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.

1. 2013 Canadian
Retail Outlook
December 2012

2. Retail sales will experience 2.7% YOY growth in 2013
Retail sales in Canada:
(rolling 3 months)
15% Rolling over a strong Spring in
2012, retail sales growth is
forecasted to be softer in early
2013 before ramping up slightly
The spike in retail sales growth was in the back half of the year
10% caused by the roll-over of low 2008 figures
YOY Growth %
Historical norm = 6 %
Recent norm = 3%
5%
0%
Retail sales declined during the recession
-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.
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
2

3. Breakdown of the underlying drivers of retail sales
Wage rates
Home turnover Household income
Unemployment
Discretionary Income Taxes
Other prices
Inflation Food prices
Population growth Cost of living
Interest rates Oil prices
Canadian
retail sales
Consumer confidence Major economic headlines
Weather
Cross-border shopping Exchange rates
Legend:
Positive impact on retail
Negative impact on retail
Varied impact on retail
Source: Fusion Retail Analytics, December 2012
3

4. 2013 forecasted impact of
underlying drivers on retail sales
Drivers vs. LY
1 Discretionary Income
2 Consumer confidence
3 Population
4
Temperature
(national weighted)
5 Home turnover
6 Cross-border shopping
Retail sales
Legend:
Trending up, positive impact Trending down, positive impact
Trending up, negative impact Trending down, negative impact
On par
Source: Fusion Retail Analytics, December 2012
4

5. Methodology overview
Forecasts are based on six factors:
1 The underlying drivers of each variable
2 Long-term trends of each variable
3 The roll-over of high/low LY figures and resulting oscillations
4 The tendency of each variable to regress to the mean
5 The lag in trends between different variables
6 External shocks (major events that can shift the economy)
These events are highly unpredictable and have not been factored into any forecasts
Notes: See methodology slides 28-32 for detailed examples of the six factors, Source: Fusion Retail Analytics, December 2012
5

6. Discretionary Income
6

7. Discretionary Income growth will continue between
1.5% to 4% as it trends towards the historical norm
Discretionary Income per household:
(rolling 4 months)
14%
12%
10%
8%
Prior to 2005, DI growth DI growth is expected to
wavered around 3% continue to trend toward
6% its pre-2005 level
YOY Growth %
4%
Historical normal = 3%
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.
Discretionary Income = Household income – Taxes – Cost of living
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
7

8. Relatively stable Discretionary Income growth
will be driven by the stability of HH income growth
Unemployment rate:
10%
Unemployment rate %
8%
The unemployment rate rose
causing income to drop below norm
6.5% threshold
6%
In order for income growth to see
As the unemployment rate was
substantial gains the unemployment
falling, income was higher than norm
level must fall below the 6.5% threshold
4%
Growth in monthly household income:
8%
Income is forecasted to stay near
6%
historical levels in 2013, preventing large
gains in Discretionary Income growth
4%
YOY Growth %
Historical norm
2%
0%
Income regressed to normal
as unemployment recovered
-2%
-4%
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.
Discretionary Income = Household income – Taxes – Cost of living
Notes: All metrics shown rolling 4 months. Source: Raw data provided by Statistics Canada; compilation and analysis by Fusion Retail Analytics, December 2012
8

9. Based on current trends, unemployment rate will not
trigger substantial gains in HH income until 2015
Unemployment rate:
10%
Based on current trends,
unemployment will not cross
Unemployment rate %
8%
the threshold until 2015
6.5% threshold
6%
4%
Jun. Dec. Jun. Dec. Jun. Dec. Jun. Dec. Jun. Dec. Jun. Dec. Jun. Dec. Jun. Dec. Jun. Dec. Jun. Dec. Jun. Dec. Jun. Dec. Jun. Dec.
'03 '04 '05 '06 '07 '08 '09 '10 '11 '12 '13 '14 '15
Notes: All metrics shown rolling 4 months. Source: Raw data provided by Statistics Canada; compilation and analysis by Fusion Retail Analytics, December 2012
9

10. Taxes as a percentage of HH income
should remain stable in 2013
Taxes as a percentage of household income: With no major tax policy
changes in 2013, taxes as
a percentage of income
26%
should continue to inch
upward as wages recover
24%
% of income
Prior to 2008, taxes were on
average 24% of income
22%
With lower incomes, taxes
dropped to 23% of income
20%
Growth in taxes paid per household:
12%
9%
Historical norm
YOY Growth %
6%
3%
0%
-3%
-6%
-9%
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.
Discretionary Income = Household income – Taxes – Cost of living
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
10

11. Cost of living growth should rise slightly in
2013, but remain below the historical norm
Growth in cost of living per household:
(rolling 4 months)
6%
Based on current trends,
cost of living will move
towards normal levels
Historical norm
4%
YOY Growth %
Below-normal growth rates
2% caused by drops in growth
of transportation and
utilities cost growth (see
appendix slides 35-36)
0%
Dip in cost of living is
caused by recession
-2%
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.
Discretionary Income = Household income – Taxes – Cost of living
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
11

12. Consumer confidence
12

13. Consumer confidence in Canada
is stabilizing around a new norm
Consumer confidence in Canada:
(rolling 3 months)
5%
Consumer confidence
spiked this spring
Historical norm = 4%
3%
The historical norm was inflated Recent norm = 1%
by the housing bubble
1%
Indexed
-1%
Consumer confidence
crashed during the The norm for the
recent recession foreseeable future is
-3% The US debt ceiling is
lower as consumers
raised causing a fall in
are wary of more
consumer confidence
swings in the economy
-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.
Source: Fusion Retail Analytics, December 2012
13

14. Temperature
14

15. Temperature moves towards the norm in 78% of months
Max temperature variance from norm in May:
(monthly max temperature in May vs. 10-year historical normal temperature in May)
4
3
2 May 2002 temperature
…so, as expected May 2003
was below normal…
Max Temperatue (⁰C)
temperature was above 2002…
1
0
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
-1
-2
…and since May 2003 was above This trend remains true
the norm, May 2004 temperature for all months except
-3
was, as expected, below May 2003 May ‘06 and ’07
All months since 2001
Months that moved towards mean 103 This trend above can be
applied to all months
Months that moved away from mean 29
% that moved towards mean 78%
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
15

16. YOY temperature forecasts can be
derived based on LY temperature
2012 max temperature variance from norm:
(monthly max temperature vs. 10-year historical normal temperature)
4
Since March 2012 was so high
above normal there is a 95%
chance March 2013 will be cooler
2
Max Temperature (⁰C)
0
-2
Because November 2012 was
significantly below normal there is 66%
chance November 2013 will be hotter.
-4
Jan '12 Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Probability
2013 will be
28% 8% 5% 59% 6% 37% 20% 16% 45% 47% 66% 31%
hotter than
2012
Implications: Without any weather forecast it is possible to calculate the probability that each month in 2013 will be hotter than 2012.
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
16

17. 2013 forecasted monthly temperatures
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
-2.0 -0.8 4.7 12.0 18.0 22.9 25.9 24.9 20.8 13.3 6.8 0.2
(norm)
Probability 2013
will be hotter 28% 8% 5% 59% 6% 37% 20% 16% 45% 47% 66% 31%
than 2012
Probability 2013
will be colder 72% 92% 95% 41% 94% 63% 80% 84% 55% 53% 34% 69%
than 2012
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

18. 2013 YOY temperature forecast
Max temperature variance from LY:
(monthly max temperature vs. LY)
4
Temperature will not
2 have a significant effect
on retail sales for the
Max Temperature (⁰C)
summer as it should be
similar to 2012
0
-2
Most retailers will have a
weak March vs. LY because
2013 will be colder
-4
Jan '13 Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
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.
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
18

19. Home turnover
19

20. A major factor in forecasting home turnover growth is
understanding the oscillations over several years
Home turnover in Canada:
(# of homes sold/purchased each month, new and existing, compared YOY, rolling 8 months)
40%
30%
When the economy is stable, a
peak in one month creates a
20%
valley in that month next year
YOY Growth %
10%
0%
-10%
-20%
-30%
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.
Source: Raw data provided by The Canadian Real Estate Association; compilation and analysis by Fusion Retail Analytics, December 2012
20

21. Home turnover has been stabilizing since 2008
Home turnover in Canada: Legend:
(# of homes purchased each month, new and existing, compared YOY, rolling 8 months) Abnormally high home turnover growth
caused by roll-over of low 2008 values 100 Area between zero
and growth line
40%
30%
Housing market now stabilizing
20%
Prior to 2008 home turnover
growth was relatively stable
YOY Growth %
10% 98
29
0%
100 53
-10%
-20%
-30%
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.
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.
Source: Raw data provided by The Canadian Real Estate Association; compilation and analysis by Fusion Retail Analytics, December 2012
21

22. Available by region*
Home turnover in 2013 will be down 4.8%
Home turnover in Canada:
(# of homes sold/purchased each month, new and existing, compared YOY, rolling 8 months)
40%
30%
20%
YOY Growth %
10%
0%
-10%
-20%
-30%
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.
*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
22

23. Cross-border shopping
23

24. The exchange rate must drop below $0.81 CAD/USD
in order to slow the trend of lost sales to the US
Legend:
Different months (from ’03 to ’12 - see slide 25)
Incremental impact of cross-border shopping on Canadian retail:
1.5% Parity ($1 USD = $1 CAD)
Cross-border shopping impact on
1.0%
0.5%
Canadian retail
0.0%
-0.5% When the Cdn. dollar drops below
$0.81 it starts to deter consumers
-1.0% from shopping in the US and
boosts retail sales in Canada
-1.5%
0.60 0.70 0.80 0.90 1.00 1.10
Exchange rate (CAD/USD)
A weak Canadian dollar deters A strong Canadian dollar drives
consumers from shopping in the US consumers to shop in the US
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
24

25. In 2013, Canadian retail sales will be down an additional
0.4% as a result of increased cross-border shopping
Legend:
Exchange rate (CAD/USD)
Incremental impact of cross-border shopping Canadian retail sales: Cross-border impact on Canadian retail sales
$1.25 1.5%
Cross-border shopping impact on Canadian retail sales
Major banks are forecasting the 1.0%
dollar to remain near par for 2013
Exchange rate (CAD/USD)
$1.00 0.5%
0.0%
$0.75 -0.5%
With the dollar at parity, Canada can -1.0%
expect to lose an incremental 0.4% of
retail sales to the US in 2013
$0.50 -1.5%
Jun. Dec. Jun. Dec. Jun. Dec. Jun. Dec. Jun. Dec. Jun. Dec. Jun. Dec. Jun. Dec. Jun. Dec. Jun. Dec. Jun. Dec.
'03 '04 '05 '06 '07 '08 '09 '10 '11 '12 '13
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
25

26. Methodology
26

27. Recall: Methodology overview
Forecasts are based on six factors:
1 The underlying drivers of each variable
2 Long-term trends of each variable
3 The roll-over of high/low LY figures and resulting oscillations
4 The tendency of each variable to regress to the mean
5 The lag in trends between different variables
6 External shocks (major events that can shift the economy)
These events are highly unpredictable and have not been factored into any forecasts
Source: Fusion Retail Analytics, December 2012
27

28. Methodology Example 1
Understanding the movement of a variable’s underlying
drivers can help predict the movement of that variable
Example, Variable X vs. Y: Legend:
14
Variable X
Variable Y
12
10
8
6
4
If Y is likely to rise in 2013,
There is a clear correlation
X is also likely to rise
between X and Y
2
0
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.
Source: Fusion Retail Analytics, December 2012
28

29. Methodology Example 2
Examining the long-term trend of a variable can give a
strong indication of how it will behave in the near future
Example, Variable Z, YOY growth %:
100
75
Z has been trending down since 2003
50
Recently the downward
trend has been weaker
25 In 2013 the downward trend will
likely continue to weaken
0
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.
Source: Fusion Retail Analytics, December 2012
29

30. Methodology Example 3
Last year’s performance plays a major
role in this year’s growth figure
Example, Variable X, absolute:
20
15
10
5
0
Example, Variable X, YOY growth %:
Though 2009 was normal, the YOY
numbers show growth. This was
150% If 2013 is a normal year,
strictly caused by a poor 2008
YOY figures will be negative
100% due to a strong 2012
50%
0%
-50%
-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.
Source: Fusion Retail Analytics, December 2012
30

31. Methodology Example 4
Many variables will inherently stabilize
around a long-term run-rate following a shock
Example, Variable Y, YOY growth %:
20%
In 2013, Y is likely to return to the
mean, despite the drop in 2012
10%
0%
-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.
Source: Fusion Retail Analytics, December 2012
31

32. Methodology Example 5
An established leading-indicator variable
can be used to predict future movements
Legend:
Variable X
Example Variable X vs. Y, absolute: Variable Y
8
7
6
5
4 Using the knowledge of Y’s
Variable X tends to lag behind variable Y
2012 movement gives a strong
3 indication of X’s 2013 trend
2
1
0
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.
Source: Fusion Retail Analytics, December 2012
32

33. Definitions
Tool Description Uses
Consumer confidence Measures the level of optimism with which consumers envision their To understand consumers’ willingness to
financial future. It indicates their willingness to incur discretionary spend based on optimism or fear of future
expenses. financial position.
Source: Fusion Retail Analytics.
Discretionary Income The amount of money consumers have available each month after To understand the income available for
paying taxes and their living costs. Canadian households to spend on
Source: Fusion Retail Analytics. discretionary items.
Temperature Average daily maximum temperature each month vs. last year To understand changing weather
(national weighted) leveraging data from 37 Environment Canada weather stations. conditions and impact on retail industry
performance.
Home turnover The number of homes sold in a given period, including both new To serve as an indicator for retail sales
and existing homes. It is essentially the amount of moves that are which will occur in the future. It is a
occurring. leading indicator, especially for the HI and
Source: CREA, Fusion Retail Analytics. 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 To serve as an input to forecast retail
excluding gas, grocery and major purchases such as vehicles. sales in Canada.
Cost of living The amount of money per household spent on items that are non- To serve as an input in calculating
discretionary. Cost of living items include food, rent/mortgage Discretionary Income.
payment, utilities, car payments, health care and gas.
Source: Statistics Canada, Fusion Retail Analytics.
Source: Fusion Retail Analytics, December 2012
33

34. Supporting slides
34

35. Declines in growth of transportation and utilities spend
explain the recent drop in cost of living growth
Transportation spend per household:
(rolling 4 months)
20%
15%
10%
YOY Growth %
5%
0%
-5%
-10%
-15%
Utilities spend per household:
(rolling 4 months)
12%
8%
YOY Growth %
4%
0%
-4%
-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.
Source: Raw data provided by Statistics Canada; compilation and analysis by Fusion Retail Analytics, December 2012
35

36. In 2013, below normal growth in transportation
and utilities spend will continue
Transportation spend per household: Legend:
(rolling 4 months)
Cost of transportation
100%
Oil prices
75%
50%
YOY Growth %
25%
0%
-25%
-50% Price of oil is predicted (by EIA)
to remain in the $89 range per
-75% barrel, leading to stable
transportation prices in 2013.
-100%
Utilities spend per household:
(rolling 4 months)
12%
8%
YOY Growth %
4%
0%
-4%
-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.
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
36

37. There is a negligible long-term weather trend
Average daily maximum temperature:
15.0
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
13.0 temperature change in the near term
Max Temperature (⁰C)
11.0
9.0
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011
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.
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
37

38. There is little evidence to support the
notion that seasons are shifting
Legend:
Hottest days
Max temperature: Coldest days
(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
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
38