Winning Bidders: Is there a strategy? Louise Brown, Stanley McGreal and Alastair Adair.
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Transcript of Winning Bidders: Is there a strategy? Louise Brown, Stanley McGreal and Alastair Adair.
Winning Bidders: Is there a strategy?
Louise Brown, Stanley McGreal and Alastair Adair
Research questions
Is there a distinct pattern emerging from the transaction histories of the winning bidders?
Does this change during different stages of the house price cycle?
Under investigation
Duration of the bidding process Observed behaviour of winning bidder through incremental
time and amount of bids.
TOM
TOM and listing strategies (Bjorklund, Dadzie,Wilhemmson,2003; Allen and Dare,2004; Knight, Sirmans and Turnbull, 1994; Asabere, Huffman and Mehidian, 200; McGreal et al,2007)
Bid-Ask Spread (Glosten and Milgrom,1985; Judd, Winkler and Kissling, 1995)
Buyer search under differing economic conditions (Baryla, Zumpano and Elder,2000; Novy-Marx, 2009)
Bidding
Evaluation of methodologies (Leung, Leong and Chan,2002; Pryce and Gibb,2006; Black et al,2003)
Extreme Bids (Levin and Pryce, 2007) Jump bidding (Raviv,2007) Aggressive bidders (De Silva et
al,2002) Individual transaction histories, UK data (Merlo and Ortalo-
Magne,2004) Bid acceptance strategies (Green and Vandell,1998) Bidding model of perfect competition (Robert Wilson,1977) Game theory and decision analysis (Howard Raiffa,various)
Lessons from other literature
Auction data and bidding at auctions (Some fundamental differences: 1)sequential open market transactions can be simultaneous 2) agreed finish time for an auction compared to unfixed duration of the bidding process.
Internet auctions reported in economic journals : cross bidders/observers (Anwar,2006; Housers and Wooders,2006)
Motivation: emotional bidders, rational/irrational exuberance (Schiller; Neo,Ong and Tu, 2008) particularly relevant in a rapid market (2006/7)
Entry point, opportunity cost of an early bid (Brosig and Reib, 2007)
Data 2001qtr3 -2009 qtr1 (31 quarters) For a metropolitan area Sample size = 991 complete cases
Source: an estate agency practice -high %sales for Belfast Metropolitan Area-spatially diverse-mass market not niche-representative of other agents-uniformity of approach across branches-long established network
year
inde
x va
lue
NI House Price Index Retail Price Index
Time period under investigation
Source: University of Ulster
Data Conceptualization
Input Variables
(Characteristics)
•Time
•Location
•House Type
•Size
•Asking Price
Output Variables
Time on the market
Difference between sales price and asking price
Difference between sales price and asking price (as a percentage of the asking price)
Process Variables
Views
Bidders
Bids
Winning Bid
Characterization of the relationship between viewing and bidding periods
Viewing but no bids
Period of viewing
Duration of bidding
Date of listing Date of first bid Date of agreement
Date of winning bid
Date of completion
Data Variables
Individual property identifier 00001Date of listing dd/mm/yyyyDate of agreement dd/mm/yyyyDate of completion dd/mm/yyyyListing Price £300,000Sales Price £315,000Full Postal Address BT37 0QBHouse Type Detached BungalowAge of Property 1960-1980Nominal Floor Area 1200sqftNumber of Bedrooms 3Number of Reception Rooms 2Bathroom YesGarage YesFull Central Heating YesIn need of modernization Yes
Bidding Information ExampleBidding Transaction Data
Individual Property Identifier 00001
Date of Listing 26/3/7 £315,000 (LP)Date of 1st Bid 2/4/7 £315,000 Bidder ADate of 2nd Bid 4/4/7 £315,500 Bidder BDate of 3rd Bid4/4/7 £316,000 Bidder CDate of 4th Bid5/4/7 £317,500 Bidder BDate of 5th Bid5/4/7 £318,000 Bidder DDate of 6th Bid13/4/7 £320,000 Bidder EDate of 7th Bid1/5/7 £322,000 Bidder Date of 8th Bid12/5/7 £330,000 Bidder G *sale agreed
Number of viewings: 44Number of bids: 8Number of bidders:7
Descriptive statistics
TOM Range = 0 to 1023 (2009 qtr1), mean =96, mode=14
No of bids Range = 1 to 11 (2006 qtr3), 90% less than or equal to 10
bids
No of bidders Range = 1 to 13 (2004 qtr1), 90% less than or equal to 4
bidders
No of viewers Range = 0-111 (2004 qtr2), 90% properties had less than or
equla to 29 viewers
Time on the market(listing to agreement)
House price cycle and viewers, bidders and bids
0
5
10
15
20
25
30
2013
2021
2023
2031
2033
2041
2043
2051
2053
2061
2063
2071
2073
2081
2083
2091
0
200
400
600
800
1000
1200
Ind
ex 1
983=
100
av no of bids per prop
av no of bidders per prop
HPI
av no of viewers per prop
Incremental timing by qtr
0
20
40
60
80
100
120
140
2013
2014
2021
2022
2023
2024
2031
2032
2033
2034
2041
2042
2043
2044
2051
2052
2053
2054
2061
2062
2063
2064
2071
2072
2073
2074
2081
2082
2083
2084
2091
days
0
200
400
600
800
1000
1200
ind
ex 1
983=
100
incremental timing of the winningbid
incremental time of the losers
HPI
-6000
-5000
-4000
-3000
-2000
-1000
0
1000
2000
3000
4000
2002 2003 2004 2005 2006 2007 2008 2009
incremental bid of the w inning bidder
incremental bid of the losers
Incremental amount by yr
Incremental time by yr
0
10
20
30
40
50
60
70
80
90
100
2002 2003 2004 2005 2006 2007 2008 2009
incremental timing fo the w inning bid
incremental time for the losers
Correlations between bidding variables
Winners Incremental timing of winning bid
LosersIncremental timing of losing bids
Winners incremental bid as a % of AP (normalised)
Losers incremental bids as a % of AP (normalised)
Sales Price -Asking Price
-.227** -.184** .299** -.178**
Time on the market
.366** .475** -.225** .417**
Findings 1
Volume of Viewers and Bidders Peak of viewers and bidders dropped before the
decrease was realised in prices therefore it could be a lead indicator.
Findings 2
Incremental amounts
Initial findings suggest that in a time of rapidly increasing prices winning bidders are prepared to go higher thereby securing the property.
Conversely in a time of rapidly decreasing prices winning bidders have >- SP-AP and still secure the property (possibly indicating better market knowledge).
Findings 3
Timing Winning bids appear to wait for a shorter time than losers
indicated by a lower incremental time between the penultimate bid and the last bid compared with the incremental time of all previous losing bids.
This may help explain the earlier finding on price. In thin markets with few sales the winning bidder bids acts more quickly but with a lower price taking advantage of market conditions.
Analysis shows that the difference between the amount of bids for winners and losers is more significant than the timing.
Bidder typologies for residential open market transactions
Data suggests three typologies:
The sole bidder The patient bidder (responsive to other bidders) The aggressive bidder
Some evidence that the sole bidder (35% cases) and the aggressive bidder are winning bidders (bids in higher increments and with shorter incremental time).
Thank you for listening. Any questions?