EconomicAnalysisReportFinalSubmitted

Ford Automobile Multiple Variable Regression Team Report
Economic Analysis & Insights
Team 15 (Isha Mehta, Seth Harris, Lamya Barazi, Tony Garcia, and Phillip Pless)
December 5, 2015
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U.S. Demand Analysis for
Vehicles and Implications for
By Team 15
Lamya Barazi, Isha Mehta, Seth Harris, Tony Garcia,
Phillip Pless
December 2015

December 5, 2015
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CONTENTS
I. Industry/Company Trends ………………………………………..……………….. 3
II. Multiple Variable Regression ……………………………………..………………. 4
a. Regression Variables ……………………………………………..………………. 4
b. Data Sources …………………………………………………………..…………….. 5
c. Multiple Variable Regression …………………………………................. 5
III. Forecasts …………………………………………………………………………………… 6
IV. Application of Forecast/Insights ………………………………………………… 8
V. Emerging Technology ………………………………………………………………… 9
VI. Automobile Demand Regression Model ………………………………….. .11
VII. References ………………………………………………………………………………. 14

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I. Industry/Company Trends
Source: U.S. Department of Commerce, Bureau of Economic Analysis, Autonews.com, and AutoData
Ford and the overall U.S. vehicle market experienced a dramatic decrease in vehicle sales during the Great
Recession. Over the past 25 years, the automobile market has undergone several periods of market growth, and
contraction. In fact, from 1990-2014, the compound annualized growth rate for Ford vehicles (cars and light trucks)
was negative 1.36%, while the overall U.S. vehicle compound annualized growth rate was 0.71%. This growth rate
accounts for annual variations in the sales growth rate, and provides a more accurate representation of Ford and the
overall U.S. market vehicle sales growth rate during this period of time. During this same time period Ford’s
average vehicle market share was 21.26% of the overall U.S. vehicle market. Ford’s market share is a significant
portion of the overall U.S vehicle market given increased competition from other automakers such as Honda and
Toyota during this time period (Cutcher-Gershenfeld, Brooks, & Mulloy, 2015).
The Great Recession of 2007-2009 had a profound impact on the automobile industry in the United States. From
2007 to 2009, sales in the U.S. automobile industry decreased dramatically. Ford’s sales decreased 22% in 2008, and
16% in 2009, while overall sales of U.S. vehicles dropped 18% and 21% respectively. Automobile sales were
depressed during this time period due to the housing market and financial market collapse that began in 2007.
Consumer sentiment is an important barometer of economic health for the automobile industry because a more
confident consumer is more likely to purchase a new or used vehicle (Levy, 2015). During the Great Recession,
consumer confidence dropped to a low of 63.8 in 2008 (“University of Michigan Consumer Sentiment”, n.d.).
Additionally, oil prices skyrocketed in 2008 which in turn caused a dramatic increase in the price of gasoline; thus,
consumers’ tastes and preference shifted from large, domestically produced vehicles to imported cars that offered
better mileage and greater fuel efficiency (Ahmadian, Hassan, & Regassa, 2013). The price of oil increased from
$37.54 to 85.34 from 2000 to 2008 (Oil Prices 1946-present, para. 6). Consequently, American car manufacturers’
market share decreased due to foreign vehicle manufacturers being more fuel efficient (Carty, 2009).
Ford Motor Company is classified as one of the largest vehicle manufacturing companies in the world. During the
1990’s, Ford’s strategy was to attract American consumers with moderate incomes, and it targeted this consumer
segment successfully (Meyer, 2015). During this same time period, Ford also acquired luxury car brands such as
Jaguar, Land Rover, and Volvo in order to diversify their targeted consumer base, and to pursue brand
diversification. (Ford Motor Company, 2015, para. 5). However, in the 2000’s, as Ford experienced financial woes,
it began to sell the companies it had acquired during the 1990’s (Ford Motor Company, 2015, para. 5). In 2008,

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when the US government offered loans to automakers to prevent bankruptcy, Ford restructured its organization and
benefitted from U.S. government programs such as “cash for clunkers” where consumers received one-time rebates
for trading in their used vehicles (Ford Motor Company, 2015). However, Ford received no bailout money from the
U.S. government. This gave Ford a positive perception with consumers, which ultimately helped Ford increase its
market share after the Great Recession (Kiley, 2009). U.S. consumers’ positive perception of Ford increased from
41% to 63% after Chrysler and GM received bailout money (Kiley, 2009). Also, because Ford did not receive
bailout money, like GM or Chrysler, it was more flexible in executing strategy because it was not limited by
government involvement in their business decisions (Schoenberger, 2011). Finally, in the past several years as the
economy has recovered, more Americans are valuing style and design, and are not as price sensitive (Amadeo,
2015). Consequently, Ford Motor Company changed its strategy to compete with other automakers by
differentiating itself through adding the features that the customers demand in their vehicles like its EcoBoost
technology.
II. Multiple Variable Regression
a. Regression Variables
Real Price (1990 $) - (Expected Negative Sign) - The expected sign between real price is projected to be negative.
An inverse relationship between real price and quantity demanded exists because as real price falls the quantity
demanded increases, and vice versa. This is the relationship between real price and quantity demanded as stated by
the law of demand.
Unemployment Rate- (Expected Negative Sign) As unemployment increases, it is expected that demand for vehicles
will decrease. Those who are unemployed will not have enough discretionary income to purchase a different vehicle,
and will be forced to keep what they currently have.
Consumer Sentiment- (Expected Positive Sign) - Consumer sentiment measures a consumer’s confidence in the
economy. As consumer confidence increases, it is expected that consumer demand for automobiles will increase as
well.
Federal Funds Interest Rate (Expected Negative Sign) - As the cost to borrow increases, the demand for vehicles is
expected to decrease. If it is more expensive for people to borrow, they will be prone to holding onto their money
until rates decrease.
GDP- (Expected Positive Sign) - Increasing GDP is expected to generate a higher demand for vehicles. The more
money that is circulating in the economy, the more confidence consumers have in making purchases.
Gasoline- Real Price- (Expected Negative Sign) Increasing gasoline prices is expected to drive demand for fuel
efficient cars. It is unclear how it would affect sales overall, because it could shift sales from bigger vehicles to
smaller vehicles (substitution effect). Overall it is anticipated it would be a negative sign because consumers most
likely will have less discretionary income for vehicle purchases in a period of high gasoline prices.
Median Household Income- (Expected Positive Sign) It is expected that as household income increases, families will
have more discretionary income to buy vehicles. Spending limits will also increase, possibly affecting the style of
car purchased.
Durable Good Expenditures (Expected Positive Sign) - Durable good expenditures are a quarterly measure of
consumer expenditures on goods with a long useful life. It is expected that durable goods expenditures will have a
positive sign with quantity demanded, because vehicles are a type of durable good. Durable goods can require a
significant investment in consumer resources and are typically signs of a strong economy.
Quarter 2 and 3 (Seasonality) Indicator Variable- (Expected Positive Sign) - Vehicle purchases have a strong
seasonal component, therefore it is expected that seasonality will play a component in the regression equation. The
expected sign for the seasonality indicator variables is expected to be positive with quantity demanded because it is
hypothesized that quarter 2 and quarter 3 have stronger sales than other quarters.
Recession (Indicator Variable)-(Expected Negative Sign) - During periods of economic recessions it is predicted
that the recession indicator variable will have a negative relationship with quantity demanded. Automobiles are

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classified as a type of durable good. During times of recession, durable goods expenditures tend to decrease. Thus, it
is expected that the recession indicator variable will have a negative sign with quantity demanded.
b. Data Sources
Monthly Civilian Unemployment Rate, Not Seasonally Adjusted, 1990-2015:
U.S. Department of Labor, Bureau of Labor Statistics. (2015, November 1). Civilian Unemployment Rate, Monthly,
Not Seasonally Adjusted [Data set]. Retrieved November 21, 2015, from
https://research.stlouisfed.org/fred2/series/UNRATENSA.
Quarterly Consumer Sentiment, Not Seasonally Adjusted, 1990-2015:
University of Michigan/Reuters. (2015, November 1). Consumer Sentiment, Quarterly, Not Seasonally Adjusted
[Data set]. Retrieved November 21, 2015, from https://research.stlouisfed.org/fred2/series/UMCSENT
Quarterly Effective Federal Funds Interest Rate, Not Seasonally Adjusted, 1990-2015:
Board of Governors of Federal Reserve System. (2015, November 1). Effective Federal Funds Interest Rate,
Quarterly, Not Seasonally Adjusted [Data set]. Retrieved November 21, 2015 from,
https://research.stlouisfed.org/fred2/series/DFF
Quarterly Gross Domestic Product, Seasonally Adjusted, Nominal Dollars, 1990-2015:
U.S. Department of Commerce, Bureau of Economic Analysis. (2015, November 1). Gross Domestic Product,
Quarterly, Seasonally Adjusted, Nominal Dollars [Data set]. Retrieved November 21, 2015, from,
http://www.bea.gov/national/index.htm#gdp
Annual Median Household Income, Not Seasonally Adjusted, 2014 Real Dollars, 1990-2015:
U.S. Census Bureau. (2015, November 1). Median Household Income, Annual, Not Seasonally Adjusted, 2014 Real
Dollars [Data set]. Retrieved November 21, 2015, from,
https://research.stlouisfed.org/fred2/series/MEHOINUSA672N
Quarterly Durable Goods Expenditures, Seasonally Adjusted, Nominal Dollars, 1990-2015:
U.S. Department of Commerce, Bureau of Economic Analysis (2015, October 29). Durable Goods Expenditures in
Billions of Dollars, Quarterly, Seasonally Adjusted, Nominal Dollars [Data set]. Retrieved November 21, 2015,
from, https://research.stlouisfed.org/fred2/series/PCEDG
Quarterly National Bureau of Economic Research Recession Indicators for the U.S. from the Period
Following the Peak through the Trough, Not Seasonally Adjusted:
Federal Reserve Bank of St. Louis, (2014, September 17). National Bureau of Economic Research Recession
Indicators for the U.S. from the Period Following the Peak through the Trough, Not Seasonally Adjusted [Data set].
Retrieved December 3, 2015, from, https://research.stlouisfed.org/fred2/series/USRECQ#
c. Multiple Variable Regression
The best demand equation to calculate the demand for the automobile sales is:
Motor Vehicle Unit Retail Sales = 5011 - 0.05929 Price (real- 1990)
- 205.4 Unemployment Rate + 12.56 Consumer Sentiment + 442.9 Quarter 2 +
272.3 Quarter 3 - 275.1 Recession

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All the independent variables are highly correlated with automobile demand/sales (dependent variable), and thus
they play an important role in driving demand. This is further indicated by low p-values. This means that each
variable is a meaningful addition to the equation. A change in any of these variables will affect demand. The VIF
values (less than 5) for the variables indicate that there is low multi-collinearity. This shows that even though the
variables affect demand individually, they don’t affect each other by a significant amount. This is important in a
good model because it indicates low redundancy.
The R-squared value is 80.18%. This means that 80.18% variation in demand can be explained by our model. The
residual plots also show that the model is approximately normal and homoscedastic. We can see that our model has
one outlier. This can be explained by an extreme event (9/11 terrorist attack). All of these factors show that the
model is a good fit.
Analyzing each variable individually, we observe
· Price has a coefficient of -0.059. This validates the assumption that consumers are price sensitive,
and thus an increase in price by one dollar reduces demand by 0.059 units when other factors remain
constant.
· Unemployment Rate has a coefficient of -205.4 which indicates that when unemployment increases,
the demand for vehicles decreases. This is true because when unemployment rises, the purchasing power
and average income of the population decreases. This causes a fall in demand for vehicles.
· Consumer sentiment is also an important factor that affects demand because it measures the
consumer’s confidence in the economy. When confidence drops, consumers will refrain from making any
big purchases, and thus the demand for automobile drops. Therefore, it has a positive coefficient.
· The sales of vehicles are seasonal. Quarter 2 and Quarter 3 (Spring and Summer time) have
historically had higher sales than Quarter 1 and Quarter 4.
· Recession plays a very important role in the demand for any product. During the time of a
recession, growth is reduced and people try to save as much as possible. The overall movement of money
in the economy slows down. This is indicated by the high negative coefficient in the model.
Our demand equation takes into account the major factors affecting demand for automobile sales and the statistics
show that it’s a good predictor for demand.
Unemployment rate and the presence of a recession seem to both have a similarly negative effect on the demand for
automobiles. Unemployment rate and the presence of a recession can be hard to measure or predict at times, but if
the information is readily available Ford can use the information to control inventory levels. Excessive inventory can
be extremely costly in a market that produces new models yearly. The presence of a recession is often not
recognized until the economy is already in one presently, or it has recently passed. With that being said, if Ford
starts to recognize indicators of a slowing economy, production across the board needs to decrease in a
corresponding manner.
As evidenced by the model, the demand for automobiles is highly seasonal in nature. This is very important because
Ford must maintain higher inventory levels in the middle two quarters of the year so that they do not lose sales due
to unavailability of inventory. It is important to not over forecast inventory, which will cause older models to still be
present on the dealer lots heading into the new year. Manufacturing efforts at the beginning of the year are very
important to set the company up for success throughout the year.
Pricing is a very important part of Ford reaching consumers in an effective manner. Although it does not drive
demand to the extent that unemployment rate, recession, and seasonality do, having an appropriate pricing strategy
will help the company drive demand. The model shows that as price increases, demand will decrease. Pricing

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vehicles to where they will sell, but not exceed production capabilities, will enable Ford to run more efficiently as an
organization.
III. Forecasts
We used the Forecasting template to determine the input values for the independent variables.
Our forecast for each of the variables is as follows:
We are almost at the end of the fourth quarter and the economy is not in a recession, therefore no recession will be
accounted for in Q4 2015. We have assumed that there will not be a recession in 2016. In the case of a recession, the
demand for vehicles will be less than the forecasted demand.
On substituting the independent variables in the demand equation
Motor Vehicle Unit Retail Sales = 5011 - 0.05929 Price (real- 1990)
- 205.4 Unemployment Rate + 12.56 Consumer Sentiment + 442.9 Quarter 2 +
272.3 Quarter 3 - 275.1 Recession
We get the following US sales:
The forecast depends on various economic, social, and political factors that can influence the independent variables.
The changes in the independent variables will affect the demand for vehicles. The graphical representation of past
sales and the forecast is as follows:

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IV. Application of Forecast/Insights
Our forecast model predicts that demand will increase in Q2 and Q3 of FY 2016. We recommend a quarterly price
increase of 0.375% (1.5% annual rate) to stay in line with the current United States inflation rate (Projected Annual
Inflation Rate, n.d.). We recommend a three percent increase in price from Q1 FY 2016 to Q3 2016. Our
recommended pricing strategy is to continue Ford’s current two-tiered pricing strategy. We recommend a market-
orientation pricing strategy across our standard Ford models such as sedans and trucks. An appropriate strategy for
our luxury vehicle lines is the premium pricing strategy; this would be suitable for us to use in the Lincoln line
(Ferguson, 2015). Our forecasting model suggests that aggregate macroeconomic factors, such as low
unemployment rates, no projected recession, and strong consumer sentiment support these pricing strategies.
In order to have enough inventory to meet the forecasted demand in Q2 and Q3 FY 2016, we recommend an
increase in production in Q1 FY 2016 to ensure inventory is on hand for our dealers. Ford is trying to take the
guesswork out of inventory ordering (Burke, 2014). Having accurate forecasts enables us to respond quickly without
distributing the required inventory. Since 2009, we have leveraged our analytics skill through the Smart Inventory
Management System to improve inventory management using data from our dealers in the United States. “The data,
stored in Ford’s supercomputers at its product development center in Dearborn, MI, are analyzed to generate
recommendations for inventory orders, including model, trim and feature combinations.” (Burke, 2014) The dealers
still retain authority to order what models, colors, and features for their dealership. The recommendations from the
Smart Inventory Management System match dealership ordering by 98 percent (Burke, 2014).
We recommend expanding their analytics capability to create greater efficiency and quality in our supply chain and
manufacturing. Ford utilizes a robust supply chain including 60 countries, 1,100 supplier companies, 4,100 supplier
manufacturing sites, and 130,000 parts being manufactured. Applying analytics to the supply chain will allow us to
be more agile to changing market demand (Ford Motor Company, 2014).

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A benefit of producing vehicles that are high quality and safe is less risk for production recalls and maintaining the
strong integrity of the Ford brand. Frequent product recalls in 2015 have negatively affected our brand. This year,
we have issued safety recalls for at least 1.509 million vehicles due to defective airbags. Additional recalls were
issued in 2015 for more than 1.038 million vehicles in the Ford Fusion, Flex, F150, Fiesta, Lincoln MKS, MKT, and
MKZ lines for a variety of issues ranging from an upper I-shaft riveted improperly, to a spring controlling the
interior door handles. (MarketLine, 2015). We are concerned about production recalls because our forecast does not
take into account recalled automobiles and how this may affect our sales. A key strategy for the company to pursue
moving forward would be to diligently track the source of product recall problems, and how this may impact sales
over time.
Our recommendations of a two-tiered pricing strategy, leveraging analytics capability to build a focus on quality in
the supply chain, and tracking the impact of product recalls on future sales will ensure Ford’s legacy as a major
player in the auto industry.
V. Emerging Technology
A new type of technology that is emerging has the potential to drastically change consumer demand for automobiles
by changing consumer behavior and even the notion of car ownership. Driverless cars, also known as autonomous
vehicles, are robotic vehicles that are designed to travel between destinations without a human operator (“Driverless
Car”, n.d.). Driverless cars use global positioning satellite (GPS) technology, advanced sensors, and artificial
intelligence to operate a motor vehicle (Pullen, 2015). Vehicle automation technology exists on a spectrum from no
automation to full automation, with no human operator required. Ford is currently working on developing a line of
Ford Fusion hybrids that will be fully operated by the vehicle, with optional driver operation (Davies, 2015). Ford’s
competitors in the market for self-driving cars include GM and other automobile companies, Google, Apple, and
Uber (Gibbs, 2015).
Driverless cars have a huge potential to shift consumer demand for automobiles, according to recent reports. While
it will take time for driverless cars to enter the market, driverless cars have the ability to transform consumer
demand in the automobile industry just like the invention of the Model T in the early 20th century. Ford and other
major automobile companies are expected to introduce this technology in a phased approach, with features of this
technology being introduced in cars in the next 3-5 years. These features are expected to lead to limited vehicle
automation, with the initial line of vehicles to aid in highway navigation, rush hour traffic, and other similar tasks
(“Self-Driving Cars Will Make Us Want Fewer Cars”, 2015). Consumer demand for these features should increase
because of the benefits that consumers will gain from these technologies. For example, if a consumer’s vehicle has
the ability to navigate rush hour traffic without the person being attentive, this person is free to engage in other
productive activities such as work-related activities, leisure, or other personal activities during a weekly commute.
Moreover, these types of technology should also cut down on accident rates for consumers given that human
operator error is one of the highest causes of automobile accidents, which would also spur consumer demand given
the importance that many consumers place on automobile safety (Francis, 2015). According to the National
Highway Traffic Safety Administration, 93% of the estimated 6 million crashes in 2010 are attributable to human
error (Silberg & Wallace, 2012). Moreover, traffic accidents cost the American economy an estimated $299.5 billion
dollars annually (Silberg & Wallace, 2012). We recommend that Ford should continue to pursue and develop
automated technologies in cars, especially given the competitive pressures that they will face from other automobile
companies, consumers, Google, Apple, and Uber.
However, driverless cars have the ability to shift consumer behavior in the long-run in a negative way for Ford. A
recent study by Barclays Capital estimates that U.S. automobile sales could plummet up to 40%, and car ownership
could be reduced by up to 50% in the next 20 years as driverless cars become dominant on the roads (Naughton,

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2015). Many industry analysts expect driverless cars to fundamentally change consumer behavior and the notion of
car ownership. Driverless cars are expected to increase the utilization of cars and consumer behavior. For example,
many industry analysts predict that some consumers may opt out of car ownership due to the creation of a shared
autonomous vehicles market, especially among younger demographics. Similar to Uber, many analysts envision a
“robotic taxi fleet” whereby consumers would order a driverless car when needed (Naughton, 2015). Many analysts
predict that when driverless cars are fully available, many households will cut down on the number of cars owned by
each individual household. Thus, analysts expect aggregate demand for cars to be reduced for two reasons: 1)
reduction in the number of cars per each household; and 2) reduction in number of car purchasers. However, there
are other analysts who predict that there will still be a market for multiple car households, especially in more rural
areas (Naughton, 2015).
What can Ford do to remain competitive in this emerging market? First of all, we recommend continued investment
by Ford into driverless car technologies, as it will help the company remain competitive in this emerging market
area. Second, paying attention to consumer demand and consumer feedback about desired car features and
technologies will allow Ford to get constant feedback about engaging consumers in product design. Marketing
research suggests that with truly innovative products, only a small percentage of people adopt new products right
away before a product becomes mainstream (Hollenberg, 2014). Recent research suggests that millennials and
digital natives are more likely to adopt to this technology over older generations (Silberg & Wallace, 2012).
Moreover, because the selling price for driverless cars are likely to be higher in the beginning, Ford should target its
products to the right consumer segment. Ford will need to pay attention to the pricing of self-driving cars and the
costs of the technologies employed as some of these technologies cost tens of thousands of dollars while consumers
are willing to pay around $3,000 for autonomous driving technologies (Silberg & Wallace, 2012). Ford will need to
strike a balance in pricing any developed self-driving cars to ensure that they are priced high enough to make a
profit while still ensuring that consumers will be willing to purchase vehicles at this price. Finally, Ford should
target consumers in densely populated markets where self-driving cars are likely to have bigger consumer benefits.
Recent research suggests that consumers in densely populated markets may be more likely to adopt to this
technology and realize the greatest benefits (in reduced congestion, improved infrastructure, and other benefits)
(Silberg & Wallace, 2012). Taking these proactive actions will allow Ford to remain competitive in this emerging
market.

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Appendix 1: Automobile Demand Regression Model
Regression Analysis:
Analysis of Variance
Source DF Adj SS Adj MS F-Value P-Value
Regression 6 24517362 4086227 64.74 0.000
Price (real- 1990) 1 3193165 3193165 50.59 0.000
Unemployment Rate 1 4053177 4053177 64.22 0.000
Consumer Sentiment 1 734294 734294 11.63 0.001
Quarter 2 1 3350526 3350526 53.09 0.000
Quarter 3 1 1263400 1263400 20.02 0.000
Recession 1 490047 490047 7.76 0.006
Error 96 6059094 63116
Total 102 30576456
Model Summary
S R-sq R-sq(adj) R-sq(pred)
251.228 80.18% 78.95% 76.28%
Coefficients
Term Coef SE Coef T-Value P-Value VIF
Constant 5011 460 10.89 0.000
Price (real- 1990) -0.05929 0.00834 -7.11 0.000 1.22
Unemployment Rate -205.4 25.6 -8.01 0.000 2.66
Consumer Sentiment 12.56 3.68 3.41 0.001 3.43
Quarter 2 442.9 60.8 7.29 0.000 1.14
Quarter 3 272.3 60.9 4.47 0.000 1.14
Recession -275.1 98.7 -2.79 0.006 1.52
Regression Equation
Motor Vehicle Unit Retail Sales = 5011 - 0.05929 Price (real- 1990) - 205.4
Unemployment Rate
+ 12.56 Consumer Sentiment + 442.9 Quarter
2
+ 272.3 Quarter 3 - 275.1 Recession
Fits and Diagnostics for Unusual Observations
Motor
Vehicle

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Unit
Retail
Obs Sales Fit Resid Std Resid
35 3736.6 4258.8 -522.2 -2.14 R
48 4357.6 3430.6 927.0 3.92 R
51 4453.8 3950.8 503.0 2.04 R
76 2469.6 3064.0 -594.4 -2.53 R
78 2598.8 3141.8 -543.0 -2.40 R
R Large residual
Residual Plots for Motor Vehicle Unit Retail Sales

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Appendix 2: Graph of Predicted vs. Actual U.S. Vehicle Sales

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