1. 26 Online Auctions 3 Aaron Schiff ECON 204 2009

2. Introduction Objectives of this lecture: Introduce issues relating to auction design such as reserve prices, and the possible tradeoffs between revenue and efficiency of auctions. Discuss various problems with online auctions and their solutions. Overview empirical evidence about behaviour of bidders in online auctions.

3. The Revenue Equivalence Theorem Recall that the expected selling prices in a first and second price auction are the same in the private values model. This turns out to be just a special case of a more general result known as the Revenue Equivalence Theorem (RET). Basically, the RET says that, under some assumptions, any auction design that satisfies some conditions will generate exactly the same expected revenue for the seller.

4. The Revenue Equivalence Theorem Assumptions about bidders: –Risk neutral (only care about expected payoff). –Have independent valuations drawn from a common distribution (do not get useful information from other bidders’ bids). Assumptions about the auction: –Is always won by the highest bidder. –Gives a zero expected payoff to the lowest bidder. All auctions that satisfy these conditions will give the same expected revenue to the seller.

5. Auction Design Auction design refers to the rules of the auction: –The way the bidders bid e.g. closed, open, ascending, descending –How the winning bidder is chosen Usually the highest bid –What the winning bidder pays First price / second price –What the other bidders pay Normal auctions: only the winning bidder pays something All-pay auction: All bidders pay their bid even if they don’t win

6. Auction Design If you are going to sell something by auction, what factors affect the seller’s choice of auction design? If the seller is simply interested in maximising the expected selling price (i.e. the seller is risk neutral) then provided the conditions of the RET are satisfied, a risk-neutral seller won’t care about the auction design. Note however that the RET says that expected revenues are the same but it does not say that the distribution of revenues is the same.

7. Auction Design It turns out that the distribution of expected revenues in a second-price auction is more ‘spread out’ than the distribution of revenues in a first-price auction. Thus a second-price auction is ‘more risky’ than a first-price auction, and a risk averse seller would prefer a first-price auction over a second-price auction.

8. Auction Design: Reserve Prices In auctions it is common for the seller to place a reserve price below which the item will not be sold. This makes sense if the object for sale has some value to the seller even if it is not sold – the seller would be stupid to sell for a price below his own value. Even if the object does not have any value to the seller, setting a reserve price may also have the effect of increasing the expected selling price.

9. Auction Design: Reserve Prices In effect, setting a reserve price is the same as the seller placing a bid on their own auction. –Reserve prices can substitute for a lack of competition among buyers if there are not many buyers. –Setting a reserve price is not so important if there are many bidders. This has two effects: –It may increase the selling price if the object is sold. –However, it may also increase the probability that the object is not sold (if the reserve price is not met). The seller must trade off these two effects when choosing the level of a reserve price.

10. Example 1 Two bidders in a second-price auction have independent private valuations that are equally likely to be 10, 20 or 30 (1/3 probability of each valuation for each bidder). Questions: –What is the expected selling price with no reserve price? –What is the expected selling price with a reserve price of 15? –What is the expected selling price with a reserve price of 25? –Compare and comment.

11. Efficiency Sometimes, the seller might care about the efficiency of the auction instead of or as well as the expected revenue. –For example, if the seller is the government. Efficiency requires that the object is sold to the bidder who values it the most. All of the simple auction forms that we have discussed are efficient, because the highest bidder always wins.

12. Efficiency vs Revenue However, there may be a tradeoff between efficiency and revenue. As we have seen, setting a reserve price can increase the seller’s expected revenue from the auction, but also introduces the possibility that the item is not sold. If at least some of the bidders value the object more than the seller does, then it is inefficient for the object to remain in the seller’s hands. When designing the auction the seller may have to weigh up their preference for efficiency or revenue.

13. Problems with Online Auctions The online environment makes it easier to get away with “bad behaviour” in auctions. Main problems associated with online auctions: 1.Fraud 2.Shill bidding 3.Bid sniping 4.Bid shielding 5.Reputation milking 6.Reputation extortion These problems can also occur in offline auctions but are less common. –Easier to create new identities in online environments. –Easier to escape direct punishment.

14. Problems with Online Auctions Fraud –Generally, in online auctions buyers send payment first and then sellers send goods (this is not a rule but seems to be a tradition that has emerged). –Sellers may send goods of worse quality than advertised or none at all. –It’s relatively easy for sellers to create new identities and repeat the trick. –Partially overcome by: Making new identities more difficult to acquire, e.g. require a credit card number. Existing consumer protection laws. Buyer awareness. Using escrow services. Reputation/feedback mechanisms.

15. Problems with Online Auctions Shill bidding –Sellers use alternate identities to place bids on their own auctions or get their friends to do it. –Why bother? Why not just set a higher reserve price? High reserve prices may put off bidders To revive “dead” bidding –Usually against the auction rules, but difficult to detect and overcome. –eBay uses computer programs to try to detect shill bidding. –Also a common problem in offline auctions, especially real-estate.

16. Problems with Online Auctions Bid sniping –Some online auctions have fixed ending times. –Bidders may wait until the last second to bid so as to avoid competing with other bidders and driving the price up. –Usually not against auction rules. –Partially overcome by: Bidders using proxy bid system. Auto-extending auctions that can be extended 5 or 10 minutes after the last bid.

17. Problems with Online Auctions Bid shielding –Online auction bidders are usually allowed to retract their bid before the auction closes. –A buyer puts in a low bid and uses another identity or gets a friend to place a very high bid to discourage other bidders. –At the last minute the high bid is retracted and the low bid wins. –Partially overcome by: Making bid retraction more difficult (need to ask seller first). Seller’s ability to remove high bids.

18. Problems with Online Auctions Reputation Milking –Sellers with good reputations are more likely to be trusted by buyers. –Sellers can generate a good reputation by selling a number of low value items honestly. –They can then take advantage of this good reputation to defraud buyers. –eBay banned auctions of digital goods because of this problem. –Difficult to overcome this problem. Reputation extortion –Good reputations are valuable to honest sellers. –Buyers may take advantage of sellers with good reputations by threatening to give them a bad rating. –Sellers can retaliate by giving the buyer a bad rating too, but sellers can’t erase ratings made by others.

19. Empirical Studies of Online Auctions Behaviour of bidders: –Most bidding takes place at the end of an auction: 50% of bids submitted in the last 10% of the auction’s duration. 32% of bids in the last 3% of the duration. 50% of winning bids come in the last 1.7% of duration. 25% of winning bids in the last 0.2% of duration. –Average of around 3-4 bidders per auction.

20. Empirical Studies of Online Auctions Factors influencing selling price: Lucking-Reiley et al examined 461 auctions for similar coins on eBay and look at the factors that affect the selling price, on average. –Read if you want: www.u.arizona.edu/~dreiley/papers/PenniesFromEBay.pdf Longer auctions get higher prices. –3-day and 5-day auctions get approximately the same prices. –7-day and 10-day auctions get prices that are 24% and 42% higher on average respectively.

21. Empirical Studies of Online Auctions Auctions that end on a weekend get prices that are 7% higher than auctions that end on a weekday. Setting a secret reserve price increases the selling price by 15%. Given that the item is sold, the starting price that the seller sets doesn’t have much effect on the selling price on average. –Provided the starting price is not too high that the item is not sold at all.

22. Empirical Studies of Online Auctions Effect of reputations / feedback ratings: –Buyers and sellers can give each other a positive or negative (or neutral) feedback rating after a trade. –A trader’s reputation score is calculated as the total positive feedbacks minus the total negative feedbacks. –Most empirical studies find that seller reputations have a small but significant effect on the selling price. –Negative feedbacks of sellers seem to matter more than positive ones.

23. Empirical Studies of Online Auctions Lucking-Reiley et al find: –A 1% increase in positive feedback of a seller yields a 0.03% increase in selling price. –A 1% increase in negative feedback of a seller yields a 0.11% decrease in selling price. Biggest reputational returns for sellers seem to come from the first few feedbacks. –Going from no positive feedback to 1 or 2 positive makes a big difference to selling prices. –Going from 100 positive feedbacks to 101 has almost no effect on selling prices. –Thus there are diminishing returns to reputations. –Should expect that sellers with many positive feedbacks will care less about maintaining their good reputation than sellers with low feedback?

24. “Auction Fever” Bidders do not always appear to behave completely rationally in online auctions. Why doesn’t everyone just submit an auto-bid at the maximum they are willing to pay and be done? Instead we observe people bidding many times and a lot of bidding action at the last minute.

25. “Auction Fever” What could explain this behaviour? The leading bidder becomes psychologically “attached” to the item and starts to regard it as their own even though they have not yet won. Bidders get some extra pleasure or utility out of “beating” other bidders. Heyman, Orhun & Ariely (2004) conducted experiments to test these hypotheses and found evidence to support them.