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From YouTube: Community Facilities Study Meeting #5 Part 2 of 6
Description
Arlington VA Community Facilities Study Committee presentation on Validating Forecast Methodologies with consultant Bob Scardamalia, RLS Demographics. Recorded April 8 2015 at Wakefield High School.
http://commissions.arlingtonva.us/community-facilities-study/
A
Demographer,
having
spent
his
entire
early
career
as
full
in
in
New,
York,
State
and
working
for
the
new
york
state,
school
system
and
retired
from
that
and
has
become
a
well
known
and
well
thought
of
consultant
on
these
issues
and
we're
delighted
to
have
him
bring
a
third
parties
perspective
to
some
of
the
demographic
issues
that
we've
been
talking
about
to
date.
Bob.
B
Thank
you
John.
Thank
you.
Thank
you
all
for
being
here
and
giving
giving
us
this
opportunity
to
talk
about
Arlington.
It's
actually
kind
of
interesting,
because
I,
my
graduate
school
work
was
done
at
Georgetown
and
lived
nearby
just
outside
near
landmark
shopping
center.
So
this
area
is
pretty
familiar
to
me.
My
son
lives
out
in
reston,
so
we're
down
here
for
grandkids
pretty
frequently.
So
it's
it's
actually
data.
You
know
kind
of
isn't
always
everybody's
first
topic.
It
is
mine
and
to
be
able
to
come
back
and
look
at
this
area
and
analyze.
B
B
What
we
are
going
to
what
our
role
here,
which
one
yep
okay,
what
our
role
in
this
process,
myself
and
Richard
Gibbs,
who
gripped,
who
I'm
going
to
introduce
in
a
few
minutes
when
I'm
done
guess
I
better
stand
over
here-
is
to
review
the
methodology
that
is
being
used
by
the
county
and
by
the
school
district
in
developing
forecasts.
My
side
of
this
is
in
that
county
forecasting
process,
which
I've
done
a
lot
of
in
New
York
State
helps
state
of
mass
New
Hampshire.
B
Do
population
forecasts
for
their
counties,
working
with
the
new
york
metropolitan
transportation
council,
the
new
york
region
in
doing
socio
economic
forecasts?
So
it's
something
that
I
have
a
great
amount
of
interest
in
expertise
in
and
what
I've
found
is.
As
a
demographer,
we
tend
to
like
demographic
methods,
cohort
component
migration
fertility
mortality
to
to
forecast
population.
That's
not
the
way
it's
done
in
Arlington,
and
it's
not
done
that
way
in
Arlington
for
I.
Think
some
very
good
reasons
and
the
method
that
is
used
here
I
think
is
appropriate
for
this
type
of
area.
B
Arlington's,
a
pretty
dynamic
place
population
movement
in
and
out
hi
college
population,
proximity
to
the
district,
heavy
commuting
patterns,
job
development,
commercial
and
residential
mix
and
so
standard
demographics,
demographic
methods,
cohort
component
methods,
I,
don't
believe,
tell
the
whole
story.
I
think
they
can
be
a
component
of
the
work,
but
they
don't
tell
the
whole
story.
So
I've
got
some
bullet
points
about
the
methodology
that
is
being
used.
You've
you've
had
materials
describing
the
methodology,
so
I
don't
want
to
spend
a
lot
of
time
on
this,
but
just
to
kind
of
provide
a
framework.
B
The
method
used
here
is
is
basically
a
land
use,
method,
housing,
residential
and
commercial
facility
based
method,
quite
appropriate.
In
an
area
like
this
new
york
city
does
something
very
similar.
You
have.
You
have
a
stock
of
residential
and
commercial
facilities
and
being
able
to
manage
that
stock.
The
changes
in
housing
and
commercial
development
is
is
a
perfectly
reasonable
way
to
forecast
to
monitor,
change
and
forecast
change,
and
I
think
that
works
here.
B
So
it's
very
different
than
what
I
would
look
at
as
a
standard
demographic
forecast,
there's
a
piece
that
it
doesn't
provide,
which
is
where
I
want
to
come
back
to
to
link
demographic
methods
with
the
housing
and
commercial
real
estate
method.
But
but
it's
a
starting
point
and
one
of
the
things
that
it's
really
that
it's
really
critical
to
have
in
such
a
method
is
quality
data,
and
so
that's
another
place
where
I
find
that
the
the
county's
methods
are
well
based.
So
it's
a
housing
and
commercial
real
estate
method.
B
It's
built
off
of
the
general
Land
Use
Plan.
It's
that
method
provides
current
estimates
of
population
of
households,
individual
housing
units
and
employment.
They
all
come
off
of
that
model
built
off
of
the
general
Land
Use
Plan
and
monitoring
change
in
approved
development
for
current
estimates,
a
couple
of
key
things:
key
drivers,
or
levers
that
monitor
that
can
be
used
to
monitor
the
method,
our
occupancy
rates
for
housing
units.
You
know.
Yes,
we
can
build
a
lot
of
housing
units,
but
are
they
occupied
number
one
question?
B
You
have
to
be
able
to
monitor
occupancy
number
two
average
household
size,
so
housing
unit
methods
are
if
they're
built
on
quality
housing.
Data
are
a
good
way
of
monitoring,
housing
and
households.
Key
unknown
factor
is
how
many
people
are
in
each
of
those
housing
units.
What's
the
average
household
size
because
that's
where
the
population
comes
from
and
as
you
have
changes
in
the
type
of
housing
single
family
versus
multifamily,
you
know
large
McMansion
type
homes
versus
apartments,
condos
that
changes
that
mix
of
housing
type.
So
those
those
two.
B
Those
are
two
key
levers:
occupancy
and
housing
unit
size,
employment
estimates
are
based
off
of
commercial
real
estate
and
again
the
county's
land
use
plan
and
permit
tracking
systems
allow
for
monitoring
proved
projects
when
they
will
come
on
when
they're
expected
to
come
online.
We
always
have
to
keep
on
mind
that,
when
the
expectation
is
isn't
always
when
things
actually
happen-
and
we
simply
don't
know-
what's
happening
in
the
future,
but
you
have
the
data
and
that's
the
important
part.
B
You
have
quality
data
that
is
built
on
a
small
geographic
level,
monitoring
individual
projects
and
development
over
time.
So
some
of
the
important
lovers
in
that
commercial
side
is,
you
know,
distribution
between
commercial
office,
space,
retail,
space,
hotels,
other
things
and
the
county
is
monitoring
that
one
of
the
issues,
though,
is
as
with
housing
occupancy
commercial
occupancy.
How
is
that
changing?
B
You
don't
have
to
look
too
far
in
this
last
recession
to
know
that
employment
and
office
space
took
some
hits
in
lots
of
different
ways,
and
so
monitoring
those
sorts
of
changes
is
not
always
an
easy
thing
to
do
and
that
that
vacancy
is
another
another
lever
in
the
method
that
can
be
used
to
monitor
change
over
time
and
essentially,
control
not
control.
It's
not
the
right
word
to
to
gauge
the
movement.
B
The
range
of
historical
values
around
which
occupancy
in
the
commercial
sector
can
can
move
in
work
in
New,
York,
City
found
very
similar
kinds
of
things.
There's
a
there's,
a
lot
of
historical
data
that
you
have
to
look
at
and
sometimes
that
historical
data
allows
you
to
look
at
trends,
but
sometimes
what
you
want
to
use
that
historical
data
for
is
just
to
control,
provide
some
bounds
on
what
is
happening.
B
Currently,
you
know
and
what
I,
what
I'm
looking
at
today
inside
those
historical
bounds
that
provides
some
confidence
and
that's
what
the
county
does
so
commercial
vacancy
rates
are
important
lovers.
Co-Star
is
a
private
provider
of
that
type
of
information.
Okay,
so
again
it
comes
comes
down
to
where
your
data
sources
koala
any
data
sources.
Very
few
folks
have
the
ability
and
the
resources
to
go
business
by
business
building
by
building
and
analyze
occupancy.
B
So
a
company
like
co-star,
that's
what
they
do,
but
the
county
also
in
their
method,
doesn't
just
use
their
data
as
it
comes
out
of
the
box.
It's
it's
got
some
levers
in
it
also
in
terms
of
looking
at
the
effect
of
brac
and
other
types
of
development
activities.
One
of
the
key
things
I
found
really
interesting
was
all
of
this
occurs
at
the
census
block
level.
So
it's
very
small
geographic
identifiers
which
has
an
advantage,
because
you
can
look
at
small
geography
and
what's
happening
there.
B
The
forecasting
process-
that's
that's
kind
of
the
estimating
process,
but
it
also
pretty
much
describes
the
forecasting
process
because,
as
you're
estimating
residential
and
commercial
activity
currently
that
same
model,
the
Land
Use
Plan
tells
us
what
is
planned
to
happen
in
the
next
three
years.
Five
years,
ten
years
what
you
can
forecast
in
terms
of
approved
plans
and
so
mathematically,
the
forecasting
process
works
in
concert
with
the
estimating
process.
B
So
you've
probably
gotten
the
idea
right
already
that
I
think
the
model
works
pretty
well,
it's
driven
by
quality
data
at
a
small
geographic
level,
it's
driven
by
a
standard
methodology
of
both
residential
and
commercial
development.
There
are
some
levers
in
there
too,
to
gauge
the
to
move
the
estimates
up
and
down.
Fortunately,
in
recent
past,
those
lovers
have
maintained
a
pretty
pretty
narrow
band
of
historical
series,
so
they
do
continuous
update
and
monitoring
of
the
permit
from
in
tracking
databases.
B
That's
in
lots
of
areas,
they
simply
don't
have
the
quality
data
you
know
issuing
of
permits
is
one
thing
actually,
knowing
something
that
was
built
and
occupied
is
something
else
you
need
to
have
those
other
two
pieces
for
the
method
to
work.
Well,
the
Arlington
method
captures
those
it's
a
bottom-up
approach,
as
I
mentioned
from
the
block
level
analysis,
and
that's
that's
good,
because
you've
got
the
quality
data.
B
After
our
review
of
this
methodology
and
speaking
with
the
county
folks
who
are
intimately
involved
in
it
a
couple
of
things
documentation,
there
are
lots
of
pieces
of
documentation
that
exists,
but
it
would
be
really
nice
to
have
a
very
comprehensive
documentation
of
this
whole
model.
So
the
the
GIS
piece,
the
database
piece,
the
estimates
piece,
the
forecast,
that's
recorded,
but
you've
got
to
go
to
a
number
of
different
places,
so
it'd
be
nice
to
have
a
consistent
and
comprehensive
documentation.
These
two
levers
the
occupancy
and
average
household
size,
as
I
mentioned.
B
Fortunately,
recent
past
says
that
they're
they're
moving
in
a
pretty
narrow
band
average
household
size
generally,
his
has
declined.
It's
been
relatively
stable.
You
have,
you
know,
changes
in
the
hundreds
of
position.
You
know
2.07
22,
point
12
the
pretty
narrow
bound,
but
we
also
have
some
changes
in
living
arrangements
in
this
country
and
in
an
arlington
I.
Think
it's
important
and
so
two
recommendations
would
be
to
utilize.
The
American
Community
Survey
data,
not
at
a
block
group
level
data.
B
You
know
the
method
is
block
oriented,
but
that
small
area
data
out
of
the
ACS
American
Community
Survey.
How
can
I
say
it?
Defecate
Lee?
If
there's
anybody
from
the
Census
Bureau
in
the
room,
it's
not
the
highest
quality
at
a
small
area
at
a
county
level
at
a
census
tract
level.
There
may
be
some
additional
information
that
that
data
can
be
valuable,
and
so
there's
a
need
to
track
that
information
and
monitor
it
a
little
more
closely
to
again
adjust
those
levers
of
occupancy
and
housing
size.
B
The
biggest
thing
that
I
think
would
be
an
advantage
in
the
method
that
would
bad
value
to
the
method
is
age
and
migration
analysis.
When
I
started
digging
into
the
method,
the
county
does
an
disaggregation
of
their
current
estimates
of
population
by
age
and
they
use
the
Census
Bureau's
data
from
2010,
the
decennial
census,
but
also
their
current
estimates
of
population
that
provide
age
distribution
data-
that's
good,
but
as
you
get
further
away
from
the
census
date,
then
the
reliability
of
those
estimates
becomes
more
and
more
questionable
and
what
I?
B
My
question
is:
what
happens
to
them?
Do
they
stay
if
they
stay,
then
you've
got
this
group
of
population
that
is
going
to
age
in
Arlington
and
in
40
years
they're
going
to
be
how
old
in
their
60s
okay.
Are
they
going
to
still
be
in
arlington?
That's
the
key
question.
So
the
the
integration
of
a
demographic
method
that
would
help
understand
this
age,
distribution
effect
and
the
migration
effect
I
think
is
an
important
additional
component
to
the
Arlington
method,
so
that
would
be
development
of
a
cohort
component
method.
B
The
county's
projections
to
2040
for
total
population
look
perfectly
reasonable
to
me,
but
what
do
they
imply
and
far
as
far
as
the
age
distribution
of
Arlington's
population,
when
we
go
out
40
years,
we'll
all
of
these
in
migrants
still
be
here,
I
think
there's
an
important
component
here.
Some
of
this
is
obviously
migration.
Arlington
also
has
a
large
college
population
college
and
rolled
population,
but
not
a
residential
they're,
not
living
in
dormitories
group
quarters
and
census
terminology,
and
so
my
question
is
what's
happening
with
them.
Are
they
part
of
this
peak
of
in
migration?
B
Should
they
be
handled
differently?
So
that's
a
long
way
of
saying,
aged
analysis
of
the
age
distribution
and
the
migration
effects
in
the
county,
I
think
are
pretty
important
as
far
as
additional
methods
and
that
kind
of
leads
to
number
eight
here,
the
development
of
an
integrated
model.
The
economic
side
of
this
comes
from
commercial,
real
estate
and
employment,
but
there's
also
larger
macroeconomic
factors
that
can
come
into
play
and
and
that's
that's
a
model.
That's
often
used
for
regional
analysis
and
the
Metropolitan
Washington
cog
is
is
using
that
type
of
model.