Microeconomic factors affecting mid-size SUV sales and pricing

¶ … microeconomic factors including inflation, SUV collateral or trade-in value, significant oil and gas price fluctuations as quantified by the average U.S. price of a gallon of gasoline, and the impact of automaker incentives on the sales of SUV sales throughout the last fourteen months. These microeconomic factors are captured in quantitative form and analyzed using a series of statistical analysis routines from the Statistical Software Package for the Social Science (SPSS). A methodology based on the use of R.L. Polk vehicle sales data in addition to use of data from the AutoData database (2008) and the Global Insight Databases (2008) which is an information service from J.D. Power. The Monthly Review (2008) is used for the specific fuel pricing used in this analysis as well. Using R.L. Polk data the high gas consumption cars will be the mid-size SUV segment. The dynamics of this market where body-on-frame vs. unibody construction make it ideal for being a significant independent variable in the analysis. For purposes of completing the R.L. Polk vehicle analysis these are the models by each type of SUV:

Body on Frame SUVs (more towing power, less fuel efficient) included in the Analysis

CHEVROLET - TRAIL BLAZER

CHEVROLET - TRAILBLAZER EXT

CHEVROLET - TRAILBLAZER SS

FORD - EXPLORER

GMC - ENVOY XL DENALI

KIA - SORENTO

NISSAN - PATHFINDER

TOYOTA - 4 RUNNER

Unibody SUVs (less towing power, more fuel efficient) included in the Analysis

CHEVROLET - EQUINOX

FORD - FREESTYLE

HONDA - PILOT

HYUNDAI - SANTA FE

NISSAN - MURANO

SUBARU - B9 TRIBECA

TOYOTA - HIGHLANDER

Methodology

R.L. Polk's Insight database (2008) was first used to gather the last fourteen months (from June, 2007 to July, 2008) of new vehicle sales and registrations for the two groups of SUVs, the median U.S. gas price using the Monthly Energy Review (2008). Using the data contained in the R.L. Polk Insight database (2008) and the J.D Power PIN database (2008) a series of data tables were constructed and analyzed. The data set of financing variables was specifically from JD Power's PIN Financing Database. Results of queries into each of these databases were extracted into Microsoft Excel files and then imported in SPSS Version 15 for Windows.

As defined the null and alternative hypotheses are presented below:

Ho: There is a negative relationship between high oil price and car sales and leasing value.

H1: There is a positive relationship between high oil price and car sales and leasing value.

Initial Results

The data sets were entered into SPSS Version 15 and a correlational analysis was completed computing distances between variables using Pearson Correlation to measure similarities. The resulting Proximity Matrix is shown below:

Average U.S. Gas Price

Table 2, Partial Correlation Descriptive Statistics shows the results of the initial analysis. With the number of periods being 14, and controlling for gas price this analysis shows that the majority of mid-size SUV vehicles buyers were financing, taking advantage of aggressive incentives on the part of manufacturers. See Figure 1 for an analysis of cash and incentive spending by SUV model.

Notice that the median level of spending is approximately $3,000 per unit for cash and incentives and with the variable entered into the analysis a correlation matrix is produced, and shown in Appendix a: Full Correlation Analysis with Cash & Incentives Included. Cash and Incentives have a.01 level of significance (.847 correlation) with motivating buyers to trade in their vehicles. Gas prices also show statistical significance in influencing cash and incentive effectiveness to in turn influence trade-ins. The bottom line is that the higher and more volatile the gas price, the more buyers rely on leasing, and initially appear to be motivated to trade-in their previous vehicles to qualify for the cash and incentives on chosen vehicles.