Why Do Vacant Houses Sell for Less

8/6/2019 Why Do Vacant Houses Sell for Less

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2011 V39 1: pp. 1943

DOI: 10.1111/j.1540-6229.2010.00285.x

REAL ESTATE

ECONOMICS

Why Do Vacant Houses Sell for Less:Holding Costs, Bargaining Poweror Stigma?

Geoffrey K. Turnbull

and Velma Zahirovic-Herbert

This article introduces Nash bargaining into a search model to identify variouschannels through which vacancy affects selling price and liquidity in the resalemarket for houses. The model shows the various vacancy effects in the formof greater seller holding cost, lower seller bargaining power and unobservednegative attributes or stigma. We use a 20-year data series on house trans-actions to test for these effects in a simultaneous model of price and liquidity,using the long data series to allow for variation across market phases. The ro-bust vacancy effects on price and liquidity across all market phases primarilyreflect greater seller holding cost and diminished bargaining power. Repeat-edly, vacant houses also exhibit significant stigma effects in the rising marketbut not in stable or declining market phases. At the same time, vacant housesenjoy stronger shopping externality effects from surrounding houses for salethan do their occupied counterparts.

There is a generally held view within the real estate brokerage industry that

vacant houses will experience longer marketing periods, lower selling prices

or both. Real estate brokersand real estate scholarsoffer two explanations

for the vacant house discount. First, empty houses do not show nearly as

well as those that are occupied, which reduces their aesthetic and emotional

appeal for prospective buyers. Second, vacancy signals a more motivated seller,thereby weakening seller bargaining power. Although vacancy has not been the

primary topic of rigorous empirical study, there is abundant empirical evidence

that vacant homes tend to sell for less. This evidence is typically a by-product

of studies focusing on other unrelated questions.1 Vacancy takes a central roleDepartment of Economics, Georgia State University, Atlanta, GA 30302-3992 [email protected].

Department of Housing and Consumer Economics, University of Georgia, Griffin,GA 30223 or [email protected].

1

See, as examples, Turnbull, Sirmans and Benjamin (1990), Asabere and Huffman(1993), Sirmans, Turnbull and Dombrow (1995), Forgey, Rutherford and Springer(1996), Springer (1996) and others. In a different vein, Zuehlke (1987) provides ev-idence of positive duration dependence for vacant homes, suggesting that owners ofvacant dwellings are more likely to reduce reservation prices as their homes remainunsold.

C 2010 American Real Estate and Urban Economics Association


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20 Turnbull and Zahirovic-Herbert

in Harding, Knight and Sirmans (2003); they rely on the logical link between

weak bargaining power of sellers and vacancy to motivate their vacancy-priceresults as a test of bargaining power effects.

This article focuses on the relationship between vacant houses, selling prices

and liquidity in the resale market for houses. It introduces Nash bargaining

into a simple search model to highlight the various channels through which

vacancy affects selling price and selling time. The theoretical model examines

the consequences of vacancy as indicating greater seller holding cost, weaker

seller bargaining power or unobserved negative house attributes or stigma.

Embedding the Nash bargaining approach within a standard search frameworkemphasizes features glossed over in rationales for the vacancy price effect

offered by both practitioners and researchers. This approach emphasizes the

difference between a seller with weaker bargaining power and a seller motivated

to accept a lower price because of higher holding costs. Although it turns out

that both tend to reduce the sellers reservation price (hence expected selling

price), the systematic changes in bargaining power associated with the various

phases of the housing market cycle alter the expected vacancy price discount

over market phases while stable holding cost will not. Further, because higher

holding costs and weaker bargaining power both prompt the seller to lower the

reservation price, they also increase the probability of an earlier sale. Therefore,if the vacant house price discount arises from motivated or weak sellers, the

model suggests that vacant houses will sell more quickly on average, rather

than more slowly as in the generally held view of professionals.

This study uses two decades of house transactions data to test the various

vacant house effects in a simultaneous model of price and liquidity. The long

sample period allows us to study possible variation across declining, stable

and rising market phases. The results show robust vacancy effects on price and

liquidity across all market phases that primarily reflect greater seller holdingcost and diminished bargaining power. At the same time, vacant houses that

are no longer fresh listings tend to enjoy stronger shopping externality effects

from surrounding houses for sale than do their occupied counterparts. The

estimates show no vacant house stigma or negative unobserved attribute or

vacant-related atypicality effects on price and selling time in declining or

trough market phases; there is, however, a significant negative stigma effect

associated with repeatedly vacant houses in rising markets.

This article adds to the existing empirical work that often treats vacancy as a

tangential feature. For example, Forgey, Rutherford and Springer (1996) focus

on the impact of marketability factors and search effort and liquidity when

they estimate a two-stage least squares model of house prices and days on

market. They use house sales data for Arlington, Texas, over 19911993. In


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Why Do Vacant Houses Sell for Less 21

this study, vacancy is one of the marketability variables. In the first stage, the

dependent variable is the log of selling time. The first-stage estimates showthat vacant homes have longer marketing times. The results also show that days

on market depends on the sellers search effort, market conditions, physical

characteristics of the property, the size of the brokerage firm and listing price.

They use the predicted values and residuals from the first-stage estimates to

create the expected days on market variable and the relative difference of the

actual and expected selling time, using these variables in the second-stage price

estimation. The results show that lower selling prices are associated with vacant

houses. Furthermore, the findings of the second stage indicate that increases in

expected selling time result in higher sales prices, supporting the notion thatthe expected sales price rises as a seller more thoroughly searches the market

for the highest offer. The second-stage estimates also indicate that deviations

from the expected time on market are inversely related to selling prices, sup-

porting the notion that buyers pay a premium for more liquid properties.

Similarly, Springer (1996) estimates impacts of seller motivations on selling

prices and marketing times using data for single-family homes sold in Ar-

lington, Texas, again over 19991993. His results show price discounts for

houses with sellers who are eager, motivated or anxious; houses with sellers

who have relocated; foreclosures and vacant houses. However, only foreclo-sure houses show the reduced marketing time expected for properties with

motivated sellers. Sirmans, Turnbull and Dombrow (1995) use Baton Rouge,

Louisiana, data for sales during 19851991 to provide evidence that owners of

vacant houses set lower reservation prices to reflect holding-period costs that

are higher than those for owner-occupied houses because the full cost of carry-

ing the vacant home has no offsetting benefits from either occupancy or rental

income.

In the closest antecedent to this study, Harding, Knight and Sirmans (2003)examine bargaining power in two separate markets (Baton Rouge, Louisiana

and Modesto, California) using vacancy as a proxy for weak seller bargaining

power. Previous research finds evidence that weak buyers pay higher prices and

weak sellers receive lower prices for their homes (Genesove and Mayer 1997,

Arnold 1999, Miceli, Sirmans and Yavas 2001, Anglin, Rutherford and Springer

2003, Harding, Rosenthal and Sirmans 2003). The main argument underlying

the Harding, Knight and Sirmans (2003) study is that sellers of vacant homes are

clearly at a disadvantage relative to other sellers, a disadvantage that presumably

weakens their bargaining power. The disadvantages come from two sources

identified at the outset: vacant homes are less appealing to potential buyers

and the higher holding cost makes their sellers more impatient to negotiate

a sale. Recognizing that price and marketing time are jointly determined in

search markets, they use instrumental variables for the selling time variable in


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the price equation. The results offer clear evidence that vacant houses sell for

lower prices, attributed to weaker seller bargaining power.

This article is organized as follows. The next section presents a simple frame-

work that integrates a Nash bargaining solution between buyer and seller into

a model of seller search behavior. The framework is simple yet sufficiently

flexible for analyzing the various channels through which vacant house status

can affect expected selling price and liquidity. The framework is useful in that

it suggests empirical proxies for measuring the relative importance of these

various channels. The empirical analysis is explained in the third and fourth

sections. The third section describes the identification problem that applies toall simultaneous models of selling price and liquidity in the course of explaining

the identification method used here. The fourth section discusses the empirical

results for the 20-year sample and studies more closely how different market

phases and degree of listing staleness affect the conclusions. The fifth section

concludes.

A Search Model with Bargaining

We assume that the selling price is determined under Nash bargaining, so

that the buyer and seller split the surplus from the transaction according to

their relative bargaining power. Suppose the sellers reservation price is r and

the buyers willingness to pay is b v, where v is a parametric shift variable

defined below. The selling price of the house under Nash bargaining is therefore

p arg max{(b v p)1 (p r) },

where the parameter reflects the sellers bargaining power or negotiating skill

relative to the buyer. Solving for p yields

p = (b v) + (1 )r. (1)

Clearly, the stronger the sellers bargaining power, the larger is and the closer

the ultimate selling price is to the buyers reservation price, b v. The weaker

the sellers bargaining power, the smaller is and the closer the ultimate selling

price is to the sellers reservation price, r. It is reasonable to assume (0, 1)

so that both the buyer and seller enjoy positive net benefits from the transaction.

The bargaining solution is easy to integrate into the simplest seller search

model. Consider a particular house that is listed for sale. The probability of

a potential buyer arriving to visit this house during a unit of time is . The

population of buyers is ordered by their willingness to pay, b, summarized

in the distribution function F(b; x). The distribution of buyers is in general a


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E[p] =

br +v [(b v) + (1 )r

] dF(b)

1 F(r + v)(5)

which yields the expected selling price comparative statics

E[p]

> 0;

E[p]

c< 0;

E[p]

v< 0;

E[p]

> 0. (6)

Substituting the equilibrium reservation price r into (2) and differentiating

yields the comparative statics on the probability of sale at a given point in time

asq

< 0;

q

c> 0;

E[p]

v< 0;

and

q

=

[1 F(r + v)]2

f(r + v)

c

f(r + v)

[1 F(r + v)]

.

The equilibrium liquidity or expected selling time equals the inverse of theinstantaneous probability of sale, 1/q, so the expected selling time effects

take the opposite signs as the q effects. The following comparative statics

immediately follow from the above

E[T]

> 0;

E[T]

c< 0;

E[T]

v= 0;

and

E[T]

0)

lowers the sellers optimal reservation price and the expected selling price,

while shortening the expected time to sell. A decrease in buyers willingness

to pay (dv > 0), from whatever source, elicits a countervailing increase in

seller reservation price, which yields a negative effect on selling price and a

zero net effect on the expected marketing time. Recall from (6) that the seller

responds to a greater buyer arrival rate by increasing the reservation price.

Therefore, the effect of a higher buyer arrival rate on expected marketing time

(d > 0) depends on the relative size of the countervailing effects from the


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higher resultant reservation price (which tends to lengthen the expected selling

time) and the direct effect of the increased arrival rate (which tends to shortenthe expected selling time). The last inequality conditions in (7) reveal that a

larger c or smaller lead to a larger value of c/ henceE[T]/ > 0.

This counterintuitive result can arise because high seller search costs or weak

bargaining power are situations in which a faster buyer arrival rate prompts the

seller to set a much higher reservation price that outweighs the effect of the

greater arrival rate on the probability of sale; in these situations, the net effect

of a faster buyer arrival rate is a longer expected marketing time even though

expected selling price rises. Similarly, a faster buyer arrival rate when seller

search costs are low or seller bargaining power is high leads to a shorter expectedmarketing time coupled with a higher expected selling price. Because seller

bargaining power is likely to vary across the declining and rising market phases

(that is, after all, what defines a buyers market and a sellers market), the

model predicts that the buyer arrival rate effect on selling time is likely to vary

as well, with a more rapid buyer arrival rate decreasing selling time in rising

markets (when is large) and possibly increasing selling time in declining

markets (when is sufficiently small).

So what do these results imply for vacant house effects on observed prices

and liquidity? The higher holding cost associated with a vacant house (dc > 0)yields a lower seller reservation price and therefore lower selling price and

shorter expected selling time (Turnbull, Sirmans and Benjamin 1990). If, at the

same time, vacancy results in lower seller bargaining power (d < 0), as in

Harding, Knight and Sirmans (2003), then the Nash bargaining model implies

lower selling price for a given seller reservation price. The implications for

seller search behavior include a lower reservation price, hence lower selling

price and shorter time on the market. The prediction is identical to the effect

of higher holding cost on price and selling time, but, as noted earlier, we

expect bargaining power to wax and wane over the market cycle, althoughwe do not expect holding costs to exhibit the same systematic pattern. When

controlling for the influence of vacant house characteristics that reduce buyers

willingness to pay and other vacant house effects identified below, the vacant

house dummy variables in the price and liquidity equations will pick up the

combined effects of holding cost and bargaining power. Only by examining

changes in the dummy variable parameter estimates over the phases of the

housing market cycle can we expect to ascertain whether the dominant vacancy

effect is from higher holding costs or weaker bargaining power.

Vacancy might also signal the presence of an unobservable factor, possibly

atypicality, that reduces buyer willingness to pay for the house. The notion

here is that vacant houses have undesirable characteristics that are observed by

sellers and buyers but are not reported in the data (condition, architecture, etc.).

The bargaining model implies that a sellers reservation price entirely counters


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the reduction in the implicit value of such unobservable factors to buyers. An

undesirable feature therefore lowers the sellers reservation price (dv > 0),which commensurately lowers the expected selling price. The comparative

static prediction (7) reveals that the combination of the reduction in buyer

willingness to pay for the vacant house and the sellers matching reduction in

reservation price induces no change in expected liquidity. An empirical test

for this effect requires controlling for Haurins atypicality that is not related to

vacancy with atypicality variables in the price and liquidity equations. To pick

up effects of unobservable factors specific to vacant houses, we identify houses

that are vacant more than once during the 20-year span for which we have

data. If there are unmeasured attributes associated with vacant houses, thenhouses that are repeatedly vacant when sold are most likely to exhibit these

undesirable characteristics. In this case, we expect repeatedly vacant houses to

have negative price and zero liquidity effects.

Finally, we need to account for the possibility that vacant houses are subject to

different shopping externality and competition effects arising from neighbor-

ing houses that are also for sale. A greater number of surrounding houses for

sale has several effects (Turnbull and Dombrow 2006). The potential shopping

externality is introduced through visits to this house by buyers that have been

either attracted to visit this neighborhood in general or some other specifichouse in the neighborhood. Thus, in our search model, the shopping externality

expresses itself (if present) through > 0 with a greater number of surround-

ing listings. The potential competition effect can also be seen through this

term. If an increase in the number of houses for sale simply dilutes the number

of potential buyers visiting each individual house in the neighborhood, then

< 0 in the search model. But if the greater number of surrounding listings

reduces potential buyers willingness to pay for a given house, then v > 0 as

well. Under the assumption that holding cost is higher and bargaining power

lower for vacant houses, the shopping externality increases the expected salesprice and decreases the expected selling time. Similarly, in such cases the spa-

tial competition effect decreases expected sales price and increases expected

selling time.

The Identification Problem

Search and matching models of the housing market envision price and selling

time as jointly determined outcomes; different market or property character-

istics typically lead to combined price and liquidity effects.3 This approach

3Lippman and McCall (1978) provide a seminal influence on search models of hous-ing markets. See Arnott (1989), Haurin (1988), Krainer (2001), Williams (1995)and Wheaton (1990) for a variety of approaches grounded in search or matchingenvironments.


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motivates the Fisher et al. (2003) notion that relatively slow seller reaction to

changing conditions explains the observed relationship between prices and liq-uidity as well as the Genesove and Mayer (1997) notion that equity-constrained

sellers are more reluctant to lower their reservation prices, instead incurring

longer selling times to await the arrival of high-bidding buyers. In general,

search theory implies that empirical hedonic price analysis should take into

account simultaneous selling time or liquidity effects whenever possible. Most

attempts to do so, however, have been hampered by the theoretical implication

that both price and liquidity are simultaneously determined by virtually identi-

cal factors and therefore represent an underidentified simultaneous system.

Many empirical studies have long used log-linear regression models to sepa-

rately estimate selling time determinants (Belkin, Hempel and McLeavey 1976,

Miller 1978, Kang and Gardner 1989, Asabere, Huffman and Mehdian 1993).

A growing number explicitly recognize that selling price and selling time are

simultaneously determined using simultaneous or two-stage models (Sirmans,

Turnbull and Benjamin 1991, Yavas and Yang 1995, Forgey, Rutherford and

Springer 1996, Huang and Palmquist 2001, Rutherford, Springer and Yavas

2001, Knight 2002, Turnbull and Dombrow 2006, Turnbull, Dombrow and Sir-

mans 2006, Zahirovic-Herbert and Turnbull 2008). These papers offer a variety

of innovative ways of dealing with the identification problem, which typicallyhinge upon a variety of rationales for why some property characteristics only

affect selling price although others only affect selling time. Still, there is as

yet no generally accepted empirical framework for dealing with endogenous

price and liquidity in a systems context. In this article, we follow the method

proposed by Zahirovic-Herbert and Turnbull (2008), using variables captur-

ing neighborhood market conditions to identify separate price and liquidity

equations.

To understand the intuition of this method, recall that the search model impliesthat expected price, E[p], and selling time or liquidity, E[T], are simultane-

ously determined. Thus, for a house with characteristics vector X and neighbor-

hood market conditions summarized in the vector C, the relationship between

expected price and selling time is implicitly defined by the surface

(E[p], E[T], X, C) = 0.

We can express the realized price and selling time surface by separate functions

with jointly distributed stochastic errors p and T.p = p(T , X, C) + p (8)

T = T(p, X, C) + T. (9)


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The vector C captures the localized competition and shopping externality ef-

fects that turn out to be essential for identifying Equations (8) and (9). Turnbulland Dombrow (2006) measure neighborhood competition from nearby houses

for sale as long each competing listed house overlaps with the period that

this house is on the market, inversely weighted by the distance between the

houses to reflect the assumption that nearby houses will have stronger effects

on the sale of this house than houses that are farther away. The days on mar-

ket or selling time is s(i) l(i) + 1, where l(i) and s(i) are the listing date

and sales date for house i. Denoting the listing date and sales date for house

j by l(j) and s(j), the overlapping time on the market for these two houses

is min[s(i), s(j )] max [l(i), l(j )] + 1. The straight-line distance in miles be-tween houses i and j is D(i, j). The measured competition for house i is

C(i) =

j

(1 D(i, j ))2{min[s(i), s(j )] max[l(i), l(j )]}, (10)

where the summation is taken over all competing houses j, that is, houses for

sale within one mile and 20% larger or smaller in living area of house i.

The other neighborhood market condition variables in (8) and (9) are con-

structed following the same approach as that taken for the neighborhood com-petition variable (10). It turns out to be useful to also define another variable,

listing density, as the measure of competing overlapping listings per day on the

market

L(i) =

j

(1 D(i, j ))2{min[s(i), s(j )] max[l(i), l(j )]}

s(i) l(i) + 1. (11)

The price and selling time Equations (8) and (9) are functions of the same

predetermined variables and so do not appear to be identified. Note that re-

gressing sales price on the right-hand side variables yields the estimated effectof competition on price as the partial derivative p/C holding selling time

constant. Changing competition while holding selling time constant, however,

is simply a change in listing density (11). Therefore, p/C = p/L so

that the price function (8) can be rewritten as a function of the listing density

(11) instead of competition (10), which means that the system of equations for

price and selling time can be expressed as

p = p(T , X, L) + p (12)

T = T(p, X, C) + T. (13)

The separate L and C variables make it possible to identify both equations

in the estimation (Zahirovic-Herbert and Turnbull 2008). As important, this


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approach also explicitly introduces empirical controls for the neighborhood

market conditions thatwhen neglectedjustify the need to correct spatialcorrelation in housing price models. This approach models the spatial compe-

tition effects directly and therefore obviates the usual rationale for applying

spatial estimation methods.

The Empirical Analysis

The Data

We use a sample comprising broker-assisted housing transactions completedbetween October 1984 and April 2005. The sample period ends 3 months before

hurricane Katrina to avoid the influence of that event on property markets. The

data are drawn from the multiple listing service (MLS) sales reports for Baton

Rouge, Louisiana, a medium-size urban area that has been the subject for much

academic housing market research. Our data cover two decades during which

the local housing market experienced a downturn (19841987) followed by

an extended market trough (19881991) and then a modestly rising market

(19922005). Therefore, this sample also allows us to investigate the extent to

which the vacancy effect on price varies over the market cycle.

We restrict our attention to detached single-family houses sold within a con-

tiguous region in the urban area. There is evidence that the prices of houses in

new subdivisions diverge significantly from the broader market until the new

development reaches a critical mass (Sirmans, Turnbull and Dombrow 1997);

we avoid this potential pricing bias from new development by including in our

sample only those houses that are at least 4 years old.4 To avoid erroneous data

entries and outlier influence on selling time estimates, we exclude from the

sample houses that take fewer than 14 or more than 400 days to sell, houses

that sell for less than $40,000 or more than $320,000, houses with unusuallysmall (less than 300 square feet) or large (greater than 4,500 square feet) living

area and similarly for the area under roof net of living area (110 and 4,000,

respectively).5 The resultant data set comprises 27,630 transactions.

Table 1 summarizes the means and standard deviations of the variables used in

the empirical models for the full sample and for vacant and occupied subsam-

ples. The sales price (Price), days on the market prior to sale (DOM), number

4This has the added advantage of eliminating from the sample houses that are vacantbecause they are newly built.

5Note that houses sold within 14 days of listing are nonetheless included in the con-struction of the New Listing Density and New Competition variables explained in theprevious section.


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Table1

Variabledefinitionsandsamplesu

mmarystatistics.

Fullsample

Vacantsubsample

Occupiedsubsample

Differenc

ein

Variablename

Definition

Mean

Std.dev.

Mean

Std.dev.

Mean

Std.dev.

meansT-test

Price

Sellingpriceofhouse

106,183.60

50,426.67

97,571.79

46,665.8311

0,001.00

5,1549.80

19.77

DOM

Daysonthemarketpriortosale

86.20

71.14

94.52

72.41

82.52

70.26

12.82

Bedrooms

Numberofbedrooms

3.32

0.60

3.29

0.60

3.34

0.60

7.3

Bathrooms

Numberofbathrooms

2.01

0.47

1.97

0.48

2.03

0.47

9.51

Livingarea

Squarefeetoflivingarea

1,929.83

586.59

1,874.25

585.77

1,954.47

585.28

10.5

Netarea

Squarefeetofotherare

a

697.31

317.33

660.31

309.74

713.72

319.27

13.1

Listingdensity

Competinglistingswei

ghtedby

days

2.46

2.064

2.42

2.05

2.48

2.07

2.18

Competition

Competinglistings

211.00

280.07

233.13

300.99

201.19

269.71

8.4

Newlistingdensity

Competingnewlistingsweighted

bydays

1.13

1.12

1.13

1.13

1.13

1.11

0.1

Newcompetition

Competingnewlistings

118.39

197.00

131.83

208.96

112.42

191.17

7.31

Vacantlisting

density

Competingvacantlistings

weightedbydays

1.01

1.10

1.06

1.4

0.99

1.08

5.1

Vacantcompe

tition

Competingvacantlistings

87.80

139.31

103.79

157.19

80.72

129.98

11.84

Larger

Positivedeviationsfrom

local

meanlivingarea

0.11

0.19

0.10

0.19

0.11

0.19

3.93

Smaller

Negativedeviationsfro

mlocal

meanlivingarea

0.07

0.11

0.08

0.11

0.07

0.10

7.06

Vacant

Vacanthousedummyv

ariable

0.31

0.46

1

0

0

0

Repeatsale

Soldmorethanoncedu

ringthe

sampleperiod

0.47

0.50

0.42

0.49

0.48

0.50

9.14

Twicevacant

Dummyindicatinghou

sesoldas

vacanttwotimesseq

uentially

0.04

0.20

0.14

0.34

0

0

36.54

Repeatvacant

Dummyindicatinghou

sesoldas

vacantmorethanonce

0.10

0.30

0.20

0.40

0.05

0.23

30.78

Observations

27,630

8,486

19,144


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of bedrooms (Bedrooms), number of bathrooms (Bathrooms), a set of dummy

variables for the age category of the house (Age i) and living area (Living Area)are drawn directly from the MLS report for each sale. The Net Area variable is

calculated as the difference between the total square footage under roof less the

square footage of living area, and it captures the size of utility rooms, garages,

covered porches, carports, etc. The house characteristics also include location,

captured by dummy variables for the set of 84 census tracts. Finally, to control

for broad housing market conditions, all of the models include year and season

fixed effects.

The models also include neighborhood market condition variables suggestedby Turnbull and Dombrow (2006). The variable Listing Density measures the

intensity of competition from other houses for sale per day on the market

(11); Competition measures the total competition from other houses over the

entire marketing time for a given house (10). These variables explained above

not only provide the means of identifying the two separate price and liquidity

Equations (12) and (13), they also convey important insights into the nature

of spatial competition. As explained earlier, a greater number of competing

houses for sale surrounding a given house increases competition for buyers,

but at the same time can lead to shopping externality effects as the greater

concentration of listings draws more potential buyers to the neighborhood. Thesign of the coefficients on the listing density and competition variables therefore

reveal the relative strength of the spatial competition and shopping externality

effects.

The variables New Listing Density and New Competition are defined anal-

ogously to the listing density and competition variables, except that they

only include newly listed houses in their calculation (i.e., houses within

their first 14 days of listing). Vacant Listing Density and Vacant Competi-

tion are constructed similarly except that they only include competing vacanthouses. The coefficients on these variables reflect the offsetting or add-on

spatial competition or shopping externality affects of newly listed and vacant

houses relative to occupied listings that have been on the market longer than

2 weeks.

These neighborhood market condition variables are based on all applicable

competing house sales (within 20% of the living area and within one mile of

the sold house), which include all relevant competing houses listed before and

after our sample period that overlap with the sample period. We use the Stata

algorithm explained in Zahirovic-Herbert (2008) to construct these variables.

We include the relative house size variables Larger and Smaller to capture

atypicality effects unrelated to vacancy. These variables measure the extent to


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which a given house is either larger or smaller than the average living area

in the surrounding neighborhood. Following Turnbull, Dombrow and Sirmans(2006), indexing all houses within a one-half-mile radius of house i by J, the

standardized measure of the relative house size is

Localsizei =

Livingareai j J

Livingareaj /Nj

j J

Livingareaj /Nj,

where Nj is the number of surrounding houses in the neighborhood J. To allow

for asymmetric relative house size effects on sales price, we define the relativesize variables Largeri and Smalleri as the absolute values of the positive and

negative values ofLocalsizei, respectively:

Largeri = |Localsize| for Localsizei > 0

= 0 otherwise;

Smalleri = |Localsize| for Localsizei < 0

= 0 otherwise.

Using the absolute value means that the variable Smalleris always nonnegative,a point to note when interpreting the empirical results.

Vacant is a dummy variable indicating an unoccupied property. When controls

for spatial competition/shopping externalities and unobserved atypicality or

undesirable attributes are included in the model, the Vacant coefficient should

primarily pick up the combined effects of higher seller holding costs and

lower seller bargaining power (recall that these two effects are observationally

equivalent in the search model). Nonetheless, to the extent that seller search

or holding costs do not vary across market phases, observed differences inthe Vacancy coefficients across the market phases reveal differences in seller

bargaining power. Table 1 shows that 31% of transactions are for vacant houses

in the sample. This proportion varies significantly over the different phases

of the local housing market cycle, with 38% vacant in the declining market

(19851988), about 35% vacant in the market trough (19881991) and 29%

vacant in the rising market (19922005).

Repeat Sale is a dummy variable for houses that sell more than once during

the sample period. Approximately 47% of the transactions involve houses that

sold more than once during the sample period. The variable Twice Vacant

is a dummy variable indicating that the house has, at some time during the

20-year sample period, been vacant two times sequentially. Repeat Vacant,

on the other hand, is a dummy variable indicating that the house (whether


15/25


currently vacant or not) has been vacant more than once during the 20-year

period, not necessarily sequentially. Although 10% of the sample compriseshouses that are sold vacant more than once during the sample period, only

4% are vacant two or more times in sequence. The Twice Vacant and Repeat

Vacant variables are included to identify houses that might have unobserved

(in the data) characteristics or atypicality not related to relative size, features

that may explain their multiple vacant status whether or not they are vacant in

the current transaction. Negative attributes of these repeatedly vacant houses

are associated with a lower buyer willingness to pay (dv > 0 in the search

model) and therefore imply negative coefficients on these variables in the price

equation and insignificant liquidity equation coefficients.

Full Sample Analysis

Table 2 reports the model estimates for the full sample. As indicated in the

first two columns, the base model specifies the natural log of sales price as

a function of the selling time, house characteristics, location and time period

dummy variables (not reported) and the set of listing density variables capturing

neighborhood housing market conditions. Liquidity or selling time is, in turn,

a function of the log of the sales price, house characteristics, location and time

dummy variables and the set of competition variables as a different measure ofneighborhood housing market conditions. The coefficients on these variables

follow expectations.

Both equations include a dummy variable for houses that are vacant during the

listing period. The price equation estimates follow popular notions as well as

what has been typically found to date: vacancy leads to lower selling price. In-

terestingly, the Vacantcoefficient in the liquidity equation indicates that vacant

houses sell more quickly on average than do their occupied counterparts, ceteris

paribus. These coefficients are significant and robust across specifications. Thesign pattern is consistent with higher holding cost or lower seller bargaining

power; the model in the first two columns, however, does not control for the

other channels through which vacancy might affect price and liquidity.

Together, the insignificant coefficient on Listing Density in the price equa-

tion and the significantly positive coefficient on Competition in the liquidity

equation indicate the presence of both spatial competition and shopping ex-

ternalities (Turnbull and Dombrow 2006). The sign pattern on New Listing

Density, New Competition, Vacant Listing Density and Vacant Competition re-

veal that new listings in the neighborhood have add-on shopping externality

effects while vacant listings increase the spatial competition effects on price

and selling time. The interaction variable, Vacant Listing Density, is signif-

icantly positive in the price equation, which means that vacant houses enjoy


16/25


Table2

Tw

o-stageleastsquaresparameterestimates.

(1)

(2)

(3)

Price

DOM

Pric

e

DOM

Price

DOM

Variables

Equation

Equation

Equation

Equation

Equation

Equation

Ln

Price

139

.

2

138

.

9

107

.

4

(7.

03)

(7.

01)

(7.

31)

DOM(103)a

0

.

257

0

.

257

0

.

263

(0.

024)

(0

.

024)

(0.

023)

Bedrooms

0

.

0103

3

.

742

0

.

0105

3

.

698

0

.

0126

2

.

455

(0.

0023)

(0.

77)

(0

.

0023)

(0.

77)

(0.

0022)

(0.

74)

Bathrooms

0.

0324

5.

137

0

.

0324

5.

120

0.

0366

4.

560

(0.

0028)

(0.

97)

(0

.

0028)

(0.

97)

(0.

0026)

(0.

94)

Livingarea(103)

0.

435

0.

0796

0

.

435

0.

0795

0.

607

0.

0872

(0.

0029)

(0.

0032)

(0

.

0029)

(0.

0032)

(0.

0044)

(0.

0047)

Netarea(103)

0.

144

0.

0184

0

.

145

0.

0183

0.

127

0.

0125

(0.

0037)

(0.

0016)

(0

.

0037)

(0.

0016)

(0.

0035)

(0.

0015)

Vacant(103

)

66

.

8

4

.

139

66

.

3

4

.

550

65

.

7

2

.

706

(2.

9)

(0.

99)

(3

.

0)

(1.

02)

(2.

8)

(0.

99)

Listing

density(103)

0.

551

0

.

508

2

.

72

(1.

3)

(1

.

3)

(1.

2)

Compet

ition

0.

0188

0.

0188

0.

0301

(0.

0046)

(0.

0046)

(0.

0045)

New

list

ingdensity(103)

6.

05

6

.

04

6.

19

(1.

9)

(1

.

9)

(1.

8)

Newcompetition

0.

233

0.

233

0.

227

(0.

0053)

(0.

0053)

(0.

0051)

Vacant

list

ing

density(103)

0

.

472

0

.

433

0.

684

(1.

7)

(1

.

7)

(1.

6)


17/25


Table2

continued

(1)

(2)

(3)

Price

DOM

P

rice

DOM

Price

DOM

Variables

Equation

Equation

E

quation

Equation

Equation

Equation

Vacantcompet

ition

0

.

0188

0

.

0187

0

.

0226

(0.

0057)

(0.

0057)

(0.

0054)

Vacant

listing

density(103)

4.32

4.

33

3.

87

(1.9)

(1.

9)

(1.

8)

Vacantcom

pet

ition

0

.

00620

0

.

00630

0

.

00820

(0.

0048)

(0.

0048)

(0.

0046)

Tw

icevacant

(103)

7.

28

2.

307

8

.

88

2.

589

(6.

4)

(2.

15)

(6.

1)

(2.

07)

Repeatvacan

t(103)

4.

82

0.

353

6.

14

0.

243

(4.

4)

(1.

48)

(4.

2)

(1.

42)

Repeatsa

le(103)

5.

57

1

.

794

3.

70

1

.

422

(2.

2)

(0.

72)

(2.

0)

(0.

69)

Larger

0

.

491

28

.

24

(0.

0089)

(4.

84)

Sma

ller

0.

193

102

.

8

(0.

013)

(4.

45)

Constant

11

.14

1,5

81

11

.

14

1,5

78

10

.

83

1,1

77

(0.014)

(77

.

6)

(0.

014)

(77

.

4)

(0.

015)

(78

.

4)

Observations

27,63

0

27,6

30

27,6

30

27,6

30

27,6

30

27,630

R2

0.86

0.

43

0.

86

0.

43

0.

88

0.

48

Notes:Whitesrobuststandarderrorestimatesinparenthesis.

Dummyvariablesfor84censustractareas,eighthouseagerangecategoriesand

seasonandyearsoldarenotreportedinthistable.

indicatessignificanceatthe10%level;indicatessignifi

canceatthe5%level;indica

tes

significanceatthe1%level.

a(103)usedonlyinthepriceequations.


18/25


additional shopping externalities from surrounding listings than do occupied

houses. The interaction variable Vacant Competition is not significant in theselling time equation, which by itself indicates no add-on competition effects

from surrounding vacant houses.

Columns (3) and (4) in Table 2 report the results when Twice Vacant, Repeat

Vacant and Repeat Sale are added to the model. As already noted, the addition

of these variables does not dramatically change the coefficient estimates on

the variables already discussed. The Repeat Sale coefficients are significantly

positive in the price equation and negative in the liquidity equation; the vacancy-

related variables, however, are not significant. The insignificant coefficients onthese variables in the liquidity equations are consistent with dv 0 in the

search model. The insignificant coefficients in the price equations, however,

indicate that they are not picking up any vacant attribute effects that reduce

buyer willingness to pay. Overall, the results are consistent with dv = 0 for

vacant houses in the search model.

Columns (5) and (6) report the estimates when the general atypicality variables

Larger and Smallerare included in the model. The coefficients on these neigh-

borhood relative house size variables are significant and indicate that larger

houses in mixed neighborhoods sell at a discount while smaller houses inmixed neighborhoods sell at a premium, both relative to homogeneous neigh-

borhoods (Turnbull, Dombrow and Sirmans 2006). More importantly for our

purposes, their inclusion in the model does not significantly alter the Vacant

effects found earlier.

When taken together, the results reported in Table 2 indicate that our Vacant

coefficient estimates are picking up primarily seller holding cost and bargaining

power effects on price and liquidity. There is no evidence of the vacant house

effect being driven by atypicality or unobserved negative attributes. The inter-action term, however, provides evidence that vacant houses enjoy an enhanced

shopping externality effect from surrounding listings.

Market Cycle and Stale Listings

We expect seller bargaining power to vary over the housing market cycle. One

advantage of this data set is that it encompasses periods covering different

market phases. Based on a residential constant quality price index, the market

phases are as follows: declining market over 19841987, market trough over

19881991 and modestly rising market over 19922005. Table 3 reports the

parameter estimates of central interest for the separate phases. The first two

columns present the full sample estimates for comparison purposes. Overall,

the Vacant effects on price and selling time are robust across market phases


19/25


Table3

Ke

yparameterestimatesacrossm

arketphases.a

FullSample

RisingMarket

MarketTrough

FallingMarket

Variables

Ln

Price

D

OM

Ln

Price

DO

M

Ln

Price

DOM

Ln

Price

DOM

Vacant

0

.

0663

4.

550

0

.

0624

1

.

352

0

.

0847

30

.

23

0

.

0691

20.17

(0.

0030)

(1.

02)

(0.

0036)

(1

.

05)

(0.

0067)

(4.

39)

(0.

0079)

(5.08)

Vacant

Listing

Density

0.

00433

0.

00862

0.

00489

0.

00478

(0.

0019)

(0.

0025)

(0.

0039)

(0.

0040)

Vacant

Com

pet

ition

0.

00630

0

.

0176

0

.

00949

0.0143

(0.

0048)

(0

.

0065)

(0.

014)

(0.013)

Repeat

Vacan

t

0.

00482

0.

353

0

.

000134

0

.

639

0.

0143

4.

958

0.

0155

0.387

(0.

0044)

(1.

48)

(0.

0050)

(1

.

49)

(0.

012)

(5.

77)

(0.

014)

(7.16)

Tw

ice

Vacant

0

.

00728

2.

307

0

.

0157

2

.

016

0

.

00257

1.

123

0.

00921

5.201

(0.

0064)

(2.

15)

(0.

0075)

(2

.

24)

(0.

016)

(8.

01)

(0.

018)

(9.20)

Observations

27,6

30

27,6

30

19,8

23

1

9,8

23

4,8

34

4,834

2,8

18

2,8

18

Notes:White

srobuststandarderrorestimatesinparenthesis.

indicatessignificanceatthe10%level;ind

icatessignificanceatthe5%level;

indicatess

ignificanceatthe1%level.

aKeyparameterestimatesfrommodel(2)in

Table2estimatedontheindicatedsubsamples.


20/25


(with the exception of the insignificant liquidity effect in the rising market).6

Somewhat surprisingly, there is no strong evidence of systematic variation inseller bargaining power for vacant houses relative to occupied houses over the

market cycle. The results are consistent with the notion that vacancy does not

reduce seller bargaining power per se, but rather increases sellers willingness

to sell in response to the relatively higher cost of holding vacant houses on the

market.

The Repeat Vacant and Twice Vacant estimates are also robust across the dif-

ferent market phases. The Vacant Listing Density and Vacant Competition

interaction terms, however, yield different results for the rising market thanthe declining and trough phases. The latter two show no additional shopping

externality or competition effects for vacant houses. In the rising market, how-

ever, the coefficients on these variables are both significantly positive. This last

result is puzzling because the positive price effect indicates a stronger shopping

externality on vacant houses from surrounding listings while the positive sell-

ing time effect indicates stronger competition from surrounding listings. These

results, in particular, deserve additional scrutiny.

To better understand these results, we next consider the possibility that vacant

house effects might vary according to listing staleness. Table 4 reports the keyparameter estimates for the complete model when the sample is partitioned

according to how fresh or stale the listing is when sold. In each case, we

partition the data into thirds according to days on the market. The lowest third

represents the fresh listing sample, the next third the average listing sample

and the highest third the stale listings sample. The partitions (in terms of days

on the market), of course, vary across the full, rising, trough and declining

market phases. Note that the number of observations in each data partition is

not precisely one third of the total because of clustering along this dimension.

The full sample results reveal some interesting differences across listing stale-

ness. The Vacantprice effect is weaker and the liquidity effect stronger for stale

than for fresh or average listings. Similar patterns emerge for the three market

phases as well. These patterns are consistent with the simple search theory:

those sellers with more modest holding costs do not reduce their reservation

prices by as much as their counterparts with higher holding costs, and as a

consequence they do not sell at as great a discount and wait longer on average

to sell their vacant houses.

6To examine the robustness of these patterns, we also estimated a model includinginteractive year and vacant dummy variables. The interactive effects in the price equationshow no systematic variation in vacant effect on price over the sample. The interactiveeffects in the selling time equation reveal no systematic variation in vacant effect onselling time beyond that already identified across market phases.


21/25


Table4

Ke

yparameterestimatesacrossm

arketphasesandlistingstaleness.a

FullSample

RisingMarket

MarketTrough

FallingMarket

Listing

Variables

Sta

leness

Ln

Price

DOM

Ln

Price

D

OM

Ln

Price

DOM

Ln

Price

DOM

Vacant

Fresh

0

.

00377

0.

140

0

.

0723

0.

852

0

.

0947

2.

682

0

.

0906

5

.

642

(0.

011)

(0.

58)

(0.

0058)

(0.

30)

(0.

011)

(1.

05)

(0.

013)

(2.

01)

Average

0

.

00390

0

.

184

0

.

0659

0.

576

0

.

0910

11.

23

0

.

0506

2

.

242

(0.

010)

(1.

00)

(0.

0059)

(0.

54)

(0.

011)

(2.

94)

(0.

014)

(2.

43)

Stale

0

.

0192

1

.

703

0

.

0510

5.

802

0

.

0627

20.

35

0

.

0552

9

.

697

(0.

011)

(3.

85)

(0.

0062)

(2.

06)

(0.

012)

(6.

35)

(0.

015)

(6.

83)

Vacant

Listing

Fresh

0.

000666

0.

00488

0.

00400

0.

00467

Density

(0.

0031)

(0.

0040)

(0.

0064)

(0.

0061)

Average

0.

00590

0.

0103

0.

00707

0

.

00225

(0.

0030)

(0.

0041)

(0.

0062)

(0.

0070)

Stale

0.

00641

0.

00852

0.

00468

0.

00480

(0.

0032)

(0.

0044)

(0.

0070)

(0.

0078)

Vacant

Fresh

0.

00699

0.

0143

0.

00483

0.

0190

Compet

itio

n

(0.

0054)

(0.

0068)

(0.

013)

(0.

016)

Average

0.

00704

0.

00723

0.

00114

0

.

0143

(0.

0044)

(0.

0056)

(0.

014)

(0.

010)

Stale

0

.

000466

0.

0127

0.

0145

0

.

000796

(0.

0061)

(0.

0086)

(0.

015)

(0.

014)


22/25


Table4

continued

FullSample

RisingMarket

MarketTrough

FallingMarket

Listing

Variables

sta

leness

Ln

Price

DOM

Ln

Price

DOM

Ln

Price

DOM

Ln

Price

DOM

Repeat

Vacan

t

Fresh

0.

00737

0.

0799

0.

00162

0.

133

0

.

000431

0.981

0.

0152

0.649

(0.

0068)

(0.

35)

(0.

0076)

(

0.

36)

(0.

018)

(1.22)

(0.

020)

(2.32)

Average

0.

00303

0.

291

0.

00575

0.

0193

0.

0112

5.015

0

.

0000134

4.877

(0.

0069)

(0.

69)

(0.

0077)

(

0.

65)

(0.

020)

(3.63)

(0.

026)

(3.74)

Stale

0.

00805

0.

248

0

.

00569

3.

558

0.

0239

7.015

0.

0344

7.731

(0.

0081)

(2.

87)

(0.

0091)

(

2.

99)

(0.

020)

(9.06)

(0.

025)

(11.5)

Tw

ice

Vacant

Fresh

0

.

00377

0.

140

0

.

00621

0.

375

0

.

00806

0.786

0.

0177

3.100

(0.

011)

(0.

58)

(0.

013)

(

0.

62)

(0.

028)

(1.85)

(0.

028)

(3.21)

Average

0

.

00390

0

.

184

0

.

0206

0.

0292

0

.

00998

5.955

0.

0176

4.073

(0.

010)

(1.

00)

(0.

012)

(

1.

00)

(0.

027)

(4.95)

(0.

033)

(4.68)

Stale

0

.

0192

1

.

703

0

.

0207

1.

909

0

.

0217

8.578

0

.

0134

4.628

(0.

011)

(3.

85)

(0.

012)

(

4.

06)

(0.

027)

(12.0)

(0.

032)

(14.3)

Observations

Fresh

9,1

44

9,1

44

6,6

82

6,6

82

1,6

22

1,622

938

938

Average

9,2

07

9,2

07

6,5

17

6,5

17

1,5

95

1,595

949

949

Stale

9,2

79

9,2

79

6,6

24

6,6

24

1,6

17

1,617

931

931

Notes:White

srobuststandarderrorestimatesinparenthesis.

indicatessignificanceatthe10%level;ind

icatessignificanceatthe5%level;

indicatess

ignificanceatthe1%level.

aKeyparameterestimatesfrommodel(2)inTable2estimatedontheindicatedsubsamples.


23/25


Twice Vacant does not affect prices or liquidity in the fresh or average listings

in the full sample, but it does lead to significantly lower prices for stale listings.Coupled with the insignificant effect on selling time, this result is consistent

with a negative stigma effect for stale vacant listings (dv > 0 in the search

model). Looking at the estimates across the different market phases, we see

that these full sample results appear to be driven by the vacant house stigma

effects for average and stale vacant houses in the rising market. No vacant

stigma or atypicality effects are evident in the trough or declining market

phases.

Turning to the interactive terms, the significantly positive coefficients in theprice equations for average and stale listings and the insignificant coefficient

for fresh listing in the full sample indicate that average and stale vacant houses

garner stronger shopping externalities from surrounding houses on the market

while fresh houses do not. The market phase estimates, however, show that this

conclusion for the full sample is primarily driven by the rising market phase.

There is no evidence of differential shopping externality or spatial competition

effects for vacant houses in the trough or declining market phases.

Conclusion

This article introduced Nash bargaining into a simple seller search model

to study the vacancypriceliquidity nexus through greater seller holding cost,

lower seller bargaining power and also unobserved property attributes or stigma

perceived by buyers. The model emphasizes the difference between a seller with

weaker bargaining power and a seller motivated to accept a lower price because

of higher holding costs. Both tend to reduce expected selling price, but the

systematic changes in bargaining power associated with the various phases of

the housing market cycle imply varying vacancy price and liquidity effectsacross the market cycle not expected for holding cost effects. Using 20 years

of house transactions data from Baton Rouge, Louisiana, the estimates show

robust vacancy effects on price and liquidity across all market phases consistent

with greater seller holding cost and diminished bargaining power that does not

vary systematically over the market cycle. At the same time, vacant houses

that are no longer fresh listings tend to enjoy stronger shopping externality

effects than do their occupied counterparts from surrounding houses for sale in

rising markets; vacant houses do not enjoy additional shopping externality or

competitive effects in the trough or declining market phases. The estimates also

show no vacant house stigma or negative unobserved attribute or vacant-related

atypicality effects on price and selling time in declining or trough market phases.

There is, however, a significant negative stigma or attribute effect associated

with repeatedly vacant houses in rising markets.


24/25


This article received the 2008 American Real Estate Society Award for Best

Paper in Valuation. We gratefully acknowledge the helpful comments and sug-gestions of session participants and the anonymous referees.

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