Preface to the 3rd (and last?) edition of Private Real Estate Investment
Before embarking on substantive comments involving content, it is important to warn the reader about the various “lives” this book has enjoyed and how that history will influence choices the reader faces in acquiring this book. The first edition was published by Academic Press in 2005 in print only. The first edition had eleven chapters and was a traditional textbook. The seven years that followed were tumultuous for both the publishing and financial industries, indeed, for the global economy. The second edition, comprising sixteen chapters, updated the subject matter to include unfolding events but also took on a new, actually several new, looks as it reaches the consumer in 2012. In 2018 a 3rd Edition appeared with a number of changes, corrections and extensions of the material including one new chapter. This, perhaps final, edition exists entirely as Wolfram CDF interactive electronic files. Due to the time-consuming, tedious conversion required it is unlikely the 3rd edition will have a hardcopy version. Having been influenced by the open source movement it is offered free to anyone with Mathematica or the free Mathematica CDF Player.
For those who like to hold a book in their hands and turn pages, the entire 2nd edition is available in print. The print version is also available in sections. Part I - The Basics contains the first five chapters, Part II - Risk Analysis is Chapters 6-10 and Part III - Special Topics has the last six chapters;
For e-readers the Second edition is also available (and cheaper) in electronic, read-only, format for the entire book or Part I, Part II or Part III separately;
For those who desire an interactive experience, the Third edition is available in the Computational Document Format (CDF) from Wolfram Research. To experience this Preface in that format, permitting interactivity, visit mathestate.com. and click on the "Resources" button.
Your author, while having nearly boundless enthusiasm for the subject, draws the line at writing three different books, one to suit each format. Thus, some compromises are necessary and I must beg the reader’s indulgence over these. It has been said many times that it is not wise to try to be all things to all people. My effort here could be criticized accordingly (although I am completely ignoring those who still prefer stone tablets, use quill pens, write on papyrus or get all their information from the little voices in their head). My hope is that, because this preface will be available to all consumers before making their decision which version to purchase, readers will be informed and satisfied buyers, having had their expectations conditioned by what appears here.
From a content standpoint, the difference in the versions is nearly none. Anyone buying any version of the Second edition will get the same information (companion Excel files are not available with the purchase of individual chapters). Those buying the print version will notice no difference in electronic form. It is the CDF version of the Third edition that offers a different experience in that many of the graphics are interactive and permit the reader to change the value of variables and see how the result changes in a particular plot. For those students who wish to work the exercises at the end of each chapter, this interactivity sometimes will be mandatory as some of the problems depend on the reader manipulating data input.
The remainder of the compromises and the consequences the reader must endure involve formatting. In order to produce only one book to fit all applications it was necessary to write the entire book using Mathematica, a product of Wolfram Research. This software enhances the author’s ability to produce quantitative output in the form of equations, data analysis and graphics. There are many occasions when any particular answer could have merely been typed into the text material. But the ability to produce them using the software within the narrative offers a number of benefits which I hope all readers enjoy.
The result of using Mathematica is the occasional disconcerting change in font or style. Mathematica uses a different font to distinguish between (a) text, (b) user input, and (c) output from the software (answers in the form of computations, equations or graphics). The reader who does not have Wolfram’s free CDF player and does not use the interactive features is requested to tolerate the change in font or paragraph formatting and merely read it as if it were part of the text. Here is an example from Chapter 13:
Substituting our data into Equation 13.2, we can obtain the amortization period for the seller financing given the payment schedule of the conventional financing.
Notice the change in font from Times New Roman in the text immediately below Equation 13.2 to the next line which is Courier. In the space between there is code that makes the calculation called for in Equation 13.2 and shows the output in Courier font. This, with much tedious effort, could have been edited for the print version to be one sentence with a single font style. Right or wrong, in the interest of programming efficiency and flexibility in revision, your author has elected to leave the slight discontinuity in style asking the reader to adapt. An advantage is that output computed by the software in which the book was written has a high probability of being correct. Luckily for those without a keen interest in mathematics, the narration of the book is written such that the equations and programming may all be ignored and the “story” remains.
Mathematica also uses "cells" to organize a document. These cells can be thought of as paragraphs in conventional publishing. Indeed, when the cell contains only text that is exactly what each cell represents, a paragraph. But cell can also contain input, output and graphics. Cells can be grouped and the group collapsed to "hide" additional code or detailed exposition. This can result in white space between paragraphs in the print and non-CDF electronic versions. For the most part all cells with text have been left "open" for all readers. But in the case of a particularly large dataset or very intricate computer code, that material will be "hidden" from the view of the reader under a collapsed cell. The words "Beneath this cell are..." alert the reader to this condition. Only readers with a full version of Mathematica will be interested in the information contained in the closed cells, all others may safely ignore what the key words “beneath this cell” refers to. Those readers who need the actual Mathematica code must request it as it is not included in any form of the book.
Here is another example, this time showing code from Mathematica input in the first line which is bold Courier, followed by output. Much of the code has been hidden from the reader’s view. Occasionally, when the context cannot be misconstrued or the code makes a contribution to the narrative, the code remains. Because the characters “/.” are Mathematicaspeak for “given that” the code below says, in words, “take the maximum of b or pv, given data in dataset 1.”
Another useful advantage of Mathematica is the ability to update electronic files by either supplementing them with new material or changing material to reflect changing times. Finally, it is much easier to correct mistakes discovered after an electronic version is published than after a paper publisher completes a 2,000 volume print run. I am indebted to those patient readers of the first edition who kindly sent me corrections for the inevitable mistakes that always survive numerous proof readings. Those corrections have been incorporated in the present edition. But as there are five chapters in the second edition that did not appear in the first edition, there has been the obvious opportunity to make gnu mistakes. My hope is that future readers will be as thorough in reporting these as their predecessors have been. If so, corrections may not be made in real time but will be made more quickly than before.
There is one last, nearly imperceptible, advantage for the rare consumer who also owns version 10 or later of the full Mathematica software package. The code for all computations and illustrations in the book is available for an additional charge (e-mail rjb at mathestate.com), providing the user with a head start should he wish to modify or expand on the subject matter within Mathematica. This will be useful only to researchers or those with a penchant for financial engineering computing applications, clearly the minority of consumers. For everyone else, the companion electronic files in Excel format (included only with the entire book, not with individual chapters) permit further exploration in a familiar environment.
The payoff for putting up with the curious result of a book written with a symbolic computing software is the ability to interact with the material. Below, from Chapter 1, is an example. If you have Wolfram’s free CDF Player installed you may visit this page to experience interactivity by moving the slider bars to change the value of variables and change the information in the plot.
Figure 1.1 Rent at point where bids are equal.
This book is designed as a handbook for individual investors in real property, institutional risk managers and as a supplementary text for upper division undergraduate and graduate real estate investment courses. The electronic files included with various chapters contain pre-written code for data analysis tailored specifically to real estate settings. The major thrust is to bridge the gap between theory and practice by showing the reader how to conceive of and implement real estate risk management 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:
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 understanding 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.
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.
These three limitations are partially overcome by higher mathematics and symbolic computing software that extends beyond spreadsheets. This book employs such software. 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.
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 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 more 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.
As a supplementary text, this book cannot cover in detail the myriad aspects of real estate investment that come before or run alongside the need to understand 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 as exhaustive.
Some fundamentals of probability and statistics are discussed but there is no attempt here to provide what excellent texts covering those subject areas offers. 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.
This book was written using Mathematica, a powerful symbolic logic computing software. For some readers, each chapter is in Computational Document Format (CDF). In electronic form it may be viewed with the free CDF Player from Wolfram Research. The reader does not have to own nor does he have to know how to program Mathematica. One who does have those qualities may immediately purchase the underlying code and expand on or test the concepts described in this book. There is a balance between displaying too much code and making the narrative unduly complex and showing enough code to provide insight into the computations and results. I have attempted to strike the right balance by "hiding" considerable code beneath the "cells" which constitute the Mathematica notebook which each chapter represents.
This book is written in modular form. The first third (Chapters 1-5) are fundamental, covering the theory of land use and rent determination, the role of government, the basics of investment property analysis and nuances and subtle traps most people overlook. Chapters 6 through 10 cover risk as an abstract concept grounded in probability theory through necessary modifications required to adapt it for real estate. Much of the material in these chapters was written prior to and actually anticipated the global financial problems that followed the events of the mid 2000s. The final chapters are special topics that include management, lending, leasing, real options and estate planning.
It is barely overstatement to say that this book is written as a template for one who wishes to “live a life of real estate” from the acquisition to the disposition phase over many decades.
Virtually all chapters, while occasionally referring to other material elsewhere in the book, are stand-alone components. At the reader’s option he may purchase only those chapters which are of specific interest. A complete table of contents is available in a number of places to guide the reader’s choice.
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 understand 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 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
San Diego, CA
January, 2018