The Probability of Sale and Price Premiums in Residential Property
Auctions: The Role of Undisclosed Reserves*
Simon Stevenson, University of Reading†
&
James Young, University of Auckland‡
Current Draft February 2012
*
The authors would like to thank Alison Stevenson for research assistance. The comments of
Geoff Meen and participants at the American Real Estate Society annual meeting and at
seminars at the University of Reading and the University of Auckland are much appreciated.
†
Corresponding Author: School of Real Estate & Planning, Henley Business School,
University of Reading, Whiteknights, Reading, RG6 6UD, UK. Tel: +44-118-378-4008,
e-mail: [email protected],
‡
Department of Property, University of Auckland, e-mail: [email protected]
1
The Probability of Sale and Price Premiums in Residential Property
Auctions: The Role of Undisclosed Reserves
1: Introduction
The last three decades have seen a large number of papers to have considered the
theoretical constructs of auctions. This literature has considered as wide range of
issues such as alternative auction methods; the preference of auction versus negotiated
sales in terms of expected revenue; risk aversion from the perspective of both sellers
and bidders; the probability of sale; the impact of the number of bidders; the nature of
the information in particular the issue of common and private information and the
importance of reserve prices. The empirical examination of these issues has often
been hampered by a lack of data, indeed it remains of the most common challenges in
the field. In particular information concerning undisclosed reserves is often hard to
obtain. Therefore, it has often been difficult to empirically consider some of the key
theoretical arguments regarding the impact and role of reserves. Riley & Samuelson
(1981) illustrate that the imposition of a reserve will maximize revenue, a result that
holds for either English or Dutch auctions. McAfee & Vincent (1992) argue that
whilst reserves should have a positive relationship with the sale price obtained but
that they will have a negative impact with the likelihood of sale. The rationale behind
this is that naturally, if the vendor imposes a higher minimum price then he is less
likely to witness a bid of that magnitude, hence a reduced probability of sale.
However, if that level is achieved, then it likely that the proceeds from the sale will be
enhanced. Riley & Samuelson (1981) also show that the optimal reserve will be
higher than the vendors own valuation and furthermore that it is independent of the
number of bidders. Finally, they note that under an assumption that all bidders share a
common cardinal utility function the optimal reserve price is a declining function of
the risk aversion of the vendor. This is a stance also argued by Hu et al. (2010), under
an assumption of symmetric and independent private values. It makes intuitive sense
in that more risk averse sellers will place greater attention on the risk of not selling.
Whilst such papers have illustrated the importance of the reserve price very little
empirical evidence has considered the issue in the context not only of real estate but
across the broader auction literature. In many cases reserve prices have not been
available (e.g. Dotzour, et al., 1998; Toda et al., 1998; Ong et al, 2005; Stevenson et
2
al., 2010). Some papers such as Ong et al. (2005) have used proxies in order to try and
capture the impact. In this case the authors use a measure they refer to as the level of
reserve price. This measure was defined as the percentage difference of an estimate of
the property’s market value relative to the opening bid1. Furthermore, many of those
papers that have been able to access reserve data have analyzed forced or distressed
sales. Whilst DeBoer et al. (1992) finds that an increased reserve reduces the
probability of sale; the data used consists of properties in Indianapolis sold due to the
nonpayment of tax. Tax sales in Indiana set the reserve, or minimum bid, at a level
equal to the tax owed. This obviously could be substantially less than the market
value of the property. The study of McAfee at al. (2002) encounters the same issue.
They estimate the optimal reserve price in the context of property auctions. Again,
these are distressed sales, in this case properties sold by the Federal Deposit Insurance
Corporation (FDIC). Across 6 FDIC auctions, including a mix of open outcry and
sealed bid, the sale price was on average 75% of appraised value. By estimating the
optimal reserve price they obtain an estimate of the lower bound also of 75%. Given
that at least in the majority of cases the winning bidder needed to have bid in excess
of the reserve, it illustrates the low levels of reserves used by the FDIC2.
Using a unique dataset, importantly including information on the undisclosed reserve
prices, this examines auctions for the residential property market in Dublin, Ireland.
An overall sample of 404 properties that were offered for sale through auction
between 1998 and 2002 is considered. The data includes not only those that were
successfully sold but also those that were sold privately prior to the auction date as
well as properties that were withdrawn at auction. Importantly, the data includes
information on variables often missing, including the undisclosed reserve, the
attendance at the auction, the number of bids and bidders and the individual
auctioneer presiding. A key advantage in this dataset is that firstly we do not have to
rely on proxies to capture the impact of different factors. Secondly, due to the nature
of the auction procedure followed in Ireland we are largely considering willing
sellers. Therefore, unlike papers such as DeBoer et al. (1992) and McAfee et al.
(2002) our reserve estimate is based on perceived market value and not related to
issues such as outstanding mortgage debt or unpaid tax.
As is common in many markets residential properties in Ireland may be sold either
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through private treaty or through auctions. In the case of properties sold through
auction the form adopted is the first-price English open-outcry method. The decision
as to the sale mechanism adopted is made by the vendor, in association with the agent,
prior to marketing. It should be noted that not only are the agents involved with
auction and private treaty sales the same firms, but that the agents act as the
auctioneer for properties sold through that method. In terms of the choice of sale
mechanism, Ireland in common with markets such as Australia (Lusht, 1996) and
New Zealand (Doutzour et al, 1998) generally sees auctions more prevalent at the
premium end of the market. This is unlike markets such as the United States (DeBoer
et al., 1992; Mayer, 1998 and McAfee at al. 2002) and Singapore (Ong et al., 2005,
Ong 2006) where a large proportion of auctioned properties are distressed sales. This
obviously leads to differences in terms of the motivation of the sellers concerned and
is of particular importance in terms of the reserve estimates, as noted previously.
Auction sales generally proceed through a similar process with a three to four week
marketing period prior to the auction. The agent will generally include in the
marketing material an estimate of the desired price, referred to as the guide or listing
price. A number of issues should be made with respect to the guide price. Firstly, it is
not a binding commitment for either the vendor or the agent as to the final sale price.
Secondly, it should be clearly noted that the listed price and the auction reserve are
not one and the same. The guide price is merely the publicly available estimate of the
value of the property as prepared by the agent/auction house. Effectively it is part of
the marketing process for the property. This was an issue that Stevenson et al. (2010)
examined. Their analysis, using a sample from the Dublin market, not only finds that
auctioned properties sell at a premium, but that the advertised guide price of
properties offered through auctions is significantly lower than for private treaty sales.
They hypothesize that this may be due to agents using the advertised guide as a
marketing tool and by discounting/underpricing the list price they are attempting to
encourage participation in the auction.
The auctioneer will agree with the vendor, generally on the day of the auction, a
reserve price that will be the minimum acceptable value which the seller will
consider. The reserve is not disclosed prior to the auction and as will be illustrated in
this paper, it is generally higher than the advertised guide price. This is unlike
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Australia, for example, where the reservation price is usually made public (Lusht,
1994, 1996). Furthermore, the reserve price is not necessarily at the level at which the
auctioneer opens the bidding. The level of reserve will in part be influenced by the
auctioneer’s assessment of the level of interest in the property during the marketing
period. A factor that aids in the gauging of potential interest arises from the fact that
successful bidders at auction are required to pay a non-refundable 10% deposit on the
day of the auction and sign contracts agreeing that the sale be closed within six weeks
of the auction date3. This means that it is imperative that potential purchasers
undertake all necessary checks and surveys, both structural and legal, prior to the
auction. It also means that financing arrangements need to be in place, particularly
with respect to the 10% deposit, at the date of the auction. However, this process
allows the auctioneer to gauge the potential interest in the property, both in terms of
the number of potential bidders, their seriousness and the possible price obtainable at
auction. As with most English outcry auctions, if the reserve price is not met, then the
vendor has the option of withdrawing the property from the market, with custom in
Ireland dictating that the right of first negotiation lies with the highest bidder.
The only point during the auction process when the reserve may be revealed is on
some occasions the auctioneer may declare that the property is ‘on-the-market’.
Whilst this is not a legal requirement, in the majority of cases in our sample the
auctioneer did declare that the property was ‘on-the-market’ at some point after
bidding had exceeded the reserve. However, it was not necessarily exactly at the
reserve price. In a number of cases bidding had risen substantially above the
undisclosed reserve prior to this declaration4. The remainder of the payout is set out as
follows. The following section briefly considers the existing empirical work to have
examined auctions in the context of housing markets. Section 3 provides information
on the dataset utilized and the methodological framework adopted. Sections 4 and 5
present the empirical results and provide concluding comments respectively.
2: Auction Literature and Housing Assets
Whilst there is a relatively large number of papers to have considered auctions in a
real estate context the majority have concentrated their focus upon the broad issue of
the preference of auctions over negotiated sales as a sales mechanism5. In part due to
5
the difficulty in obtaining sufficiently in-depth data, empirical work on other issues
has been relatively limited. With respect to those papers that have considered the
broad sale mechanism issue, the evidence presented has been very mixed, particularly
when considering the results across different markets. The contrast in results obtained
is of interest in the context of the broad auction literature. For example, papers such as
Wilson (1977) and Milgrom (1989) argue that for goods that are of high quality, are
not standardized and where the market clearing price is unstable, then auctions will be
preferred over negotiated sales. Furthermore, when goods are heterogeneous then
prices will display greater uncertainty as well (Milgrom, 1989 and Bulow &
Klemperer, 1996). In the case of English auctions with no reserve price Bulow &
Klemperer (1996) show that a simple competitive auction with one additional bidder
will result in high revenue in comparison to an optimally structured negotiated sale
that has one less bidder. They thus argue that the benefit of having additional bidders
is greater than the value of negotiating skill. Even in the case where the cost of
auctioning an asset may be high, auctions may be the preferred route. Wang (1993)
argues that if the marginal revenue curve associated with the valuation the bidder
attaches to the asset is sufficiently steep then auctions will be the optimal sale
mechanism6.
Despite this evidence, a large proportion of the empirical literature to have examined
housing markets have provided evidence that properties sold through auction sell at a
discount in comparison to private treaty sales. This is certainly true from the majority
of studies to have considered data from the United Sates. Papers such as Mayer (1995,
1998) and Allen & Swisher (2000) all report discounts. Mayer (1995) argues that as
vendors selling privately are not time constrained they can wait longer in order to find
a buyer more matched in terms of price. This is a similar argument to that presented in
Adams et al. (1992) albeit in the context of slow Dutch auctions, and one that Mayer
(1995) refers to as the Cost of Liquidity. In the context of auctions, there is a greater
risk of mismatch, hence auction participants do not bid up the price. Furthermore,
Mayer (1995) argues that properties that display less heterogeneity have a lower Cost
of Mismatch and should therefore sell for a lower discount. Likewise, for more
heterogeneous properties, they will be higher mismatch costs. Ong et al (2005) in
their empirical examination of auctions in Singapore provide results that support the
theoretical constructs of Mayer (1995). They find that apartments and condominiums,
6
which can be viewed as being more homogeneous in nature than detached or semidetached houses, have a highly probability of sale7.
In many markets however, the evidence is counter to that provided in the United
States. Studies such as Newell et al. (1993) and Lusht (1996) for Australia, Dotzour et
al. (1998) in the case of New Zealand and Stevenson et al. (2010) with Ireland have
all provided evidence that indicates the presence of an auction premium. Stevenson et
al. (2010) is of particular relevance given that it considers sales for the same market,
Dublin, and over a similar time-period, 1997 to 2004. Based on a sample of 2,657
sales a significantly positive coefficient, of 0.3157, is reported with respect to auction
sales. Whilst this does at first sight appear to contradict Mayer (1995) it is not
necessarily so. Mayer (1995) does acknowledge that his model assumes that a seller
cannot adjust the price in the face of two bidders willing to pay the guide price.
Therefore, in booming markets, where this may frequently occur, auctions may
provide an opportunity to maximize revenue further as bidders can raise prices in
excess of the guide price. In addition, the auction discount would be reduced in
circumstances where a greater number of bidders attended. This would effectively
reduce the cost of mismatch. The empirical evidence provided in Mayer (1998) does
support the arguments relating to market conditions. Whilst discounts ranging from
9% to 21% are reported for properties sold in Dallas during the housing crash of the
late eighties, a Los Angeles based sample from the mid-eighties see discounts range
from 0% to 9%, i.e. whilst discounts were still observed they were of a lower
magnitude during boom conditions.
Mayer (1998) notes the issue of market conditions may be a key reason as to the
discrepancy in the findings, in line with his rationale as detailed above. Rising
markets could also lead to buyers overbidding, resulting in a form of the ‘Winner’s
Curse’8. Indeed, for most of the studies that have reported auction premiums, the
sample used is frequently dominated by sales from rising, or least non-falling,
markets. Certainly in an Irish case the use of auctions has largely ceased in recent
years following the collapse of the market in 2007. Mayer also argues that issues such
as the media attention that the use of auctions receives may also influence findings
and encourage a short-term non-sustainable premium. Again, there can some
supporting evidence with regard to those papers that have provided evidence of an
7
auction premium. Stevenson et al. (2010) report that in their tests for selectivity bias
the time dummies are increasingly negative. They argue that this is due to a fall in the
popularity in the use of auctions later in the sample. In part this may have resulted
from the relatively weak market conditions in 2001, a fall in the sale rate at auction
and growing adverse media coverage in Ireland concerning the high premiums over
valuation being obtained9.
However, whilst it would appear that market conditions do play a role in the success
or otherwise of auctions, the differences observed across countries may be due to an
additional factor, namely the type of properties sold through auction. As previously
noted, in markets such as Australia, New Zealand and Ireland, those properties sold
through auction are generally those at the higher end of the market. Certainly during
the sample period considered in this paper auctions were the sale method of choice by
willing vendors of higher value properties in Dublin. The extent of this can be seen in
Stevenson et al. (2010) where over 60% of the properties in the highest value decile of
their sample were sold through auction. Indeed, based on their sample it can also be
seen that 43.66% of auctions were in the highest decile and 78.95% in the top three
deciles by price. Dotzour et al. (1998) note that significant premiums were only
observed in those submarkets with above average house prices, while the areas in
which no discernable differences were observed are those with below average house
prices. A similar finding was also reported in Stevenson et al. (2010). Using a
relatively crude method of simply dividing the sample into two based on a cut-off
point €500,000 the results reveal that the auction premium is only present in the
higher half.
As previously observed papers such as Wilson (1977) and Milgrom (1986) argue that
auctions will be preferred in the case of high quality goods and those that are less
standardized. Higher value properties would be a typical case of such a class of assets.
This is to some degree counter to the argument of Mayer (1995) in that the auction
discount will be less in properties that display greater homogeneity. One possible
reason behind this disparity is the issue of scarcity. French & McCormick (1984)
argue that a vendor will opt for a negotiated sale when they can either identity the
highest potential bidder in advance or when they is reduced dispersion in the value,
i.e. greater homogeneity. In contrast, where greater heterogeneity is observed then
8
auctions may be preferred. Dotzour et al. (1998) illustrate this argument directly in the
context of housing. As noted above, they found that auction premiums were not
observed in submarkets with below average prices. They use French & McCormick’s
(1984) rationale to explain this. In these relatively cheaper areas there will be
increased market activity in terms of the number of possible properties on the market,
therefore a greater availability of comparable sales information. This has two
implications. Firstly, that there should be a higher level of certainty regarding the true
value of the property. Secondly, the increased supply of close substitutes means that
bidders have greater choice. In contrast, higher value and more heterogeneous
properties effectively contain a higher degree of private value relating to the specific
characteristics of the property. Effectively, a scarcity issue comes into play10. This
could lead to more determined bidding and thus the greater efficiency of auctions. A
further factor that may also play a role at the higher end of the market relates to
financing constraints. It may be the case that bidders at the lower end of the market
are subject to greater constraints in this regard and therefore an auction premium is
not observed.
In markets such as the United States and also Singapore where a large proportion of
auctions involve distressed sales the situation may be quite different. As Ong et al.
(2005) note the sellers of distressed properties may be more willing to sell quickly and
at a discounted price in order to recoup some of their investment. In contrast willing
sellers will be less willing to either sell quickly or at a discount. If an auction market,
such as Ireland, is dominated by willing sellers then this does make a distinct
difference based on the nature of the sellers concerned and a possible reduced
likelihood of an auction discount being observed, particularly during strong housing
market conditions.
The influence of market conditions may also have an impact in another respect. This
is concerned with the nature of the bids themselves and the information revealed
during the auction process. Lusht (1996) notes the importance of the actual auction
process when bids are not independent but rather affiliated. In a broader context
Goeree & Offerman (2002, 2003) highlight that in practice few auctions can be neatly
categorized as display solely either private or common values11. In the context of
residential property it is clearly the case that bidder’s will take into account both
9
common information (e.g. potential price appreciation) and private information (e.g.
location and architectural preferences). The point that Lusht (1996) makes is that the
actual auction process reveals additional information that would be otherwise
unobserved in the case of pure independent bids. It has the effect that a bidder’s
private value is influenced by the information revealed at the auction in terms of the
behavior of other bidders. It may therefore lead to a bidder upwardly revising their
valuation. The combination of this use of other bidder’s revealed valuations, together
with the price rule ensures that there is a preference in terms of revenue over sealed
bid auctions (Levin et al., 1996). Milgrom & Weber (1982) argue that English open
outcry auctions will be revenue maximizing if bids are affiliated rather than
independent in comparison to sealed bids as a high valuation assigned by one bidder
makes higher valuations by other bidders more likely12. Goeree & Offerman (2002,
2003) note that the combination of common and private values can lead to
inefficiencies. One of the examples that they note is whereby a party with a lower
private value then another bidder may still end up purchasing the asset/good in the
case where they have placed a higher valuation on the common valuation. Whether
this higher assessment of the common value is realistic or not is irrelevant. They
argue that auction efficiency will increase, and therefore the vendor’s revenue, with
increased certainty concerning common values. The argument is based upon the
premise that uncertainty regarding the common value can, in the limit, override
bidder’s private information. At the opposite end of the spectrum, if there was no
uncertainty regarding the common value, then the auction reduces to a pure private
value auction13.
3: Data
The data analyzed in this paper consists of a total of 404 residential properties that
were offered for sale through auction. The properties were all located in the Greater
Dublin metropolitan area of Ireland and the auction date for each property was
between 1998 and 2002. The data was obtained from one of the largest
auctioneers/estate agents in Dublin and includes all information contained in their
auction book14. The sample includes properties that were successfully sold at auction
and properties that were withdrawn. The withdrawn sample effectively includes three
types. The first is those properties initially marketed as being offered at auction but
10
that were sold prior. The second sub-sample consists of those properties that were
withdrawn at auction and subsequently sold through private treaty negotiations. The
final set contains properties that did not successfully sell after being withdrawn at
auction. It should be mentioned that none of the withdrawn properties were reauctioned; rather all of them were offered for sale through private treaty. Where the
guide price for the property was altered following the auction that is also noted. This
information not only therefore allows us to analyze behavior at the auction itself but
also to examine the withdrawn properties in a similar fashion to papers such as Ong
(2006).
For 240 of the properties we also have the undisclosed reserve price. For those
properties for which this information was not available it is either a case that the
property was sold prior to auction, and therefore the reserve not set, or that simply the
information was not recorded in the auction book. In addition to price data we also
have information on the number of bids, number of bidders, the attendance at the
auction, whether multiple auctions were held that day, the auctioneer who conducted
the auction and the location of the final bidder in the auction room15.
Prior to discussing the characteristics of the sample it is important to note two issues.
Firstly, as noted in the introduction, the majority of properties sold through auction in
Ireland are at the higher end of the market. The second issue that requires noting is the
sample period considered. The 1998 through 2002 period was one characterized by
rapid upward movement of house prices. Previous papers, such as Mayer (1995), have
illustrated the importance of controlling for market conditions. In the context of the
current paper, whilst we do include time dummies in the specifications, there is less
need to control for distinct market conditions. However, the results are indicative of a
strong upward market and may not necessarily apply in the context of weaker market
conditions. In addition, the broader comments of Mayer (1995) with respect to the
optimality of auctions versus negotiated sales in boom markets needs to be taken into
account.
With regard to property specific variables we have access to the number of bedrooms,
number of bathrooms and whether the property had parking facilities. In the absence
of reliable square footage data, the bedroom and bathroom variables have to act as a
11
proxy for the size of the property. In all of the tests conducted we control for the time
of sale and the location and type of the property. Given the small sample it was not
feasible to use time dummies at a greater frequency than annual. For location the
properties are divided into the Central City, South City, North City, South County and
Periphery of Dublin. The Central City is defined by the postcodes (zip codes) D1, D2,
D7 and D8. The remaining areas within the City of Dublin are divided based on the
River Liffey into North and South. The use of even numbered postcodes south of the
river and odd numbered ones north of the river makes this divide very easy. South
County Dublin contains all areas within the County of Dublin south of the River
Liffey but not formally within the City of Dublin. The peripheral grouping contains
all remaining areas in the County of Dublin and also those properties in neighboring
countries that can viewed as part of the greater metropolitan area, including parts of
Wicklow, Kildare, Meath and Louth. Property type variables are included for the
following types: detached, semi-detached house, bungalow (ranch), terrace/mews and
apartment. The location dummy excluded is the north city, while bungalows are the
excluded property type. For the time dummies 2000 was the excluded year16.
The number of bidders has been a key component in the auction literature, especially
those papers which have considered the likelihood of a successful auction sale. Whilst
the role of the number of bidders in obtaining higher prices at auction is shown in
papers such as Vickrey (1961), Holt (1979), Harris & Raviv (1981) and McAfee &
McMillan (1987), empirical evidence is more thinly spread. Non-property papers such
Saidi & Marsden (1992) and Chen et al. (2003), together with those to have directly
considered real estate such Ching & Fu (2003), Ong et al. (2005) and Ooi et al. (2006)
have all found some form of evidence that the number of bidders significantly
impacts, in a positive sense, upon either the price obtained or the probability of a sale
being achieved. Ong (2006) finds that the number of bidders plays a significant role in
the price obtained even in the case of properties that were initially withdrawn at
auction and subsequently sold privately. Whilst not directly examining the number of
bidders, the role of turnout and the need to encourage as many participants as possible
does play a role in the findings of Stevenson et al. (2010). As previously noted they
report results that imply that not only do auctioned properties sell at a premium, but
that the listing price for auctioned properties is significantly lower than for properties
sold privately. They argue that this may be attributed to agents using the advertised
12
guide as a marketing tool. By effectively underpricing the properties they aim to
encourage a greater number of bidders to participate17. Although in a different
context, Gilbert & Klemperer (2000) illustrate that it may be more profitable for a
seller to ration output, thus selling at a fixed price at a level at which demand exceeds
supply, rather than selling at a higher price that clears the market. The underlying
rationale here relates to the idea that the offering of lower price acting as an incentive
for more buyers to enter the market. In the context of our paper this does have a direct
relevance; furthermore, it can be tied to the arguments of Glower et al. (1998) who
note that vendors convey information about their desire to sell the property through
the listing price that is set. The role of bidders can in some respects also be tied to the
issue of market conditions, as it is likely that auctions undertaken during stronger
conditions will see a higher number of potential bidders. Goeree & Offerman (2002,
2003) explicitly argue that efficiency and therefore the revenue generated will be
higher when more bidders enter the auction18. Mayer (1995) argues that an increase in
bidders also increases the likelihood of the participation of high-value bidders. In an
auction context, this could be viewed as those with higher private values of the
property concerned. The recent work of Chow et al. (2011) also notes this. They argue
that auctions will be the preferred sale mechanism in a case where bidders with higher
valuations participate19.
One of the key features of the current study is however the examination of how the
reserve price impacts upon both the price obtained and the probability of sale at
auction. As with the issue of the number of bids, auction theory has highlighted the
importance of a reserve, in particular that whilst reserves should lead to an increase in
the revenue obtained, a higher reserve will also reduce the probability of a successful
sale20. An additional issue in this regard is whether the reserve is disclosed or not.
Vincent (1995) argues that in a common-value auction the non-disclosure of the
reserve can increase the price obtained. The rationale in this respect is that disclosing
the reserve may lead to non-bidding as potential participants may be discouraged if
the reserve was either above or even at their potential maximum bid. The argument in
favor of non-disclosure is based on the premise of encouraging a greater number of
bidders. As Vincent (1995) argues, if they did not participate then the information that
their bids would have revealed is lost. It is effectively the same arguments relating to
the impact of an increased number of bidders21.
13
The setting of the reverse price has parallels in the general housing literature in terms
of listing prices. A number of papers have provided evidence that the setting of a
guide price, in a private treaty context, has similar effects as with the reserve in an
auction setting. Papers such as Haurin (1988), Mayer (1995) and Genesove & Mayer
(1997) argue that there exists a trade-off resulting from the setting of the guide
price22. A higher guide will, on the one hand increase possible revenue but it will
reduce the speed of arrival of bids and therefore generally lead to an increase in the
amount of time the property is on-the-market23. Merlo & Ortalo-Magne (2004) also
illustrate that the setting of the listing price affects the arrival of bids and thus the
time-on-market. The longer the time-on-the-market then there is a fall in the ‘arrival
rate’ of bids. It also has the impact that the probability that the listing price is adjusted
and that there is a negative effect on both the amount of offers and the sale price
relative to the listing price. However, it does, increase the probability that a match is
successful24.
In a broader sense there is a major point of differentiation between list prices for
private sales and either list or reserve prices in an auction setting. As papers such as
Haurin (1988), Horowitz (1992) and Yavas & Yang (1995) argue, private treaty guide
prices indicate what the reservation price of the seller is and more importantly they
indicate an upper bound to that reservation price. Unless a vendor is in a situation
where competing bidders are present at the same point in time the listing price
effectively precludes the possibility of selling the property at a higher price. Indeed, it
would be relatively rare that the list price is exceeded in a negotiated sale. However,
not only will the sale price in a successful auction generally exceed the guide, but the
reserve can be exceeded. This impact may be enhanced in the context of a sample
such as ours where the reserve is undisclosed. The listing and reserve prices, in the
context of our sample, therefore no longer act as an upper bound.
Tables 1 through 2 provide summary statistics for the sample. Table 1 contains details
of the number of observations. Of the total of 404 properties, 198 successfully sold at
auction, 15 were sold prior and the remaining 191 were withdrawn at auction. Of the
withdrawn properties we have details for 77 of them of their subsequent sale. It can
also be seen that the spread of the sample across property type is relatively even, with
14
only the apartment sector seeing a small number of properties sold through auction,
only 25. The location of properties is dominated by two key submarkets, namely
South City and South County Dublin. These are the two most highly priced
submarkets in Greater Dublin and confirm the comment made earlier that auctions
were used primarily at the premium end of the market, particularly during the period
covered in this sample. The dummy variables, for which the count is reported in Table
1, with respect to the location within the room and the auctioneer, are defined as
follows. We have information relating to the location in the auction room of the
winning. This is defined quite broadly as being at the front, center or rear of the room.
We will include these variables in a number of the models but it is interest that the
number of successful bids increases as you move backwards in the room. For the
auctioneer variable we have details on the individual person who conducted the
auction. Three auctioneers conducted the majority of the proceedings and presided
over far more of the auctions than others. These three are denoted as 1, 2 and 3
respectively. The variable Auctioneer 4 combines all remaining auctioneers.
Table 2 reports the main summary statistics from the data, whilst Table 3 details the
average of the percentage differences across the different prices for the individual
properties25. From the two tables it can be seen that not only was the average sale
price achieved above the reserve, but that the reserve itself was on average higher
than the guide price26. For those properties sold at auction for which complete
information is available as shown in Panel C, the average guide price was
€393,493.33. In contrast the average undisclosed reserve was €394,943.33 and the
average sale price was €450,326.67. This highlights a number of issues that will be
addressed in a more robust manner shortly. Firstly, that as with the sample used by
Stevenson et al. (2010) it would appear that vendors and auction houses discounted
the listing prices, possibly in order to attract additional participants to the auction.
Secondly, the fact that sale prices are on average higher than the reserve would
indicate that the auction process does add an element of value to the sale process.
These effects can also be seen from the statistics displayed in Table 3 which reports
the percentage differences across the guide, opening bid, reserve and sale prices. Here
it can be clearly seen that on average the reserve was 15.15% above the guide price.
Furthermore, in only four out of 240 cases was the reserve set below the guide.
15
Indeed, the high proportion found here is repeated elsewhere. On average the
achieved sale price was 14.18% above the reserve, with only six out of 150
observations being negative, and 31.97% above the listing price, with only three
negative observations. The fact that not only is the average sale price higher than the
reserve but that a positive result is achieved in virtually all of the cases highlights the
key difference between auction and private treaty sales noted earlier. Whilst the
setting of the reserve higher than the guide may initially appear to be in line with the
argument of Riley & Samuelson (1981) in that the optimal reserve should be higher
than the vendors own valuation, it is important to remember that the guide is not
necessarily a pure estimate of valuation. It is also of interest to consider the behavior
of the auctioneers by looking at the opening bids. The auctioneer can open the bidding
at any price and is not constrained by either the advertised listing price or the
undisclosed reserve. Table 3 shows that on average auctioneers open the bidding
6.59% below the advertised guide price, a figure equivalent to the reserve being
24.48% higher. Indeed, this trend was followed with over 70% of the observations
seeing the opening bid below the guide. In only four out of 199 observations was the
bidding opened above the reserve. The overall finding was that the average
percentage price movement from the opening bid to the sale price was in excess of
40%.
4: Empirical Analysis
4.1: Hedonic Models of Sale and Guide Prices
The broad methodological framework adopted is similar to that adopted in many
previous papers, such as Lusht (1996), Mayer (1998), Ong et al. (2005) and Stevenson
et al. (2010). Initially we consider the characteristics of the sample, specifically
examining whether the final sale mechanism impacted upon the price achieved. The
model used was a standard log hedonic model as displayed in Equation (1).
ln( sale price) i    X   i
(1)
X represents the hedonic characteristics of the property. We include dummy variables
for whether the property was successfully sold at auction or whether it was sold prior.
16
The missing dummy in this case referred to withdrawn properties. The base model
then included the number of bathrooms, bedrooms and a dummy indicating whether
the property had parking facilities. Groups of dummies were included to capture the
impact of property type, location and the year of sale. The base property was one an
apartment, located in North City Dublin that sold in 2000.
The results, reported in Table 4 do report a positive coefficient for properties selling
at auction; however it is not statistically significant at conventional levels. The
interpretation of such a finding does have to be carefully considered, particularly in
light of the findings of Stevenson et al. (2010) who found evidence of an auction
premium. A key element that differentiates the two samples is Stevenson et al. (2010)
examined a sample containing both auction and private treaty sales. In contrast, our
sample is limited to only those that were at least initially put up for sale via auction.
Therefore, it needs to be considered that when considering the sold at auction dummy
it is relative to the sale price achieved by properties withdrawn and subsequently sold.
This is an issue that we will return to, in that it may be that although the withdrawn
properties did not sell at auction, their final realized price was influenced by the
auction process, hence the lack of a significant coefficient in this regard. The
remaining coefficients are generally in line with expectations. Significant positive
coefficients are reported with respect to the number of bedrooms and bathrooms,
detached properties and those located in South City and South County Dublin. In
terms of the year dummies they reflect the strong upward movement in the market
during the sample period with a significant negative coefficient for 1998 and positive
findings for 2001 and 2002.
Panel B of Table 4 repeats the hedonic model but substitutes the log of the listing
(guide) price in the place of the realized sale price. The results for the majority of the
variables are as reported when sale prices were considered. It is however with respect
to the auction dummy that an interesting finding is observed. The properties that were
successfully sold at auction appear to have been advertised at a significantly lower
guide than those that did not sell and were withdrawn. Again, the comparison with
Stevenson et al. (2010) is not a simple confirmation as our sample is constrained to
solely those properties that were advertised as being sold through auction. In this case
it may be that the guide price plays a more subtle role. The result can be interpreted
17
that properties whose relative guide price is lower are more likely to sell. Therefore,
in that sense the results support the view of Stevenson et al. (2010) in that the guide is
being used as a marketing tool in order to encourage participation in the auction and
hence increase the likelihood of a successful sale. We will return to this issue shortly
to consider more explicitly whether the guide plays a role in the probability of a sale
at auction.
Table 5 reports the results from modifications of Equation (1). By excluding those
properties that were sold prior to the auction we can include additional variables that
specifically refer to the auction process. We therefore add to the variables the
attendance at the auction; the number of bids; number of bidders; a dummy variable
indicating whether multiple properties were put up for sale at the auction and
dummies for Auctioneers 1 through 3. Auctioneer 4, which as noted earlier represents
all other persons who presided over the auction, was the excluded dummy. Finally we
also consider the ordering of the auction and the whether the previous auction had
been successful. For the ordering we quantify this by simply counting the order of the
property in that day’s auction. For the previous auction we include a dummy variable
that equals unity if the previous property on offer that day sold and zero otherwise.
The models are estimated firstly using a combination of those properties that sold and
those that were withdrawn and secondly solely looking at those that were successful.
In comparison with the results in Table 4 in the majority of cases similar results are
reported, particularly with respect to bathrooms, bedrooms, detached properties and
the location and time dummies. The coefficient relating to whether a property sold at
auction is again insignificant.
There are however significant findings in both specifications relating to Auctioneer 1.
In both cases a significant positive coefficient is reported. This would appear to
initially imply that Auctioneer 1 displays enhanced skill in obtaining a higher sale
price. However, the interpretation of this finding does require careful consideration. It
is possible that mis-specification leads to this implication when the reality may be that
the auctioneer in question tended to preside over those auctions involving the most
highly valued properties27. If one considers the raw data it can be seen that the
average guide and sale prices for those properties sold by the first auctioneer are
€468,837 and €504,897 respectively. In comparison the corresponding guide prices
18
for auctioneers 2 and 3 are €294,913 and €498,200, whilst the figures in terms of sale
prices are €345,661 and €519,500. Therefore, Auctioneer 3 on average also presides
over high value properties. In order to more closely look at this we re-estimate the
hedonic models for the auctioned properties using both the log of the guide and
reserve prices as the dependent variables. In each specification a similar finding is
found to that reported initially. The coefficient for Auctioneer 1 is both positive and
significant. In the case of the Guide Price the coefficient is 0.1411, with a t-statistic of
1.8950, whilst the corresponding figures with the Reserve Price are 0.2288 and
2.2706 respectively28. These therefore would provide a modicum of support for our
initial suspicions. If auctioneering skill was the sole reason behind the initial result we
would not have observed similar results with respect to the guide or reserve.
4.2: The Probability of Sale
To more explicitly consider those features and characteristics of the process on the
success of the auction we estimate probit models, similar in form to those utilized in
papers such as Ong et al. (2005). The dependent variable is a binary variable taking
the value of unity if the property was sold and zero otherwise. The independent
variables include the property specific variables incorporated in the hedonic model
previously discussed. Whilst the importance of these would obviously impact upon
both the listing and sale prices, as already illustrated, papers such as DeBoer et al.
(1992) and Ong et al. (2005) show that factors such as location can also impact upon
the probability of sale. The auction variables are primarily as used in the
specifications reported in Table 5. We include variables that relate to the attendance at
the auction, the number of bidders, the number of bids and whether multiple auctions
were held that day. These variables obviously relate to the large literature discussed
previously that has illustrated the importance of bidders in both obtaining higher
prices at auction and leading to an increased probability of sale29. Ong et al. (2005)
explicitly highlights the role of the number of the bidders. Their variable turnout is
positive and statistically significant at conventional levels in their probit model of
Singaporean residential property auctions. Our more detailed data allows us to include
not only overall attendance but also the specific number of both bids and bidders.
In addition to the above we include dummy variables relating to the auctioneer who
19
presided over the sale, a dummy variable as to whether the previous auction that day
had resulted in a sale and the ordering of the auction in the day. We also have
information as to the location in the room of the winning bidder. We group the
winning bidders into front, center and rear, excluding the front dummy from the
estimated model. The results from the model are reported in Table 6. Model I refers to
the base specification. Models II, III and IV are augmented specifications that in
addition to the core variables add the following. Model II includes the Guide Price.
The rationale here is that a lower guide may lead to an increased probability of sale,
an argument related to that used by Stevenson et al. (2010) with respect to agents
using the listing price as a marketing tool. In addition, it allows us to further consider
the result reported in Table 5 in that properties that successfully sold at auction had a
significantly lower guide price. Model III includes the Reserve Price, where available,
and also the percentage premium of the reserve over the guide. This obviously relates
to the large literature concerning the role of the reserve in both having a positive
impact upon revenue but likewise having a negative impact upon the likelihood of
sale (Riley & Samuelson, 1981; McAfee & Vincent, 1992; Hu et al., 2010)30. The
final specification, Model IV, includes the Opening Bid and the premium of the
reserve over it.
The results, reported in Table 6 provide empirical evidence to support a number of the
theoretical arguments thus far presented. As would be anticipated the majority of the
property specific variables are not significant. This makes intuitive sense as the
primary area where they come into play is in terms of the price. However, as with the
Ong et al. (2005) study we do find significant coefficients with respect to some of the
location variables. It is shown in three of the four specifications that properties in the
South City display a significantly greater probability of sale. Furthermore, properties
in South County Dublin do likewise in Model IV. This illustrates that whilst the
location obviously plays an important factor in the price of the property it also
influences the success of the auction. Interestingly the South City and South County
of Dublin are the prime residential areas. In relation to the variables concerned with
bids no specification fails to find significant findings. The Number of Bids and the
Number of Bidders both significantly increase the probability of sale in the first three
models, whilst the number of bids is also significant in Model IV. This therefore
provides supporting evidence for the theoretical arguments relating to the importance
20
of bidders. Whilst Auction Attendance isn’t itself significant, the dummy variable
relating to whether multiple sales took place is in all but Model IV. This would imply
that attendance does perhaps play a role, although as noted previously the properties
auctioned on the same day frequently displayed characteristics that were quite
divergent. Of interest is that the dummy variable concerned with the winning bidder
being in the center of the room was significant in two of the four models. In both
Models II and III, this increased the probability of sale to a statistically significant
extent31.
The augmented specifications all provide an element of significant findings. In Model
II the Guide Price is negative and significant at a 99% level. We had previously found
that properties that sold at auction had a significantly lower guide price and these
findings provide an additional level of support in terms of the role of the guide price
in the auction process. The Guide Price is found to be significantly negative, implying
that an increase in the listing price significantly reduces the probability of sale. It
could be argued that it plays an important role, as implied by Stevenson et al. (2010),
in the marketing of the property and the encouragement of participation in the auction.
The general housing literature does find a parallel finding in that an increase in the
listing price reduces the speed at which bids arrives and increases the time-on-the
market.
Model III incorporates the Reserve Price into the specification. This is one of the key
elements of this paper given the rarity at which information pertaining to undisclosed
reserves is available for empirical testing. We find that the reserve also has a
significant negative coefficient with respect to the probability of sale. This provides
empirical support for the theoretical arguments made in papers such as Riley &
Samuelson (1981), McAfee & Vincent (1992) and Hu et al. (2010). It also illustrates
the importance of having available the actual reserve prices. Papers such as Ong et al.
(2005) were forced to use a proxy for the reserve; however it did not result in
significant findings.
The final variables are the Reserve-to-Guide premium in Model III and the Opening
Bid and Reserve-to-Opening Bid premium in Model IV. Whilst the percentage
premium of the Reserve to Guide is negative it is not so at significant levels.
21
Likewise, the Opening Bid is not statistically significant. It may be, in this case, that
bidders are very aware that the opening bid provides limited information. As
previously reported on average the opening bid was 6.59% lower than the advertised
guide. Therefore, bidders may simply accept that the auctioneer will start the bidder at
a low level in order to encourage initial bids. However, whilst the Opening Bid itself
was not significant, the premium of the reserve over it was. A significant negative
coefficient was found in this case. This does make intuitive sense as this measure
captures the increase in the bidding necessary for the property to be sold.
4.3: Analysis of Sale Premiums
The third element of the empirical analysis considers an analysis of the sale
premiums. Percentage changes of the sale price relative to the opening bid, guide
price and undisclosed reserve are analyzed in turn. The models are specified in a
similar manner to those previously discussed and the results are reported in Table 7.
In terms of the headline findings one of the key elements is that across all three
models the role of the bidders yet again comes into play. For each specification both
the Number of Bids and the Number of Bidders is positive and significant. This again
supports the existing literature in terms of the importance of encouraging
participation. The results here would indicate the increased participation results in a
significantly higher premium, irrespective of whether the base figure is the opening
bid, guide price or reserve. In addition, when considering the Sale-Reserve premium
overall Auction Attendance is also positive and significant.
Given the nature of the dependent variable in these cases the interpretation of the
findings has to be done so carefully, as the results may not necessarily imply a higher
sale price obtained. The findings with respect to auctioneer dummy variables are a
case in point in this regard. In the analysis of the Sale-to-Opening Bid, Auctioneer 2
and Auctioneer 3 are both negative and significant. Furthermore, Auctioneer 3 reports
a significant positive coefficient when the Sale-to-Guide Price is modeled as does the
Auctioneer 2 variables in the Sale-to-Reserve specification. With regard to the
premium over the opening bid, these findings may simply imply that the second and
third auctioneers tended to open the bidding at a higher price. It does not necessarily
imply that they sold the properties for lower prices. Likewise, given that the
22
auctioneer will be involved throughout the sale process and therefore have a role in
the setting of the guide price a similar interpretation could be lent to the second
model. In this case a significant positive result was found with respect to Auctioneer
3. This could imply that the third auctioneer tended towards a lower guide price,
perhaps due to the previously evidenced role that the guide can play. Furthermore,
this viewpoint would be consistent with a counter interpretation in the case of the
third model. As the reserve price will be set by the auctioneer in conjunction with the
vendor and will be the undisclosed minimum acceptable price to the seller, there is
less room for this variable to be affected by the strategies adopted by the auctioneer.
Therefore, the significant positive coefficient reported in relation to Auctioneer 2
when the Sale-to-Reserve premium is modeled could be interpreted as an indication of
the skill of the auctioneer in question in achieving a higher sale price specification.
These findings do have to be considered though in light of those reported earlier
where significant positive coefficients were reported with respect to Auctioneer 1 for
sale, guide and reserve prices32.
A number of other variables provide significant findings in the analysis of the sale
premiums. It is of interest that in a number of cases property specific variables report
significant coefficients. This is true of the Bedrooms (positive) and Semi-Detached
(negative) variables in the modeling of the Sale-to-Opening Bid and the
Terrace/Mews (negative), Detached (negative), South City (positive) when the Saleto-Guide premium is examined. As with the analysis of the results for the auctioneers
these findings do require carful interpretation. In some cases it may be that the auction
process leads to a premium that can be attributed to certain characteristics. This may
be true say for example, in terms of the positive coefficient for the South City. As
seen from three of the probit model specifications this variable did have a significant
positive effect on the likelihood of sale. However, it is interesting that no such
property specific variables are significant in the case of the premium over the reserve
price. As the reserve price is the minimum acceptable price on the part of the seller it
may be that it is a more accurate measure of the anticipated price than either the
opening bid or guide. In contrast, to varying degrees and in slightly different contexts,
both the opening bid and guide prices are part of the auction process and may be
adapted by the agent/auctioneer in order to encourage participation and bidding.
Therefore, information other than that which reflects the ‘true’ estimated value of the
23
property may be incorporated into them.
A number of the year dummies are also significant in the various specifications.
Specifically 1999 is significantly positive when looking at the premium over the guide
price, whilst a similar result is reported for 2002 when the reserve price is considered.
Again these findings may reveal more about the base price rather than the achieved
sale price. It may be that guide prices were relatively undercooked in 1999, as were
reserves in 2002. This explanation would make some degree of intuitive sense given
the market conditions in Ireland in this period. The fast rate of house price
appreciation in the late nineties may have meant that despite the short time horizon
under consideration here, the market was moving so fast in 1999 to give the
impression of an exaggerated Sale-to-Guide premium. Likewise, the brief respite in
the market that occurred in 2001 may have resulted in agents and sellers being more
cautious in their assessment of achievable prices in 2002.
4.4: Withdrawn Properties
The final section of the empirical analysis considers the influence of the auction
process on the prices achieved subsequently for properties that were withdrawn. Very
few papers have considered this, the two primary exceptions in a real estate context
being Ashenfelter and Genesove (1992) and Ong (2006). Ashenfelter and Genesove
(1992) found evidence that those properties that were not sold at auction and
subsequently sold privately achieved a premium in their price. The Ong (2006) paper
extended upon this to find that issues such as the attendance at the auction and
whether no bids were submitted at auction have a positive and negative impact
respectively on the probability of subsequent sale33. Our withdrawn sample includes a
total of 191 properties. Of these 77 were subsequently sold, 114 remaining unsold. As
with the overall sample property specific characteristic data is available, as are the
details from the auction itself. The analysis effectively replicates the probit model on
the probability of sale and the estimations using the sale price premiums previously
used and reported above. The models used are of a similar form; however they are
estimated solely using the withdrawn sample. It is therefore necessary to exclude
some of the variables. For example, without a ‘winning bid’ the location within the
room variables cannot be included. It was also decided, given that the specific
24
auctioneer will not necessarily be involved in the subsequent private marketing, to
exclude the dummy variables relating to them. We do however add in an additional
variable which takes the value of unity if no bids were made at auction and zero
otherwise.
Table 8 reports the findings from the Binary Probit model. These models take three
slightly alternative specifications. In addition to the core property and auction
variables they also include in turn the New Guide-to-Old Guide premium, the New
Guide-to-Reserve premium and the Reserve Price. None of the variables relating to
attendance at auction and the number of bids/bidders are significant in this case. This
is in contrast to Ong (2006). However, Ong’s sample of Singaporean properties
primarily consisted of apartments and contained a large number of distressed sales.
This more homogeneous sample may explain the difference in the findings reported.
However, the No Bids dummy is significant in two of the models, and is of the
anticipated sign, i.e. negative. Therefore, if no bids were achieved at auction it
reduced the likelihood of a subsequent negotiated sale. The other coefficients that
report significant results from the probit are those relating to the year dummies. The
dummy for 1998 is significant in each specification, as are those for 1999 and 2001 is
Model I. This is probably largely due to the strong market conditions during this
period.
The results also show that the Reserve Price has a significant negative impact in the
third specification. This may be reflecting an over-valuation of the property that
despite the failure to sell at auction continues into the subsequent private negotiations.
However, this result does need to be viewed also in the context of that found for the
New Guide-to-Old Guide premium. In the second model a significant positive
coefficient is found for this variable. This would imply that if the guide price was
revised upwards following the auction it significantly increased the probability of a
subsequent sale. Whilst this may appear counter intuitive two issues need to be made
clear. Firstly, any upward movement in the guide following the auction may reflect an
indication of demand for the property, even if this demand may not have resulted in a
successful sale. Secondly, it needs to be remembered that the guide price was
originally set with an eye towards an auction sale and the aim of encouraging
participation in the auction. Therefore, an increase in the guide may be a reflection of
25
a more realistic assessment of the value of the property. This can be seen be looking
at the average figures from the raw data. From our withdrawn sample the average
initial guide price is €487,584 whilst the average revised, post auction, guide
increases to €509,410. Out of 190 observations, 86 properties see an upwards
adjustment, only 4 see a reduction and the remainder see no change. If you constrain
the sample to only consider those properties that the reserve prices are available
situation becomes even clearer. In this case out of 88 properties the average initial
guide is €423,864, rising to €453,205 post auction. Again in the majority of cases the
guide was adjusted upwards, in this case in 52 instances, with only one example of a
downward adjustment. However, the average reserve price of the 88 properties was
€478,807. Therefore, whilst a property may have seen its guide increased, it was still
on average less than the reserve. Indeed, the average percentage difference between
the revised guide and the reserve is -4.31%. Hence, whilst the guide has been adjusted
upwards this is perhaps reflective of an admission that the initial guide was not a true
indication of the underlying value. The fact that on average the revised guide is less
than the reserve does show the impact of a failure to sell and the use of a perhaps
more realistic assessment for subsequent negotiations.
Table 9 presents the final set of empirical findings which relate the analysis of the sale
premiums achieved in the case of the withdrawn properties. As with the properties
sold at auction the dependent variable is the percentage premium of the achieved sale
price relative to, in this case, the opening bid, guide price and the reserve. The
primary issue of note in these results relates to the renewed importance of the auction
process with respect to participation. The Number of Bidders is significant in two of
the three specifications, whilst Auction Attendance and the Number of Bids are
significant in one model each. These findings highlight the importance of the auction
process, in terms of encouraging interest and participation and are in line with the
findings of Ong (2006). However, the findings do have to be considered with a degree
of caution. It is important to remember that the opening bid and the guide price
contain information that does not necessarily reflect an estimate of market value.
Therefore, any discounting of these figures and therefore a higher premium does have
to be viewed in that light. The result with respect to the Reserve Price is however
more robust as it should more accurately reflect the agents and vendors assessment of
market value. Therefore, any significant findings here can be viewed, at least with a
26
greater degree of certainty, that the Number of Bids is significant in the price achieved
post auction.
5: Conclusion
One of the common problems that the empirical literature on auctions has encountered
is the unavailability of key elements of data. In particular information concerning
issues such as the number of bids/bidders and the undisclosed reserve price is often
not available leading to either their exclusion from empirical work or the use of
proxies. This paper has the advantage in that the data set available provides a rich vein
of information relating to the auction process. The results reported are broadly
supportive of the theoretical auction literature. The impact of the undisclosed reserve
plays an important role with evidence that whilst it may increase the revenue obtained
from the auction a higher reserve does reduce the probability of sale. Throughout the
analysis the role of auction participation is highlighted. This is clearly evident in that
the number of bidders/bids is generally significant not only in the probability of sale
at auction but also with respect to premium of the sale price achieved, both in the
context of auction and subsequent sales. In addition, more subtle effects are
highlighted. The role of the guide price as a marketing tool in order to encourage
bidding is also noticeable. Auctioned properties are found to have a significantly
lower guide price than those properties that do not successfully sell and the listing
prices play a significant role in the likelihood of sale. One that was raised from a
number of results concerned the role of the individual auctioneer. A number of
significant findings were reported. It is intended that future research pursue this
avenue.
27
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32
Tables & Figures
Table 1: Observations for Dummy Variables
Number
198
15
77
114
123
25
104
96
110
69
27
45
161
133
38
155
111
69
41
31
120
274
43
97
114
86
115
130
73
Sold at Auction
Sold Prior
Withdrawn then Sold
Withdrawn Unsold
Parking
Apartment
Terrace/Mews
Semi-Detached
Detached
Bungalow
Central Dublin
North City
South City
South County Dublin
Periphery
1998
1999
2000
2001
2002
No Bids
Multiple Auctions
Front of Room
Centre of Room
Back of Room
Auctioneer 1
Auctioneer 2
Auctioneer 3
Auctioneer 4
Notes: Table 1 displays the number of observations of unity reported for each of the dummy variables
used throughout the empirical analysis.
33
Table 2: Summary Statistics
Mean
Median
Panel A: Overall Sample
Sale Price
452,034.91
358,000.00
Guide Price
418,542.08
327,500.00
Reserve Price
424,125.00
340,000.00
Withdrawn Price
498,388.24
350,000.00
Number in Attendance
20.37
14.00
Number of Bids
12.32
8.00
Number of Bidders
2.25
2.00
Number of Auctions on Day
2.32
2.00
Time After Auction to Subsequent Sale
9.88
1.00
Bathrooms
1.73
1.50
Bedrooms
3.57
3.00
Panel B: Properties Sold at Auction
Sale Price
462,436.87
364,500.00
Guide Price
353,914.14
290,000.00
Reserve Price
394,493.33
325,000.00
Panel C: Properties Sold at Auction for Whom Reserve Available
Sale Price
450,326.67
359,000.00
Guide Price
341,700.00
280,000.00
Reserve Price
394,493.33
325,000.00
Number
289
404
240
85
404
404
404
404
77
404
404
198
198
150
150
150
150
Notes: Table 2 reports summary statistics for some of the key variables. The summary statistics are
reported on the basis of the overall sample, for those properties that sold at auction and, in Panel C, for
those sold properties for whom all data was available.
34
Table 3: Summary Statistics for Price Premiums
Reserve-Open Reserve-Guide Open-Guide Sale-Reserve Sale-Open Sale-Guide
Average
24.48%
15.15%
-6.59%
14.18%
40.60%
31.97%
Count
199
240
283
150
198
198
No. Positive
193
227
38
143
197
195
No. Zero
2
9
45
1
1
0
No .Negative
4
4
200
6
0
3
Maximum
125.00%
141.67%
108.33%
65.22%
140.00%
192.50%
Minimum
-10.00%
-9.91%
-60.00%
-14.67%
0.00%
-53.33%
Notes: Table 3 provides information on the average percentage differences between the various prices
used in the study.
35
Table 4: Sale Mechanism Tests
Coefficient
Panel A: Dependent Variable: Sale Price
Constant
11.8826
Sold at Auction Dummy
0.0608
Sold Prior Dummy
0.0515
Bathrooms
0.2279
Bedrooms
0.5577
Parking
0.0593
Apartment
0.1633
Terrace/Mews
-0.0407
Semi-Detached
-0.0293
Detached
0.2850
Central Dublin
0.0632
South City
0.2206
South County Dublin
0.1998
Periphery
0.0471
1998
-0.3108
1999
-0.1137
2001
0.3623
2002
0.4854
R2 adjusted
0.5116
Observations
289
Standard Error
T Statistic
0.1609
0.0542
0.1154
0.0641
0.0987
0.0542
0.1191
0.0797
0.0774
0.0776
0.1162
0.0779
0.816
0.1137
0.0757
0.0814
0.1093
0.1083
73.8596***
1.1218
0.4463
3.5538***
5.6521***
1.0940
1.3710
-0.5110
-0.3790
3.6701***
0.5435
2.8310***
2.4478**
0.4140
-4.1048***
-1.3968
3.3161***
4.4808***
Panel B: Dependent Variable: Guide Price
Constant
11.7187
0.1495
78.3962***
Sold at Auction Dummy
-0.1097
0.0504
-2.1786**
Sold Prior Dummy
0.0019
0.1072
0.0176
Bathrooms
0.2556
0.0596
4.2901***
Bedrooms
0.6124
0.0917
6.6795***
Parking
0.0739
0.0503
1.4682
Apartment
0.1420
0.1107
1.2836
Terrace/Mews
0.0006
0.0740
0.0084
Semi-Detached
-0.0096
0.0719
-0.1331
Detached
0.3232
0.0721
4.4807***
Central Dublin
0.0898
0.1080
0.8314
South City
0.2019
0.0724
2.7879***
South County Dublin
0.1860
0.0758
2.4525**
Periphery
0.0824
0.1057
0.7799
1998
-0.3312
0.0703
-4.7087***
1999
-0.1786
0.0756
-2.3603**
2001
0.3637
0.1015
3.5827***
2002
0.4184
0.1006
4.1576***
R2 adjusted
0.5780
Observations
289
Notes: Table 4 reports the coefficients estimated from a log hedonic model using Sale Prices, Panel A,
and Guide Prices, Panel B, as the dependent variables. * indicates significance at 10%, ** at 5% and
*** at 1%.
36
Table 5: Sale Price Tests on Auctioned Properties
Dependent Variable: Sale Price
Auctioned Properties
Auction Sales Only
Coefficient
Standard Error
T Statistic
Coefficient
Standard Error
T Statistic
Constant
11.6959
0.1928
60.6566***
11.6144
0.2781
41.7578***
Sold at Auction Dummy
-0.0936
0.0954
-0.9810
Bathrooms
0.2336
0.0679
3.4402***
0.1855
0.0786
2.3594**
Bedrooms
0.5866
0.1052
5.5752***
0.6304
0.1186
5.3154***
Parking
0.0514
0.0569
0.9035
0.0162
0.0656
0.2472
Apartment
0.1536
0.1229
1.2501
0.1338
0.1605
0.8333
Terrace/Mews
-0.0500
0.0829
-0.6028
-0.1059
0.0976
-1.0852
Semi-Detached
-0.01634
0.0797
-0.2050
-0.0298
0.0916
-0.3253
Detached
0.2624
0.0811
3.2350***
0.1356
0.0970
1.3969
Central Dublin
0.1028
0.1231
0.8347
0.1167
0.1488
0.7842
South City
0.2354
0.0800
2.9444***
0.2255
0.0909
2.4800**
South County Dublin
0.1719
0.0851
2.0195**
0.1451
0.0976
1.4864
Periphery
0.0738
0.1181
0.6248
0.0720
0.1372
0.5245
1998
-0.3123
0.0812
-3.8453***
-0.3509
0.097
-3.8697***
1999
-0.1063
0.0836
-1.2718
-0.0518
0.0949
-0.5457
2001
0.4071
0.1178
3.4552***
0.3825
0.1385
2.7617***
2002
0.5046
0.1133
4.4521***
0.5033
0.1196
4.2081***
Auction Attendance
0.0111
0.0327
0.3391
-0.0391
0.0455
-0.8601
Number of Bidders
0.0374
0.0468
0.7993
0.0679
0.0596
1.1401
Number of Bids
0.0607
0.0823
0.7383
0.0857
0.0866
0.9895
Multiple Auctions
0.0438
0.0632
0.6933
0.0580
0.0764
0.7590
Auctioneer 1
0.1474
0.0798
1.8461*
0.2713
0.0989
2.7435***
Auctioneer 2
-0.0016
0.0772
-0.0203
0.0520
0.0913
0.5694
Auctioneer 3
0.0387
0.0834
0.4633
0.1187
0.0963
1.2331
Previous Auction Sold
0.0524
0.0759
0.6899
0.0778
0.0874
0.8897
Order of Auction
-0.0140
0.0698
-0.2006
-0.0672
0.0794
-0.8459
R2 adjusted
0.5147
0.5086
Observations
275
198
Notes: Table 5 reports the coefficients estimated from a variation of the log hedonic models on sale prices reported in Table 4. These specifications constrain the sample to
consider firstly those properties that actually went to auction and secondly just those that sold at auction. * indicates significance at 10%, ** at 5% and *** at 1%.
37
Table 6: Probit Models Examining the Probability of Sale
Model I
Model II
Model III
Model IV
Coefficient
T Statistic
Coefficient
T Statistic
Coefficient
T Statistic
Coefficient
T Statistic
Constant
-4.2435
-4.5097***
1.2736
2.4067**
4.1290
0.6818
2.5740
0.3897
Bathrooms
0.1622
1.0037
0.0196
0.7942
0.1893
0.7096
0.2487
0.8355
Bedrooms
0.0097
0.0726
0.0327
1.5193
0.3577
1.5831
0.2732
1.1069
Parking
0.4678
1.5976
0.0319
0.8506
0.6590
1.4684
0.6585
1.3012
Apartment
-0.3052
-0.4509
-0.0272
-0.3265
-0.6292
-0.6597
-0.8021
-0.7791
Terrace/Mews
0.2216
0.4652
0.0324
0.5828
0.1428
0.2025
0.2336
0.3112
Semi-Detached
0.0088
0.0201
-0.0008
-0.0151
-0.3354
-0.5239
-0.0969
-0.1433
Detached
0.1290
0.3138
0.0289
0.5391
0.3142
0.4718
0.5955
0.8437
Central Dublin
0.8783
1.2731
0.0974
1.1369
1.0496
1.1735
0.9965
0.9956
South City
0.7793
1.7601*
0.0568
1.0299
1.1852
2.1291**
1.2726
2.0674**
South County Dublin
0.6856
1.5434
0.0339
0.5992
1.0778
1.8530*
1.1084
1.6833*
Periphery
0.8826
1.5610
0.0414
0.5549
0.4996
0.6175
0.6364
0.7292
1998
0.5080
1.2985
-0.0286
-0.5201
0.8064
1.3133
1.0925
1.7281*
1999
0.0517
0.1380
-0.0553
-1.0837
0.2129
0.3888
-0.0937
-0.1575
2001
0.3108
0.6068
0.0181
0.2692
0.5702
0.6799
0.3054
0.3483
2002
0.7888
1.4152
0.0850
1.1461
1.2889
1.3700
1.2744
1.0941
Auction Attendance
0.0008
0.1080
-0.0003
-0.3007
0.0073
0.7230
0.0007
0.0598
Number of Bidders
0.2775
1.9106*
0.0971
7.2227***
0.5132
2.4609**
0.3829
1.6177
Number of Bids
0.1709
6.1232***
0.0143
7.9551***
0.1648
4.8812***
0.1736
4.7473***
Multiple Auctions
0.7947
2.3768**
0.0829
1.9327*
0.8034
1.9109*
0.6785
1.5147
Auctioneer 1
-0.4724
-1.1348
-0.0064
-0.1151
-0.3096
-0.4230
-0.3706
-0.4767
Auctioneer 2
-0.4176
-1.0044
-0.0364
-0.6946
-0.2713
-0.3995
-0.5334
-0.7374
Auctioneer 3
-0.1709
-0.3736
-0.0250
-0.4461
0.5002
0.6421
0.0445
0.0537
Centre of Room
0.3961
1.5913
0.0755
2.1630**
0.7782
2.2189**
0.3465
0.7972
Rear of Room
-0.0978
-0.3667
0.0160
0.4326
-0.0415
-0.1132
0.0633
0.1495
Previous Auction Sold
-0.0988
-0.2640
-0.0145
-0.2981
-0.0145
-0.0265
-0.1037
-0.1588
Order of Auction
0.0569
0.1622
-0.0143
-0.3138
0.4532
0.9244
0.7418
1.1650
Guide Price
-0.1128
-2.6032***
Reserve Price
-0.8779
-1.6468*
Reserve-Guide (%)
-1.2759
-0.9930
Opening Bid
-0.6327
-1.0724
Reserve-Opening Bid (%)
-3.7565
-3.2473***
McFadden R2
0.7261
0.6397
0.7151
0.6629
Observations
389
389
239
199
Notes: Four alternative specifications of a Binary Probit model are estimated where the dependent variable in a binary variable. It takes the value of unity if the property sold at auction and zero
otherwise. * indicates significance at 10%, ** at 5% and *** at 1%.
38
Table 7: Sale Premiums on Properties Sold at Auction
Sale-Open Bid (%)
Sale-Guide (%)
Sale-Reserve (%)
Coefficient
Standard
T Statistic
Coefficient
Standard
T Statistic
Coefficient
Standard
T Statistic
Error
Error
Error
Constant
0.1122
0.0912
1.2297
0.0772
0.0972
0.7945
-0.1154
0.0595
-1.9394*
Bathrooms
-0.0116
0.0192
-0.6054
-0.0210
0.0204
-1.0247
-0.0199
0.0122
-1.6286
Bedrooms
0.0279
0.0146
1.9146*
-0.0074
0.0155
-0.4736
0.0141
0.0094
1.5033
Parking
-0.0069
0.0288
-0.2381
-0.0483
0.0307
-1.5724
-0.0060
0.0187
-0.3233
Apartment
-0.0383
0.0687
-0.5572
0.0707
0.0731
0.9669
0.0531
0.0436
1.2195
Terrace/Mews
-0.0583
0.0431
-1.3543
-0.0875
0.0459
-1.9075*
0.0079
0.0276
0.2876
Semi-Detached
-0.0675
0.0404
-1.6707*
-0.0686
0.0431
-1.5935
-0.0288
0.0269
-1.0734
Detached
-0.0165
0.0425
-0.3877
-0.0755
0.0453
-1.6693*
0.0202
0.0293
0.6873
Central Dublin
0.7031
0.0658
1.0690
0.0167
0.0700
0.2385
-0.0605
0.0410
-1.4759
South City
0.0330
0.0402
0.8220
0.0851
0.0428
1.9878**
-0.0220
0.0237
-0.9256
South County Dublin
-0.1892
0.0431
-0.4392
0.0587
0.0459
1.2801
-0.0096
0.0255
-0.3769
Periphery
-0.0412
0.0603
-0.6826
-0.0356
0.0643
-0.5545
-0.0332
0.0384
-0.8634
1998
0.0392
0.0402
0.9753
0.0182
0.0428
0.4247
0.0071
0.0289
0.2460
1999
0.0199
0.0420
0.4735
0.1042
0.0447
2.3319**
0.0369
0.0289
1.2792
2001
0.0689
0.0610
1.1301
0.0297
0.0650
0.4579
0.0592
0.0425
1..3929
2002
0.0296
0.0527
0.5615
0.0800
0.0561
1.4259
0.1003
0.0382
2.6258***
Auction Attendance
0.0015
0.0008
1.9521*
-0.0004
0.0008
-0.5121
0.0010
0.0005
2.1505**
Number of Bidders
0.0267
0.0093
2.8814***
0.0223
0.0099
2.2667**
0.0242
0.0062
3.9187***
Number of Bids
0.0059
0.0011
5.2421***
0.0065
0.0012
5.3766***
0.0035
0.0007
5.2700***
Multiple Auctions
0.0037
0.0335
0.1099
0.0167
0.0357
0.4677
-0.0058
0.0209
-0.2790
Auctioneer 1
-0.0041
0.0436
-0.0930
0.0499
0.0465
1.0736
0.0388
0.0289
1.3427
Auctioneer 2
-0.1246
0.0405
-3.0794***
0.0305
0.0431
0.7088
0.0552
0.0251
2.2007**
Auctioneer 3
-0.0911
0.0425
-2.1443***
0.0873
0.0453
1.9292*
0.0228
0.0283
0.8052
Previous Auction Sold
0.0145
0.0385
0.3776
0.0665
0.0410
1.6200
0.0628
0.0240
2.6169***
Order of Auction
-0.0247
0.0350
-0.7056
-0.0318
0.0372
-0.8550
-0.0473
0.0212
-2.2326**
2
R adjusted
0.2956
0.3159
0.4342
Observations
198
198
150
Notes: Three alternative OLS models are estimated with the percentage change of the sale price over the opening bid, guide and reserve price. * indicates significance at 10%,
** at 5% and *** at 1%.
39
Table 8: Probit Models Examining the Probability of Sale of Withdrawn Properties
Coefficient
Constant
Bathrooms
Bedrooms
Parking
Apartment
Terrace/Mews
Semi-Detached
Detached
Central Dublin
South City
South County Dublin
Periphery
1998
1999
2001
2002
Auction Attendance
Number of Bidders
Number of Bids
Multiple Auctions
No Bids
Order of Auction
New Guide-Guide (%)
New Guide-Reserve
(%)
Reserve Price
-0.5082
-0.2130
-0.0593
-0.0665
0.0790
-0.2066
-0.2597
-0.1464
0.9068
0.5240
0.4158
0.3193
1.5586
0.9531
0.7615
-0.7513
0.0025
0.1654
0.0049
-0.4421
-0.6345
0.2202
1.3031
0.2500
190
Model I
Standard
Error
0.7513
0.1702
0.1404
0.2527
0.561
0.3866
0.4000
0.3509
0.5852
0.3978
0.3952
0.5103
0.3499
0.3495
0.4061
0.7838
0.0083
0.2847
0.0460
0.3052
0.3849
0.2825
1.2018
-
T Statistic
Coefficient
-0.6764
-1.2519
-0.4222
-0.2632
0.1408
-0.5345
-0.6493
-0.4173
1.5495
1.3173
1.0523
0.6258
4.4539***
2.7267***
1.8753*
-0.9585
0.3051
0.5811
0.1060
-1.4486
-1.6485*
0.7795
1.0843
1.1998
-0.1900
-0.3204
-0.9375
0.1118
-0.3412
0.0108
-0.1424
1.0040
0.2235
-0.0933
0.0235
2.2090
0.9884
0.9020
0.0549
0.0044
0.4309
-0.0282
-0.8268
-1.0280
0.4990
-
-
6.3389
0.3664
88
Model II
Standard
Error
1.2909
0.2820
0.2544
0.4559
1.3551
0.6539
0.6264
0.6478
1.1274
0.6283
0.6679
0.9363
0.7500
0.7231
1.0132
1.9018
0.0160
0.3908
0.0633
0.5760
0.5870
0.4607
3.1271
-
T Statistic
Coefficient
0.9294
-0.6736
-1.2595
-2.0565**
0.0825
-0.5219
0.0172
-0.2200
0.8905
0.3558
-0.1397
0.0251
2.9454***
1.3700
0.8903
0.0289
0.2724
1.1026
-0.4458
-1.4354
-1.7513*
1.0832
-
3.8125
-0.0438
-0.0015
-0.2393
0.1133
0.0115
0.0552
0.1201
0.0507
0.0273
-0.0526
0.0568
0.4580
0.2410
0.2032
0.1169
-0.0012
0.0274
0.0018
-0.1214
-0.2506
-0.0035
-
2.0271**
-
-0.2573
0.3938
89
Model III
Standard
Error
1.8028
0.0871
0.0864
0.1353
0.2995
0.1802
0.1845
0.1871
0.3102
0.1818
0.1921
0.2703
0.1815
0.1740
0.2475
0.3604
0.0041
0.1087
0.0172
0.1681
0.1625
0.1288
0.1523
T Statistic
2.1148**
-0.5036
-0.0169
-1.7688*
0.3782
0.0640
0.2989
0.6421
0.1633
0.1501
-0.2739
0.2101
2.5230**
1.3856
0.8212
0.3243
-0.3037
0.2524
0.1022
-0.7224
-1.5422
-0.0272
-1.6895*
McFadden R2
Observations
Notes: Three alternative specifications of a Binary Probit model are estimated on those properties that were withdrawn at auction. The dependent variable in a binary variable which takes the
value of unity if the property sold following the auction and zero if it remained unsold. * indicates significance at 10%, ** at 5% and *** at 1%.
40
Table 9: Sale Premiums on Withdrawn Properties
Sale-Open Bid (%)
Sale-Guide (%)
Sale-Reserve (%)
Coefficient
Standard
T Statistic
Coefficient
Standard
T Statistic
Coefficient
Standard
T Statistic
Error
Error
Error
Constant
0.2157
0.1821
1.1842
-0.0855
0.1104
-0.7741
-0.0221
0.0517
-0.4276
Bathrooms
0.0180
0.0451
0.3990
0.0107
0.0253
0.4231
-0.0069
0.0122
-0.5632
Bedrooms
-0.0279
0.0418
-0.6686
0.0079
0.0219
0.3604
-0.0036
0.0110
-0.3266
Parking
-0.0283
0.0630
-0.4495
-0.0081
0.0392
-0.2077
0.0044
0.0273
0.1615
Apartment
0.0291
0.1010
0.2880
0.1023
0.0622
1.6450
0.0302
0.0320
0.9411
Terrace/Mews
0.0375
0.0939
0.3989
0.0419
0.0534
0.7849
0.0079
0.0269
0.2919
Semi-Detached
-0.0067
0.1161
-0.0578
0.0527
0.0588
0.8969
0.0025
0.0273
0.091
Detached
-0.0320
0.0988
-0.3234
0.0192
0.0493
0.3897
0.0068
0.0234
0.2928
Central Dublin
-0.0574
0.2146
-0.2673
-0.0874
0.0836
-1.0447
-0.0788
0.0440
-1.7908*
South City
0.0370
0.0994
0.3723
-0.0065
0.0636
-0.1019
0.0107
0.0290
0.3683
South County Dublin
0.0633
0.1076
0.5882
0.0347
0.0681
0.5094
0.0161
0.0378
0.4257
Periphery
0.0083
0.1430
0.0579
0.0152
0.0883
0.1721
0.0239
0.0428
0.5583
1998
-0.0835
0.1164
-0.7174
0.0016
0.0740
0.0222
0.0370
0.0379
0.9779
1999
-0.0969
0.1216
-0.7971
-0.0267
0.0736
-0.3626
0.0129
0.0396
0.3264
2001
-0.1413
0.1449
-0.9753
-0.0615
0.0828
-0.7425
-0.0884
0.0526
-1.6799
2002
-0.1740
0.2766
-0.6290
-0.0064
0.1597
-0.0400
0.0490
0.0700
0.6998
Auction Attendance
0.0002
0.0018
0.1273
0.0022
0.0011
2.0021*
0.0007
0.0006
1.1317
Number of Bidders
0.0881
0.0417
2.1158**
0.0726
0.0291
2.4951**
-0.0138
0.0127
-1.0799
Number of Bids
-0.0018
0.0069
-0.2578
-0.0012
0.0049
-0.2414
0.0047
0.0021
2.2726**
Multiple Auctions
0.0055
0.0654
0.0836
-0.0741
0.0391
-1.8965*
-0.0120
0.0201
-0.9921
No Bids
0.1103
0.0471
2.3408**
0.0001
0.0220
0.0048
Previous Auction Sold
0.0425
0.1008
0.4221
-0.0248
0.0542
-0.4571
-0.0264
0.0258
-1.0256
Order of Auction
-0.0654
0.1046
-0.6250
0.0663
0.0510
1.2999
0.0138
0.0254
0.5418
R2 adjusted
0.0879
0.2119
0.3855
Observations
48
77
45
Notes: Three alternative OLS models are estimated with the percentage change of the sale price over the opening bid, guide and reserve price. The sample is constrained to
just consider those properties that were withdrawn at auction. * indicates significance at 10%, ** at 5% and *** at 1%.
41
Endnotes:
1
This proxy was not adopted in the current study for three primary reasons. Firstly, the
undisclosed reserve was available. Secondly, the current lack of a comparable sample of pure
private treaty sales limits the ability to provide an estimate of market value. Finally, the
opening bid, as we will see, bears limited relation to the valuation of the property.
2
The finding that the optimal reserve should be in excess of those currently observed in also
found in non-real estate papers such as McAfee & Vincent (1992), Paarsch (1997) and Li et
al. (2003).
3
In comparison, for negotiated private treaty sales the deposit is refundable up until the point
when the contracts agreeing the sale are signed, which is typically 4 to 6 weeks after the price
is agreed. Fees, commissions and transaction costs are however, identical for both sale
methods.
4
Initial specifications of the models used in this paper did attempt to incorporate this through
the use of a dummy variable to signify when an auctioneer stated that the property was onthe-market. However, its use was not feasible. This was particularly so in the case of the
probit models adopted to examine the probability of sale. Obviously, if a property did not sell
it was because the reserve had not been breached, therefore, the auctioneer would not have
made the statement. Likewise, in the vast majority of cases that the property sold it was stated
at some point after bidding had exceeded the reserve.
5
In addition to the literature considered below there are a number of papers to have examined
the timing of auctions, both sequentially and across different auctions, e.g. Ashenfelter
(1989), Ashenfelter & Genesove (1992) and Vanderporten (1992).
6
See also papers such as Harris & Raviv (1981), McAfee & McMillan (1988) and Campbell
& Levin (2006).
7
Ashenfelter & Genesove (1992) is one of the few US studies to provide evidence of an
auction premium. However, what is of interest is that the sample examined consisted of
identical condominiums units in New Jersey, thereby leading to a very homogeneous sample.
Care does however have to be taken in the interpretation of the Ashenfelter & Genesove
(1992) findings. The paper found evidence that the auctioned properties saw an average 13%
premium over those sold privately. However, the results do not necessarily imply that
auctioned properties sell for more than those properties that were never placed before an
auction. This is because all of the private sale sample consisted of units that were initially
offered at auction, left unsold and then subsequently sold.
8
The issue of the Winner’s Curse is however not straight forward in a residential property
context, especially in markets with high owner-occupier rates. In this case bidders will not
generally be looking to sell in the short-term and therefore the impact of any overbidding is
not as immediate. Furthermore, there are issues other than financial ones that will come into
play in determining both the type/location of property purchased and its price. This is a point
that also arises with respect to the issue of private and common values when discussing
residential property auctions. See, for example, Eklof & Lunander (2003).
9
Mayer (1998) also argues that the presence of a premium may be due to econometric issues
and specifically omitted variable bias. However, most of the papers of the papers, such as
Lusht (1996) and Stevenson et al. (2010) do explicitly test for selectivity bias in their
empirical work.
10
The arguments laid out by Quan (2002) can also be assessed in this context. Quan (2002)
illustrates that bidders with higher search costs will tend to participate in auctions. Properties
at the premium end of the market would fit this description. Quan (2002) also provides
42
evidence that the price obtained should be enhanced in an auction context.
11
See also papers such as Maskin (1992).
12
It should be noted that the Milgrom & Weber (1982) model that shows the preference over
sealed bids is illustrated in the case of ‘irrevocable exit’, whereby bidders who withdraw from
the auction cannot subsequently re-enter. Sealed bid auctions will maximize revenue in the
case of independent bids and risk-averse bidders.
13
Under an assumption of independent private values this therefore reverts to the Vickery
(1961) structure whereby the good is purchased by the bidder who has the highest private
value.
14
Part of the conditions agreed to when obtaining the data was that the auction house remain
anonymous. Furthermore, the sensitive nature of the data meant that no data after 2002 was
released.
15
With regard to the multiple auctions dummy it should be noted that when multiple auctions
took place it did not necessarily mean that the properties were similar, or substitutable. In
many instances the properties concerned display a wide range of characteristics in terms of
property type, guide price, size and location.
16
An earlier version of the paper followed convention more closely and excluded the earliest
period, i.e. 1998. However, due to the strong upward movement in house prices during the
1998 to 2002 period this resulted in a number of the models reporting significant positive
coefficients for the time dummies included. This did lead to some misunderstandings.
Therefore to avoid possible confusion and so that the role and interpretation of the time
dummies is more easily determined, the models were re-estimated with the middle year, 2000,
excluded. The broad findings were unaltered and these alternative specifications are available
from the authors on request.
17
This would be consistent with previously noted points regarding negative publicity in
Ireland regarding the premiums obtained at auction. If not only were properties selling at a
premium but the advertised valuations were discounted it would give an additional element to
the perceived premiums observed.
18
Pesendorfer & Swinkels (2000) note that a large number of bidders can lead to a
resumption of efficiency, even in the case of a two-signal auction.
19
On a more general note Chow et al. (2010) also support the previously cited work relating
to the importance of market conditions and that auctions are more likely to outperform during
periods of strong house price appreciation.
20
See the previously cited Riley & Samuelson (1981), McAfee & Vincent (1992) and Hu et
al. (2010). See also papers such as Maskin & Riley (1983), Waehrer et al. (1998) and Haile &
Tamer (2003) with respect to the issue of risk aversion and the setting of optimal reserve
prices.
21
Papers such as Eklof & Lunander (2003) do however argue in favour of disclosure of the
reserve. The Eklof & Lunander (2003) paper specifically considers residential properties, in
this case apartments in Stockholm. Using private values, thus assuming a motive of
consumption, and assuming that the number of bidders is exogenous and known, the evidence
provided shows that a strategy of non-disclosure of the reserve secret yields lower expected
revenue. It is important to note however that during the sample period considered the
Stockholm market was undergoing a downturn. Furthermore, the properties considered were
not generally in prime areas. This does therefore reduce the possibility of price appreciation,
in the form of common values playing a major role. In addition, as the authors note, they only
have access to information contained in the winning bid. This therefore excludes the
43
information contained in other bids which would also be an important element in common
values. Furthermore, the assumption that the number of bidders is exogenous and known
eliminates the possibility that the disclosure of the reserve may lead to a reduction in the
number of bids (Vincent, 1995). This point is also discussed in Goeree & Offerman (2002,
2003). They argue that revealing information about common value will increase revenue as
without this information there may be large degree of uncertainty regarding the value of the
good/object. This uncertainty, added to by the infrequent nature of such auctions, leads to the
loss of efficiency as if less uncertainty is present bidding will be more aggressive, However,
Goeree & Offerman (2002, 2003) also illustrate that revenue will increase with the number of
bidders. Furthermore, this effect is stronger than the effect of revealing information.
Therefore, if revealing information through the disclosure of the reserve reduces participation,
it may lead to an overall loss of efficiency.
22
See also papers such as Yavas (1992), Sirmans & Turnbull (1993), Chen & Rosenthal
(1996a, 1996b), Springer (1996), Knight et al. (1998), Arnold (1999), Taylor (1999), Knight
(2002), Anglin et al. (2003).
23
Genesove & Mayer (1997) specifically illustrate this in the context of vendors with high
loan-to-value ratios. Due to the need for increased revenue in order to comply with their
mortgage financing obligations they have a greater tendency to set a higher listing price.
24
In this sense the findings here can be related back to Mayer’s (1995) and the general
advantage of a negotiated sale as it removes the time constraints present in an auction and
therefore allows vendors more time in order to find a bidder who matches their valuation of
the property.
25
The figures for the withdrawn price only incorporates those properties that went to auction,
i.e. were not sold prior, but also those where there was actual bidding. In a large number of
cases properties were withdrawn after no bidding. Hence in those cases we have no actual
withdrawn price or an opening bid. Indeed, the only reliable prices are the listing price and
the reserve where available.
26
Note that whilst prices were denominated in Irish Pounds prior to the introduction of the
coinage of the Euro in January 2002 all prices used in the study are quoted in Euro’s.
27
Informal inquiries would confirm this. The person in question was the senior auctioneer and
was more likely to preside over highly valued properties.
28
The complete estimates from these hedonic specifications are available from the authors on
request.
29
See previous cited papers such as Vickrey (1961), Holt (1979), Harris & Raviv (1981) and
McAfee & McMillan (1987) in relation to the theoretical arguments as to the role of bids in
auction prices also to more recent papers such as Mayer (1995) and Goeree & Offerman
(2002, 2003).
30
As previously noted there is a similar argument relating to the setting of list prices with
respect to negotiated sales in housing. See for example Haurin (1988), Mayer (1995),
Genesove & Mayer (1997) and Merlo & Ortalo-Magne (2004).
31
This is an element of the study that we intend to consider further in future work.
32
It is the intent of the authors to examine in more depth the strategies of the individual
auctioneers in future research.
33
The current lack of a large scale comparable sample of pure private treaty sales does limit
somewhat what can be considered in this section. Therefore, the empirical analysis
concentrates upon the impact of factors from the auction in both the sale price achieved and
the probability of sale.
44