Agents, Firms, and the
Real Estate Brokerage Process*
Geoffrey K. Turnbull**
Georgia State University
Jonathan Dombrow
DePaul University
March, 2005
Abstract. This study examines how individual agents affect house selling prices and time
on the market while controlling for brokerage firm-specific effects as well as supply and
demand conditions that vary by neighborhood. Firm size effects disappear once agent
characteristics are taken into account but geographic concentration by firms leads to
higher selling prices. For individual agents, neither sex nor selling own listings affects
price or selling time, but there are gains from partnering transactions across firms. Agents
who specialize in listing properties obtain higher prices for their sellers while those who
specialize in selling obtain lower prices for their buyers. Houses nearer to other
transactions of an agent sell for higher prices. Finally, greater scale of listing and selling
activity by an agent tends to lower selling price or lengthen the time on the market.
KEYWORDS: real estate agents, real estate brokers, housing market
*This research was supported by the National Association of REALTORS® National
Center for Real Estate Research. The conclusions and opinions are not necessarily those
of the NAR or the NCRER. The authors thank the individual agents and office managers
who generously shared their experience, insights, and information. The authors are
responsible for any errors.
**Communications to: Geoffrey K. Turnbull, Fiscal Research Center, PO Box 3992,
Georgia State University, Atlanta, GA 30302-3992; fax 404-651-0416; phone 404-6510419; email: [email protected]
1. Introduction
This study examines how individual agents affect house selling prices and time on the
market while controlling for brokerage firm-specific effects as well as supply and
demand conditions that vary by neighborhood. The existing theoretical and empirical
research on real estate brokerage tends to blur the distinction between the firm and
individual agents within firms. As a consequence, the empirical literature studying the
housing market-broker nexus deals primarily with identifying firm level effects in real
estate transactions and the growing stock of empirical evidence offers only limited insight
into how individual agent characteristics or strategies affect sales outcomes.1
This study examines the effects of agents’ unique skills and revealed strategies in
the market as reflected in various degrees of agent specialization by function (e.g., listing,
selling, partnering, or by geographic area). The empirical model is based on the
simultaneous equations model of sales price and marketing time developed in Turnbull
and Dombrow (2005). The model is appropriate for answering questions about individual
agent effects on transaction outcomes for several reasons. First, it controls for localized
demand and supply conditions surrounding the house being sold, an innovation shown to
improve the empirical performance of the hedonic price approach. This is particularly
important for our application since it allows us to separate the effects of localized market
conditions from the effects of agent specialization across locations on prices and
marketing times. Second, this approach models selling price and marketing time using a
simultaneous system of equations. This enables us to pick up the different effects of firms
and agents on both house sales price and sales time. Allowing firms and agents to
simultaneously affect these price and selling time in different ways is important inasmuch
as both are jointly determined key outcomes that are important to sellers.
The paper is organized as follows. Section 2 discusses existing empirical evidence
as it relates to agents’ roles in the sales process, emphasizing that different agents may
have comparative advantages in different parts of the sales sequence and may therefore
find it beneficial to specialize their functions to some degree. Whether such specialization
yields price or selling time performance advantages to individual house sellers, however,
1
See Benjamin, Jud and Sirmans (2000) for a summary of the empirical literature.
1
is addressed in the empirical study. Section 3 explains the empirical model, the variables
and their interpretation for firm and agent effects. Section 4 describes the data set and
discusses the empirical results. Section 5 summarizes our findings.
2. Agents and the Sales Process
How does a real estate agent affect the sales process? Roughly, the sales process can be
broken down into the following sequence.
Listing phase. House sellers typically interview several potential agents (often from
different firms) before choosing the one who will represent them in the market. Nelson
and Nelson (1991) find that the seller's ability to be comfortable with the agent, the
reputation of the brokerage firm, and the knowledge and qualifications of the agent to be
the most important factors in the selection of the brokerage firm. The survey by Johnson,
Nourse, and Day (1988) indicates that individuals emphasize the agent rather than the
firm in their listing decision. The results also indicate that sellers are primarily concerned
with the agent’s prior experience, knowledge of the market, and his or her ability to
“understand the client.” These seller concerns are what we expect, since the agent is
someone being chosen to represent the seller and to work in the seller's best interests. But
these survey results also point to a direction largely neglected in the agent-pricing nexus
literature: experience and knowledge of the market are attractive features from the
seller’s perspective. The existing published research, however, has not yet picked up on
this point or taken it very far.
When setting the asking price, the agent educates the seller about market
conditions, the underlying value of the house and surroundings, and discusses realistic
discounts from asking price in order to influence the seller’s reservation price in light of
market realities. The agent can play a pivotal role at this stage, defining marketing
strategy options, timing, and identifying property improvements and presentation issues
that the seller can resolve. This is also the point at which the agent begins to establish
rapport with the seller, building the credibility and goodwill that might be needed to
finalize negotiations and close the sale if difficulties arise latter in the process.
2
Search/matching phase. A popular perspective of agent matching activity in the
academic literature envisions a house seller whose agent matches the seller with a buyer
from the population of potential buyers for a house, typically by influencing the seller’s
asking price (Salant, 1991). At the same time, we can also view the agent engaged with a
buyer—whether a formal buyer’s agent or a traditional seller’s agent—as assisting the
buyer with their search, matching the buyer with a house from the population of available
houses (Baryla and Zumpano, 1995). The second perspective emphasizes that different
skills or information expertise are helpful in the search/matching phase when compared
with the listing phase. In this case, we expect agents with strong comparative advantages
in the market to emphasize the function(s) in which they hold the advantage. 2 Firms can
be thought of as portfolios of individual agents so that, even though individual agents
may specialize in listing or selling, we do not expect firms to specialize to the same
extent.
Negotiation phase. For the seller, the starting point established at the listing stage is
going to be important in the negotiation stage. The agent’s ability to reinforce a realistic
perspective for the seller’s beliefs about the market value of the house affects the seller’s
assessment of finding a better offer from a better buyer at this stage. The level of trust
established by the listing agent through the entire process to this point can help the seller
make objective assessments as well as overcome incipient seller’s remorse or falling back
to unrealistic valuations of specific house features to which the seller may have become
especially attached over time.
For the buyer, the base of knowledge about market conditions and neighborhoodspecific factors that affect value enters into the offer formulation. If the buyer is using a
buyer’s broker, the real estate agent serving that role can help with this information as
well as help design negotiation strategy. If the buyer is not using a buyer’s broker, the
real estate agent still shapes the buyer’s perception of market conditions and
neighborhood effects during the search phase, organizing and presenting relevant
2
This is one function of the real estate brokerage firm, to provide a venue for such talent pooling and agent
interaction as well as an environment for long term cooperation among agents working in the firm.
3
information to the buyer throughout the process, information that helps the buyer to
update or modify unrealistic preconceptions about the market and the target property.
Individuals’ negotiation skills matter for both sides of the transaction (Harding,
Rosenthal, and Sirmans, 2003). The agent’s ability to project market realities to both
parties is critical at the negotiation stage as is the agent’s role as a dispassionate
professional helping to navigate around potential impasses that often occur during the
process, common hitches that can frustrate a seller or buyer and prompt them to terminate
what could have been a successful negotiation sequence. Still, the overall difficulty of the
negotiation process can affect prices (Sirmans, et al., 1992). The implication of these
empirical studies relevant to our purposes is that agents who can reinforce the bargaining
power of their party or generally smooth the negotiation process will affect selling prices.
Closing phase. Although most transactions that reach the point of an accepted sales
contract close without major mishap, real estate agents have a role and incentive to help
resolve problems that can arise or generally offer support to their seller (or buyer, in the
case of buyer’s agents) to complete the transaction. The agent can help both buyer and
seller by coordinating the final tasks required to fulfill the sales contract, for example,
prompting the buyer and seller to ensure required inspections are completed in timely
fashion and even helping to schedule required repairs or serve as an observer or
representative while repairs are being made. The agent can also educate the parties about
low cost methods of resolving sales contract performance issues, provide guidance about
conventions that may vary by state or jurisdiction like filing for homestead property tax
benefits, etc., and serve as a conduit between the parties that each can use to convey
information about their own readiness to close the transaction at the specified time as
well as possible options in the event the closing cannot proceed as originally planned.
Finally, agents play another important role throughout all stages of the selling
process: helping both sellers and buyers avoid inadvertently violating sundry federal,
state, and local laws, regulations or conventions regarding real estate transactions,
ranging from fair housing regulations to required disclosures—the details of which
individuals outside the real estate profession may not be fully aware.
4
The most popular economic models of the housing market typically focus on the
search/matching stage (Wu and Colwell, 1986; Yavas, 1992, 1996; Yinger, 1981). Yet, as
we argue here, agents have more channels through which to influence the transaction.
Our summary touches on the variety of different agent skills or traits that enter at
different phases of the process. Existing research tends to ignore that individual agents
can vary how they split time among competing activities and that some may have
comparative advantages and therefore specialize more in one part of the transaction
process. In contrast, this observation motivates our effort to measure how market
outcomes are affected by individual agent’s decisions to specialize in listing- or sellingoriented activities or concentrate their investment in market information to focus on
specific neighborhoods or market segments.
Firms versus individual agents. The real estate agency relationship is a complicated
multi-faceted set of principal-agent relationships: between the seller and agent, of course,
but also between the brokerage firm and its associated agents as well as between buyers
and their agents where allowed. This suggests that we need to include variables in the
empirical model to capture both firm level and individual agent level effects on observed
transactions. We do so in this study, estimating a series of models with firm effects and
those with both firm and individual agent effects.
Despite the importance of the topic, published real estate research does not offer
much solid evidence about how individual real estate agents affect property markets.
Much work has been done on real estate brokerage in general, both theoretical and
empirical. The theoretical models, however, typically assume no distinction between a
firm and its individual agents. Indeed, the terms “firm” and “agent” are sometimes used
interchangeably (Turnbull, 1996; Yang and Yavas, 1995). And while there is a growing
literature concerning individual real estate agents, the bulk of it focuses on agents’
earnings rather than their role in the market. The review articles by Yavas (1994) and
Benjamin, Jud, and Sirmans (2000) provide detailed summaries of the main categories
covered in individual studies. The number of hours worked, the experience of the agent,
the size of the brokerage firm, the agent's level of education, whether one works in a
larger metropolitan area or not, and having an ownership interest are all widely supported
5
as having a positive effect upon an individual agent's income. This literature points to
several agent characteristics that can lead to success. While the number of hours worked
and experience affect agent income, this does not give any insights into strategies of
successful agents: specialization by tasks, geographic areas, or partnering. Further, the
focus is on agent income, not observable market performance from the individual seller’s
(and buyer’s) perspective: how much the house will sell for and how long it will take to
sell.
The empirical literature examining price or selling time effects tends to focus on
firms. Sirmans, Turnbull, and Benjamin (1991), Turnbull and Sirmans (1993) find some
firm size and co-brokered sales effects on the sales price and marketing time. Jud and
Winkler (1994) find higher prices for co-brokered houses. Hughes (1995) finds that
houses listed with larger brokerage firms tend to sell for higher prices. It is not clear
whether the empirical estimates of firm size effects in any of these studies reflect
economies of scale or other inherent advantages associated with firm size or marketing
advantages from established name brand recognition from the wider market presence.
The branch of literature looking at how individual agents affect transactions
outcomes has not yet yielded much in the way of insights. Yang and Yavas (1995),
Zumpano, Elder, and Baryla (1996), Jud and Winkler (1994), Jud, Seaks, and Winkler
(1996), and Munneke and Yavas (2001) generally conclude that the characteristics of
individual agents have insignificant effects on selling prices or time on the market. Black
and Nourse (1995) similarly find no significant price effect from introducing buyers’
brokers into the residential property market. None of these studies, however, address the
aspects of agent strategy examined here; whether or not greater specialization by function
or market segment improves performance.
It is convenient at this point to raise a caveat with respect to empirical studies of
firm and agent behavior and common inferences about economic efficiency. It is
sometimes claimed that the primary benefit to using brokers or agents in any market can
be measured by the higher return or quicker selling time that sellers obtain. This is not
true in general. Observed price effects do not generally reflect the net advantage of
broker-assisted sales in the market because professional agent assistance lowers search
and transactions costs for both buyers and sellers, which yields an ambiguous net effect
6
on selling price.3 More generally for our study, this implies that we need not observe
price effects from firms or agents even when firms or agents yield net transactions cost
savings to the buyers and sellers. For our purposes, it is worth emphasizing that, while
improved price or marketing time performance provides evidence of net advantages to
buyers or sellers, the absence of observed price or marketing time effects does not by
itself imply zero net advantages to buyers and sellers working with specific brokerage
firms or agents. This also explains why differences in observed selling price and
marketing time performance can persist across agents or different size firms even in a
highly competitive industry like residential real estate brokerage.
3. The Empirical Model
Sirmans, Turnbull, and Benjamin (1991) develop an empirical multi-equation model of
the joint housing and brokerage services markets. Turnbull and Dombrow (2005) modify
the framework into a simultaneous model of sales price and marketing time that
specifically controls for the effects of competition among nearby houses for sale. We
extend the framework to measure separate firm and agent effects on selling price and
marketing time.
Denoting selling price (Price) and time on the market (TOM), the model can be
summarized as
(1)
lnPrice = f(TOM, H, M, C, F, A) + u
(2)
TOM = g(lnPrice, h, m, c, F, A) + v
3
Although the commission rate by itself increases average selling price by a greater or less extent as house
supply is less or more elastic than demand, it can be shown that lower sellers’ transactions costs (including
search costs) by itself lowers average selling price while lower buyers’ transactions costs by itself raises
average selling price so that the combined effect on price is ambiguous a priori. See, for example, the
housing and brokerage markets model in Turnbull (1996). This provides one rationale for the mixed
empirical evidence on price effects of brokers. For example, Jud and Frew (1986) find broker-assistance
increases the selling price relative to FSBO but Kamath and Yantek (1982) find no net difference.
Nonetheless, regardless of the effect on sales price, the effect of brokerage on economic efficiency is clear:
lower transactions costs unambiguously expand the market and improve efficiency.
7
where H and h are vectors of house characteristics, M and m are vectors of variables
capturing overall market conditions, C and c are vectors of variables capturing
neighborhood supply and demand conditions, F and A are vectors of firm and agent
characteristics, respectively, and u and v are the stochastic error terms. This model
recognizes that price and time on the market are simultaneously determined by market
conditions and firm and agent factors. We use the two stage least squares (2SLS)
estimation method in order to remove the effects of endogenous TOM in the lnPrice
equation and endogenous lnPrice in the TOM equation. The different elements in the
house characteristics, market conditions and neighborhood supply and demand conditions
vectors across the two equations ensure that both regression equations are identified in
the 2SLS estimation. The specific variables used in the empirical models and their
construction are discussed below; Table 2 reports summary statistics for the variables
used in the empirical analysis.
A. The Price Equation.
The selling price equation (1) extends the standard hedonic framework to include
measures of competition from surrounding houses on the market and the variables we
introduce to control for firm and agent effects.
House characteristics. The house characteristics include many that have become
standard in hedonic models: Living Area, other area under roof (Net Area = total area
under roof – living area), and Age of the structure. Areas are measured in thousands of
square feet and age is in years. The house characteristics also include location, which is
indicated by dummy variables for Multiple Listing Service (MLS) areas.
Market conditions. Any systematic changes in the broad metropolitan housing market
conditions are captured by the quadratic monthly trend variables in the equation, Month
and Month2. The dummy variable Vacant is included to capture different seller incentives
or market conditions facing sellers of vacant property arising from higher holding costs
for sellers (Sirmans, Turnbull and Dombrow, 1995).
8
Localized market conditions. Capturing the effects of neighborhood market conditions
is more difficult. We follow the approach taken in Turnbull and Dombrow (2005). The
number of houses for sale in a small neighborhood surrounding a particular house can
have localized effects on the balance of potential buyers and sellers. A greater number of
houses for sale increases the competition among sellers for buyers who choose to visit the
neighborhood in their house search process (this is the “spatial competition” effect). But
at the same time, a greater number of houses for sale may draw a greater number of
potential buyers to visit the neighborhood, thereby increasing the likelihood of matching
a particular house with a feasible buyer (this is the “shopping externality effect”).
Following Turnbull and Dombrow (2005), the variables measuring the number of
competing houses for sale in the surrounding neighborhood account for the number of
days that competing houses actually overlap, weighted by the distance between them.
Denote the listing and selling date for house i by L(i) and S(i). The time on the market
this house is TOM(i) = L(i) – S(i) + 1. The overlapping time on the market between
houses i and j is min[S(i),S(j)] – max[L(i),L(j)]. The geographic distance D(i,j) between
the two houses is found from the geocoded data. For each house i, the number of
distance-weighted competing houses days on the market is therefore
(3)
Listing Density = Σ(1-D(i,j))2 {min[S(i),S(j)] – max[L(i),L(j)]}/TOM(i)
where the summation is taken over all competing houses j within one mile of house i.
This variable controls for the window of opportunity open to potential buyers to buy this
or competing houses that are available at the same time. The distance weighting captures
the notion that competing houses that are close by will have stronger competition or
shopping externality effects than houses that are farther away.
There is a widely held belief that newly listed houses attract more visits than
houses that have been for sale a while. This can lead to stronger shopping externality
effects for competing new listings than for neighboring stale listings (Turnbull and
Dombrow, 2005). Similarly, vacant houses usually are associated with higher holding
costs and therefore might indicate a motivated seller. Thus, surrounding vacant houses for
9
sale should be expected to have different spatial competition or shopping externality
effects on a particular house. To take the new listings and vacant listings into account, we
include in the price equation (1) the variables New Density and Vacant Density, which are
constructed parallel to the Listing Density definition (3), where a new listing is a house
that has been listed 14 days or less and a vacant house is one that is vacant at time of
listing.
A positive Listing Density coefficient in the price equation indicates a shopping
externality effect that is stronger than the spatial competition effect from nearby
surrounding houses. In this case, a greater number of surrounding houses for sale attracts
enough additional potential buyers to increase the likelihood of matching a given house
with a buyer who is willing to offer more for the house. On the other hand, a negative
coefficient indicates a spatial competition effect that is stronger than the shopping
externality effect. In this case, a greater number of houses for sale in the neighborhood
increases the competition for potential buyers, thereby reducing the likelihood of
matching a given house with a buyer who is willing to offer more for the house.
The coefficients on the New Density and Vacant Density variables reflect the
differences in competition (if negative) or shopping externality effects (if positive) for
these types of properties relative to stale owner occupied competing houses.
Firm characteristics. The effects of the brokerage firm on selling price are captured by
variables measuring the firm size (based on number of transactions), whether the
transaction was co-brokered within the same firm, and the strength of the firm’s presence
in the neighborhood of the house sold. Big List Firm and Big Sell Firm are dummy
variables for listing and selling firms indicating one of the two largest firms in the sample
market, each of which individually account for ten to fifteen percent of MLS sales in the
area. Medium List Firm and Medium Sell Firm are dummy variables indicating one of the
four intermediate size firms in the market, each of which represents from four to ten
percent of local MLS sales. The remaining firms are defined as small firms, the omitted
dummy variables categories in the price equation.
10
Positive coefficients on the big or medium size firm variables indicate economies
of scale, firm benefits from local brand recognition by buyers or sellers, or both. Negative
coefficients indicate diseconomies of scale in listing or selling functions.
The dummy variable Own Firm Sale indicates that the transaction was listed and
sold by agents within the same firm (not necessarily by the same agent). This variable
captures the effect of partnering among specialized listing and selling agents within a
single firm relative to partnering between agents from different firms. Because this
variable should be interpreted in conjunction with the agent variable introduced below,
we defer further discussion concerning interpretations until then.
The strength of a firm’s presence in the neighborhood of the sold house provides
one measure of how broadly or narrowly a firm specializes in the market. If a greater
local share of listings or sales reflects a successful firm strategy to garner greater
localized expertise or information advantages then we expect to find positive coefficients
on the relevant variables in the price equation. Our measures of the listing and selling
firms’ presences in the neighborhood are, respectively, Firm Local Listing Concentration
(List Firm Conc) and Firm Local Selling Concentration (Sell Firm Conc). To construct
these variables, we use the geocoded data to identify both the listing and selling firms for
every MLS property sold within one half mile of a given property during the sample
period. Note that a given firm can be present in a particular transaction as the listing firm,
selling firm, or both; our measure of total transactions captures both sides of the
transaction. The Firm Local Listing Concentration is calculated as the number of firm
listings as a percentage of all firms’ listings and sales located within one half mile of the
sold house. Firm Local Selling Concentration is similarly calculated for the firm’s sales
transactions. These variables provide combined measures of neighborhood market share
by activity (listing or selling) as well as overall neighborhood market share. Our
construction of the two variables takes into account the possibility that some firms might
specialize more in one function than the other, with the specialization varying across
neighborhoods.
Positive coefficients on these variables in the price equation are consistent with
greater localized expertise and/or neighborhood presence as a listing or selling firm,
respectively, yielding higher selling prices.
11
Agent characteristics. Our goal is to evaluate whether agent characteristics (sex),
functional specialization strategies (primarily listing, selling, or a balanced mix), and
investments in localized market knowledge (specialization by area and neighborhood)
affect selling price.
While there are popular beliefs regarding the relative merits of male and female
agents, there is no solid empirical evidence favoring one over the other. To address this
open issue, we use the dummy variables Male Listing and Male Selling to indicate the
presence of a male listing or selling agent, respectively.
Some agents are primarily listing agents while others are primarily selling agents.
We use the dummy variables Mainly Listing and Mainly Selling to indicate relative
specialization based on observed performance. An agent is designated as a mainly listing
agent if over 2/3 of his or her total transaction activity is on the listing side. An agent is
defined as mainly a selling agent if 2/3 of his or her activity is on the selling side. We
note that Mainly Selling agents includes both agents who primarily function as formal
buyer’s agents as well as including traditional seller’s agents working through a cobrokerage agreement either within the same firm or across firms. The sign of the
coefficients on the listing agent variable indicates whether or not the specialized listing
skills enhance the ultimate outcome for the seller. A negative (positive) coefficient on the
selling agent variable indicates whether a greater proportion of the selling specialists in
our sample behave as buyer (seller) agents.
Agent Own Listing is a dummy variable indicating that the listing agent was also
the selling agent. It is important to note, however, that the coefficient of this variable
cannot be interpreted independent of the Own Firm Sale variable, since Agent Own
Listing = 1 implies Own Firm Sale = 1 by definition.4 Therefore, we use the difference
between the two estimated coefficients of these variables in our inferences about agents’
performance when selling their own listings. When the coefficient on the agent variable
minus the coefficient on the firm variable is positive, then this implies that agents enjoy
economies of scope across the listing and selling functions for a given property. These
4
This does not imply that the two variables are collinear because many houses sold within a firm (Own
Firm Sale =1) are listed and sold by two different agents (Agent Own Sale = 0).
12
economies of scope are productivity advantages that make it easier for agents to sell their
own listings—productivity advantages that we expect to be reflected in higher selling
prices.
When the coefficient on the agent variable minus the coefficient on the firm
variable is negative, though, then this implies that there are diseconomies of scope across
the listing and selling functions for a given property. These diseconomies make it more
difficult for agents to sell their own listings, a productivity disadvantage that we expect to
see reflected in lower selling prices for houses listed and sold by the same agent.
Finally, we consider how an agent’s investment in specialized market knowledge
or expertise affects observed sales outcomes. The basic question concerns agents’
decisions to focus on developing knowledge and expertise in geographically concentrated
market segments as well as their overall scale of activities as an individual agent.
Consider first how narrowly or widely the agent focuses his or her activities across the
geographic market. Since listing and selling skill sets may differ, our model uses separate
variables to measure agent listing and selling activities. Both variables are based on how
far a given house is from other transactions in which the agent has (or will have) taken
part. For a given house, we define Ave Listing Distance as the average distance between
the listed house sold and other transactions by the same agent during the sample period.
Thus, a large average listing distance indicates that the agent either tends to cover a wide
area of the market or that if he or she tends to focus more narrowly on a particular area,
this house lies outside that area. In either case, if market experience translates into
information or expertise then a negative coefficient on average listing distance in the
price equation indicates that agents who spatially concentrate their activities on market
segments are able to translate their informational advantage to sellers in the form of
higher realized prices. If, on the other hand, the coefficient is positive then listing agents
who are active across a wide territory in the market perform better than those who focus
on a narrow territory. A positive coefficient on this variable indicates that specialized
market information does not improve selling price performance.
We similarly define the Ave Selling Distance variable as the average distance
between the given house and other transactions involving the selling agent. The
interpretation of the estimated coefficient on this variable resembles that for the listing
13
variable case, except that now the relevant skills or information are those that are related
to the matching and negotiation phases of the selling process.
We also include another set of variables measuring a different dimension of how
intensively the agent has invested in localized market information: Listing Agent Local
Concentration (List Agent Conc in the tables) and Selling Agent Local Concentration
(Sell Agent Conc in the tables). For a particular house, Listing Agent Local Concentration
is calculated as the number of other transactions by the listing agent within one half mile
of the sold house, as a percentage of all transactions within that radius. Selling Agent
Local Concentration is constructed similarly for the transactions of the selling agent.
There is a complication, however. Let ni be the agent density, that is, the agent’s
market share of neighborhood transactions, and nT his or her share of all transactions in
the market. nT is a measure of the agent’s scale of operation and is not directly observed
in our data. Let Di denote the average distance between the house and the agent’s other
transactions in the neighborhood and Do denote the average distance between the house
and the agent’s other transactions outside the neighborhood. Using this notation and some
manipulation, we can express agent average distance as
(4)
Ave Listing Distance = Do + (Di − Do )Ni (ni/nT)
where Ni is total neighborhood sales by all agents as a proportion of all transactions in the
market. The above relationship shows that increasing the agent density ni while holding
Ave Listing Distance constant implies a concomitant greater scale of agent activity nT for
a given Ni. Thus, increasing the agent density variable ni while holding the agent average
distance variable constant captures the combined effects of more focused agent activity in
the neighborhood and the effects of increasing the agent’s overall scale of operation.
(Similarly for the selling agent.) This means that the estimated coefficients on the agent
local concentration variables capture two separate effects: the intensity of agent activity
in the immediate neighborhood of the house in question and the overall effects of the
agent’s scale of operation (i.e., whether there are economies or diseconomies of scale for
individual agents). The question is, how can we sort out these two effects in the empirical
estimates?
14
Scale effects can reinforce or offset the observed effect of greater agent
productivity from more concentrated market knowledge. Economies of scale are reflected
in improved agent performance as the agent increases the number of transactions in
which he or she is involved; expected selling price rises with the scale of agent activities.
In contrast, diseconomies of scale are reflected in poorer agent performance as the agent
increases his or her scale of activities; in this case, expected selling price falls with the
scale of agent activities.
In order to interpret the concentration variable coefficient estimates in terms of
geographic concentration and scale effects, recall that a negative coefficient estimate for
the average distance variables implies that geographic specialization by agents increases
selling prices, which is labeled conclusion (a) in the first row of Table 1. Consider the
case where the coefficient estimate for agent local concentration is also negative,
indicating that the combined effect of greater agent concentration in the neighborhood
and a larger scale of agent activity together lead to lower selling prices. But the average
distance variable coefficient already implies that greater geographic specialization by
itself increases price, so the scale effect must be lowering price by a greater extent. Since
diseconomies of scale result in greater scale lowering selling price, the negative
concentration coefficient implies that there are diseconomies of scale present at the agent
level. Thus, when we can conclude (a) in the first row of Table 1, then a negative
coefficient on the local concentration variable implies that increasing the agent’s scale of
operation lowers selling price—which is the first case in the fourth row of Table 1.
In contrast, a positive agent local concentration variable coefficient might simply
reflect the conclusion derived from the negative average distance coefficient, that a
narrower geographic focus by the agent increases selling prices. In this case, scale effects
might or might not be present; if they are, they can take either sign (either economies or
diseconomies of scale) as long as they are not large enough to overshadow the direct
effects of geographic concentration on selling prices. This is the possibility listed as the
first case in the third row of Table 1—a negative average distance coefficient coupled
with a negative local concentration coefficient allows us to conclude (a) that there are
positive returns to geographic concentration by agents but does not allow us to make any
solid conclusions about agent scale effects.
15
Table 1 summarizes the conclusions that we can make from all possible
combinations of coefficient estimates. The other cases in the table can be traced through
similarly.
B. The Time on the Market Equation
The time on the market equation (2) uses the living area, net area, and house age as house
characteristics variables. Of course, it includes the MLS area dummy variables to control
for location effects. The overall market conditions are captured by the quadratic monthly
trend and the vacant house dummy variable, as in the price equation, and Discount
(percentage discount of selling price from list price).
The Listing Density and related variables that measure localized market
conditions in the price equation are not appropriate for the TOM equation because they
include the TOM for each sold house in their denominators. In order to avoid the
problems that can arise from this induced functional relationship, Turnbull and Dombrow
(2005) suggest using the following measures of localized competition instead:
(5)
Competition = Σ{(1-D(i,j))2 {min[S(i),S(j)] – max[L(i),L(j)]}
with New Listing Competition and Vacant Competition appropriately defined following
(5). Recalling that we are looking at marketing time, a positive competition coefficient
indicates stronger spatial competition than shopping externality effects, as more houses
for sale in the surrounding neighborhood lengthens the time it takes to sell a house for a
given expected price, other things equal. A negative coefficient indicates a stronger
shopping externality effect. The new listing and vacant competition coefficients identify
the differential effects of competing houses in those categories over and above occupied
houses that have been on the market more than two weeks.
The time on the market equation includes the same firm and agent variables
identified earlier. When interpreting the coefficients, however, it is important to note that
selling price and marketing time represent two inversely related margins along which the
market can adjust. In this model a factor that increases selling price for given marketing
16
time can be expected to shorten TOM for given expected selling price and vice versa.
Thus, even if there are downward price rigidities from equity constrained sellers or target
return strategies, the firm and agent effects will be visible through the TOM equation.
4. Empirical Analysis
We use a sample of single-family, owner occupied, broker-assisted housing transactions
to examine the effects of agent specific influences and brokerage firms in the selling
process. The sample covers transactions occurring within a two year period, the short
horizon minimizing biases introduced by agents moving from one firm to another, firms
merging operations, as well as the natural changes in individual agent productivity as
they acquire human capital with greater experience.5
The sample draws from the Multiple Listing Service (MLS) sales reports for Baton
Rouge, Louisiana, comprising 1,395 transactions during the period beginning January
1996 through September 1997.6 The sample includes all MLS transactions for which
sufficient information about individual agents could be obtained and matched with the
MLS data. In addition, in order to enhance the comparability and homogeneity of the
houses, we restricted our attention to a contiguous region within the urban area which
accounts for approximately one half of all the broker-assisted single family house sales in
the Baton Rouge Metropolitan Statistical Area during the sample period. We omit sales
using below-market financing arrangements, a standard precaution implemented to avoid
biased estimates from the capitalized benefits of such arrangements. In addition, 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
5
Additional biases in the data can also arise for lengthier sample periods—changing geographic or
functional specializations by agents over time. We found no systematic change in agent specializations
during our sample period, which suggests that the two year horizon is short enough to minimize this
potential problem.
6 The calculations described earlier in the paper for all of the density and competition variables include all
MLS sales for the entire metropolitan area over the period July 1995 through March 1998. Therefore, these
variables include all relevant out-of-sample transactions, that is, houses in areas bordering on our sample
geographic areas and houses listed before or after the sample time period which overlap with our sample
period.
17
Dombrow, 1997). Our sample avoids this pricing bias associated with new development
by including only those houses that are at least two years old, a period long enough to
build out the newer subdivision filings in the sample. We also exclude from the sample
houses that take more than six months to sell in order to avoid undue outlier influence on
selling time estimates.7
Table 2 reports the summary statistics for the variables used in the estimation.
Among other things, the summary statistics show that the combined market share of the
largest firms in our sample is approximately 33% of listings and 37% of sales.
Intermediate size firms account for 16% of listings and 18% of sales. The smallest firms
as a group differ from the large and medium firms in that they generate more listings
(51%) than sales (45%) in the market. For firms as a whole, only 28% of transactions are
co-brokered within the same firm. Both listing and selling firm concentrations range from
about 0.2% to 62% of neighborhood transactions, with an average of 14% for listings and
12% for sales.
Male agents are a distinct minority in the residential house market, accounting for
only 18% of listings and 20% of sales. Over one third of agents specialize in listing or
selling activities; 19% specialize as mainly listing and 19% specialize as mainly selling
agents. Most transactions involve more than one agent; only about 17% of houses are
listed and sold by the same agent. The average distances between transactions for both
individual listing and selling agents is around 3.5 miles, but the distance varies widely
across agents and observed sales, ranging from about a quarter mile to about 10 miles.
One average, each individual listing agent has 1.9% of the surrounding neighborhood
transactions while each selling agent has 1.4% of the surrounding neighborhood selling
transactions. The variations in listing and selling agent local concentrations are wide,
however, ranging from 0.2% to 50% of neighborhood transactions.
The model with only firm effects. We begin by introducing the firm level variables into
the price and time on the market system of equations. Tables 3 and 4 report the estimated
coefficients for the firm variables in the two equations. In the first model in Table 3, firm
7
The transactions included in our sample account for approximately 95% of all MLS transactions in the
areas covered by our sample. A Chow test for data pooling implies that the 5% of sold houses with
marketing times longer than six months should not be pooled with the rest of the sample.
18
size and listing/selling function variables indicate an effect on price and selling time. The
Big Selling Firm and Big Listing Firm coefficients in the price equation show a net
performance advantage for the largest firms, although the effects are not the same for
selling and listing functions. While the largest firms increase selling price, houses sold by
the largest firms have about one third greater price increments as houses listed by the
largest firms when compared with medium and small firms. Taking the firm size dummy
variables as a set, there appear to be performance advantages associated with selling by
and certainly listing with large firms in the market. These results add to the mix of
existing results on firm size effects referenced earlier. Taken at face value, the pattern of
estimates indicate economies of scale, but only for the largest real estate brokerage firms
in the sample. We caution against this interpretation at this point, however, because this
version of the model does not control for other aspects of firm strategy. We shall return to
this issue shortly.
Before doing so, however, it is interesting to note that the Own Firm Sale
coefficient is significantly negative in the price equation (and insignificant in the TOM
equation). This indicates, other things equal, lower selling prices for houses listed and
sold by members of the same firm when compared with houses listed and sold by agents
who are members of different firms. There appear to be performance advantages from
agents partnering transactions across different firms. Overall, these estimates indicate that
while firm size does not matter, the nature of the firm’s involvement in the transaction
can matter.
The second model in the tables include the surrounding share of market by the
listing firm (List Firm Concentration) and the surrounding share of market by the selling
firm (Sell Firm Concentration) variables. Recall that these variables are calculated for
each individual house sold in the sample, and capture the listing or selling firm’s market
share of activity within one half mile of this house during the sample period. We include
these variables in the model to control for the effects of firms concentrating their
investment in expertise for specific market segments. It is clear from the table that the
firm size variables become insignificant one the geographic concentrations are included
in the model. The large firm effects on price found in the first model are evidently
attributable to the information advantages from concentrating on market segments, rather
19
than pervasive economies of scale at the firm level. Taken together, selling firms that
concentrate their investment in market expertise over a smaller number of locations are
able to sell houses at a higher price (although, the premium is less than 0.1%). Listing
firms that concentrate their efforts, on the other hand, see no such price advantage. This
latter effect is somewhat surprising given the advantages of geographic concentration
evident in the selling function.
The model with individual agent effects. Tables 5 and 6 report the firm and agent
coefficient estimates for the models including agent variables. The first model in each
table includes only agent variables, the second includes both the firm spatial
concentration and all of the agent variables, and the third includes all of the firm and
agent variables. The estimates are remarkably robust across the three specifications.
Compared with the second firm model in Tables 3 and 4, including controls for agent
effects introduces important qualitative changes in the estimates of firm size effects.
There are no significant firm size effects on either price or selling time in any of the agent
models in Tables 5 and 6; there is no evidence of strong scale effects for firms in the
market.
Looking at the results for the two spatial concentration variables for the firms,
Firm Local Listing Concentration and Firm Local Selling Concentration, we find that the
insignificant former and marginally significant latter variables in the previous firm model
are now both positive and statistically significant when included with the base agent
model in the price equation. When we look at the full agent and firm model though only
the Firm Local Selling Concentration remains significant in both the price and TOM
equations. At less than 0.1%, the price effect of selling firm concentration is extremely
modest, but it is highly stable across the sample nonetheless.
With respect to the last firm variable in Table 5, as in Table 3, the Own Firm Sale
dummy variable indicates that houses sold within a single firm tend to sell for less. This
result shows that selling agents who partner with selling agents in other firms tend to
obtain higher prices (although the marketing time effect is not significant in Table 6).
This is consistent with the notion that agent specialization in listing or selling functions
affects outcomes. But recall as in our earlier discussion that conclusions about agent
20
specialization should not be inferred from this variable without reference to the individual
agent variable.
The Agent Own Listing dummy variable is included in the models reported in
Table 5 to control for differences in performance when the individual is both the listing
and selling agent. In the last two models in Table 5, this dummy variable has a significant
positive coefficient in the price equation. When interpreting this variable, however, recall
that when an agent sells his or her own listing then Agent Own Listing = 1, but this also
means that the agent’s firm has sold its own listing as well so that Firm Own Listing = 1,
too. Therefore, the effect of an agent selling his or her own listing on price is the
combined effect of the firm and agent own listing coefficients, which is about -0.5%, but
not significantly different from zero. There appears to be no systematic price and market
time difference for houses listed and sold by the same agent.
Now turning to the other variables controlling for agent-level effects, the dummy
variables for Male Listing Agent and Male Selling Agent are both insignificant at the 5%
or lower level in all of the price and marketing time equations. Contrary to many widelyheld beliefs, we do not find any observable difference between the selling performances
of men and women, whether as listing or selling agents.
What about how specialization in listing or selling functions affect the sales
outcome? Recall that the Mainly Listing and Mainly Selling dummy variables reflect the
observed behavior of individual agents as primarily listing or selling in the market. In all
cases in Tables 5 and 6, agents who specialize in listing see their houses sell at a
significantly positive premium relative to all others. On the other hand, houses that end
up being sold by primarily selling agents sell at a significant discount relative to all
others. These estimates are not surprising. We expect agents who have a comparative
advantage in the listing phase of the selling process to concentrate more heavily on
listing. The listing function is an important part of the transaction process, as argued
earlier; a good listing agent sets the stage for a successful sale. The positive listing
specialist coefficient is consistent with our contention on this point.
The negative coefficient on the Mainly Selling dummy variable is also not
surprising. In our data we are unable to distinguish agents who are formal buyer’s agents
and those who are working with buyers but follow the traditional formal function as a
21
seller’s agent. We expect successful buyer’s agents to help the buyer ultimately obtain a
house at a lower price than without buyer agent assistance,8 hence we expect to find a
negative coefficient. The observed result is consistent with stronger price effects of true
buyer’s agents outweighing the countervailing price effects of seller’s agents (who are
not the listing agent) or true buyer’s agents and non-listing seller’s agents who behave
like buyer’s agents.
Now consider the effect of agents who choose to specialize in smaller areas
within the broader market. We use the Ave Listing Distance, Ave Selling Distance, List
Agent Conc, and Sell Agent Conc variables to sort out the net effects of such
specialization and scale as explained earlier. The coefficients on all of these variables are
robust across the models reported in Tables 5 and 6; while including or excluding agent
variables alters firm variable coefficient estimates, including or excluding firm variables
does not appreciably affect agent variable coefficient estimates.
The Ave Listing Distance and Ave Selling Distance price coefficients are all
negative and the listing average distance is only marginally significantly positive in one
TOM equation. Other things being equal, listing and selling agents who concentrate their
activities on geographic market segments sell houses at higher prices or in shorter
marketing times. There is a measurable effect of specialization in localized expertise on
price and selling time.
The agent local concentration estimated coefficients are also robust across
specifications. The selling agent concentration has a negative effect while the listing
agent concentration has no significant effect on price. At the same time, note in Table 6
that selling agent concentration has no effect while listing agent concentration has a
significantly positive effect on time on the market for given expected price. Referring
back to the interpretation of coefficients summarized in Table 1, since the average
distance coefficients indicate a positive value to sellers of agents who acquire localized
market knowledge, these local concentration coefficient estimates indicate diseconomies
of scale for individual agents in both the selling and listing activities. As a consequence,
sellers may be better off in terms of price and selling time working with agents who list
8
Recall, however, our caveat regarding the fact that broker or agent assistance benefits buyers and sellers
even when there are no observable effects on selling price.
22
or sell many houses, but not necessarily with agents who are involved in the greatest
number of transactions in the market.
5. Conclusion
This study examined the effects of individual agents and real estate brokerage firms on
house selling prices and time on the market. The empirical model is an extension of the
simultaneous equations model of sales price and marketing time developed in Turnbull
and Dombrow (2005), which controls for localized demand and supply conditions
surrounding the house being sold as well as individual firm and agent attributes.
When excluding individual agent controls from the empirical models we find that
brokerage firms may be able to exploit expertise in specific market segments or locationspecific information advantages to affect selling price and time on the market. Adding
individual agent controls to the models leads to stronger conclusions: there is no evidence
of pervasive economies of scale at the firm level for either listing or selling functions, but
firm specialization in market segments leads to modestly higher selling prices for given
time on the market.
Looking at individual agent effects, we find no difference attributable to the sex
of the agent in either listing or selling functions. On the other hand, agents who specialize
more heavily in listing functions obtain higher prices as do listing agents (specializing or
not) who complete a transaction with selling agents in other firms. We interpret the latter
effect as evidence of a well-functioning MLS system that makes it easier for individual
agents to partner transactions across firms. Agents who specialize more heavily in selling
appear to enhance buyer bargaining power in our sample, leading to lower selling prices.
We also find strong evidence that specialization in market segments by listing and
selling agents yields returns to sellers in the form of higher prices; houses closer to all of
the other transactions of the listing and selling agents obtain higher selling prices. At the
same time, though, listing and selling agents appear to be subject to diseconomies of
scale as greater scale—while holding market specialization constant—yields lower
expected selling price or longer time on the market.
23
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25
Table 1. Interpreting Agent Information Effects from Estimated Coefficients
Variable
Coefficient Sign (price equations)*
Implication of coefficient sign
(-)
(a) Positive value of agent localized market information to the
seller.
Average Distance
 0
 
 
(b) No value of agent localized market information to the seller.
If (a) above also holds then can make no conclusions about scale
effects at the agent level.
(+)
If (b) above also holds then can conclude that there are economies of
scale at the agent level.
Agent Local Concentration
 0
 
 
If (a) above also holds then can conclude diseconomies of scale at
the agent level.
If (b) above also holds then can make no conclusions about scale
effects at the agent level.
* Opposite signs in the time on the market equations.
26
Table 2. Description of Variables and Summary Statistics
Variable
Sales Price
Time on Market (TOM)
Living Area (1000 sq. ft.)
Net Area (1000 sq. ft.)
Age
Area 42
Area 43
Area 52
Area 53
Area 61
Area 62
Vacant
Month
Listing Density
New Density
Vacant Density
Competition
New Competition
Vacant Competition
Big List Firm
Big Sell Firm
Medium List Firm
Medium Sell Firm
Own Firm Sale
Agent Own Listing
List Firm Conc (%)
Sell Firm Conc (%)
Male Listing
Male Selling
Ave Listing Distance (mi.)
Ave Selling Distance (mi.)
List Agent Conc (%)
Sell Agent Conc (%)
Mainly Listing
Mainly Selling
Mean
Standard
Deviation
120267.87
49.431
1.997
0.714
16.460
0.188
0.447
0.119
0.166
0.039
0.038
0.188
10.458
6.105
0.945
1.749
289.150
52.745
84.025
0.328
0.374
0.160
0.178
0.278
0.167
14.136
12.125
0.181
0.196
3.451
3.677
1.906
1.405
0.188
0.192
45564.18
43.155
0.575
0.256
7.929
0.391
0.497
0.324
0.372
0.194
0.192
0.391
6.059
2.854
0.598
1.132
294.179
59.062
94.719
0.469
0.484
0.367
0.383
0.448
0.373
13.173
12.720
0.385
0.397
1.272
1.323
3.308
2.868
0.391
0.394
27
Minimum
Maximum
43900.00
0
1.016
0.113
2
299000.00
179
4.431
1.558
35
1
0.011
0
0
0.061
0
0
21
17.581
4.543
8.212
1905.140
398.926
668.332
0.217
0.197
62.328
62.328
0.280
0.257
0.207
0.197
10.371
9.974
50.000
50.000
Table 3. 2SLS Results for Log of Sales Price Equation
Base Firm Model With
Firm Concentrations
Base Firm Model
Variable
Intercept
TOM
House
Characteristics
Living Area
Living Area2
Net Area
Net Area2
Age
Age2
Area 42
Area 43
Area 53
Area 61
Area 62
Estimate
10.312
-0.0005
t-Statistic
232.59 ***
-4.60 ***
1.013
-0.114
0.182
-0.043
-0.027
0.0005
-0.107
-0.081
-0.103
0.033
-0.006
26.60
-13.96
2.68
-1.04
-16.22
10.87
-8.18
-6.89
-7.50
1.64
-0.28
Market Conditions
Month
Month2
Vacant
0.004
-0.00002
-0.038
1.65
-0.22
-4.32
*
Localized Market
Conditions
Listing Density
New Density
Vacant Density
0.003
0.002
-0.009
1.67
0.23
-1.94
*
0.019
0.028
-0.011
0.014
-0.018
2.30
3.53
-1.16
1.46
-2.21
**
***
Firm Characteristics
Big List Firm
Big Sell Firm
Medium List Firm
Medium Sell Firm
Own Firm Sale
List Firm Conc
Sell Firm Conc
***
***
***
***
***
***
***
***
***
*
**
Estimate
10.300
-0.0004
t-Statistic
231.79 ***
-4.49
***
1.010
-0.113
0.177
-0.042
-0.027
0.0005
-0.099
-0.075
-0.094
0.042
0.003
26.56
-13.89
2.62
-1.04
-16.01
10.68
-7.41
-6.25
-6.74
2.04
0.13
0.004
-0.00003
-0.037
1.72
-0.26
-4.26
*
0.004
0.001
-0.009
1.80
0.19
-2.06
*
0.009
0.004
-0.011
0.008
-0.022
0.0006
0.001
0.89
0.29
-1.13
0.85
-2.66
1.47
2.16
Significance: *** 1 percent level; ** 5 percent level; * 10 percent level.
28
***
***
***
***
***
***
***
***
**
***
**
***
**
Table 4. 2SLS Results for Time on the Market Equation
Base Firm Model
with Firm
Concentrations
Base Firm Model
Variable
Intercept
Ln Price
Estimate
841.620
-73.662
t-Statistic
6.35
***
-6.02
***
House
Characteristics
Living Area
Net Area
Age
Age2
Area 42
Area 43
Area 53
Area 61
Area 62
39.658
5.004
-2.828
0.054
-20.285
-19.505
-25.509
-1.193
-2.112
6.41
1.35
-6.26
5.02
-7.53
-8.64
-9.17
-0.31
-0.55
***
Market Conditions
Month
Month2
Vacant
-1.200
0.0402
-0.447
-2.50
1.96
-0.26
Localized Market
Conditions
Competition
New Competition
Vacant Competition
0.0533
0.240
0.081
6.39
7.41
5.42
2.464
-0.452
-2.648
-1.718
-0.065
1.60
-0.30
-1.40
-0.93
-0.04
Firm Characteristics
Big List Firm
Big Sell Firm
Medium List Firm
Medium Sell Firm
Own Firm Sale
List Firm Conc
Sell Firm Conc
Estimate t-Statistic
849.044
6.36
***
***
-74.381
-6.02
39.959
5.089
-2.853
0.054
-20.193
-19.394
-25.332
-0.857
-1.943
6.40
1.37
-6.31
5.06
-7.48
-8.54
-9.08
-0.22
-0.50
***
**
*
-1.197
0.040
-0.371
-2.49
1.96
-0.21
**
*
***
***
***
0.054
0.239
0.081
6.41
7.36
5.34
***
***
***
1.363
0.071
-2.693
-1.666
0.045
0.062
-0.033
0.68
0.03
-1.42
-0.88
0.03
0.85
-0.35
***
***
***
***
***
Significance: *** 1 percent level; ** 5 percent level; * 10 percent level.
29
***
***
***
***
***
Table 5. 2SLS Results for Log of Sales Price Equation
Base Agent Model
Variable
Intercept
TOM
House Characteristics
Living Area
Living Area2
Net Area
Net Area2
Age
Age2
Area 42
Area 43
Area 53
Area 61
Area 62
Market Conditions
Month
Month2
Vacant
Localized Mkt Conditions
Listing Density
New Density
Vacant Density
Estimate
9.452
-0.0004
t-Statistic
4.18
***
-4.47
***
0.969
-0.107
0.162
-0.037
-0.028
0.0005
-0.127
-0.090
-0.085
0.002
-0.026
26.13
-13.44
2.48
-0.94
-17.09
11.31
-9.96
-7.85
-6.28
0.13
-1.31
***
***
**
0.004
-0.00003
-0.037
1.78
-0.26
-4.33
*
0.002
0.002
-0.010
0.79
0.26
-2.36
Base Agent Model with
Firm Concentration
Full Agent and Firm
Model
Estimate
10.524
-0.0004
Estimate
10.523
-0.0004
t-Statistic
216.99 ***
-4.37
***
0.964
-0.105
0.160
-0.037
-0.028
0.0005
-0.115
-0.083
-0.079
0.017
-0.016
26.19
-13.40
2.46
-0.94
-17.26
11.37
-8.93
-7.25
-5.81
0.86
-0.81
***
***
**
1.90
-0.33
-4.32
*
***
0.005
-0.00003
-0.037
**
0.002
0.001
-0.011
0.97
0.14
-2.54
***
***
***
***
***
0.965
-0.106
0.166
-0.041
-0.028
0.0005
-0.115
-0.082
-0.079
0.017
-0.015
26.15
-13.39
2.54
-1.04
-17.18
11.34
-8.83
-7.13
-5.78
0.88
-0.76
***
***
**
1.90
-0.33
-4.38
*
***
0.0047
-0.00003
-0.037
**
0.002
0.001
-0.011
0.94
0.13
-2.47
***
**
***
0.007
-0.010
-0.013
0.002
-0.029
0.0005
0.001
0.66
-0.77
-1.31
0.17
-3.13
1.16
2.48
-0.011
-0.005
0.019
-0.022
0.023
-0.012
-0.018
-0.0003
-0.007
-1.24
-0.57
2.20
-2.52
2.05
-3.64
-5.93
-0.14
-3.66
***
***
***
***
***
Firm Characteristics
Big List Firm
Big Sell Firm
Medium List Firm
Medium Sell Firm
-0.029
0.0007
0.0009
Own Firm Sale
List Firm Conc
Sell Firm Conc
-3.24
2.48
3.03
t-Statistic
215.42 ***
-4.44
***
***
***
***
***
***
***
**
***
**
Agent Characteristics
-0.016
-1.80
-0.013
*
-0.009
-1.01
-0.004
0.018
2.08
0.019
**
-0.026
-3.06
-0.022
***
0.0008
0.08
0.024
Agent Own Listing
Ave Listing Distance
-0.013
-3.93
-0.011
***
Ave Selling Distance
-0.019
-6.47
-0.018
***
List Agent Conc
-0.00006
-0.03
-0.0005
Sell Agent Conc
-0.006
-3.05
-0.007
***
Significance: *** 1 percent level; ** 5 percent level; * 10 percent level.
Male Listing
Male Selling
Mainly Listing
Mainly Selling
30
-1.46
-0.52
2.23
-2.57
2.10
-3.53
-6.05
-0.29
-3.52
**
***
**
***
***
***
**
**
**
***
***
***
Table 6. 2SLS Results for Time on the Market Equation
Base Agent Model with
Firm Concentration
Base Agent Model
Variable
Intercept
Ln Price
House Characteristics
Living Area
Net Area
Age
Age2
Area 42
Area 43
Area 53
Area 61
Area 62
Estimate
454.773
-39.079
t-Statistic
3.19
***
-3.03
***
23.337
-0.035
-1.645
0.031
-15.753
-16.648
-21.835
-1.060
-1.568
3.68
-0.01
-3.48
2.91
-5.59
-7.41
-8.23
-0.29
-0.43
***
-1.325
0.040
1.292
-2.90
2.03
0.78
0.056
0.230
0.089
7.01
7.48
6.16
Estimate
442.998
-37.919
Full Agent and Firm
Model
t-Statistic
3.08
***
-2.90
***
22.632
-0.087
-1.637
0.031
-15.921
-16.554
-21.823
-1.271
-1.665
3.53
-0.02
-3.43
2.92
-5.71
-7.39
-8.28
-0.34
-0.45
***
***
**
-1.357
0.041
1.419
-2.97
2.10
0.86
***
***
***
0.056
0.230
0.089
7.01
7.47
6.13
***
***
***
***
***
Estimate
453.999
-38.757
t-Statistic
3.16
***
-2.97
***
22.919
0.440
-1.727
0.034
-16.342
-16.688
-22.348
-1.683
-2.341
3.58
0.12
-3.61
3.11
-5.84
-7.43
-8.46
-0.46
-0.64
***
***
**
-1.382
0.042
1.298
-3.03
2.14
0.78
***
**
***
***
***
0.055
0.231
0.089
6.96
7.51
6.15
***
***
***
2.743
2.808
-1.790
-0.602
1.286
-0.027
-0.189
1.37
1.16
-0.97
-0.33
0.73
-0.36
-1.96
**
-0.775
-0.998
-2.266
0.853
-2.85
1.115
0.176
0.821
0.564
-0.46
-0.63
-1.40
0.53
-1.34
1.77
0.28
2.56
1.47
***
**
***
***
***
***
***
***
***
***
Market Conditions
Month
Month2
Vacant
Localized Market
Conditions
Competition
New Competition
Vacant Competition
Firm Characteristics
Big List Firm
Big Sell Firm
Medium List Firm
Medium Sell Firm
Own Firm Sale
List Firm Conc
Sell Firm Conc
1.504
0.050
-0.103
0.88
0.91
-1.77
Agent Characteristics
-1.303
-0.78
-1.247
-0.74
-1.193
-0.76
-1.464
-0.93
-1.978
-1.24
-1.993
-1.25
1.507
0.94
1.165
0.73
-1.898
-1.07
-2.911
-1.37
Agent Own Listing
Ave List Distance
1.021
1.64
1.153
1.84
Ave Sell Distance
0.428
0.69
0.304
0.49
List Agent Conc
0.775
2.45
0.745
2.35
**
Sell Agent Conc
0.421
1.14
0.510
1.35
Significance: *** 1 percent level; ** 5 percent level; * 10 percent level
Male Listing
Male Selling
Mainly Listing
Mainly Selling
31
*
**
*
**