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The Effect of Free Internet Advertising on the Pricing of Used Cars Daniel A. Epstein January 30, 2007 This paper examines the question of whether sellers of used automobiles mark up the value of their product differently when advertising is free than when advertising is costly. The advent of the internet has produced a forum that allows sellers to advertise to a large number of people for no monetary cost. Craigslist.org, dubbed “the world’s biggest free bulletin board,” is an extremely popular website forum that does not charge patrons to advertise. This new method of advertising may have an influence on the pricing of the goods advertised, and incentives may be different in the world of free advertising. This paper intends to compare the prices and values of similar goods when advertising is free and when advertising is costly to see if there is any difference in markup percentage. This research is of interest as it examines the impact of the internet on pricing policies and sheds light on the role of information’s affect on market dynamics. Literature: No research has attempted to answer whether similar goods are priced differently dependant upon whether they have been advertised freely or at cost. There is, however, literature on the effect of advertising on prices, on price as a signal of quality, on the role of information gatekeepers on the internet in competitive markets, on auction theory, and on the role of information dispersal on prices; all of which are relevant to my research. Benham concluded in his article, “The Effect of Advertising on the Price of Eyeglasses,” that advertising does influence pricing, and when advertising was allowed in the case examined, lower prices followed. He concludes that consumer’s knowledge of market information was more important than previously thought, and that the role of advertising cost was less important than previously thought. Benham’s research compared pricing in markets that allowed advertising and markets that did not. My research compares pricing in markets that advertise over different advertising mediums. According to George Stigler, price dispersion in a market is evidence of ignorance of market alternatives. With perfect perception of the market (i.e. costless information), consumers would surely buy their used car at the lowest price available; but since buyers do not have perfect information, and since search within the market place is usually costly and hence incomplete, sellers are able to sell their cars at varying prices within what is a “homogenous” market. This might not be a perfect assumption since sellers that advertise at cost often provide extra services like warrantees, financing, and quality inspections; however, Stigler argued for this assumption when he wrote that, “it would be metaphysical, and fruitless, to assert that all dispersion is due to heterogeneity.” Stigler recognized the importance of advertising in eliminating buyer ignorance. Laurent Linnemer and Asher Wolinsky had similar ideas when they wrote on price as a signal of quality. They understood that pricing was dependant upon the existing proportion of informed consumers. They also suggested that a high price can signal high quality to otherwise uninformed consumers. This is important to remember in the used car marketplace (which is notorious for information asymmetry between buyers and sellers). Similarly, they saw that markups were greater as information was poorer. Auction theory applies to my hypothesis. The Dutch auction (also known as the descending auction) is specifically relevant. The Dutch auction starts at a prohibitively high price. Then the auctioneer slowly decreases the price until eventually a buyer bids. That first bid wins and ends the auction. The Dutch auction is most commonly seen in IPOs (and most famously with Google), but is applicable elsewhere. Methodology: I considered two markets that differed in the cost of their advertising medium. I examined the used car market as advertised in newspapers and magazines (which is costly for the advertiser) and as advertised over craigslist.org (which is free for the advertiser). From the car’s advertised value, I subtracted the appraised value (according to the Kelly Blue Book). Then I took the difference and divided that by the appraised value to determine the percent by which the value was marked up over the appraised value. Determining a percentage rather than a level number is important, because value may be correlated to the amount the good is marked up. I performed this measurement using advertised values from sources like newspapers and magazines, where advertising is not free as well as using advertised values from craigslist.org, where advertising is free. I then took these two groups of values and did a two sample t-test with unequal variances to test the means. (paid advertising value) – (appraisal) (free advertising value) – (appraisal) -------------------------------------------- vs. -------------------------------------------- (appraisal) (appraisal) Ho: µmarkup%@cost - µmarkup%free = 0 Ha: µmarkup%@cost - µmarkup%free ≠ 0 A simple statistical test of the null hypothesis that these groups of values are equal answered whether the advertised values of both of these groups are marked up equally or not. Hypothesis: I expect to find that the automobiles advertised on craigslist.org are marked up slightly more than those advertised through costly mediums. The logic behind this expectation is the following: For every day that a seller has their ad in the paper, they pay a fee. Thus, they have an incentive to have their ad in the paper for as short a time as possible. One way to minimize this time is to lower the asking price on the automobile (thus increasing the crop of potential buyers and increasing the appeal of the good). If however, the seller incurs no monetary cost in advertising, then there is less incentive to have a lower asking price because they lose less each day they do not sell their product. So a seller can cast a high price into the market and wait. If there is no response, they can lower the price marginally until they catch a buyer. This would essentially mirror a Dutch Auction model. If we graphed accumulated cost over time (which includes advertising and waiting costs), the curve of the seller that advertises freely should have a slope that is slightly positive (only accounting for the cost of waiting). The curve of the seller that advertises through costly mediums should have a more positive slope. If we graph the advertised price of the good over time, we should see a slightly downwards-sloping line (the slope is negative due to the seller’s scarce patience and the depreciating value of their product). Now if we superimpose the curve of the advertised price onto the graph of the accumulated cost over time, we see two converging curves. The seller wants to maximize the gap between the advertised price (approximate revenue) and the accumulated cost. Thus, other things being equal, the seller has an incentive to sell their good as close to time=0 as possible. Contrarily, for the seller that advertises freely, the only determinant of their asking price is their own patience (Graphs 1 & 2). If the seller advertises a 1981 Honda for $384,058, the likelihood of them selling that car within their lifetime is almost nil. So we see that the seller that advertises at no cost essentially faces an expected value problem. They must multiply their asking price by the probability of selling the car at that price within a desirable time frame. E[S|P] where S is the probability of selling the car within the seller’s desirable time frame and P is the selling price of the car. If the seller has perfect perception of the market, they will be able to maximize their profit within their desired timeframe. However, perfect perception is a false assumption. So as I said, I expect the reality to be that free advertisers cast their marked up price into the market and wait; lowering the price marginally until they catch a buyer. So then the slope of the seller’s advertising price should be negative over time; determined by the depreciating value of their good, their desired time frame for selling their good, their patience, and their perception of the probability of selling their good at a given price within their desired time frame. This model assumes that the price of advertising at cost is not negligible with respect to the good for sale and that the cost of advertising at no cost is negligible with respect to the good for sale. Further research might determine the magnitude to which these assumptions are or are not true, and the extent to which they change the slope of the selling price over time. Results: Table 1 Ho: µmarkup%@cost - µmarkup%free = 0 Ha: µmarkup%@cost - µmarkup%free ≠ 0 t-Test: Two-Sample Assuming Unequal Variances Markup Markup percentage of Percentage of cars advertised cars advertised @ cost freely Mean 0.423 0.042 Variance 0.452 0.045 Observations 39 39 Hypothesized Mean Difference 0 df 45 t Stat 3.373 P(T<=t) one-tail <0.001 t Critical one-tail 1.679 P(T<=t) two-tail <0.010 t Critical two-tail 2.014 Discussion: In looking at the results, we can reject the null hypothesis that the mean markup percentages in both groups are equal. We can reject the null hypothesis with extremely high confidence. The resulting P-value would satisfy an α of 0.01. Though the mean markup percentages are not equal, the results are inconsistent with my hypothesis. The results show that the mean markup percentage of cars advertised at cost are almost an order of magnitude larger than the mean markup percentage of cars advertised freely. Why do cars advertised at cost tend to be marked up more than those advertised freely? What are the alternative explanations and what are the implications of these hypotheses? There are a number of potential answers to this question. I think that a big reason for the markup disparity is a different group of sellers using the different advertising mediums. While there is an overlap in the mediums, I think that those who advertise through costly mediums tend to be expert sellers, and those who advertise freely tend to be amateur sellers. The expert sellers are well versed in the market and have better perception of the limit to which they can markup their products. I think that the amateur sellers have poor perception of the market, and as such do not realize how much they can markup their product. Furthermore, those non-expert sellers may look for a guide to find out how valuable their product is. I think that that guide is the Kelley Blue Book (or some similar valuation resource). In other words, it is possible that the amateur sellers, in their ignorance of the market, look to the Kelley Blue Book value to figure out what price they will advertise their car at. If this were true, the mean markup percentage of used cars advertised over craigslist.org would not be statistically unequal to 0. This is exactly what we see in the statistical test in table 2. In running this test we see that we cannot reject the null even at α = 0.2. This means that the mean markup percentage of cars advertised freely is statistically not unequal to the Kelley Blue Book value. It is possible that with a greater sample set, this would not be the case, but it seems apparent that at least a certain portion of “craigslisters” are using the Kelley Blue Book (or some similar valuation resource) in their pricing. This raises another issue. Buyers are most likely segregated, creating a selection bias among buyers. It is possible that two different buyer populations conduct their product search via the two different advertising mediums in question. Perhaps those who conduct their product search through costly advertising mediums are willing to pay more above Kelley Blue Book value than are those who conduct their product search over craigslist.org. Clearly this must be the case; otherwise there would be no reason that cars with high markup percentages would ever be sold. That is, unless the products advertised over different mediums were not perfectly equal. The inequality of these products (or heterogeneity) is another issue. Stigler took on this issue by saying that we cannot learn anything from our observations unless we assume homogeneity, but in this case I think it is important to acknowledge the fact that those who advertise at cost are oftentimes offering a slightly different product. Used cars advertised in the newspaper or auto trader magazines oftentimes offer warranties, safety inspections, and a certain sense of credibility in their operation. Dealers do advertise over free forums like craigslist.org, and individuals do advertise at cost, but craigslist.org is dominated by individual sellers, and costly advertising is dominated by dealers. I tried to account for selection bias in products by comparing markup percentages as opposed to advertised prices, but to fully account for extras like warranties and safety inspections, more thorough research is needed. Xavier Gabaix and David Laibson see these warranties and safety inspections as noise that expert sellers insert into their product to increase ignorance, thus allowing for greater markups and greater profitability. With data on the value of these add-ons, multi-linear regression might be able to get a more accurate measurement of the disparity of mean markup percentages. Another point of note is seen when observing the actual advertisements on craigslist.org. Many of them suggest that they are selling their product so cheap because they need to get rid of their product quickly (due to change of location, divorce, loss of job, etc.). I addressed this in my hypothesis when I discussed the desirable time frame of sale. I think I underestimated the number of people who had a relatively short time frame. In comparison, used-car dealers have a very large time frame since they have storage space and are operating in scale. Changing the time frame assumptions changes the whole dynamic of my hypothesis and pricing in general. Craigslist.org’s extremely easy access may also be a factor in significantly more competitive pricing. The sheer number of different sellers that advertise on craigslist.org (many of whom are not solely interested in maximizing profit) can drive down prices. Newspapers and magazines may offer more total cars, but many of those cars are advertised by the same seller. Craigslist.org is packed with individual non-colluding sellers, all competing with each other. The internet is a goldmine of market information. Consumers can gain a much more accurate perception of the market with an equal level of effort by using the internet than by searching newspapers and magazines. This assertion matches “expert buyers” with amateur sellers and “amateur buyers” with expert sellers. It is no wonder then that we see the mean markup percentages diverging in the direction that we do. It should be noted though that some newspapers do post their “Classifieds” section on the internet, which may bring into question the power of this assertion. Negotiation expectations should also be considered. Unlike many goods, car prices are starting points of negotiation. The realities of negotiation impart some complex dynamics with respect to pricing; especially considering the fact that many cars bought from dealers are sold as a part of a deal that includes a trade-in. These alternative hypotheses were not directly tested, but the magnitude of the difference in mean markup percentage between these two groups appears to be an anomaly. Conclusion: My initial hypothesis was that pricing for used cars advertised freely over the internet would operate like a Dutch auction, resulting in high initial offers. I looked at 39 cars advertised freely over the internet and 39 cars advertised through costly newspapers and magazines and found the mean markup percentages to be 4.2% and 42.3% respectively; a disparity of 38.1%. This result proved wrong my initial hypothesis. 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Appendix: Graphs 1 & 2 Table 1: Ho: µmarkup%@cost - µmarkup%free = 0 Ha: µmarkup%@cost - µmarkup%free ≠ 0 t-Test: Two-Sample Assuming Unequal Variances Markup Markup percentage of Percentage of cars advertised cars advertised @ cost freely Mean 0.423 0.042 Variance 0.452 0.045 Observations 39 39 Hypothesized Mean Difference 0 df 45 t Stat 3.373 P(T<=t) one-tail <0.001 t Critical one-tail 1.679 P(T<=t) two-tail <0.002 t Critical two-tail 2.014 Table 2: Ho: µmarkup%free = 0 Ha: µmarkup%free ≠ 0 t-Test: Two-Sample Assuming Unequal Variances Markup Percentage of Cars 0 Advertised Freely Mean 0.042 0 Variance 0.045 0 Observations 39 39 Hypothesized Mean Difference 0 df 38 t Stat 1.250 P(T<=t) one-tail 0.110 t Critical one-tail 1.686 P(T<=t) two-tail 0.219 t Critical two-tail 2.024 Tables 3 & 4: Descriptive Statistics of price when advertised at cost Mean 8939.051 Standard Error 1078.629 Median 6977 Standard Deviation 6736.034 Sample Variance 45374152.37 Kurtosis -0.434 Skewness 0.768 Range 23140 Minimum 1350 Maximum 24490 Sum 348623 Count 39 Confidence Level(95.0%) 2183.570 Descriptive Statistics of price when advertised freely Mean 8919.513 Standard Error 1172.387 Median 6000 Standard Deviation 7321.552 Sample Variance 53605126.62 Kurtosis 1.335 Skewness 1.221 Range 32000 Minimum 500 Maximum 32500 Sum 347861 Count 39 Confidence Level(95.0%) 2373.373 Tables 5 & 6: Descriptive Statistics of Kelley Blue Book value of cars advertised at cost Mean 7247.821 Standard Error 992.896 Median 5420 Standard Deviation 6200.636 Sample Variance 38447889.2 Kurtosis 0.618 Skewness 1.155 Range 22320 Minimum 525 Maximum 22845 Sum 282665 Count 39 Confidence Level(95.0%) 2010.014 Descriptive Statistics of Kelley Blue Book value of cars advertised freely Mean 8416.205 Standard Error 1094.188 Median 6040 Standard Deviation 6833.200 Sample Variance 46692628.22 Kurtosis 1.482 Skewness 1.348 Range 28760 Minimum 1030 Maximum 29790 Sum 328232 Count 39 Confidence Level(95.0%) 2215.067 Tables 7 & 8: Descriptive Statistics of markup percentage of cars advertised at cost Mean 0.423 Standard Error 0.108 Median 0.264 Standard Deviation 0.672 Sample Variance 0.452 Kurtosis 10.590 Skewness 2.942 Range 3.801 Minimum -0.475 Maximum 3.326 Sum 16.507 Count 39 Confidence Level(95.0%) 0.218 Descriptive Statistics of markup percentage of cars advertised freely Mean 0.042 Standard Error 0.034 Median 0.041 Standard Deviation 0.212 Sample Variance 0.045 Kurtosis 0.474 Skewness -0.188 Range 0.985 Minimum -0.515 Maximum 0.470 Sum 1.655 Count 39 Confidence Level(95.0%) 0.069