2013 Canadian Retail Outlook

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


Consumer confidence

Population

Temperature (national weighted)

Home turnover

Cross-border shopping

Retail sales

1

2

4

5

6


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


6

-6%

-4%

-2%

0%

2%

4%

6%

8%

10%

12%

14%


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%


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


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%


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


-2%

0%

2%

4%

6%


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


Consumer confidence

12

-5%

-3%

-1%

1%

3%

5%


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%


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


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


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


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%


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%


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


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


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


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


Z has been trending down since 2003

Recently the downward

trend has been weaker

In 2013 the downward trend will

likely continue to weaken



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%


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



Last year’s performance plays a major

role in this year’s growth figure

-10%

0%

10%

20%


31

In 2013, Y is likely to return to the

mean, despite the drop in 2012

Example, Variable Y, YOY growth %:



Many variables will inherently stabilize

around a long-term run-rate following a shock

32

0

1

2

3

4

5

6

7

8


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



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.


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%


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%


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


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


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:

Date post:	01-Nov-2014
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2013 Canadian Retail Outlook

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