Asymmetric Information and List
Price Reductions in the Housing
MarketErik R. de Wit∗

Bas van der Klaauw†

April 2010

Abstract
In housing markets with asymmetric information list prices may signal
unobserved properties of the house or the seller. Asymmetric information is the starting point of many models for the housing market.
In this paper, we estimate the causal effect of list-price reductions totest for the presence of asymmetric information. We use very rich andextensive administrative data from the Netherlands. Our empiricalresults show that list-price reductions significantly increase the probability of selling a house, but also the rate of withdrawal from themarket increases.
Keywords: Market microstructure, asymmetric information, duration models∗

Finance Group - University of Amsterdam, Tinbergen Institute. e.r.dewit@uva.nl
Department of Economics - VU University Amsterdam, Tinbergen Institute, and
CEPR. bklaauw@feweb.vu.nl
†

1

1

Introduction

Heterogeneity in the housing market is often considered to be quite substantial. Houses, for example, differ in size, location and quality. Many of suchhouse characteristics are revealed to potential buyers when a house is put up
for sale on the market. In fact, real estate brokers often add an extensive listof house characteristics including pictures to their advertisements of houses
for sale. However, there may remain characteristics, which are known to theseller, but unobserved by potential buyers. This may not only be characteristics of the house, but may also relate to the seller. Sellers may, for example,
differ in risk preference, financial constraints and patience. An importantquestion is to which extent such information asymmetries are important in
the housing market.
In this paper, we focus on how changes in the list price affect the timea house remains on the market. List prices are not binding in the housing
market. In the Netherlands list prices have no formal role, and by law sellers
have to provide all relevant information about the house. The lack of any
legal commitment implies that if the market is characterized by symmetric
information between buyers and sellers, the list price will not have any effecton outcomes. However, in case of asymmetric information, the list price may
signal some unobserved properties of the house or the seller (e.g. Albrecht,
Gautier and Vroman, 2010). More patient sellers may, for example, set ahigher list price than desperate sellers.
Estimating the causal effect of the list price on outcomes in the housing
market is complicated. There may be characteristics which are observed byboth buyers and sellers, but which are unobserved by the econometrician. For
example, the thinness of the market for a particular house affects both the list
price and the probability of selling the house (e.g. Lazear, 1986). Therefore,
we focus on the effect of changes in list prices while a house is on the market,
rather than the initial level of the list price. However, Lazear (1986) showsthat also changes in the list price are not exogenous, market thinness may
also affect list price changes. We use a timing-of-events duration model toestimate the causal effect of a list price change on the time a house is on themarket. The empirical model builds on Abbring and Van den Berg (2003)2

who show that identification of the causal effect of a list price change depends
on the market not anticipating the exact moment at which the list prices isreduced. No anticipation only implies that buyers do not know the exact
moment at which the list price is lowered. The actual list price reduction
thus causes a shock to the market. Buyers may, however, know that certain
houses are at risk of lowering the list price or that list prices are likely to belowered in certain time periods. No anticipation thus does not imply thatlist price reductions are exogenous, or that the rate at which list prices are
reduced is the same over time. Houses may have different rates at whichhouse prices are reduced, and it may be that during particular periods ahouse price reduction is more likely than in other periods. We thus explicitly
allow for selection on unobservables. Also if some houses never lower the list
price (i.e., the duration distribution until lowering the list price is defective),the model is identified.
We not only focus on the time until selling the house, but also allow for
the option to withdraw a house from the market. Withdrawing a house fromthe market is not exogenous (e.g. Taylor, 1999). We explicitly incorporate
this in our model by having competing risks. This implies that we estimate
the causal effect of a list price change on both the probability of selling thehouse and the probability of withdrawing the house from the market.
We use a unique administrative data set from the Dutch NVM (Association of Real Estate Brokers and Real Estate Experts) on houses put for saleon the Dutch housing market during the period 2005–2007. The data containdaily information on the time the house was on the market. Also the reason
for leaving the market is recorded, so we also observe houses withdrawn fromthe market by the seller. Such information is not trivial. Caplin and Leahy(1996) discuss the consequences of self selection in markets with frictions
such as real estate markets, when only sales are observed. Withdrawal datacontain important information about the selling process (e.g. Taylor, 1999).
The data contain not only the initial list price, but are also informative on the
moments and magnitudes of all list-price changes. Furthermore, we observea very extensive set of characteristics of each house.
Our paper contributes to two earlier studies considering list price changes.
Both Knight (2002) and Merlo and Ortalo-Magné (2004) provide descriptive3

evidence on list price reductions. Knight (2002) suggests that high initial list
prices are costly to the seller. Those houses with large reductions in initiallist prices take longer to sell and ultimately sell at lower prices. Merlo andOrtalo-Magné (2004) establish a number of stylized facts of bidding behavior
and list price changes.
The remainder of the paper is organized as follows. Section 2 describes theinstitutional setting of the Dutch market for owner occupied houses. Section
3 describes our empirical model that is inspired by the theoretical literature.
Section 4 describes the unique administrative data set. Section 5 presents
the estimation results and some sensitivity analyses. Section 6 concludes.

2

Owner Occupied Housing Market in the
Netherlands

In this section, we describe some institutions of the Dutch housing market.
We mainly focus on owner occupied sector, and highlight aspects relevant
for our purposes. According to Statistics Netherlands in 2006, 56% of theseven million households in the Netherlands were living in an owner occupied
house. The average price of owner occupied houses was e235,842, which is
4.57 times the average household income.
During the 1990s the Dutch housing market experienced a large real priceincrease. Apart from a growth in real income, this price increase is often
explained from population growth, stringent spatial planning policies reducing the construction of new houses, reduced interest rates on mortgages, andchanges in the Dutch mortgage finance market.1 Usually banks restrict mortgages to 4.5 times the household income, but in exceptional cases they givehigher mortgages. There is no restricting on the loan-to-value ratio. Ball(2009) indicates that in 2007 the loan-to-value ratio of first-time buyers was
114%.
There are substantial transaction costs associated to purchasing a house.
1
Since 1990 mortgages can be based on total household income rather than the incomeof the highest earner. Furthermore, during the 1990s new mortgage products were introduced which exploited more the existing tax benefits. Interest payments on mortgages are
currently for 30 years 100% deductable.

4For existing houses there is a transaction tax of 6%, which is absent for
new houses. Broker costs are between 1% and 3% of the selling price, andmortgage often have a 1% to 1.5% initiation fee. Furthermore, there arenotary fees and possible intermediary fees. Total transaction costs are approximately 10% of the selling price, and are often financed by including
them in the mortgage principle.
Usually when selling a house, the seller approaches a real estate broker.
Most real estate brokers are connected to the NVM. Actually, about 70% of
all houses offered for sale are offered through a member of the NVM. The
broker advises the seller on an appropriate list price, but the seller determines
the list price. The real estate broker adds the house on a publicly available
website with a list price, a detailed description on the characteristics andsome pictures. This website also contains information about socioeconomic
characteristics of the neighborhood. The seller can also choose to advertisein media such as local newspapers. An interested potential buyer contacts
the broker for information on the house or to visit the house. A visit to thehouse is usually hosted by the broker, and the seller will not be present.
In the Netherlands, list prices do not have a formal role as list prices are
not binding. So even if a buyer is willing to pay the list price, a seller canrefuse or try to negotiate a higher price. There are some rules for negotiatingwith potential buyers. Potential buyers communicate their bids to the broker.
The broker will then contact the seller and this starts the negotiation process.
It is not allowed to negotiate with multiple buyers at the same time or toreveal bids to other buyers. Furthermore, the seller should negotiate withpotential buyers in the order in which they made their first bid. Finally, if
after selling the house it turns out that there are defects to the house, thebuyer can hold the seller liable for the costs of repairing (even in case thebuyer inspected the house during the sale). The seller thus has by law the
obligation to reveal all information about the house.

53

The Model

3.1

Theoretical Framework

An early model for the housing market was developed by Olsen (1969). Because it is a model of perfect competition and symmetric information, list
prices play no role. Lazear (1986) provides a two-period model in which list
prices are actually important. Sellers face uncertainty about buyers’ valuations, and learn in the first period. Therefore, second-period list prices arelower than in the first period. The price setting is related to market thinness. In a thin market, sellers receive less strong signals about the buyers’
valuation. Therefore, they start with a lower initial price and prices decrease
less rapidly. However, the model imposes that the object is sold to the firstbuyer who is willing to pay the list price. This assumption makes the model
less suitable for the housing market. However, it indicates that list prices
and list-price changes are often not exogenous.
Also Taylor (1999) develops a two-period model, but he allows for twosided asymmetric information. Also in this model list prices are binding.
The model is specified such that sellers cannot use the list price to signal thequality of their house. Houses that remain on the market for a long time
become stigmatized, and can eventually be removed from the market as sale
becomes unlikely. This is the consequence of buyer herding and informationcascades, which can arise if second-period buyers do not observe list-pricehistories and inspections are not public. The latter might imply that highquality sellers will reveal as much information as possible to the market, andlow-quality sellers mimic their behavior. Indeed, empirical evidence confirmsthat the probability of sales decreases with time on the market (e.g. De
Wit, 2009; Huang and Palmquist, 2001; Pryce and Gibb, 2006; and Zuehlke,1987). However, withdrawal from the market is not exogenous to selling thehouse, and empirical analyses should take this into account.
Both Horowitz (1992) and Haurin, Haurin, Nadauld and Sanders (2006)
presents a model in which buyers never bid above the list price. In both models bids arrive and sellers have to decide to reject or accept a bid immediately.
Haurin, Haurin, Nadauld and Sanders (2006) assume that the rate at which

6bids arrive reduces in the level of the list price. Horowitz (1992) imposes thatthe distribution of bids depends on the list price, which could be interpreted
as the seller revealing information about the (unobserved) reservation priceto the market. Both models explain why sellers choose list prices, and expressions for the optimal list price are derived. However, the bidding behavior ofbuyers is very mechanic, and not driven by any underlying optimization.
Albrecht, Gautier and Vroman (2010) explicitly model the behavior ofbuyers in the housing market. They allow list prices to be used as signals.
In their model sellers differ in the reservation value for their house, but thisis private information. Such heterogeneity might arise because some sellers
already hold a new house (e.g. Wheaton, 1990). Search of buyers is directed,
so based on the list price they determine which house to visit. After visitinga house, buyers receives a match-specific value. In equilibrium, list prices
reveal the seller’s type. Sellers with a higher reservation value set a higher
list price. Even though list price are not binding, the list price has both aneffect of the selling price and the probability of sale. In particular, sellers who
post a higher list price are less likely to sell their house, but receive a higher
selling price. This only holds if sellers are heterogenous, and the source ofheterogeneity is unobserved by buyers. If sellers would be homogenous, there
would not be any role for list prices.
The key conclusion is that if list prices are not binding, in the absence ofasymmetric information list prices do not play any role (Albrecht, Gautierand Vroman, 2010). This implies that the level of the list price does notaffect the probability of sale. It is important to distinguish between information known to the market and to the econometrician. For example, market
thinness within particular segments may be known to both buyers and sellers, but is unknown to the econometrician (Lazear, 1986). Furthermore, theeconometrician should explicitly take account of withdrawing as this might
not be exogenous in a housing market of asymmetric information (Taylor,1999).

7

3.2

Empirical Model

The main conclusion from the theoretical literature is that if list prices are notbinding, they only play a relevant role in the case of asymmetric information.
In particular, Albrecht, Gautier and Vroman (2010) show that list prices areused for signaling the (unobserved) seller type, and list prices are thus related
to the probability of sale. The key empirical problem is that list prices areendogenous. There may be relevant market characteristics that are observed
by both the buyer and the seller, which are unobserved by the econometrician.
Therefore, we focus on list-price changes rather than the level of the list price.
We use the timing-of-events model to estimate the causal effect of a list-pricechange on the probability of selling the house (e.g. Abbring and Van den
Berg, 2003). Furthermore, we jointly model withdrawals from the market,
as Taylor (1999) shows that withdrawing a house may not be exogenous ina market of asymmetric information.
Before we provide the details of our econometric model, we first give a
brief outline of the data. Our data contains houses which became for salebetween January 2005 and December 2007. For each house we observed theexact date of entering the market, the date of leaving the market, and thereason for leaving the market (sale or withdrawal). Furthermore, we observe
the initial list price, and the date and magnitudes of all possible list-pricechanges while the house was on the market. It should be noted that weonly focus on the first list-price change. About 20% of the houses entering
the market change list price, but only in 0.76% of the case the list price ischanged more than once.
Consider a house which is put on the market at (calender) date τ0 . Ourmodel is a continuous-time duration model in which t denotes the time ahouse is already on the market and tp the moment of changing the list price.
Let θs denote the rate at which houses are sold, and θw the rate at whichhouses are withdrawn from the market. These transition rates can dependon the duration the house is already on the market t, calendar time τ0 +t, observed characteristics x, some characteristics v which are observed by
the market but unobserved to the econometrician, and a variable indicating
whether a list price was lowered I(tp < t) (with I(·) the indicator function).

8Lowering a list price has a permanent effect on the rate at which houses are
sold and the rate at which houses are withdrawn. We relax this assumption
in Subsection 5.2 when we perform a number of sensitivity analyses.
We denote the unobserved term v in the rate of selling the house by vs , andin the rate of withdrawing by vw . These terms are allowed to be correlated
to each other, but are assumed to be independent of x and τ0 . Since thevariables in x are mainly used as control variables, and we will not causallyinterpret their covariate effect, this is not a strong assumption. Conditionalon τ0 , x, vs and tp , the rate at which a house is sold after t periods on themarket follows a familiar mixed proportional hazard specificationθs (t|x, vs , tp ) = λs (t)ψs (τ0 + t) exp(x′ βs + δs · I(tp < t) + vs )And a similar specification is used for the rate at which houses are withdrawn
from the market
θw (t|x, vw , tp ) = λw (t)ψw (τ0 + t) exp(x′ βw + δw · I(tp < t) + vw )In these specification ψs (τ0 + t) and ψw (τ0 + t) are genuine calendar-time
effects modeled by dummies for each quarter. The functions λs (t) and λw (t)represent duration dependence, which might, for example, be the consequenceof stigmatization. Although, it should be noted that in a housing market withsymmetric information, time on the market does not provide a signal to themarket and the duration dependence term should thus be constant. The
parameters δs and δw are the key parameters of interest as these denote thecausal effect of a list-price reduction on the rate at which houses are sold andwithdrawn. In case of symmetric information, list prices should be irrelevant.
Our test for symmetric information thus consist of testing if δs and δw are
equal to zero.
The timing of list-price reductions tp are most likely not exogenously
determined. Therefore, we jointly model the timing of list-price reductions
also using a mixed proportional hazard specification
θp (t|x, vp ) = λp (t)ψp (τ0 + t) exp(x′ βp + vp )9The rate θp thus denotes the rate of lowering a list price if no list-pricereduction has yet occurred while the house was on the market. The ratedepends on the same set of observed characteristics x as the rate at whichhouse are sold and withdrawn.
Now consider the joint distribution of ts , tw and tp . Conditional on τ0 ,x, vs , vw and vp , the only possible relation between (ts , tw ) and tp is by wayof the direct effect of a list-price change on the selling rate and the rateof withdrawal. In case of independence between (vs , vw ) and vp , we would
have a standard competing-risks model for (ts , tw )|x, τ0 , tp with I(tp < t)a time-varying regressor which is orthogonal to the unobserved heterogeneity (vs , vw ). However, if (vs , vw ) and vp are not independent, inference on(ts , tw )|x, τ0 , tp has to be based on (ts , tw , tp )|x, τ0 . Let G(vs , vw , vp ) be thejoint distribution function of the unobserved characteristics (vs , vw , vp ).
It is straightforward to derive the likelihood contributions from the specifications of the different hazard rates. The use of a flow sample of houses
entering the market implies that we do not have any initial conditions problems. The right-censoring in the data is exogenous, and is, therefore, solvedin a straightforward manner. In particular, let cs equal one if a house isobserved to be sold, cw is one if the destination state was withdrawal, and cp
indicates if the list price was reduced. If i = 1, . . . , n denote the observations,then the loglikelihood function equals
log ℓ =

n
X
i=1

log

(Z Z
vs

vw

Z

θs (ti |xi , τ0,i , vs , tp,i )cs,i θw (ti |xi , τ0,i , vw , tp,i )cw,ivp

 Z tiexp −θs (z|xi , τ0,i , vs , tp,i ) + θw (z|xi , τ0,i , vw , tp,i )dz0 Z tp,icp,iθp (tp,i |xi , τ0,i , vp ) exp −θp (z|xi , τ0,i , vp )dz dG(vs , vl , vp )0

If the house was still on the market at the end of the observation period(cs = cw = 0), then t equals the duration until right-censoring. Furthermore,
if during the time on the market no list-price reduction has been observed(cp = 0), then tp is set equal to the time t the house was on the market(which is the moment of censoring the duration until a list-price reduction).
Abbring and Van den Berg (2003) provide an extensive discussion on
10

the identification of such models. The key identifying assumptions for thecausal effects δs and δw of list-price reductions is that such reductions are notanticipated. Formally, no anticipation of the exact moment of the list-pricereduction implies that for td 6= t′d and t < td , t′d , it should be thatθs (t|x, vs , td ) = θs (t|x, vs , t′d ) and θw (t|x, vw , td ) = θw (t|x, vw , t′d )This thus implies that conditional on both observed and unobserved characteristics, the current selling rate and the current rate of withdrawal do notdepend on the exact moment of a future list-price reduction. No anticipation
thus does not imply that list-price reductions are exogenous, or that the rate
at which list prices are reduced is continuous over time. Houses (based on
both observed and unobserved characteristics) may have different rates atwhich list prices are reduced, and it may be that during particular periods alist-price reduction is more likely than in other periods. We thus explicitly
allow for selection on unobservables. Also if some houses never lower the list
price (i.e., the duration distribution until lowering the list price is defective),the model is identified.
It may be clear that in this setting the assumption of no anticipation
is satisfied. Obviously buyers are not informed a priori about a list-pricereduction. List-price reductions thus come as a shock. Furthermore, sellers
do not know when potential buyers arrive and bid for their house. If theassumption of no anticipation is satisfied, no exclusions restrictions or verystrong functional-form restrictions are necessary to identify the causal effects
of list-price reductions. To provide some intuition for the identification, firstnote that the data can be broken into two parts: (i) a competing-risks partfor the duration until a house leaves the market (after being sold or taken off
the market) or a lowering of the list price, whichever comes first, and (ii) the
residual duration from the moment of lowering the list price until the houseleaves the market. From Heckman and and Honoré (1989) it follows that
under general conditions the whole model except for δs and δw is identified
from the data corresponding to the competing-risks part. Subsequently, δsand δw are identified from the data corresponding to part (ii) of the model,
i.e. the residual duration on the market after a list-price reduction. Abbring11

and Van den Berg (2003) show that the causal effects of list-price reductions
δs and δw are allowed to depend on t, τ0 , x and v. We exploit this is in thesensitivity analyses discussed in Subsection 5.2.

3.3

Parameterization

For the duration dependence functions and the trivariate unobserved heterogeneity distribution we take the most flexible specifications used to date(e.g. Heckman and Singer, 1984). We take λs (t), λw (t) and λp (t) to have apiecewise constant specification,
λi (t) = exp

X

λij Ij (t)j=1,2,...

!

i = s, w, p

where j is a subscript for duration intervals, and Ij (t) are time-varying
dummy variables that are one in consecutive time intervals. Note that withan increasing number of intervals any duration dependence pattern can beapproximated arbitrarily closely. We normalize the pattern of duration dependence by fixing λi1 = 0.
We take the joint distribution of the unobserved heterogeneity terms vs ,vw and vp to be trivariate discrete with unrestricted mass-point locations foreach term. In particular, we allow for K terms
Pr(vs = vsk , vw = vwk , vp = vpk ) = pk

for k = 1, . . . , K

with p1 + . . . + pK = 1. For K ≥ 2 this specification allows for dependence
between the different unobservable heterogeneity terms. The degree of flexibility increases with K. We do not restrict the locations of the mass points,
but instead we normalize the model by not including an intercept in thevector of observed characteristics x.

4

The Data

Our data contain all houses and apartments offered for sale through all realestate brokers associated to the Dutch NVM between January 2005 and12December 2007. This covers about 70% of all houses and apartments offered
for sale in the Netherlands. For each house (and apartment) we observe theexact date when it was put on the market, and the initial list price. We
also observe the exact date at which the house was sold or was taken off themarket. If it was still on the market on January 1, 2008, the time on themarket is exogenously right censored. Furthermore, we observe the exact
dates and the sizes of all revisions of the list price.
For each house, we observe a rich set of characteristics. There is information on the type of house (12 types), the construction period (5 periods),
parking facility (4 types), garden location (9 types), and region (76 regions).2
The data also include several size characteristics such as the floorsize, lotsize(in square meters), and the number of rooms in the house. Furthermore,
we observe whether the dwelling is well isolated, type of heating system (3types), location next to a quiet road, possible groundlease, presence of anelevator in the apartment building, and two variables measuring inside andoutside quality on a discrete scale from 1 to 9. These quality measures are
determined by the real estate broker selling the house.
Table 1 presents some details of the data. In total our data contain498,369 houses put on the market. For 369,611 houses we observe a sale,
51,092 houses were taken off the market, and 77,666 houses were still on
the market at the end of the observation period. On average, the initial list
price is e274,367 (although the average initial list price for houses which
did sell was substantially lower at e259,410), and the average selling priceis e246,614. About 89% of the houses are sold below the list price. It
should, however, be noted that the average list-price premium for houses
which experienced a list-price reduction was 1.82% versus -1.14% for houses
which did not experience a list-price reduction.3
2

Within a NVM region 80% of the families changing house stay within the region.
The list-price premium is the difference between the log of the initial list price of thehouse and the predicted log value of the initial list price of the dwelling. The predicted log
value of the list price is based on standard loglinear regressions separately performed foreach year (e.g. Rosen, 1974). The R-squared for these regressions are 79.8%, 80.1% and80.2% for 2005, 2006 and 2007, respectively. This approach is identical to that in Merlo
and Ortalo-Magné (2004). The variable gives us a measure of overpricing or underpricingof the house based on what would be a “normal” list price for the house based on observed
characteristics.
3

13

Table 1: Some characteristics of the data set.
Number of observations
498,369
Number of sales
369,611
Number of withdrawals
51,092
Number of right censored
77,666
Number of list-price reductions
Average list-price reduction
Average selling price
Average list price

101,8965.5%
e246,614
e274,367

Average list price premium:for houses which list price was reducedfor houses which list price was not reduced

1.82%-1.14%

Note.–List-price premium is the difference between the log of the initial list price of thehouse and the predicted log value of the initial list price of the house. The predicted log
value of the list price is based on a log-linear hedonic regression.

Figure 1 shows the Kaplan-Meier estimates for the survival function for
selling the house and withdrawing from the market (without sale). Whenestimating the survivor function for selling the house, withdrawing is considered to be exogenous, and vice versa. This also implies that the probability
that a house is still on the market after some duration is the product of thesurvivor to selling the house and the survivor for withdrawal. If no houses
would have been withdrawn before, about 50% of the houses is sold after
108 days. Withdrawal is a much slower process, it takes 665 days before theprobability of withdrawn reached 0.5.
In total, for 101,896 dwellings we observe a list-price reduction during
the period the dwelling was on the market. Only 0.76% of the dwellings forwhich a list price has been reduced, experience a subsequent reduction in thelist price. Therefore, in the empirical analyses we only focus on the first listprice reduction. Figure 2 shows the survivor function for repricing the house.
Within the first 100 days less than 13% of the houses is repriced. After that
period the probability of repricing increases. However, it still takes 299 days
before the probability of repricing reaches 0.5. If a list price is reduced, the14

Figure 1: Kaplan-Meier estimates for the survivor function to selling thehouse and withdrawal.
average reduction is 5.5%.

5Estimation Results

5.1

Parameter Estimates

In this section we discuss the results of our empirical analyses. First, we
present the parameter estimates of our baseline model and provide somemodel simulations, while in the next subsection we perform sensitivity analyses.
For the piecewise constant duration dependence we choose the followingintervals: 0-30 days, 31-60 days, 61-120 days, 121-180 days, 181-270 days,
271-360 days, 361-720 days, and beyond 720 days. However, in the hazard towithdrawing we merge the last two intervals to one interval beyond 361 days,
and in the repricing hazard we merge all intervals beyond 181 days. For theunobserved heterogeneity we have three mass points (K = 3). We allowed
for additional mass points, but the probability mass associated to a fourth15

Figure 2: Kaplan-Meier estimates for the survivor function for reducing thelist price.
point converged towards 0, and the loglikelihood function did not show any
improvement. The vector of observed characteristics includes 61 variables.
Table 2 presents the parameter estimates of our baseline model. The
main parameters of interest are δs and δw , which represent the effect of alist-price reduction on, respectively, the rate at which a house is sold andwithdrawn from the market. The parameter estimates show that a list-pricereduction has both a positive and significant effect on selling the house andwithdrawing the house from the market. The estimated value of δs is 0.606,
which implies that after a list-price reduction the rate at which the house issold increases with (exp(0.606) − 1) × 100% = 83%. A list-price reduction
increases the rate at which the house is withdrawn from the market with(exp(0.366) − 1) × 100% = 44%. Recall that in a housing market withsymmetric information, there is no important role for list prices. Our results
indicate that list-price reductions have very substantial effects. This should
thus be interpreted as evidence in favor of asymmetric information between
sellers and buyers in the housing market.
The distribution of unobserved heterogeneity shows three mass points.
16

Table 2: Estimation results of the baseline model.
Sale Hazard θs
Effect of repricing δ
0.606 (0.026)
Unobserved heterogeneityυ1−2.739 (0.970)
υ2−2.789 (0.756)
υ3−4.120 (0.764)
p1
0.834 (0.355)
p2
0.103 (0.017)
p3
0.062 (0.754)
Duration dependence
λ0 (0-30 days)0
λ1 (31-60 days)
0.033 (0.016)
λ2 (61-120 days)−0.268 (0.017)
λ3 (121-180 days)−0.441 (0.022)
λ4 (181-270 days)−0.502 (0.027)
λ5 (271-360 days)−0.498 (0.037)
λ6 (361-720 days)−0.448 (0.049)
λ7 (> 720 days)−0.524 (0.125)
Calendar time effects
2005-Q1
0
2005-Q2
0.055 (0.039)
2005-Q3
0.144 (0.039)
2005-Q4
0.211 (0.038)
2006-Q1
0.239 (0.039)
2006-Q2
0.196 (0.038)
2006-Q3
0.155 (0.039)
2006-Q4
0.140 (0.039)
2007-Q1
0.209 (0.039)
2007-Q2
0.177 (0.038)
2007-Q3
0.083 (0.039)
2007-Q4
0.067 (0.039)
List price premium−0.632 (0.031)
Additional controls
Yes
Log likelihood
Observations

Exit Hazard θw
0.366(0.075)

Repricing Hazard θp−13.901−7.720−7.847(1012.179)(1.650)(1.639)−12.577−12.146−13.675

0
0.069
0.322
0.595
0.698
0.833
0.977
...

(0.062)(0.058)(0.067)(0.078)(0.096)(0.122)

0
1.167
1.874
2.193
2.383
...
...
...

0
0.109
−0.185−0.0350.142
0.038−0.064
0.034
0.133
0.087
0.110
0.107
0.106

(0.135)(0.134)(0.131)(0.129)(0.130)(0.130)(0.129)(0.128)(0.128)(0.128)(0.128)(0.076)Yes-3,401,344.37
498,369(1.898)(1.403)(1.406)(0.053)(0.049)(0.053)(0.061)

0
0.226
0.035
0.094
0.107
0.249
0.034
0.093
0.171
0.301
0.038
0.143
0.228

(0.107)(0.105)(0.105)(0.105)(0.104)(0.105)(0.104)(0.104)(0.104)(0.105)(0.104)(0.057)
Yes

Note.–List-price premium is the difference between the log of the initial list price of thehouse and the predicted log value of the initial list price of the house. The predicted
log value of the list price is based on a log-linear hedonic regression. Additional controls
are for number of rooms, log(lotsize), log(lotsize) squared, log(floorsize), log(floorsize)
squared, construction period, type of house (or type of apartment), presence of a lift in
the apartment building, parking facility, garden location, isolation, location to busy roads,
groundlease, inside and outside quality of the house, and regions. Standard errors are inparentheses.

17

Most probability mass (83%) is located at houses which are sold relatively
fast and have a very low rate of withdrawal. This might be regular houses
where the seller is determined to sell the house, for example, because theseller already obtained another house. These houses have an average rate ofrepricing. The second mass point (10% probability) describes houses which
both have a high rate of sale and of withdrawal, and also a high rate ofrepricing. The final 7% probability is located at the third mass point, which
describes houses with a low rate of sale and also with a low rate of repricing,but with a high rate of withdrawal. These might be relatively patient sellers
or seller preferring to sell their house first (for a sufficiently high price) before
buying a new house. The main conclusion is that there are relevant unobserved characteristics which causes dependency between the three hazards.
There is significant and substantial negative duration dependence in therate of selling, so it becomes less likely to sell the house the longer it is on
the market. This might indicate that houses get stigmatized once they arefor sale for a longer period. Stigmatization would imply the presence of acharacteristic unobserved by buyers which might, for example, be revealed
during inspections (e.g. Taylor, 1999). This might suggest asymmetric information, but it is not necessary that the seller has more information. An
alternative explanation could be that a house which is new on the market
is considered by the current stock of all potential buyers. Once they havedecided against buying the house, the house can only be sold to new buyers
entering the housing market. This argument is similar to stock-flow matching models for the labor market (e.g. Coles and Smith, 1998). The duration
dependence in the rate of withdrawing a house from the market is positiveand significant. So the longer the house is on the market the more likely
it becomes that the seller withdraws the house. Also there is positive andsignificant duration dependence in the rate or repricing. Our paper is thefirst showing empirical evidence in favor of positive duration dependence inrepricing. This is consistent with the theory in Lazear (1986), showing thatprices decline in time on the market.
To capture the effects of business cycles, we included a flexible time trendcontaining indicators for each quarter. The parameter estimates show an increasing trend in the rate of selling a house during 2005 and a decreasing trend18

starting in the second quarter of 2007. There are no significant calendar-time
effects in the rate of withdrawing a house. In the rate of repricing a house,
there are only significant increases in each second quarter of the year. This is
usually the quarter of the year, which real estate brokers consider as the best
moment of selling a house. It might be that, therefore, real estate brokers
advise sellers to reduces their list price in this quarter.
In the estimation we also included the list-price premium as explanatory
variable. Recall that the list-price premium is defined as the list price of thehouse compare to an hedonic list price for the house. A positive list-pricepremium thus implies that the house is priced higher than comparable housesin the market. It should be stressed that no strong causal interpretation
should be given to the covariate effects of the list-price premium. However,
the estimation results are consistent with most theoretical predictions (e.g.
Lazear, 1986). Houses with a higher list-price premium are less likely to be
sold, are more likely to be withdrawn from the market, and more likely to be
repriced. These results (and also all other parameter estimates) are robust
against alternative specification of the list-price premium, for example, thelist price relative to the hedonic selling price.
A list-price reduction has a positive and significant effect on both therate at which are sold and withdrawn from the market. The changes in these
rates are quite substantial. In Table 3 we present how these effects translate
in the probability of selling and withdrawing a house from the market. In
particular, we consider list-price changes after a house has been on the market
for one month (30 days), one quarter (91 days) or half a year (182 days), andwe focus on selling or withdrawing the house within one quarter, half a year
and one year (365 days). In the model calculations we only take into accounthouses entering the market in 2005 and 2006.4 Column (1) shows that if list
prices would never be reduced about 44% of all houses are sold within one
quarter (and 3.2% of the houses are withdraw from the market). A list-pricereduction after one month increase the percentage of houses sold within one
quarter to almost 57% (see Column (2)). This is substantial, particularly
4

In the model calculations we follow houses for at most one year. Since we modeledbusiness-cycle effects using quarterly dummies, we cannot say anything beyond January 1,
2008 without imposing some arbitrary extrapolation. Therefore, in the model calculations
we ignore houses entering the market in 2007.

19

Table 3: Predicted probabilities for the baseline model.
(1)
never

Moment of repricing
(2)
(3)
(4)
30 days 91 days 182 days
79.4%
52.8%
33.5%

Sold within 91 daysWithdrawn within 91 days

44.0%
3.2%

56.7%
4.2%

44.0%
3.2%

44.0%
3.2%

Sold within 182 daysWithdrawn within 182 days

60.7%
5.9%

73.9%
7.2%

68.6%
7.1%

60.7%
5.9%

Sold within 365 daysWithdrawn within 365 days

74.8%
9.7%

83.5%
10.3%

81.6%
11.0%

79.5%
11.0%

In market at repricing

Note.–Only houses entering the market in 2005 and 2006 are taken into account.

since after 30 days only about 79% of the houses are still on the market.
A list-price reduction also increases withdrawing the house from the market.
However, a late list-price reduction 182 days compared to 30 days has a more
substantial effect on withdrawing the house than selling the house within 365
days (see Column (2) and (4)). In the next subsection we focus more on theimportance of the timing of a list-price reduction.

5.2

Sensitivity Analyses

In this subsection we examine the robustness of our parameter estimateswith respect to the model specification. We provide a number of sensitivity
analyses, and mainly focus on the effects of list-price reductions.
In the first sensitivity analysis we consider the importance of allowing for
dependency between the different hazard rates. Independent hazards implies
that the unobserved heterogeneity components in the three hazard rates are
independent of each other. Note that this does not mean that unobserved
heterogeneity is absent from the model. From the first plane in Table 4 it canbe seen that not correcting for dependency between the hazards causes anincrease in the estimated effects of the list-price reductions. Compared to thebaseline model the effect of a list-price reduction on selling the house increases
from 0.606 to 0.771, and given the small standard errors these effects are20

statistically different from each other. The effect on withdrawing increases
from 0.370 in the baseline model to 0.405. These results show the importanceof self selection in the decision to reduce the list price. Obviously houses
with a lower rate of selling are more likely to be repriced, which confirms theargument of Caplin and Leahy (1996) that self selection matters in markets
with frictions.
Next, we consider the size of the list-price reduction. Therefore, we interact the effect of the list-price reductions with the magnitude of the list-pricereduction, measured as fraction decrease in the list price. The specificationof the effect of a list-price reduction is thus δ0 + δ1 ∆P , where ∆P is the sizeof the list-price reduction. The second plane in Table 4 shows that the sizeof the list-price reduction has a positive and significant effect on selling thehouse. If the list price is more substantial the effect of the list-price reduction
is larger. Recall from Section 4 that the average list-price reduction in thedata is 5.5%. So for the average list-price reduction the effect on the hazard of sale is 0.630, which is not very different from the homogeneous effectestimated in our baseline model. The size of the list-price reduction does
not have a significant effect on the rate of withdrawing the house from themarket. It is important to stress that although these results provide someindication about the effects, it is difficult to draw strong causal conclusions.
This is the case because the size of the list-price reduction is most likely
endogenous which is not taken into account in our model.
Next, we want to know if the timing of the list-price reduction is important. So does a list-price reduction if the house is only shortly on the market
have a different effect than a list-price reduction if the house is already forsale for a longer period. Therefore, we alow the effect depends on time on themarket. In particular, we allow the effect to be different within three timeintervals, (i) within the first 60 days, (ii) between 60 days and 182 days, and(iii) after 182 days on the market. The third plane in Table 4 shows thatthe effect of a list-price reduction on the hazard of selling the house increases
in time on the market, while the opposite is the case in the hazard of withdrawing. However, recall that we found negative duration dependence in the
selling hazard and positive duration in the rate of withdrawing the housefrom the market. So in absolute terms the change in selling and withdrawing
21

Table 4: Sensitivity analyses on the effect of list-price reductions.

22

Sale Hazard θs

Exit Hazard θw

Independent hazards
Effect of repricing: δ

0.771(0.016)0.405(0.036)

Repricing interacted with magnitude
Effect of repricing: δ0
Interaction with magnitude δ1

0.506
2.270

(0.025)(0.290)0.370−0.164(0.060)(0.600)Moment of repricing
Repricing within 60 days δ1
Repricing between 60 and 182 days δ2
Repricing after 182 days δ3

0.464
0.649
0.762

(0.032)(0.026)(0.034)

0.650
0.330
0.150

(0.076)(0.060)(0.072)0.603(0.020)0.400(0.046)

Model with transaction priceEffect of repricing δ1
Days on the market δ2

Transaction price

0.308(0.020)0.00216 (0.00008)

Note.–Similar specification and controls as in the baseline model. Full sets of parameter estimates are available on request. Standard
errors are in parentheses.

probabilities due to list-price reductions do not vary that much in the timingof the list-price reduction.
A list-price reduction reduces the average duration until selling a house.
In a housing market with asymmetric information, time on the market may
be a negative signal and thus negatively affect the selling price. Other than
this indirect effect, list-price reductions may also have a direct effect on thetransaction price simply because a list-price reduction provides a signal to themarket. A first indication is that houses with an observed list price reduction
are sold, on average, 3.6% below their hedonic value, while houses without
a list price reduction are sold 0.9% above their hedonic value. However,
we showed above that there are also unobserved characteristics affecting thedecision to reduce the list price. To take this into account we extend ourmodel with a model for transaction prices. We use also a hazard rate modelfor the transaction prices p, with the density function
 Z p

ft (p|tp , t, τ0 , x, vt ) = θt (p|tp , t, τ0 , x, vt ) exp −θt (s|tp , t, τ0 , x, vt )ds0withθt (p|tp , t, x, vt ) = λt (p)ψt (τ0 + t) exp (x′ βt + δt,1 · I(tp < t) + δt,2 · t + vt )So ψt (τ0 +t) denotes calendar-time effects at the moment of selling the house,
I(tp < t) describes if the list price was reduced while the house was on themarket, and t is the time the house was on the market before being sold. The
unobserved heterogeneity term vt can be correlated to the unobserved heterogeneity terms in the selling, withdrawing and repricing hazard to accountfor endogeneity. The parameters of interest are the direct effect of a listprice reduction δt1 , and δt,2 capturing the effect of time on the market on thetransaction price. Using a hazard rate model for transaction prices follows
Donald, Green and Paarsch (2000) who present hazard rate specifications asvery flexible models for wages.
The bottom plane of Table 4 shows the estimation results for the main
parameters of interest. First, it should be noted that jointly modeling thetransaction price hardly changes the parameter estimates obtained from the23baseline model. The parameter estimate of a list-price reduction on thehazard for the transaction price is positive, which implies that a list-pricereduction reduced the expected transaction price. There is thus a substantial disadvantage to the seller of reducing the list price. However, also thecoefficient of the time on the market is positive, implying that the expectedtransaction price reduces in the time the house was on the market before theselling it. However, only if the list-price reduction reduces the time on the
0.308
market with more that 0.00216≈ 143 days, the indirect effect dominates the
direct effect on the transaction price.
Finally, Haurin (1988) distinguishes between regular houses and atypicalhouses. We also considered this distinction. First, we included a dummy
variable for atypical houses (using the Haurin, 1988; measure) in our model.
This did not change any of the estimation results. In particular, the effectsof the list-price reductions were unaffected. Second, we estimated the model
again without atypical houses, which excluded 10% of the data. The effectof the repricing slightly changed to 0.598 (s.e. 0.028) on the hazard of saleand 0.364 (s.e. 0.078) on the hazard of withdrawal. This indicates that our
results are not driven by atypical houses changing list prices, but also holdin the market for regular houses.

6

Conclusions

The main focus of this paper is on the effect of lowering list prices on thetime at which a house is on the market. In our empirical model we explicitly
allow for selectivity in list-price reductions. We also take into account thathouses can also be taken off the market without being sold and that such exits
might not be exogenous. Our model is a timing-of-events model described in
Abbring and Van den Berg (2003).
Our empirical results show that list-price reductions significantly increase
the hazard of sale, but also increase the hazard at which the house is taken
off the market. The effects are very substantial. A list-price reduction raises
the selling rate by 83%, and the rate of withdrawing by 44%. Since list prices
do not have any formal legal role in the Dutch housing market, list prices canonly be used by the seller to provide signals to the market. In a market with24

symmetric information, signals do not add any information. Therefore, weinterpret the substantial and significant effect of the list-price reductions asevidence in favor of the presence of asymmetric information in the housing
market.
In the sensitivity analyses, we have also shown that the timing and themagnitude of the list-price reduction matter. Furthermore, we have stressed
the importance of allowing for selectivity in list-price reductions and taking
withdrawals from the market into account. Our results confirm the argument
made by Caplin and Leahy (1996) that self selection-effects matter in markets
with frictions. Finally, we have investigated the effect of list-price reductions
on the transaction price. List-price reductions reduce the expected transaction price, which is the direct effect. However, also the time on the market
before selling the house has a negative effect on the transaction price. The
indirect effect of a list-price reduction is thus that it reduced the time on themarket which again increases the expected transaction price.
We have found negative duration dependence in the hazard of sale, whichmight also be the consequence of houses getting stigmatized due to the presence of asymmetric information. The parameter estimates show positive duration dependence in the rate of withdrawal and the repricing hazard. This
paper is actually the first finding empirical evidence in favor of positive duration dependence in the hazard of repricing, which is consistent with Lazear(1986). Also the finding that higher list prices increase the likelihood of listprice reductions is consistent with Lazear (1986), although our estimate forthe latter is merely an association than an causal effect.

7

Acknowledgements

We are grateful to the Amsterdam School of Real Estate and the NVM(Dutch Association of Real Estate Brokers and Real Estate Experts) for
providing the data.

25

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27