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The housing market and real estate agents

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Title:
The housing market and real estate agents
Creator:
Mantrala, Suryamani, 1961-
Publication Date:
Language:
English
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x, 81 leaves : ill. ; 29 cm.

Subjects

Subjects / Keywords:
Brokers ( jstor )
Housing ( jstor )
Housing market ( jstor )
Market prices ( jstor )
Multiple listing service ( jstor )
Prices ( jstor )
Real estate ( jstor )
Real estate industry ( jstor )
Sales commissions ( jstor )
Sales representatives ( jstor )
Dissertations, Academic -- Economics -- UF
Economics Thesis Ph.D
Real estate agents -- Salaries, etc ( lcsh )
Real property -- Marketing ( lcsh )

Notes

Thesis:
Thesis (Ph.D.)--University of Florida, 1992.
Bibliography:
Includes bibliographical references (leaves 77-80).
General Note:
Typescript.
General Note:
Vita.
Statement of Responsibility:
by Suryamani Mantrala.

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University of Florida
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Copyright [name of dissertation author]. Permission granted to the University of Florida to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.
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AJG4350 ( NOTIS )

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THE HOUSING MARKET AND REAL ESTATE AGENTS















BY

SURYAMANI MANTRALA


A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE
UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE
REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA

MAY 1992































Copyright
by
Suryamani Mantrala
May 1992






























I dedicate this dissertation to my parents Alapati Narasinga Rao and Sugunamani.














ACKNOWLEDGEMENTS


This dissertation could not have been successfully completed without the support


and guidance of Professors Edward Zabel and G.


their usefu


Maddala.


wish to thank them for


comments, insights and faith in me which kept me on track whenever


was


faltering.


would also like to thank


Professors


David


Denslow,


David Sappington,


Lawrence Kenny and Stephen Donald for their invaluable comments and suggestions.


am also greatly indebted to the illustrious economics faculty at the University of


Florida, for contributing in one way or another to my development in the Ph.


D. program.


In particular


shall cherish my


association with Professor Steven Slutsky whose


zeal for teaching and research has inspired me immensely.


am grateful also to Professor Barton Weitz of the Marketing Department for being


on my dissertation committee despite his extremely busy schedule. Many thanks are due

to Pam Dampier for helping me with all my wordprocessing needs and to Dian Studstill

and Joan Feuston for invaluable office assistance.


Finally,


could not have endured the Ph.


D. program without continual support


from my family.


thank my parents Narasinga Rao and Sugunamani and Puma Rao and


Satyavati and my brother and sister-in-law Surya Rao and Sundari for their support and


encouragement throughout my education.


Syamala and brother-in-law Ram for their confidence in me.


also thank my sisters-in-law Annapurna,


especially thank my mother-


I -.. .1 S, A, a. Ba! a I aI -~ a lrr A~ a r llr Ir Ilr .1I. a J aa Laa taa Alr.&


rnl:u,, J LLr


;=







started or successfully concluded my doctorate. Last, but definitely not the least, the very


presence of my lovely daughter Vidya kept me going during the final years.


It is their


pride in me today that is the greatest reward for me.














TABLE OF CONTENTS


PAGE


ACKNOWLEDGEMENTS

LIST OF TABLES ...

ABSTRACT ......


CHAPTERS


INTRODUCTION


OPERATION OF THE HOUSING MARKET


LITERATURE REVIEW

THEORETICAL MODEL


. S S U S S U S S S S S C S S C I S S S S S S

S S C S S S S U S S S S S S S S S S S S S S U S C C S S S


Review of Theoretical Models
Model Description and Specifics
Introduction ..


ation . .


. .. 27


Specification of the Model with a Single Broker
Specification of the Model with MLS Brokers


EMPIRICAL MODEL AND ESTIMATION


Review of an Empirical Model ..
Model Description and Specification

DATA AND EMPIRICAL RESULTS .


Data


Empirical Results


SUMMARY AND CONCLU


SIONS


. . 46


. . 5 6
. . 5 8

. . 6 4








APPENDICES


MATHEMATICAL APPENDIX


ANTITRUST ISSUES AND REAL ESTATE BROKERS .... .....


DEPARTMENT OF HOUSING AND URBAN DEVELOPMENT


SETTLEMENT STATEMENT HUD-1 FORM


RESULTS FROM SEPARATE ESTIMATION OF


EQUATIONS (23),


(24) AND (25)


. . 75


REFERENCES


*5 S S a a a S S S U S S S A S S S S S S S S S C 7P7~


B IOGR1APHIICAL SKETCH . . .













LIST OF TABLES


TABLES


PAGE


ESTIMATED REDUCED FORM TOBIT


EQUATION FOR COMMISSION RATE .. .. .......


ESTIMATED REDUCED FORM PROBIT
EQUATION FOR BROKERED SALE DUMMY


. .. 61


ESTIMATED REDUCED FORM EQUATION FOR PRICE .... ..

ESTIMATED STRUCTURAL TOBIT
EQUATION FOR COMMISSION RATE... ...........

ESTIMATED STRUCTURAL PROBIT
EQUATION FOR BROKERED SALE DUMMY .. .. ....

ESTIMATED STRUCTURAL EQUATION FOR PRICE ....













Abstract of Dissertation Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Doctor of Philosophy

THE HOUSING MARKET AND REAL ESTATE AGENTS

BY


SURYAMAN


MANTRALA


MAY 1992


Chairman:


Professor Edward Zabel


Major Department:


Economics


This dissertation studies the interaction of buyers,


in the housing market.


sellers and real estate agents


Most of the current work on housing markets has ignored the


importance of real estate agents.


Buyers


sellers


have


incomplete


information


about


each


other


availability of houses.


"marketplace."


Spatial fixity of houses also implies the lack of an organized


The main role of a real estate agent is to provide information and bring


buyers and sellers together thus making the market.


a commission.

United States.


For these services the realtor is paid


Commission rates have been in the neighborhood of 6% across the

This has led to charges of noncompetitive commission rates fixing.


The present thesis disputes the argument of commission rate fixing.


In a competi-


tive market with incomplete information characterized by a large number of buyers and


sellers with


little or no market power, a market maker'


ability to charge high prices is







by the market maker then tend to be very close to the competitive equilibrium prices.


This implies here that the observed commission rates are


kely to be the competitive rate


of return for the real estate agents' services.


present a theoretical model which considers the sellers, buyers and brokers of


housing in an integrated manner.


to be a function of the seller'


The optimal commission rate set by brokers is shown


decision rule for using a broker in selling his house.


deriving


outcome


examine


a variety


models


consider


various


implications of incomplete information.


then develop an empirical model that simultaneously estimates the commission


rates and probabilities of brokered sales of housing,


states across the U


n 540 housing transactions in fifteen


My hypotheses regarding the commission rate are validated by


this model.













CHAPTER


INTRODUCTION


Expenditures on housing are a large component of household spending.


They


are also an important economic indicator in a major industrialized economy like the U


Despite the obvious importance of the housing market,


an analysis of how this market operates.


little attention has been given to


The focus in this dissertation is the interaction


of various agents


n the operation of this market.


There are three kinds of agents that we


will analyze--the seller of a house, the buyer and the real estate agent.


Real estate agents


provide a variety of services to both the buyers and the sellers of housing and charge a


commission rate for their services


, usually from the sellers.


These services are mainly


those


providing


information


about


available


houses,


prices,


financing


assistance


n arranging matches between buyers and sellers.


As producers and sellers


of information about the housing


market, real estate brokers perform the important


function of "making the market" for buyers and sellers of housing.


This market-making


function


of brokers has not been


given the


importance it deserves in the


existing


literature.

When a seller wants to sell a house, he can do so via two channels--(i) on his own


and (ii) through a real estate broker by listing with a brokerage firm.


The two channels


of sale differ in the costs involved and may differ in the expected time on the market.









the seller sells the house himself he has to


ncur the cost of searching for buyers (e.g.


advertising), showing the house to prospective buyers and the cost of holding the house


until a sale occurs.


If the house is sold through a real estate agent, on the other hand,


the seller has to pay the broker a percentage of the sale price of the house as a


commission.


A rational seller will choose the better alternative.


A buyer searches for a house which meets his requirements at the least price.


this process of search,


he too has various options-(i) to go directly to individual sellers,


(ii) to use the services of a brokerage firm or (iii) to build a house.


Usually a buyer would


use both alternatives (i) and (ii) for an already existing house since the costs of both

alternatives are similar--the time costs of visits to a seller or the broker.

Brokers produce and sell information to sellers and offer this information without

charge to buyers of houses and aim to maximize returns from their activities.

The objective of the present dissertation is to critically evaluate the theoretical and

empirical work previously done on the housing market and suggest ways in which the

analysis would be enriched when the importance of a real estate agent as a market maker


incorporated in the analysis.


In doing so an empirical model of the price making


behavior in the housing market is developed.


The rest of the dissertation is organized as follows.


describe the operation of the


housing market and the role of real estate agents in the next chapter.


Chapter


examines the existing literature.


In Chapter


examine a theoretical model.


develop


an empirical model and describe estimation procedures in Chapter V.


Data sources and


empirical


results


are discussed


Chapter


Finally,


present


a summary













CHAPTER II

OPERATION OF THE HOUSING MARKET


The housing market involves interactions among three types of economic agents-

sellers wanting to sell houses, potential buyers and real estate agents who facilitate

transactions by providing information about available houses to buyers and about the


pool of buyers to sellers.


For the function of providing


information the broker charges a


commission


, usually from the seller.


The seller of a house wants to sell his house at a reasonable price with minimum


delay.


This involves searching for buyers by spending time and money to attract them.


A larger number of potential buyers implies


a higher probability of getting a better price


for any house and a higher probability of attracting a buyer within any time interval.


seller may undertake this search himself or employ the services of a real estate agent.


These two channels of search differ


n the costs involved and the rational seller would


choose the better alternative.

A buyer's objective is to buy a house with a set of characteristics at a minimum


price


Thus a buyer searches over the set of available houses in the market.


Buyers


have the option of getting information from any or all of the following sources--through


advertisements, word of mouth or a real estate agent.


A buyer may choose a real estate


agent along with any of the other alternative sources since in most cases the cost of
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4
The main function of real estate agents is matching prospective buyers and sellers.

This function assumes great importance in the housing market due to several distinguish-

ing characteristics of the commodity being traded which makes the existence of a usual


marketplace impossible.


These characteristics are (i) fixed


locations of houses,


heterogeneity of houses, (iii) durability of houses,


(iv) infrequent transactions among


buyers and sellers and (v) complexity arising from the financial and legal dimensions of

the transactions.

The function of real estate brokers is to collect and maintain information about the


prospective supply of and demand for houses.


The buyer


search is thus expedited by


such availability.


This information is useful to the seller in determining (i) whether to sell


the house and (ii) the initial asking price.


buyers and sellers.


The broker's information facilitates trade among


There are several benefits of involving a real estate broker in the


process of buying or selling a house.


First, real estate brokers have more expertise and


familiarity with general bargaining and specific real estate transactions.

may have the potential ability to mediate differences between principals,


Secondly, they

which may be


more difficult if buyers and sellers try to negotiate directly. Finally, the broker may be able

to expedite the transaction by providing guidance and information about the settlement


process.

commissic


For all the important functions listed above,


, usually from the seller (see Crockett,


the real estate agent charges a


1982).


Commission


rates charged


by the


estate


agents are


influenced


by the


characteristics of the market and the resulting behavior of buyers and


sellers.


From the


perspective of both buyers and sellers,


the commission rate represents an opportunity


rn









through


an intermediary.


High


commission


rates are also likely to


buyers to


negotiate directly with sellers as they hope to find lower prices than those listed with the


broker


Even though the seller technically pays the commission, the incidence may be


on the buyer, implying that buyers have some prospect of obtaining a price between the


price received by sellers who list with brokers and that paid by buyers.


Thus, the ability


of buyers and sellers to transact directly among themselves exerts pressure on the


commission rates charged by the broker.


When choosing commission rates,


real estate


agents must strike a balance between two opposing forces--i.e. providing a reasonable

opportunity costs to buyers and sellers while ensuring a sufficient return on their own


investments.


Hence, the existence of potential traders outside the real estate agent'


activities implies considerable competitive pressure on the choice of commission rates

by market-making real estate agents.

The operation of the housing market as described above provides an explanation

for the empirical observation that commission rates do not differ much from region to


region in the United States.


These rates tend to vary only in a small


interval around six


percent with some variation


depending


on the


price of the


house.


apparent


uniformity has lead to concerns about price fixing by real estate brokerages and violations


of antitrust regulations (People vs. National Association of Realtors,


1981


Owen


1977


Crockett,


1982; Miller and Shedd,


1979).


Several Federal Trade Commission Reports


have attributed the observed pricing behavior to the cooperative marketing arrangements


by the local multiple listing services (see e.g.


, 1984).


Some recent studies have


disputed this argument


of price fixing


as there


are a large


number competitive


number


competitive









Association of Realtors (Crockett,


1982).


In particular the characteristics of the housing


market suggest that it is the competitive pressures exerted by the buyers and sellers who

have the ability to transact among themselves, rather than those exerted by other brokers,


that lead to near competitive commission rates.


Jud (1983) has also observed that real


estate agents lack any monopoly power in influencing the sales prices of houses.


The argument that commission rates tend to be competitive,


however,


does not


rule out some variation in commission rates due to differing costs incurred by the real


estate agents on different transactions.


depending on the costs of acquiring


Commission rates may vary within regions


information and characteristics of houses.


extensive study covering over 7000 housing transactions in the United States in 1975,


1978 and 1979 was conducted by Carney.


He uses an economic search model and


various brokerage cost assumptions to derive brokerage pricing implications and argues


that relative search cost differences


mply that commission rates will be lower on (i) sales


of higher-priced homes, (ii) sales of new relative to existing homes and (iii) nonco-op


relative to co-op


sales


(Carney,


1982).


Carney's


findings


are consistent with


argument that commission rates are kept in check due to the competitive pressures

because these variations are caused by genuine cost differentials rather than the power

a real estate agent possesses as a market maker.


The above explanation of the features of the rea


estate market is similar in spirit


to the analysis of securities exchanges using the specialist system provided by Bradfield


and Zabel (1979) and Zabel (1981).


The former introduces explicit price making behavior


in competitive markets.


The specialist


n the securities exchange is a price maker (akin









to our real estate agent).


The existence of such a price making agent challenges the


notion that all agents in a competitive market must be price takers.


In an extension of this


model, Zabel (1981) shows that even though a securities exchange specialist possesses

some monopoly power in choosing the spread between the ask and the bid price of

stock, this power is restricted by the prospect of traders trading among themselves within


the spread.


sales.


This prospect induces the specialist to reduce the spread to avoid losing


Similarly real estate agents' fees are limited by the competition among various


brokers and also because sellers and buyers can deal directly with each other.

Despite the similarities between the securities market and the market for real


estate, importance differences also exist.


The specialist guarantees buy or sell orders


since he satisfies excess demand by trading for his own account (i.e. from the inventories


he holds or by selling short).


Also


, in the securities market buyers and sellers do not


have to search for each other and the commodity being traded (shares in corporations)


is homogeneous

much different.


In the housing market the informational and availability aspects are


'S


One of the important features of this market is the heterogeneity of the


commodity being traded.


The characteristics of houses are usually difficult to assess.


Another uncertainty is introduced due to the difficulty


buyers and sellers.


n determining the availability of


Thus it is essential to search for information about housing charac-


teristics and buyers and sellers.


n this securities market.


In most cases there is no buyer or seller of last resort


Real estate agents only provide intermediation between


buyers and sellers because holding of inventories in this market is costly and sellers have

to bear the cost of holding a house while awaiting a sale.








8
Thus, in the real estate markets while competition leads to some uniformity in


commission rates across regions, characterist


of regional markets affect the decisions


of buyers and sellers in regard to trading outside the market or with real estate agents.

One of the important characteristics of the market which determines whether buyers and

sellers trade among themselves or through real estate agents is whether the market is


active or inactive.


In a market with a large number of buyers,


., an active market, the


likelihood of a seller finding an interested buyer is high and thus the cost of searching for


buyers would be relatively low.

estate agent would be low. To


Thus the probability that a seller would list with a real


a buyer the active market would thus indicate a situation


where negotiating directly with the seller or seeking the services of a broker may be


equally


attractive.


an inactive


market,


however,


number


of buyers


is small


compared


to the


number


houses


available


thus


requiring


sellers


to expend


considerable amount of time and money in finding a prospective buyer


Thus the


likelihood of sellers engaging the services of a broker in such a market will be relatively

high.

Since housing is an infrequently traded commodity and buyers and sellers do not

have the advantage of extensive experience, they might revise their decision about trading


with the help of real estate agents.


Initially, a seller may attempt to sell a house without


intermediation but, as the burden of holding a house increases, may later shift to a


brokerage


listing.


Thus


a seller'


behavior has


a dynamic element attached to it.


Similarly,


buyers too may need to revise their decision to approach a broker after they


have entered the market.









prior work


n this area reveal


that either the relationship between agents (i.e. sellers,


buyers and brokers) has been completely ignored, or modeled very inadequately.


next chapter


In the


examine and review existing literature on theoretical as well as empirical


issues in this area.













CHAPTER II


LITERATURE REVIEW


In this chapter


give a broad overview of research on the housing market.


In the


next chapter and in Chapter V


consider some of the studies most relevant to my


research in more detail.


A recent survey of economic models of the housing market by Smith,


Fallis (1988) suggests a shortage of analytica


Rosen and


and empirical work incorporating the


extremely important role played by real estate agents in the market for housing.


literature on models of the housing markets is divided according to which of the several


special features of housing are emphasized.


heterogeneity, durability,


These special characteristics of housing are:


spatial fixity and the extensive government involvement in the


housing


sector.


almost


impossible


to build


a realistic


economic


model


incorporating all these features, each strand of the literature deals with one or the other


However


, models are now becoming more general and thus many of these strands


overlap.

Some of the earliest economic models started out ignoring most of the special

characteristics of the housing market (Muth 1960; Olsen 1969) by assuming the existence

of an unobserved commodity called housing service which takes care of the heterogene-


ity feature.


Further, these studies dismiss any problems involving durability of housing


-- ~









Deaton and Muellbauer,


1980).


These models also ignored intertemporal and spatial


issues.


Questions of interest were: How does an increase in income affect the price of


housing services? How do increases in prices of building materials affect housing output


and prices


Early empirical models mainly study the estimation of price and income


elasticities of demand for housing and the production function on the supply side.


Leeuw 1971


, Quigley 1979 and Mayo


survey demand side issues; supply side


issues are discussed by Muth 1960, de Leeuw and Ekanem 1971


1983 and Bruce Smith 1983.


Arnott and Griesen


McDonald 1981 and Edelstien 1983 survey the literature on


production functions.)

Starting from these simple models several modifications are introduced to make


analysis


more realistic


to study


some of the


special


features


of housing.


introducing durability in these basic models makes the distinction between housing stock


and housing services important.


Durability is introduced into the simple models of


housing explicitly by assuming that the market adjusts in a stock-flow manner. These

models assume that the short-run supply of housing stock is perfectly inelastic. Then it

is the demand for housing that determines the equilibrium price in this market. (See


Duesenberry 1958; Muth 1960; Lawrence Smith 1969; and Olsen 1969 for a discussion


of the stock-flow models of housing.)

macroeconomic models of the housing


Similar stock-flow models were also used in

market. These are surveyed by Grebler and


Maisel (1963),


Fair (1972) and Fromm (1973).


explanation for tenure choice,


choice betw


Durability of housing also presents an

'een renting and owning a house, as


markets for housing stock (i.e. ownership) and for housing services (rental market) now


1981









analyses


must


now


consider


discrete


choice of


owning


or renting


as well


as the


continuous choice of quantity.

Empirical analyses including durability mainly involve estimating expected user


cost of housing which are used to test hypotheses about tenure choice (e.g.,


Diamond 1980).


services


Douglas


The durability issue also focuses on the production process for housing


., owning a house implies home production of housing services and renting


implies buying these services in the market (Weiss 1978).


Spatial fixity


another important attribute of housing which has received extensive


attention in the urban and regional economics


literature as it has important implications


in studying effects of race and racial discrimination in urban housing markets.


analysis of housing demand in this case includes location


n the utility function.


Utility


maximizing households must choose location as well as quantity of housing.


involving location choice issues are surveyed by McDonald (1979),


The mode


Wheaton (1979) and


Henderson (1985).

Heterogeneity of housing deters formation of an organized commodity market

since accurate price-characteristics information is not readily available in this market.

Information gathering involves expensive search on the part of buyers and sellers of


housing,


thus making real estate agents central to this market.


However adequate


attention has not been paid to this


information generating function of real estate agents


in the housing market.

The surveys by Smith, Rosen and Fallis (1988) and by Muth and Goodman (1989)

seem to suggest that studies which deal with the economics of housing markets do not









has become customary to distinguish a housing market which deals with the demand for

and supply of housing and a brokerage market which deals exclusively with the demand


for and supply of brokerage services with


little concern for the interaction of these


markets.


Zumpano and


Hooks (1988) survey theoretical


and empirical models on the


market for real estate brokerage services.


They present and critically evaluate past


studies which attempt to explain the structure and performance of this market.


the research has sought to


market.


Much of


justify or challenge certain popular observations about this


It has been charged that this market is inefficient, that it is monopolistic and most


importantly that local multiple listing services lead to little competition and hence too high


commission rates.


Even this literature surveyed here does not provide any formal


treatment of the alternative methods of search for individual buyers and sellers in the

market for real estate.

One of the firMt theoretical models of real estate broker behavior was developed


by Yinger (1981).


The primary goal of this study was to promote an understanding of the


market for rea


estate broker services under uncertainty.


He formulates the problem as


one of broker's search for buyers and sellers in order to maximize his income.


Income


is derived


by the


broker


as a commission


successfully


making


a match


succeeding


making


a sale).


Yinger


derives


equilibrium


amounts


of search


commission rate and the expected number of matches through partial equilibrium analysis


and suggests comparative static exercises for changes in the equilibrium values.


looks at the effects of MLS on the variables


of interest and studies the welfare and policy









Among other conclusions, considering the entry of brokers,


Yinger argues that


entry


shifts


the supply


of listings


downwards and


raises


commission


comparing the system with and without MLS,


he concludes that MLS increases the


average housing price, decreases resources devoted to broker search and increases


brokers'


income but that the effect on the commission rate is ambiguous.


Finally,


concludes that there may be substantial welfare gains from government intervention in

this market.

Recent work by DeBrock (1988, 1989) is also a theoretical analysis of the market


for broker services.


As in Yinger'


study,


DeBrock does not allow direct sales between


buyers and sellers.


In contrast to Yinger, DeBrock considers interaction among brokers


to be a non-cooperative game.


He considers a two-stage game in which prices are set


in the first period and marketing effort occurs in the second period.

He demonstrates that under MLS the Bertrand-Nash outcome is that the prices of

houses are bid up (possibly to the monopoly level) and not down to cost (this conclusion


is exactly the opposite of the usual Bertrand-Nash outcome).


leads to an unambiguous


He also shows that MLS


increase in commissions and an increase in the pricing of


houses with a decrease in the return to the house owner.


From the point of view of


efficiency,


he states that the MLS system may attract too much or too


little co-brokerage


efforts thus leading to lower than maximum industry profits.


general notion that MLS is a beneficial

of view of increased information, is inco


Thus he concludes that the


institution in the housing market, from the point


rrect in that other effects negate this positive one.


In contrast to


Yinger


DeBrock,


Wu and


Colwell


(1986)


introduce direct









equilibrium) and simultaneously (general equilibrium).


equilibrium in the housing market, an


For example, considering partial


increase in the commission rate has no effect on


total housing supply but reduces the quantity and price of housing.


In the general


equilibrium


analysis


an increase


search


listings


decreases


commission rate but the effect on the price of housing is


inconclusive.


Similarly,


increase in


the cost


of search


for buyers decreases


price of housing with an


inconclusive effect on the commission rate.


n their analyst


, they conclude that an MLS brokerage system wil


increase the


price


housing,


, like


Yinger


, they


too conclude


effect


MLS


commission rates is ambiguous.


MLS each broker wil


Contrary to Yinger, Wu and Colwell conclude that with


devote more resources to search than an independent broker.


Most of the claims by Yinger, DeBrock and Wu and Colwell, some conflicting, have


not been subjected to empirical testing.


However, whatever empirical evidence does exist


in this area does not corroborate any of the claims made by them (Jud,


1982).


1983; Carney,


More perspective on these models is presented in the next section which provides


a model directly


incorporating interaction among buyers, sellers and rea


estate agents.


On the empirical side,


there have been only two studies which have explicitly


included the importance of real estate agents in the housing market.


The main emphas


of the study by Carney (1982) is to derive implications for brokerage pricing under various


cost assumptions.


He describes a search model showing a relation between the search


and the expected maximum price for a house,


of the model.


but he does not provide a formal analysis


He argues that the maximum price that a seller can obtain increases at a









also argues that commission rates will decrease with mean home price,


increase with


variance in home prices and decrease with the cost of search.

Empirical evidence is presented to corroborate the above observations and it is

found that commission rates are lower on (i) higher priced houses, (ii) on sales of new


relative to existing homes and (iii) on non-MLS (or


to MLS sales.


co-op as he refers to them) relative


To obtain these results, he regressed commission rates on prices of


houses and on dummy variables for households (as a proxy for existing versus new

homes) and for MLS sales.

The second empirical study which appears in the current stream of literature is one


by Jud (1983).


He develops a model of demand for broker services by both sellers and


buyers of housing.


The basic idea


that the seller'


demand for brokerage services is


a negative function of commission rates and a positive function of transaction costs and


the price of the house.


The demand for broker services by buyers is similarly a negative


function of existing stock of market information and a positive function of the opportunity


cost of time.


The probability that a seller will decide to employ a broker and the


probability that a buyer will choose to buy with the help of a broker are then estimated.

Both transaction costs and the sale price were found to be significant in predicting the


probability


of seller


employing


a broker


The results


on the


probability


of buyers


transacting through brokers were more ambiguous than those on seller probabilities with


income being taken as a measure of buyer'


opportunity cost of time.


As to market


information


, new buyers were considered as dummy variables and they are expected to


have less i


information than experienced buyers,


and resident buyers who buy the house









Another finding


is that listing with brokers was not statistically significant in


explaining variations in prices, thus suggesting that brokers do not possess any special

monopolistic advantage which enables them to extract more for a house (thus getting a

higher commission) than if it were sold by the seller himself.













CHAPTER IV

THEORETICAL MODEL


In this chapter


present an integrated model of seller, buyer and broker behavior


which captures the essential features of the housing market as described in the previous


sections


Before setting-up the model, however, it is appropriate to examine existing work


in this area. As mentioned earlier, there are very few studies incorporating the importance

of real estate brokers in the housing market.


Review of Theoretical Models


In a pioneering paper


Yinger (1981) studied the "search and match" behavior of


real estate brokers in the housing market.


In this model


, brokers face three types of


uncertainty about (i) the number of buyers in the market (ii) available


istings and (iii)


matches between buyers and sellers. In formulating the problem, he makes the following

assumptions:


Brokers try to influence the average price of housing but not the distribution


of housing prices.


The average price becomes a choice variable in that the probability


that a buyer will accept the asking price is a function of the average price.


Brokers consider each time period to be homogeneous in that the number of


buyers and sellers in the system is the same in each time period.








19
According to Yinger both of these assumptions imply that there are no bargains

or overpriced houses in some time periods and each buyer and seller encounters the

same prospects in each period.


The broker's


Traditionally,


income comes from the commission payments made for his services.


the commission is a percentage of the sale price of the house and in


Yinger'


model the commission rate is also a choice variable.


Clearly, for such a broker,


the larger the number of customers, the larger is the expected income.


However, the


broker has to incur costs to attract customers. Thus the objective of the broker is to

maximize the expected income net of the costs of search. The choice variables are, then,


the commission rate, the average value of the house and units of search levels for buyers


sellers of housing.


Yinger abstracts from dynamic considerations,


resorting to


common


demand


supply


static


analyses focussing


on market


clearing


choice


variables.


Characteristics of demand and supply functions derive from probabilistic


analysis of interaction of buyers and sellers and various assumptions about demand and


supply behavior.


For example, the probability 6 that a buyer will accept the asking price


is a diminishing function of the average price of housing.


However, the paper does not


distinguish buyers and sellers who arrange trades without using the broker's


intermedia-


tion.


Thus


, all sellers and buyers use the


ntermediation services of a broker.


In a market without a multiple listing service (MLS),


expected income Y


a single broker maximizes


where


Y=(cV-a)NP8[1


-Sp P1


- TBPBNB


with respect to the commission rate c, the average price of housing V,


units of search for


-~TBSB-ILSL









attracting a buyer

in the system, re


PL is the probability of attracting a listing,


is the price of a unit of search for buyers,


N, is the number of listings

rL is the price of a unit of


search for listings and T, is the showing cost per buyer.


The term


n brackets [1-(1-


6P)NL] is the probability that the broker can match a buyer with a


sting in the system.


Yinger then examines the equality first order conditions to determine the characteris-


tics of the system.


He does not explicitly consider circumstances causing competitive or


noncompetitive outcomes,


but does make the following distinction.


If the market for


broker services is competitive and it is not costly for sellers of housing to find buyers, the

(aPL/c) is negative infinity and the broker accepts the market-determined commission.


Also, if the housing market is competitive,


and it is not costly for buyers to find houses,


then


, (6/V) is negative


nfinity and the broker has no


influence on the price of housing.


Moreover


, he also claims that high search costs for the seller of housing can generate


market power for brokers but does not demonstrate how this result occurs.

In the event the market is competitive, outcomes are determined by equality of


demand and supply functions for matches in conjunction with first order conditions.


difficult


summarize


outcomes


since


Yinger


derives


results


under


a variety


assumptions.


downward


variables rg, rL


For example,


sloping with


and o.


with simplified assumptions,


respect to the commission


Considering entry of brokers,


the supply curve of listings is


independent of the


Yinger argues that entry shifts the


supply of listings downward and raises the equilibrium commission.

Yinger next considers an MLS system where a broker makes matches with his own


listings or


other


br6i~er's


listings


which


commissions


are then


shared.


Without








21
the commission rate remains the same is ambiguous, but if brokers have market power,


MLS will increase V.


With or without entry, fewer resources are devoted to search with


an MLS than without one and, taking into account other considerations (e.g. an increase


in the average housing price),


MLS boosts brokers' income.


Hence, brokers who set up


an MLS have a strong economic incentive to keep other brokers out of it.


With additional


assumptions he draws some conclusions about the share of a commission a broker


receives


matching


another


brokers


listing.


example,


more


heavily


influenced by experience and prestige than P8,


then established brokers prefer a lower


value of this share than new brokers.

With regard to public policy Yinger argues that his analysis suggests that the

optimal level of broker search is less than the level generated by a system with no MLS


or even an unregulated MLS.


Citing arguments by Stigler (1961) that competition should


lead to price variation,


a finding


Owen and Grunfest (1977)


about uniformity of


commission rates in California, and price fixing cases brought against real estate brokers,

Yinger also claims that this evidence implies that brokers have considerable market

power. Hence, because MLS reduces search relative to the non-MLS system, government


should encourage an MLS system,

laws to prevent market power. Ying


accompanied by vigorous enforcement of anti-trust


jer concludes by stating that his paper suggests that


the welfare gain from government intervention in the market for real estate broker service

could be substantial.


In contrast to Yinger, Wu and Colwell (1986)


introduce direct exchanges between


sellers and buyers as well as transactions with real estate brokers as intermediaries.









housing.


The real estate brokerage market determines the equilibrium commission rate.


Equilibrium in these two markets residually determines equilibrium in the market of direct


transaction.


Circumstances which determine whether buyers and sellers trade in the


brokerage market or the market of direct transactions are not considered.

In analyzing the brokerage market Yinger and Wu and Colwell make a number of


similar assumptions.


Major difference in assumptions concern characteristics of housing


housing


prices


choice


variables.


Colwell


assume


houses


homogeneous with sellers'


asking prices specified


by some probability


distribution.


Brokers do not choose the average price of a house or the commission rate.


choice variables are units of search for listings and buyers.


notation


The only


In the Wu and Colwe


, without ML;, the ith brokers profit function becomes


= (cV


- o)NP,[1


8)1411]


- rbSbl


- tPbN,


- rs~


Most variables have the same definition as


n the Yinger model.


Here 6 is the probability


of matching a buyer with a seller and is equivalent to 6PL in the Yinger equation. A major


difference is the interpretation of V which is now a parameter given to the broker.


V is the expected selling price per housing unit with broker intermediation.


Here,


The expected


selling price is related to an expected price determined in buyer search for low housing

prices. According to Wu and Colwell, V may or may not equal this expected search price

depending on whether buyers only search in the brokerage market, search only in the


direct transaction market or search in both.

determination of V is not explained. A similar


function.


If V does not equal this expected price the


r problem arises in the owner-seller objective


The objective function iri is maximized with respect to Sbi and Si where the








23
In the market for direct transactions the jth owner-seller maximizes the net price

of a house by choosing the optimal search for buyers in the objective function


- u')PbiN,b


- rbSb/


- td,/N,


where the primes onh ariables distinguish owner-seller variables from broker's variables.


In particular, V


is the expected selling price per housing unit with owner-seller transac-


tions and, once more, equality first order conditions equate appropriate marginal returns


and marginal costs.


The buyer's optimization problem is a key element in the analysis


since, as noted, this problem determines


an expected minimum price which either equals


V orV


or is somehow related to these parameters.


The buyer uses Stigler (1961)


search with recall.


The buyer visits a number of houses for sale and then chooses to


purchase


house


lowest


observed


price.


The optimal


number


of visits


determined by equating the marginal return to search and of a marginal cost to search.

That is, the buyer equates the reduction in the expected minimum price by one more


search to the cost of visiting a house, obtaining an optimal sample size,


houses to visit.


the number of


The details of the problem are well known and need not be repeated.


nonoptimality of Stigler


optimal


sample


search


is also well


known


, being


dominated by sequential search in which a buyer visits houses until a price is observed

which equals or is less than a reservation price determined in the analysis of search.

However, correcting the Wu and Colwell model to introduce sequential search would not


change qualitative outcomes in the model.


The parameters V and V


would now be


related to an expected minimum price determined by sequential search.

Wu and Colwell then complete their analysis by an examination of partial general


(V'









of housing.


In the general equilibrium analysis an increase in the cost of search for


istings


decreases


commission


, but the


effect


on the


price


of housing


inconclusive.


Similarly, an increase in the cost of search for buyers decreases the price


of housing with an inconclusive effect on the commission rate.


In an analysis similar to Yinger's,


Wu and Colwell consider the system with MLS.


With MLS, the partial equilibrium price of housing is higher and the commission rate is


lower.


However, in the general equilibrium analysis the effect on the commission rate is


inconclusive


In analyzing the ratio of the split between the brokers,


extreme assumptions, they argue that the split would be close to,


using various


but higher than,


than


the equal split observed in most real estate markets.


In comparing search levels, Wu and


Colwel


conclude


, contrary to Yinger'


results


, that, under MLS each broker will devote


more resources to search than an independent broker.


They also conclude that with MLS


the price of housing is higher and the


evel of search by owner-seller is greater.


Wu and


Colwell refrain from drawing conclusions about public policy issues.


The papers by DeBrock (1988,


1989) are similar to Yinger's


study in that direct


sales between buyers and sellers are not allowed.


But, whereas Yinger uses competitive


marginal


analysis and


comparative statics


to obtain


outcomes,


DeBrock


considers


interactions among brokers to be a non-cooperative game. DeBrock also explicitly allows

dynamic elements of the trading process by considering probabilistic features of the time


it takes to sell a house.


We consider outcomes in his second paper which is a revision


and extension of the first paper.

As do other authors, DeBrock considers the brokerage system with and without MLS,








25
Real estate agents buy houses from sellers at the price P and then resell houses to


buyers at the full price p


= (1 +pP where p, is the commission rate.


Hence pi


gives the commission income on the sale of a house.


Each agent also invests an amount


of marketing expenditure m, per house at time t


n the system without MLS,


m, is assumed fixed, so that an agent's only decision


variable is the commission rate pi which is chosen to maximize discounted expected profit


on sales.


Without MLS the objective function for the ith agent is then


- f~p,-rF'.Jq,(p,,p,)h(t n)e~d


- m7q,(p/.pl)


where ci is the fixed closing cost per sale,


qi(pi, e) is buyers'


demand for houses listed


with agent


, which depends on own price p, and the vector of prices of rival sellers 9, h(t,


m) is the density function of time to sale where h diminishes with increases in marketing


expenditure m,


r is the discount rate and Fi is a fixed cost.


Seeking a noncooperative


Nash


solution


prices,


DeBrock


asserts


one


derive


common


non-


cooperative equilibrium prices pi.

In the MLS system with joint marketing DeBrock poses the problem as a two-stage


game.


In stage one an agent chooses the price p, and


n stage two the marketing level


He assumes an equal split in the event an agent, who is not the listing agent, sells


the house.


Later in the paper he considers non-equal splits.


The objective function is


now modified to take into account the split and probabilities of a listing being sold by rival


agents.


For the ith agent the objective function becomes











Vnats


-H(t, m))K-'


e-rdt


- mq,


P)q,(K-1)h(t,m)(1


Here (1


- H(t,m))KI1 is the probability that the K-1 rivals will not sell an agent


listing by


time t where H is the distribution function associated with the density h.


Here F


includes fixed cost of membership in the MLS.

To obtain a non-cooperative solution to this problem and to compare the outcome


with the system without MLS,


DeBrock


(1989) assumes all agents are identical,


density function h is exponential and the demands qi are quadratic.


He then shows that


the equilibrium commission rate is larger in the MLS system,


where M


represents the MLS system and


the non MLS (independent) system.


Moreover, MLS


agents' profits increase relative to the system without MLS.


DeBrock also compares the


MLS non-cooperative system to the MLS collusive system where agents jointly maximize


industry profit.


Outcomes depend on the parameter values. According to DeBrock, there


exist a wide range of parameter values under which an MLS would price above the


collusive price level.


Moreover


, MLS may lead to over-marketing or under-marketing


relative to the collusive outcome.


In considering the choice of a split variable he argues


that the outcome is also parameter dependent.


Finally,


DeBrock briefly considers cases


where the supply function of houses is upward sloping and a case in which houses are

supplied by a monopolist. Again, results are parameter dependent. However, to consider

a case of monopolistic supply of houses which contradicts facts in the real world does


-(2p-


P)qh(t, m)(l


-I~r~ m))K-le-'~dt









Model Description and Specification


Introduction


The model to be developed focuses on the interaction between owner-sellers and


real estate brokers. Yinger and DeBrock ignore the possibility that sellers may attempt

to market their own houses, and Wu and Colwell only assume the separate existence of


owner-seller and real estate brokerage markets without explicitly considering the interac-

tion of these markets.

Rather than distinguishing the housing market, the brokerage market and market


of direct transactions (as in Wu and Colwell's


paper),


which has become common in the


literature on housing,


the emphasis here is that there is a housing market in which


transactions are arranged either by direct interactions between sellers and buyers or by


brokers acting as intermediaries.


In this perspective the housing market


incorporates


strong purely competitive features.


There exists a large stock of houses, of various ages


and characteristics


, individually owned,


a fraction of which are available for resale.


home building industry consists of many builders with little impediment to entry.


Hence,


housing prices,


both new and old, tend to be competitively priced.


literature, as discussed in Chapter III,


The earlier empirical


which ignored marketing of houses,


emphasized


the competitive structure of the housing market.


Given the strong competitive feature of


the housing market, methods by which houses are bought and sold would affect housing

prices mainly through'the spread between ask and bid prices, that is, between the prices


paid by buyers and the prices received by sellers.


In transactions with brokers the








28
in the housing market, then, is whether the commission rates are determined competitive-


ly or noncompetitively,


as attested by numerous antitrust suits against brokerage firms.


issue


not directly


studied


Yinger


Colwell


, though


Yinger


nevertheless, concludes that the evidence implies that brokers have considerable market

power and encourages vigorous enforcement of antitrust laws to prevent market power.

Using game theoretic analysis of brokerage firms, DeBrock qualifies his results, but does

emphasize noncompetitive outcomes among brokerage firms and specifies conditions

under which the equilibrium price that arises under MLS would be higher than the price


that a monopolistic (cooperative) brokerage market would charge,


even though the


individual brokers act noncooperatively under MLS.


Specification of the Model with a Sina.


Broker


Specification of the model with identical sellers


The housing market is characterized by several classes of houses with various


characteristics.


Housing


prices


reflect


only the


class


houses


region


differences such as land prices,


labor costs and differential market activity,


generally


affecting regional demand and supply of houses.


However, since the emphasis here is


the spread between ask and bid price as determined by interaction among sellers, buyers


and brokers


, I assume, as does DeBrock, that there is a single class of houses with a


given competitively determined price P.


A second major assumption is the heterogen


ity of houses,


even though the


houses have the same listing price.


Houses have different characteristics due to location,


state of repair, arrangement of rooms, exterior building material,


landscaping and other


- w









As do other authors,


assume that brokers receive a commission p for their


services


, as is customary in the real world.


do not attempt to explain this method of


payment, other than to note that the use of a commission rate has some support in the


principle-agent literature (e.g.,


Harris and Raviv


, 1979).


Since sellers cannot directly


monitor the broker'


activities, payment linked to success provides an


incentive for


brokers to consummate sales.


assume as does


Yinger explicitly and DeBrock implicitly that the number of


sellers and buyers (M,


N) remains the same in each period.


Hence sellers and buyers


consummating sales in a period are replaced next period to maintain (M,


N) constant.


Initially it is assumed that sellers have identical costs even though they may be


ng houses with differing characteristics and there exists a single broker


Hence. if one


seller decides to list (or not) with the broker, the others will also do the same.


Thus


wish to compare a situation where all sellers list with the broker or none list with the


broker


. Thus, when sellers do not list with the broker, all buyers only visit owner-sellers,


and when all list with the broker all buyers only visit the broker.


The object then is to


determine a broker commission rate which will induce all sellers to list with the broker.


assume both sellers and brokers wish to maximize the expected return over an


ndefinite horizon.


First, the random process for an owner-seller is considered.


Suppose


an indefinite horizon is divided


nto discrete time periods,


a week.


Potential buyers


visit the house randomly during the week.


per week.


Let 1 be the expected arrival rate of buyers


Moreover, suppose a fraction p of these buyers offer to purchase the house.


Then the expected number of offers per week is ip.









situations involving random arrival rates (Parzen,


1960 and Ross,


1972).


If P(k) is the


probability that k buyers arrive and make offers during the week, then


P(k)


= e- ~ WP)k


= 0,1,2,...


Then P(0)


= eX'P is the probability that no offers are made during the week.


Since 1-P(O)


is the probability that one or more offers will be made during the week and since the

owner-seller would accept any offer, the relevant probabilities for the owner-seller are P(O)


and 1


- P(0).


In this framework


, P(0) represents a failure and 1


- P(0) a success for the


owner-seller


Then


, the probability for a first success in period t would be


[P(O)-'.(1


-P(O))]


= [e-(


-e-ap)]


= (eP-_l)e-l t


which is the probability of failure in the first t-1 periods times the probability of success

in period t.

In a more general framework, F(t,m,M,N) would represent the probability of a first


success


n period t, indicating the dependence of F on marketing expense m and (M,N)


the number of sellers and buyers in the market.


It would be reasonable to assume that


the sum of these probabilities, over any finite number of initial periods, would increase


with an increase in m and N


, and decrease with an increase in M (and, of course, the


sum of these changes would go to zero as the number of initial periods goes to infinity).


In Appendix A it is shown that, in the Poisson process,


this outcome is obtained if


d(ap)/dm


> 0, d(Xp)/dN


> 0 and d(lp)/dM


< 0, which are reasonable assumptions.


In formulating the seller'


of a house, the expected time of


problem of maximizing the expected return in the sale

sale, or, equivalently, the expected time of a first offer,


-r)~p~(l











= (LP"


Using the properties of infinite sequences,


- 1) te-pt


it easily follows that


= 1/(1


- e-^ .1


However


, in a general framework, such a convenient expression


would not be available.


In the Poisson case, given the assumptions d(Ap)/dm


> 0, d(Xp)/dN


> 0 and


d(Xp)/dM


< 0, it then follows that the expected time on the market E(t) diminishes as m


and N increase and increases as M increases.


Given the assumption about F(t,m,M,N)


in the general case, the expected time on the market has the same properties as in the

Poisson case.

Owner-seller's Problem


The owner-seller'.


problem,


then


, is to maximize the net expected value of the


house over an indefinite horizon.


The sale price of the house is assumed to be fixed by


the competitive forces in this market.


The owner-seller also


ncurs various costs.


These


costs are:


Marketing costs per period to attract buyers, e.g.,


advertising in the local


newspaper.




The outcome described by E(t) is analogous to a similar result obtained in the


continuous time exponential process where the distribution function G(t)


the density function g(t)


= XpeA"" for t


Here, the expected value,


S


=,1
;ay,


I eX" and
E(t) equals


- A









Holding costs per period.


house as an asset.


Maintenance expense, interest and user cost of


Holding costs strongly depend on whether the owner


lives in the house, the house is rented or it is vacant during time on the

market.


Showing cost per prospective buyer.

to the house by an interested buyer.


This is the opportunity cost of a visit

The expected cost per period is As.


Closing costs.


Fixed costs.


These are the costs of preparation of the house for sale.


For the owner-seller the net expected value is then given by


= E p'-1[P


where p is the discount factor and ot


- c-(m + h + As)oF(t,m,M,N)


= (1 -fit)/(1 -p)(3t-1).


When the seller decides to sell the house through a broker, the costs that need


to be incurred are different.


The advertising and other related costs are now borne by


the broker.


Therefore m need not be spent by the seller.


The seller also need not incur


showing costs, since now that is the responsibility of the broker. However, the holding

cost until the sale is finalised is still the responsibility of the seller. Similarly, the closing

costs and the fixed costs are also the obligation of the seller. For the services of the


broker, the seller needs to pay him pP upon completion of the sale,


commission rate.


where p is the


The net expected value to the seller in this case is then











-p)P


- c-ho ] Fb(t, m, M,N)


where Fb(t,mb,M,N) is the probability of a first success in period t if the property were sold


by the broker.


owner-seller.


Here, we distinguish between the marketing cost for the broker and the


Thus the property will be listed with the broker if Vb


-p)P


r '-1iP


- c- ho ] F(t mb, M, N)


- c- (m+h+Als) o,] F(t, m, M, N)


= F then the constraint reduces to


Broker'


problem


The broker considers (M,N),


the numbers of sellers and buyers


n the market,


given the assumed renewal properties,


to be the same


for each house there is a probability of no offers


n each period.


n each period (i.e.,


In each period,


a failure),


Pb(O), and


a probability of a success,


1-Pb(0),


allowing for the possibility that probabilities for the


broker and owner-seller may differ.


Then the process of selling houses


n each period


is described by the binomial distribution.


Hence, the expected number of houses sold


each period equals M times the probability of success or M(1-Pb(O)),


recalling that Pb(O)


depends on (M,N).


In the general framework, note that (1-Pb(0))


= Fb(1,mb,M,N),


the probability of a


= E P"[(1


, .e.,


p t-'[pP
1


-(m+As)ot]F


8"'[(1









In selling houses, the broker incurs various costs.


These costs are


Marketing expenditures


per period to


generate


listings and to attract


buyers.


Showing costs per prospective buyer per house.


The expected showing


cost per house for the broker is AbSb where 1b is the number of expected

buyers visiting the broker per period per house.

Db Fixed costs in establishing a real estate agency.

The objective of the broker is to maximize the net expected profit from selling


houses, subject to the constraint that any seller would use the broker only if Vb


problem can be written as

V= )"p[pP


- .bsbI MF(1 ,mM, N)


-E_ Pt- m


subject to


E p-[(1


SPt'[P


-p)P


- c-(m +h +Xs) oj]F(t, m,M, I')


where the choice variables are the commission rate and the marketing rate (p,mJ.


constraint (9) does allow some variation in p depending on the choice of mb. An increase

in mb would decrease the expected time to sale and would allow an increase in p.


Nevertheless


, the constraint (9) limits the choice of the commission rate.


Hence, the


constraint on p,


imposed by a seller's option of selling directly to buyers, induces a


competitive outcome in this market, even with a single real estate agency, since a seller


D,


- c- ha,] Fb(tl mb' M, N)








35
maker for a security, the specialist, chooses the spread, the difference between the ask


and bid prices.


However,


the traders are not obligated to use the services of the


specialist.


Thus, if the spread is sufficiently large, traders may arrange mutually beneficial


exchanges among themselves inside the

reduce the spread to avoid losing sales.


This prospect induces the specialist to


In the real estate market, with the constraint on


the choice of the commission rate, sellers are not able to arrange mutually beneficial

exchanges with buyers inside the "spread", the difference between the ask price of Pand


the bid price (1-p)P.


The specialist would welcome a rule which requires all traders to


arrange exchanges at the ask and bid prices he calls,


for then he could exploit his


monopoly power, as a single price-maker, in the choice of the spread.


Similarly, the real


estate broker would Welcome a similar rule as the intermediator in the real estate market.


The broker's problem illustrates another feature of the housing market.


The broker


interacts with various sellers and buyers,


market infrequently.


week by week,


whereas a seller enters the


Some implications of this feature are discussed


n the next section.


Specification of the model with heterogeneous sellers


Here


, once again it is assumed that there is a single broker and a single class of


houses.


However, now suppose sellers are


not all identical with


respect to costs.


Differences among sellers arise for several reasons.

purchase a house depends on its characteristics. Sc


First, the willingness of buyers to


>me houses are more "salable" than


others. For sellers with less desirable houses the probability of an offer p would be lower,

the expected time to sale higher and the net expected value of the house would be lower.


Second


, the cost of showing the house to prospective buyers may also be different.








36
selling differ widely depending on whether the owner lives in the house, rents it or leaves

it vacant during the time that it is up for sale.

Differential costs among sellers would imply a distribution of net expected values


V,, where


..., M distinguishes different sellers.


Thus, assuming the broker knows


the distribution over Vi the problem is to choose the commission rate and marketing rate


(p,mb) to capture enough of the sellers with low V


to ensure a viable operation.


With a choice of (p,m) made by the broker, a fraction of the M sellers would list

their houses with the broker and the remainder would choose to sell themselves.


With respect to the number of buyers N,


then


, a fraction would only visit owner-


sellers


, a fraction would only visit the broker and a fraction would visit both.


Owner-seller Problem


To be more specific


write the problem in terms of the Poisson distribution.


Now,


in a situation where the sellers are heterogeneous in costs, the expected net value of the

house for the ith seller would be given by
-


i rpt[P


Here, I.i,


seller


-1 )e-Iplt


the expected number of prospective buyers per period who visit the ith owner-


, depends on some fraction of M (possibly all M) and a fraction of N who visit the


owner-seller houses.


seller


The probability of an interested buyer making an offer to the ith


, P,, depends on housing characteristics.


As in the earlier case, the objective of the seller is to maximize the net expected


value of the house.


Hence, the owner will choose to list with the broker only if


D,


- c-(m, + h, + ~.ls3 al(e~lPI











L KI
E t'-1[(1
1
a


-p)P


-c- h, t] (e -Pb 1)e-9li


where,


distinguish


broker


probabilities from owner probabilities and the


expression on the left equals Vbi.

Broker's problem


Unlike the situation with homogeneous sellers, now only a part of the total number

of sellers list with the brokers and similarly not all the buyers in the market will seek the


help of the broker.


Thus


Snow


the broker's


prospects are (Nb,Mb) where Nb is the


number of buyers going to the broke


and Mb is the number of sellers listing with the


broker and both of these numbers are functions of the commission rate p.

expected net value which the broker seeks to maximize is
-a


= p -'[ pP


The broker will then choose (p,


is an equality.


Hence, the


*Ae-bPh)


- bS]Mb(1


mb) such that, for one or more owners, the inequality (11)


That is,


Pt'-[(1

1pr-[p
1


-p)P


-c-hiotJ(elAb


-c-(mL +hL+XL


-1)9-AbPbt


-ALPLt


s) oJ,](LLP"


where L specifies an owner for which the equality is satisfied.


expected values greater than VL


Hence, owners with net


will become owner-sellers and those with net expected


=VL


- c (mi h,+ ~, rs3 o,] (eL 9~-l)e-~IP~'


_ 1-1


- Db









values less than V, will list with the broker.


Thus, the broker'


optimization problem can


be written as


-,sb]JM,(1-e*P )


1
ri-


-p)P


EKI
E P'-'[(1
1.


In this problem the choice of (p,


-hL,o (eLPb


-l(-""


-o-(mL+ hL +XrSdua(eLdPL


mb) also selects L, taking into account the distribution


of net expected values Vi.


Whether


broker


survive,


given


fixed


alternative


opportunities for employment, depends on the distribution of V


for owner-sellers


if the


distribution has large weight on high V,


too low for survival.


then the broker may be forced to choose a p


In small communities, there may be no broker or a broker may need


to attempt to cover more than one community.


The number of sellers satisfying V,


may be too small to support a p for the broker to survive,


, to find a p such that V is


greater than the opportunity cost for the broker.

The problem for the broker is now more realistic and much more difficult for the


broker to solve, but the competitive feature of the previous problem remains.


In principle,


it is not difficult to specify the first-order equality conditions and to examine the trade off


between the choice variables (p,


mb) but not much is gained by this exercise.


Rather,


assuming viability, it is more useful to examine insights about information, time on the


=
= p-[pP
1


-Db








39
A broker whose income depends on being well informed about the housing

market will tend to be better informed than an individual seller about the state of the


market, desirable characteristics of houses and market price P.


Even if well-informed


about (M,N) and the arrival properties of prospective buyers, the seller may misjudge the

desirability of his house and overestimate pi and, hence, underestimate expected time on


the market and overestimate the return V,.


Over time, as the owner-seller becomes better


informed about p, and with no success in the sale of the house, he may decide to list with

the broker.


A similar


situation arises about estimating the state of the market, whether it is


strong or weak.


The owner-seller


here


, may underestimate M and/or overestimate N.


Again the seller gains information over time if unable to sell the house


then


, may


decide to list with the broker.


The seller may also overestimate the price of the house R


This overestimation will also lead to the same process of updating information and then


revising the choice of the channel of sale.


Of course


, the seller may also underestimate


P and be rewarded with a quick sale (which is part of the motivation of buyers'


visits to


owner-seller houses).


All the analysis presented


n this section assumes the existence of only one


broker.


In the next section


relax this


assumption and analyse the model with more than


one broker having access to a common pool of information,


such as the multiple listing


service.

Specification of the Model with MLS Brokers


In the previous sections, it was assumed that there was onlv one broker orovidinn









institutional arrangement among brokers,


.e. the multiple listing service (MLS), where two


or more brokers agree to poo


first shows h


information about listings of available houses.


"own" listings to prospective buyers.


A broker


This ensures the broker the full


commission from the seller if a sale is

However if the buyer rejects these house

showing the buyer other listings in the h


made to a prospective buyer from this list.

ies, then the broker has the opportunity of

ILS. If the buyer selects a house listed by


another broker, the selling broker is paid a pre-arranged proportion of the commission


by the listing broker.


In presenting the case involving more than one broker,


will assume


that there are two identical brokers who are a part of an MLS (this assumption is for ease

of notation alone and the logic can be easily extended to more than two brokers).


Second


, I first analyze the case of identical sellers.


As noted earlier, in this case


sellers have identical costs even though they may have houses with different character-


istics.


Again,


if one seller decides to list with a broker, the others wi


do the same and


buyers only visit brokers when sellers decide to use brokerage services.


Third


, I assume that the two identical brokers act as Nash competitors.


In a Nash


equilibrium the two competitors wi


choose a common commission rate and marketing


expense (p,


m) and agree upon a split rate 0


< 1 where OpP is the commission


earned by a broker when selling a listing of the other broker.

would be indifferent to listing with either broker and, on the a*

would list with one broker and half with the other broker. Sit


In this situation a seller


average, half of the sellers

nilarly, half of the buyers


would visit one broker and half with the other.









Specification of the model with identical sellers


Owner-seller'


problem


The owner-seller'


problem here remains the same as in the previous case with


identical sellers.


As in equation (4),


the net expected value for the seller is given by


= B pt'-[p


- (m+h+X s)o,]F(t,m,M,N)


In selling the house through a broker,


broker has the listing,


nce it is a matter of indifference which


the net expected value to the seller is analogous to the result in


equation (5)


= p'-'[(1


-p)P


- hO]Fb(t, mb.M,N)


where Fb(t, mb, M, N) is the probability of a first success in period t if the property were


sold by a broker.


Here,


Fb need not be identical to Fb in equation


(5) since total


marketing expense by the two brokers may exceed the marketing expense of a single


broker


Broker'


Again, a seller will list with a broker if Vb


problem


n the previous case (M,


is the same in each period.


the number of sellers and buyers in the market,


Moreover, since on the average half the sellers list with each


broker,


the probability of any house being sold by any broker is Fb(1


mb, M,


N)/2.


Hence


, the expected number of houses sold each period by a broker from hi


own


listings is M/2.Fb(1


m, M


, N)/2.


However, under the MLS system, a broker may also sell


the other broker's


listings so that the expected number of houses sold,


listed with the










M/2.Fb(1


m, M


houses


period or,


in total


, MFb(1


m, M, N)


is the expected total


number of houses

Moreover.


sold by both brokers,


in the MLS system,


case


a broke


of the


receives


single broker.


income


from three sources.


Income is received from selling his


own listings,


from selling the


other broker'


listings,


and from the other broker'


sales


of his listings.


net expected value to be


maximized by a broker is


- XbSb


,m, M, N)


- yt1 .m


+ t-z[0PP
1


+ Pt'[(1
fp-1i


-1 bSb]
2


, mb,IM, N)


, mb, N)


-6)p PM2
2


subject to the


constraint that Vb


n equation (18)


the first term gives


the expected return on the broker'


own


listings,


seco


nd term gives


discounted marketing expense


per period mb and Q


is the fixed cost where


indicates an added


harge for establishing th


MLS


system.


fourth term gives


the expected value from selling the other broke


's listings.


final term gives


expected rev


nue fro


m the


other broker'


sales of the given


broker'


listings.


immediately obvious


relevant.


from equation


What a broker expected to gain from th


18) that the


choice of the split rate 0 is


sales of the other broker'


listings is


offset by the rev


rse operation by that broker


ence


why not ch


oose


which is


the common choice in an MLS


system? Whatever the choice of 0, equation (18) reduces


=- p- [pp












= rE t-'[pP


.Fb(,m, M,N)


- P'-1 b


- Db,.


Thus, when the brokers maximize equation (19)


subject to Vb


_ V, the problem and


hence the solution is analogou


to the previous


case


with identical


sellers and a single


broker


. Once


again,


previous


case


with a


single broker, the constraint Vb


limits the choice of th


common commission rate chosen by th


brokers.


next section


consider


case


with heterogeneou


sellers and examine


implication


of the existence


of two identical MLS brokers.


Specification of the model with heteroq


eneo


us sellers


Once again,


re th


lers differ from each other


n the


costs that they face


ng the house.


Thus


differential costs


mply


a distribution of the net expected values


where


distinguishes the selle


marketing


expenditure


brokers must now choose th


so as to maximize


commission rate


net expected earnings while


capturing enough of the low Vi sellers to en


ure viability of operation.


As in the previous


case


, only a fraction of the total sellers will list with


broker (either of th


two brokers)


and similarly a fraction of th


total buyers


would vi


brokers.


Owner-seller'


problem


Here


net expected


of th


house


for the ith


selle


is given


by the


following


= E P'-'[p


- (m,+h,+ X


ao Fi(t, m, M, N)


-Di












- P9


- h;.o] aFb(t, mb,M,N)


-g


Hence


, as previously,


the ith seller chooses to list with the broker only if Vbi


Broker'


Problem


In thi


case


, only a fraction of th


total sellers will list with brokers.


Thus


of that


number Mb,


on an ave


rage each broker expects to have


Mb/2


list with him.


Then each


broker'


expected net valu


will be


Mb


f13 t-l[pP
1PM1pp
1


Fb(l ,mb, Mb, N)


where again the choice of the split rate 6 is


-E P


relevant.


-D,


brokers wil


now choose (p,


uch that fo


one or


more owne


rs Vbi


Again,


each of th


two brokers will satisfy


above constraint and will be limited


in the


choice of the commission rate.


Hence


, the general outcomes


are similar to ones


obtained


in the discussion


heterogeneous sellers and a single broker


. However, the choice (p


m,) may differ in the


case


more


than


broker


Since


broker


now has


fewer


listings,


a Nash


equilibrium may dictate a decision to


increase


total number of


listings with brokers by


lowering the commission rate.


investigate this


prospect would require more detailed


information about the distribution of the net expected values V


i to weigh the gain


listings against th


Moreover


n revenue


isting.


, in the real world it would be difficult to argue


that brokerage agencies


are i


identical.


Brokers acquire reputation


whether


ustifi


d or not.


As noted earlier


= E P'[(1










arrangements


title search


companies.


Howeve


as noted


in Appendix


argument is that these arrangements tend to increase, rather than decrease, closing costs


and are subject to antitrust proceeding


Reputation


seem to be acquired through


marketing costs.


n all previous models


have


assumed that marketing costs for brokers


are a fixed amount per period,


independently of the number of listings.


While there is


some truth


n this assumption,


since


some


costs are


independent of the number of


listings,


advertisements


n local newspapers,


part of marketing cost may be more


directly associated with listings and potential


buyers.


Brokers display varying degrees of


creativity


and costs)


in newspaper


rtisements to attract listings and potential buyers.


Some


brokers


feature


open


houses


expense)


more


than


others.


Some


emphasize canvassing neighborhoods by mailing residents information about their local


sales record and market conditions


n the local area (including asking prices of houses


for sale


n the neighborhood).


Some brokers are more solicitou


than others,


n direct


dealings with sellers and buyers.


All attempt,


in one way o


another


, to use marketing


cost to enhance the


reputation


leading to some variation


n marketing costs across


brokers.


some


monopolistic


competitive


markets


, attempts


brokers


differentiate their


products may lead to excessive marketing expense


as they compete to


attract listings and buyers from a common pool.


n the next chapter


present an


mpirical model and its estimation taking


consideration theoretical issues


discussed in the present chapter














CHAPTER V

EMPIRICAL MODEL AND ESTIMATION


In this chapter,


first examine


Jud (1983) model


n detail


and then present an


alternate approach to th


mpirical estimation.


Review o


an Empirica


Jud stud


the role of real estate b


rokers


in the


housing market and develops an


empirical model of demand for broker


services by


both buyers and sellers.


Seller'


demand for


broker


serve


is presented


as function


commission


rates


transaction costs for the


seller (ts) and sales price of th


property (p).


Formally,


= D,(c, t,, p)


where


< 0, (aD,/at,)


> 0, and


A buyer's


demand for broker


services


depend


nt on his pre-existing stock


of market


information (


and opportunity cost of his time (tb)


b = Db(


where


aDb/ai)


< 0 and (aDjatb)


assumes


that horn


sellers are price takers (given the


prevailing commission










and sale price alone.


Since the dependent variable D


- is a dichotomus variable, ordinary


least squares estimation methods are not appropriate.


Therefore


, he estimates the D,


equation as a probability function.


described as logistic,

prob(Listing)


probability that a seller lists with a broker is


= 1/{1 +exp[-(a++p3t+pp)]}


n[ prob(Listing)
1 -prob(Listing)


=D,=a + pt+ Pip


which he estimates using conditional logit techniques.


The variables he used are as


follows


Listing


= one


if the house was


sold through a broker


= zero


erwise;


Trans


Cost


= one


if the seller moved out of th


county


= zero


, otherwise;


Price


= the sale price of the property (in $


000s


The dichotomous variable "Listing" was


used to construct the dependent variable (D).


It was


found that both transaction cost


and sale price variables were statistically


significant (at the 1

with a broker. As


level


n predicting th


transaction costs


probability that a seller will list his property


increase


probability of a seller using a broker


increases.


As th


price of the property


increases,


probability of selling through a


broker increases.


Jud then estimates an equation for


demand for broker


services by buyers, also











Broker


= one


, if the buyer consulted a real estate broker


to aid him


zero


n his housing search,


other


New Buyer


= one, if the buyer n


ever owned a h


ome


zero


other


= one


, if buye


lived previously in the


same


county,


zero, otherwise;


Black


= one, if the buyer was


black


zero


erwise;


New


= one


,if buyer purchased


a new h


ome,


zero


other


= buyer


annual


income


in $1


O00s


The dichotomous variable Broker was


used to


construct the


logistic dependent variable.


The variables "New Buy


r" and "Res" were used to provide measures


of the buyer


existing knowledge of the housing market.


New buy


ers are mo


re likely to consult brokers


since


they hav


very little informati


on and


similar


y resident buyers have


more information


so their


need to consult a broker is


less.


Estimated coefficients of both these variables


had the expected sign,


but only th


variable


es" was


statistically significant.


variable "Black" was


included to


ncor


porate racial


differences


but was found to be


insignificant.


"Inc" was


also not


gnifican


"New" was


with its negative


coefficient


which was


expected since brokers


are less active


new h


ome


market.


It sri thna vanraeoarlr tt nrr'a ni I,


;!. h rn arrthaehrl 4kr'M. in,,k


A..A A


I


r~\


' r


n n ~ n rk ntk h I ~k


kni rFn


rnl i










variables were statistically significant except the listing variable.


This result seems to


suggest that the brokers do not possess any special monopolistic advantage which


enables them to extract more for for


a house


han if it were sold by the owner.


agrees with ou


intuition about the market.


present an empirical model of th


housing market with buyers,


sellers and real


estate agents.


There are


several shortcomings of Jud'


study which


hope to resolve in


this dissertation.


First, he


assumes


that sellers are price takers given that the commission


rates are fixed.


However,


as argued


in the previous


section


, the ability of sellers to deal


outside the market does


influence


the commission rates.


sellers are not entirely


passive price takers.


Moreover


evidence


about th


fixity of commission rates does


indicate some variation


n rates (see Carn


, 1982)


as outlined


n Chapter


As noted


there


, Carney finds that commission rates are low


r on (i) higher priced houses,


(ii) on


sales of new relative


to existing homes


i) on non-MLS (or,


co-op


as he refers to


them) relative to MLS sales.


Model Description and Specification


n the present study


include the


estimation of commission rates and explicitly


consider the simultaneous


interaction between the three


types of agents,


., sellers,


buyers and real estate brokers.


commission rates are included


n the model then Jud'


estimation procedure


no longer correct.


This is because


now there is a simultaneity


in the system.


Commission rates are affected by buyer


and seller behavior which in turn


VI%~~~kl tjI+4 t1 I flf rn v,,.I t at~ an v 'r :r- t. I t.n *l .:i Znna .a. jil t.AArj~


ArA ACIAA)Arl k~l(kA AAMMlnnln~ rA(AA


*










simultaneou


equation


model.


discuss


my model and estimation procedure in the rest


of this section.


The main interest


n this dissertation is


in how commission rates affect and are


affected by the behavior of buyers and sellers and their


estate brokers.


ability to deal outside the real


Clearly these effects need to be determined simultaneously.


The commission rate that a real estate agen


can charge depends on several


variables


including the housing price,


seller probabilities of


housing characteristics


seeking the serv


are not available


housing


characteristics


of a broker


in the data


in the


the best


market


environment and


sale of the house.


can do is


Since


assume the mix


of housing characteristics is constant.


housing market consists of new houses as


as existing ones.


Usually, th


new properties are adv


ertised and sold by the builders


and developers of such properties.


Thus brok


ers face competition from new housing


construction


therefore consider the number of housing permits


issued


as an i


ndex of


construction


activity


capture


measure


competition


from


new


construction.


However, new construction may also provide


some


means


ure of whether the housing


market is active


Finally


native


seller probabilities o


so that th


outcome of using housing p


listing the property with a broker


ermits may be mixed.


influence


the commission


rate that can be charged by th


broker


How


ever


these


probabilities are also determined


by commission rates along with th


characteristics


of th


sellers and buyers (to be


described next).


A seller's


nf lietinn th


decision to make the


sale through a broker


nrnnof'r t lA/ith a hrnlter\ r4onQnrt


---


-"-A'--,


(and hence


A- ---


the probability


- AL ..


ni I11 If L0 r1aELr tnI ggtyt '' 0 lrr fY n


I


o


nT TB










costs.


These are all transaction costs


involved


n completing the sale


which could be


viewed as opportunity costs of time).


transaction


costs are


kely to be highly


correlated with the price of the house,


higher the sale price of the house,


more likely it is that the seller


income


is high (which


in turn


mplies that his opportunity


cost would be high).


Thus


only consid


er the


sale price of th


house


in the actual


estimation of this model.


The market condition


determining the probability of a house


being listed with a broker relate to the numbers of buyers compared to the numbers of


sellers (as explained


n the previous section


which


in turn


influence th


expected time to


In the estimation


use the un


employm


ent rate


as an i


ndex of the market activity.


n areas with high un


employment, the number of buyers is likely to be less than the


number of sellers, making it more probable for


sellers to list with brokers.


The probability


of a sale taking place through a broker would also depend on th


opportunity costs of


the buyer


because


what is observed is the


actual sal


through a broker and not just the


seller'


probability of listing with a broker


rese


nt paper


the opportunity cost


faced by the buyer are m


measured


as th


cash paym


ent by th


buyer at the time of


, including the down payment and other


costs.


Finally,


price


house


was


assumed


previous


section


exogenously determined by the market


upply of and d


mand for housing.


However,


while empirically studying the housing market,


interesting to consider the effect that


brokers may have


on the price of houses.


price of a house would also depend on


the geography


location of th


property and th


une


employment


n that area.










Thus


key point in this model is that th


commission rates and the probability


brokered


sales


are interdependent


variables


hence


must


determined


simultaneously.


There are two opposing fo


rces


which are at play here.


First, under the


conditions described abov


market is ve


ry active,


it is not costly fo


the seller


to search for buye


rs him


self and undertake to sell the house


directly, and the demand fo


services of a broker


This feature tends to dampen the commission rate.


However


, this dampening effect


encourages


seller to


mploy a broker and hence


increase the probability of a brokered


Therefore


estimating


commission rates and


the probability of brokered sales separately w


would yield biased


results.


Also


, the price


of th


house


must be estimated simultan


eously


n this


system because


am i


interested


effect of a brokered sale on th


price of the


house.


problem can then be


appropriately formulated


as th


following


system of equations


COM


+ P12(B)


+ P13(


11 (HF')


-Cc21


+ 1P1(COM)
+ P31(COM)


+ Y22()


+ p23(
+ M2(B*)


+ Y23( 4
+ Y34(G2)


'2 (24)


35(03)


The reduced forms for the abov


system of


quations are


COM
B'
P


K2i
-It3


+ a12n(HP)
+ 22(HP)
+ "32(HP


+ 7ni(D)
+ X23(D)
+ %733(0)


C4U
+714U
+ ,U


+ 15(G2)
+ "is(G
+ 25(G2)
+ "xz(G2)


+ "16(G3)
+ X26(G3)
+ i3(G3)


+ w3










The variables


n the above


system of equations are defined


as follows


sale price of the property (in $


probability that th


,000s)


property would be sold through a broker (which is a


latent variable)


if the house


is sold thro


ugh a broker


zero


otherwise


cash down payment plu


other costs paid by th


buyer


(in $s)


COM


commission rate


employment rate


number of housing permits by


if the house is located


zero


issuing location


eastern U


otherwise


if the house


is located


western U


zero,

I G


otherwise

- G3 specifies location in the Central U


The simultaneous system of equation


and (25)


involves


the unobserved


dependent variable B (


probability


of a brokered


sale)


instead we observe


another variable B*


(where B


= 1 ifB


zero


rwise


we must use a two-


stage limited depend


reduced form equation (26)


nt variable estimation method.


by tobit since


first stage consists of estimating


commission rate variable COM is not


observed for all


cases


., it is not observed for houses sold by own


r-sellers


rall ira rl- frrm nis latifnn 1/07\ bK th" a nrnhit KIll rm thth


one


~in~a


;t a~fllall\r th~ nmkak;l;br











dependent variables into th


structural equations


and once


again estimating equation (23)


by tobit, (24) by probit ML method and (25) by OLS (see


Maddala, 1983 for a description


of two-stage


estimation


methods


simultan


eous


equations


involving


unobserved


dependent variables).


Using th


rank


condition


for identification of


our system of


equation


find that the above


system of equation


is identified.


above


system


equation


for the


results


to be


cons


istent with


theoretical


model


, the hypoth


eses


regarding


structural


coefficients would


be as


follows:


(the coefficient of B


n equation (23)


should be close to zero and P13,


coefficient of P must be negative


received


because


as commission would be higher


r high priced houses,


making the


"absolute" amount


brokers willing to accept a lower


"rate" of commission.


n equation


the structural coefficient of the commission


rate variable must be negative


and p23 must be positive.


n equation


24), p31


coefficient of the variable COM will give


commission rate is on th


an indication of whether th


seller or on the buyer and 32 would be posit


incidence of the

ive if brokers are


able to obtain higher prices than do own


r-sellers.


The othe


variables are expected to hav


the following


The sign of y,, is


is not certain


, as argued earlier, because


on the one hand the number of housing permits


issued


indicates competition to brokers fr


om new


construction.


On the other hand the


new construction may be a m


measure of an activ


or inactiv


market.


The higher the level


of new construction


easier it would be for a seller to make a direct sale with a buyer.


Thus. the effect of HP on the commission rate will be uncertain.


Turning to equation (24),


Snn


n~t.,., ai~ +knrtln;~ an.%,'n4 .ntnn+-4


-"I


1- -


n nrA 4kl :n :tlnr an Itrr Irst* a knt


TJ


1~ -


I


rr


tnh nl nnr


r^ f










positive influence on the likelihood of a brokered sale.


If unemployment


n an area is


high,


the number of buyers


in that area is likely to be low


. Thus it is more


important for


the sellers to seek the


n equation (24),


services of a broker.


the sign of y


Thus


, the sign of y


is likely to be negative,


is likely to be positive.


because


in areas with high


unemployment leve


ere are


kely to be


few buyers


relative


to sellers) and thus the


prices of houses are lower


. The signs


and y


will give


impact of geographical


location of the property on the prices of houses


(taking Central U.S.


The model presented here differs from Jud's


as a base).


following ways.


First, he


ignored


commission


rates


in his


estimation


procedure,


which


takes account


of the


nterrelation


betw


een agents in th


housing market.


Secondly, by


including the index


of market activity (i.e.


unempioym


ent rate


allow for


differences


n seller behavior in


decision


about sale through brokers.


n the next chapter


discuss


data


sources


and results from the estimation


procedure described in this section.


also discuss


seven


ral alternative


data sources which


might be better suited to the analy


presented here.














CHAPTER VI

DATA AND EMPIRICAL RESULTS


Data


In estimating the system of equation


described


n Chapter V


used data from


540 housing transaction


in cities


states


United States which took place


early months of 1989.


The data on six of th


variables-


COM


P, B, D, G, and G3 were


obtained from actual Settlement Statement forms HUD-1


(or RESPA forms) which are


signed by the parties


involved (


buyers, sellers and lenders at the time of closing of


a deal)


One observation about this data set is that it covers only low and medium priced


properties


This is because


obtained th


data from the U


Department of Housing and


Urban Development regional offices and these


offices only deal


in transactions financed


through federal and Veterans Administration loan


These


federal loan


have an upper


limit and thus the data that


could obtain is only for


low and medium priced houses.


Thus the results apply only to moderately priced properties.


For the


ndex of market


activity


, I have used unemployment rates.


my study com


from the Bureau of Labor Statistics.


data on unemployment rates that


These


use i


figures are annual averages


1989


which


have


been


disaggregated


at the


county


level.


Since


housing


transactions data is at a city level,


cities and counties are related by the information


available


in statistical abstracts of each


individual state.


The problem with this process










available.


Also some cities are part of two (or more) counties.


Fortunately there were


very few


uch cities.


Construction activity


n the area of


interest is considered as another index of


market activity.


measure construction activity by using the number of housing permits


issued


n that yea


n the particular area.


This was obtained from Current Construction


Reports published by the U


Departm


nt of Comm


erce, Bureau of the Census (the


particular report was titled Housing Units Authorized by Building Permits).


to date numbers from the November 1989


used the year


issue.


The main problem with the


above


study is th


unavailability of any organized form


of data for the type of problem


n the real estate market that


have


have described in


this pape


. Due to this problem


had to collect the data from several different sources


and combine them.


This makes the


data ve


ry noisy because,


the different data were


available at different levels of disaggregation.


For instance


the RESPA data from the U


Department of Housing and Urban Development (for th


variables COM


, B, P and D) is


actual


transaction


data


is disaggregated


at th


level.


data


unemployment rate were obtained from stat


offices of Bureau of Labor Statistics and


these were mainly at county levels


although th


data was


available fo


some of the larger


cities


Thus for the smaller cities


had to relate the RESPA data to the


relevant county.


This is obviously an approximation,


which has


some bearing on the validity and the


quality of the results.


Similarly


the data on housing permits is published by the


ThiQ tnnn in an annrvimatinn fr th0 QmaIllo


I


tnr\nc


permit issuing offices.


hinQ anrI ritiio AAhir, Sh mor nft hHao a










One of the sources of data which would have been useful is data from


brokerage houses.


individual


This would have made it possible to obtain data on a wider range of


prices of housing,


the corresponding comm


issio


n rates, probable time


-on-the-market as


well as some housing characteristics


as the age of the house,


the size of the house


etc. Unfortunately, the real estate brokerage industry has


antitrust allegations which has lead to extra cautiou


spoke with several brokers at


been particularly vulnerable to


behavior by brokerage houses.


several large brokerage houses and also with the research


departments of the National Association of Realtors and Florida Association of Realtors


as well


as Gainesville Board of Realtors and each time was


told that there is no available


source


information


commission


data


as it


could


interpreted as non-


competitive price fixing by the FTC.


Another


, appropriate method of data collection is to undertake a survey of house-


owne


rs (those who have sold houses


as well


as those


who have bought hou


ses-


directly


or through brokers) and brokers.


This method w


would actually be the best for


the type of


empirical work undertaken


1983 (see


n this study


FTC Staff Report 1983) but


. This type


was


survey was


unable to obtain this


done for the FTC in


survey data for use in


the present research as my communication with th


organizer of this


urvey revealed that


those data tapes were destroyed.


Empirica


results


The results from the


two stage estimation of the


simultaneous equations system


(usina LIMDEP package for estimation of limited dependent variables) defined in Chaoter










coefficients for equations (26),


(27) and (28) respectively


Tables IV, V and VI present the


corresponding structural coeffici


ents.


The estimated structural coefficients presented


n tables IV


, V and VI give some


conflicting evidence about the hypoth


eses


presented earlier


R2s are not high.


However, this result is not uncommon with large


samples and particularly with noisy data


have available.


The coefficients of th


two i


important variables,


B and COM


have


the following signs.


The coefficient of B


equation (


23) gives


the expected result- it is


found to satisfy the hypothesis that the


coefficient is not


significantly different from zero.


other


words


, the


probability of sell


rs and


buyers of housing


seeking


brokerage


services does not hav


a significant effect on commission rates.


This is consistent with


the theory presented


in the


previous


sections.


How


ever


coefficient of COM


in the


equation (24) has a positive sign (although not significant).


positive


sign if


significant


would have implied that commission rates hav


a positive


effect on the decision of sellers


and buyers)


n choosing to employ a broker


(it was


expected that coefficient of COM in


equation


24) would be negative because


higher th


commission rate


the lowe


was


the probability of a brokered sale).


coefficien


of P in equation (23) has the expected


negative


n equation (24),


In equation


the sign of the coefficient of P is positive as expected.


the coefficient of D which is indicative of buyer'


opportunity cost


and thu


expected to be positive,


turns


out to be negative


but it is not significant.


Finally,


coefficient


of th


unemloym


nt variable


expected


positive


significant.


Next. the results from the


estimation of the rice of housina (i


-t.


equation (251 are










negative coefficient, suggesting that the


ncidence


of the commission is not on the buyer


of the housing.


The unemployment rate has


as expected and it is also significant.


Finally


a negative


influence on the price of housing


both G2 and G3


are significantly positive


suggesting that


n the present sample,


prices


of ho


uses


in both the Eastern U


Western U


are s


significantly higher than


in the


ntral U.S.


, the results provide only a partial corroboration of the hypoth


eses


in this


paper.


But this is expected given that many of th


variables


have used are proxies and


the variables that are not proxies are


subject to large measurement


errors.


The estimation


limited dependent variable models with m


easurement


errors is extremely complex,


(especially with a particularly noisy data set like mine) (see Stapleton and Young,


1984)


and hence


did not attempt that procedure.


How


ever


feel that there is much research


to be done


n this area


and an


important part of my future research agenda would be to


obtain cleaner data


possibly from primary


survey


ys) and study the housing market in a


much greater detail.










TABLE


ESTIMATED REDUCED FORM TOBIT EQUATION FOR COMMISSION RATE


VARIABLE DESCRIPTION COEFFICIENT
ONE 4.90
(26.45)
DOWN PAYMENT -0.29E-01
(-1.71)
UNEMPLOYMENT -0.25E-03
(-0.02)
HOUSING PERMITS -0.16E-03
(-2.27)
GEOGRAPHIC LOCATION (EAST) -0.96E-03
(-0.29)
GEOGRAPHIC LOCATION (WEST) 0.94E-01
(0.38)
[t-ratios are provided in parentheses]


TABLE II

ESTIMATED REDUCED FORM PROBIT EQUATION FOR BROKERED SALE DUMMY


VARIABLE DESCRIPTION COEFFICIENT
ONE 1.26
__________________________________(9.00)
DOWN PAYMENT -0.26E-02
(-0.29)
UNEMPLOYMENT 0.16E-01
(1.48)
HOUSING PERMITS -0.25E-04
(-0.68)
GEOGRAPHIC LOCATION (EAST) -0.47
(-2.31)










TABLE


ESTIMATED REDUCED FORM EQUATION FOR PRICE


VARIABLE DESCRIPTION


COEFFICIENT


ONE 56.72
(40.79)
DOWN PAYMENT 1.32
(10.17)
UNEMPLOYMENT -0.53
(-5.29)
HOUSING PERMITS 0.12E-02
(2.27)
GEOGRAPHIC LOCATION (EAST) -0.12
(-4.93)


GEOGRAPHIC LOCATION (WEST)


[t-statistics presented in parentheses.]

TABLE IV

ESTIMATED STRUCTURAL TOBIT EQUATION FOR COMMISSION RATE


VARIABLE DESCRIPTION


COEFFICIENT


ONE 5.93
(7.91)
BROKERED SALE DUMMY (FITTED) -0.64E-03
(-0.51)
PRICE -0.18E-01
(-1.54)
HOUSING PERMITS -0.14E-03*
(-1.96)


[t-ratios are provided


n parentheses.


* indicates significance at


level.]








63

TABLE V

ESTIMATED STRUCTURAL FORM PROBIT EQUATION FOR BROKERED SALE DUMMY


VARIABLE DESCRIPTION


COEFFICIENT


ONE 1.64
(-1.13)
COMMISSION RATE (FITTED) 0.32
(1.45)
PRICE 0.19E-01
(1.43)
DOWN PAYMENT -0.20E-01
(-1.01)
UNEMPLOYMENT 0.14E-01 *
(12.90)
[t-ratios are provided in parentheses. indicates significance at 5% level.]

TABLE VI

ESTIMATED STRUCTURAL EQUATION FOR PRICE


VARIABLE DESCRIPTION COEFFICIENT


ONE


-163.05


(-3.31)
COMMISSION RATE (FITTED) -92.47*
(-8.46)
BROKERED SALE DUMMY (FITTED) 533.09*
(6.74)
UNEMPLOYMENT -9.23*
(-7.14)
GEOGRAPHIC LOCATION (EAST) 250.4*
(6.74)
GEOGRAPHIC LOCATION (WEST) 36.98*
(8.86)














CHAPTER VII

SUMMARY AND CONCLUSIONS


n the present study,


attempt to provide an understanding of the housing market


re buyers, sellers and real estate agents interact with each other while making optimal


decisions for


themselves.


The housing market has


several distinct characteristics which


makes this market different from most other markets.


spatial fixity, durability and the extensive


These


features are heterogeneity,


involvement of the government.


These attributes


and imperfect information among buyers and sellers provide an opportunity for real estate


agents to serve


n this paper


as market-makers arranging transaction


develop a theoretical model of the


between buyers and sellers.


interaction among sellers, buyers


and real estate agents.


Taking


nto account an uncertain


date of sale and various


periodic costs the seller must decide whether


to list the


house


with an agent or to attempt


to sell the house without an agent'


intermediation.


rs may visit owner-sellers and/o


use the services of an agent.


estate agents must ch


oose


a commission rate with


the understanding that buy


rs and


sellers


may arrange


transactions directly without


ntermediation.


mode


explicitly


considers


dependent


random


processes


involving


decision


of buy


rs to visit houses and make offers.


n this un


certain


environment sellers


must determine


the expected gain by attempting to attract buyers directly or by listing










estate agents.


Real estate agents maximize expected returns under the constraint that


they choose commission rates which are


The model


efficiently favorable to attract listings by sellers.


initially considers a market with a single real estate agent and then extends


results to introduce numerous agents with a multiple listing service.


In either case the


outcomes are strongly


influenced by the competitive pressures


imposed by sellers with


the option of arranging trades without real estate agent


intermediaries.


In the remainder of the paper


taking into account outcomes


n the theoretical


model


, I develop an empirical model which simultaneously estimates the commission rate


and sellers'


and buyers'


interaction with a broker


which is


indicated by brokered sales


and non-brokered sales


use a two stage simultaneous


model which estimates system


of equations consisting of commission rates and the


probability of brokered sales.


n the


first stage, the reduced form equation


are estimated with the commission rate equation


determined by tobit (because commission rate is not observed for all cases) and the


probability of brokered sale by probit procedures.


Then the reduced form parameters are


substituted


the structural form


and once


again,


the commission


rate equation


estimated by the Tobit procedure and the probability of brokered sales is estimated by


probit.


use data consisting of 540 housing transactions


from fifteen states across the


U.S. in the estimation.


Although the results from the estimation procedure are mixed and


somewhat


capturing


disappointing,


essence


the procedure


itself


consumer


an important


estate


agent


first step


behavior


in correctly


the housing


markets.


Also


one of the main contribution


of the present pape


is to develop an


mnnrnflrh uwhirh vnlieitflw rnncirr


intorrntinne


hotfA/oon hi i/oror anr4 collore nrf nrinr














APPENDIX A

MATHEMATICAL APPENDIX


Let F(t, m,


= (e-P


- 1)e-Apt


Then


F(t, m,


*=


increases with


increase


nm or N


and decreases with an


increase


n M if d(Xp)/dm


> 0, d(Xp)/dN


> 0 and d(Xlp)/dM


Proof:


Note that


d( t, m,M,N)


d(Xp)


= eXP(e


-te-At^


= e- pt[ep(i


Next, use


mathematical


induction to v


rify that


-z


d(Xp)


dF(t,m,M,N)


Cr e-1


Pt(eAP(1


d(Ap)


-t) +t)


= T'-rp


We hav


ume


= (T-1)


-(T-i)Xp


Then


+ T]


=


= Te-


(Note that


-TAp < 1/-2 for


efficiently large.


-Tip < Tr2


ence


= 1/T for T


sufficiently large


-* CO)


Finally,
T
dIE F(t,m,M,N)


= Te-T


/fgd~p}


= S,[dm(p
dm


+ e-


d[t) F(t, m, M, N)]


pi d( IP











if d( p)
dN


I~=Ft ,M )


<0; if d(p)
dM


d[~ ~ t, m, M, N)1














APPENDIX B

ANTITRUST ISSUES AND REAL ESTATE BROKERS


The real estate brokerage industry has


endured frequent antitrust litigations.


Most


of these


cases


have


involved fixing of commission rates by loca


boards of realtors in


order to restrain trade and competition but other


issues hav


also arisen.


An examination


of the antitrust


implication


of brokerage activity


led to three main concerns.


are the actions relating to barriers to


entry


into th


brokerage


industry which might lead


to limited competition.


Seco


nd are th


xing of commission rates by th


local real estate


boards. Finally, there are concern


about the


tie-ins


among brokers and other real estate


conveyancing services.


Here


look at some landmark


cases


dealing with the abov


issues


and note the


udgments pronounced in each


case.


majority


antitrust


violation


private


industry


considered under the stipulations of th


erma


n Antitrust Act of 1860.


This act outlaws


three types of practices,


of monopoly power


namely, (i) possession and willfu


n the relevant market


acquisition and maintenance


ntent to fix prices and eliminate


competition and


ii) collusion among firm


with the


ntent to monopolize.


Apart from the


Sheman Act there are two other acts which govern antitrust


ues-


Clayton Act


1914)


and the Robinson-Patman Act (1936).


The former prohibits m


ergers which would lead to










Barriers to Entry


Under the


Sherman Act (as wel


as under the other two acts)


it is illegal to restrict


entry to an industry in order to monopolize it.


of the main con


cerns that this issue


creates


real estate brokerage industry has


been that brokers who are not members


of the local board of realtors are not allowed


access


to the


multiple listing service


n the


area.


One of the most important


cases


concerned with barriers to entry is Grille Vs.


Board of Realtors of Plainsfield Area (9


Super 202,


21 8-219,


219 A.2d 635, 1966).


The case


n question was


that a N


ew J


ersey brok


r who was


not a membe


of the


Plainsfield Areas Board of Realtors complained that the


Board of Realtors prohibited non-


members from using th


Board'


multiple


listing


service.


This case was a bemnchmark


case


for all


cases


in the future.


The verdict


in the case was that it is illegal to restrict th


use of MLS only to board members (unless


membership to a board is open to anyone


who is


interested


under th


statutes of the


Sherman Act.


There have


been


some


cases


re th


judg


eme


nt was


n favor of the defending


board of realtors.


However


circumstances


in these


cases


were peculia


(see Miller


Shedd (1979),


a discussion of these


cases).


Based on the Grille


case


and a vast majority of the


cases


, it is now well


accepted that the use


of any MLS


information


should be


open to any


licenced brokers


n the area whether the broker


belongs to the


board of realtors or not.


Commission Rate Fixinq


The real estate brokers have eni


nucar4 f~i


j III


v uniform commission rates since the


uV.










suits.


Under the


Sherman Act it is stated that any contract or conspiracy that results


in restriction of


interstate trade is illegal.


The landmark case about commission rate fixing is the United States Vs.


National


Association of Real Estate Boards (339 U


1950


n this case the Washington


Board of Real Estate required that th


members could not accept lower commission


than those


imposed by the Board.


Here, the U


. Supreme Court ruled that the Board


of Realtors was voilating the stipulation


of th


Sherman Act and that it is illegal to


restrain trade through price fixing,


indicated by the mandatory commission rate.


Since


the ruling of the Suprem


Court


n 1950


real estate boards


nthe U


not have any prescribed or mandatory commission rates mentioned


in their


code of


After 1950, commission rates for member realtors were "recommended" by most


of th


local board of realtors.


For example,


until 1972


they were recommended by the


New York Board and thereafte


code of ethics manual of the N


merely declared tha

ew York Board of R


such rates w


ere "fair"


ealtors all rates w


In the 1974


re removed


Barasch


1974


for a detailed discu


Convevancinq Costs


Excessiv


cost of conveyancing


serve


is also


subject to antitrust statutes.


joint report by the U


Department of Housing and Urban Development and the Veteran'


Administration


1972)


indicated that these


costs


are too high


n many areas in the U.


Conveyancing costs are th


closing


costs of


serve


Excessive costs were attributed to


provided to custom


an elaborate


rs at the time of


system of rebates,


kickbacks










and referral fees


n this


industry, (ii) high level of duplication and (iii) other


nefficiencies


present in this


industry.


The antitrust concern


costs are that brokers receive rebates from title search


insurance) companies and hence designate such companies to buyers and reduce


competition among firms providing closing


services.


Despite the system of rebates and tie-ins,


it has been felt that there has not been


much attention given to these aspects of closing costs


Owen and Grumfest


,1977).


They


suggest a reform


n the


egal structure to


corporate these


antitrust issues.


Epley and


Parson


1976


present a list of practices


of transaction


charged to be illegal because


of their anticompetitive nature.


The discussion presented


n this Appendix


suggests that the brokerage industry


has endured antitrust


litigation for half a century and thus that brokers are extremely


cautious in dealing with each other


as well


as with buye


rs and sellers of real property.














APPENDIX


U.S. DEPARTMENT OF HOUSING


AND URBAN DEVELOPMENT


SETTLEMENT STATEMENT HUD-1 FORM















A. Settlement Statement


U.S. Departmem of Houing
end Urlb n Demoeant


OMB No. 2502-0265 (Exo. I


i3r
2-31486)


B. Tyn of Loan
1. O FHA 2. O FmHA 3. ] Conv. Ungn.. '"U Nus. -Lo r-n- S. Motn- ---. c"m N--ge
4. 0 VA 5. C Cov. ins.

C. No This form s fumished to giv you a statement of actual settlement costs. Amounts paid to and by the settlement agent are
shown. Items marked "(p.o.c.)" wer paid outside the closing; they are shown henre for informational purpose and are not
included in the totals.___
D. Nna ad AdMOre @o4 onfl U. Nam wd AW d nl SE 5l4 F. Nf~ w Ad Lam@4f Ian






G Prowlv uLOan n H. kS re( AgM

Pi o gImmeiM I. Sstwmnt4 O.




J. Summry o narrow's Trnsctlosm K. Summe ot kow's Tnnmesfon
100. Grom Amoum Due PFrm em _r _400. OGre Amou l Due To l841w
101. Contract saMe price 401. Contract saMles pce
102. Personal properly_ 402. Personal popn
103. Settlement chare to boower 104. 404.____
105. 4.____
Ad Ummue tow items mMs e i. enmMce Ad mls te s pad by ssll In Sfencl
106. Citytown taxes to 406. Cl town taxes to
107. County taxes to 407. Count taxes to
106. Aslasnments to 4_0. Assessments to
109. 409_
110. 10._______
111. 411.
112. 412.

120. Grou Amoust De From onsmse 420. Grow Amman Oue To SMIw
200. Anmues Phid Otr DIen el Of S Son o00. Rdulmns hi Amuam Due To. S6ll
201. Deposit or eanmet money 501. Excess dol fte Ie tnstructions)
202. Princp amount of new a 502. Settlement char s to sllr (line 1400 ____
203. Existing loIsI) takes to S03. Existing olon(s) tten sub ict to _____
204. 504. Payoff of firnt mortgage 10m
205. _os. Pay ff second modta loan
206 506.
207 7.
208 .
209 .
Ador Ahf u npm id by A_ *domen tr ba a *-if
210. City/town taxes Ito 10. Cl/town taxes to
211. County taxe to_ 511. CounIt taxes_ _to
212, AslesMlmens to_ 512. Assessments to
213. 13.
214. 514.
215. 15.
216, _516.
217. 51.
218. 518.
219. 1.














L Settlement Charg4es --------
700. Total $Sallrok'a ComnsrMI be on Pd From Paid From
Division of Commission line 700 as follows:orrowr Sler
701. $ to Funds *t Funds at
Settlement Settlement
702. S to
703. Commission d at Settlement
704. ___________
300. Items Pyable In Connetion Wlh Laan
801. Loan Origination Fee %
BP I oan Discount %
803. A grasa Fee to
804. Credit Report to
805. Lender's Inspection Fee _
806. Mortgae Insurance Appication Fee to
807. Assumption Fe

809___
810.
011.
BOO, Iem Required By Lender To Be Paid In Advewne
901. Interest from to IdG
902. Mortgage Insurance Premium for month to
903. Hazard Insurance Premium for e ar to ____
e9t. o _to

1000. Reumyen 1elltod With Ladr
1001. Hazard insurance month$ er month ____
1002. Mortgage insurance monthsfl cmpr month
1003. City prop taxes month S r month
:004. Count propr taxes month r month _____ __
1005. Annual assessments monthaS T month _____
100e. monthelr er month
1007. month per month ____
1008. monthast pr month
1100. Title Chu__
1101. Settlement or closing fee to_
1102. Abstract or title search to
1103. Title examination to
1104. Title tnurance binder to
1105. Document preparation to
'06. Notary fees to________
1107. Attomey 's fees to
(includes above items numbe:____
1108. Title insurance to
includess above items number: _
1 109. Lender's coveraGe S_
1110. Owner's cover S___
1111.
1112.
1113.
1200. Gewanomet Mrin and Tranufer C lens ___ ,,,
1201. Recording tees: Deed S ; Mortage a ; Reeaes t _
1202. City/county taxstamps: Deed : Mortgage S
1203. State tax/tamp: Deed S ; Mortga ge ___
1204.
1205.
1300. Adchlonel SethmeeM CMgee
1301. Survey to














APPENDIX D


RESULTS FROM SEPARATE ESTIMATION OF EQUATIONS


(23), (24) AND (25)


Estimated Separate Tobit Equation for Commission Rate


VARIABLE DESCRIPTION COEFFICIENT
ONE -0.26
(-0.77)
BROKERED SALE DUMMY 6.6
(24.72)
PRICE -0.1 3E-01
(-3.95)
HOUSING PERMITS -0.69E-04
(-1.55)
t-ratios are provided in parentheses.]


Estimated Separate Form Probit Equation for


Brokered Sale Dummy


VARIABLE DESCRIPTION COEFFICIENT

ONE 0.17
(5.11)
COMMISSION RATE 0.12
(31.17)
PRICE 0.20E-02
(4.72)
DOWN PAYMENT -0.30E-03
(-0.21)
UNEMPLOYMENT -0.13E-02
I c r,\










Estimated Separate Equation for Price


VARIABLE DESCRIPTION COEFFICIENT
ONE 63.63
(25.27)
COMMISSION RATE -3.52
(-5.2)
BROKERED SALE DUMMY 21.67
(4.61)
UNEMPLOYMENT -0.56
(-5.22)
GEOGRAPHIC LOCATION (EAST) -0.11
(-4.05)
GEOGRAPHIC LOCATION (WEST) 9.02
(4.43)
t-ratios are provided in parentheses.]














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BIOGRAPHICAL


SKET


Suryamani Mantrala was born


ew Delhi


India


in 1961


After graduating from


Univers


ity of Delh


economics


with Bachelo


taught at the


of Arts (Honors) and Master of Arts degrees in


rsity of Delh


in 1983-1984.


She joined the Unive


rsity of


nois at Chicago


re sh


received a


Master


of Arts degree


economy


entered th


doctor


program in


economy


at the Univ


rsity of Florida in


1986 and expects to receive the degree of


Doctor of Philosophy in M








certify


have


read this


study


acceptable standards of scholarly presentation


and that


opinion


and is fully adequate,


t conforms to
in scope and


quality


as a dissertation for the degree of Doctor of Philosophy.


trc~cef


Edward Zabel


Chiman


Matherly Professor of Economics


certify


have


read this


study


acceptable standards of scholarly presentation and


t in my
is fully


opinion
adequate,


conforms


in scope and


quality,


as a dissertation for the degree of Doct r of Philosophy.

IJ-


Maddala


Graduate Research Professor of Economics


certify that


have


read th


acceptable standards of scholarly


study


opinion


presentation and is fully adequate,


conforms


in scope and


quality,


as a dissertation for the degree of Doctor of Philosophy.


David Denslow


Professo


of Economics


certify


have


acceptable standards of


quality


read


study


scholarly presentation


as a dissertation for the degree of Do


or of


it in my opinion
is fully adequate,
philosophy. .4


conforms


in scope and


Barton Weitz


J. C. Penney Professor of Marketing

This dissertation was submitted to the Graduate Faculty of the Department of


Economics in the College of Business


was accepted
Philosophy.


Administration and to the Graduate School and


as partial fulfillment of the requirements


for the degree of Doctor of


May,


1992


Dean


Graduate School

































UNIVERSITY OF FLORIDA
HIIII Il i11 in1 I2 ill lln I
3 1262 08556 7153




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