Private Real Estate Investment: Data Analysis and Decision Making_1

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Nội dung Text: Private Real Estate Investment: Data Analysis and Decision Making_1

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 Amsterdam Boston Heidelberg London New York Oxford Paris San Diego San Francisco Singapore Sydney Tokyo
Elsevier Academic Press 200 Wheeler Road, 6th Floor, Burlington, MA 01803, USA 525 B Street, Suite 1900, San Diego, California 92101-4495, USA 84 Theobald’s Road, London WC1X 8RR, UK This book is printed on acid-free paper. Copyright ß 2005, Elsevier Inc. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Permissions may be sought directly from Elsevier’s Science & Technology Rights Department in Oxford, UK: phone: ( þ 44) 1865 843830, fax: ( þ 44) 1865 853333, e-mail: permissions@else- vier.com.uk. You may also complete your request on-line via the Elsevier homepage (http:// elsevier.com), by selecting ‘‘Customer Support’’ and then ‘‘Obtaining Permissions.’’ Library of Congress Cataloging-in-Publication Data British Library Cataloguing in Publication Data ISBN: 0-12-137751-2 ISBN: 0-12-088532-8 (CD-ROM) For all information on all Academic Press publications visit our Web site at www.academicpress.com Printed in the United States of America 04 05 06 07 08 09 98 7 6 5 4 3 2 1
‘‘Is life mathematics or is it poetry?’’ ´` Roger Magueres
CONTENTS Preface xiii Acknowledgements xix 1. Why Location Matters: The Bid Rent Surface and Theory of Rent Determination Introduction 1 Classical Location Theory 2 Notation Guide 2 The Model 3 Example #1—Two Competing Users in the Same Industry 3 Example #2—Several Competing Users in Different Industries 5 Is the Bid Rent Curve Linear? 7 Empirical Verification 8 An Economic Topographical Map 12 Relaxing the Assumptions 13 A Window to the Future 16 References 17 2. Land Use Regulation Introduction 19 Who Shall Decide—The Problem of Externalities 20 The Idea of Utility 23 The Model 24 Optimization and Comparative Statics 27 A Graphic Illustration 28 Implications 32 A Case Study in Aesthetic Regulation 32 vii
viii Contents Conclusion 36 References 37 Appendix: Comparative Statics for Chapter 2 37 3. The ‘‘Rules of Thumb’’: Threshold Performance Measures for Real Estate Investment Introduction 39 Threshold Performance Measures 40 A General Caution 42 The Gross Rent Multiplier (GRM) 43 What Not to Do 44 What Should be Done 45 Capitalization Rate (CR) 49 The Three Bad Assumptions 49 Capitalization Rate and Discounted Cash Flow Analysis 50 Monotonic Growth 52 The Expense Ratio and the ‘‘Honest’’ Capitalization Rate 54 The Normal Approach to Data 56 Questioning the Assumption of Normality 60 The Stable Approach to Data 62 Linear Relationships 62 Linear Transformations 64 Spurious Relationships 64 Cash-on-Cash Return (C/C) 67 Price Per Unit (PPU) 67 Other Data Issues 71 References 72 4. Fundamental Real Estate Analysis Introduction 73 The Role of Computational Aids 73 Deterministic Variables of Discounted Cash Flow Analysis 75 Single Year Relationships and Project Data 76 Multi-Year Relationships 78 Sale Variables Relationships 79 The Net Present Value 81 Insight into the Analysis 82 An Illustration of Bargaining 87 Another Growth Function 90 Data Issues 93 Conclusion 98 References 98
ix Contents 5. Chance: Risk in General Introduction 99 Objective and Subjective Risk 100 Games of Chance and Risk Bearing 101 The Utility Function Revisited 104 The ‘‘Certainty Equivalent’’ Approach 107 Multiple (More than Two) Outcomes 111 The Continuous Normal Case 112 Conclusion 116 References 117 6. Uncertainty: Risk in Real Estate Introduction 119 Non-normality—How and Where Does it Fit? 119 The Continuous Stable Case 121 Producing a Stable pdf 123 Still More Distributions? 126 Enter Real Estate 127 Determinism 128 Determinism and House Prices 131 Determinism and Real Estate Investment 135 Risk and Uncertainty 138 Rolling the Dice 141 Real Estate—The ‘‘Have it Your Way’’ Game 145 The Payoff 147 Data Issues 150 Conclusion 152 References 153 7. The Tax Deferred Exchange Introduction 157 Taxes are Less Certain for Real Estate Investors 158 Variable Definitions 160 The Structure of the Examples 161 The Base Case: Purchase–Hold–Sell 162 Example 1—Modifying the Growth Projection 163 Example 2—The Tax Deferred Exchange Strategy 167 Exchange Variable Definitions 168 The Value of Tax Deferral 173 The Sale-and-Repurchase Strategy: Tax Deferral as a Risk Modifier 176 The Sale-and-Better-Repurchase Strategy: The Cost of Exchanging 178
22 x Contents Example 3—Exchanging and The Plodder 182 Data Issues 185 Conclusion 186 References 188 8. The Management Problem Introduction 189 The Unavoidable Management Issue 189 The Property Manager’s Dilemma 190 Is Building Size Really Important? 193 The Property Owner’s Dilemma 195 The ‘‘No Vacancy Rate’’ Approach 195 Enter the Vacancy Rate 197 Reconciling the Two Problems 198 Data Issues 201 Conclusion 202 References 203 Appendix: A Caution On the Use of Data to Construct Theories 204 Making Vacancy and Rental Rates Reasonable 204 The Model to End all Models 205 9. The Lender’s Dilemma Introduction 209 Lenders and their Rules 209 Appraisal Techniques 210 The Capitalization Rate Approach Versus the Mortgage Equity Approach 210 The Lender’s Perspective 212 The Borrower’s Perspective 212 Irrational Exuberance and the Madness of Crowds 213 Bubble Theory—How High is Up? 217 Positive Leverage 217 The Lender as Governor 221 Resolving the Conflict 222 Three Two-Dimensional (2D) Illustrations 224 Endgame 227 Data Issues 231 Conclusion 235 References 235
xi Contents 10. The Private Lender Introduction 237 The ‘‘Hard Money’’ Loan Versus the ‘‘Purchase Money’’ Loan 238 The Diversification Problem 238 Other Possibilities 239 Did We Make a Loan or Did We Buy the Property? 240 The Installment Sale 242 The Motivation of the Parties 244 The Buyer 244 The Seller 245 The Installment Sale Transaction 248 Is the Seller’s Financing a Good Deal for The Buyer? 248 The NPV Test 249 An IRR Test 250 A Simple ‘‘Tax Blind’’ Test 250 A Prepayment Penalty 253 Conclusion 255 Reference 257 11. Creative Financing Introduction 259 Retirement and Creative Financing 259 A Life Estate 260 A Zero Coupon Bond 261 The Retiree’s Dilemma 261 The Conventional Arrangement 262 The Reverse Amortization Mortgage 265 Intra-Family Alternatives 267 The Income Viewpoint 268 The Larger House Viewpoint 269 The Remainderman’s Position 271 The Income Case 271 The Larger House Case 272 Conclusion 273 References 274 Index 279
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22 PREFACE This book is designed as a supplementary text for upper division under- graduate and graduate real estate investment courses. The CD-ROM included with the book contains spreadsheets for data analysis tailored speciﬁcally to real estate settings. The major thrust is to bridge the gap between theory and practice by showing the student how to implement his real estate education in the real world. The study of real estate follows long traditions grounded in Urban Economics and Finance. There is, however an inherent conﬂict between the twin realities that the ﬁnance market is efﬁcient and the real estate market is not. Practitioners in the real world know, or at least act as if they know, that real estate is very different from ﬁnance. No investment real estate broker gets up in the morning and does anything even remotely resembling what a stockbroker does. While anecdotal evidence suggests that the two activities are different, until very recently academic theory supporting such a belief has been underdeveloped and has suffered from a lack of data to test hypotheses. The data are growing around us every day as the industry converts real estate information into digital form. It may be that this will improve real estate market efﬁciency. It may also lead us to conclude that real estate is different from 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 in that market. Now, robust databases are available for many different types of real estate. For the ﬁrst time, databases covering private real estate investment have breadth (large number of observations in relatively small geographic areas) and depth (long histories of data covering the same property). xiii
22 xiv Preface 2. Closed-form analytical techniques for risk modeling either (a) have been exhausted and/or (b) because of institutional factors are inappli- cable to real estate. Hence, risk modeling using fast, numerically intensive simulation with bounded datasets offers a signiﬁcant improvement over the present ad hoc real estate methods. 3. Considerable recent progress has been made in mathematical software and algorithms that permit one to access, combine, and integrate real estate 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 consumers and researchers in market economies of any country interested in land use and real estate investment. Empirical risk analysis improves the under- standing of markets in general. Real estate is not different in this regard. Each day thousands of bright, entrepreneurial souls arise and make dramatic contributions to our built environment, heretofore without data or database analysis techniques. This book hopes to add a suite of tools that will sharpen their vision and understanding of that process. READERS OF THIS BOOK Academic 1. Undergraduate students will ﬁnd the narrative and examples in the text manageable without higher mathematics or an understanding of programming. The assumption is that students have had at least a semester of calculus and have for reference a primary real estate investment text. 2. Graduate students with some background in statistics will take the sample data provided and exercise their empirical skills in the context of real estate data limitations. This will enhance understanding of how real estate adds to and ﬁts into the overall economic picture. Practitioners 1. Lenders and managers of large real estate portfolios, many of whom originate real estate data, will be able to incorporate these tools into their daily real estate risk management activities. 2. The most sophisticated investors and their advisors will use these tools for due diligence in an environment of professional liability and a rising standard of care.
22 xv Preface 3. Investment real estate brokers in the MAI and CCIM category will ﬁnd the narrative and illustrations helpful in explaining investment risk/ return tradeoffs to clients. Investors For investors (and all others) familiar with the spreadsheet environment, numerical analysis and sensitivity testing is often done via example for which a spreadsheet is well suited. The use of the tools provided here can enhance the investor’s experience by providing better understanding of his advisor’s recommendations. However, all should recognize the inherent limitations of any spreadsheet approach. 1. The use of a spreadsheet implies (but does not require) two dimensions. 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 that exhibit non-decreasing functions. These are misleading as the world is neither linear nor do the economic variables in the real world constantly increase. 3. There is a static aspect to spreadsheets that fail to fully consider the time dimensions of any model. These three limitations are partially overcome by higher mathematics and symbolic computing software that extends beyond spreadsheets and the scope of this book. The reader is urged to develop an appreciation for these advanced tools and recognize the elementary nature of spreadsheet modeling and the complexity such simpliﬁcation overlooks. A PRACTICAL GUIDE TO INVESTMENT REAL ESTATE Perhaps the ﬁrst manual for the private real estate investor was William Nickerson’s How I Turned $1,000 into a Million in Real Estate in My Spare Time based on his real estate investments in the 1930s. Despite the complexities of modern day life, thousands of real estate investors still practice his teachings each day. This book updates Nickerson’s timeless message and elaborates it in a rigorous framework that describes how individual real estate investors make decisions in the 21st Century. Underlying most successful folklore is a sound
22 xvi Preface theory. Private real estate investors follow well-developed and widely respected micro-economic theory in that they are proﬁt seeking, risk averse, utility maximizers. However, their approach differs from that of their brethren in ﬁnancial assets. Privately owned real estate offers an opportunity 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 route taken by the average stock market investor. After decades of thinking of a database as three comparable sales, real estate 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 the basement, either completely ignored or dealt with subjectively in a variety of ad hoc ways. Despite this, over the long run the monetary performance of real estate investments appears to compete favorably with that of ﬁnancial assets, an outcome that could not have been achieved without addressing risk along the way. However, little analysis of this process exists beyond applying mainstream ﬁnance models, often with apologies for how poorly the square peg of real estate ﬁts through the round hole of ﬁnance. Private real estate investment opportunities offer a different kind of risk, a non-linear variety characterized by observations often far from the mean. The persistence 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 holding period 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 in real estate’’ comes a different view of risk in this unique market. The goal of this book is, therefore, threefold: First, updating Nickerson’s widely respected work, it will apply mathematical rigor to the various homilies and truisms that have characterized private real estate investment for decades. Second, at a time when the industry is digitizing and databases deliver more objective information about the private real estate investment market, it will incorporate appropriate yet innovative ways to use this new data. Third, combining the ﬁrst two, it will uncover a way of viewing risk in real estate that is intuitively appealing, theoretically sound and supported by empirical 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 to understand risk and use data. Early chapters lay foundation to some degree
22 xvii Preface but the reader is cautioned not to take the contents of a book this short as exhaustive. 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 asymptotic properties of likelihood functions, pervade the subject of statistics. While practitioners can often get by without an intimate knowledge of such things, they exist and should not be ignored. Practical limitations prevent a thorough discussion of these subtleties here. The illustrations in this book offer guidelines about locating a path, they are not a road map with a certain destination. Indeed the subject of risk and data is about uncertainty. The most a book such as this can offer is a framework for thinking about problems involving uncertainty. Hopefully the illustrations 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 between academics and practitioners. At times each side considers the other to be either irrelevant or the enemy. This behavior is not productive. Academics need practitioners mucking around in a messy real world producing observations that in the aggregate provide empirical evidence to support or contradict theory. Practitioners need academics to articulate theory that constitutes 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 the importance of each other’s contribution. To that end, I counsel patience on the part of practitioners who quickly grow weary of the pedantic formalism of mathematics and on the part of academics who become impatient with examples that may seem superﬁcial and anecdotal. These sentiments may be summarized in a metaphor from another ﬁeld. Very few people are interested in the inner workings of the highly mathematical model that sequences the human genome. Even fewer under- stand it. Similarly, only a few people are interested in models describing the general nature of how real estate markets work. On the other hand, we all have a common and usually strong interest in being healthy. Thus, after the doctor listens very carefully to the patient’s description of his symptoms, the patient, otherwise disinterested in biology, listens very carefully as a doctor explains how a particular form of gene therapy may preserve and extend his
22 xviii Preface life. In the spirit of this analogy it may be equally well for academics to observe how real world investors make money as it is for practitioners to learn the mathematics that underlies whatever science there is in real estate investing. Now let us begin sequencing the genome of real estate investing. Roger J. Brown Alpine, CA January, 2005
22 ACKNOWLEDGMENTS When this book is made into a movie and wins an Academy Award, my acceptance 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 than importance 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 wonderful math teachers from Sister Fridolene, my ﬁrst grade teacher through Monty Fones in high school. I was twice blessed when it all had to be done again thirty years later at Penn State. Ed Coulson and Herman Bierens were both patient and talented professors who, in restoring my ancient math skills, gave more of their time than I deserved. Four people made important contributions to the sort of business understanding one can only obtain in the real world. Chilton and Bryan Jelks 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 right track 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 a lot harder. The graduate school part of this quest began in 1992 at San Diego State University under the wise guidance of Bob Wilbur, Andy Do, and Milton Chen. In 1995 these ﬁne academics breathed a sigh of relief and handed me off to Professors Ken Lusht and Jeff Sharp at Penn State, two superb gentlemen did their best, given the raw material they had to work with, to ﬁnish the job. While at Penn State I was also fortunate to receive vital help at crucial times from Cemile Yavas, Tom Geurts, Jim Jordan, xix