private real estate investment data analysis and decision making

Private Real Estate Investment Data Analysis and Decision Making... Why Location Matters: The Bid Rent Surface and Theory of Rent Determination 2.. Uncertainty: Risk in Real Estate Deter

Private Real Estate Investment Data Analysis and Decision Making

Private Real Estate Investment

Data Analysis and Decision Making

Roger J Brown, PhD

Director of Research Real Estate and Land Use Institute

San Diego State University San Diego, California

Paris San Diego San Francisco Singapore Sydney Tokyo

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Printed in the United States of America

‘‘Is life mathematics or is it poetry?’’

Roger Mague´re`s

1 Why Location Matters: The Bid Rent Surface and

Theory of Rent Determination

2 Land Use Regulation

Conclusion 36

Appendix: Comparative Statics for Chapter 2 37

3 The ‘‘Rules of Thumb’’: Threshold Performance Measures

for Real Estate Investment

4 Fundamental Real Estate Analysis

Deterministic Variables of Discounted Cash Flow Analysis 75

Single Year Relationships and Project Data 76

5 Chance: Risk in General

The ‘‘Certainty Equivalent’’ Approach 107

6 Uncertainty: Risk in Real Estate

Determinism and Real Estate Investment 135

Real Estate—The ‘‘Have it Your Way’’ Game 145

Example 1—Modifying the Growth Projection 163

Example 2—The Tax Deferred Exchange Strategy 167

The Sale-and-Repurchase Strategy: Tax Deferral as a Risk Modifier 176The Sale-and-Better-Repurchase Strategy: The Cost of Exchanging 178

Example 3—Exchanging and The Plodder 182

Appendix: A Caution On the Use of Data to Construct Theories 204

9 The Lender’s Dilemma

Irrational Exuberance and the Madness of Crowds 213

10 The Private Lender

The ‘‘Hard Money’’ Loan Versus the ‘‘Purchase Money’’ Loan 238

Is the Seller’s Financing a Good Deal for The Buyer? 248

This book is designed as a supplementary text for upper division graduate and graduate real estate investment courses The CD-ROM includedwith the book contains spreadsheets for data analysis tailored specifically

under-to real estate settings The major thrust is under-to bridge the gap between theoryand practice by showing the student how to implement his real estateeducation in the real world

The study of real estate follows long traditions grounded in UrbanEconomics and Finance There is, however an inherent conflict between thetwin realities that the finance market is efficient and the real estate market isnot Practitioners in the real world know, or at least act as if they know, thatreal estate is very different from finance No investment real estate broker gets

up in the morning and does anything even remotely resembling what astockbroker does While anecdotal evidence suggests that the two activitiesare different, until very recently academic theory supporting such a belief hasbeen underdeveloped and has suffered from a lack of data to test hypotheses.The data are growing around us every day as the industry converts realestate information into digital form It may be that this will improve real estatemarket efficiency It may also lead us to conclude that real estate is differentfrom finance for reasons we previously had not considered

Three significant ideas motivate this book:

1 Until recently, data on real estate was available only for large,institutional grade properties and its use limited to those who work inthat market Now, robust databases are available for many differenttypes of real estate For the first time, databases covering private realestate investment have breadth (large number of observations inrelatively small geographic areas) and depth (long histories of datacovering the same property)

xiii

2 Closed-form analytical techniques for risk modeling either (a) havebeen exhausted and/or (b) because of institutional factors are inappli-cable to real estate Hence, risk modeling using fast, numericallyintensive simulation with bounded datasets offers a significantimprovement over the present ad hoc real estate methods.

3 Considerable recent progress has been made in mathematical softwareand algorithms that permit one to access, combine, and integrate realestate databases in ways that make possible visual, spatial represen-tations Such demonstrations are now accessible to a much larger, and

at times less sophisticated, audience

It has been estimated that one-half of the world’s wealth is in real estate

A book such as this offers tools to enhance decision-making for consumersand researchers in market economies of any country interested in land useand real estate investment Empirical risk analysis improves the under-standing of markets in general Real estate is not different in this regard Eachday thousands of bright, entrepreneurial souls arise and make dramaticcontributions to our built environment, heretofore without data or databaseanalysis techniques This book hopes to add a suite of tools that will sharpentheir vision and understanding of that process

READERS OF THIS BOOK

Academic

1 Undergraduate students will find the narrative and examples in thetext manageable without higher mathematics or an understanding ofprogramming The assumption is that students have had at least asemester of calculus and have for reference a primary real estateinvestment text

2 Graduate students with some background in statistics will take thesample data provided and exercise their empirical skills in the context

of real estate data limitations This will enhance understanding of howreal estate adds to and fits into the overall economic picture

Practitioners

1 Lenders and managers of large real estate portfolios, many of whomoriginate real estate data, will be able to incorporate these tools intotheir daily real estate risk management activities

2 The most sophisticated investors and their advisors will use these toolsfor due diligence in an environment of professional liability and arising standard of care

3 Investment real estate brokers in the MAI and CCIM category will findthe narrative and illustrations helpful in explaining investment risk/return tradeoffs to clients.

However, all should recognize the inherent limitations of anyspreadsheet approach

1 The use of a spreadsheet implies (but does not require) twodimensions Certainly the graphics produced by the average spread-sheet program model only two variables

2 Very often spreadsheet use in limited to linear models or models thatexhibit non-decreasing functions These are misleading as the world isneither linear nor do the economic variables in the real worldconstantly increase

3 There is a static aspect to spreadsheets that fail to fully consider thetime dimensions of any model

These three limitations are partially overcome by higher mathematics andsymbolic computing software that extends beyond spreadsheets and thescope of this book The reader is urged to develop an appreciation for theseadvanced tools and recognize the elementary nature of spreadsheetmodeling and the complexity such simplification overlooks

A PRACTICAL GUIDE TO INVESTMENT

REAL ESTATE

Perhaps the first manual for the private real estate investor was WilliamNickerson’s How I Turned $1,000 into a Million in Real Estate in My Spare Timebased on his real estate investments in the 1930s Despite the complexities ofmodern day life, thousands of real estate investors still practice his teachingseach day

This book updates Nickerson’s timeless message and elaborates it in arigorous framework that describes how individual real estate investors makedecisions in the 21stCentury Underlying most successful folklore is a sound

theory Private real estate investors follow well-developed and widelyrespected micro-economic theory in that they are profit seeking, riskaverse, utility maximizers However, their approach differs from that oftheir brethren in financial assets Privately owned real estate offers anopportunity to add the value of one’s entrepreneurial effort to one’s portfolio.Such a process provides an avenue to success quite different from the routetaken by the average stock market investor.

After decades of thinking of a database as three comparable sales, realestate investors today suddenly find that they have access to plentiful data.Large data sets light the way to a host of objective ways of viewing real estate.Until now, the thorny issue of risk has been real estate’s crazy aunt in thebasement, either completely ignored or dealt with subjectively in a variety of

ad hoc ways Despite this, over the long run the monetary performance of realestate investments appears to compete favorably with that of financial assets,

an outcome that could not have been achieved without addressing risk alongthe way However, little analysis of this process exists beyond applyingmainstream finance models, often with apologies for how poorly the squarepeg of real estate fits through the round hole of finance

Private real estate investment opportunities offer a different kind of risk, anon-linear variety characterized by observations often far from the mean Thepersistence of such outliers bespeaks of a need for a new approach to risk.Also, as a result of (1) a fixed supply of land, (2) an adjustment in holdingperiod when needed, and (3) the addition of labor, real estate investors live in

a market where the size of their return may be uncertain but the sign is likely

to be positive With empirical support for the maxim ‘‘You can’t go wrong inreal estate’’ comes a different view of risk in this unique market

The goal of this book is, therefore, threefold: First, updating Nickerson’swidely respected work, it will apply mathematical rigor to the varioushomilies and truisms that have characterized private real estate investment fordecades Second, at a time when the industry is digitizing and databasesdeliver more objective information about the private real estate investmentmarket, it will incorporate appropriate yet innovative ways to use this newdata Third, combining the first two, it will uncover a way of viewing risk inreal estate that is intuitively appealing, theoretically sound and supported byempirical evidence

WHAT THIS BOOK IS NOT

As a supplementary text, this book cannot cover in detail the myriad aspects

of real estate investment that come before or run along side the need tounderstand risk and use data Early chapters lay foundation to some degree

but the reader is cautioned not to take the contents of a book this short asexhaustive.

Some fundamentals of probability and statistics are discussed but there is

no attempt here to provide what excellent texts in those subject areas offer.The subtleties of such topics as leptokurtosis, ergodicity and the asymptoticproperties of likelihood functions, pervade the subject of statistics Whilepractitioners can often get by without an intimate knowledge of such things,they exist and should not be ignored Practical limitations prevent a thoroughdiscussion of these subtleties here

The illustrations in this book offer guidelines about locating a path, theyare not a road map with a certain destination Indeed the subject of risk anddata is about uncertainty The most a book such as this can offer is aframework for thinking about problems involving uncertainty Hopefully theillustrations stimulate thinking about how people, property and numbers can

be combined in the presence of uncertainty to make good decisions

A FINAL THOUGHT ON PURPOSE

There is an undertone of indifference and occasional hostility betweenacademics and practitioners At times each side considers the other to beeither irrelevant or the enemy This behavior is not productive Academicsneed practitioners mucking around in a messy real world producingobservations that in the aggregate provide empirical evidence to support orcontradict theory Practitioners need academics to articulate theory thatconstitutes a base of knowledge from which to launch successful careers One

of the most ambitious goals of this book is to speak the language of both sides

in a way that the separate camps understand each other and appreciate theimportance of each other’s contribution

To that end, I counsel patience on the part of practitioners who quicklygrow weary of the pedantic formalism of mathematics and on the part ofacademics who become impatient with examples that may seem superficialand anecdotal

These sentiments may be summarized in a metaphor from another field.Very few people are interested in the inner workings of the highlymathematical model that sequences the human genome Even fewer under-stand it Similarly, only a few people are interested in models describing thegeneral nature of how real estate markets work On the other hand, we allhave a common and usually strong interest in being healthy Thus, after thedoctor listens very carefully to the patient’s description of his symptoms, thepatient, otherwise disinterested in biology, listens very carefully as a doctorexplains how a particular form of gene therapy may preserve and extend his

life In the spirit of this analogy it may be equally well for academics toobserve how real world investors make money as it is for practitioners to learnthe mathematics that underlies whatever science there is in real estateinvesting.

Now let us begin sequencing the genome of real estate investing

Roger J BrownAlpine, CAJanuary, 2005

When this book is made into a movie and wins an Academy Award, myacceptance speech will be long:

At the end of a project like this there are so many people to thank I hope

I don’t leave anyone out The order of mention is more about chronology thanimportance for all were vital to my education and intellectual development.All, in some way, made an important contribution to this book

From my earliest childhood days I was inspired and guided by wonderfulmath teachers from Sister Fridolene, my first grade teacher through MontyFones in high school I was twice blessed when it all had to be done againthirty years later at Penn State Ed Coulson and Herman Bierens were bothpatient and talented professors who, in restoring my ancient math skills, gavemore of their time than I deserved

Four people made important contributions to the sort of businessunderstanding one can only obtain in the real world Chilton and BryanJelks provided years of practical guidance outside of real estate I have

Dr David K Hostetler and Jim Darr to thank for keeping me on the righttrack over three decades of real estate practice For me, these four were Deans

of the School of Hard Knocks without whom the knocks would have been alot harder

The graduate school part of this quest began in 1992 at San DiegoState University under the wise guidance of Bob Wilbur, Andy Do, andMilton Chen In 1995 these fine academics breathed a sigh of relief andhanded me off to Professors Ken Lusht and Jeff Sharp at Penn State, twosuperb gentlemen did their best, given the raw material they had towork with, to finish the job While at Penn State I was also fortunate toreceive vital help at crucial times from Cemile Yavas, Tom Geurts, Jim Jordan,

xix

Norm Swanson, and Paul Claar Since I left PSU the help and thoughtfulcomments of Richard Graff, Colin Rose, Stephen Roulac, Hu McCulloch,Tim Riddiough, Norm Miller, George Matysiak, and Ted Ersek were alsoappreciated.

The technical aspects of this book, especially the inclusion of veryartful Mathematica code that produced many of the insights, most of thegraphics and a number of the technical goodies on the CD-ROM, wouldnot have been possible without the tireless efforts of Marlyn L Hickswhose clear-headed, agnostic view of mathematics is reflected in everychapter Marlyn was a wonderful sounding board, a voice of reason neverswayed by academic history or politics A book of this nature needs areal mathematician and Marlyn played that role magnificently

Tom Zeller and Andreas Lauschke are two very talented Mathematicaprogrammers at Wolfram Research who always came through with importantassistance when I needed it

The inspiration for the Risk in Real Estate models of Chapter 6 comesfrom John Nolan whose help over many years enriched my understanding

of the strange world of Stable Distributions This area in the book alsobenefited from the long discussions of life, investing, probability andmathematics with Robert Rimmer, MD, who very generously gave of histime, providing important interpretations that may have been missed and alsoassisting in the Mathematica programming This book is supported by aninteractive web site, www.mathestate.com, where many of the routinesdescribed here can be immediately implemented My thanks to the WorldFamous Cosmo Jones and his more talented sidekicks, Lorenzo Ciacci andChris Sessions for building that site and to Tom Compton and D JacobWildstrom for their help in maintaining it

My contemporaries in the business world were also very supportive JohnBoyle, Terry Moore, Richard Schneider, Howard Wiggins, Chuck Wise, andseveral anonymous reviewers either read portions of the book or providedhelpful suggestions Data provided by the CoStar Group was made possiblethrough the gracious assistance of Craig Farrington Craig and I spent many

a lunch over most of a decade dreaming about new and different ways inwhich data will be used in future real estate decisions Pat Barnes and BruceHowe provided vital data for Chapter Two that made the case study at the end

of that chapter possible

No book ever sees the light of day without a superb editor who believes

in the project Scott Bentley was that person for me His easy-going mannerand always helpful suggestions made this process a pleasure

Many books leave some innocent soul waiting No married author everreaches the end of a process like this without owing yet another un-repayable

debt of gratitude to a long-suffering spouse This author is no different.Without the world’s most perfect woman exhibiting the sort of understandingthat passes all understanding this book would not exist Thank you most ofall, Bonnie Jean.

C H A P T E R 1

Why Location Matters

The Bid Rent Surface and Theory of

Rent Determination

If you do not rest on the good foundation of nature, you will

labor with little honor and less profit

Leonardo da Vinci (1452–1519) quoted in Mathematics

for the Non-Mathematician (Kline, p 204)

INTRODUCTION

One of the oldest cliche´s we hear is: ‘‘The three most important things in realestate are location, location and location.’’ Most cliche´s become truisms for agood reason If the value of location is universally acknowledged, there may

be some strong underlying theory that can be represented mathematically.That theory is found in the construction of a ‘‘bid rent curve.’’ The generalnotion is that land users ‘‘bid’’ or ‘‘offer’’ to pay rent to land owners based

on the renters’ ability to efficiently use the land Those who can use itmost efficiently offer to pay the highest rent If value is based on income,the highest land values should occur where users are willing to pay thehighest rent

In this chapter we will:

Determine how the market allocates land between consumers

Build a model that tells us who will locate where

Compute the bid rent curve, the rate at which rents fall for a particularuse as one moves away from the center of the city

Consider how the appropriate use of real estate data permits us toconfirm the actual shape of the rent gradient

Reach conclusions about another commonly used term in real estate: thepath of progress

Discuss how the use of data improves the location decision

1

CLASSICAL LOCATION THEORY

Certain tenants, categorized by the way they use land, have certain needs.Common to all of them is the need to locate in a specific place That specificplace may be dependent on proximity to their customers, suppliers, rawmaterial, transportation arteries, or any number of other attributes of a landmarket The key word here is ‘‘proximity.’’ Hence, location obtains its valuefrom the notion of ‘‘closeness.’’ We need an analytical way to determine howone parcel of land is better or worse than another by virtue of its location,its closeness to some particular desirable other place

Theory predicts that rents (and therefore values) will be highest whereeconomic activity is most intense and productive, hence profitable Profits arewhat we observe when land is used efficiently Therefore, if one landconsumer can achieve a greater profitability on a parcel of land than anothercan, the consumer who can use the land most profitably pays the highestprice Efficient outcomes are achieved as property rights in land gravitate

to the highest bidder To illustrate this theory and build a workable model,

we make several simplifying assumptions:

The urban area is monocentric, that is, all activity takes place at thecenter There are no suburbs

The land is a flat, featureless, uniform plane over which movement isequally possible in all directions The only variation between differentplaces is the distance from the center of the city

No input substitution or scale economies are possible For instance, youcan’t substitute cheaper capital for expensive labor; neither can youreduce transportation costs per unit by carrying larger loads

Transportation costs are uniform in all directions These costs are linear

in distance based on a cost per unit with no initial fixed cost

The urban area contains the textbook competitive market (many sellers,all price takers, identical products, no monopoly, no transaction costs,

no economic profits)

These are, admittedly, very restrictive assumptions We will relax some ofthem later, but for now this is what is required to establish a baselineunderstanding of how location relates to value

NOTATION GUIDE

R ¼ Rent, formally ‘‘Ricardian Rent’’ after David Ricardo who firstobserved the nature of rent determination

p ¼ Economic profits, assumed in the competitive model to be zero forany individual land consumer

p ¼ Price of goods produced and sold by land user

a ¼ Amount of goods sold by the land user

w ¼ Fixed inputs such as wages, interest, and raw materials; this includesmanagerial profits, but excludes rent

t ¼ Transportation costs, a rate per unit of distance for each unit ofproduct

m ¼ Distance from the center of the city in the same units as used for t

THE MODEL

As always, profits are what is left after subtracting all expenses fromrevenue, so,

p ¼ pa  w  tam  R ð1-1ÞSince p, by assumption, is zero, we can move R to the left, leaving

The above supports the general belief that rent constitutes the residual.That is, land consumers can pay rent in the amount of whatever is left afterall other operating costs have been deducted from revenue For simplicitybelow, we will take the net income before transportation costs (pa  w) as aconstant, leaving the result that rent is a linear function of distance

EXAMPLE #1—TWO COMPETING USERS IN THE

SAME INDUSTRY

We now take two land users, both in the same industry (farming), each with adifferent product We assume certain fixed values for the inputs, noting that

a pea farmer has higher transportation costs (t) than a wheat farmer

In Figure 1-1 we plot rent against distance for each user, noting the difference

in the slopes

By setting rent equal to zero, inserting the fixed inputs for each land user,and solving for m, we can determine the farthest distance from the center ofthe city each user can afford to locate This asks the question: At what

distance from the center are all revenues exhausted? Locating outside of thatdistance would produce negative revenue, an economic consequence thatprevents a user from locating there Notice that, given the inputs, the wheatfarmer can afford to locate farther away Stated differently, the pea farmerMUST locate closer in.

R ¼ pa  w  tam ¼ 0

R ¼ 1010  50  :510m ¼ 0 R ¼ 1510  75  110m ¼ 0

By assuming an arbitrary value for m and solving for t, we can determinethe slope of each party’s bid rent curve Notice that the pea farmer’s slope isgreater What does this mean to the way both parties will bid for land closer tothe center of the city?

R ¼ 1010  50  t1010 ¼ 0 R ¼ 1010  50  t107:5 ¼ 0

t ¼ :5 ¼ Slope of bid rent curve t ¼ 1 ¼ Slope of bid rent curve

Placing them both on the same plot is useful at this stage, noting that thepoint where the curves cross is the point on the land where the bids are equal.Prior to that point, the pea farmer is willing to pay the most for the land;beyond that point, the wheat farmer bids more than the pea farmer Settingthe two rent equations equal to each other, inserting the fixed inputs, andsolving for m tells us the location on the land of the crossover point Figure 1-2shows the point on the land where both parties bid an equal rent and theamount of that rent

Distance

20406080100

Pea Farmer

1010  50  :510m ¼ 1510  75  110m m ¼ 5

A little experimentation with different values for the fixed inputs leavesone with the insight that (in our stylized example) nothing matters buttransportation cost Mathematically, this can be verified by taking the firstderivative of R with respect to m, with the quantity produced standardized to 1

5Distance

25

bids are equal

peawheat

rent surface or rent gradient Note in Figure 1-3 that the largest land mass istaken by residential Why might that be so?

Following our wheat/pea farmer procedure, we can solve for each over point Table 1-1 reflects these values

cross-We can link the crossover points to the change in use on the land byconnecting the points to the perimeters of the appropriate circle (Figure 1-4)

A different perspective is provided by placing them all on the same plane(Figure 1-5) The amount of land devoted to each use is dependent upon thesize of the circles conscribing it We can compute the total area of eachconcentric ring, noting that in this example land mass devoted to each usegenerally increases as we move away from the center (Table 1-2).1

140

10490

3010

AgriculturalIndustrial IIResidentialIndustrial ICommercial

land use changes

1 It is, of course, possible to make a simple supply and demand argument for lower rent for sectors

in which more acreage is available.

IS THE BID RENT CURVE LINEAR?

Joining the crossover points creates a bid rent surface for the entire city(Figure 1-6) Note that for the aggregate of these user classes, the bid rentsurface is non-linear

It is clear from the plot in Figure 1-6 that multiple classes of users with asequence of crossover points produce a bid rent surface for the entire city that

5 25 35

Distance

140

10490

3010Rent

Distance

140

10490

3010Rent

is not strictly linear, but appears linear on a piecewise basis The aggregation

of various uses, each with a different transportation cost (and, therefore, adifferent slope), creates this shape From this we may speculate that differentindividual users within any one sector each may also have slightly differenttransportation costs, and the aggregate of the linear bid rent curves of thesedifferent users produces a curve for any specific use that is also not a straightline (Figure 1-7) Under these conditions one might reasonably assume thatthe functional form of the bid rent curve for all individual users would be

R ¼ eax, where x is distance from the center of the city, the exponent a is adecay rate that may be observed in the market as one moves away from thecenter, and e is the base of the natural logarithm

3010Rent

such as reflected in Table 1-3 The first element in each pair is the distancefrom the center, the second is the rent paid at that point, and the third is thenatural log of the rent, a useful conversion for further analysis.

A plot of the distance and rent data in Figure 1-8 shows a nearly lineardecay in rent as distance increases We are interested in the relationshipbetween distance and rent A common method for investigating therelationship between two variables is linear regression analysis For this,

we use the natural log of rent as the dependent variable

Figure 1-9 shows a plot of the data in Table 1-3 Not surprisingly, itappears linear because taking the natural log of a curved function has theeffect of ‘‘linearizing’’ the function

We then fit the regression model (Equation 1-3):

Log R½  ¼Log ke xd

¼Log k½  xd ð1-3Þwhere k is the regression constant, x is the slope, and d is distance from thecenter The intercept and slope terms are shown in the regression equation:

Log R½  ¼6:71003  0:0155191x(A complete regression analysis appears among the electronic files for thischapter.)

Exponentiating2 both sides of the regression equation produces theconclusion that one may estimate rent based on a fixed intercept multiplied

0.20.40.60.81Rent

R= e−ax

2 There is some doubt that ‘‘exponentiating’’ is a word The Oxford English Dictionary does not carry ‘‘exponent’’ as a verb However, we need a word for the cumbersome statement ‘‘using each side of the entire equation, each, as an exponent for the base of the natural log .’’ For this we press ‘‘to exponentiate’’ into service.

TABLE 1-3 Rent Data

times the base of the natural logarithm taken to an exponent that is composed

of the product of the decay rate (as a negative number) and the distance

The same curve is more pronounced over a longer distance (Figure 1-11)

So we see that while the curve is a function of the decay rate, for small decayrates its curvature is only apparent over longer distances

Distance6.4

6.456.56.556.66.656.7

AN ECONOMIC TOPOGRAPHICAL MAP

The world is not flat and neither are its land economics The story becomesmore realistic when one considers the theory in three dimensions After all,there are an infinite number of directions away from any particular high rentlocation One would expect the decay rate to vary in different directions

A stylized version of this uses the trigonometry employed in topography.3

760780800820Rent

R=820.597e−ax

Distance200

400600

800Rent

R=820.597 e−axDistance 0–200

www.mathestate.com.

The so-called ‘‘path of progress’’ is the direction in which the decline inrent is the slowest, thus the decay rate is the slowest because higher rent ispersistent in that direction In that direction the decline is relatively flat Theopposite case is that of the steepest decay rate As rents decline fastest, thedecay rate is larger in the direction people are not locating.

The three-dimensional parametric plots in Figure 1-12 show the economictopography where a ¼ 1 (Figure 1-12a) or a ¼ 02 (Figure 1-12b) to simulatethe way rent changes as one travels around the land

RELAXING THE ASSUMPTIONS

All models are only approximations of reality Unfortunately, we attemptbetter approximations at the expense of generality Nonetheless, the exercise

of testing the model under more realistic assumptions is useful

One way to move closer to what we actually observe is to relax some ofthe assumptions The first might be the idea that the urban business environ-ment is monocentric In Figure 1-13a we see the potential for two high rentareas in a given market This representation suggests that the secondary point

of high activity might be somewhat flat at the top, representing an mic oasis of activity where rents are generally high in a small area This isthe relaxation of the assumption that the greatest activity takes place at theabsolute center Rotating Figure 1-13a to see the rear of it in Figure 1-13breveals an area of depressed rent Clearly, there are as many portrayals ofthis condition as there are different cities on earth

econo-Figure 1-13 could also depict the relaxation of the no transaction costsassumption Zoning, a constraint on freedom of choice in how one uses one’sland, is essentially a transaction cost If government imposes zoning thatprohibits land use in a certain area, the consequence can be higher rent forthat use in the area where that use is permitted Another explanation for a plotlike Figure 1-13 might be non-uniform transportation costs in one directioncaused by natural barriers such as a river or mountain that must be crossed.One might also see an impact on the rent gradient as transportation costsdiffer in directions served by mass transit

Whether these graphical depictions represent reality is an interestingdebate One can challenge the notion that the market is symmetrical around apoint, calling into question whether the most intense activity takes place on asingle spot Clearly, over time ‘‘clusters’’ of similar businesses gather in certainareas Particular areas become ‘‘attractors’’ for certain kinds of industries Thelist of exceptions to the basic theory is long The primary value of the sort ofanalysis undertaken in this chapter is to provide a logical framework forlocation decisions and guide the thoughtful land consumer to a rational

choice of location As one delves more deeply into the exceptions to thegeneral principal, one gets closer to what we observe in practice at theexpense of a loss of generality Regardless, with each special case we seerepeated the importance distance plays in the decision Apparent exceptionsoften just change the place from which we are distant, not the actual

North–South(a)

–200

20East–West

00.250.50.751

Rent

North–South(b)

−50

−2502550East–West

00.250.50.751

Rent

–25 0 25North–South

(a)

–250

25East–West

0.250.5

0.75Rent

025

−25025

0.250.5

0.75Rent

−25North–South

(b)

East–West

importance of distance Thus, the connection between location and distanceremains key.

This book will discuss the careful use of data often In the case of marketrents, one must be mindful of the fact that no dataset supplants a carefulmarket survey in the local area of a target acquisition However, as real estatemarkets become more efficient and data is more robust, the sort of modelsdeveloped here will assist buyers in ‘‘getting up to speed’’ in an unfamiliarmarket Having been instructed by the CEO of an REIT or real estate fund tovisit a new city and investigate real estate opportunities there, an acquisitionteam may first consult data before landing in a market where local playersdominate transactions

A WINDOW TO THE FUTURE

Table 1-3 shows rent data collected along a line stretching away from a highrent location Real estate data always has some location attribute In the pastthat attribute was its street address Later, a zip code was added Recently,longitude and latitude points have been included Each of these steps moves

us closer to a time when the theoretical graphs shown in this chapter can bedisplayed as actual data points and the economic topographical map willrepresent a real world situation

Data represents reality, and there will be times when reality conflicts withtheory In Figure 1-14a we see a void where a lake, a public park, or a block ofgovernment buildings might be In Figure 1-14b we see a number of missingdata points throughout, each of which represents a location where rent is notreported One of these could be owner occupied housing, another a church or

a school, but some will be where rent is being paid and no inquiry has beenmade In time as data collection is more streamlined and coverage is morecomplete, the grid will become finer and the picture more complete.There are a number of excellent data gatherers and providers; some areindependent firms, and some are in-house for major real estate companies It

is to these industry support groups we direct a final appeal As real estate databecomes more plentiful, observations of rent across the land will becomemore compact, filling in the grids necessary to describe the actual shape of thebid rent surface For highly developed countries with efficient markets infinancial assets, one would expect that real estate data gatherers and providerswill deliver not only the raw information, but analytics based on thatinformation For countries with nascent market economies where datacollection is just beginning, one hopes that those interested in marketdevelopment will use the models above as templates to guide their databasedesign at the early stages

1 Alonzo, W Location and Land Use Cambridge, MA: Harvard University Press.

2 Geltner, D M., & Miller, N G Commercial Real Estate Analysis and Investments Upper Saddle River, NJ: Prentice Hall.

3 Kline, M., Mathematics for the Non-Mathematician New York: Dover Publications, Inc.

4 von Thunen, J H (1966) The Isolated State New York: Pergamon Press.