Forecasting demand in a cyclical business such as housing components is difficult & tricky. In this case, we developed an econometric model for housing components driven by order backlogs, housing starts, remodeling spending and pricing.

1. Alpha Construction
Group
Housing Component Products
Demand Forecast
1

2. Forward
• Many businesses are affected by the cyclical effects of
housing and remodeling demand, yet they have not
developed formal tools to allow these data to drive
their business plans and forecasts.
• Bottom-Line Analytics has significant experience in this
and can provide clients with an interactive tool which
will help them more accurately forecast and plan their
business performance.
• The following is a case using an econometric model to
forecast demand for a housing component
manufacturer in 2015.
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3. Content
• Forecast Model Description
• Sales Predictions v. Actuals
• Forecast Summary and Assumptions for 2015
• Drivers of Growth for 2014
• Model Driver Relative Importance
• Model Driver Impacts and Sensitivities
– Recurring Seasonality
– Housing Starts
– Remodeling Spending
– Order Backlog
– Price to Customer
• Forecast-Simulator Demonstration
• Summary
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4. • The forecast model covers monthly periods from Jan 2009 to present. Four positive
drivers and one negative driver were used.
• The positive drivers include recurring seasonality, housing starts, Home Remodeling
Spend & Order Backlog. The negative driver was average price to the customer.
Home
Remodeling
Spend
Order
Backlog
Price-to-
Customer
Monthly
Unit
Demand
Housing
Starts
Recurring
Seasonality
4

5. 0
2,000,000
4,000,000
6,000,000
8,000,000
10,000,000
12,000,000
Jan.2006
May.2006
Sep.2006
Jan.2007
May.2007
Sep.2007
Jan.2008
May.2008
Sep.2008
Jan.2009
May.2009
Sep.2009
Jan.2010
May.2010
Sep.2010
Jan.2011
May.2011
Sep.2011
Jan.2012
May.2012
Sep.2012
Jan.2013
May.2013
Sep.2013
Jan.2014
May.2014
Sep.2014
Jan.2015
May.2015
Sep.2015
Forecast/Simulated Sales Actual Sales
Forecast & Actual Sales Plot
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Model R2=94.0%, Mean Absoluter Error +/- 4.1%
From the bottom of the recession in 2009, Components have been growing at a +9.5% annual rate per year.
The shaded area depicts the recession of 2008-2011.

6. Forecast Summary & Assumptions
• With modest but slightly improving growth in key drivers, plus flat pricing,
we would expect 2015 components demand to improve to about +7.2% in
2015.
• This would generate near-term record components sales of over 116 MM.
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7. Sources of Growth
-1.0%
-0.4%
-0.04%
2.0%
2.2%
-1.5% -1.0% -0.5% 0.0% 0.5% 1.0% 1.5% 2.0% 2.5%
Price to Cust
Drawings
Repairs
Housing Starts
Baseline Momentum
Growth Impact
Growth Impact
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• Based on the model, here is the contribution to Housing Components overall
annual growth of +2.8%

8. Relative Impact Importance of Housing Component Business
Drivers
33%
6%
25%
1%
35%
Importance of Sales Impact
Recurring Seasonality
Price to Customer
Housing Starts
Order Backlogs
Remodel Spend
Remodel Spend, followed by Seasonality and Housing Starts comprise the dominant drivers o
components’ demand.
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9. Impact of Recurring Seasonality
87
84
88
99 101
109 111
114 113 111
98
83
4,000,000
4,500,000
5,000,000
5,500,000
6,000,000
6,500,000
7,000,000
7,500,000
8,000,000
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC
Average Sales Impact of Recurring Seasonality
The “high season” for component demand is June-October
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10. Price-to-Customer Demand Sensitivity
8,600,000
8,700,000
8,800,000
8,900,000
9,000,000
9,100,000
9,200,000
9,300,000
9,400,000
-6.0% -4.0% -2.0% 0.0% 2.0% 4.0% 6.0%
MonthlyDemand
Change in Price
Monthly Demand Impact from Change in Price to Customer
• The sensitivity of price-to-customer is slightly “inelastic” at -0.6, meaning a 1%
increase would yield about a -0.6% decline in sales.
• This sensitivity indicates that price increases are likely to be modestly profitable.
On average there is also about a 3 month lag between the largest change in sales
and the change in price.
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11. Housing-Starts Demand Sensitivity
5,000,000
6,000,000
7,000,000
8,000,000
9,000,000
10,000,000
11,000,000
-60.0% -40.0% -20.0% 0.0% 20.0% 40.0% 60.0%
MonthlyDemand
Change in Housing Starts
Monthly Demand Impact from Change in Housing Starts
• Housing starts can vary significantly from period to period. Overall, every 10%
increase in starts generates about a +2.8% increase in Components sales.
• On average there is also about a 3 month lag between the largest change in sales
and the change in housing-starts.
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12. Remodeling Spend Demand Sensitivity
5,000,000
6,000,000
7,000,000
8,000,000
9,000,000
10,000,000
11,000,000
-60.0% -40.0% -20.0% 0.0% 20.0% 40.0% 60.0%
MonthlyDemand
Change in Drawings
Monthly Demand Impact from Change in Remodel Spend
• Remodeling Spending by Home Owners has the largest single impact of any driver.
Every 10% change is connected to a 4.6% increase in component sales.
• On average there is also about a 3 month lag between the largest change in sales
and the change in remodeling spending
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13. Monthly Order Backlog Demand Sensitivity
9,000,000
9,010,000
9,020,000
9,030,000
9,040,000
9,050,000
9,060,000
9,070,000
9,080,000
9,090,000
9,100,000
-60.0% -40.0% -20.0% 0.0% 20.0% 40.0% 60.0%
MonthlyDemand
Change in Repairs
Monthly Demand Impact from Change in Order-Backlogs
• Backlogs have the least impact of any driver.
• There are also no lead-lag relationships between demand and
changes in repairs
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14. Forecast-Simulation Tool
• Below is a rendering of the interactive-forecasting tool which
will be demonstrated
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15. Summary
• Across 5 key business drivers, we have derived a model
which connects to monthly Components demand with a
93% fit.
• For 2014, prior sales momentum and housing starts are the
two leading positive drivers of annual +3.1% sales gain.
• With “modest” assumptions about the growth in key
drivers, and flat pricing, for 2015, we expect to see demand
growth of about +7.2% versus about +3.1% growth in 2014.
• Drawings, recurring seasonality and housing starts form the
most dominant drivers of components monthly demand
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