecast
should
be
used
to
inform
decisions,
not
dictate
budget
planning
or
decisions.
Budget
Property Tax Revenue
•
Importance:
Primary
local
funding
source
for
operations
(with
the
exception
of
certain
systems
that
have
access
to
sales
taxes
for
operations)
•
Data
Sources:
Digest
values,
observed
home
sales,
info
from
property
tax
assessor’s
office
•
Volatility:
Low,
relatively
easy
to
forecast
–
Exception:
The
Great
Recession
created
declining
statewide
property
tax
digests.
•
Method
to
Try:
Linear
trend
forecast
(ordinary
Property Tax Revenue
$500 $700 $900 $1,100 $1,300 $1,500 $1,700 $1,900 $2,100 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014Net
M&O Dig
est in $ MIllions
Tax Year
District A
Property Tax Revenue
$500 $700 $900 $1,100 $1,300 $1,500 $1,700 $1,900 $2,100 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015Net
M&O Dig
est in $ MIllions
Tax Year
District A With Moving Average Forecast
g
g
Two period
moving average
Which Line fits the data better?
otherwise known as “goodness of fit”
Sales Tax (ESPLOST)
•
Importance:
A
major
fund
source
for
capital
projects
–
Districts
enter
into
five
year
plans
based
in
part
on
expected
collections
•
Data
Sources:
Sales
tax
collections
(GDOR)
and
information
on
community
business
activity
•
Volatility:
High,
forecast
results
in
a
wide
range
–
Seasonality
and
other
trends
generally
are
incorporated
into
forecast.
•
Method
to
Try:
An
observed
average
over
a
long
Sales Tax (ESPLOST)
40 50 60 70 80 90 100 110ESPLOST Re
ve
nu
e $ in
Thousands
Sample District, 1999-2015
g
1999-2015 monthly
average
G d
f f ? N b d
d
average
Goodness of fit? Not bad and
its just the long term monthly
Sales Tax (ESPLOST)
40 50 60 70 80 90 100 1101-Jan-99 1-Jun-99 1-No
v-99
1-Apr-00 1-Sep-00 1-Feb-01 1-Jul-01 1-Dec-01 1-May-02 1-Oct-02 1-Mar-03 1-Aug-03 1-Jan-04 1-Jun-04 1-No
v-04
1-Apr-05 1-Sep-05 1-Feb-06 1-Jul-06 1-Dec-06 1-May-07 1-Oct-07 1-Mar-08 1-Aug-08 1-Jan-09 1-Jun-09 1-No
v-09
1-Apr-10 1-Sep-10 1-Feb-11 1-Jul-11 1-Dec-11 1-May-12 1-Oct-12 1-Mar-13 1-Aug-13 1-Jan-14 1-Jun-14 1-No
v-14 1-Apr-15 1-Sep-15
