Determinants of the House Bidding Process:

Approximating the Seller’s Surplus?

Sotirios Thanos, David Watkins, & Michael White

Heriot-Watt University, Edinburgh

The Scottish Housing Market Context A system of single sealed bids (usually) over the asking price

The bidding process takes place when at least two bids have been received

The seller is informed of the bids after the conclusion of the process and is not legally bound to accept highest the bid

Through this process we have information about the asking price that is not adjusted to the magnitude of the bids

The seller can at any time in the process switch to direct negotiations and/or a fixed price scheme

Research Questions and Definitions For the purposes of this discussion we define the BID as: BID = Selling Price – Asking Price Is the asking price just a “marketing tool” or does it have

another “economic” significance? Are the determinants of the BID different to the asking

price?Hypothesis: Asking price = f[Structural Characteristics + Neighbourhood

(accessibility, environmental) characteristics + Seller’s socioeconomic characteristics + Market Conditions at the time of price setting]

BID = f(Asking price + Time on the Market + Expectations of future market conditions + Buyer’s preferences towards the characteristics of the “housing services” + Bidding frequency)

Data and Mean Prices The data consists of 19290 transactions in Aberdeen from ASPC

93.16% (17970) of the properties were sold through the bidding process

3.4% (616) of these attracted a negative BID

6.84 % (1320) of the properties were sold at a fixed price

Socioeconomic variables were introduced at the level of census output areas (COA) and accessibility variables were calculated using GIS

Table 1: Mean Selling Price

   

 Mean Selling

Price (£) Std. Err. Statistical Tests t-stat P>tWhole Sample 140844.7 739.3

Positive Bids (PB) 139829.5 779.1 Ho: mean(PB) - mean(FP)=0 -1.0869 0.277Negative Bids (NB) 164834 4756.5 Ho: mean(PB) - mean(NB)=0 -5.9092 0.000

Fixed Price (FP) 142996.2 2582      

Model Description 3 Initial Regression Models: Model 1: Dependent variable the natural logarithm of selling price Model 2: Dependent variable the natural logarithm of asking price Model 3: Dependent variable the BID as a proportion of asking priceModelling details Semi-log specification fitted the data best for Models 1 and 2 White’s correction was employed to correct for the heteroscedasticity

detected in the models 69 variables were used to control for socioeconomic, accessibility, structural,

submarket and temporal characteristicsGoals: To determine whether “asking price” and “proportional bid” models are

different to a typical HP approach (Model 1) Get any information about the hypotheses in the previous slide concerning

the BID function

Table 2: Indicative Results from the Regression Models Variable Model 1 Model 2 Model 3

R2: 0.8717 R2: 0.8709 R2: 0.336Dwelling Density -.00084216*** -.0009765*** .01771238***Double_Glazing -.01834765** -0.00311337 -2.2352805***TOM -.00040808*** -.00007142* -.04113134***NEWBUILT .31533861*** .43758321*** -14.625904***Unemployment -.00678595*** -.00697908*** 0.0480763Detached .24786663*** .27184801*** -2.8346032***No-Detached .12247761*** .12521139*** -0.31317574BASEMENT -.15976582** -.14907471* -1.7786615FLOOR2 .01678761* .02194743** -0.83369105FLOOR3 0.05073933 .06821761* -2.4925233FLOOR4PL 0.08959663 0.11662584 -3.2414365DIST_Airport .02981054*** .02994713*** 0.01499035DIST_Station -.02889319*** -.02721614*** -.23625004**GARAGE .08063188*** .07554665*** 0.61953705GasHEATING .09385462*** .09050692*** 0.35033745GARDEN .04176949*** .02273177*** 2.465611***Bedroom1 -.49016294*** -.52615028*** 4.9190326***Bedrooms2 -.13839069*** -.15170499*** 1.8279956***Bedrooms4 .1520955*** .17054399*** -2.3547978***Bedrooms5 .31162695*** .33329854*** -2.8985522***

* p<.05; ** p<.01; *** p<.001

2SLS Model The asking price is endogenous to the bid, it is instrumented in the

2SLS model The selection of the instruments is informed by Model 2 Model 3 informed the decision of selecting independent variables for the

BID model in conjunction with the standing hypothesis, namely: Dummies for the year and quarter the sale took place Double Glazing Dwelling density TOM

To test that the 2SLS model was specified correctly, a simple regression model with the same variables also was run and a Hausman Test was employed

The hypothesis that “the difference in coefficients between the two models is not systematic” was rejected at the 99% level [χ2(19)= 295.72]

Table 3: Model 4 (SLS Regression) Results LNBID Coef. Std. Err. t P>t

Ln(asking price) 0.8490103 0.0096997 87.53 0Y04Q1 -0.4433784 0.0234611 -18.9 0Y04Q2 -0.1418022 0.0215355 -6.58 0Y04Q3 -0.4198439 0.0246898 -17 0Y04Q4 -0.5912017 0.0256659 -23.03 0Y05Q1 -0.4485817 0.026508 -16.92 0Y05Q2 -0.2348433 0.0244407 -9.61 0Y05Q3 -0.2624129 0.0246814 -10.63 0Y05Q4 -0.2931563 0.025332 -11.57 0Y06Q1 -0.0692977 0.0266178 -2.6 0.009Y06Q2 0.1219391 0.0240112 5.08 0Y06Q3 0.1626833 0.0236937 6.87 0Y06Q4 0.2183823 0.02509 8.7 0Y07Q1 0.4496888 0.0259151 17.35 0Y07Q2 0.4801289 0.0236749 20.28 0Y07Q3 0.2881602 0.0241673 11.92 0DBL GLAZING -0.0844976 0.0139821 -6.04 0TOM -0.0026863 0.0000796 -33.76 0Dwelling density 0.0008539 0.0000966 8.84 0_cons 0.4843715 0.1185452 4.09 0

Observations: 17354 Adj R-squared=0.5629

Comparing Model Results Comparing Tables 2 and 3: Dwelling density coefficient is positive and highly significant for Model 4,

possibly reflecting bidding frequency Double Glazing is not significant in Model 2. The marginal effect of this

variable is 4.6 times higher to the BID (Model 4) than to the selling price (Model 1)

Table 4 demonstrates the significantly higher sensitivity of BID to TOM compared to Selling Price (Model 1) and even to Model 3

Table 4: "Time on the Market" Point Elasticity at the Mean Model TOM Point Elasticity z P>zModel 1 -0.0189619 -6.25 0Model 2 -0.0033186 -2.15 0.032Model 3 -0.0679644 -4.81 0Model 4 -0.1248216 -33.76 0

Northern Rock Crisis

Discussion The BID may depend on environmental preferences, as the

double glazing variable might have indicated As expected TOM is a strong determinant of the BID TOM has been found in the literature to depend upon market

conditions (e.g. Pryce and Gibb, 2006) We have found that the BID is also highly dependent on market

conditions, reflecting buyer expectations for future market movements

The asking price could be interpreted as a signal of the sellers reserve price to the buyer. Hence, the BID could operate as a proxy to the seller’s surplus.

Some weak evidence of a time lag by which the asking price is adjusted to previous bidding processes (witnessed by real estate agents / solicitors)

Further Research

Logit models to address the choice of selling method (fixed price or bidding) is one research avenue.

Stated preference experiments might also prove enlightening with regard to this question

Noise measurements will be included in the models, determining whether the purchaser’s environmental preferences are reflected in the BID

We recognise that the treatment of TOM is simplistic here and a more “state of the art” approach is the next step

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