1. Determinants of the House Bidding Process: Approximating the Seller’s Surplus? Sotirios Thanos, David Watkins, & Michael White Heriot-Watt University, Edinburgh

2. 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

3. 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)

4. 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 Testst-statP>t Whole Sample140844.7739.3 Positive Bids (PB)139829.5779.1 Ho: mean(PB) - mean(FP)=0 -1.08690.277 Negative Bids (NB)1648344756.5 Ho: mean(PB) - mean(NB)=0 -5.90920.000 Fixed Price (FP)142996.22582

5. 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 price Modelling 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 characteristics Goals:  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

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

7. 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 [ χ 2 (19)= 295.72]

8. Table 3: Model 4 (SLS Regression) Results LNBIDCoef.Std. Err.tP>t Ln(asking price)0.84901030.009699787.530 Y04Q1-0.44337840.0234611-18.90 Y04Q2-0.14180220.0215355-6.580 Y04Q3-0.41984390.0246898-170 Y04Q4-0.59120170.0256659-23.030 Y05Q1-0.44858170.026508-16.920 Y05Q2-0.23484330.0244407-9.610 Y05Q3-0.26241290.0246814-10.630 Y05Q4-0.29315630.025332-11.570 Y06Q1-0.06929770.0266178-2.60.009 Y06Q20.12193910.02401125.080 Y06Q30.16268330.02369376.870 Y06Q40.21838230.025098.70 Y07Q10.44968880.025915117.350 Y07Q20.48012890.023674920.280 Y07Q30.28816020.024167311.920 DBL GLAZING-0.08449760.0139821-6.040 TOM-0.00268630.0000796-33.760 Dwelling density0.00085390.00009668.840 _cons0.48437150.11854524.090 Observations: 17354Adj R-squared=0.5629

9. 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 ModelTOM Point ElasticityzP>z Model 1-0.0189619-6.250 Model 2-0.0033186-2.150.032 Model 3-0.0679644-4.810 Model 4-0.1248216-33.760

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11. 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)

12. 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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