Estimating good buying and selling prices on eBay using eBay sales history

Question

I am trying to create the algorithm that will tell me the good price to buy stuff and a correct re-selling price based on eBay item history.

The main variables would be the auction type (buy now or bidding), price, if the item was sold and some sort of margin that has to be made on each transaction (in percents or dollar value)

This is the spreadsheet with example data: Excel sheet. The first few rows appear below.

 AUCTION_TYPE PRICE   DATE_SOLD    SOLD_Y_N
 BUY-NOW      $300.00 May-10 22:26 Y
 AUCTION      $300.00 May-13 09:11 Y
 AUCTION      $300.00 May-13 09:12 Y
 BUY-NOW      $474.89 May-14 12:30 N

It is a table of Auction_Type (a binary covariate equal to "Auction" or "Buy now"), Date_Sold (to the minute), Price (consistently in American dollars)--which is an outcome for auctions but is predetermined for "Buy now" sales--and Sold, a binary outcome indicating whether the item was sold.

Herein is one of the principal complications: Price, which is something I would like to estimate, functions sometimes as an "independent" variable and sometimes as a "dependent" variable, depending on the type of auction. This situation therefore does not seem to fit within a standard regression setting. A good solution may require some innovative statistical thinking.

Well, I think that I we need to establish some sort of MINIMUM amount of sold/unsold items per week. For example, it is hard to predinct anything if there are 5 records exist. But if we would work with 50 records per week and with the period of coupld of weeks, we could narrow the probability.

In addition, autioned items have the higher probability to be sold for lesser value, than the buy-it now. But, on another hand, some unsold auctions can be a warning sign that indicated that this item might not be that valuable.

What I am trying to figure out- is there any resonable solution can be created that would allow to buy things based on the item selling/unselling history, and then to be able to resell the same item with the profit and with very high probability of success.

Answer 1

This is non-trivial exercise since there's lots of dynamic strategic behavior. Your bid depends on what the other bidders are doing (and vice versa). For example, say you stumbled on an auction for a Suny DVD player. You are probably the only participant, and your bid just has to exceed the minimum, since you're not worried about the others.

Here are some examples of auctions with new digital cameras (Vogt et al, 2005), mp3 players and DVDs (Zeithammer, 2005), and a method to back out demand from bid data (Backus and Lewis, 2010). I think all of these assume a unit demand, meaning that each buyer purchases only a single item, so your problem, where reselling is an option, is considerably harder.

Answer 2

You might look at a Springer Series that published a title something like "Examing the Maximization of Return in Online Auctions" - a small study regarding the use of strategic knowledge aggregation to maximize the quantity of customer surplus (c.f., seller surplus). The papers lists a methodology / sample of prices. Most importantly, I believe price in ebay is irrelevant and the quantity of roi and etc. - here most relevantly consumer surplus as a buyer. I will look up the book and get you the name. Jeff Delezen