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Amid continued global economic uncertainty, Canadian retail sales are expected to continue their slow but steady climb next year. Fusion Retail Analytics is forecasting retail sales growth of 2.7% for 2013, maintaining the 2.5% growth pace set in 2012 . Retailers can expect a slower start to 2013 as we roll over a very strong Spring ’12 but YOY retail sales growth is expected to recover in the back half of the year. When looking at the underlying drivers of retail sales, their trends, oscillations, LY performance, lag impacts and tipping points it is unlikely that retail sales growth will deviate from its recent run-rate of 2-4%. Retail is unlikely to see a break in this pattern until we see a dramatic rise in Discretionary Income – the amount of money Canadians have left after paying their taxes and cost of living expenses. The key variable to watch is unemployment; if this drops below its tipping point of 6.5%, Canada will see accelerated wage gains, pushing Discretionary Income up and flowing into retail sales. Discretionary Income in expected to grow in 2013, lifting retail sales, but this positive impact will be tempered by lower home turnover, cooler temperatures, and ever-increasing cross-border shopping in 2013. Growth in cost of living is forecasted to remain below the historical run-rate, primarily due to the expected low price of oil and cooler temperatures, which will curb increases in transportation and utilities costs, respectively. Slow growth in cost of living, combined with steady growth in income and taxes, will drive Discretionary Income growth back to historical, pre-recession levels. Those who have recently moved purchase from more categories and spend more than other retail consumers, making home turnover – a measure of recent movers – a major factor in overall retail sales growth. Unfortunately, due to fairly strong growth in early 2012 and new mortgage rules, home turnover in 2013 in expected to trail 2012, maintaining the downward trend into the Spring before starting to recover later in the year. Warmer temperatures can jump-start the Spring season, a key sales period for many retailers. This is exactly what happened in 2012, which included the warmest month of March in over 10 years, pulling seasonal sales forward and kicking off a warmer-than-average year. With average temperatures expected and only 2 months forecasted to be warmer in 2013 than they were in 2012, retailers will likely not have the benefit of a hot 2013, cooling our retail sales forecast. American retailers continue to slowly eat away at Canadian retail sales. With the Canadian dollar expected to stay around parity with the US dollar, the trickle of Canadians travelling across the border will continue to increase in 2013, stealing an additional 0.4% sales growth from Canadian retailers. The exchange rate would have to fall to under $0.80 to deter most Canadians from crossing the border and keeping their purchases in Canada.
Citation preview
2013 Canadian Retail Outlook December 2012
-5%
0%
5%
10%
15%
Jun. '03 Dec. Jun. '04 Dec. Jun. '05 Dec. Jun. '06 Dec. Jun. '07 Dec. Jun. '08 Dec. Jun. '09 Dec. Jun. '10 Dec. Jun. '11 Dec. Jun. '12 Dec. Jun. '13 Dec.
YO
Y G
row
th %
2
Retail sales in Canada: (rolling 3 months)
Retail sales declined during the recession
The spike in retail sales growth was
caused by the roll-over of low 2008 figures
Historical norm = 6 %
Recent norm = 3%
Source: Retail sales excludes auto, grocery and gas, reported from Statistics Canada’s Monthly Retail Trade Survey up to Nov ’11, with a 13 month delay allowing Statistics Canada to
make revisions; retail sales within the most recent 13 months reported from Fusion Retail Analytics
Retail sales will experience 2.7% YOY growth in 2013
Rolling over a strong Spring in
2012, retail sales growth is
forecasted to be softer in early
2013 before ramping up slightly
in the back half of the year
Source: Fusion Retail Analytics, December 2012
Breakdown of the underlying drivers of retail sales
Canadian
retail sales
Population growth
Weather
Discretionary Income
Consumer confidence
Home turnover Household income
Taxes
Cost of living
Wage rates
Unemployment
Interest rates
Inflation
Oil prices
Food prices
Other prices
Cross-border shopping Exchange rates
Major economic headlines
Positive impact on retail
Negative impact on retail
Varied impact on retail
3
Legend:
Drivers vs. LY
Discretionary Income
Consumer confidence
Population
Temperature (national weighted)
Home turnover
Cross-border shopping
Retail sales
1
2
4
5
6
Source: Fusion Retail Analytics, December 2012
3
4
2013 forecasted impact of
underlying drivers on retail sales
Trending up, positive impact
Trending up, negative impact
Trending down, positive impact
Trending down, negative impact
On par
Legend:
5
Forecasts are based on six factors:
1 The underlying drivers of each variable
2
6
5
4
3
Notes: See methodology slides 28-32 for detailed examples of the six factors, Source: Fusion Retail Analytics, December 2012
Methodology overview
Long-term trends of each variable
The roll-over of high/low LY figures and resulting oscillations
The tendency of each variable to regress to the mean
The lag in trends between different variables
External shocks (major events that can shift the economy) These events are highly unpredictable and have not been factored into any forecasts
Discretionary Income
6
-6%
-4%
-2%
0%
2%
4%
6%
8%
10%
12%
14%
Jun. '03 Dec. Jun. '04 Dec. Jun. '05 Dec. Jun. '06 Dec. Jun. '07 Dec. Jun. '08 Dec. Jun. '09 Dec. Jun. '10 Dec. Jun. '11 Dec. Jun. '12 Dec. Jun. '13 Dec.
YO
Y G
row
th %
DI growth is expected to
continue to trend toward
its pre-2005 level
Notes: Discretionary Income is the amount of money consumers have available each month after paying taxes and their living costs. Cost of living items include groceries, rent, utilities,
health care and gas. Source: Raw data provided by Statistics Canada; compilation and analysis by Fusion Retail Analytics, December 2012
Discretionary Income = Household income – Taxes – Cost of living
Prior to 2005, DI growth
wavered around 3%
Discretionary Income per household: (rolling 4 months)
7
Historical normal = 3%
Discretionary Income growth will continue between
1.5% to 4% as it trends towards the historical norm
4%
6%
8%
10%
Unem
plo
ym
ent ra
te %
-4%
-2%
0%
2%
4%
6%
8%
Jun. '03 Dec. Jun. '04 Dec. Jun. '05 Dec. Jun. '06 Dec. Jun. '07 Dec. Jun. '08 Dec. Jun. '09 Dec. Jun. '10 Dec. Jun. '11 Dec. Jun. '12 Dec. Jun. '13 Dec.
YO
Y G
row
th %
Income regressed to normal
as unemployment recovered
Notes: All metrics shown rolling 4 months. Source: Raw data provided by Statistics Canada; compilation and analysis by Fusion Retail Analytics, December 2012
Income is forecasted to stay near
historical levels in 2013, preventing large
gains in Discretionary Income growth
Unemployment rate:
Growth in monthly household income:
In order for income growth to see
substantial gains the unemployment
level must fall below the 6.5% threshold
As the unemployment rate was
falling, income was higher than norm
The unemployment rate rose
causing income to drop below norm
8
6.5% threshold
Historical norm
Relatively stable Discretionary Income growth
will be driven by the stability of HH income growth
Discretionary Income = Household income – Taxes – Cost of living
4%
6%
8%
10%
Jun.'03
Dec. Jun.'04
Dec. Jun.'05
Dec. Jun.'06
Dec. Jun.'07
Dec. Jun.'08
Dec. Jun.'09
Dec. Jun.'10
Dec. Jun.'11
Dec. Jun.'12
Dec. Jun.'13
Dec. Jun.'14
Dec. Jun.'15
Dec.
Unem
plo
ym
ent ra
te %
Notes: All metrics shown rolling 4 months. Source: Raw data provided by Statistics Canada; compilation and analysis by Fusion Retail Analytics, December 2012
Unemployment rate:
9
Based on current trends,
unemployment will not cross
the threshold until 2015
6.5% threshold
Based on current trends, unemployment rate will not
trigger substantial gains in HH income until 2015
20%
22%
24%
26%
% o
f in
com
e
-9%
-6%
-3%
0%
3%
6%
9%
12%
Jun. '03 Dec. Jun. '04 Dec. Jun. '05 Dec. Jun. '06 Dec. Jun. '07 Dec. Jun. '08 Dec. Jun. '09 Dec. Jun. '10 Dec. Jun. '11 Dec. Jun. '12 Dec. Jun. '13 Dec.
YO
Y G
row
th %
Notes: Taxes as a percentage of income shown rolling 12 months, taxes per household shown rolling 4 months. Source: Raw data provided by Statistics Canada; compilation and analysis
by Fusion Retail Analytics, December 2012
Prior to 2008, taxes were on
average 24% of income
Taxes as a percentage of household income:
Growth in taxes paid per household:
With lower incomes, taxes
dropped to 23% of income
With no major tax policy
changes in 2013, taxes as
a percentage of income
should continue to inch
upward as wages recover
10
Historical norm
Taxes as a percentage of HH income
should remain stable in 2013
Discretionary Income = Household income – Taxes – Cost of living
-2%
0%
2%
4%
6%
Jun. '03 Dec. Jun. '04 Dec. Jun. '05 Dec. Jun. '06 Dec. Jun. '07 Dec. Jun. '08 Dec. Jun. '09 Dec. Jun. '10 Dec. Jun. '11 Dec. Jun. '12 Dec. Jun. '13 Dec.
YO
Y G
row
th %
Notes: Cost of living items include groceries, rent, utilities, health care and gas. Forecast based on the trend of most items regressing towards normal with exceptions noted in slide 36.
Source: Raw data provided by Statistics Canada; compilation and analysis by Fusion Retail Analytics, December 2012
Dip in cost of living is
caused by recession
Below-normal growth rates
caused by drops in growth
of transportation and
utilities cost growth (see
appendix slides 35-36)
Growth in cost of living per household: (rolling 4 months)
11
Historical norm
Cost of living growth should rise slightly in
2013, but remain below the historical norm
Based on current trends,
cost of living will move
towards normal levels
Discretionary Income = Household income – Taxes – Cost of living
Consumer confidence
12
-5%
-3%
-1%
1%
3%
5%
Jun. '03 Dec. Jun. '04 Dec. Jun. '05 Dec. Jun. '06 Dec. Jun. '07 Dec. Jun. '08 Dec. Jun. '09 Dec. Jun. '10 Dec. Jun. '11 Dec. Jun. '12 Dec. Jun. '13 Dec.
Indexed
13
The historical norm was inflated
by the housing bubble
Consumer confidence
crashed during the
recent recession
Consumer confidence in Canada: (rolling 3 months)
Consumer confidence
spiked this spring
The norm for the
foreseeable future is
lower as consumers
are wary of more
swings in the economy
The US debt ceiling is
raised causing a fall in
consumer confidence
Historical norm = 4%
Recent norm = 1%
Source: Fusion Retail Analytics, December 2012
Consumer confidence in Canada
is stabilizing around a new norm
Temperature
14
-3
-2
-1
0
1
2
3
4
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
Max T
em
pera
tue (⁰C
)
15
Source: Environment Canada data from 37 weather stations representing the largest metropolitan areas in Canada; data is updated on a weekly basis; using Fusion's proprietary weather
model, the data is compressed into national numbers expressing the total precipitation and maximum temperature experienced by the average Canadian consumer, December 2012
Max temperature variance from norm in May: (monthly max temperature in May vs. 10-year historical normal temperature in May)
…so, as expected May 2003
temperature was above 2002…
…and since May 2003 was above
the norm, May 2004 temperature
was, as expected, below May 2003
May 2002 temperature
was below normal…
This trend remains true
for all months except
May ‘06 and ’07
All months since 2001
Months that moved towards mean 103
Months that moved away from mean 29
% that moved towards mean 78%
This trend above can be
applied to all months
Temperature moves towards the norm in 78% of months
-4
-2
0
2
4
Jan '12 Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Max T
em
pera
ture
(⁰C
)
16
Source: Environment Canada data from 37 weather stations representing the largest metropolitan areas in Canada; data is updated on a weekly basis; using Fusion's proprietary weather
model, the data is compressed into national numbers expressing the maximum temperature experienced by the average Canadian consumer, December 2012
2012 max temperature variance from norm: (monthly max temperature vs. 10-year historical normal temperature)
Probability
2013 will be
hotter than
2012
28% 8% 5% 59% 6% 37% 20% 16% 45% 47% 66% 31%
Since March 2012 was so high
above normal there is a 95%
chance March 2013 will be cooler
Because November 2012 was
significantly below normal there is 66%
chance November 2013 will be hotter.
Implications: Without any weather forecast it is possible to calculate the probability that each month in 2013 will be hotter than 2012.
YOY temperature forecasts can be
derived based on LY temperature
Jan '13 Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
2012 -0.4 1.5 8.5 11.9 19.9 23.5 27.3 25.9 21.2 13.2 5.6 1.5
2013 forecast
(norm) -2.0 -0.8 4.7 12.0 18.0 22.9 25.9 24.9 20.8 13.3 6.8 0.2
Probability 2013
will be hotter
than 2012 28% 8% 5% 59% 6% 37% 20% 16% 45% 47% 66% 31%
Probability 2013
will be colder
than 2012 72% 92% 95% 41% 94% 63% 80% 84% 55% 53% 34% 69%
Notes: TY forecast based on Fusion’s proprietary weather model triangulated with Environment Canada’s seasonal weather outlook. Source: Environment Canada data from 37 weather
stations representing the largest metropolitan areas in Canada; data is updated on a weekly basis; using Fusion's proprietary weather model, the data is compressed into national numbers
expressing the maximum temperature experienced by the average Canadian consumer, December 2012
17
2013 forecasted monthly temperatures
-4
-2
0
2
4
Jan '13 Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Max T
em
pera
ture
(⁰C
)
18
Notes: Temperature forecast triangulated The Weather Network’s 2013 Winter Outlook and Environment Canada’s Seasonal weather forecasts. Source: Environment Canada data from
37 weather stations representing the largest metropolitan areas in Canada; data is updated on a weekly basis; using Fusion's proprietary weather model, the data is compressed into
national numbers expressing the maximum temperature experienced by the average Canadian consumer, December 2012
Max temperature variance from LY: (monthly max temperature vs. LY)
Implications: All else being equal, months in which retailers will have difficulty matching LY sales in 2013 can be forecasted with
LY temperatures. Due to cooler temperatures, there will be less demand for retail in March 2013 than in March 2012. These sales
will likely be pushed to April or May so YOY retail is expected to have a relatively poor March and stronger April/May.
Temperature will not
have a significant effect
on retail sales for the
summer as it should be
similar to 2012
Most retailers will have a
weak March vs. LY because
2013 will be colder
2013 YOY temperature forecast
Home turnover
19
-30%
-20%
-10%
0%
10%
20%
30%
40%
Jun. '03 Dec. Jun. '04 Dec. Jun. '05 Dec. Jun. '06 Dec. Jun. '07 Dec. Jun. '08 Dec. Jun. '09 Dec. Jun. '10 Dec. Jun. '11 Dec. Jun. '12 Dec.
YO
Y G
row
th %
20
Home turnover in Canada: (# of homes sold/purchased each month, new and existing, compared YOY, rolling 8 months)
When the economy is stable, a
peak in one month creates a
valley in that month next year
Source: Raw data provided by The Canadian Real Estate Association; compilation and analysis by Fusion Retail Analytics, December 2012
A major factor in forecasting home turnover growth is
understanding the oscillations over several years
-30%
-20%
-10%
0%
10%
20%
30%
40%
Jun. '03 Dec. Jun. '04 Dec. Jun. '05 Dec. Jun. '06 Dec. Jun. '07 Dec. Jun. '08 Dec. Jun. '09 Dec. Jun. '10 Dec. Jun. '11 Dec. Jun. '12 Dec.
YO
Y G
row
th %
21
Implications: The area under the curve has been decreasing with each cycle since 2008 as home turnover is beginning
to stabilize. The positive oscillations are not as high as the negative oscillations which also indicates a downward trend.
Home turnover in Canada: (# of homes purchased each month, new and existing, compared YOY, rolling 8 months)
Prior to 2008 home turnover
growth was relatively stable
Abnormally high home turnover growth
caused by roll-over of low 2008 values
Housing market now stabilizing
100 53
29
98
Area between zero
and growth line 100
Source: Raw data provided by The Canadian Real Estate Association; compilation and analysis by Fusion Retail Analytics, December 2012
Home turnover has been stabilizing since 2008
Legend:
-30%
-20%
-10%
0%
10%
20%
30%
40%
Jun. '03 Dec. Jun. '04 Dec. Jun. '05 Dec. Jun. '06 Dec. Jun. '07 Dec. Jun. '08 Dec. Jun. '09 Dec. Jun. '10 Dec. Jun. '11 Dec. Jun. '12 Dec. Jun. '13 Dec.
YO
Y G
row
th %
22
*On an extended analysis project.
Notes: Fusion forecast based on the trend, oscillation and stabilization, triangulated with CREA, MLS and TD Canada projections. Source: Raw data provided by The Canadian Real
Estate Association; compilation and analysis by Fusion Retail Analytics, December 2012
Home turnover in Canada: (# of homes sold/purchased each month, new and existing, compared YOY, rolling 8 months)
Available by region*
Home turnover in 2013 will be down 4.8%
Cross-border shopping
23
-1.5%
-1.0%
-0.5%
0.0%
0.5%
1.0%
1.5%
0.60 0.70 0.80 0.90 1.00 1.10
Cro
ss-b
ord
er
shoppin
g im
pact on
Canadia
n r
eta
il
Exchange rate (CAD/USD)
24
When the Cdn. dollar drops below
$0.81 it starts to deter consumers
from shopping in the US and
boosts retail sales in Canada
A strong Canadian dollar drives
consumers to shop in the US
A weak Canadian dollar deters
consumers from shopping in the US
Parity ($1 USD = $1 CAD)
Different months (from ’03 to ’12 - see slide 25) Incremental impact of cross-border shopping on Canadian retail:
Notes: Cross-border shopping impact on Canadian retail is the amount that retail sales are affected as a result of increased cross-border shopping. Source: Raw data provided by
Statistics Canada and Bank of Canada; compilation and analysis by Fusion Retail Analytics, exchange rate forecast from TD and CIBC, December 2012
The exchange rate must drop below $0.81 CAD/USD
in order to slow the trend of lost sales to the US
Legend:
-1.5%
-1.0%
-0.5%
0.0%
0.5%
1.0%
1.5%
$0.50
$0.75
$1.00
$1.25
Jun.'03
Dec. Jun.'04
Dec. Jun.'05
Dec. Jun.'06
Dec. Jun.'07
Dec. Jun.'08
Dec. Jun.'09
Dec. Jun.'10
Dec. Jun.'11
Dec. Jun.'12
Dec. Jun.'13
Dec.
Cro
ss-b
ord
er s
hoppin
g im
pact o
n C
anadia
n re
tail s
ale
sE
xchange r
ate
(C
AD
/US
D)
With the dollar at parity, Canada can
expect to lose an incremental 0.4% of
retail sales to the US in 2013
25
Exchange rate (CAD/USD)
Cross-border impact on Canadian retail sales
Major banks are forecasting the
dollar to remain near par for 2013
Incremental impact of cross-border shopping Canadian retail sales:
Notes: Cross-border shopping impact on Canadian retail is the amount that retail sales are affected as a result of increased cross-border shopping. Source: Raw data provided by
Statistics Canada and Bank of Canada; compilation and analysis by Fusion Retail Analytics, exchange rate forecast from TD and CIBC, December 2012
In 2013, Canadian retail sales will be down an additional
0.4% as a result of increased cross-border shopping
Legend:
Methodology
26
27
Forecasts are based on six factors:
1 The underlying drivers of each variable
2
6
5
4
3
Source: Fusion Retail Analytics, December 2012
Recall: Methodology overview
Long-term trends of each variable
The roll-over of high/low LY figures and resulting oscillations
The tendency of each variable to regress to the mean
The lag in trends between different variables
External shocks (major events that can shift the economy) These events are highly unpredictable and have not been factored into any forecasts
28
Example, Variable X vs. Y:
0
2
4
6
8
10
12
14
Jun. '03 Dec. Jun. '04 Dec. Jun. '05 Dec. Jun. '06 Dec. Jun. '07 Dec. Jun. '08 Dec. Jun. '09 Dec. Jun. '10 Dec. Jun. '11 Dec. Jun. '12 Dec. Jun. '13 Dec.
Variable X
Variable Y
There is a clear correlation
between X and Y
If Y is likely to rise in 2013,
X is also likely to rise
Methodology Example 1
Source: Fusion Retail Analytics, December 2012
Understanding the movement of a variable’s underlying
drivers can help predict the movement of that variable
Legend:
29
Example, Variable Z, YOY growth %:
0
25
50
75
100
Jun. '03 Dec. Jun. '04 Dec. Jun. '05 Dec. Jun. '06 Dec. Jun. '07 Dec. Jun. '08 Dec. Jun. '09 Dec. Jun. '10 Dec. Jun. '11 Dec. Jun. '12 Dec. Jun. '13 Dec.
Z has been trending down since 2003
Recently the downward
trend has been weaker
In 2013 the downward trend will
likely continue to weaken
Methodology Example 2
Source: Fusion Retail Analytics, December 2012
Examining the long-term trend of a variable can give a
strong indication of how it will behave in the near future
-100%
-50%
0%
50%
100%
150%
Jun. '03 Dec. Jun. '04 Dec. Jun. '05 Dec. Jun. '06 Dec. Jun. '07 Dec. Jun. '08 Dec. Jun. '09 Dec. Jun. '10 Dec. Jun. '11 Dec. Jun. '12 Dec. Jun. '13 Dec.
30
Example, Variable X, absolute:
Example, Variable X, YOY growth %: Though 2009 was normal, the YOY
numbers show growth. This was
strictly caused by a poor 2008 If 2013 is a normal year,
YOY figures will be negative
due to a strong 2012
0
5
10
15
20
Methodology Example 3
Source: Fusion Retail Analytics, December 2012
Last year’s performance plays a major
role in this year’s growth figure
-10%
0%
10%
20%
Jun. '03 Dec. Jun. '04 Dec. Jun. '05 Dec. Jun. '06 Dec. Jun. '07 Dec. Jun. '08 Dec. Jun. '09 Dec. Jun. '10 Dec. Jun. '11 Dec. Jun. '12 Dec. Jun. '13 Dec.
31
In 2013, Y is likely to return to the
mean, despite the drop in 2012
Example, Variable Y, YOY growth %:
Methodology Example 4
Source: Fusion Retail Analytics, December 2012
Many variables will inherently stabilize
around a long-term run-rate following a shock
32
0
1
2
3
4
5
6
7
8
Jun. '03 Dec. Jun. '04 Dec. Jun. '05 Dec. Jun. '06 Dec. Jun. '07 Dec. Jun. '08 Dec. Jun. '09 Dec. Jun. '10 Dec. Jun. '11 Dec. Jun. '12 Dec. Jun. '13 Dec.
Example Variable X vs. Y, absolute:
Variable X
Variable Y
Variable X tends to lag behind variable Y Using the knowledge of Y’s
2012 movement gives a strong
indication of X’s 2013 trend
Methodology Example 5
Source: Fusion Retail Analytics, December 2012
An established leading-indicator variable
can be used to predict future movements
Legend:
33
Tool Description Uses
Consumer confidence Measures the level of optimism with which consumers envision their
financial future. It indicates their willingness to incur discretionary
expenses.
Source: Fusion Retail Analytics.
To understand consumers’ willingness to
spend based on optimism or fear of future
financial position.
Discretionary Income The amount of money consumers have available each month after
paying taxes and their living costs.
Source: Fusion Retail Analytics.
To understand the income available for
Canadian households to spend on
discretionary items.
Temperature
(national weighted)
Average daily maximum temperature each month vs. last year
leveraging data from 37 Environment Canada weather stations.
To understand changing weather
conditions and impact on retail industry
performance.
Home turnover The number of homes sold in a given period, including both new
and existing homes. It is essentially the amount of moves that are
occurring.
Source: CREA, Fusion Retail Analytics.
To serve as an indicator for retail sales
which will occur in the future. It is a
leading indicator, especially for the HI and
furniture industries as people continue to
make purchases months after a move.
Cross-border shopping The amount of money Canadians spend shopping in the US
excluding gas, grocery and major purchases such as vehicles.
To serve as an input to forecast retail
sales in Canada.
Cost of living The amount of money per household spent on items that are non-
discretionary. Cost of living items include food, rent/mortgage
payment, utilities, car payments, health care and gas.
Source: Statistics Canada, Fusion Retail Analytics.
To serve as an input in calculating
Discretionary Income.
Source: Fusion Retail Analytics, December 2012
Definitions
Supporting slides
34
Source: Raw data provided by Statistics Canada; compilation and analysis by Fusion Retail Analytics, December 2012
Transportation spend per household: (rolling 4 months)
Utilities spend per household: (rolling 4 months)
-8%
-4%
0%
4%
8%
12%
Jun. '03 Dec. Jun. '04 Dec. Jun. '05 Dec. Jun. '06 Dec. Jun. '07 Dec. Jun. '08 Dec. Jun. '09 Dec. Jun. '10 Dec. Jun. '11 Dec. Jun. '12 Dec.
YO
Y G
row
th %
-15%
-10%
-5%
0%
5%
10%
15%
20%
YO
Y G
row
th %
35
Declines in growth of transportation and utilities spend
explain the recent drop in cost of living growth
-100%
-75%
-50%
-25%
0%
25%
50%
75%
100%
YO
Y G
row
th %
-8%
-4%
0%
4%
8%
12%
Jun. '03 Dec. Jun. '04 Dec. Jun. '05 Dec. Jun. '06 Dec. Jun. '07 Dec. Jun. '08 Dec. Jun. '09 Dec. Jun. '10 Dec. Jun. '11 Dec. Jun. '12 Dec. Jun. '13 Dec.
YO
Y G
row
th %
Notes: Oil price forecast based on the Energy Information Administration (EIA) projections. Utilities forecast based on expected utilities cost if historical normal weather occurs. Source: Raw
data provided by Statistics Canada and the Federal Reserve Bank of St. Louis; compilation and analysis by Fusion Retail Analytics, oil price forecast provided by EIA, December 2012
Cost of transportation
Oil prices
Price of oil is predicted (by EIA)
to remain in the $89 range per
barrel, leading to stable
transportation prices in 2013.
36
Transportation spend per household: (rolling 4 months)
Utilities spend per household: (rolling 4 months)
In 2013, below normal growth in transportation
and utilities spend will continue
Legend:
9.0
11.0
13.0
15.0
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011
Max T
em
pera
ture
(⁰C
)
37
Source: Environment Canada data from 37 weather stations representing the largest metropolitan areas in Canada; data is updated on a weekly basis; using Fusion's proprietary weather
model, the data is compressed into national numbers expressing maximum temperature experienced by the average Canadian consumer
Average daily maximum temperature:
The 10-year weather trend is slightly
negative. However, the R square of the
line is 0.019, indicating a negligible
relationship between time and
temperature change in the near term
Implications: Since there is essentially no trend to absolute weather in the long run, the
best way to predict YOY temperature is to focus on the values this year will be rolling over.
There is a negligible long-term weather trend
38
Source: Environment Canada data from 37 weather stations representing the largest metropolitan areas in Canada; data is updated on a weekly basis; using Fusion's proprietary weather
model, the data is compressed into national numbers expressing the maximum temperature experienced by the average Canadian consumer
Hottest days
Coldest days Max temperature: (daily max temperature rolling 30 days)
Jan Feb Mar Apr May Jun July Aug Sep Oct Nov Dec
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
There is little evidence to support the
notion that seasons are shifting Legend: