The Value of Automotive Factors Between Segments
Total Page：16
File Type：pdf, Size：1020Kb
Robert Heeter Econ 499H Senior Honors Thesis May 1, 2008 The Value of Automotive Factors between Segments The goal of this thesis will be to research, analyze, and present the influence of car attributes on their price in the market. A number of studies exist that involve hedonic pricing of automobiles in industrial organization studies, from Berry, S., J. Levinsohn, and A. Pakes to Triplett or the original Court, A. but I have not seen a study yet focusing on the comparisons of coefficients of the value derived by attributes across different segments. In some cases, the hedonic studies are done to compare the increase in automotive prices to price indexes or to determine if colluding had occurred. Instead of determining the change in quality and price over years, I will focus on the change of coefficients for effects of attributes on price across vehicle market segments. BACKGROUND INFORMATION I have for a long while have been intrigued by cars, since middle school, and have been familiar with automotive terms and the industry atmosphere. However, in order to prepare some unfamiliar readers, I ought to go into a little background information on the subject. In my procedure area, I have included brief descriptions of the segments and variables, but it is important to also understand the automotive industry basics. There are several EPA classes for cars, ranging from minicompact to large, with subcompact, compact, and midsize in-between. These are grouped by size generally and would not have leant to very interesting regressions without dummies for all their standard features, as much of the past studies relied on physical dimensions and performance/feature specification and segmenting by EPA class would lose half of that. The government on fueleconomy.gov also breaks down classes by market class, which includes sporty/sports cars and luxury/upscale. This sort of segmenting would help group cars of similar quality and features while describing their variance using physical dimensions as well as other specifications. For objectively defining performance, I chose to use a calculation of hp/ton and then chose to use my own definition of luxury in order to include the luxury wagons that the fueleconomy.gov separated as its own category with other passenger car station wagons. PAST RESEARCH Before I begin discussing my approach, let me review the studies I have read and have taken into consideration. In the study of the 1955 Price War, Bresnahan [1987] modeled that consumers minimize the price of a good that will satisfy their desire for quality of a certain product, as Pj – vxj. Applying this to vehicular specifications, a consumer seeking a group of features in a car would choose the car that fulfilled his needs for the least amount. If there were multiple products of varying quality and different prices, then at different quality levels, a consumer would select other alternatives. He would be indifferent if vh = (Pi – Ph / Xi – Xh) as paying an unit more for a unit more of quality makes him just as satisfied. If v increases, then the consumer is more fond of an expensive car. The slope is Ph – Vxh. The cost to produce would be the fixed cost plus marginal cost to increase quality. The max profit comes from the decision to stick to the minimum of what will make the costumer satisfied. The focus of this research was the more competitive segments of smaller cars where multiple makes existed. Even today, many people have a horse in the subcompact or compact categories where larger vehicles may be skewed. His variables included weight, length, horsepower, number of cylinders, and a hardtop as well as make dummy variables. These will help guide my selection as well as a preliminary regression to see how modern cars are differentiated. I am as well using Automotive News for my data. Triplett’s study [1969] looked into whether or not the price index was upwardly biased by not accounting for increases in quality. If the cars price rose and xh did as well by the same amount (borrowing terms from Bresnahan), then the real price of the car and its quality remained neutral. He based quality on factors of horsepower, width, length, and then dummy variables for V8, hardtop, automatic, power steering/brakes, and compact. Comparing adjacent years, any residual not accounted for by these variables was chalked up to the years and real price increases. In the years of my study, the majority of cars now have steering/brakes standard, but I will have a hatchback variable to help describe differences between a traditional car and a hatchback which are typically associated with cheaper models, but perhaps controlling other factors it may not. In Boyle and Hogarty’s research [1975] the coefficients can represent cost of producing characteristics if industry is perfectly competitive and have the same processes. They relied on indexes based on MSRP which I have done. (It would have been great to get the type of data Agarwal and Ratchford found along with mystery shopping to get real dealership pricing but for the simplicity, I have assumed MSRP are representative enough). The principal characteristics of quality desired by car buyers are comfort, durability, economy, maneuverability, performance, safety, and style. All of the statistics were significant except for economy. (But this could have been because when weight, cylinders, and horsepower are fixed it may have collinearity). B&H based their regression upon front seat room, horsepower, and weight. The Information Disclosure Act helped pricing become more transparent to consumers and make assumptions of complete information more plausible (although not as much so as today with the internet and comprehensive guides available). Agarwal and Ratchford [1980] expanded on Rosen’s model [1974] which look into price elasticities of price attributes with attention paid to the varying preferences of the consumer and their demographics, life cycles, etc. Instead of simply finding evidence that the price one is willing to pay is sensitive to attributes, this model built upon it by including whether the car would be used for long distance travel for example. And also add dummy variables in order to capture if manufacturers had preferences, or were capable of providing X attribute easily or cheaply compared to the competition. Both the consumer and producer were assumed to be making decisions in order to build the market interactions. A&R had objectives of physical specifications and Consumer Reports as well as 27 respondents which answered on 34 constraint ratings that gave perceptual insight. Some attributes included were displacement, luggage capacity, rear leg room, 45-65 passing speed, handling, ride from Consumer Reports ratings, and 2dr or 4dr. But there were issues present with how to rate styling objectively, and the affect of coefficients may have been overstated because of explaining other elements remaining in u but correlated, such as in my first regression which had a great negative coefficient for number of cylinders with a given horsepower rating which could have brought in influence from an unaccounted for fuel mileage. When factoring in curb weight and several characteristics of the engine, fuel mileage should be somewhat included in the model. Agarwal and Ratchford’s econometric study also included consumers and how many storerooms they visited, the number of cars looked at, the type of driving they were planning, how often they were getting a new car, their amount of do-it-yourself work, income level, education, occupation, family size, number of cars, life-cycle stage, and home/garage ownership. For the producers and dealership activity, a brand dummy was used. This model assumed consumer had complete information, there were no economies of scale in models or trims, and that the industry was perfectly competitive. The hedonic equation was the natural log of price = .0349 displacement + .1492 handling + .2391 ride + .2664 (1/passing time) + .0334 luggage capacity + .2674 rear leg room. R squared was .684. When there was only a primary car or one for long distance travel being shopped for, people preferred larger, more comfortable cars but when it was a short trip secondary car, it was smaller. They also calculated the resulting utility taking the hedonic price of the car and subtracting it from the actual. Griliches and Ohta [1986] researched the adjustment of buying preferences after the increase in gas prices. The hypothesis was that Americans shifted towards smaller cars and drove their prices up as larger cars decreased in numbers from the producers, and mpg was a more attractive attribute while engine size and weight were undesired. This model assumed that automotive prices are a function of the characteristics of it. Utility from the automobile was a combination of its speed, room, comfort, and handling. Gas mileage was not necessarily a part of utility in this discussion as it was only part of the budget constraint. The regression used displacement, cylinder number, weight, wheelbase x width, less than four doors or more than four, Automatic, power steering, and A/C. In my primary regression, a dummy variable for hatchback is sometimes significant, and probably should not be grouped with coupes in which during this era may have been much more expensive while hatches are economical. Griliches and Ohta described speed with the engine description, comfort with the size and weight, doors and A/C for ride quality, and Automatic and PS for ease of drive. Although these seem like great extrapolations to be making, Taylor in 1994 simply focused horsepower, luggage room, and gas mileage to represent interests. Two final studies I have looked out were Bajic [1988] and Bordley [1993]. Bajic includes helpful suggestions of excluding small-production expensive luxury cars and using one representative model when multiple trims are present.