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- Herausgeber: John Wiley & Sons
- Kategorie: Fachliteratur
- Sprache: Englisch
Provides a revolutionary conceptual framework and practical tools to quantify uncertainty and recognize the value of flexibility in real estate development
This book takes a practical "engineering" approach to the valuation of options and flexibility in real estate. It presents simple simulation models built in universal spreadsheet software such as Microsoft Excel®. These realistically reflect the varying and erratic sources of uncertainty and price dynamics that uniquely characterize real estate. The text covers new analytic procedures that are valuable for existing properties and enable a new, more profitable perspective on the planning, design, operation, and evaluation of large-scale, multi-phase development projects. The book thereby aims to significantly improve valuation and investment decision making.
Flexibility and Real Estate Valuation under Uncertainty: A Practical Guide for Developers is presented at 3 levels. First, it introduces and explains the concepts underlying the approach at a basic level accessible to non-technical and non-specialized readers. Its introductory and concluding chapters present the important “big picture” implications of the analysis for economics and valuation and for project design and investment decision making.
At a second level, the book presents a framework, a roadmap for the prospective analyst. It describes the practical tools in detail, taking care to go through the elements of the approach step-by-step for clarity and easy reference.
The third level includes more technical details and specific models. An Appendix discusses the technical details of real estate price dynamics. Associated web pages provide electronic spreadsheet templates for the models used as examples in the book.
Some features of the book include:
• Concepts and tools that are simple and accessible to a broad audience of practitioners;
• An approach relevant for all development projects;
• Complementarity with the author's Commercial Real Estate Analysis & Investments—the most-cited real estate investments textbook on the market.
Flexibility and Real Estate Valuation under Uncertainty: A Practical Guide for Developers is for everyone studying or concerned with the implementation of large-scale or multi-phase real estate development projects, as well as property investment and valuation more generally.
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Seitenzahl: 420
Veröffentlichungsjahr: 2018
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Table of Contents
Cover
Title Page
Foreword
Authors’ Preface
What this book is about, and how to use it.
The Value Proposition
Accessibility
How to Use this Book
Acknowledgments
About the Companion Website
1 Discounted Cash Flow Valuation
1.1 Why the Focus on the Discounted Cash Flow Model?
1.2 Structure of a Discounted Cash Flow Spreadsheet
1.3 The Cash Flow Projection
1.4 Discount Rate
1.5 Market Value and Forward‐Looking (Ex‐Ante) Analysis
1.6 Backward‐Looking (Ex‐Post) Analysis
1.7 Conclusion
2 Economics of the Discounted Cash Flow Valuation Model
2.1 Choice of Discount Rate
2.2 Differences between Discount Rate, Opportunity Cost of Capital, and Internal Rate of Return
2.3 Net Present Value
2.4 Relationship between Discount Rate, Growth Rate, and Income Yield
2.5 Relationship between Discount Rate and Risk
2.6 Conclusion
3 Future Scenarios Matter
3.1 The Standard Discounted Cash Flow Model Appears to be Deterministic
3.2 We Live in a World of Uncertainty
3.3 Discounted Cash Flow Pro Forma Cash Flows Are Expectations
3.4 Flexibility and Options
3.5 Conclusion
4 Scenario Analysis
4.1 Discounted Cash Flow Scenario Analysis
4.2 Scenarios Affect Value
4.3 Flexibility Has Value
4.4 Conclusion
5 Future Outcomes Cover a Range of Possibilities
5.1 Distribution of Future Outcomes
5.2 Quantifying Input Distributions
5.3 Distributions of Outcomes Differ from Distributions of Inputs
5.4 Flaw of Averages
5.5 Conclusion
6 Simulation of Outcomes
6.1 Generating Scenarios
6.2 Real Estate Simulation in a Nutshell
6.3 Simulation Is an Efficient Process
6.4 Number of Trials
6.5 Conclusion
7 Modeling Price Dynamics
7.1 Pricing Factors
7.2 Random Walks
7.3 Real Estate Pricing Factor Dynamics
7.4 Conclusion
8 Interpreting Simulation Results
8.1 Target Curves
8.2 Comparing Target Curves
8.3 Value at Risk
8.4 Scatterplots
8.5 Conclusion
9 Resale Timing Decision: Analysis
9.1 The Resale Timing Problem
9.2 Extending the Time Horizon of the Discounted Cash Flow Model
9.3 IF Statements
9.4 Trigger Value for Stop‐Gain Rule
9.5 Value of Example Stop‐Gain Rule
9.6 Conclusion
10 Resale Timing Decision: Discussion
10.1 Sensitivity Analysis
10.2 When to Use the Stop‐Gain Rule
10.3 Implications of Flexibility for Property Valuation
10.4 Conclusion
11 Development Project Valuation
11.1 Time‐to‐Build Difference between Development Projects and Existing Assets
11.2 Lower Opportunity Cost of Capital for Construction Costs
11.3 Illustrative Example
11.4 Residual Value of Development Land
11.5 Investment Risk in Development Project
11.6 Conclusion
12 Basic Flexibility in Development Projects
12.1 Review of Call (and Put) Options
12.2 Land as a Call Option on Development
12.3 Drivers of Option Value
12.4 A Practical Example of a Call (and Put) Option
12.5 Flexibility and Scenario Analysis for Development Projects
12.6 Conclusion
13 Option Dichotomies
13.1 Three Dichotomies for Thinking Generally about Development Options
13.2 Defensive versus Offensive Options
13.3 Options “On” and “In” Projects
13.4 Timing Options versus Product Options
13.5 Conclusion
14 Product Options in Development
14.1 Concept of Base Plan
14.2 Product Expansion Flexibility
14.3 Product Mix Flexibility
14.4 Conclusion
15 Timing Options in Development
15.1 Project Start‐Timing Flexibility (The Delay Option)
15.2 Project Production Timing Flexibility
15.3 Modular Production Timing Flexibility
15.4 Phasing Timing Flexibility
15.5 Types of Phasing
15.6 Recognizing Defensive and Offensive Options in Simulation Results
15.7 Conclusion
16 Garden City: An Example Multi‐Asset Development Project
16.1 Overview of Multi‐Asset Development Project
16.2 Structure of a Realistic Multi‐Asset Spreadsheet Pro Forma
16.3 Cash Flows for the Example Pro Forma
16.4 Temporal Profile for Base Case
16.5 Expected Economics of the Garden City Project
16.6 Conclusion
17 Effect of Uncertainty without Flexibility in Development Project Evaluation
17.1 Modeling Uncertainty for the Multi‐Asset Development Project
17.2 Generating Random Future Scenarios
17.3 Outcomes Reflecting Uncertainty for the Multi‐Asset Development
17.4 Effect of Different Probability Inputs Assumptions
17.5 Conclusion
18 Project Start‐Delay Flexibility
18.1 Project Start‐Delay Option
18.2 Option Exercise Decision Rule
18.3 Defining “Profit” in the Decision Model
18.4 Value of Start‐Delay Flexibility in the Garden City Project
18.5 Conclusion
19 Decision Rules and Value Implications
19.1 Simple Myopic Delay Rule
19.2 Trigger Values
19.3 Value Implications of the Decision Rules
19.4 Effect of Trigger Values (Start or Delay Bias)
19.5 Review the Meaning of Flexibility Value
19.6 Conclusion
20 Modular Production Timing Flexibility
20.1 Modular Production Timing Flexibility
20.2 Modeling the Modular Production Option
20.3 Value of Modular Production Timing Flexibility
20.4 Effect of Trigger Values (Bias toward Pause or Continue)
20.5 Effect of Combining Start‐Delay and Modular Production Delay Flexibility
20.6 Conclusion
21 Product Mix Flexibility
21.1 Product Mix Flexibility
21.2 Modeling the Product Mix Option
21.3 Value of Product Mix Flexibility
21.4 Effect of Combining Product Mix Flexibility and Timing Options
21.5 Effect of Correlation in the Product Markets on the Value of Product Mix Flexibility
21.6 Effect of Volatility on the Value of Flexibility
21.7 Conclusion
22 Project Phasing Flexibility
22.1 Modeling the Sequential Phase Delay Option
22.2 Modifying the Garden City Project Plan
22.3 Project Economics
22.4 The Delay Decision Model
22.5 Exploring the Value of Project Phasing Flexibility
22.6 Conclusion
23 Optimal Phasing
23.1 Effect of Increasing the Number of Phases
23.2 Principles for Optimal Phasing
23.3 What Is the Difference between a Phase and an Expansion Option?
23.4 Conclusion
24 Overall Summary
Appendix Quantifying Real Estate Uncertainty
A.1 The Real Estate System
A.2 Sources of Uncertainty and Some Practical Advice for Simulation
A.3 The Nature of Real Estate Price Dynamics and Uncertainty
A.4 Putting It All Together
Glossary
Acronyms and Symbols
Index
End User License Agreement
List of Tables
Chapter 01
Table 1.1 Illustrative “pro forma” spreadsheet for the DCF valuation of a rental property.
Chapter 02
Table 2.1 Repeating Table 1.1 pro forma spreadsheet (r=7.0%).
Chapter 03
Table 3.1 Pro forma spreadsheet for the DCF valuation of a rental property.
Chapter 04
Table 4.1 Three DCF scenarios
Table 4.2 Comparison of scenarios and their average values.
Chapter 05
Table 5.1 Outcome and input distributions differ.
Chapter 09
Table 9.1 Example effect of IF statement.
Chapter 11
Table 11.1 Essential parameters for the example investments.
Chapter 12
Table 12.1 Essential parameters for the example investments.
Table 12.2 Comparison of decision with and without flexibility.
Chapter 16
Table 16.1 Pro forma base plan and base case for Garden City.
Table 16.2 Extract from Garden City pro forma.
Chapter 17
Table 17.1 Garden City base case (top) and example random future scenario pricing factors and resulting outcome (bottom).
Chapter 22
Table 22.1 Pro forma base plan for two‐phase Garden City project.
List of Illustrations
Chapter 01
Figure 1.1 Present value of a single $100 future cash flow promised at various future times and discounted at various rates.
Chapter 05
Figure 5.1a Bell‐shaped distribution.
Figure 5.1b Skewed distribution.
Figure 5.2 Expert forecasts often differ greatly from what actually occurs.
Chapter 07
Figure 7.1 Pricing factors for six future scenarios, based on the random walk.
Figure 7.2 Pricing factors for six future scenarios, based on real estate parameters (random walk with autoregression, cyclicality, and mean‐reversion).
Chapter 08
Figure 8.1 Cumulative target curve for a two‐state probability distribution.
Figures 8.2–8.3
Figures 8.4–8.5
Figure 8.6 Scatterplot of simulated IRR differences.
Chapter 09
Figure 9.1 Logic of IF statement for stop‐gain sale.
Figure 9.2 Cumulative PV target curve comparison of rental property with and without resale timing flexibility.
Figure 9.3 Cumulative IRR target curve comparison of rental property with and without resale timing flexibility.
Chapter 12
Figure 12.1 Bentall 5 tower in Vancouver expanding vertically.
Chapter 13
Figure 13.1 Vertical expansion of the HCSC building in Chicago (center of images). Phase 1 (left) and Phase 2 (right).
Chapter 14
Figure 14.1 A flexible design for Court Square Two, New York City.
Chapter 15
Figure 15.1 Harvard University’s Allston Science Complex, delayed 6 years.
Figure 15.2a General effect of “call” options on development project as seen in cumulative target curves.
Figure 15.2b General effect of “call” options on development project as seen in frequency target curves.
Figure 15.3a General effect of “put” options on development project as seen in cumulative target curves.
Figure 15.3b General effect of “put” options on development project as seen in frequency target curves.
Chapter 16
Figure 16.1 Concept of the Garden City project.
Figure 16.2a Pro forma temporal profile of project construction.
Figure 16.2b Expected net cash flow gross of land cost.
Chapter 17
Figures 17.1–17.2
Figures 17.3–17.4
Figure 17.5 Garden City simulation results reflecting uncertainty without flexibility: IRR as a function of NPV.
Figures 17.6–17.7
Figures 17.8–17.9
Chapter 18
Figures 18.1–18.2
Figures 18.3–18.4
Figure 18.5 Scatterplot of IRR outcomes for start‐delay flexibility (Aggressive Developer Rule).
Chapter 19
Figures 19.1–19.2
Figures 19.3–19.4
Figure 19.5 Scatterplot of IRR outcomes for start‐delay flexibility (Simple Myopic Delay Rule).
Figure 19.6 Effect of changing level of trigger value for start‐delay flexibility.
Figure 19.7 Effect of changing level of trigger value on downside results for start‐delay flexibility.
Chapter 20
Figure 20.1a Production schedule for the Garden City project’s base plan.
Figure 20.1b Production schedule for one future scenario with the option to delay (modular production timing flexibility).
Figures 20.2–20.3
Figures 20.4–20.5
Figure 20.6 Scatterplot of IRR outcomes for modular production timing flexibility (Simple Myopic Delay Rule, trigger = 0).
Figure 20.7 Effect of changing level of trigger value for modular production timing flexibility (Simple Myopic Delay Rule).
Figure 20.8 Effect of changing level of trigger value on downside results for modular production timing flexibility.
Figures 20.9–20.10
Figures 20.11–20.12
Chapter 21
Figures 21.1–21.2
Figures 21.3–21.4
Figure 21.5 Scatterplot of IRR outcomes for product mix flexibility versus no flexibility at all.
Figures 21.6–21.7
Figures 21.8–21.9
Figures 21.10–21.11
Figures 21.12–21.13
Figures 21.14–21.15
Figures 21.16–21.17
Chapter 22
Figure 22.1a Garden City base plan: projected production, two‐phase project.
Figure 22.1b Garden City base case: projected net cash flow, two‐phase project.
Figures 22.2–22.3
Figures 22.4–22.5
Figure 22.6 Scatterplot of IRR outcomes for flexible versus inflexible two‐phase Garden City project.
Chapter 23
Figure 23.1 Scatterplot comparing NPV for three versus two phases.
Figure 23.2 Scatterplot comparing IRR for three versus two phases.
Figure 23.3 Four archetypical base plan temporal production profiles for major real estate development projects.
Appendix
Figure A.1 The real estate system.
Figure A.2 Simplifying uncertainty for simulation modeling.
Figure A.3 Commercial property prices, United States, 1969–2016: long‐term trend.
Figure A.4 Commercial property prices, United States, 1969–2016: volatility.
Figure A.5 Commercial property prices, United States, 1969–2016: cyclicality and mean reversion.
Figure A.6 Commercial property prices, United States, 1969–2016: Inertia (autoregression).
Figure A.7 Price paths of 20 property investments as of December 2016: idiosyncratic drift.
Figure A.8 Effect of the global financial crisis on price paths of all US real estate investment trusts: Black swan event (Financial Crisis, October 2008–March 2009).
Figure A.9a Pricing factors for six future scenarios based on typical price dynamics: stock market (simulations based purely on the random walk).
Figure A.9b Pricing factors for six future scenarios based on typical price dynamics: real estate (simulations based on random walk and cyclicality, mean‐reversion, and autoregression).
Guide
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Flexibility and Real Estate Valuation under Uncertainty: A Practical Guide for Developers
David Geltner
Massachusetts Institute of TechnologyCambridge, MA, USA
Richard de Neufville
Massachusetts Institute of TechnologyCambridge, MA, USA
This edition first published 2018© 2018 John Wiley & Sons Ltd
All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, except as permitted by law. Advice on how to obtain permission to reuse material from this title is available at http://www.wiley.com/go/permissions.
The right of David Geltner and Richard de Neufville to be identified as the authors of this work has been asserted in accordance with law.
Registered OfficesJohn Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USAJohn Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex, PO19 8SQ, UK
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Library of Congress Cataloging‐in‐Publication Data
Names: Geltner, David, 1951– author. | De Neufville, Richard, 1939– author.Title: Flexibility and real estate valuation under uncertainty : a practical guide for developers / by David Geltner, Massachusetts Institute of Technology, Richard de Neufville, Massachusetts Institute of Technology.Description: Hoboken, NJ : John Wiley & Sons, [2018] | Includes bibliographical references and index. |Identifiers: LCCN 2017051519 (print) | LCCN 2017054517 (ebook) | ISBN 9781119106456 (pdf) | ISBN 9781119106487 (epub) | ISBN 9781119106494 (pbk.)Subjects: LCSH: Real property–Valuation. | Real estate development. | Real estate investment.Classification: LCC HD1387 (ebook) | LCC HD1387 .G45 2018 (print) | DDC 333.33/2–dc23LC record available at https://lccn.loc.gov/2017051519
Cover Design: WileyCover Image: © Lava 4 images/Shutterstock
to Debby and Ginger
Foreword
Major leaps forward in real estate thought leadership are few and far between, such as the application of discounted cash flow to real estate valuation in the 1970s, and of portfolio theory to property investment in the 1980s and 1990s. This seminal book by Professors Geltner and de Neufville provides the vanguard for the next major leap in thought leadership, being optionality in real estate.
While optionality, as distinct from option pricing theory, has been conceptually discussed by the world’s leading real estate academics for the last few years, including at the landmark RICS Foundation Global Symposium on “Optionality” in early 2012, little has been written until now.
Similarly, while the use of spreadsheets for development cash flows and the application of probability analysis and scenario analysis are not new ideas, their combination within a framework of flexibility or optionality is a new concept. This changes the way we think about real estate valuation, with their guided application through the use of spreadsheets rendering them accessible to all practitioners. Such techniques as probability analysis and scenario analysis are no longer possible add‐ons to development cash flows, but now have a central role.
Henry Ford is often misquoted as saying:
If I had asked people what they wanted, they would have said “faster horses.”
This was academia’s previous response to the real estate industry and profession through such developments as financial management rate of return and the modified internal rate of return for discounted cash flow. This book does not provide a faster spreadsheet or a simpler probability application, but instead provides a new way to conceptualize real estate as a bundle of opportunities with positive or negative contributions to value that can be combined to optimize value to an individual party or to a market. Significantly, rather than the traditional view of land value theory, this book views land as a call option on development. As the authors note in the Preface:
You can think of our approach as providing a way for decision‐makers—with more, or less, experience—to transform their intuitive sense of managing risks and exploiting opportunities into more solid, defensible quantitative economic valuations of real estate options.
Optionality in real estate valuation essentially addresses the three realms made famous by former US defense secretary Donald Rumsfeld—known knowns, known unknowns, and unknown unknowns—providing a framework within which each may be quantified and modelled. It is optionality manifest in flexibility arising from uncertainty in the investment and development process, which is the focus of this book.
This book logically and sequentially moves through three phases—acquainting the reader with the quantitative foundations, then introducing the concept and framework of optionality, before combining these through application to an increasingly detailed example of a real estate project.
Chapter 1 sets out the rationale for and structure of the cash flow model, with a focus on retrospective and prospective assessment, while Chapter 2 extends the discussion to net present value (NPV) and internal rate of return (IRR), with a focus on the opportunity cost of capital and the discount rate. Chapters 3–8 introduce the recognition of uncertainty through probabilities and scenarios, simulation, pricing factors, and random walks, importantly recognizing the differences between inputs and outputs. Chapters 9 and 10 apply these quantitative foundations to a simple example of optionality in real estate, being the sale timing decision. Chapter 11 moves to the development process and the opportunity cost of capital for construction, while Chapter 12 focuses on the decision of whether and/or when to develop.
The concept and framework of optionality in the context of real estate are introduced in Chapter 13, with Chapter 14 focusing on product options and Chapter 15 on timing options. The quantitative foundations and concept and framework of optionality are then combined through application to the large‐scale Garden City residential development project. Chapter 16 presents the traditional cash flow, with uncertainty (but without flexibility) discussed in Chapter 17. Both uncertainty and flexibility are examined in Chapters 18 and 19 through the start date, in Chapter 20 through modular production timing flexibility, in Chapter 21 through product mix flexibility, and in Chapters 22 and 23 through sequential phasing delay. The use of the same example evolving through a series of chapters allows the reader to easily understand the respective applications and their differing impacts on the outputs of the analysis.
Chapter 24 then summarizes the book. Text boxes throughout provide illuminating commentary, and the Appendix provides key information on the real estate system, uncertainty, and the eight components of real estate price dynamics. The accompanying website provides very helpful sample spreadsheets and supporting material. Usefully, each chapter is bite‐sized, being generally 15 pages or less and written in a conversational style that makes reading both quick and enjoyable, satisfying the author’s intention to present:
… common‐sense methods rooted in the spirit of engineering, rather than highly complex models typical of academic literature in the field of economics. (Chapter 24)
Significantly, the book bridges concepts of value with development analysis. Rather than the traditional focus on concepts of market value, the book views development through the lens of investment value (or its economic equivalent, private value) from the perspective of the developer, which is contingent on the ability and willingness of the developer to exercise the flexibility being modelled. A key output of this book for valuation theory is the bridging of investment value and market value in a development context:
Furthermore, at a deeper level, private valuations underlie, and determine, market values. Buyers will not pay more than their private valuations for an asset, and sellers will not take less than their private valuations for assets they own. The equilibrium prices that we define as market values evidenced by consummated transaction prices can therefore reflect private valuations. (Chapter 19)
The overall contribution of this book to an understanding of flexibility and optionality in real estate is aptly summarized by the authors:
We can distill the results of our analysis into a general rule: Make as much of the project as flexible as possible, as early as possible, but think about the implications of the market cycle.
Professors Geltner and de Neufville are to be commended for their contribution to the next major leap in real estate thought leadership, being optionality in real estate.
April 2017
Professor David Parker
Professor of PropertyUniversity of South Australia
Authors’ Preface
What this book is about, and how to use it.
This book is a groundbreaking text for real estate developers and investors. It is about uncertainty: “unknown unknowns.” It shows how the flexibility that exists in real estate investments and development projects unlocks hidden value, and it provides easy‐to‐use tools to quantify that hidden value. If you are a developer or investor, you know that uncertainty pervades your decision‐making, and you intuitively realize the importance of flexibility for dealing with unexpected future outcomes. Flexibility includes such capabilities as the ability to sell a property whenever you choose, to delay a second phase of construction, or to change from building a hotel to building apartments. This book describes an approach to realistically quantifying the nature and effect of future uncertainty, and to putting a monetary value on these types of flexibilities.
Our approach is easy to use because it is based on the industry‐standard spreadsheets of discounted cash flow analysis. It efficiently calculates the values of flexibilities and options, and quantifies the nature of risks and opportunities. In contrast to the complex, highly mathematical procedures that academics and some Wall Street or City “rocket scientists” often use to calculate option value, the approach we present is intuitive, transparent, easy to implement, and, we think, more informative for real estate decision‐makers. The procedures described in this book are direct analogs of management decision‐making, not academic economic models of market equilibrium. In the real world of real estate investment decision‐making, this approach adds fundamental and crucial aspects of reality that are currently too much ignored or treated only with seat‐of‐the‐pants intuition. Namely, we include the explicit and quantitative consideration of uncertainty and flexibility.
The text presents and describes in detail this innovative and simple approach to valuing the types of real estate flexibility that commonly exist in real‐world investment and development. Building naturally and easily on the familiar current practice of project valuation and financial analysis, the procedure we present completes the analysis and makes it much more powerful and useful. It enhances one’s capability to evaluate the multiple, interacting options and contingencies that arise from market changes. Importantly, it does more than calculate the expected, or average, value of real estate options. It describes the range of possible outcomes, and so informs users about the possible risks and rewards, quantifying the “downside” and the “upside.” We believe it does so with sufficient depth and realism to usefully inform project planning and design decisions.
Essentially, we exploit the power of modern personal computers, combined with knowledge derived from newly available empirical data about real estate markets. Instead of using complex mathematical computations based on limited assumptions about the nature of uncertainty (for example, the random walk assumption), we use laptops to explore in detail what may actually occur. The procedure simulates the effects of the many different kinds of uncertainties that may exist, and considers the implications of a range of possible decisions that managers might take. This enables users to explore strategies of management and development in the light of a sophisticated valuation of the flexibility that exists. You can think of our approach as providing a way for decision‐makers—with more, or less, experience—to transform their intuitive sense of managing risks and exploiting opportunities into more solid, defensible, quantitative economic valuations of real estate options. Our approach is:
Valuable
: It unlocks added value by exploring options that might provide a significant increase in expected value; this may be done by exploiting upside opportunities, avoiding downside risks, and, in some cases, decreasing initial costs;
Practical
: It simply extends the standard spreadsheet‐based discounted cash flow analyses for valuing real estate projects that practitioners are already at ease with, requiring no additional special software; and
Realistic
: It builds on over 30 years of collaboration between the commercial real estate industry and the Center for Real Estate at the Massachusetts Institute of Technology (MIT), and over 90 years of combined professional investment, engineering, and teaching experience of the authors.
The Value Proposition
The book enables real‐world practitioners—managers, investors, and developers of real estate properties and projects—to evaluate their real estate options quantitatively. Practitioners can use this book to identify opportunities to increase their expected value using various types of flexibilities that can exist in real estate investment, and particularly in development projects. These opportunities arise from the possibilities to:
Time the start, stop, or sale of developments or investments to their advantage;
Change the mix of uses, or even the scale and density, in a development in accordance with changing market priorities; and
In general, manage and develop properties’ flexibilities to exploit opportunities as they arise, while also avoiding risks that may crop up.
Taken together, these options allow developers to deal proactively with the many uncertainties that inevitably confront the development of real estate projects.
The use of options in real estate can significantly increase the expected value of real estate development in three ways. It can enable decision‐makers to:
Exploit new opportunities arising from favorable upticks in market conditions;
Reduce or avoid the downward consequences associated with unfavorable circumstances; and
Increase the rate of return while reducing the risk in the return on investments, or reduce their initial capital requirements—for example, by delaying the implementation of project stages until the market becomes more favorable, or by resizing the initial investment to consider future expansion possibilities.
Users taking advantage of the flexibility of real options can put themselves ahead of the competition. The ability to identify greater value in investment and development projects will enable practitioners to win more opportunities. The capability to deploy innovative designs that enhance the value of new developments by incorporating valuable options should improve investment performance.
Using this approach to quantitatively document the value of options can strengthen the case for certain projects. In other cases, scenario exploration can reveal cautionary considerations that are important for investors and principals to take into account before launching the project. The methods in this book can help test the intuitive sense of opportunities and, where appropriate, demonstrate the value of options that developers are considering for a project. Reducing uncertainty by shining a more quantitative “light” on the nature of the risks faced by the developer, scenario and simulation analysis can better facilitate financing of potential projects (or weed out more risky projects). Overall, the solid analysis and the use of real estate options give practitioners an advantage over any competitors who ignore this new capability.
This innovative book is inherently future‐oriented. It describes how real estate valuation can evolve, learning from, but not repeating, the past. It’s for the new generation used to living in a world of “big data.”
Accessibility
Our approach is eminently accessible. It builds on the standard spreadsheet analyses that industry practitioners already use to evaluate projects. It extends this method to the valuation of options through commonsense and logic. It simply exploits the power of modern computers to search through a range of possibilities, to calculate the results of alternative actions, and to display those results in intuitively understandable ways. The process thus avoids the use of complicated mathematics.
We have designed the presentation for easy learning and adoption. We provide a suite of practical, realistic tools to value real estate options in different circumstances. We present this material in easy‐to‐read, bite‐sized steps. These steps build up from simple demonstrations to examples that have a degree of realism useful for actual business decision‐making. Illustrative cases and simple worked‐out examples guide users through the process. Beyond the book, an accompanying website provides spreadsheet templates that practitioners can download and adapt to their own needs.
The approach presented here results from the collaboration of two leading teachers at MIT, David Geltner and Richard de Neufville. Professor Geltner is the principal author of a leading industry text, Commercial Real Estate Analysis and Investments (3rd edition, OnCourse Learning). Professor de Neufville is the lead author of Flexibility in Engineering Design (MIT Press) and six other textbooks on systems analysis, planning, and design.
How to Use this Book
Before diving in, please take a moment to look at how we have structured the text. We have designed this book to smoothly and easily introduce real estate managers, investors, and developers to new ways to evaluate and improve their projects. The book presents the new approach one concept at a time. It builds up your understanding, step by step, in short chapters that you can cover in about an hour each. We illustrate topics with practical examples. And we have written the text in straightforward language. The goal is to help you quickly understand the concepts and the principles of how you can use flexibility and options to create and increase value in real estate.
The basic idea is to imagine what could happen and then examine the consequences. It’s a “what if?” analysis. We show how to do this simply and quickly using laptops. A computer simulates the possibilities and the consequences rapidly—thousands of times in just seconds. The process then compares the results to identify the benefits of possible flexible strategies and options.
The process mechanics should be accessible to practicing real estate analysts. The calculations build on the standard financial spreadsheets (such as Microsoft Excel®) that are almost everywhere in real estate valuations. The approach does not involve fancy mathematics—we just calculate possible values many, many times. Nor does it require special software beyond standard spreadsheets such as Microsoft Excel®. We use a disciplined approach structured to provide the results numerically and graphically.
In addition, the analyses are transparent. The spreadsheets clearly display inputs and assumptions and allow users to change them easily. Users are not required to assume that uncertainties and trends are stationary (that is, do not alter over time), a commonly required assumption in academic economic options models.
We describe (and provide freely via the web) a series of spreadsheet templates to simulate a plausible range of possible uncertain outcomes based on our knowledge of real estate markets, and to calculate the resulting distribution of the investment performance outcome for a typical illustrative project. The electronic templates effectively supplement the examples in the text for readers who want to replicate the examples or build on them to create their own applications. Thus, the web material, which is well annotated at the “nuts‐and‐bolts” level in Microsoft Excel®, can be used by readers who want to value flexible real estate strategies themselves. The web material is also suitable for students, either in class or for self‐study.
In a nutshell, the way to access the material is to:
Read the text for overall comprehension of our approach;
Dip into the web material for detailed explanations as desired; and
Draw on the web material for detailed examples and templates you can use as starting points for your own projects.
Acknowledgments
The authors gratefully acknowledge the professional support and advice of Professor David Parker of the University of South Australia and Dr. Paul McNamara of Linden Parkside Ltd in the UK; the technical support of our graduate assistants Nick Foran, Saurabh Jalori, Eric Mo, and Qing Ye; the helpful feedback of the hundreds of MIT and Harvard students who have followed our class at MIT over the years; our home copy editor Susan Matheson; our Wiley‐Blackwell editor Paul Sayer; and of course the Wiley‐Blackwell production team in the UK and India, including Blessy Regulas, Adalfin Jayasingh, Shalisha Sukanya Sam and Aravind Kannankara.
About the Companion Website
This book is accompanied by a companion website:
www.wiley.com/go/geltner-deneufville/flexibility-and-real-estate-valuation
The website includes:
Excel files
1Discounted Cash Flow Valuation: The Basic Procedures and Concepts Underlying Spreadsheet Valuation Constitute the Springboard to our Approach of Analyzing Flexibility Under Uncertainty
LEARNING OBJECTIVES
Share the reasons why we focus on the discounted cash flow (DCF) model;
Establish the basic terminology and setup that we use throughout the text;
Review the mechanics of the DCF valuation model;
Understand the use of the DCF model for prospective (ex‐ante) and retrospective (ex‐post) valuations.
OUTLINE OF THE CHAPTER
1.1 Why the Focus on the Discounted Cash Flow Model?
1.2 Structure of a Discounted Cash Flow Spreadsheet
1.3 The Cash Flow Projection
1.4 Discount Rate
1.5 Market Value and Forward‐Looking (Ex‐Ante) Analysis
1.6 Backward‐Looking (Ex‐Post) Analysis
1.7 Conclusion
The focus of this book is on the valuation of properties and development projects in the face of uncertainty. We concentrate particularly on management and design flexibility, which is the ability to respond to circumstances in order to reduce downside risks and take advantage of upside opportunities.
To this end, everything in this book builds on and uses the basic discounted cash flow (DCF) model. So, to ensure we’re all on the same page, and using the same terminology and basic understanding about this tool, this first chapter introduces and reviews the DCF model, and thereby sets the stage for all that follows.
First, we discuss why it makes sense for us to focus on the DCF model. What makes the DCF model so appropriate for our purpose? (See Section 1.1.)
Second, we describe the essential elements of the DCF model. We define both the terminology and structure of the model that we use throughout the book. We do this to establish a common vocabulary and to avoid confusion that might arise from different professional practices. This quick review also introduces DCF modeling for those who might not be familiar with the procedure (see Sections 1.2–1.4).
Third, we illustrate the two basic ways to use the DCF model to value projects. We can use it to value projects prospectively—in advance of making investments, as an aid to decision‐making. We can also use it retrospectively—to assess the actual past performance of a real estate asset and investment, to help diagnose the causes of success and failure (see Sections 1.5 and 1.6).
1.1 Why the Focus on the Discounted Cash Flow Model?
Three factors make the DCF model the most appropriate basis for valuation of real estate properties and developments in the face of uncertainty:
DCF is based on fundamental financial economic theory, explicitly recognizing and valuing, based on opportunity cost, the three seminal considerations in investment:
cash flow
,
time
, and
risk
.
The DCF model is already the analytic workhorse for valuation of real estate investments. Many of you are probably already familiar with it.
Implemented in modern computer spreadsheet software, the DCF model is very efficient and widely applicable. The relevant calculations usually take seconds or less. And the model can realistically represent a wide range of complex situations useful for valuing flexibility under uncertainty.
Allow us to elaborate briefly on these three points.
The focus of DCF on cash contrasts with a focus on accounting metrics such as net income. Of course, such accounting metrics are very important, and DCF models often include and use accounting metrics. Nevertheless, cash is what you can actually use—in investments, in business, in life. The accounting metrics are indirect representatives, reflections, or predictors of the existing or future cash flow that ultimately matters.
The “D” part of “DCF” is how we account for time and risk in the valuation. Future money is worth less than present money for two reasons:
You could be using the money in the meantime (maybe to spend on consumption, maybe to earn returns in investment); and
The future money might not materialize in full or at all, since the future is uncertain (no one has a crystal ball).
The discount rate by which the DCF procedure reduces future expected cash flow to present value (PV) accounts for both of these considerations. It does this accounting based on the fundamental economic principle of opportunity cost, using a discount rate that reflects what the investor could expect to earn by investing in a similar investment of similar risk. In short, the DCF model is solid, elegant, and intuitive.
The DCF model is not just sound from an economic theory perspective—its use too is widespread. DCF models operate using computer spreadsheets, which are a common way to organize data for valuation analysis. Spreadsheets are everywhere in financial analysis. Business analysts and decision‐makers worldwide use common spreadsheet programs such as Microsoft Excel®. Such spreadsheet software is, in effect, a common language in the business and financial world. This can greatly facilitate communication, transparency, understanding, and use.
Finally, DCF models based on computer spreadsheets have tremendous range and flexibility in what they can do for us analytically, especially in our quest to bring uncertainty and flexibility explicitly into valuation. Spreadsheets take in numerical data and calculate outputs, allowing us to easily change one or more entries and recalculate to see the results instantaneously. Spreadsheets have two special capabilities that enable us to represent uncertainty and flexibility. These capabilities are easy to implement and use, and are essential to the approach we present in this book.
First, we can easily use spreadsheets to calculate thousands of variations of the same problem automatically, in seconds. This feature allows us to deal with the range of possible economic and other variations that could affect the performance of an investment and hence the valuation of the real estate. This frees us from the need to confine our analysis to just a few estimates of the possible future. It enables us to look at probabilistic distributions of possibilities in detail, such as the effects of business cycles and market movements—a step that is necessary for the proper valuation of flexibility. This capability is available through the random number generation capability and the “data table” function in Microsoft Excel
®
.
Second, we can set up the spreadsheet to represent the actions of a decision‐maker choosing to take appropriate actions under the conditions we specify. In effect, we can create an analog model of the investment and decision‐making process. Thus, we can program potential decisions to take advantage of the flexibility to sell a property under favorable circumstances, or to delay development in a down economic cycle, for example. This capability enables us to represent and quantify the advantages of certain options and certain types of decision flexibility. This approach also enables us to value several options simultaneously, a capability largely beyond the ability of many of the formal academic models of option valuation. In essence, we employ “IF statements” in Microsoft Excel
®
formulas.
1.2 Structure of a Discounted Cash Flow Spreadsheet
Let’s now review the widely accepted and somewhat standardized structure and procedure for the DCF analysis we use for valuation, arguably a canonical framework in real estate. As we have said, this structure is tailor‐made for spreadsheets (and vice versa). It is usual to call this setup and framework a “pro forma analysis,” or, simply, a DCF “pro forma.”
Table 1.1 presents a simple numerical example of such a DCF valuation for a stylized commercial rental property. As Table 1.1 illustrates, the DCF pro forma is a table (or matrix) showing the state of an investment over time in two dimensions (rows and columns). The overall structure is that:
The columns specify different periods, for example, years. For generality, a usual practice is to number the columns starting with “0,” which refers to the present—that is, the time when the analysis and evaluation are applicable. “Column 1” is the first year (or period) in the future, “Column 2” the second, and so on.
The rows represent revenue and expenditure items relevant for analysis and valuation. We speak of these as the “cash flow over time” of the line item represented in the row. The normal convention is to assume that cash flows occur “in arrears”—that is, as of the end of the indicated period. Some rows are sometimes used to display the underlying physical source amounts, such as the quantity of units sold or the square meters of space occupied.
Table 1.1 Illustrative “pro forma” spreadsheet for the DCF valuation of a rental property.
1.3 The Cash Flow Projection
In this and the next sections, we review the essential mechanics of the DCF valuation procedure. We first focus on the future stream of cash flow we expect from the property—that is, the estimates of its future revenues and expenditures, period by period. It’s important to note that we specify these estimates by period. It isn’t enough to estimate the overall revenues and expenses; one must assess how they evolve over time. This is because the discounting process noted previously (and as elaborated in the following section) will give different present values to future cash flows, depending on how far in the future they occur.
We base cash flow projections on a variety of sources. These include:
Knowledge of fixed contractual obligations (such as mortgage payments and lease terms);
Informed best estimates of specific income and expenses; and
Assumptions about the relevant real estate market and overall economic conditions, such as future prices.
Table 1.1 presents a simple numerical example of a commercial rental property, in which the cash flows are all speculative estimates. You might think of the property as an apartment property.
Later in this book, we focus primarily on development projects—that is, investments that require considerable construction up front as well as possibly in later stages. Development projects can have major net negative cash flows for extended periods or at different times during the project’s life. But, to begin, we focus on a simpler and more fundamental type of capital asset, as represented by the fully operational rental property depicted in Table 1.1. (A fully operational property occupied to a normal level is also referred to as a “stabilized” property.)
The property owner takes in rental revenue and pays out cash to cover operating and capital expenses. The difference between the money in and the money out is the “net cash flow.” This can be either positive or negative in any given period. Positive cash flow means that the property owner receives money, net, from the asset. Negative cash flow means that expenditures exceed revenues, and that the property owner must somehow provide cash to the property.
While we have not labeled the cash flows in any particular currency, we refer to them in “dollars” for illustrative purposes. Note also that Table 1.1 uses real estate terms typical for the United States.
Table 1.1 projects all the estimated cash flow components in our property to grow at a steady rate. This practice does occur in the real world as a simplification, but here this simplification is for ease of illustration, to allow us to make some essential points more clearly. For the property in our example, we use a projected growth rate of income and expenses of 2% each year, as indicated at the top of the spreadsheet. While this might be a typical rate of growth for rental property in a mature economy, it is just an example of growth projection.
It is usual to structure the rows so that annual revenues are generally at the top of each column, followed by costs, leading to net cash flow for the year at the bottom. Table 1.1 is reasonably standard in this respect. To see how this works, consider the entries in the column for Year 3. Taking each row in turn, we have:
Potential gross income
(PGI): This refers to the revenue that the property would generate if it were fully occupied. (This is also sometimes referred to as
Gross Revenue
, or
Rent Roll
.) We project this as $104.04 in Year 3, which is the $100 of Year 1 grown by 2% over 2 years.
Vacancy allowance
: This implies that some fraction (which we assume to be 5%, or $5.20) of the potential revenue will
not
be generated, owing to vacancy in the property during the year.
Effective gross income
(EGI): $98.84 equals PGI minus vacancy allowance.
Operating expenses
: This refers to the estimated regularly recurring costs for operating the property, such as utilities, insurance, property taxes, maintenance, and management costs.
Net operating income
(NOI): $62.42 equals EGI minus operating expenses.
Capital improvement expenditures
(“Capex”): This refers to the longer‐term, less regularly recurring expenditures incurred to improve the property and keep it running—for example, a new roof, new heating or air conditioning system, repaving the parking lot or re‐landscaping the grounds, refurbishing and refitting apartments with new appliances, etc.
Net cash flow
: $52.02 equals NOI minus Capex; this is the overall difference between the money in and the money out, at the property level. (In this book, we focus on the asset level, not considering specifically investor‐level cash flows such as debt payments or income taxes—although, of course, the DCF model may also be applied at that level.) Net cash flow is our “bottom‐line” projection for operations in Year 3. As noted, the net cash flow can be either positive or negative in any given year.
A complete DCF valuation, in addition to its ongoing annual cash flows, has to account properly for the projected value of the asset at the time it might be sold. We do this by projecting what real estate analysts call the “reversion” cash flow. This amount corresponds to what in other fields of capital budgeting might be termed “terminal value” or “salvage value.” In real estate, this is the expected resale price for the property.
