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An interesting article about the ecomonics of

our market and the seasons it goes through.

 

Published by the BCREA and written by

Brendon Ogmundson, BCREA Economist

 

What is a seasonal adjustment?

 

“BC Multiple Listing Service® (MLS® )
home sales fell 27 per cent in December!...
BC home sales rose 49 per cent in March!”
Both of these statements are true, but are
they meaningful? Not really.

 

The type of variation we see in some monthly data is
produced by a normal and predictable
seasonal pattern. Which is why whenever
the media reports economic statistics such
as how many jobs were created or houses
sold each month, it is very likely that the
statistics they are reporting have been
“seasonally adjusted.” But what exactly
does that mean?
Before we get into how and why seasonal
adjustment is done, it will be useful to
defi ne a few terms:


Time series: a time series is a set of data
points of some economic (or other) variable
observed through time. For example MLS®
home sales from January 1980 to
December 2011.


Seasonality: a time series displays
seasonality if there is, within the calendar
year, repetitive and predictable movement
around an economic variable’s trend.


Trend: the trend is the long-term
movement in a time series after other
components, such as cyclical fl uctuations,
have been accounted for.


Cycle: the cycle component of a time series
is the fl uctuation around the long-term
trend that occurs at a period of longer than
one year.


Irregular: a time series may also have what
is called an irregular component consisting
of whatever variation remains once trend,
cycle and seasonality have been
accounted for. It can be the result of
one-time events like extreme weather
conditions or other unpredictable events.
Many economic data are infl uenced by
recurring seasonal factors.

 

Whether from weather, holidays or other recurring
calendar events, these seasonal factors
often obscure the underlying movement
of an economic variable and make data
analysis more challenging. Consider the
increase in retail sales during the Christmas
shopping season or home sales in the
spring and summer months when the sun
is shining and fl owers are blooming. If one
were to look at the raw data for these series,
you would observe spikes in the level of
the data that could mask a meaningful
underlying trend.


The challenge for economists and other
users of data is to isolate movements in a
time series that are due solely to seasonality
and not to other important economic factors
that might be impacting trends in the data.
To accomplish this, various statistical
methods have been developed to decompose
a time series into its trend, cycle,
irregular and seasonal components. The
time series decomposition of MLS® home
sales is shown in the accompanying graph.
Most of the monthly fl uctuation in sales are
due to the long-term trend and mediumterm
cyclical economic factors. However,
there is a pronounced seasonal factor as
well. Once the seasonal factor has been
removed, it is much easier to see smaller
movement in the underlying data that
were previously masked by seasonal
fl uctuations.


As we have seen, seasonal adjustment is
an invaluable tool for data analysis that
can signifi cantly enhance understanding
and communication of the month to 

month changes in the housing market.

 

Provided by Darin Germyn

 

Darin Germyn sells homes in White Rock, Surrey, Delta and Langley

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