2013 Canadian Retail Outlook

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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.

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2013 Canadian Retail Outlook

  1. 1. 2013 CanadianRetail OutlookDecember 2012
  2. 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 tomake revisions; retail sales within the most recent 13 months reported from Fusion Retail Analytics 2
  3. 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 retailSource: Fusion Retail Analytics, December 2012 3
  4. 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 parSource: Fusion Retail Analytics, December 2012 4
  5. 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 forecastsNotes: See methodology slides 28-32 for detailed examples of the six factors, Source: Fusion Retail Analytics, December 2012 5
  6. 6. Discretionary Income 6
  7. 7. Discretionary Income growth will continue between 1.5% to 4% as it trends towards the historical normDiscretionary 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 livingNotes: 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. 8. Relatively stable Discretionary Income growth will be driven by the stability of HH income growthUnemployment 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. 9. Based on current trends, unemployment rate will not trigger substantial gains in HH income until 2015Unemployment rate: 10% Based on current trends, unemployment will not crossUnemployment 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. 10. Taxes as a percentage of HH income should remain stable in 2013Taxes 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. 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 livingNotes: 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. 12. Consumer confidence 12
  13. 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. 14. Temperature 14
  15. 15. Temperature moves towards the norm in 78% of monthsMax 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 Fusions 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. 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 DecProbability2013 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 Fusions proprietary weathermodel, the data is compressed into national numbers expressing the maximum temperature experienced by the average Canadian consumer, December 2012 16
  17. 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 2012Notes: TY forecast based on Fusion’s proprietary weather model triangulated with Environment Canada’s seasonal weather outlook. Source: Environment Canada data from 37 weatherstations representing the largest metropolitan areas in Canada; data is updated on a weekly basis; using Fusions proprietary weather model, the data is compressed into national numbersexpressing the maximum temperature experienced by the average Canadian consumer, December 2012 17
  18. 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 from37 weather stations representing the largest metropolitan areas in Canada; data is updated on a weekly basis; using Fusions proprietary weather model, the data is compressed intonational numbers expressing the maximum temperature experienced by the average Canadian consumer, December 2012 18
  19. 19. Home turnover 19
  20. 20. A major factor in forecasting home turnover growth is understanding the oscillations over several yearsHome 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. 21. Home turnover has been stabilizing since 2008Home 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. 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. 23. Cross-border shopping 23
  24. 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 USNotes: 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 byStatistics Canada and Bank of Canada; compilation and analysis by Fusion Retail Analytics, exchange rate forecast from TD and CIBC, December 2012 24
  25. 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 13Notes: 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 byStatistics Canada and Bank of Canada; compilation and analysis by Fusion Retail Analytics, exchange rate forecast from TD and CIBC, December 2012 25
  26. 26. Methodology 26
  27. 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 forecastsSource: Fusion Retail Analytics, December 2012 27
  28. 28. Methodology Example 1 Understanding the movement of a variable’s underlying drivers can help predict the movement of that variableExample, 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. 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. 30. Methodology Example 3 Last year’s performance plays a major role in this year’s growth figureExample, Variable X, absolute: 20 15 10 5 0Example, Variable X, YOY growth %: Though 2009 was normal, the YOY numbers show growth. This was150% If 2013 is a normal year, strictly caused by a poor 2008 YOY figures will be negative100% 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. 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. 32. Methodology Example 5 An established leading-indicator variable can be used to predict future movements Legend: Variable XExample 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. 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. 34. Supporting slides 34
  35. 35. Declines in growth of transportation and utilities spend explain the recent drop in cost of living growthTransportation 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. 36. In 2013, below normal growth in transportation and utilities spend will continueTransportation 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: Rawdata 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. 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 Fusions proprietary weathermodel, the data is compressed into national numbers expressing maximum temperature experienced by the average Canadian consumer 37
  38. 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 2012Source: Environment Canada data from 37 weather stations representing the largest metropolitan areas in Canada; data is updated on a weekly basis; using Fusions proprietary weathermodel, the data is compressed into national numbers expressing the maximum temperature experienced by the average Canadian consumer 38

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