Price In Online Auctions Research Articles

Functional forms in studies of reputation in online auctions

Many studies have examined how various factors affect prices in online auctions. These studies assume that the relationship between price and the seller's reputation take a variety of functional forms, most frequently linear or linear-log. Others divide the sellers into categories by their reputations, and control for dummy variables indicating the seller's category. Identifying the correct functional form is a critical issue for research on any topic involving online auctions. Studies that assume the wrong functional form run the risk of generating biased and inconsistent estimates of the effect of their variables of interest. In this study, the price-reputation relationship is estimated under each of these functional forms using data from auctions of three different products. The estimated effect of reputation on price is substantially larger when using a categorical specification. The models are then subjected to specification tests which suggest that the categorical model is the most appropriate choice.

Market structure and quality uncertainty: a theoretical framework for online auction research

Given large numbers of buyers and sellers, with access to a wide variety of information, economic theory suggests that online auction markets should provide an efficient mechanism for establishing equilibrium prices. Previous research on online auction prices, however, is far from conclusive, having produced mixed findings. The seemingly inconsistent and sometimes contradictory results make it very difficult to integrate empirical findings into a coherent body of knowledge. The purpose of this paper is to present a framework that can reconcile previous findings and provide direction for future research. Accordingly, we propose a simple theoretical framework with two dimensions—market structure (thick vs. thin) and quality uncertainty (high vs. low). By examining the literature in the context of market structure and quality uncertainty we find that previous studies are not necessarily at odds, but that there is actually a fairly consistent pattern of results.

AN ANALYSIS OF INTERNATIONAL PRICE DIFFERENTIALS ON EBAY

I. INTRODUCTION No one likes to pay too much. In the language of economists, this translates into the of one which essentially says that if different prices are charged for similar goods at different locations, some clever agent will engage in arbitrage. By buying goods at cheap locations and selling them where prices are high, he forces prices to converge. Despite the intuitive appeal of the law of one price, empirical evidence is mixed. Prices are found to differ within cities, between cities, and between countries. (1) Of particular interest for this study is that empirical analysis has found evidence of effects, that is, prices between countries differ by more than prices within a country. (2) The border is typically explained by lack of transparency, that is, exchange rates hampering trade, as they require tedious conversion of foreign prices into home currency (Parsley and Wei, 1996). Are there on the Internet? The Internet has changed the way consumers and sellers interact. The introduction of modern communication technologies has increased price transparency: online shops in foreign countries can be browsed free of charge, Internet Web sites aid consumers to find the cheapest prices, and ample opportunities exist for comparing prices across countries. Moreover, payment sites as PayPal.com facilitate cross-border purchases. Online markets are a step closer to an economist's ideal of a frictionless, competitive market. A related of the Internet is that it offers new ways to measure prices. Online auctions provide economists with data that can be used to measure prices and investigate price formation. Using data from eBay, we investigate price differentials in Europe. Of all Internet auction sites, eBay is the largest one, and its (economic) importance is rapidly growing: According to its annual report, 180.6 million users from more than 150 countries listed 1.9 billion items on eBay in 2005. The total value of goods sold that year reached nearly 44 billion U.S. dollars, representing a 30% increase over 2004. Compared with, for example, scanner data from supermarkets, Internet auctions have a number of advantages: first, prices for a broad range of goods are easily observable. Second, all items are shipped by surface mail and thus highly tradable. Third, goods do not require costly display in shops. This reduces the non-tradable cost component, relative to traditional retailers. And lastly, comparing online auction prices is simply more realistic than investigating international price differentials in supermarkets: few, say, Italian consumers would drive to the United Kingdom to do grocery shopping, but it is not unreasonable to assume that they buy an English copy of a Harry Potter book at eBay from a British seller. We examine two issues: First, comparing prices consumers pay for tradable goods in different countries, we establish that online consumers pay different prices for similar goods. Second, we want to get insights into the causes of these price differentials. Does the effect stem from countries, having different currencies, or from having national borders? We do this by comparing prices between the United Kingdom and the euro area and within the euro area. Price differentials within the euro area cannot stem from different currencies, as all countries share the euro as common currency. To preview the conclusions, we find evidence of substantial differences in price levels. In addition, price dispersion seems to be related to whether or not countries share a common currency. Hence, we find evidence that national borders create frictions (which result in price differentials), and the magnitude of the differential is increasing if countries do not share a common currency. This study contributes to the literature in several ways. First, we use eBay to evaluate a macroeconomic concept. Second, this is one of the first studies to use data from the euro area and the United Kingdom to analyze the source of the effect. …

The Rugby Player and the Handbag

Research into the determinants of online auction prices has tended to group them into buyer factors, seller factors and site factors. A case is presented which records how a $30 handbag was sold for $ 22,750 in an online auction shortly after a national sport final. Analysis of the case indicates additional factors which can exert a considerable influence on the final auction price. A model is proposed which depicts five groups of factors impacting the final price: buyer factors, seller factors, site factors which are expanded to include timing of the “action”, and site brand strength; product factors which include product features, brand strength, and brand extension/association; and promotion, which includes media publicity. While not all factors will impact on every auction, due consideration should be accorded each of them.

Reserve prices as reference points – Evidence from auctions for football players at Hattrick.org

We study the impact of sellers’ reserve prices on transfer prices in online auctions of virtual football players at Hattrick.org. We introduce an empirical model that distinguishes between two separate effects from public reserve prices: (1) a mechanical effect, which is driven by the design of the English auction and (2) a psychological reference-dependence effect through reserve prices serving as reference points. The psychological effect has recently been introduced in behavioral models of situations where agents are uncertain about their own willingness-to-pay, while the mechanical effect is well captured by standard auction theory. Controlling for censoring when players are not sold, both effects are observed. Once we account for the potential endogeneity of reserve prices, however, we do not find evidence for reference dependence in Hattrick auctions.

Real-time forecasting of online auctions via functional K-nearest neighbors

Forecasting prices in online auctions is important for both buyers and sellers. With good forecasts, bidders can make informed bidding decisions and sellers can select the right time and place to list their products. While information from other auctions can help forecast an ongoing auction, it should be weighted by its relevance to the auction of interest. We propose a novel functional K -nearest neighbor (fKNN) forecaster for real-time forecasting of online auctions. The forecaster uses information from other auctions and weights their contributions by their relevance in terms of auction, seller and product features, and by the similarity of the price paths. We capture an auction’s price path by borrowing ideas from functional data analysis. We propose a novel Beta growth model, and then measure the distances between two price paths via the Kullback–Leibler distance. Our resulting fKNN forecaster incorporates a mixture of functional and non-functional distances. We apply the forecaster to several large datasets of eBay auctions, showing an improved predictive performance over several competing models. We also investigate the performance across various levels of data heterogeneity, and find that fKNN is particularly effective for forecasting heterogeneous auction populations.

Real-Time Forecasting of Online Auctions via Functional K-Nearest Neighbors

Forecasting the price in online auctions is important for buyers and sellers. With good forecasts, bidders can make informed bidding decisions and sellers can select the right time and place to list their products. While information from other auctions can help forecast an ongoing auction, it should be weighted by its relevance to the auction of interest. We propose a novel functional K-nearest neighbor (fKNN) forecaster for real-time forecasting of online auctions. The forecaster uses information from other auctions and weighs their contribution by their relevance in terms of auction, seller and product features, and by similarity of the price paths. We capture an auction's price path borrowing ideas from functional data analysis. We propose a novel Beta growth model, and then measure distances between two price paths via the Kullback-Leibler distance. Our resulting fKNN forecaster incorporates a mixture of functional and non-functional distances. We apply the forecaster to several large datasets of eBay auctions, showing improved predictive performance over several competing models. We also investigate performance across various levels of data heterogeneity, finding that fKNN is particularly effective for forecasting heterogeneous auction populations.

Seller Search and Market Outcomes in Online Auctions

It has become widely accepted that information technology (IT) has had a profound influence on market transactions, with lower search costs for buyers often considered the key to lower prices and higher consumer welfare. However, sellers also use online channels to search for buyers whose product preferences and valuations match the sellers' offerings, and limited research has examined the impact of IT on sellers' search strategies or associated market outcomes. In this study, we investigate how market characteristics, the length of time the seller searches the auction marketplace for a buyer, and seller search in other websites influence the final sale price in online auctions. The online auction market for used vehicles provides a valuable opportunity to observe search processes of sellers, as the unique vehicle identification number associated with each vehicle enables the tracking of an individual item across both subsequent auctions and across other websites. We find that the market characteristics, duration of search, and search in other websites are each associated with a higher final sale price. Our findings highlight the importance of examining both buyer and seller search behaviors to further our understanding of the impacts of IT on market outcomes and efficiency.

Agents for Predicting Online Auction Closing Prices

No accessInternational Journal of e-Business ManagementOther Journal Article01 May 2008Agents for Predicting Online Auction Closing Prices Authors: Deborah Lim; Patricia Anthony; Chong Mun Ho Authors: Deborah Lim University of Malaysia Sabah Google Scholar More articles by this author ; Patricia Anthony University of Malaysia Sabah Google Scholar More articles by this author ; Chong Mun Ho University of Malaysia Sabah Google Scholar More articles by this author SectionsAboutPDF/EPUBExport CitationsAdd to FavouriteAdd to FavouriteCreate a New ListNameCancelCreate ToolsTrack CitationsCreate Clip ShareFacebookTwitterLinkedInEmail Abstract Online auctions are a very popular method for buying and selling items on the internet. It is very hard to speculate the winning bid of any particular auction, since it is very dynamic in that the bid prices change from time to time until the close of the auction. The closing price of an auction is not known and is dependent on several factors such as the number of auctions selling the same item, the number of bidders participating in that auction, as well as the behaviour of every individual bidder. In addition, there can be numerous auctions happening simultaneously. To be involved in an online auction, a person has to decide which item he or she will bid on, at what price, and when to bid. Generally, this preparation takes time, and has no certainty of success. If the closing price of an auction is known, then the bidders only need to determine which auction to bid on and at what price. This paper explains one of the methods used in forecasting the closing price of an auction, the Grey System Theory. This method has been known to accurately speculate values in areas where the information is insufficient. We also compare this with another forecasting method, the Time Series. Evaluations on the effectiveness of these methods in speculating the closing price in online auctions is discussed and elaborated upon. Previous article Next article RelatedDetails View PUBLICATION DETAILSDate of Publication:May 2008Journal:International Journal of e-Business ManagementISSN:1835-5412Volume:2Issue:1Page Range:20-38First Page:20Last Page:38Source:International Journal of e-Business Management, Vol. 2, No. 1, May 2008: 20-38Date Last Modified:13 June 2019 16:18Date Last Revised:21 September 2010 Original DOI: 10.3316/IJEBM0201020SubjectInternet auctionsTime-series analysis METRICS Downloaded 0 times Copyright© RMIT Publishing, 2008Download PDFLoading ...

Explaining and Forecasting Online Auction Prices and Their Dynamics Using Functional Data Analysis

Online auctions have become increasingly popular in recent years, and as a consequence there is a growing body of empirical research on this topic. Most of that research treats data from online auctions as cross-sectional, and consequently ignores the changing dynamics that occur during an auction. In this article we take a different look at online auctions and propose to study an auction's price evolution and associated price dynamics. Specifically, we develop a dynamic forecasting system to predict the price of an ongoing auction. By dynamic, we mean that the model can predict the price of an auction “in progress” and can update its prediction based on newly arriving information. Forecasting price in online auctions is challenging because traditional forecasting methods cannot adequately account for two features of online auction data: (1) the unequal spacing of bids and (2) the changing dynamics of price and bidding throughout the auction. Our dynamic forecasting model accounts for these special features by using modern functional data analysis techniques. Specifically, we estimate an auction's price velocity and acceleration and use these dynamics, together with other auction-related information, to develop a dynamic functional forecasting model. We also use the functional context to systematically describe the empirical regularities of auction dynamics. We apply our method to a novel set of Harry Potter and Microsoft Xbox data and show that our forecasting model outperforms traditional methods.

The influence of seller strategy on the winning price in online auctions: A moderated mediation model

This paper examines the impact of seller strategy on winning prices in online auctions. In our conceptual model, three strategic choices made by the seller – minimum opening price, auction length, and use of a hidden reserve price – are mediated by the number of bids placed during the auction and moderated by product type. Our tests analyze eBay auction data for four consumer products through two matched studies (two products per study). In particular, we compare products for which value is easily determined with those for which value is less clear. Overall, we find strong evidence of the effects of minimum opening price and hidden reserve prices on final winning prices. The impact of auction length on winning price is less clear. In general, our tests support the idea that potential buyers rely more on signals such as opening price and reserve price for products for which reference prices are less available.

PENNIES FROM EBAY: THE DETERMINANTS OF PRICE IN ONLINE AUCTIONS*

This paper presents an exploratory analysis of the determinants of prices in online auctions for collectible United States one‐cent coins at the eBay web site. Starting with an initial data set of 20,000 auctions, we perform regression analysis on a restricted sample of 461 coins for which we obtained estimates of book value. We have three major findings. First, a seller's feedback ratings, reported by other eBay users, have a measurable effect on her auction prices. Negative feedback ratings have a much greater effect than positive feedback ratings do. Second, minimum bids and reserve prices have positive effects on the final auction price. In particular, minimum bids appear only to have a significant effect when they are binding on a single bidder, as predicted by theory. Third, when a seller chooses to have her auction last for a longer period of days, this significantly increases the auction price on average.

Modelling Concurrency of Events in On-Line Auctions via Spatiotemporal Semiparametric Models

Summary We introduce a semiparametric approach for modelling the effect of concurrent events on an outcome of interest. Concurrency manifests itself as temporal and spatial dependences. By temporal dependence we mean the effect of an event in the past. Modelling this effect is challenging since events arrive at irregularly spaced time intervals. For the spatial part we use an abstract notion of ‘feature space’ to conceptualize distances among a set of item features. We motivate our model in the context of on-line auctions by modelling the effect of concurrent auctions on an auction's price. Our concurrency model consists of three components: a transaction-related component that accounts for auction design and bidding competition, a spatial component that takes into account similarity between item features and a temporal component that accounts for recently closed auctions. To construct each of these we borrow ideas from spatial and mixed model methodology. The power of this model is illustrated on a large and diverse set of laptop auctions on eBay.com. We show that our model results in superior predictive performance compared with a set of competitor models. The model also allows for new insight into the factors that drive price in on-line auctions and their relationship to bidding competition, auction design, product variety and temporal learning effects.

Temporary and Permanent Buyout Prices in Online Auctions

Buyout options allow bidders to instantly purchase at a specified price an item listed for sale through an online auction. A temporary buyout option disappears once a regular bid is submitted, whereas a permanent option remains available until it is exercised or the auction ends. Such buyout price may be static and remain constant throughout the auction, or dynamic and vary as the auction progresses. We formulate a game-theoretic model featuring time-sensitive bidders with independent private values and Poisson arrivals but endogenous bidding times to answer the following questions: How should a seller set the buyout price (if at all)? What are the implications of using a temporary buyout option relative to a permanent one? What is the potential benefit associated with using a dynamic buyout price? For all buyout option types we exhibit a Nash equilibrium in bidder strategies, argue that this equilibrium constitutes a plausible outcome prediction, and study the problem of maximizing the corresponding seller revenue. Our numerical experiments suggest that when any participant is time sensitive, the seller may significantly increase his utility by introducing a buyout option, but that dynamic buyout prices may not provide a substantial advantage over static ones. Furthermore, whereas permanent buyout options yield higher predicted revenue than temporary options, they also provide additional incentives for late bidding and may therefore not be always more desirable.

Modeling On-Line Art Auction Dynamics Using Functional Data Analysis

In this paper, we examine the price dynamics of on-line art auctions of modern Indian art using functional data analysis. The purpose here is not just to understand what determines the final prices of art objects, but also the price movement during the entire auction. We identify several factors, such as artist characteristics (established or emerging artist; prior sales history), art characteristics (size; painting medium--canvas or paper), competition characteristics (current number of bidders; current number of bids) and auction design characteristics (opening bid; position of the lot in the auction), that explain the dynamics of price movement in an on-line art auction. We find that the effects on price vary over the duration of the auction, with some of these effects being stronger at the beginning of the auction (such as the opening bid and historical prices realized). In some cases, the rate of change in prices (velocity) increases at the end of the auction (for canvas paintings and paintings by established artists). Our analysis suggests that the opening bid is positively related to on-line auction price levels of art at the beginning of the auction, but its effect declines toward the end of the auction. The order in which the lots appear in an art auction is negatively related to the current price level, with this relationship decreasing toward the end of the auction. This implies that lots that appear earlier have higher current prices during the early part of the auction, but that effect diminishes by the end of the auction. Established artists show a positive relationship with the price level at the beginning of the auction. Reputation or popularity of the artists and their investment potential as assessed by previous history of sales are positively related to the price levels at the beginning of the auction. The medium (canvas or paper) of the painting does not show any relationship with art auction price levels, but the size of the painting is negatively related to the current price during the early part of the auction. Important implications for auction design are drawn from the analysis.

Temporary and Permanent Buyout Prices in Online Auctions

Buyout options allow bidders to instantly purchase at a specified price an item listed for sale through an online auction. A temporary buyout option disappears once a regular bid is submitted, while a permanent option remains available until it is exercised or the auction ends; such buyout price may be static and remain constant throughout the auction, or dynamic and vary as the auction progresses. We formulate a game-theoretic model featuring time-sensitive bidders with independent private values and Poisson arrivals but endogenous bidding times to answer the following questions: How should a seller set the buyout price (if at all)? What are the implications of using a temporary buyout option relative to a permanent one? What is the potential benefit associated with using a dynamic buyout price? For all buyout option types we exhibit a Nash equilibrium in bidder strategies, argue that this equilibrium constitutes a plausible outcome prediction, and study the problem of maximizing the corresponding seller revenue. Our numerical experiments suggest that when any of the participants are time-sensitive, the seller may significantly increase his utility by introducing a buyout option, but that dynamic buyout prices may not provide a substantial advantage over static ones. Furthermore, while permanent buyout options yield higher predicted revenue than temporary options, they also provide additional incentives for late bidding and may therefore not be always more desirable.

The effects of shilling on final bid prices in online auctions

An increasing number of reports of online auction fraud are of growing concern to auction operators and participants. In this research, we discuss reserve price shilling, where a bidder shills in order to avoid paying auction house fees, rather than to drive up the price of the final bid. We examine the effect that premium bids have upon the final selling price, since they are linked with reserve price shill bids. We use 10,260 eBay auctions during April 2001, and identify 919 auctions involving 322 sellers and 1583 bidders involved in concurrent auctions for the exact same item. We find that premium bidding occurs 23% of the time, in 263 of the 919 auctions. Using a theoretical perspective involving valuation signals, we show that other bidders may view high bids as signals that an item is worth more. Thus, they may be willing to pay more for the items than others that do not receive premium bids. The implications are disturbing in that sellers may be more motivated to enter a shill bid in order to drive up the final price in an online auction. We also examine and report on alternative hypotheses involving winner’s curse and the possibility of reserve price shill bids. Our results are developed in the context of a weighted least squares regression model that predicts an item selling price-to- average selling price ratio.

Buy prices in online auctions: irrationality on the internet?

Buy prices are puzzling: it makes sense for a seller at auction to set a minimum bidding level (i.e., a reserve price), surely, but a maximum? We explore the use of maximum bidding levels (buy prices) in online auctions and provide a rational explanation for this seemingly irrational auction mechanism. We show that augmenting an English auction with a buy price can improve the seller’s profits by partially insuring (and therefore increasing the expected payment from) some risk-averse bidders. Perhaps more surprising is that the English auction augmented with a buy price can also be superior even to the first-price sealed-bid and Dutch auctions when bidders are risk averse.