Determinants of Real Estate Agent Compensation Choice
Lenny V. Zumpano
The College of Commerce and Business Administration
The University of Alabama
lzumpano@cba.ua.edu
Ken H. Johnson
College of Business Administration
Florida International University
kenh.johnson@fiu.edu
Randy I. Anderson
College of Business Administration
University of Central Florida
anderson@bus.ucf.edu
Abstract
This research seeks to determine what factors are decisive when an agent chooses
between a 100% payout and the more traditional split-commission arrangement with his
firm. Besides the expected positive relationship between income and the 100% payout
election, tolerance for risk, a complementary sales force, and experience also influence
the choice of compensation arrangement of agents. These finding suggest that
compensation arrangements may not always be effective markers of agent productivity
and that compensation incentives alone may not elicit greater effort and output.
I. Introduction
The impact of real estate brokerage intermediation has been the subject of much study
over the past decade. Most of this research looks at either differential market outcomes
(price and marketing time) across brokerage categories or outcome differences between
brokered and non-brokered transactions. Recently, the compensation arrangement
between an agent and his firm has come under investigation with conflicting results. In
particular, claims are made by some researchers that differences in skill levels among
agents can be discerned by the type of compensation arrangement (100% vs. Spilt)
between the agent and his firm1, suggesting the possibility that some agents can sell
properties at premium prices and/or over shorter time horizons than is the case with other
agent-assisted transactions handled by less skilled or motivated agents (Munneke and
Yavas, 2001; Allen, Faircloth, Forgey, and Rutherford, 2003; and Johnson, Zumpano,
and Anderson, 2008). Such findings have important efficiency implications, as most
studies of the market for brokerage services have assumed no differences in agent skill
levels.
2
So far, however, no study addresses the more fundamental, prerequisite question of what
factors determine an agent’s choice of compensation arrangement between himself and
his firm. If these factors are positively associated with agent productivity and motivation,
then one might reasonably expect differences in agent performance to be systematically
linked to the types of compensation incentives available.
The goal of this research is to model the choice of compensation arrangement between an
agent and his firm.2 The theoretical and empirical literature is examined to help identify
important determinants of this choice. Empirically, compensation choice is modeled as a
Probit estimate using the National Association of Realtors® 2001 Membership Survey,
which provides a national, cross-section profile of NAR member characteristics.
The next section of this paper examines the relevant literature on agent performance.
Sections on methodology and data, empirical analysis, and concluding comments follow
in order.
II. Literature Review
IIa Buyer Search and Market Intermediation
Many researchers have examined the impact of broker intermediation in the marketplace.
There is a large body of research on the search process in the real estate market, much of
it focuses on the seller’s side of the market, and is commonly referred to as the pricing
and time on market literature3. Other research, however, has begun to look at buyer
3
search and the role of the intermediary. Early studies by Jud (1983) and Jud and Frew
(1986) suggested that intermediation by real estate agents influences search time and
increases the demand for housing. In the latter study, Jud and Frew find that agentassisted buyers have a greater demand for housing than buyers who search without
benefit of an agent. They attribute this outcome to the informational impact of agents,
offering the analogy of the effect of advertising in markets with imperfect information.
A paper by Baryla and Zumpano (1995) finds that broker intermediation reduces search
time for all classes of consumers, regardless of their demographic characteristics.
Zumpano, Elder, and Baryla (1996) examining the decision to use an agent subsequently
find that buyers with high opportunity costs (travel, time, and information) of search are
the most likely to use agents, which in turn reduces search time. Additionally, Elder,
Zumpano, and Baryla (1999) determine that agents, by reducing within-period search
costs, increase buyer search intensity, thereby reducing search duration.4
In a more recent paper, Elder, Zumpano, and Baryla (2000) find that the type of agent has
a discernable effect on the search duration of buyers. Specifically, the authors find buyer
agents are more effective at reducing search time for their clients than more traditional
seller agents or non-agent facilitators. As is the case in most earlier studies, agents do not
affect price, no matter the type of agent. While this line of research does generally
support the idea that agent actions can affect market outcomes, it does not differentiate
agent performance on the basis of compensation.
4
IIb Incentives and Agent Performance
Recently, three papers (Munneke and Yavas (2001), Allen, Faircloth, Forgey, and
Rutherford (2003), and Johnson, Zumpano, and Anderson (2008)) appeared which try to
assess whether different agent compensation arrangements could serve as proxies for
differences in skill/motivation levels of agents as reflected by differences in time on the
market and selling price of properties market by this classification of agent. However,
their empirical findings are not reconcilable. Munneke and Yavas find that full-payout,
REMAX agents, presumed to be more skilled than their split-commission counterparts,
have no long-term impact on selling price or property marketing time. Allen et al., on the
other hand, using a similar construct but different data, find that residential properties
marketed by 100% agents are sold more quickly and at a premium relative to homes sold
by “less productive” agents. Johnson et al. find that 100% agents reduced search time,
but have no effect on selling price.
In all three studies the way in which the agent is compensated is singled out as the
appropriate productivity marker or signal. Productive agents are differentiated from their
less productive counterparts by the way they are compensated by their firm. The 100%
payout election, such as that offered by REMAX, is deemed to attract more productive
agents because they will earn more money than if they share their commissions with their
broker-owners.
5
In the case of the Munneke and Yavas and the Allen, et al., neither study actually links
compensation plans to agent output or actual compensation differences among agents.
Instead they look at whether selling prices and marketing times are related in a systematic
way to agent compensation structure. Both papers assumed that all 100% agents work
for REMAX firms and all split-commission agents are employed by other companies.
Segregating the sample by firm rather than at the agent level may, therefore, create
specification problems because some split–commission companies also have 100%
commission agents on their payroll.5
More importantly, the papers cited above either do not model the choice of compensation
plans or employ one dimensional, self-selection criteria that makes the choice of
compensation arrangement a simple financial decision. Munneke and Yavas, for
example, consider it a simple adverse selection problem. Agents base their decision as to
which type of compensation arrangement to choose using a simple break-even analysis.
In this setting, a highly skilled or motivated agent maximizes his earning by choosing a
100% commission arrangement as his expected production is such that his gross income
from his total closed volume, less his periodic payments to the firm, will exceed what he
is expected to earn with a split-commission plan.
In point of fact, there are many other factors that could influence agent productivity
besides compensation arrangements, including, an agent’s risk tolerance, years of work
experience, education, current and other income levels, the location and size of the
market, and the size of the firm where the agent works. Until the link between
6
compensation arrangements and productivity is proven, nothing definitive can be said
about how alternative compensation plans impact agent performance and market
outcomes.
IIc Human Capital, Earnings Models, and Agency Theory
A number of studies have examined the determinants of real estate agent earnings6.
Numerous papers by, among others, Follain, Lutes, and Meir (1987), Glower and
Hendershott (1988), Crellin, Frew, and Jud (1988), find education, experience, size of
firm, and the number of hours worked per week, all have a positive impact on agent
income. Abelson, Kacmar, and Jackofsky (1990) and Sirmans and Swicegood (1997)
also find a positive relationship between non-pecuniary factors such as job satisfaction
and agent earnings. None of these papers, however, directly examine the link between
income and agent performance.
Additional insights into the linkages between agent compensation choice and agent
characteristics can be gleaned from the vast amount of research on the moral hazard
problem that arises when agent actions cannot be monitored. Commonly referred to as
the principal-agent problem, much of this literature examines agent performance and the
use of incentive compatible contracts, such as percentage commissions, to better align
principal’s and agent’s interest. Some of this research indicates that neither flat fees nor
percentage commissions perfectly align the interests of both principal and agent. 7 For
example, Holmstrom (1979) shows that when an outcome is a function of both effort and
7
a random state of nature, basing an agent’s compensation on outcomes alone is a secondbest solution8.
Other research [Frederickson (1992) and Stewart (1999)] examines the efficacy of
alternative payment arrangements, such as relative performance measures and contests, as
ways to either improve risk sharing between the principal and agent without reducing
agent effort, or alternatively, provide greater agent incentives without imposing
additional risks on the agent. In a real estate brokerage context, when the variance of
outcomes is high due to factors beyond the control of the agent, such as changes in
mortgage interest rates or the state of the economy, risk-averse agents would be
encouraged to seek improved risk-sharing arrangements with their companies.
Still other researchers have criticized principal-agent theory that is based solely on
economic or financial incentives. Two papers [Frey (1997) and Fehr and Falk (2002)]
present work that suggests that there are also intrinsic, non-pecuniary incentives and
rewards that can influence work effort and performance; a finding consistent with some
of the real estate agent earnings research cited above. In some cases, economic
incentives complement intrinsic incentives such as job satisfaction, social approval, and
worker morale, while in other cases financial incentives (or penalties) can “crowd out”
such intrinsic incentives, thereby reducing performance. Financial incentives may also
be counterproductive if they lead to undesirable behavior (sabotage of co-worker
performance) by employees. These authors point to studies showing a weak relationship
between financial incentives and worker performance as support for their hypotheses.
8
III. The Model and Methodology
IIIa The Variables
This study seeks to determine whether any considerations besides productivity may cause
an agent to choose a full-payout compensation arrangement versus a split-commission
arrangement with his firm. To date, there does not appear to be any studies that explicitly
model how and why an agent, when given the choice, chooses a specific compensation
arrangment. The previous literature review does, however, provide insights into some of
the factors that may influence this choice. For organizational purposes three broad, nonmutually exclusive, categories (job characteristics, agent characteristics, and risk
tolerance) of explanatory variables are created.
IIIa1 Job Characteristic
Job-related characteristics include SalesStaffOffice, which represents the number of other
agents in the agent’s office. This study hypothesizes that the larger the sales force, the
greater the marketing impact of the agent’s firm and the greater the probability of
closings, both co-op and the typically more lucrative in-house sale, which should make it
easier for an agent to meet and exceed the periodic fees associated with a 100%
commission structure. Therefore, SalesStaffOffice is expected to be positive and
significantly related to choosing a 100 % contract. Similar synergies may be present as
the firm itself gets larger spatially, increasing its market area which may allow agents to
reduce the risk associated with the 100% contract structure. To capture this effect, the
9
total number of offices (Offices) for a given firm is specified. In the interest of brevity,
Exhibit 1 provides definitions and abbreviations for these, as well as, all other variables
employed herein.
IIIa2 Agent Characteristics
Experience, represents the total number of years an agent has practiced in residential real
estate. It is conjectured that this variable will have a positive relationship with an agent’s
choice to become a 100% agent. With job tenure, agents gain experience and knowledge,
which are critical to an agent’s ability to generate the listings and sales necessary to cover
the fixed costs associated with full-payout compensation arrangements. However, it also
seems logical that there are diminishing returns associated with experience; therefore,
ExperienceSqrd is specified in the Probit estimation to control for the decreasing
marginal impact of agent experience on choice of compensation arrangement. If these
suppositions are correct, Experience will sign positive and significant while
ExperienceSqrd will sign negative and significant.
The agent earnings and human capital literature suggests, in addition to experience, that
agent-related compensation decision parameters may also include education (Education)
and agent age (AGE), both of which are included in the model. If education improves
agent performance it should be positively associated with the choice of the full-payout
compensation arrangement. If knowledge increases with age, agent age should be
positively related to the 100% commission choice. On the other hand, older agents may
10
be more or less risk-averse than younger agents due to their personal wealth level making
it difficult a priori to determine the sign of AGE.
Finally, a number of studies have argued that full-payout compensation arrangements are
positively linked to agent productivity and work effort. Accordingly, HoursWorked,
representing the average number of hours worked per week by the agent, is entered as a
proxy for agent effort in the compensation choice model. A priori, it seems reasonable to
assume a positive relationship between this proxy and the choice of compensation
arrangement.
IIIa3 Risk Tolerance Vatiables
Risk tolerance should play a role in the choice of compensation package. With the
traditional split-commission arrangements, an agent is assured of some income, even at
low levels of productivity. In contrast, full-payout agents will receive no income until
their commission production exceeds their payments to the firm. Although this form of
operating leverage can prove very profitable for highly risk tolerant agents seeking to
maximize their incomes, more risk-averse agents may opt out for less risky income
sharing arrangements.
Risk preference can be proxied by such things as marital status, gender, how often an
agent changes jobs, and both agent and other income noted in the model and exhibits as
Married, Male, YearsCurrentFirm, Income, and IncomeDifference, respectively. The
11
potential marginal impact of each of these risk tolerance variables on agent compensation
choice is discussed in turn.
Married agents, especially those with children, might be expected to be more risk-averse
than single agents as more people are dependent upon their earnings. As a result, they
may prefer the risk-sharing attribute of the more traditional split-commission agent
arrangement. If this reasoning is sound, Married should enter as a negative predictor of
compensation choice.
Psychology studies have shown that in areas such as finance, men are more overconfident
than women. In particular, they are more overconfident about their abilities, knowledge,
and future prospects. Empirical support for the contention that males tend to be more
overconfident than their female counterparts is provided by Barber and Odean (2001)
who examined the stock trading records of over 35 thousand households finding the
turnover rate for men was nearly one and a half times greater than that of female stock
traders. Such overconfidence could easily transfer to compensation choices as well
resulting in Male having a positive affect on compensation choice.
Additionally, a causal relationship is expected between agent income and the choice of
compensation plan. If productive agents are more likely to choose full-payout plans, then
income earned as an agent should be positively and significantly related to this choice.
Interest here, however, is in the degree to which past income proxies anticipated future
income earning ability, and, therefore, an agent’s ex ante estimate of the likelihood of
12
covering the periodic fee associated with 100% payouts. Thus, the higher the agent’s last
year’s income, the lower the risk of not being able to meet the periodic fee. Accordingly,
increases in Income should be associated with a greater likelihood of becoming a 100%
agent. For similar reasons, the total household income of an agent, another risk proxy,
should also be positively related to the choice of a 100% commission payout. As outside
incomes increase, agents become less dependent upon commission income. Thus,
IncomeDifference is included as a variable representing the difference between an agent’s
annual income from their residential practice and their gross annual reported income,
which should sign positive and significant in the Probit estimation.
Non-pecuniary factors, such as job satisfaction and social approval can reasonably be
expected to influence compensation arrangement choice. Such incentives, however, are
difficult to proxy given the database, although years with the current firm
(YearsCurrentFirm) may reflect the attractiveness of the work environment, separate and
apart from productivity. As an alternative explanation, the amount of time spent with a
given firm could also act as a proxy for willingness to bear the risk of becoming a 100%
agent. In particular, it seems plausible that non-risk takers will be more likely to remain
at the same firm while risk takers, seeking ever better employment opportunities, will
change jobs more frequently and, hence, have shorter job tenure. Therefore, all else
being equal, it is reasonable to expect YearsCurrentFirm to be inversely related to
becoming a 100% agent.
IIIb The Model
13
The dependent variable of this study is agent compensation choice (CC). It is a
categorical variable coded as either a 1 or a 0. Agents that choose to pay their firm a
periodic fee in exchange for retaining 100% of their generated commissions, receive a 1
for identification purposes. Agents that choose the more traditional split-commission
arrangement with their firm are coded with a 0 designation. Split-commission agents
differ from their full-payout counterparts in that they do not pay a periodic fee to their
firm. Instead, they split their generated commissions with their firm on a negotiated
percentage basis with 50/50, 60/40, and 70/30 sharing arrangements being commonly
agreed upon splits. 100% agents, on the other hand, pay their firm a periodic fee in
exchange for all of their earned commissions.
Following the arguments outlined above the following Probit estimation is specified to
model an agent’s compensation choice:
CC = f (YearsCurrentFirm, Experience, ExperienceSqrd, HoursWorked, Income,
IncomeDifference, Male, SalesStaffOffice,Age, Married, Education, Offices)
(1)
IIIc The Data
The data for this study comes from the National Association of Realtors® 2001
Membership Survey. The 2001 survey is chosen because it appears to be more
representative of a typical agent force participating in a constrained market place such as
the one faced by agents today as opposed to the makeup of the agent population during
the recently past, rapidly escalating property market. Said another way, the typical agent
14
force seems more likely to look like that of 2001 than that of recent years making it
sensible to use prior data.
The Membership Survey is a national survey of NAR members that contains questions on
business activity, technology use, office information, agent production, agent income,
and other agent demographic information. The raw data set consists of 7,440 responses.
However, many of the responses were either incomplete, provide erroneous information
such as negative income, or were completed by individuals who do not practice in
residential real estate on a daily basis, making the assignment of variability in the model
suspect due to the loss of degrees of freedom fostered by these absences. Thus, these
incomplete responses are eliminated in order to avoid this potential problem. Next, since
the survey also indicates that very few agents are on straight salaries, this choice is
excluded from the analysis that follows. Therefore, the observations are limited to those
agents that have sufficiently completed the questionnaire, i.e. provided answers to all the
necessary variables employed herein.
After making these adjustments to the data set, a final sample of 1,853 observations
remain for analysis. Copies of the full sample and the Membership Survey itself are
available from the National Association of Realtors. Descriptive statistics for the
examined sample are provided in Exhibit 2. Additionally, descriptive statistics split by
compensation choice are available in Exhibit 3. A casual review of these split statistics
suggests that many of the proposed modeling arguments seem warranted. For example,
casual observation (i.e., without statistical validation) suggests that the means for Income
15
and IncomeDifference appear to be higher for 100% agents than those of Split-agents.
The next section discusses the descriptive statistics in additional detail and provides a
review of the empirical findings.
IV. Empirical Results:
IVa Analysis of Descriptive Statistics
Examining the basic demographic information in Exhibit 2 reveals that less than half the
sample are male, while over three-quarters of the sample are married. Additionally, the
average sample respondent is nearly 45 years of age and has almost 15 years of
education, which translates into the average sample agent having some college or an
associate’s degree. The mean years of work experience is slightly over 11, with slightly
under 6 of those years being at the respondent’s current firm. The average real estate
derived income is slightly more than $123,000 per agent per year. And,
IncomeDifference reveals that the mean difference between what an average agent
produces in commission income and their total household income is slightly over
$87,000.
Next, some casual empirical results of the descriptive statistics are examined by dividing
the sample into those respondents that are full-payout agents and those who contract as
split-commission agents. Note that in Exhibit 3 a value of 1 in the column for
compensation choice indicates that the summary statistic refers to the subsample of fullpayout agents, while 0 refers to the subsample of agents on a commission split.
16
Contrary to expectations from reasoning provided in Barber and Odean (2001), it seems
that males make up slightly less than half the 100% subsample. Additionally, as
mentioned above, the income variables are noticeably higher for the 100% subsample.
Finally, there are a number of variables that appear, at least anecdotally, to have no
impact on the choice of compensation method. In particular, an agent’s marital status,
age, and education are virtually the same for 100% and split-commission agents.
While these casual statistics are suggestive, they are not statistically verifiable; therefore,
it is necessary to model these indicators in the presence of one another in order to
determine their actual impact on agent compensation choice. The next subsection
discusses the results for the specified compensation choice model.
IVb Binary Probit Regression Results
The estimation of compensation choice is formally reported in Exhibit 4. Experience and
ExperienceSqrd, both sign as hypothesized as does YearsCurrentFirm. Thus, it appears
that an understanding of the business that tends to comes with experience makes it more
likely for an agent to become a 100%er, while job satisfaction (or simply the familiar
environment of the more common split-commission arrangement) makes it less likely for
an agent to become a 100%er.
17
Not surprisingly, Income and IncomeDifference are both positively related to CC. The
higher the agent’s real estate derived income the more likely the 100% commission
compensation structure is chosen. Furthermore, the larger an agent’s total income, as
indicated by the difference between an agent’s total income and his real estate derived
income, the less dependent the agent is on commission income, and the greater the
probability that the agent chooses the 100% commission option.
It was anticipated that male agents having, on average, a greater propensity to accept risk
would be more likely to chose the 100% payout commission structure. The empirical
analysis confirms this reasoning with Male being a significant and positive predictor in
the choice of the 100% compensation arrangement. Interestingly, an agent’s age has a
negative affect on the probability of choosing to become a 100% agent. Thus, it appears
that younger agents, on average, are more willing to take on the additional risk of
operating as a full-payout agent.
The model, also, finds that the probability of choosing to be a full-payout agent is
positively and significantly related to the size of the sales staff (SalesStaffOffice) in the
agent’s office, suggesting that synergies are in play within an office. Specifically, it
appears that a large sales staff surrounding an otherwise productive agent leads to a
higher probability that this agent chooses a 100% compensation arrangement with their
firm. These synergies do not seem to extend to the number of branch offices in an
agent’s firm as the number of branch offices does not have a statistical impact on
compensation choice. A possible explanation for this outcome comes from a profit
18
maximizing strategy employed by many successful agents. Specifically, it is common for
successful agents to establish a “farming” area. These “farms” are areas in which an
agent specializes and has significant name recognition and reputation. Obviously, these
“farms” are geographically constrained making additional offices in distant locations
irrelevant. Thus, assuming 100% agents (being successful) have a propensity to “farm”,
additional branches do not enter into their decision to become a full-payout agent.
Some of the other variables included in the model did not prove statistically significant,
despite the apparent importance of these variables in earnings studies. Education and
Married are not important determinants of compensation choice. The reasoning for these
outcomes could be couched in the unique nature of the industry. Specifically, it may be
such that the demographics of the agents within the industry are significantly different
from that of the overall population resulting in non-influential outcomes. Regardless, the
explanation for the lack of statistical significance is beyond the scope of this current
study.
V. Summary and Conclusions
There has been a great deal of interest in how compensation choice among real estate
agents signals differences in agent productivity. Do only the more highly motivated and
productive agents choose 100% commission arrangements, based solely on anticipated
compensation, or are there other factors that go into this choice? This paper attempts to
19
make the initial investigation into that determination by modeling the choice of
compensation scheme between the agent and his firm.
Findings suggest that younger, male agents with significant past commission income and
considerable experience in the industry working in a large office tend to favor the 100%
compensation arrangement. Other income sources also favorably influence the choice of
becoming a full-payout agent. On the other hand, the length of stay at an agent’s current
firm lowers the probability of becoming a full-payout agent. Interestingly, the number of
hours worked by an agent, his education level, and his being married does not impact
compensation arrangement choice.
From an industry standpoint, agents face this choice of compensation arrangement
typically once a year. Firm’s regularly struggle with the decision to offer 100%
compensation arrangements, the optimal mix of 100ers and split-agents, and the type of
agent that is most likely to succeed as a 100%er. Hopefully, the findings in this initial
study will facilitate answers to these questions.
From an academic standpoint, this study provides the initial investigation into the
determinants of agent compensation choice. The findings suggest that compensation
arrangements may not always be effective markers of agent productivity and that
compensation incentives alone may not elicit greater effort and output as a number of
prior studies suggest and/or implicitly assume.
20
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Exhibit 1: Variable Legend
VARIABLE
YearsCurrentFirm
Experience
ExperienceSqrd
HoursWorked
Income
IncomeDifference
Male
SalesStaffOffice
Age
Married
Education
Offices
CC
DEFINITION
The number of years the agent has worked with their current firm
The number of years the agent has worked in residential real estate
The square of the number of years the agent has worked in
residential real estate
The number of hours worked per week by the agent
Last annual income of the agent from their residential real estate
practice
Last other income available to the agent from additional sources,
spouse, investments, etc.
1 if the respondent is male, 0 otherwise
The number of agents practicing in a given office
The age of the agent
1 if the respondent is married, 0 otherwise
The education level of the agent with each year completed equal to 1
The number of branch offices in the agent’s firm
1 if the agent chooses the 100% compensation plan, 0 otherwise
25
Exhibit 2: Descriptive Statistics – Full Sample
Variable
YearsCurrentFirm
Experience
ExperienceSqrd
HoursWorked
Income
IncomeDifference
Male
SalesStaffOffice
Age
Married
Education
Offices
CC
N
Mean
StDev
5.923
5.384
11.202
8.807
203.000
290.420
41.824
14.725
123281.000 166510.000
87482.000 124447.000
0.435
0.496
31.926
21.277
44.700
11.097
0.751
0.432
14.617
2.603
51.270
342.820
0.184
0.388
1853
26
Exhibit 3: Descriptive Statistics by Compensation Choice Subsamples
Variable
YearsCurrentFirm
Experience
ExperienceSqrd
HoursWorked
Income
IncomeDifference
Male
SalesStaffOffice
Age
Married
Education
Offices
CC N
0 1512
1 341
0 1512
1 341
0 1512
1 341
0 1512
1 341
0 1512
1 341
0 1512
1 341
0 1512
1 341
0 1512
1 341
0 1512
1 341
0 1512
1 341
0 1512
1 341
0 1512
1 341
Mean
StDev
5.907
5.459
5.997
5.045
10.815
8.831
12.915
8.503
194.910
292.610
238.900
278.100
41.247
14.889
44.384
13.706
105794.000 148476.000
200820.000 213684.000
84956.000 119176.000
98683.000 145206.000
0.422
0.494
0.493
0.501
30.938
21.058
36.300
21.720
44.802
11.253
44.252
10.377
0.753
0.431
0.742
0.438
14.590
2.613
14.739
2.555
49.600
336.100
58.700
371.600
27
Exhibit 4: Binary Probit Compensation Choice Model
Dependent Variable – CC (Compensation Choice)
Variable
Coefficient Standard Error
Constant
-2.0215
0.5019
YearsCurrentFirm
-0.0547
0.0144
Experience
0.1217
0.0265
ExperienceSqrd
-0.0026
0.0008
HoursWorked
0.0036
0.0047
Income
0.0001
0.0000
IncomeDifference
0.0001
0.0000
Male
0.3698
0.1291
SalesStaffOffice
0.0080
0.0030
Age
-0.0184
0.0069
Married
-0.1260
0.1473
Education
-0.0110
0.0246
Offices
0.0001
0.0002
N
1853
Log Likelihood
-820.869
Significance Level
.0001
T
-4.030
-3.790
4.590
-3.320
0.760
6.280
2.980
2.860
2.690
-2.660
-0.850
-0.440
0.410
P
0.001
0.001
0.001
0.001
0.445
0.001
0.003
0.004
0.007
0.008
0.393
0.657
0.680
28
Endnotes
1
100% compensation arrangements are also commonly referred to as full-commission, full-payout, and
100% agents in both the literature and in practice. In a 100% compensation arrangement, the agent pays
his firm a periodic fee in exchange for 100% of the earned commissions. This arrangement stands in
contrast to the more traditional Split-compensation arrangement where the agent receives a percentage of
the earned commission but does not face a periodic fee to his firm.
2
Throughout this study the term “agent” is used to represent the sales force of a firm with one exception.
Specifically, in the literature review section, the use of “agent” is referenced when discussing the body of
study commonly referred to as the Principal-Agent literature. No where in this study does the term “agent”
represent any sort of fiduciary duty (or lack of duty) from the real estate firm through their sales force to
their clients or customers.
3
See Jud, Seaks, and Winkler (1996), Johnson, Springer, and Brockman (2005), and Rutherford, Springer,
and Yavas (2007) for relevant citations and a discussion of this literature.
4
The model used in Elder, Zumpano, and Baryla (1999) to estimate search intensity is based upon optimal
search theory models and, in particular, the model employed in Morgan and Manning (1985).
5
The Johnson, Zumpano, and Anderson paper uses intra-firm data. In their estimations, agents are actually
classified as full-payout or split-commission agents based upon an actual determination of each agent’s
specific compensation arrangement. The firms the agents work for are not used to separate the sample.
6
See Benjamin, Jud, and Sirmans (2000) for a good review of this literature.
7
See Holstrom (1979) and Zorn and Larsen (1986) for good examples of this type of article.
29
8
Furthermore, Holmstrom shows that in such situations payment contracts based solely on outcomes
impose more risks on agents and result in less effort. More specifically, payment schemes that are based
upon outcomes and signals that provide more information about an agent’s actions, or the random state of
nature, create greater effort incentives than contracts based upon performance alone.
30