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?