Real Estate Investment Market [1 ed.] 9781617283437, 9781616683955

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Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.

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FINANCIAL INSTITUTIONS AND SERVICES

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REAL ESTATE INVESTMENT MARKET

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FINANCIAL INSTITUTIONS AND SERVICES

REAL ESTATE INVESTMENT MARKET

SOFIA M. LOMBARDI

Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.

EDITOR

Nova Science Publishers, Inc. New York

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Copyright © 2010 by Nova Science Publishers, Inc. All rights reserved. No part of this book may be reproduced, stored in a retrieval system or transmitted in any form or by any means: electronic, electrostatic, magnetic, tape, mechanical photocopying, recording or otherwise without the written permission of the Publisher. For permission to use material from this book please contact us: Telephone 631-231-7269; Fax 631-231-8175 Web Site: http://www.novapublishers.com NOTICE TO THE READER The Publisher has taken reasonable care in the preparation of this book, but makes no expressed or implied warranty of any kind and assumes no responsibility for any errors or omissions. No liability is assumed for incidental or consequential damages in connection with or arising out of information contained in this book. The Publisher shall not be liable for any special, consequential, or exemplary damages resulting, in whole or in part, from the readers’ use of, or reliance upon, this material. Any parts of this book based on government reports are so indicated and copyright is claimed for those parts to the extent applicable to compilations of such works.

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Independent verification should be sought for any data, advice or recommendations contained in this book. In addition, no responsibility is assumed by the publisher for any injury and/or damage to persons or property arising from any methods, products, instructions, ideas or otherwise contained in this publication. This publication is designed to provide accurate and authoritative information with regard to the subject matter covered herein. It is sold with the clear understanding that the Publisher is not engaged in rendering legal or any other professional services. If legal or any other expert assistance is required, the services of a competent person should be sought. FROM A DECLARATION OF PARTICIPANTS JOINTLY ADOPTED BY A COMMITTEE OF THE AMERICAN BAR ASSOCIATION AND A COMMITTEE OF PUBLISHERS. Additional color graphics may be available in the e-book version of this book. LIBRARY OF CONGRESS CATALOGING-IN-PUBLICATION DATA Real estate investment market / editor, Sofia M. Lombardi. p. cm. Includes index. ISBN:  (eBook) 1. Real estate investment. 2. Housing--Finance. 3. Mortgage loans. I. Lombardi, Sofia M. HD1382.5.R385 2010 332.63'24--dc22 2010015621

Published by Nova Science Publishers, Inc.

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CONTENTS   vii 

Preface Chapter 1

Chapter 2

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Chapter 3

Chapter 4

Chapter 5

Chapter 6

Chapter 7

Chapter 8

Addressing the Ongoing Crisis in the Housing and Financial Markets Douglas W. Elmendorf  

1 

Value Versus Growth Real Estate Investment Strategy: Is the Win a Flash in the Pan? Kwame Addae-Dapaah, Hin/David Kim Ho, and Yan Fen Tan  

31 

Restructuring Real Estate Market Information Management to Facilitate Land-Based Investment Activities in Ghana Raymond T. Abdulai and Felix N. Hammond 

75 

Investment Characteristics of Housing Market: Focusing on the Stickiness of Housing Rent Chihiro Shimizu  

105 

Fannie Mae and Freddie Mac: Changes to the Regulation of Their Mortgage Portfolios N. Eric Weiss  

127 

Overview of the Securities Act of 1933 as Applied to Private Label Mortgage-Backed Securities Kathleen Ann Ruane  

139 

Examining the Continuing Crisis in Residential Foreclosures and the Emerging Commercial Real Estate Crisis: Perspectives from Atlanta Jon D. Greenlee   Short Communication: Diversification in Listed Real Estate Investment Fund Reporting in South Africa Valmond Ghyoot  

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151 

161 

Contents

vi Chapter 9

Chapter 10

Should Banking Powers Expand into Real Estate Brokerage and Management? Walter W. Eubanks   Emerging Economies and Secondary Mortgage Markets Raymond T. Abdulai and Frank Gyamfi-Yeboa

169  177 

181

Index

183 

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Chapter Sources

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PREFACE The turmoil in the international financial markets since the subprime loan crisis has had a significant effect on the real-estate investment market around the globe. This suggests that the real estate investment market is becoming part of the financial market. This book reviews current data on real-estate investing including topics such as the investment characteristics of the housing market; real estate markets in developing sub-Saharan Africa; ascertaining whether the superiority of "value" over "growth" real estate investment is unsustainable; emerging economies and secondary mortgage markets; a CBO report on the ongoing crisis in the housing and financial markets; changes to the regulation of Fannie Mae and Freddie Mac; an overview of the Securities Act of 1933 as it applies to private label mortgage-backed securities and others. Chapter 1- Chairman Conrad, Senator Gregg, and Members of the Committee, I welcome the opportunity to discuss the turmoil in our nation’s housing and financial markets and some options for additional action by policymakers. A strong financial sector is a necessary component of a robust economy. Financial markets and institutions channel funds from savers to borrowers who need the money to build businesses and hire workers and to buy homes and other goods and services. Indeed, credit is often required to support the ordinary operations of businesses—for example, to finance their inventories and to meet payrolls before payments are received. If the customary means of obtaining credit break down, the disruption to households’ and businesses’ spending can be severe. Thus, the ongoing crisis in the U.S. financial system has significantly depressed economic activity during the past year and a half, and it poses a serious threat to the nation’s ability to quickly return to a path of solid economic growth. Losses on mortgages, on assets backed by mortgages, and on other loans to consumers and businesses, together with an associated pullback from risk taking in many credit markets, have raised the cost and reduced the availability of credit for borrowers whose credit ratings are less than the very highest. To be sure, among the fundamental causes of the crisis was the provision of too much credit at too low a price as well as insufficient capital. However, the sudden shift to a much higher price for risk taking has led to a significant reduction in wealth and borrowing capacity; it has also forced a number of financial institutions to close and others to be merged with stronger operations. Those forces, in turn, are weighing heavily on consumption, the demand for housing, and businesses’ investment.

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Sofia M. Lombardi

Chapter 2- The superiority of the contrarian investment strategy, though well attested in the finance literature, is being questioned in some quarters on the pretext that the gap between the performance of value and growth investment narrows over time. If this is proven to be true, it would imply that value real estate investment may not be advisable given that real estate is a medium to long term investment. This paper uses empirical real estate investment return data from 1985Q1 to 2005Q3 for US, and some Asia Pacific cities to ascertain whether the superiority of “value” over “growth” real estate investment is a “flash” in the pan, i.e. unsustainable. The office, industrial and retail property investments are examined in the context of the value-growth paradigm, and complemented with mean reversion and stochastic dominance tests. In addition to confirming the relative superiority of “value” over “growth” property investment, the results show that office and industrial property investments exhibit return reversal. This implies that the “win” is sustainable. Although the returns from retail property investment display inertia, the results of stochastic dominance test validate the relative superiority of “value” over “growth” property investment for all the three sectors. This implies that fund managers who traditionally have been favoring prime (i.e. growth) property investment may have to reconsider their investment strategy if they want to maximize their return. Chapter 3- Real estate is so important a subject that it cannot be left out any serious macroeconomic deliberation and the collective quest for investment, wealth creation, poverty alleviation and economic development. This is amply demonstrated by the negative effects that the current real estate market downturn is having on every facet of the economies of rich nations. The role played by, especially, private real estate in the economic development of the advanced world is well documented. The importance of well established real estate markets that operate efficiently cannot, therefore, be over-emphasised. One area that has and continues to dominate discussions relates to how real estate market information should be organized and managed to guide participants in the markets to make efficient purchase, sale and investment decisions. It is often the responsibility of the state to organize and manage real estate market information through implementation of land registration programmes. In Ghana, despite 126 years of unbroken history of implementing land registration programmes, it is estimated that only 8% of real estate ownership has been registered. It is important to properly comprehend this problem and its fundamental causes in order to proffer the appropriate remedies. Using the quantitative research methodology, this study seeks to offer explanations of the large lag in land registration in Ghana. It has been established that the fundamental root cause of the problem is the fact that the operation of Ghanaian state agencies that are responsible for the organization, management and dissemination of real estate market information is not based on clear economic principles. As a starting point, it is recommended that a nationwide timed-bound real estate ownership census akin to the survey conducted in Britain that resulted in the Domesday Book of 1086 be carried out and it should be financed by the government. From then onwards, it should be in the interest of the state to ensure that every real estate ownership or transaction is recorded by instituting an incentive package that would attract people to register; after all, such information would be sold to the public at a price. In this way a viable real estate ownership information system would be created, which would enable the real estate market to operate efficiently. Chapter 4- The turmoil in the international financial market since the subprime loan crisis has had a significant effect on the real-estate investment market in Japan, particularly the Japan real-estate investment trust (J-REIT) market. This suggests that the real-estate

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Preface

ix

investment market is becoming part of the financial market. It is necessary to precisely understand the mechanism of risk generation and cash flow in the real-estate market to understand the characteristics of the real-estate investment market. The purpose of this study is to statistically clarify the characteristics of the five problems that have been recently pointed out as risk factors in the real-estate investment market for housing. Specifically, we have attempted to clarify the following five intrinsic problems, which are considered to be characteristics of the housing market: 1) the return problem, 2) the small-scale investment problem, 3) the risk associated with the adjustment of rent, 4) the key tenant problem, and 5) the inflation problem, all of which have been pointed out to be problems in the housing and commercial property markets. Regarding the risk associated with the adjustment of rent, we investigated the actual situation in the housing market by considering the decrease in housing rent with the age of the building and the adjustment of housing rent when a new contract is concluded between a landlord and a new tenant. The results indicated that the yearly rate of decrease in housing rent for nontimbered houses is as high as approximately 6% over the first five years after construction, but decreases to 2.6% over the 5th to 10th years and 2.5% over the 10th to 20th years, indicating that the long-term rate of decrease in housing rent is small. The probability of no change in rent was converted to a yearly value of 0.6585, which means that the revenue from the housing rent of 65% of leasehold properties does not change. This result revealed that housing rent in the Japanese market is extremely sticky compared with that in the US. Regarding the risk associated with the adjustment of rent, the probability of downward adjustment of the housing rent should be considered; however, in most cases, the housing rent is left unchanged. Even when the housing rent is adjusted downward, decreases of more than 10% comprised only 11.2% of all the adjustments. Also note that the occurrence of rent adjustment is random with respect to time; the housing rent market is not strongly affected by the economic environment, in contrast to the market for office buildings; a turnover of residents occurs because of events such as marriage, childbirth, and relocation, regardless of the economic cycle, causing the housing rent to change. Chapter 5- This chapter analyzes the costs and benefits of the Fannie Mae’s and Freddie Mac’s retained portfolios while they remain under conservatorship. Increasing numbers of homeowners are threatened with foreclosure because of interest rate resets on subprime mortgages, combined with stagnant or falling home prices. Congress responded to this situation by passing the Housing and Economic Recovery Act of 2008 (H.R. 3221, P.L. 110-289), which uses the congressionally chartered, stockholder-owned government-sponsored enterprises (GSEs), Fannie Mae and Freddie Mac, to lead the market in providing more affordable mortgages. The GSEs have retained mortgage portfolios with a combined value of more than $1.4 trillion. The size of the portfolios, past management problems, risks to the financial system, and potential cost to the taxpayer led, in part, to provisions of the Housing and Economic Recovery Act that changed the rules governing the activities and regulation of Fannie Mae and Freddie Mac. The bill created the Federal Housing Finance Agency (FHFA) and authorized it to regulate the size of the GSEs’ retained mortgage portfolios; it also raised the conforming loan limit in certain high-cost areas, thereby allowing the GSEs to purchase larger mortgages in these areas. Previous regulatory actions have affected the GSEs’ portfolios. In 2006, following discovery of accounting and management problems, the GSEs agreed to restrictions on their

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retained portfolios. In 2007, the Office of Federal Housing Enterprise Oversight (OFHEO), now the Federal Housing Finance Agency (FHFA), denied requests from both Fannie and Freddie to raise or eliminate the caps, but these restrictions were relaxed shortly afterwards. On September 6, 2008, the GSEs were placed in conservatorship (government management). One condition of the conservatorship set the portfolio limit to $850 billion as of December 2009, with a 10% yearly decline until the portfolios reach $250 billion. The GSEs’ portfolios include mortgages and mortgage-backed securities (MBS) that are subject to financial risks. When these risks are not managed properly, or if market movements turn dramatically against the GSEs, the government faces two unsatisfactoryalternatives: eitherlet the GSEs go into default and work to control the financial repercussions, or step in and assume payments on the GSEs’ debt at a significant cost to taxpayers. The GSEs and their supporters argue that the profits generated by the investment portfolios enhanced the GSEs’ ability to support affordable housing programs and reduce mortgage interest rates. Chapter 6- Mortgage-backed securities that are packaged and issued by private industry participants are required to comply with the Securities Act of 1933. Issuers of so-called private label mortgage-backed securities must either register these securities pursuant to the rules the Securities and Exchange Commission has set forth, or obtain an exemption from registration. Failure to register or fall under an exemption could result in liability for the issuer and other parties involved in the offering. Furthermore, material misstatements or omissions in the offering materials may also result in liability under the Securities Act. This chapter will provide an overview of the Securities Act of 1933 as it may be applied to mortgage-backed securities issued by private industry participants. Chapter 7- Chairman Kucinich, Ranking Member Jordan, and members of the Subcommittee, I appreciate the opportunity to appear before you today to examine several issues related to the condition of the banking system. First, I will discuss credit conditions and bank underwriting standards, with a particular focus on commercial real estate (CRE), and I will briefly address conditions in the state of Georgia. I will then describe Federal Reserve activities to enhance liquidity and improve conditions in financial markets. Finally, I will discuss the ongoing efforts of the Federal Reserve to ensure the overall safety and soundness of the banking system, as well as actions taken to promote credit availability. Chapter 8- The study set out to determine the extent to which diversification, as promoted in the financial literature, is actually implemented by institutional investors in South Africa. Diversification theory is encapsulated in a conceptual model of potential diversification strategies. The universe of listed real estate investment trusts in South Africa (Property Unit Trusts and Property Loan Stock Companies) was evaluated in 2004 and the study was updated in 2009. Content analysis was used to compare the conceptual model of potential diversification strategies with the annual reports of the listed real estate investment funds. The study finds that in 2004 few of the available diversification strategies were reported on. By 2009, reporting was more comprehensive. The study also explores focused strategies as an alternative to diversification. Chapter 9- In late 2000, the Federal Reserve and the Treasury proposed to increase banking powers. They proposed allowing banking companies to engage in real estate brokerage and management, as activities that are financial in nature. The substantiative issues are the respective nature of banking and of real estate activities and the potential impact on consumers. Procedural questions involve the intent of Congress in P.L. 106-102, which delegated authority to both agencies to issue new regulations. The reintroduced Community

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Choice in Real Estate Act, H.R. 111/S. 413, 110th Congress, would permanently remove these real estate activities from consideration under the marketadaptive powers of the regulators. In the mean time, Treasury spending bills have forestalled any such regulations for six fiscal years, most recently in P.L. 110-5. Chapter 10- Access to long-term credit remains one of the major obstacles to solving the perennial housing problems in many emerging economies. These countries have been making serious attempts at developing their mortgage markets in recent times. There is a general consensus on the need for emerging economies to develop housing finance systems that would ensure easy, affordable and sustainable accessibility to credit. The exact nature and the elements of such a system are still subject to debate. In this commentary, we argue for the institution of secondary mortgage markets but recommend the use of mortgage credit institutions in the short to medium term.

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Chapter 1

ADDRESSING THE ONGOING CRISIS IN THE HOUSING AND FINANCIAL MARKETS

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Douglas W. Elmendorf Chairman Conrad, Senator Gregg, and Members of the Committee, I welcome the opportunity to discuss the turmoil in our nation’s housing and financial markets and some options for additional action by policymakers. A strong financial sector is a necessary component of a robust economy. Financial markets and institutions channel funds from savers to borrowers who need the money to build businesses and hire workers and to buy homes and other goods and services. Indeed, credit is often required to support the ordinary operations of businesses—for example, to finance their inventories and to meet payrolls before payments are received. If the customary means of obtaining credit break down, the disruption to households’ and businesses’ spending can be severe. Thus, the ongoing crisis in the U.S. financial system has significantly depressed economic activity during the past year and a half, and it poses a serious threat to the nation’s ability to quickly return to a path of solid economic growth. Losses on mortgages, on assets backed by mortgages, and on other loans to consumers and businesses, together with an associated pullback from risk taking in many credit markets, have raised the cost and reduced the availability of credit for borrowers whose credit ratings are less than the very highest. To be sure, among the fundamental causes of the crisis was the provision of too much credit at too low a price as well as insufficient capital. However, the sudden shift to a much higher price for risk taking has led to a significant reduction in wealth and borrowing capacity; it has also forced a number of financial institutions to close and others to be merged with stronger operations. Those forces, in turn, are weighing heavily on consumption, the demand for housing, and businesses’ investment. Policymakers have responded to the turmoil with a set of unprecedented actions. Thus far, a systemic collapse of the financial system has not occurred, and conditions have improved noticeably in some financial markets. Nevertheless, according to some analysts, U.S. banks and thrift institutions could be facing more than $450 billion in additional estimated losses on their assets—on top of the approximately $500 billion that has already

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Douglas W. Elmendorf

been recognized. The scale of those losses suggests that many financial institutions and markets will remain deeply troubled for some time, which will keep borrowing exceptionally costly for many borrowers and thereby dampen spending by households and businesses. Challenging conditions seem likely to persist for some time in the housing and mortgage markets as well. Housing sales remain weak, and construction activity continues to decline. With the housing market’s large glut of vacant properties, the prices of homes are likely to fall considerably further, pushing the value of more borrowers’ homes below the value of their outstanding mortgages. As more of those “underwater” borrowers experience losses of income during the current recession, rates of delinquency and foreclosure on residential mortgage loans are likely to rise further. A crucial and challenging question for policymakers is, What further actions can be taken to normalize the financial and housing markets so as to spur economic activity? A separate but equally important question—though not one considered in this testimony—is, What can policymakers do to reduce the risk of a financial crisis in the future? I will make four major points in this testimony: • •

•

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•

Turmoil in the housing and financial markets is likely to continue for some time, even with vigorous policy actions and especially without them. Most economists think that to generate a strong economic recovery in the next few years, further actions to restore the health of the housing sector and the financial system are needed. An effective policy to ensure the availability of credit for qualified borrowers probably requires a multifaceted strategy that uses a range of tools to address the different aspects of financial distress. The costs to federal taxpayers of actions to reduce mortgage foreclosures and improve financial conditions are highly uncertain and may be large, but the economic consequences of doing nothing may be even greater.

THE ECONOMY’S CONTINUING FINANCIAL PROBLEMS The vigorous monetary and financial policy actions of the past year and a half represent a graduated response to the unfolding crisis.1 When the first signs of financial turmoil emerged, it was not clear either to policymakers or to most other observers just how serious the crisis would become. The Federal Reserve first began to supply additional liquidity to credit markets in August 2007 as pressures from losses on mortgage-related assets unexpectedly began to mount. In the following year and a half, the central bank greatly increased the funds it was providing by creating a number of new lending facilities to address emerging problems among financial institutions and in certain markets (such as those for commercial paper, money market mutual funds, and mortgages). It also expanded arrangements (known as currency swaps) to provide U.S. dollars to a number of foreign central banks and slashed the federal funds rate, which banks charge each other for overnight loans of their monetary reserves, almost to zero by late last year. 1

Tables 1 through 3 on page 26 describe those actions in more detail.

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Policymakers also took a series of significant steps to prevent the problems with solvency that a number of major financial institutions were experiencing from further destabilizing markets. •

•

•

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•

•

•

The Federal Reserve, in consultation with the Department of the Treasury, facilitated the sale of the investment bank Bear Stearns to the commercial bank JPMorgan Chase, in March 2008, by lending $29 billion to a newly formed limited liability company (LLC), Maiden Lane, against a $30 billion portfolio of Bear Stearns’s less liquid assets. (An LLC, like a corporation, offers protection from personal liability for debts incurred by a business.) The Federal Housing Finance Agency (FHFA)—the regulator of Fannie Mae, Freddie Mac, and the 12 Federal Home Loan Banks—put Fannie Mae and Freddie Mac into conservatorship, and the Treasury provided an initial pledge to inject up to $100 billion of capital into each of the institutions by purchasing an equity share, or ownership interest, in each company.2 The Federal Reserve extended a $60 billion line of credit to the insurance company American International Group (AIG). Additionally, the Federal Reserve Bank of New York arranged to lend up to $52.5 billion to two newly formed LLCs to fund purchases of residential mortgage-backed securities and collateralized debt obligations from AIG’s securities portfolio. The Emergency Economic Stabilization Act of 2008 (Division A of Public Law 110343) created the $700 billion Troubled Asset Relief Program (TARP), which began purchasing preferred stock of commercial banks in late October. (Preferred stock refers to shares of equity that provide a specific dividend to be paid before any dividends are paid to common stockholders and that take precedence over common stock in the event of a liquidation.) The law also temporarily raised the ceiling on deposit insurance from $100,000 to $250,000 per depositor. The Treasury, the Federal Reserve, and the Federal Deposit Insurance Corporation (FDIC) jointly announced agreements with Citigroup and Bank of America to provide each with a package of asset guarantees, access to liquidity, and capital. The FDIC created the Temporary Liquidity Guarantee Program in October 2008 to strengthen confidence and encourage liquidity in the banking system. The program guarantees certain newly issued unsecured debt of banks, thrift institutions, and certain holding companies and provides full deposit insurance coverage for certain checking and non-interest-bearing deposit accounts, regardless of dollar amount.

The actions mentioned above have improved conditions in some financial markets and thus far reduced the risk of a financial meltdown. The interbank market for short-term loans, which had virtually seized up, has improved markedly in recent months, as indicated by the spread, or difference, between the interest rates banks pay to borrow from each other and their expectations about the federal funds rate. (The spread reflects the risk that banks will not 2

Fannie Mae and Freddie Mac were originally created as federally chartered institutions but were privately owned and operated. Designed to facilitate the flow of investment funds, they pool mortgages purchased from mortgage lenders and sell them as mortgage-backed securities, collecting annual guarantee fees on the mortgages they securitize. Conservatorship is the legal process in which an entity is appointed to establish control and oversight of a company to put it in a sound and solvent condition.

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repay the loan.) That spread can be measured by the difference between the key interbank lending rate, the three-month Libor (the London interbank offered rate), and the average expected federal funds rate over the next three months.3 The spread has fallen to about 1 percentage point, which is roughly where it was before the failure of the investment bank Lehman Brothers (though still well above its historical norm) and well below its peak of 3.6 percentage points in October 2008. Transactions in the interbank market for short-term loans have picked up, and loans are being extended to somewhat longer terms than those seen recently, signaling that the crisis of confidence among financial institutions is continuing to ease. Conditions have also improved in the market for commercial paper, as indicated by a smaller spread between the interest rate on commercial paper and the rate on three-month Treasury bills. (Commercial paper is a kind of short-term borrowing that provides credit to financial and nonfinancial firms.) The spreads for commercial paper that represents higherquality credit have fallen substantially; in the case of paper with the highest credit rating, spreads have returned to the levels observed before September 2007—that is, before the financial crisis began to emerge. Moreover, the amount of commercial paper issued by financial firms has mostly recovered after a sharp decline last fall (the amount of nonfinancial commercial paper has changed little during the crisis).Those improvements, however, do not imply that private lending has returned to normal; rather, the Federal Reserve has provided extensive financial support to this market, particularly for paper that carries longer maturities, whose spreads remain elevated. Indeed, the amount of outstanding asset-backed commercial paper has yet to recover from the sharp drop that occurred in September 2007, and markets for lower-quality commercial paper no longer extend beyond a 90-day maturity. Credit difficulties are much more severe for companies with low credit ratings. Firms’ issuance of investment-grade (high-quality) debt was robust in the fourth quarter of 2008, and the interest rates that AAA-rated firms—those with the highest credit ratings—are paying to borrow money are 2 percentage points lower than at the height of the crisis, in October. (The spread of the AAA rate over the interest rate on 10-year Treasury notes nevertheless reached historic highs at the end of last year, indicating that the convulsions in financial markets and the recession have affected the cost of credit even for firms with the highest credit rating.) Conditions are more difficult for firms that have lower credit ratings—there has been little issuance of below-investment-grade debt. In addition, spreads on junk bonds have widened since September 2008, in part reflecting the difficulties that continue to beset the economy. Although some financial conditions have improved significantly since September and October of last year, the flow of credit from banks remains constricted. A recent study showed that apart from preexisting lines of credit, bank lending to large borrowers dropped sharply during the September-to-November period.4 Moreover, the senior loan officer opinion survey conducted by the Federal Reserve in October 2008 shows that banks continued to tighten lending standards and terms in the third quarter of 2008. About 80 percent of large banks tightened lending standards for commercial and industrial loans, an important source of

3

The Federal Reserve attempts to achieve a target value of the federal funds rate in its conduct of monetary policy. The expected federal funds rate is measured by the overnight index swap contract. 4 Victoria Ivanova and David Scharfstein, “Bank Lending During the Financial Crisis of 2008” (working paper, Harvard Business School, December 15, 2008). Real Estate Investment Market, Nova Science Publishers, Incorporated, 2009. ProQuest Ebook Central,

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Addressing the Ongoing Crisis in the Housing and Financial Markets

5

credit for investment.5 In addition to their applying more rigorous standards for borrowers to qualify for such loans, more than 90 percent of banks (on net) raised their interest rates on commercial and industrial loans relative to their cost of funds. Lending standards for mortgages have tightened as well, with 100 percent of the respondents in the Federal Reserve’s October survey saying they were applying more rigorous standards to subprime loans (loans made to borrowers with low credit scores or other impairments to their credit histories), 90 percent saying they had tightened standards on nontraditional mortgages (such as alt-A loans, which are riskier than prime loans), and 70 percent reporting having tightened standards for prime borrowers (those considered least at risk of default). In light of the past excesses in mortgage lending, some tightening in standards had been expected. Since October, interest rates on jumbo mortgages (generally loans of more than $417,000) and on conventional 30-year mortgages have fallen, but the spreads between those rates and the interest rate on 10-year Treasury notes rose. Those spreads fell in January, however, due in part to the Federal Reserve’s actions to support the mortgage market (discussed later). Lenders have also tightened standards and terms for consumer loans. In the third quarter of 2008, 58 percent of respondents to the Federal Reserve’s survey reported tightening standards on credit cards, compared with 67 percent reporting such tightening in the second quarter. Interest rates on credit cards have begun to move down modestly over the past several months, but given the much lower Libor rates, the interest rate spread has, in fact, widened. Tighter standards for lending, declines in employment, and a large drop in consumer confidence have contributed to a marked slowing in the growth of consumer credit. By November 2008, the amount of consumer credit had grown by only 2¼ percent relative to the amount in November 2007, compared with growth of more than 5½ percent in the previous year. Much of the slowdown in growth in the past year occurred after July 2008, when the financial turmoil began to intensify. Continuing declines in house prices and the ongoing recession are likely to worsen the financial condition of banks. Delinquency rates on residential mortgage loans continued to rise through the third quarter of last year (the latest available data), and foreclosure rates have remained high. Delinquency rates on commercial real estate loans and consumer installment loans at commercial banks have also risen sharply over the same time span. According to the latest compilation by the Bloomberg financial information network, financial institutions worldwide have recognized losses of about $1 trillion since the third quarter of 2007, primarily because they held securities based on residential real estate. Analysts with Goldman Sachs estimate that banks worldwide are likely to experience about another $1 trillion in losses on residential mortgages, loans for commercial real estate, credit cards, auto loans, commercial and industrial loans, and corporate bonds.6 In an attempt to deal with such losses, financial institutions have been reducing their leverage—that is, their use of borrowed funds—by holding a greater amount of capital in relation to their assets. In 2007 and early 2008, many banks seemed to have little difficulty in deleveraging because they could obtain additional capital from private sources through 5 6

Board of Governors of the Federal Reserve System, The October 2008 Senior Loan Officer Opinion Survey on Bank Lending Practices (November 2008). See Jan Hatzius and Michael Marschoun, Home Prices and Credit Losses: Projections and Policy Options, Goldman Sachs Global Economics Paper 177 (New York: Goldman Sachs, January 2009).

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offerings of common and preferred stock. As the solvency of more and more financial institutions has been tested, however, those private sources of capital appear to have dried up.7 (Another way to increase capital would be to cut dividends, but most banks are reluctant to do so because that could deter new and existing shareholders from holding the stock.) The interventions by the Treasury and the Federal Reserve in the past several months have been largely directed toward counteracting the contraction of credit that results from banks’ deleveraging. The Federal Reserve, through its holdings of assets and by direct lending (for example, in the commercial paper market), has provided credit that private institutions previously would have provided. In addition, most of the first half of the $700 billion in TARP funding has been used to supply banks directly with capital.

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THE NEED FOR A MULTIFACETED STRATEGY TO ADDRESS THE FINANCIAL CRISIS Economists and financial experts widely agree that the financial markets are likely to remain severely stressed for some time and that additional action is desirable now to promote their recovery and hence the economy’s return to more vigorous growth. With the economy weakening, losses on loans are likely to continue to deplete the capital of financial institutions for the foreseeable future. Such conditions raise the prospect of a vicious cycle of loan losses, leading to further reductions in the availability of credit, weaker economic activity, more loan losses, and so on. Stimulus from fiscal policies can strengthen the economy and, as a result, complement policies directed specifically at strengthening the financial sector. Many analysts agree that a broader, clearer strategy is necessary to help return the flow of credit to a more normal state and support the recovery of overall economic activity. Some critics of the actions taken to date say those interventions have been confusing to markets and have given the impression that the government is “playing favorites” (because different forms and amounts of support have been given to different financial institutions).8 Private investors are chary of providing capital to banks in part because of uncertainty about banks’ financial positions and future government actions. Moreover, banks may be postponing actions to resolve their financial problems in anticipation of receiving additional support from the government. Therefore, one advantage of a more clearly enunciated strategy would be that financial markets would be more certain about future policy steps.

7

8

Because new capital would largely help to shore up balance sheets, new investors would expect existing shareholders to accept a dilution of their ownership. Existing shareholders would rather take the gamble of not raising new capital than suffer an immediate reduction in wealth. Economists refer to that reluctance of distressed firms to raise equity capital as a “debt overhang” problem. See Shadow Financial Regulatory Committee, An Open Letter to President-Elect Obama, Statement No. 264 (Washington, D.C.: American Enterprise Institute, December 8, 2008), available at www.aei.org/ docLib/20081208_StatementNo.264.pdf; and Luigi Zingales, “Yes We Can, Mr. Geithner,” available at www.voxeu.org/index.php?q=node/2807.

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Principles for Crafting a Strategy Several principles can be used to craft a sound strategy for further assisting the recovery of the financial markets: •

•

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•

•

•

•

•

Effective strategies would have some degree of flexibility so actions can be adjusted to changing and unexpected circumstances. There is enormous uncertainty not only about the future course of this crisis but also about its impact on economic activity, the degree of success that might be expected from different policy actions, and the amount of resources to devote to those actions. A degree of flexibility would allow policymakers the leeway to shift gears so as to regain traction in a crisis that might continue to unfold in unexpected ways. Flexibility that is governed by principles that are understood by the private sector could reduce uncertainty about the government’s interventions, which can freeze actions by the private sector. A sound strategy would determine an appropriate price for the assistance given to financial institutions. Such pricing should give financial institutions an incentive to solve their problems on their own if they are in a position to do so and should mean shuttering institutions that have little prospect of recovery. Underpricing the support would profit creditors, executives, or workers in the financial system at the expense of taxpayers. As a result, it would increase the likelihood that they would continue to take excessive risks in the future or become too large and important an institution to be allowed to fail (a phenomenon known as moral hazard). However, overcharging would delay the system’s and the economy’s recovery. An effective strategy would encourage the participation of private capital. Having a role for private capital is important both because the government cannot provide enough money itself and because private market signals regarding the long-term viability of specific institutions can be valuable. Encouraging private capital means not only that the strategy must provide clear guidance, but also that it must avoid as much as possible a lack of clarity and especially incentives that encourage private capital to sit on the sidelines and wait for government to act. As the financial system is rebuilt, private creditors will have to take some losses; and some banks may have to fail: It is neither necessary nor desirable for government to take on all the losses from bad assets. An efficient strategy would distort the supply of credit as little as possible. Distortions could arise, for example, if policies picked winners and losers—that is, if they treated financial institutions in similar circumstances differently or focused on certain types of credit at the expense of others with similar needs. A sound strategy would coordinate the activities of government agencies (including the Federal Reserve, the Treasury, the FDIC, the FHFA, and the Securities and Exchange Commission) to avoid overlapping actions and initiatives that operate at cross purposes. A successful strategy would be implemented quickly to reduce the chances of a vicious cycle of losses on loans, reductions in the availability of credit, weaker economic activity, more loan losses, and so on.

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Evidence from Other Crises Previous financial crises in the United States and other countries highlight the risks and greater costs that come from implementing only partial measures in the hope that time and economic growth will quickly resolve problems in the banking system. The savings and loan crisis in the United States in the late 1980s illustrates the costs of delaying action. The ultimate cost to taxpayers for cleaning up the thrift crisis was estimated to be about 2 percent of gross domestic product (GDP), and an analysis by the Congressional Budget Office (CBO) found that delays in closing and resolving insolvent thrifts doubled the costs to taxpayers.9,10 At the time of the crisis, some regulators thought that the problems facing the thrift institutions were temporary and that, given time, the institutions could be restored to solvency through the profits gained in their operations and a recovery in the value of their assets. In effect, though, that forbearance by regulators led many insolvent institutions to take greater risks in the hope of becoming solvent, a phenomenon known as “gambling for resurrection.” Because most of their deposits were federally insured, the institutions could acquire additional funds to make speculative investments by offering somewhat higher interest rates than solvent institutions had to pay. In the end, the costs to taxpayers spiraled, eventually resulting in the Financial Institutions Reform, Recovery, and Enforcement Act of 1989 and the creation of the Resolution Trust Company. Financial conditions in Japan in the 1990s were probably closer to current conditions in the United States. In the 1980s, Japan experienced both stock market and real estate bubbles that by 1992 had burst. Initially, Japanese authorities encouraged the formation of private asset management companies that would purchase troubled assets from banks, but as the financial problems deepened, public funds were also used to purchase assets. In 1997, a credit crisis began with the bankruptcy of a major bank and a securities firm. Like the United States, Japan faced highly elevated interbank lending rates after those events, reflecting a lack of confidence in its financial institutions. In the midst of the crisis, the government changed the accounting rules governing banks’ financial statements, allowing banks to choose whether to value assets at their historical book value or to use “mark-to-market” accounting.11 As a result, Japanese banks could report earnings using the accounting method that was more favorable to them. Using the book value of assets also gave Japanese banks an incentive to offer additional credit to troubled borrowers rather than to healthier firms “to avoid the realization of losses on their own balance sheets.”12 Furthermore, as was the case during the U.S. thrift crisis, regulators allowed and even encouraged the practice of forbearance. By late 2002, Japan had finally begun to address the problems caused by forbearance, and its regulators were pressuring banks to improve their balance sheets. Japan’s financial sector improved, but whether the more effective regulation of banks or the global economic boom

9

Timothy Curry and Lynn Shibut, “Costs of the Savings and Loan Crisis: Truth and Consequences,” FDIC Banking Review (2000). 10 Congressional Budget Office, The Cost of Forbearance During the Thrift Crisis (June 1991). Note that the costs cited for resolving previous financial crises are generally stated in cash terms. 11 Takeo Hoshi and Anil Kashyap, Will the U.S. Bank Recapitalization Succeed? Lessons From Japan, NBER Working Paper 14401 (Cambridge, Mass.: National Bureau of Economic Research, December 2008). 12 Joe Peek and Eric Rosengren, “Unnatural Selection: Perverse Incentives and the Misallocation of Credit in Japan,” American Economic Review, vol. 95 (2005).

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that began in 2003 had the greater impact is difficult to determine. The costs associated with the rescue of Japan’s financial system have been estimated at about 25 percent of GDP.13 The lack of support provided to the financial system during the Great Depression differed from the policy of forbearance that characterized the U.S. thrift crisis and Japan’s financial turmoil, and some economists view that laissez-faire approach to the widespread bank failures that occurred during the Depression as an example of being too strict. According to Federal Reserve Chairman Ben Bernanke, policymakers thought at the time that “to weed out weak banks was a harsh but necessary prerequisite to the recovery of the banking system.”14 By contrast, in Bernanke’s view, the Federal Reserve should have increased the monetary base either by supplying more funds to banks or by increasing the currency in circulation to limit the adverse effects of bank failures on borrowers and depositors. Those funds could then have been used to pay off depositors and curtail runs on banks. A more successful outcome emerged from the response of Swedish policymakers to the financial crisis of 1992, which came on the heels of a speculative bubble in Swedish real estate. By 1991, the cost of the reunification of Germany had caused interest rates in Europe to increase sharply. In addition, international growth was slowed by a recession in the United States, and the combination of those factors led Sweden’s real estate bubble to burst. The steep decline in the value of real estate in turn impaired the value of the assets held by many Swedish banks. The crisis was exacerbated by attempts to defend Sweden’s currency: Sweden’s central bank, the Riksbank, let overnight rates rise as high as 500 percent to prevent the outflow of the Swedish currency. In that difficult environment, Sweden’s economy fell into recession, and banks’ losses increased rapidly. The Swedish government insisted that banks value loans and assets on their balance sheets using mark-to-market accounting standards.15 Under those rules, the values prevalent in the financial markets were applied, even though many participants believed that the current conditions in those markets temporarily understated the values of the assets. That policy led to large losses for the banks, but authorities considered such a policy necessary to restore confidence in the financial system. After the banks’ assets were marked to market, banks identified as having good prospects for surviving were helped, and the rest were either merged with stronger banks or liquidated. As was the case in the savings and loan crisis in the United States, Sweden formed assetmanagement companies to deal with the assets from the liquidated banks. No measures were adopted to support nonfinancial companies, and the number of bankruptcies rose markedly. Sweden placed limits on the Riksbank in its dealings with the banks, basically allowing the central bank only to provide liquidity and not to take risks with taxpayers’ funds. Sweden’s financial sector began to recover about a year after the crisis reached its peak, in late 1993, at a cost of about 4 percent of its gross national product. The experiences of previous financial crises highlight the risks to nations’ economies and the costs to taxpayers when governments delay action to bolster their financial systems in the 13

14 15

See Luc Laeven and Fabian Valencia, Systemic Banking Crises: A New Database, IMF Working Paper WP/08/224 (Washington, D.C.: International Monetary Fund, November 2008); and Anil Kashyap, “Sorting Out Japan’s Financial Crisis,” Federal Reserve Bank of Chicago Economic Perspectives, vol. 26 (2002, Fourth Quarter). Ben S. Bernanke, “Money, Gold, and the Great Depression” (remarks at the H. Parker Willis Lecture in Economic Policy, Washington and Lee University, Lexington, Va., March 2, 2004). See Lars Heikenstein, Deputy Governor, Risbank, “Financial Crisis—Experiences from Sweden” (speech in Seoul, Korea, July 15, 1998).

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hope that economic growth will resolve banks’ problems. Of course, some cases could be cited in which global economic growth has allowed financial systems to recover without special government action. Yet once the problems of such systems became as severe as in the United States’ current situation, economists and financial experts generally agreed that additional action was desirable to promote a system’s recovery. Successful approaches have entailed forceful action by government authorities to uncover the true financial condition of each bank, to close banks in the worst shape, and to provide support to banks that appear viable in the long run.

Possible Elements of a Rescue Strategy

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Several complementary approaches might be used to further assist the recovery of the financial system. Some extend or continue current interventions; others attack the crisis from different angles. Inject more equity into financial institutions The government could further strengthen the financial system by taking a larger ownership interest in some financial institutions through the purchase of more equity. That could be accomplished by continuing the Capital Purchase Program (CPP) under the TARP, an approach that was widely supported by economists when it began. In the eyes of some observers, the government’s further purchases of equity in banks would bring the government closer to nationalizing a major portion of the banking system. However, additional purchases may be appropriate if conditions in the financial markets worsen. The main advantage of this approach is that it would provide banks with a greater capacity to absorb further losses, which would help stabilize the banking system and in so doing support banks’ lending. Another advantage of injecting equity is that it would maintain existing channels of borrowing and lending. Such channels cannot be created overnight, and the use of existing pathways would allow lending to pick up again more quickly. Some observers have criticized the CPP because they believe that banks that have received money from the equity purchases have not increased their lending sufficiently. That criticism is difficult to evaluate because it is very hard to trace the use of particular funds in large and complex banks, and it is very hard to know what bank lending would have been in the absence of equity injections by the TARP. In addition, many banks currently have good reason not to boost lending. To the extent that they need to reduce their own leverage, they can do that either by lending less or by getting more capital. The government’s capital injection may thus mean that banks do not have to cut their own lending as much, but that may not mean they can actually increase lending. Moreover, even without the need to delever, the slow growth of lending reflects banks’ unwillingness to increase risky lending in the current recession or a lower demand for borrowing as a result of the slowdown. A further criticism of the CPP is that it is purchasing equity from banks at very favorable terms for the banks. The program requires all banks to pay a dividend of 5 percent on the government’s preferred shares for the first five years—even though banks that have other outstanding preferred shares currently pay the owners of those shares a higher dividend.

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Moreover, the subsidy that the government’s purchase represents varies by bank, because it depends on the market’s assessment of the riskiness of investment in the bank. Injecting more equity into financial institutions raises the risk of propping up banks that should be allowed to fail. By supporting weak banks, the government may be allowing them to take excessive risks in hopes of resurrecting themselves. If they are successful, they stay in business; if they are not successful, their mistakes are paid for by the federal deposit insurance system or by taxpayers. Government equity injections are, moreover, unlikely to be sufficient to fill all the capital needs of banks if they are to provide a level of lending that is sufficient for a growing economy after the recession ends. Policy therefore needs to be designed to encourage private investors to supply some of the new capital. A clear, principled policy can reduce the incentive for private investors to sit on the sidelines, waiting to see how much money the government will commit and which institutions will be supported. One possible approach to determining which banks should receive funds and the price they should pay for them, while at the same time encouraging private participation in recapitalization, would be to match the government’s equity contributions or loans to private equity purchases. The involvement of private investors would solve the pricing problem because they would inject capital into firms only on terms that provided an adequate return on their investment. Policymakers could require that any injections of public capital be matched by private investors’ equity purchases and that the dividend rate that banks pay on their new public capital equal the rate they must pay on the new private capital. In that way, taxpayers would receive a return on their investment that more closely reflected the risks they were assuming. However, the management and shareholders of distressed firms are unlikely to agree to take equity infusions without some federal subsidy because the injection of new equity capital on market terms usually benefits the firm’s debtholders at the expense of its shareholders.16

Address troubled assets The government could facilitate the removal of troubled assets from the balance sheets of some institutions. Such a removal could clarify the true value of institutions’ balance sheets by removing the difficult-to-value assets from some institutions and by establishing a market price that other institutions could use in their own valuations. That step might improve the solvency of some institutions by establishing a price for troubled assets that exceeds both the value of those assets on the institutions’ books and the price that investors are currently willing to pay for them. That would leave those institutions in a better position to raise capital and make new loans. At the same time, establishing a market price could force some institutions to recognize losses, because of the accounting rule that most assets held for sale must be marked to market. Moreover, removing troubled assets would allow the managers of financial institutions to focus on new lending rather than on cleaning up previous mistakes. One approach that is currently much under discussion is to set up an “aggregator bank” that would purchase risky assets that are not actively traded from troubled institutions and then dispose of them, leaving the balance sheets of the banks clean so that they could then return to lending. That is similar to Sweden’s approach, described earlier. 16

That phenomenon is termed the “debt overhang” problem (see, for example, S. Myers, “Determinants of Corporate Borrowing,” Journal of Financial Economics, vol. 5 (1977).

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The first problem to be encountered is how much to pay for those assets. Because they are not actively traded, the assets do not have readily observable market values. Paying too much would help recapitalize the banks, but it would reward risky behavior and leave taxpayers with a large bill from the losses on the assets. Erring on the low side— such as by buying assets at fire-sale prices—would run the risk of forcing banks to mark down the assets to unrealistically low prices, making more banks insolvent than perhaps needed to be. As with capital injections, the government could partner with private investors to determine a market price for asset purchases. In that case, the government could partially finance (through loans) the purchase of troubled assets by private investors. Because the investors’ profits would depend on accurate pricing, they would help determine the assets’ fair market prices. The government would not finance the full cost of the purchases so that private investors would have to put up—in essence, risk—some of their own money for the transaction. The government could help protect taxpayers’ money in a number of ways: by requiring that the private investors take losses before the government, by holding the purchased assets as collateral, or by using recourse arrangements for the loans (essentially collateralizing the loan with the investors’ other assets). However, without some federal subsidy, private investors might find few willing sellers of such assets. Alternatively, instead of buying assets, the government could guarantee portfolios of assets; that is, provide insurance against some losses on the assets. An asset guarantee would shift the risk of loss from the financial institutions to the federal government, just as if the government had taken direct ownership of the troubled assets. With guarantees in place, financial institutions would more easily borrow and raise capital. Determining the price of the guarantee would not be easy, and the government could experience large losses if the price was too low, or fail to attract participation if the price was too high. Yet another approach, known as “good bank/bad bank,” tries to isolate troubled assets in a different way. An existing bank that has a large amount of troubled assets is split into two new banks—one (a “good” bank) with all of the good assets and lending operations and the other (a “bad” bank) with all of the bad assets.17 Mellon Bank used that approach to deal with its soured energy and real estate loans (without government support) in 1988, and the Swiss government used it last year to deal with the problems of the bank UBS. This approach essentially forces the stockholders and creditors of the bank to absorb the losses from the bad assets while creating a new bank with a clean, transparent balance sheet that should be able to borrow and lend in a normal way. In principle, that approach does not require government funds, although as a practical matter, such funds may well be necessary. Dividing assets and putting them into separate entities has the advantage of providing greater clarity and less uncertainty about the financial health of the new good banks than are offered by the more subtle approaches of guarantees or selective asset purchases. Consequently, the good banks would be more willing to lend to each other (although there might be some reluctance if the existing management team remained in place) and more able to raise new capital from private investors to support new lending. Because this approach would effectively quarantine the bad bank away from the greater financial system, the 17

Chairman Ben S. Bernanke, “The Crisis and the Policy Response” (the Stamp Lecture, London School of Economics, January 13, 2009). See also Zingales, “Yes We Can, Mr. Geithner.” Zingales also proposes a prepackaged bankruptcy option that would allow banks to restructure their debt and restart lending. He describes that option in fuller detail in “Plan B,” The Economists’ Voice (October 2008), available at www.bepress.com/ev.

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approach would also allow for a more orderly liquidation of the bad bank’s assets. Such a process would probably obtain higher prices for the assets than those achievable through a fire-sale liquidation. However, even though Mellon Bank managed to split itself into a good and a bad bank in 1988, many securities lawyers are skeptical that similar splits could be accomplished now without government support or perhaps legislation, because of the competing interests of debt and equity holders. Those competing interests could come into play because some ways of accomplishing the split could favor stockholders over creditors by allowing stockholders a share of profits in the new good bank. Approaches that inject capital and purchase troubled assets could be used together. The government could pay market prices for the assets and then help banks cover their losses through a program of capital injections. In that way, the prices of the assets would not be distorted, but the banks would receive some assistance. That approach, however, has the disadvantage of potentially providing the most government capital to the banks that made the worst business decisions and therefore have the greatest volumes of toxic assets on their balance sheets.

Provide credit directly The government could increase its direct lending to consumers, homeowners, and businesses by expanding existing programs or starting new ones. That approach would increase the availability and lower the cost of credit for those borrowers. For example, the Federal Reserve could expand its Term Asset-Backed Securities Loan Facility (TALF). The TALF is designed to help participants in the market meet the credit needs of households and small businesses by supporting the issuance of asset-backed securities that are collateralized by student loans, auto loans, credit card loans, and loans guaranteed by the Small Business Administration. The TALF is expected to begin lending in February 2009; at that point, the Federal Reserve Bank of New York will lend up to $200 billion on a nonrecourse basis to holders of certain AAA-rated securities that are backed by newly and recently originated consumer and small business loans. The Federal Reserve Bank of New York will lend an amount that is less than the market value of the securities; the loans will be secured at all times by those securities. The Treasury—under the TARP—will provide $20 billion of credit protection to the Federal Reserve Bank of New York in connection with the TALF. The Federal Reserve could expand the TALF by buying securities backed by other types of assets, such as mortgages on commercial properties. The Federal Reserve also could expand its Commercial Paper Funding Facility (CPFF), which is designed to provide a liquidity “backstop” to U.S. issuers of commercial paper. The CPFF is intended to improve liquidity in short-term funding markets and thereby contribute to greater availability of credit for businesses and households. Under the CPFF, the Federal Reserve Bank of New York finances the purchase of highly rated unsecured and asset-backed commercial paper from eligible issuers via primary dealers.18 In expanding the facility, the Federal Reserve could purchase more paper from eligible issuers and expand the program to include lower-rated paper.

18

Primary dealers are firms that trade in U.S. government securities with the Federal Reserve System. There are currently 17 primary dealers.

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Another alternative would be for the government to attempt to broadly lower the cost of mortgage loans. In lowering the cost of borrowing, such a program would raise the demand for houses, but it would be unlikely to boost house prices significantly, given the large overhang of vacant houses. Programs of that kind would also help reduce unnecessary foreclosures by increasing opportunities to refinance unaffordable loans. Several programs are already in place to lower the cost of prime conforming loans (loans of up to $417,000—higher in high-cost areas—that are eligible to be purchased by Fannie Mae and Freddie Mac). •

•

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•

The Housing and Economic Recovery Act of 2008 authorized the Department of the Treasury to buy obligations and securities issued by Fannie Mae and Freddie Mac. About $70 billion of residential mortgage-backed securities had been purchased as of December 31, 2008. Over the next several quarters, the Federal Reserve, through competitive auctions, will purchase up to $100 billion in debt issued by the three government-sponsored enterprises for housing—Fannie Mae, Freddie Mac, and the Federal Home Loan Bank System.19 Over the next several quarters, the Federal Reserve will purchase up to $500 billion in mortgage-backed securities issued by Fannie Mae, Freddie Mac, and the Government National Mortgage Association (Ginnie Mae).20

The government could begin a similar program to help thaw the market for jumbo mortgages and stimulate originations of jumbo loans. Under such an approach, the government could purchase securities that are backed by jumbo loans either directly or through Fannie Mae and Freddie Mac. Policymakers have also worked to improve the supply of student loans. In May 2008, lawmakers enacted Public Law 110-227, the Ensuring Continued Access to Student Loans Act, which allowed the Department of Education to offer buyer- and lender-oflast-resort programs to lenders in the Family Federal Education Loan Program (or FFELP). Lenders in the FFELP program, who finance the loans they make to students in private capital markets (with federal assistance), have seen their financing costs increase sharply since the financial market turmoil began.Under the new programs, which apply to loans issued before July 1, 2010, lenders may obtain temporary financing from the Education Department at attractive borrowing terms (that is, at financing rates higher than those that might be considered normal but lower than the rates they could get in the current credit markets), or they may sell their loans to the department (at close to face value). Without the additional federal assistance, those higher funding costs would have forced lenders to cut back on their lending in the 20082009 school year and beyond. To date, the actions of the department have been successful in ensuring the continued availability of student loans. The Department of Education has provided temporary financing of $8.7 billion, which covers almost half of the loans originated in the 2008–2009 school 19 20

Unlike Fannie Mae and Freddie Mac, the Federal Home Loan Bank System, which provides lowcost loans to home mortgage lenders, has not been taken over by the government. Ginnie Mae, a government-owned corporation, guarantees securities backed by federally insured or guaranteed loans, mainly loans insured by the Federal Housing Administration or guaranteed by the Department of Veterans Affairs.

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year. Loans worth approximately $62 million have thus far been sold to the department under the purchase programs. In November, the Department of Education announced new programs that broadened eligibility for funding and purchases of loans to those originated before 2008. (Before that announcement, only loans that were originated in the 2008–2009 and 2009–2010 school years were eligible for purchase or financing.) Lenders may also be eligible to finance their student loans under the Federal Reserve’s TALF. Assist troubled businesses and governments As part of a broader strategy to support the overall economy rather than just the financial sector, the government could assist nonfinancial industries, as it has started to do with some of the major U.S. automobile manufacturers, whose possible failure appears likely to worsen the ongoing recession. Policymakers used some of the funds provided through the TARP to support General Motors and Chrysler and their financial arms. However, other industries have also sought assistance, putting policymakers in the position of picking winners and losers in the current economic downturn. That situation raises issues of fairness, prompting questions about why some workers and firms receive assistance but others do not. It also raises issues of economic efficiency because assisting troubled businesses could keep labor and capital from moving to other businesses and industries that might better be able to use them. That problem may not seem severe during a recession, when there are unused resources. But to the extent that businesses that would otherwise have failed are still around, and failing to thrive, after the recession, resources will be misallocated and the productivity engine of the economy will be compromised.

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Promoting Actions to Lessen the Number of Mortgage Foreclosures The government could help mortgage borrowers and lenders, and improve conditions in the housing market, by more vigorously supporting efforts to reduce the number of avoidable foreclosures. In 2007, about 1.6 million foreclosures were initiated; the first nine months of 2008 alone saw 1.7 million foreclosures. Moreover, with house prices likely to continue to fall and with the recession pushing down family incomes, analysts expect the number of foreclosures to remain high during the next two years. (CBO expects that the prices of houses will decline by another 14 percent, and some forecasters in the private sector are looking for even bigger slides.) Some analysts are now suggesting that the prices of houses in some markets are back to or near their fundamental values; however, another possibility is that prices could overshoot on the downside by 10 percent or more.21 Many of the coming foreclosures are unavoidable because the borrowers cannot afford a refinanced loan that would also be profitable for lenders (that is, the profits from the modified loan are less than the amount that the lender would earn through foreclosure). However, some of those foreclosures might be avoided if distressed borrowers were given the opportunity to refinance their loans on more favorable terms. If government policies do not address the foreclosure problem, the additional excess supply of houses could further push up expected mortgage losses, which already exceed $1 trillion, according to some analysts.

21

For example, see Hatzius and Marschoun, Home Prices and Credit Losses.

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The benefits of preventing unnecessary foreclosures are considerable, not only for lenders and borrowers but also for the economy. The cost of a foreclosure may range from 30 percent to 60 percent of the value of a property, and by contributing to neighborhood blight, foreclosures have additional negative spillover effects on local economies. A reduction in the number of avoidable foreclosures would complement other actions to strengthen the financial sector because it would shore up the values of mortgage loans on lenders’ books—although not by enough to resolve the problems with solvency in the financial system. A smaller number of foreclosures would also provide some support for house prices, but probably not enough to reverse their ongoing decline. Modifications of mortgage loans have increased in the past year, but the approach has met with limited success—in part because a large percentage of loans that had been modified have subsequently redefaulted.22 However, the streamlined modification plan used by the FDIC to modify loans made by the failed lender IndyMac may have more success because it targets a substantial reduction in the borrower’s monthly payments and repayment burden.23 Efforts to reduce foreclosures face a number of obstacles. Lenders are afraid that if they modify some loans, borrowers who otherwise might meet their contractual mortgage payments will ask for loan modifications as well. Lenders also may be waiting to see what mitigation strategies the government settles on. Further complicating modifications of loans in many instances are second mortgages and home-equity loans and lines of credit. When first liens are underwater—the value of the house is less than the balance on the mortgage—any second liens are almost valueless. In that circumstance, modifying the first lien—especially reducing the principal on an underwater loan— may do the borrower no good if it simply increases the value of the second mortgage. Thus, meaningful loan modification may require the cooperation of second lien holders, which can be difficult to arrange. Modifications of loans held in “pools” that back securities face additional obstacles. (Rates of foreclosure on loans that have been held by the lenders and not securitized are about 20 percent to 30 percent lower than the rates experienced by third-party servicers.) Thirdparty servicers have little or no financial incentive to modify mortgages because they will not be adequately compensated for their costs. In addition, legal constraints and uncertainties in the pooling and servicing agreements for mortgage-backed securities may inhibit modifications. Servicers may be prohibited from performing modifications that improve the net returns to all investors collectively if some investors (typically those holding the lowestpriority claims on the securities’ returns) are made worse off by the modification. Consequently, a number of proposals have been advanced to overcome those obstacles. To align the incentives of servicers more closely with those of investors and borrowers, servicers could be paid a fee for each successful loan modification. Alternatively, investors could be given an incentive to be more receptive to loan modifications. Under some

22

More than 37 percent of the loans modified in the first quarter of 2008 were more than 30 days delinquent after three months, and 55 percent were more than 30 days delinquent six months later. See Office of the Comptroller of the Currency and the Office of Thrift Supervision, OCC and OTS Mortgage Metrics Report, Third Quarter 2008 (December 2008). Also see the remarks of John Dugan, Comptroller of the Currency, at the Office of Thrift Supervision’s Third Annual National Housing Forum, Washington, D.C., December 8, 2008. 23 The FDIC contends that systematic loan modifications can still make good business sense even with a default rate of 40 percent. See the statement of John F. Bovenzi, Deputy to the Chairman and Chief Operating Officer, Federal Deposit Insurance Corporation, before the House Committee on Financial Services, January 13, 2009.

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proposals, those fees would be subsidized by the government. Currently, Fannie Mae and Freddie Mac are increasing the payments they make for loan modifications. Alternatively, a number of proposals would change the legal constraints that inhibit loan modifications on securitized loans. For example, one would eliminate explicit restraints on modifications and create a “safe harbor” from lawsuits in the case of modifications that raise the overall net returns to investors.24 However, such proposals might raise constitutional issues unless compensation was provided to some of the parties who lost out. And that kind of approach would require using taxpayers’ money to compensate the holders of the riskiest “slices” of mortgage-backed securities. Another important obstacle to actions to promote loan modifications is that a large number of distressed borrowers have “negative home equity”—that is, balances on their loans that exceed the homes’ value. By the middle of last year, an estimated 10.5 million borrowers had a total of about $850 billion in negative home equity with an average amount of more than $75,000.25 Those borrowers do not have the necessary equity to qualify for a refinanced loan with a private lender.26 To address that problem, policymakers created the Hope for Homeowners program under the Federal Housing Administration (FHA) to encourage private lenders to refinance loans of borrowers with negative home equity. The FHA will guarantee new 30-year fixed-rate mortgages under the plan if the loans meet a number of criteria. One criterion, that the new loan be between 90 percent and 97 percent of the home’s current appraised value, has limited lenders’ interest in the program because it requires them, in some cases, to “recognize” (record on their balance sheets) a substantial loss on the original loan. To date, no modifications have been completed under this program, and the number of applications has been minimal. Reducing the size of that write-down or subsidizing it (or both) would encourage more lenders to participate, but it would also shift more costs to the government. Other proposals for limiting foreclosures would shift more costs to taxpayers either through federal loan guarantees or direct purchases of loans and their modification by the government. For example, a proposal by the FDIC would result in the government’s guaranteeing modified loans. Under the proposal’s streamlined approach to modifications, modified mortgages would include a reduction in interest rates, an extension of loan terms to 40 years, and forbearance on repayment of the principal, all of which would be designed to reduce a borrower’s monthly cost for housing to as low as 31 percent of his or her monthly income. If the loans subsequently redefaulted, lenders would recover up to 50 percent of the loan from the government, subject to some restrictions. A proposal modeled after the approach taken by the Depression-era Home Owners Loan Corporation (HOLC) would have the government purchase and then refinance mortgages that were in or near default. A new agency would be created that would buy mortgages from lenders at some discount to the mortgages’ book values (the values for the loans that lenders 24 25

26

Statement of Christopher J. Mayer, professor, Columbia Business School, before the House Committee on Financial Services, January 13, 2009. Christopher Mayer and R. Glenn Hubbard, “Home Prices, Interest Rates, and the Mortgage Market Meltdown” (working paper, Columbia University Business School, October 2008), available at www2.gsb.columbia .edu/faculty/cMayer/Papers/Mayer_Hubbard_BEP_10_2008_v7.pdf. Private lenders have generally avoided writing down the principal of mortgage loans in favor of either forbearance on payment of the principal or reductions in interest rates. In part, they fear that write-downs will encourage borrowers to behave strategically to qualify; in part, they also hope that housing prices will recover in the future.

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carry on their balance sheets) and then refinance them at interest rates tied to the government’s borrowing rates (that is, the rates on Treasury securities). Buying up all of the troubled loans, however, would require hundreds of billions of dollars, and determining prices that would provide enough protection to taxpayers would be particularly challenging. Dangers include the likelihood that the worst mortgages would be sold to the government and that a lack of funding would allow only some borrowers to be helped. Although the HOLC returned a small amount of funds to the Treasury when it was liquidated, the program was not costless to taxpayers, who were not compensated for the risks they bore. Another concern is how to target any subsidies that are offered and avoid the problem that some borrowers might be helped a great deal and others only slightly. (Tying the subsidy to the size of the mortgage, for example, would provide greater help to those with bigger mortgages.) Given the aggregate amount of negative equity, such proposals could cost the government hundreds of billions of dollars, even with the private sector absorbing a good portion of the losses. A different approach to encouraging loan modifications would be to change federal bankruptcy laws. Bankruptcy judges could be allowed to restructure certain mortgages on principal residences under Chapter 13—for instance, by limiting a mortgage to the current value of a home (known as “cram down”) or by changing the terms of a loan. Under current law, Chapter 13 halts foreclosure proceedings by lenders, giving homeowners an opportunity to restructure their financial arrangements. Although Chapter 13 currently gives courts the leeway to adjust many financial obligations, it does not generally allow the terms of a mortgage on a principal residence to be modified.27 Changing that provision of Chapter 13 would allow bankruptcy courts to treat mortgages on a primary residence in the same way they treat secured debts on other items, such as motor vehicles, vacation homes, investment properties, and personal businesses. (In practice, bankruptcy judges seldom restructure mortgages on vacation or investment properties.) Allowing a bankruptcy court to modify the amount or terms of a mortgage changes incentives for both borrowers and lenders. It gives borrowers an incentive to file for bankruptcy as a way to lower their mortgage payments and avert foreclosure. Consequently, lenders would have a greater incentive to restructure loans voluntarily. Lenders would also have a stronger incentive to be more prudent in making loans, which could help avoid future excesses in the mortgage markets. In doing so, lenders might raise mortgage rates, particularly for high-risk borrowers, to offset any expected additional losses from loan modifications in bankruptcy. However, some research indicates that in the past, the terms and availability of mortgages that could be modified in bankruptcy were not too different from those that bankruptcy did not cover.28 The increase in mortgage rates might be limited in part

27

11 U.S.C. §1322(b)(2). Furthermore, the Supreme Court has held that even when the value of the debt exceeds the value of the property—a partially secured debt—courts may not modify that debt. See Nobleman v. Am. Savings Bank, 508 U.S. 324 (1993). Conversely, when a second (or third) mortgage is wholly unsecured because the value of the property is insufficient to satisfy the first mortgage, such subordinated debt may be discharged. Tanner v. FirstPlus Fin. Inc. (In re Tanner), 217 F. 3d 1357 (11th Cir. 2000) announced what has become the dominant view among the circuitcourts of appeals. Hence, the claims of partially secured creditors are protected by bankruptcy law, but the claims of unsecured creditors are not. 28 See Adam J. Levitin and Joshua Goodman, The Effect of Bankruptcy Strip-Down on Mortgage Markets, Business Economics and Regulatory Policy Working Paper No. 1087816 (Washington, D.C.: Georgetown University Law Center, February 6, 2008). See also Michelle J. White, Bankruptcy: Past Puzzles, Recent Reforms, and the Mortgage Crisis, Working Paper No. 14549 (Cambridge, Mass.: National Bureau of Economic Research, December 2008).

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because lenders might also change other lending terms to reduce their exposure to losses. Changing the bankruptcy law could also add to the caseload of the bankruptcy court system.

THE BUDGETARY COSTS OF THE FINANCIAL RESCUE The ultimate costs of the actions taken in response to the turmoil in the financial markets are uncertain, but they could be quite large. Those costs derive from the policy actions of various parts of the government—the Federal Reserve, the Treasury, and other federal agencies. Many of the actions involve the purchase of assets or loans by the government; as a result, some portion of the current funding being directed toward the crisis (perhaps most of it) is likely to be recouped in the future. However, given the fragility of the financial sector and the riskiness of the assets being purchased or guaranteed—as well as the social purposes underlying the policy responses—the federal government can expect some net losses from its transactions. (Tables 1 to 3 contain details of those actions.)

Costs to the Taxpayer

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Most of the policy actions taken in response to the financial turmoil have been more like investments than like cash outlays. Both the Federal Reserve and the Treasury have been purchasing financial instruments (for example, mortgage-backed securities) in an effort to boost liquidity in the market; at some point in the future, many of those instruments will be redeemed by their issuers or sold to other buyers. Because such investments were not made purely with the goal of making a profit, they could reasonably be expected to result in some losses.

The Federal Reserve Activities of the Federal Reserve are not directly recorded in the federal budget. Rather, each year its net earnings—generated by interest on its holdings of securities; income from foreign currency holdings; fees received for services provided to institutions that accept monetary deposits from consumers (such as check clearing, funds transfers, and automated clearinghouse operations); and interest on loans to such institutions—are remitted to the Treasury and recorded in the budget as revenues. That income is typically in the range of $20 billion to $30 billion a year.29 Thus, recent actions by the Federal Reserve to address the turmoil in the markets may affect federal revenues through their impact on the amount of earnings that the central bank remits to the Treasury. Those earnings will be diminished by any losses that resulted from creditors being unable to repay loans or from assets that the Federal Reserve acquired proving to be worth less than the cost to acquire them. The central bank has committed nearly $2.3 29

The Federal Reserve is now paying interest on required reserves and excess balances held on behalf of financial institutions. The interest rate paid on those deposits is currently set at 0.25 percent; CBO estimates that the Federal Reserve will incur interest costs of less than $5 billion in 2009. Authorization to pay interest on such reserves came from the Emergency Economic Stabilization Act of 2008, which advanced the effective date of a provision of the Financial Services Regulatory Relief Act of 2006 that was slated to take effect in 2011.

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trillion to its programs, but the assets purchased through those programs are backed by collateral. Still, CBO estimates that the Federal Reserve will incur modest losses, although it is expected to eventually recoup nearly all of its investments. Nevertheless, losses are possible; for example, the Federal Reserve has already written down—by about $2 billion— the value of the assets it acquired in the takeover of Bear Stearns. The Troubled Asset Relief Program CBO records spending for the TARP on a risk-adjusted discounted-present-value basis rather than on a cash basis.30 That is, CBO accounts for the costs resulting from interest subsidies, potential defaults on lending, and other factors. As is the case with the Federal Reserve’s transactions, the principal of most of the assets acquired under the TARP should be repaid over time. Of the $700 billion that the TARP is expected to disburse before the end of December of this year, CBO anticipates that the subsidy cost (after adjusting for market risk) will be about $200 billion.

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Purchases of Mortgage-Backed Securities The Treasury is also purchasing mortgage-backed securities in the private market. Again, those transactions are basically an exchange of assets—the Treasury has used cash to buy the securities and will receive cash upon the sale of the asset or at its maturity. Because there is no statutory provision for an alternate treatment, the cost of purchases of mortgage-backed securities is computed using standard credit reform procedures.31 To date, the net cost of those purchases is close to zero.

Fannie Mae and Freddie Mac In CBO’s baseline projections of the federal budget, most of the cost recorded in 2009 for Fannie Mae and Freddie Mac stems from the existing assets and liabilities of the two GSEs at the time of their takeover. CBO estimates that the value of the GSEs’ mortgage loans and guaranteed assets falls short of their liabilities by about $200 billion (on a present-value basis); that amount is included in CBO’s estimate of the deficit as calculated for 2009. Nearly $40 billion in 2009 and smaller annual amounts thereafter represent the estimated annual subsidy costs (on a net-present-value basis) associated with the GSEs’ new business after the takeover. The decline in the annual subsidy reflects CBO’s forecast that the housing and mortgage markets will stabilize over the next several years. CBO has long held that the federal government has subsidized the operation of Fannie Mae and Freddie Mac by providing what some have called an “implicit guarantee” of the GSEs’ debt.32 However, the federal government has never recognized the cost of the subsidy in its budget. The value of that guarantee (the existence of which has now been demonstrated 30

The Administration is accounting for capital purchases made under the TARP on a cash basis rather than the present-value basis adjusted for market risk that was specified in the Emergency Economic Stabilization Act of 2008. (Present value is the value on a given date of a future payment or series of future payments, discounted using an appropriate interest rate to reflect the risk and term to maturity of the underlying asset.) The Administration’s treatment will show more outlays than will CBO’s treatment for the TARP this year and then will show cash receipts in future years. 31 For an explanation of credit reform, see Congressional Budget Office, Policy Options for the Housing and Financial Markets, Box 3-2 (April 2008). 32 See Congressional Budget Office, Assessing the Public Costs and Benefits of Fannie Mae and Freddie Mac (May 1996), and Federal Subsidies and the Housing GSEs (May 2001).

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by the Treasury) is a large component of the estimated cost of the GSEs’ operations that CBO has included in its baseline budget projections.

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Other Agencies A few other agencies have also taken actions in response to the turmoil in the markets, either through existing authority or on the basis of recent legislation. The FDIC has temporarily raised the limit on insurance coverage—from $100,000 to $250,000 per depositor—and has established a program to enhance liquidity by guaranteeing debt issued by banks as well as deposits in checking accounts and other non-interest-bearing accounts. The FDIC will also provide assistance to Citigroup in conjunction with the TARP and the Federal Reserve. Financial turmoil has also affected credit unions. As a result, the National Credit Union Administration, or NCUA (the federal agency that charters and supervises federal credit unions and insures deposits) has created programs to ensure the liquidity of its member institutions. The costs incurred by the FDIC and NCUA are treated in the budget on a cash basis. The Department of Housing and Urban Development (HUD) has established several programs in an attempt to reduce foreclosures and address other issues in the housing market. Many of those programs were created by the Housing and Economic Recovery Act of 2008, but HUD has also used existing authority to create the FHA Secure program. HUD’s programs are also treated in the budget on a cash basis. Differences between CBO and the Administration in the Treatment of Policy Actions in the Budget By this point, two major differences have arisen between CBO and the Administration in their treatment of policy actions taken in response to the financial crisis. One involves the recording of the budgetary costs of the TARP, and the other deals with the costs related to the conservatorship of Fannie Mae and Freddie Mac. The Troubled Asset Relief Program Section 123 of the Emergency Economic Stabilization Act of 2008 states that the federal budget should display the costs of purchasing or insuring troubled assets by using procedures similar to those specified in the Federal Credit Reform Act but with an adjustment to account for market risk. Under that procedure, the federal budget would not record the gross cash disbursement for the purchase of a troubled asset (or the cash receipt for its eventual sale) but instead would reflect the market value of the asset or an estimate of the government’s net cost (on a present-value basis) for the purchase. Broadly speaking, the net cost is the purchase cost minus the present value—calculated using an appropriate discount factor that reflects the riskiness of the underlying cash flows associated with the asset—of any estimated future earnings from holding the asset and the proceeds from its eventual sale. Following that directive, CBO has estimated that the net costs of the TARP’s activities through January 22, 2009 (with $293 billion disbursed), total $94 billion. That calculation implies a subsidy rate of 32 percent—that is, the net subsidy (in 2009 dollars) amounts to an estimated 32 percent of the government’s initial expenditures. CBO and the Administration’s Office of Management and Budget do not differ significantly in their assessments of the

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subsidy cost of those transactions but vary in their judgment as to how the transactions should be reported for budgetary purposes. OMB submitted its first report to the Congress on the costs of the Treasury’s purchases and guarantees of troubled assets on December 5, 2008;33 at the time that the report was compiled (November 6, 2008), the TARP had disbursed $115 billion to eight large banks in exchange for preferred stock and warrants (securities that entitle the holder to buy stock of the company that issued them at a specified price). OMB maintains that the Federal Credit Reform Act applies only to direct loans and loan guarantees and that the reference in the Emergency Economic Stabilization Act does not require the use of credit reform procedures for other types of transactions. As a result, it budgeted for those initial TARP disbursements on a cash basis rather than by reporting the estimated subsidy cost. In its December report on the TARP, however, OMB also provided two alternative estimates of the subsidy cost of that first set of disbursements. One such estimate was valued using procedures similar to those specified in the Federal Credit Reform Act (discounting future cash flows using a risk-free rate), and the other estimate was calculated using an approach similar to the way CBO treats the TARP (discounting future cash flows while adjusting for estimated market risk). OMB’s second alternative calculation is comparable to CBO’s assessment of the cost of the first $115 billion of transactions. Using a modified credit reform basis (that is, adjusting for risk), OMB estimated those costs to be $25.5 billion, or a subsidy rate of 22 percent, and CBO arrived at a cost of $20.5 billion, or a subsidy rate of 18 percent. Most of that difference is probably explained by such factors as the discount rate used (which is affected by when the estimates were made) and the volatility of stock prices (which affects the potential value of the warrants).

Fannie Mae and Freddie Mac CBO has concluded that because of the extraordinary degree of management and financial control that the government has exercised, Fannie Mae and Freddie Mac should now be considered federal operations. Although the GSEs are not legally government agencies and their employees are not civil servants, CBO believes it is appropriate and useful to policymakers to account for and display the GSEs’ financial transactions alongside all other federal activities in the budget. However, the Administration continues to treat the two organizations as separate from the government. As a result, it has so far recorded the cash infusion that the Treasury provided to Freddie Mac ($13.4 billion) as an outlay. By contrast, CBO considers such payments as intragovernmental transfers that have no net effect on the budget.

33

Office of Management and Budget, “OMB Report Under the Emergency Economic Stabilization Act, Section 202,” letter to the Honorable Nancy Pelosi (December 5, 2008), available at www.whitehouse.gov/ omb/legislative/eesa_120508.pdf.

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Addressing the Ongoing Crisis in the Housing and Financial Markets Table 1. Actions Taken by the Federal Reserve in Support of the Housing and Financial Markets as of January 22, 2009 (Billions of dollars) Action Reductions in Interest Rates

Funding Committed Potentiala to Date n.a. n.a.

Loans to Financial Institutions Primary and 63 Secondary Credit Programs

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Term Auction Facility

416

Takeover of Bear Stearns Backed assets to 29 facilitate takeover of Bear Stearns by JP Morgan Chase

Unknown

600

29

Description The target for the federal funds rate (the interest banks charge on loans to other banks) was reduced 10 times between September 2007 and December 2008, falling from 5.25 percent to between zero and 0.25 percent. Through the primary and secondary credit programs, the Federal Reserve disburses short-term loans to banks and other institutions that are legally allowed to accept monetary deposits from consumers. The term of the loan may be as long as 90 days. The Term Auction Facility (TAF) allows banks and other financial institutions to pledge collateral in exchange for a loan from the Federal Reserve. The interest rate on the loan is determined by auction; such auctions are conducted biweekly for loans with a maturity of either 28or 84 days. The maximum size of each auction is $150 billion, although accepted bids for most recent auctions have been considerably smaller. The Federal Reserve created Maiden Lane I, a limited liability company (LLC), to acquire certain assets of Bear Stearns at a cost of $29 billion. (An LLC offers protection from personal liability for business debts, just like a corporation. The profits and losses of the business pass through to its owners, as they would if the business was a partnership or sole proprietorship.) The LLC will manage those assets to

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Action

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Support for AIG Acquired control of nearly 80 percent of the insurance company

Table 1. (Continued) Funding Description Committed Potentiala to Date maximize the likelihood that the investment is repaid and to minimize disruption to financial markets. The current value of the portfolio on the Federal Reserve’s balance sheet is $27 billion. 82

113

Support for Short-Term Corporate Borrowing Commercial 351 1,800 Paper Funding Facility

The Federal Reserve agreed to loan AIG $60 billion and acquired control of nearly 80 percent of the company. In addition, the Federal Reserve Bank of New York bought $19.5 billion of residential mortgage-backed securities from AIG’s portfolio through an LLC and another $24.5 billion of collateral-lized debt obligations (CD-Os) on which AIG wrote contracts for credit default swaps through another LLC. (CDOs) are complex financial instruments that repackage assets such as mortgage bonds, loans for leveraged buyouts, and other debt— including other CDOs— into new securities. A credit default swap is a type of insurance arrangement in which the buyer pays a premium at periodic intervals in exchange for a contingent payment in the event that a third party defaults. The size of the premium paid relative to the contingent payment generally increases with the likelihood of default.) The Commercial Paper Funding Facility (CPFF) finances the purchase of commercial paper (securities sold by large banks and corporations to obtain funding to meet short-term borrowing needs, such as payroll) directly from eligible issuers. Securities purchased under this program may be backed by assets or unsecured; they must be highly rated, denominated in U.S. dollars, and have a maturity of three months. The program is in effect through April 30, 2009.

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Addressing the Ongoing Crisis in the Housing and Financial Markets Committed Potentiala to Date Support for Money Market Mutual Funds Money Market 0 540 Investor Funding Facility

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Asset-Backed Commercial Paper Money Market Mutual Fund Liquidity Facility

15

Support for Primary Dealers Term Securities 133 Lending Facility and TSLF Options Program

Primary Dealer Credit Facility

33

Unknown

200

Unknown

The Money Market Investor Funding Facility (MMIFF) is designed to restore liquidity to money markets by purchasing certificates of deposit, bank notes, and commercial paper from money market mutual funds and other similar investors. The authority to purchase assets is in effect through April 30, 2009. The Asset-Backed Commercial Paper Money Market Mutual Fund Liquidity Facility (AMLF) provides funding to U.S. depository institutions and bank holding companies to finance their purchases of high-quality assetbacked commercial paper (ABCP) from money market mutual funds under certain conditions. The program is intended to assist money market funds that hold such paper in meeting demands for redemptions by investors and to foster liquidity in the ABCP market specifically and money markets generally. The program is in effect through April 30, 2009. The Term Securities Lending Facility (TSLF) offers to lend Treasury securities held by the Federal Reserve for a one-month term in exchange for other types of securities held by the 17 financial institutions, known as primary dealers, that trade directly with the Federal Reserve. The TS-LF Options Program (TOP) offers options on short-term TSLF loans that will be made on a future date. (An option is a contract written by a seller that conveys to the buyer the right—but not the obligation —to buy or sell a particular asset.) The Primary Dealer Credit Facility (PDCF) provides overnight loans in exchange for eligible collateral to fina-ncial institutions that trade directly with the Federal Reserve. The program is in effect through April 30, 2009.

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Douglas W. Elmendorf Table 1. (Continued) Funding Action Description Committed Potentiala to Date Support for the Mortgage Market Purchases of the 23 100 The Federal Reserve will purchase debt of the up to $100 billion in debt issued housing-related by three government-sponsored governmententerprises GSEs)—Fannie Mae, sponsored (Freddie Mac, and the Federal enterprises Home Loan Banks—through competitive auctions over the next several quarters. Purchases of 53 500 Over the next several quarters, the mortgage-backed Federal Reserve will purchase up securities to $500 billion in mortgagebacked securities (MBSs) issued by GSEs and the Government National Mortgage Association (Ginnie Mae). Support for Consumer and Small Business Lending Term Asset0 200 Through the Term Asset-Backed Backed Securities Securities Loan Facility (TALF), Loan Facility the Federal Reserve Bank of New York will lend up to $200 billion to holders of certain AAA-rated asset-backed securities (consumer and small business loans), and the Troubled Asset Relief Program will provide $20 billion of credit protection (protection against debtors that do not pay because of insolvency or protracted default) for those loans. The TALF is expected to begin lending in February 2009; the authority expires on December 31, 2009. 0 234 The Federal Reserve will absorb Assistance to 90 percent of any losses resulting Citigroup from the federal government’s guarantee of a pool of Citigroup’s assets after payouts have been made by Citigroup, the Troubled Asset Relief Program, and the Federal Deposit Insurance Corporation. 0 87 The Federal Reserve will absorb Assistance to 90 percent of any losses resulting Bank of America from the federal government’s guarantee of a pool of Bank of America’s assets after payouts have been made by Bank of America, the Troubled Asset Relief Program, and the Federal Deposit Insurance Corporation.

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Addressing the Ongoing Crisis in the Housing and Financial Markets

Currency Swaps

Committed to Date At least 500

27

Potentiala Unlimited

In response to strong demand for dollars from abroad, the Federal Reserve has contracted with 14 foreign central banks to make U.S. dollars available temporarily. After a specified period of time, the original amounts of dollars will be returned in exchange for the foreign currency.

Source: Congressional Budget Office based on information from the Federal Reserve. Note: n.a. = not applicable. “Potential funding” refers to the size of the market or the maximum amount of lending under the program.

Table 2. Actions Taken by the Treasury in Support of the Housing and Financial Markets as of January 22, 2009 (Billions of dollars) Disbursements

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Action

Subsidy b

To Date

Potential

Troubled Asset Relief Program

293

700

(Credit basis) 94

HousingRelated Tax Provisions

0

12

n.a.

a

Description The Emergency Economic Stabilization Act of 2008 (Division A of P.L. 110-343) granted authority to the Treasury to purchase $700 billion in assets through a new program, the Troubled Asset Relief Program (TARP). The second $350 billion will become available on January 27, 2009. As of January 22, the program had disbursed $293 billion. The subsidy cost estimated by the Congressional Budget Office— about $94 billion to date—is computed using the modified credit reform procedure (that is, accounting for market risk) specified in P.L. 110-343. The Housing and Economic Recovery Act of 2008 (P.L. 110289) authorized a refundable tax credit for first-time home buyers (to be repaid, without interest, over

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Douglas W. Elmendorf Table 2. (Continued) Disbursements Subsidy b

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Action

To Date

Potential a

Description

(Credit basis)

Purchases of Obligations and Securities Issued by Fannie Mae and Freddie Mac

71

Unlimite d

-1

Conservator ship for Fannie Mae and Freddie Mac

14

200

n.a.

Temporary Guarantee Program for Money Mar-ket Funds

Unk now n

3,000

n.a.

a 15-year period) and contained other housing-related tax provisions. The Housing and Economic Recovery Act of 2008 authorized the Department of the Treasury to buy obligations and securities issued by Fannie Mae and Freddie Mac. About $71 billion of residential mortgage-backed securities (securities whose value is derived from an underlying pool of mortgages) had been purchased as of December 31, 2008. Authority to make such market purchases expires on December 31, 2009. The subsidy cost recorded in the budget is computed using standard credit reform procedures. The Treasury received senior preferred equity shares and warrants in exchange for any future contributions necessary to keep the two entities solvent. (Preferred equity shares provide a specific dividend to be paid before any dividends are paid to common stockholders and take precedence over common stock in the event of a liquidation; a warrant is a security that entitles the holder to buy stock of the company that issued it at a specified price.) The Treasury will gu-arantee investors’ shares as of September 19, 2008. The guarantee is in effect through April 30, 2009, but can be extended through September 18, 2009. Participating funds pay a fee of 1.5 or 2.2 basis points times the number of shares outstanding. (A basis point is onehundredth of a percentage point.) The Treasury is borrowing from the public to assist the Federal Reserve.

Supplement 175 Unlimite n.a. d -ary Financing Program Source: Congressional Budget Office based on information from the Department of the Treasury.

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Addressing the Ongoing Crisis in the Housing and Financial Markets

29

Note: n.a. = not applicable. a. “Potential disbursements” refers to the maximum amount of spending under the program or the maximum amount of outstanding assets available for guarantee. b. “Subsidy,” broadly speaking, refers to the purchase cost minus the present value of any estimated future earnings from holding those assets and the proceeds from the eventual sale of them.

Table 3. Actions Taken by Other Agencies in Support of the Housing and Financial Markets as of January 22, 2009 (Billions of dollars) Disbursements To Date Potentiala Federal Deposit Insurance Corporation n.a. 700 Temporarily Raised the Basic Limit on Insurance Coverage from $100,000 to $250,000 per Depositor

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Action

Temporary Liquidity Guarantee Program

n.a.

1,450

Assistance to Citigroup

0

10

Description The Emergency Economic Stabilization Act of 2008 (Division A of P.L. 110-343) temporarily raised the limit on deposit insurance through December 31, 2009. That action is estimated to increase the amount of insured deposits by about $700 billion, or 15 percent. The Temporary Liquidity Guarantee Program has two components. The first—the debt guarantee program—aims to enable participating institutions to borrow and lend money more readily. It fully protects certain newly issued senior unsecured debt (securities that are not backed by collateral and have priority over all other debt in rank-ing for payment in the event of default) with a maturity of more than 30 days, including promissory notes, commercial paper (securities sold by large banks and corporations to meet short-term needs, such as payroll), and interbank funding. The guarantee applies to debt that is issued by June 30, 2009, and matures no later than June 30, 2012. Participating institutions pay fees based on the maturity of the debt. To date, the Federal Deposit Insurance Corporation (FDIC) has guaranteed $238 billion of new debt; potential guarantees could total $1 trillion. The second component provides full guarantees for certain checking and other noninterest-bearing accounts through December 31, 2009. Participat-ing institutions also pay fees for this guarantee, which could total $450 billion. The FDIC may absorb up to $10 billion in losses resulting from the federal government’s guarantee of a pool of Citigroup’s assets after payouts have been made by Citigroup and the Trou-bled Asset Relief Program. As a fee for the guarantee, the FDIC will receive $3 billion in preferred stock (shares of equity that provide a specific dividend to be paid before any dividends are paid to common stockholders and that take precedence over common stock in the event of a liquidation).

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30

Douglas W. Elmendorf Table 3. Continued Department of Housing and Urban Development 0 4 Redevelopment of Abandoned and Foreclosed Homes

HOPE for Homeowners Program

0

1

FHA Secure

n.a.

1

Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.

National Credit Union Administration Streamlined Modification Unknown Program

Unknown

Credit Union Homeowners Affordability Relief Program and Credit Union System Investment Program

5

41

Temporary Corporate Credit Union Liquidity Guarantee Program

n.a.

Unknown

The Housing and Economic Recovery Act of 2008 (P.L. 110-289) provided $4 billion in funding to state and local governments to purchase and rehabilitate foreclosed and abandoned homes. The HOPE for Homeowners program permits home mortgages to be refinanced through private lenders with a guarantee from the Federal Housing Administration. The new loans must have a loan-to-value ratio that is no greater than 90 percent of the property’s appraised value. FHA Secure was a temporary initiative to permit lenders to refinance non-FHA (Federal Housing Administration) adjust-table-rate mortgages. The pro-gram has made about 4,000 loans since fall 2007 The Streamlined Modification Program is intended to avoid foreclosures by creating a fast-track method for reducing monthly payments on mortgages. The program will restrict payments to 38 percent of a household’s gross monthly income by reducing the interest rate, extending the life of the loan, or deferring principal. That policy applies to loans held by Fannie Mae and Freddie Mac and was launched on December 15, 2008. These two loan programs are operated through the National Credit Union Administration’s Central Liquidity Facility and are financed by borrowing from the Federal Financing Bank. The Credit Union Homeowners Affordability Relief Program (CU HARP) will provide subsidized funding intended to help credit unions modify mortgages. The Credit Union System Investment Program (CU SIP) seeks to facilitate lending by shoring up corporate credit unions (which primarily provide financial resources and services to other credit unions). The Temporary Corporate Credit Union Liquidity Guarantee Pro-gram guarantees certain unsec-ured debt of participating corporate credit unions that was or will be issued between October 16, 2008, and June 30, 2009. Such debt must mature by June 30, 2012. Participating institutions pay annualized fees for the guarantees. To date, this program has guaranteed $5 billion in debt.

Source: Congressional Budget Office based on information from the Federal Deposit Insurance Corporation, the Department of Housing and Urban Development, the Federal Housing Finance Agency, and the National Credit Union Administration. Note: n.a. = not applicable. a. “Potential disbursements” refers to the maximum amount of spending under the program or the maximum amount of outstanding assets available for guarantee.

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In: Real Estate Investment Market Editors: Sofia M. Lombardi, pp. 31-74

ISBN: 978-1-61668-395-5 © 2010 Nova Science Publishers, Inc.

Chapter 2

VALUE VERSUS GROWTH REAL ESTATE INVESTMENT STRATEGY: IS THE WIN A FLASH IN THE PAN? Kwame Addae-Dapaah*, Hin/David Kim Ho, and Yan Fen Tan ABSTRACT

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The superiority of the contrarian investment strategy, though well attested in the finance literature, is being questioned in some quarters on the pretext that the gap between the performance of value and growth investment narrows over time. If this is proven to be true, it would imply that value real estate investment may not be advisable given that real estate is a medium to long term investment. This paper uses empirical real estate investment return data from 1985Q1 to 2005Q3 for US, and some Asia Pacific cities to ascertain whether the superiority of “value” over “growth” real estate investment is a “flash” in the pan, i.e. unsustainable. The office, industrial and retail property investments are examined in the context of the value-growth paradigm, and complemented with mean reversion and stochastic dominance tests. In addition to confirming the relative superiority of “value” over “growth” property investment, the results show that office and industrial property investments exhibit return reversal. This implies that the “win” is sustainable. Although the returns from retail property investment display inertia, the results of stochastic dominance test validate the relative superiority of “value” over “growth” property investment for all the three sectors. This implies that fund managers who traditionally have been favoring prime (i.e. growth) property investment may have to reconsider their investment strategy if they want to maximize their return.

*

Corresponding author: E-mail: [email protected], Department of Real Estate, School of Design & Environment, National University of Singapore 4 Architecture Drive Singapore 117566 Telephone: ++ 65 6516 3417 Fax: ++ 65 6774 8684.

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Kwame Addae-Dapaah, Hin/David Kim Ho, Yan Fen Tan

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INTRODUCTION The choice of an investment strategy is an important step in the decision-making process of fund managers and large institutional investors. In view of this, growth stock investment strategy and value stock investment strategy have received considerable attention in the finance literature. The growth stock investment strategy is frequently associated with investments in “glamour” stocks that have relatively high price-to-earnings ratios (i.e. high gross income multiplier in real estate terms). On the other hand, value stock investment strategy usually involves investing in “gloomy” stocks that characteristically have relatively low market prices in relation to earnings per share (EPS), cash flow per share, book value per share, or dividend per share (i.e. low gross income multiplier). They are often less popular stocks that have recently experienced low or negative growth rates in corporate earnings. Notwithstanding their relative unpopularity with investors, studies have shown that investments in value stocks, commonly known as contrarian investment strategy, have outperformed growth stocks in major markets (see for example, Fama and French [1993, 1995, 1996, 1998], Capual et al. [1993], Lakonishok et al. [1994], Haugen [1995], Arshanapali et al. [1998], Levis and Liodakis [2001], Badrinath and Omesh [2001] and Chan and Lakonishok [2004]). However, Jones (1993) reports that the profitability of contrarian portfolios is a pre-WW II phenomenon that has since largely disappeared. Furthermore, Kryzanowski and Zhang (1992) find that the Canadian stock market exhibits significant price inertia, which negates the relative superiority of contrarian investments. These contrary findings have been refuted (see for example, Bauman and Miller [1997]). In view of the overwhelming evidence in support of the superior performance of contrarian investment in the finance literature, there appears to be a prima facie case for expecting contrarian real estate investment to do likewise (Addae-Dapaah et al. (2002)). Growth stock is analogous to prime properties as both have relatively low earnings-toprice ratio (i.e. low initial yield) and investors in both investment media pin their hopes on a relatively high potential price or capital appreciation. Similarly, value stock that provides high income is comparable to high income-producing properties such as lower grade properties and properties in secondary locations. In relation to real property, the contrarian strategy implies that value properties with high running yield could outperform growth properties with low running yield. Thus, the objectives of the study are: i)

to ascertain the comparative advantage(s), in terms of performance, of contrarian real estate investment; ii) to evaluate the relative riskiness of value properties and growth properties; iii) to establish whether excessive extrapolation and expectational errors characterize growth and value strategies; and iv) to ascertain the sustainability of the relative superiority (the “win”) of contrarian real estate investment if such superiority is established. In view of this, the next section provides a brief review of the finance literature on the contrarian investment strategy after which, a specific set of research hypotheses are formulated. This is followed by a discussion on data management and sourcing, and the

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Value Versus Growth Real Estate Investment Strategy

33

contrarian strategy model. The next section is devoted to the empirical model estimation which is followed by a post-model estimation. The last section deals with concluding remarks.

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LITERATURE REVIEW According to Dreman (1982) a contrarian investor is an investor who goes against the “grain”. Thus, contrarian investment strategy simply refers to investment in securities which have lost favor with investors. It covers various investment strategies based on buying/selling stocks that are priced low/high relative to accounting measures of performance – earnings-toprice ratios (E/P), cash flow-to-price ratio (C/P) and book value-to-price ratio (B/P) – as well strategies based on low/high measures of earning per share (EPS) growth (Capual, 1993). In simple terms, the contrarian investment strategy refers to the value/growth stock paradigm. While there is substantial empirical evidence supporting the efficient market hypothesis that security prices provide unbiased estimates of the underlying values, many still question its validity. Smidt (1968) argues that one potential source of market inefficiency is inappropriate market responses to information. The inappropriate responses to information implicit in Price-Earnings (P/E) ratios may be indicators of future investment performance of a security. Proponents of this price-ratio hypothesis claim that low P/E securities tend to outperform high P/E stocks (Williamson, 1970). Basu (1977), Jaffe et al. (1989), Fama and French (1992, 1998), Davis (1994), Lakonishok et al. (1994), Bauman et al. (1998), Badrinath and Omesh (2001) and Chan and Lakonishok (2004) show a positive relationship between earnings yield and equity returns. However, as a result of the noisy nature of earnings (i.e. the category of stocks with low E/P include also stocks that have temporarily depressed earnings), value strategies based on E/P give narrower spreads compared to other simple value strategies (Chan and Lakonishok [2004]). Furthermore, in view of the noise in reported earnings that results from Japanese accounting standards (i.e. distortions in the earnings induced by accelerated depreciation allowances), Chan et al. (1991) find no evidence of a strong positive earnings yield effect after controlling for the other fundamental variables. Rosenberg et al. (1985) show that stocks with high Book Value relative to Market Value of equity (BV/MV) outperform the market. Further studies, e.g. Chan et al. (1991) and Fama and French (1992), confirm and extend these results. In view of the highly influential paper by Fama and French (1992), academics (e.g. Capaul et al., 1993; Davis, 1994; Lakonishok et al., 1994; La Porta et al., 1997; Fama and French, 1998; Bauman et al., 1998 and 2001; Chan et al., 2000; and Chan and Lakonishok, 2004) have shifted their attention to the ratio of BV/MV as one of the leading explanatory variables for the cross-section of average stock returns. Although BV/MV has gained much credence as an indicator of value-growth orientation, it is by no means an ideal measure (Chan and Lakonishok (2004)). BV/MV is not a ‘clean’ variable uniquely associated with economically interpretable characteristics of the firm (Lakonishok et al. (1994)). Many different factors are reflected in this ratio. For a example, low BV/MV may describe a company with several intangible assets that are not reflected in accounting book value. A low BV/MV can also describe a company with attractive growth opportunities that do not enter the computation of book value but do enter the market price. A

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Kwame Addae-Dapaah, Hin/David Kim Ho, Yan Fen Tan

stock whose risk is low and future cash flows are discounted at a low rate would have a low BV/MV as well. Finally, a low BV/MV may be reminiscent of an overvalued glamour stock. The shortcomings of accounting earnings have motivated a number of researchers to explore the relationship between cash flow yields and stock returns. High Cash Flow to Price (CF/P) stocks are identified as value stocks because their prices are low per dollar of cash flow, or the growth rate of their cash flows is expected to be low. Chan et al. (1991), Davis (1994), Lakonishok et al. (1994), Bauman et al. (1998), Fama and French (1998), and Chan and Lakonishok (2004) show that a high ratio of CF/P predicts higher returns. This is consistent with the idea that measuring the market’s expectations of future growth more directly gives rise to better value strategies (La Porta (1996)). Fama and French (1998) and Bauman et al. (1998) use the ratio of Dividends to Price (D/P) as a proxy for the market’s expectations of future growth. Firms with higher ratios have lower expected growth and are considered to be value stocks. They show that the performance of the value stocks based on dividend yields is quantitatively similar to the performance based on the prior categorizations (i.e. P/E, BV/MV and CF/P). Finally, instead of using expectations of future growth to operationalize the notions of glamour and value, Davis (1994) and Lakonishok et al. (1994) use past growth to classify stocks. Davis (1994) and Lakonishok et al. (1994) measure past growth by Growth in Sales (GS) to conclude that the spread in abnormal returns is sizeable. To the extent that the different valuation indicators of value-growth orientation are not highly correlated, a strategy based on information from several valuation measures may enhance portfolio performance. Lakonishok et al. (1994) explore sophisticated twodimensional versions of simple value strategies. According to the two-way classification, value stocks are defined as those that have shown poor growth in sales, earnings and cash flow in the past, and are expected by the market to continue growing slowly. Expected performance is measured by multiples of price to current earnings and cash flow. La Porta et al. (1997) form portfolios on the basis of a two-way classification based on past GS and CF/P introduced by Lakonishok et al. (1994). Using robust regression methods, Chan and Lakonishok (2004) estimate cross-sectional models that predicted future yearly returns from beginning-year values of the BV/MV, CF/P, E/P and the sales to price ratio. The use of the multiple measures in the composite indicators boosts the performance of the value strategy (see Gregory et al. [2003]). In contrast to the above findings, Jones (1993) reports that the profitability of contrarian portfolios is a pre-WW II phenomenon that has since largely disappeared. However, this has been refuted by later studies which include post-war data. Also, Kryzanowski and Zhang (1992) suggest that positive profits resulting from the use of the contrarian investment strategy are limited to the U.S. stock market. When applied to the Canadian stock market, the DeBondt and Thaler (1985) do not produce favorable results. Instead of finding significant price reversals, Kryzanowski and Zhang (1992) find that the Canadian stock market exhibits significant price continuation behavior, which does not support contrarian investments. This is also refuted by later studies that conclude mean-reversion tendency (see for example, Bauman and Miller [1997]). In view of the accumulated weight of the evidence from past studies, the finance academic fraternity agrees that value investment strategies, on average, outperform growth investment strategies. The only polemical issue about the contrarian strategy is the rationale for its superior performance.

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Rationale for Superior Performance of Contrarian Strategies Competing explanations for contrarian supremacy include risk premiums (Fama and French, 1993, 1995, 1996; Petkova and Zhang, 2004), systematic errors in investors’ expectations and analysts’ forecasts – i.e. naïve investor expectations of future growth and research design induced bias (see for example, La Porta et al., 1997; Bauman & Miller, 1997; La Porta, 1996; Dechow & Sloan, 1997; Lakonishok et al., 1994; Lo and MacKinlay, 1990; Kothari et al., 1995) and the existence of market frictions (Amihud and Mendelson, 1986) .The traditional view, led by Fama and French (1993, 1995, 1996), is that the superior performance is a function of contrarian investment being relatively risky (see also Chan, 1988; Ball and Kothari, 1989; Kothari and Shanken, 1992.). However, Lakonishok et al. (1994), MacKinley (1995), La Porta et al. (1995, 1997), Daniel and Titman (1996) have found that risk-based explanations do not provide a credible rationale for the observed return behaviour (see Jaffe et al., 1989; Chan et al., 1991; Chopra et al., 1992; Capaul et al., 1993; Dreman and Lufkin, 1997; Bauman et al., 1998, 2001; Nam et al., 2001; Gomes et al., 2003 and Chan and Lakonishok (2004)). The behavioral finance paradigm recognizes psychological influences on human decision-making in which experts (in this case, investors) tend to focus on, and overuse, predictors of limited validity (i.e., earnings trend in the recent past) in making forecasts (see Covel and Shumway, 2005). In view of systematic errors in investors’ expectations and analysts’ forecasts, it has been argued that a significant portion of value stocks’ superior performance is attributable to earning surprises (see De Bondt and Thaler, 1985; Lakonishok et al., 1994; La Porta, 1996; Chan et al., 2000, 2003; Chan and Lakonishok, 2004; Jegadeesh et al., 2004). According to Dreman and Berry (1995) and Levis and Liodakis (2001), positive and negative earnings surprises have an asymmetrical effect on the returns of value and growth stocks. Positive earning surprises have a disproportionately large positive impact on value stocks while negative surprises have a relatively benign effect on such stocks (see also Bauman and Miller, 1997). Furthermore, analysts and institutional investors may have their own reasons for gravitating toward growth stocks. Analysts have self-interest in recommending successful stocks to generate trading commissions and more investment banking business. Moreover, growth stocks are typically in ‘promising’ industries, and are thus easier to promote in terms of analyst reports and media coverage (Bhushan, 1989; and Jegadeesh et al., 2004). These considerations play into the career concerns of institutional money managers (Lakonishok et al., 1994). Another important factor is that most investors have shorter time horizons than are required for value strategies to consistently pay off (De Long et al., 1990; Shleifer and Vishny, 1990). In addition, institutional investors act in a fiduciary capacity. Pension fund trustees, in particular, are expected to behave as an “ordinary man of prudence”. This implies that they must go with the crowd (i.e. opt for glamour stocks. The result of all these considerations is that value stocks/glamour stocks become under-priced/overpriced relative to their fundamentals. Due to the limits of arbitrage (Shleifer and Vishny (1997)), the mispricing patterns can persist over long periods of time. A third hypothesis that has been postulated for the superiority of the contrarian strategy is that the reported cross-sectional return differences is an artifact of the research design and the database used to conduct the study (Black, 1993; Kothari et al., 1995). Thus, the abnormal returns would be reduced or vanish if different methodology and data were used. Such

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Kwame Addae-Dapaah, Hin/David Kim Ho, Yan Fen Tan

researchers argue that the superior returns are the result of survivor biases in the selection of firms (Banz and Breen, 1986), look-ahead bias (Banz and Breen, 1986), and a collective datasnooping exercise by many researchers sifting through the same data (Lo and MacKinlay, 1990). Finally, the database is limited to a relatively short sample period (Davis, 1994). The data-snooping explanation has been controverted by Lakonishok et al. (1994), Davis (1994, 1996), Fama and French (1998), Bauman and Conover (1999), Bauman et al., (2001), and Chan and Lakonishok (2004) who used databases that are free of survivorship bias and/or fresh data that previously have not been used for such analysis to confirm the superior performance of value strategy. Furthermore, two features of value investing distinguish it from other possible anomalies. According to Chan and Lakonishok (2004), many apparent violations of the efficient market hypothesis, such as day-of-the-week patterns in stock returns, lack a convincing logical basis and the anomalous pattern is merely a statistical fluke that has been uncovered through data mining. The value premium, however, can be tied to ingrained patterns of investor behavior or the incentives of professional investment managers. In view of the analogy between value stock and high income producing property (henceforth called value property), the features of the contrarian investment strategy may apply to property investment. Therefore, it is hypothesized that: a). b). c). d).

value properties generate higher returns than growth properties; value property investment is riskier than growth property investment; investors naively extrapolate past performance into future expectations; and the returns of value and growth properties are mean-reverting.

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These hypotheses will be operationalized through statistical tests, and where possible, stochastic dominance test.

DATA SOURCING AND MANAGEMENT A growth real estate investor prefers properties with a low initial yield to properties with high initial yield. The investor chooses to exchange immediate cash flows for higher future cash flows (in the form of potential capital appreciation and/or rental growth) that are worth more at the date of the purchase, depending on the investor’s opportunity cost of capital. On the other hand, a value property investor prefers to receive a high initial yield rather than to wait for future income or uncertain capital growth. The paper uses the Jones Lang Lasalle Real Estate Intelligence Service-Asia (JLL REIS-Asia), the Property Council of New Zealand, the Property Council of Australia and NCREIF property databases to classify 73 office property sub-markets, 52 industrial property sub-markets and 48 retail property submarkets into value/growth sub-markets on the bases of yields (see Appendix A), i.e. E/P ratio. The data for the office and industrial property markets are from 1985Q1 to 2005Q3 while the retail property market data are from 1992Q1 to 2005Q3. JLL REIS-Asia dataset consists of ex post quarterly (since 1994) and ex-ante annual (forecasts for the next 4 years) capital and rental values of prime commercial properties for 16 Asia real estate market sectors (i.e. eight retail sectors and eight office sectors). The capital

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and rental values of commercial real estate assets (office and retail) in the eight cities are based on a basket of 30 prime commercial buildings per sector in each city. Rental values are based on actual rents while the capital values are based on transactions and estimated valuations. The JLL REIS-Asia ex ante data are derived from JLL’s proprietary quantitative forecasting and the consensus views of the JLL network of branch offices in Asian cities, namely: Singapore (the Raffles Place CBD), Beijing, Shanghai, Hong Kong (the Central & major business districts), Bangkok, Manila (Makati CBD), Kuala Lumpur and Jakarta3. The criteria for selecting investment grade offices for the dataset are the same for all the markets in the sample. Thus, the dataset provides a basis for comparing like with like. Similarly, the data from the Property Council of New Zealand and the Property Council of Australia are based on market rentals and valuations. The quality of these data is attested by the fact that they have been subsumed by the IPD. All the datasets are extensively used by researchers. The only caveat about the use of different datasets is that one cannot guarantee that the quality of all the datasets is the same. However, the datasets are of very good quality to provide credible results. The initial yields are measured in U.S. dollars. Decile portfolios are formed on the basis of the end-of-previous-quarter’s initial yield. The top decile of the sample with the highest initial yield is classified as value property (Vp) portfolio while the bottom decile with the lowest initial yield is classified as growth property (Gp). Each decile is treated as a portfolio composed of equally weighted properties. The portfolios are reformulated only at the end of each holding period. This system of classification is consistent with the finance literature (see for example, Chan et al. [1991] and Bauman et al. [1998, 2001]). The classification of the property sub-markets into Vp and Gp portfolios is followed by an examination of the relative performances of the portfolios. If there is evidence of a value premium in any of the sampled property sector markets, the underlying reasons behind the relative superiority of Vp will be discussed.

THE CONTRARIAN STRATEGY MODEL The performances of both the value and growth properties for the office and industrial sectors are compared on a 5-year, 10-year, 15-year and entire holding-period (of up to 83 quarters) horizons while those for the retail sector are compared on 5-year, 10-year and entire holding-period (of up to 55 quarters). Medium and long term investment horizons are the focus of analyses as real estate investors usually invest long (Ball, 1998). Periodic (i.e. quarter-by-quarter) return measure is used in the evaluation of the relative superiority of the performance of Vp and Gp portfolios. The periodic returns are quantified as simple holding period returns. Thus, the simple holding period returns are calculated for each quarter and compounded to obtain the multi-year holding-period (e.g. 5-year investment horizon) returns as defined in equation (1).

rt = [(1 + r1 )(1 + r2 )...(1 + rm )] − 1 (Levy, 1999), where r1, r2…rm = return for each quarter of the period m. m = number of quarters for the holding period.

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(1)

38

Kwame Addae-Dapaah, Hin/David Kim Ho, Yan Fen Tan

Compared to simply adding the returns for all quarters of a given period, equation (1) is more accurate (Sharpe et al., 1998). The periodic quartile returns for each holding- period horizon are averaged across the full period of study to determine the time-weighted average return. Arithmetic mean is most widely used in forecasts of future expectations and in portfolio analysis (Geltner and Miller, 2001). Each value-growth spread (i.e. value premium) is then computed by subtracting the mean return on a Gp portfolio from that on the corresponding Vp portfolio. The pooled-variance t test and separate-variance t test are then used to determine whether there is a significant difference between the means of the Vp and Gp portfolios. If the p-value is smaller than the conventional levels of significance (i.e. 0.05 and 0.10), the null hypothesis that the two means are equal will be rejected:

H 0 :μ value = μ growth H 1 : μ value ≠ μ growth The next step is to determine whether any difference in returns is a function of variation in risk, using a more direct evaluation of the risk-based explanation that focuses on the performance of the value and growth properties in ‘bad’ states of the world. Traditional measures of risk such as standard deviation of returns, risk-to-return ratio (i.e. coefficient of variation – CV) and return-to-risk ratio will be utilized. The Levene’s Test is used to test the equality of the variances for the value and growth properties:

H 0 : σ 2 value = σ 2 growth

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H 1 : σ 2 value ≠ σ 2 growth

Performance in ‘Bad’ States of the World According to Lakonishok et al. (1994), value strategies would be fundamentally riskier than glamour strategies if: i) they under-perform glamour strategies in some states of the world; and ii) those are on average ‘bad’ states of the world, in which the marginal utility of wealth is high, making value strategies unattractive to risk-averse investors. Periods of severe stock market declines are used as a proxy for ‘bad’ states of the world. This is because they generally correspond to periods when aggregate wealth is low and thus the utility of an extra dollar is high. The approach of examining property performance during down markets also corresponds to the notion of downside risk that has gained popularity in the investment community (Chan and Lakonishok, 2004). If the above tests confirm the superiority of value properties, stochastic dominance will be used to ascertain the optimality of the value property investment strategy.

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STOCHASTIC DOMINANCE The most widely known and applied efficiency criterion for evaluating investments is the mean-variance model. An alternative approach is the stochastic dominance (SD) analysis, which has been employed in various areas of economics, finance and statistics (Levy, 1992; Al-khazali, 2002; Kjetsaa and Kieff, 2003). The efficacy and applicability of SD analysis, and its relative advantages over the mean-variance approach have been discussed and proven by several researchers including Hanoch and Levy (1969), Hadar and Russell (1969), Rothschild and Stiglitz (1970), Whitmore, 1970, Levy (1992), Al-khazali (2002) and Barrett and Donald (2003). According to Taylor and Yodder (1999), SD is a theoretically unimpeachable general model of portfolio choice that maximizes expected utility. It uses the entire probability density function rather than simply summarizing a distribution’s features as given by its statistical moments.

Stochastic Dominance Criteria The SD rules are normally specified as first, second, and third degree SD criteria denoted by FSD, SSD, and TSD respectively (see Levy, 1992; Barrett and Donald, 2003; Barucci, 2003). There is also the nth degree SD. Given that F and G are the cumulative distribution functions of two mutually exclusive risky options X and Y, F dominates G (FDG) by FSD, SSD, and TSD, denoted by FD1G, FD2G, and FD3G, respectively, if and only if, F ( X ) ≤ G ( X ) for all X (FSD)

(2)

∫ [G(t ) − F (t )]dt ≥ 0 for all X (SSD)

(3)

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x

−∞

∫ ∫ [G(t ) − F (t )]dtdυ ≥ 0 for all X, and x

υ

−∞ −∞

E F ( X ) ≥ E G ( X )(TSD )

(4) The FSD (also referred to as the General Efficiency Criterion – Levy and Sarnat, 1972) assumes that all investors prefer more wealth to less regardless of their attitude towards risk. The SSD is based on the economic notion that investors are risk averse while the TSD posits that investors exhibit decreasing absolute risk aversion (Kjetsaa and Kieff, 2003). A higher degree SD is required only if the preceding lower degree SD does not conclusively resolve the optimal choice problem. Thus, if FD1G, then for all values of x, F(x) ≤ G(x) or G(x) - F(x) ≥ 0. Since the expression cannot be negative, it follows that for all values of x, the following must also hold:

∫ [G(t ) − F (t )]dt ≥ 0 ; that is, FD G (Levy and Sarnat, 1972; Levy, 1998) x

−∞

2

Furthermore, the SD rules and the relevant class of preferences Ui are related in the following way: Real Estate Investment Market, Nova Science Publishers, Incorporated, 2009. ProQuest Ebook Central,

40

Kwame Addae-Dapaah, Hin/David Kim Ho, Yan Fen Tan FSD: F ( X ) ≤ G ( X ) ∀X ⇐⇒ E F U ( X ) ≥ EGU ( X ) SSD:

∫

TSD:

x

∀u ∈ U 1 ,

F (t )dt ≥ ∫ G (t ) dt ∀X ⇐⇒ E F U ( X ) ≥ EGU ( X ) ∀u ∈ U 2 , x

−∞

x

−∞

υ

∫ ∫

−∞ −∞

F (t ) dtdυ ≥ ∫

x

∫

x

−∞ −∞

(5) (6)

G (t ) dtdυ ∀X ⇐⇒ E F U ( X ) ≥ EGU ( X )

∀u ∈ U 3 , and E F ( X ) ≥ EG ( X ) ,

(7)

where U i = utility function class (i =1, 2, 3)

U 1 includes all u with u '≥ 0 ; U 2 includes all u with u '≥ 0 and u ' ' ≤ 0 ; and U 3 includes all u with u '≥ 0 , u ' ' ≤ 0 and u ' ' ' ≥ 0 . In other words, a lower degree SD is embedded in a higher degree SD. The economic interpretation of the above rules for the family of all concave utility functions is that their fulfilment implies that E F U ( x ) > E GU ( x ) and E F ( x ) > E G ( x ) ; i.e. the expected utility

and return of the preferred option must be greater than the expected utility and return of the dominated option.

Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.

EMPIRICAL MODEL ESTIMATION – A TEST OF THE EXTRAPOLATION MODEL Following the evaluation of the risk characteristics of the Vp and Gp portfolios, the next task is to investigate the relationship between the past, the forecasted, and the actual future growth rates. This relationship is largely consistent with the predictions of the extrapolation model. The essence of extrapolation is that investors are excessively optimistic about growth properties and excessively pessimistic about value properties. A direct test of extrapolation (Lakonishok et al. (1994)), then, is to look directly at the actual future rental income and capital growth rates of value and growth properties, and compare them to: a) past growth rates and b) expected growth rates as implied by the initial yields. If naïve extrapolation is established, the variance ratio test will be used to show that naïve extrapolation is a credible explanation to the relative superiority of the contrarian strategy.

Variance Ratio Test The variance ratio, which measures the randomness of a return series, is calculated by dividing the variance of longer intervals’ returns by the variance of shorter intervals’ returns

Real Estate Investment Market, Nova Science Publishers, Incorporated, 2009. ProQuest Ebook Central,

Value Versus Growth Real Estate Investment Strategy

41

(for the same measurement period. The result is normalized to 1 by dividing it by the ratio of the longer to the shorter interval. The test assumes that if a return series follows a random walk, the variance of its k-differences should be k times the variance of its first difference (Poterba and Summers, 1988). Assuming that yt denotes a time series consisting of T observations, the variance ratio of the k-th difference is calculated as follows (see Lo and MacKinlay, 1988; Poterba and Summers, 1988; Belaire-Franch and Oppong, 2005): VR(k ) =

σ 2 (k ) , σ 2 (1)

(8)

where VR(k): is the variance ratio of the series k-th difference

σ 2 (k ) : is the unbiased estimator of 1/k of the variance of the series k-th difference σ 2 (1) : is the variance of the first–differenced return series

k: is the number of the days of the base observations interval, or the difference interval. The estimator of the k-period difference,

σ 2 (k ) , is computed as:

σ 2 (k ) = 1 ∑ ( y t + ... + y t − k +1 − kμˆ ) T

T

(9)

t =k

where

Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.

μˆ =

1 T ∑ yt ; while the unbiased estimator of variance of the first difference, T t =1

σ 2 (1) = 1 ∑ ( yt − μˆ ) 2 T

T

σ 2 (1) , is:

(10)

t =1

A variance ratio greater than 1 suggests that the shorter-interval returns trend within the duration of the longer interval (i.e. the return series is positively serially correlated). Conversely, a variance ratio less than 1 implies that the return series is negatively serially correlated (i.e. the shorter-interval returns are mean reverting within the duration of the longer interval.

Performance of the Contrarian Strategy Exhibits 1 to 4 clearly demonstrate the superiority of the contrarian strategy in each of the holding periods under consideration. The value portfolio for each property sector outperformed the corresponding growth portfolio. The value industrial property portfolio, in particular, recorded 100% positive value-growth spread for all the investment formation horizons (Exhibits 1-4a). In other words, the value industrial property portfolio outperformed

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42

Kwame Addae-Dapaah, Hin/David Kim Ho, Yan Fen Tan

its growth counterpart in every holding period. The mean value/growth industrial portfolio returns for the 5, 10, 15 and more than 15 years holding periods are 163.59%/40.77%, 405.55%/107.46%, 1023.36%/187.18% and 1992.29%/258.69% respectively (Exhibit 5 – full details are obtainable from authors). This implies that an investor who adopted the contrarian strategy over the more than 15-year holding period would have earned, on average, 1733.6% more on each dollar invested than the one who invested in glamour industrial properties over the same period. Similarly, the value retail property portfolio had spectacular performance by registering 100% value-growth spread for the 10 and more than 10 years holding periods (Exhibits 2 and 4a). Over the 5-year investment formation horizons, however, the value retail property portfolio outperformed its glamour counterpart in 35 of the 36 holding periods (Exhibit 1). The mean value/growth retail property portfolio returns for the 5, 10 and more than 10 years holding period are 201.54%/65.62%, 810.85%/143.7% and 980.84%/203.76% respectively (Exhibit 5 – full details are obtainable from authors).  

 

Exhibit 1: Value-Growth Spread (5-Year Holding Period 600.00

500.00

Cumulative Spread

300.00

200.00

100.00

0.00

-100.00

-200.00 85 q1 -8 9q 85 4 q4 -9 0q 86 q3 3 -9 1q 87 2 q2 -9 2q 88 q1 1 -9 2q 88 q4 4 -9 3q 89 3 q3 -9 4q 90 q3 2 -9 5q 91 2 q2 -9 6q 92 q1 1 -9 6q 92 4 q4 -9 7q 93 q3 3 -9 8q 94 2 q2 -9 9q 95 q1 1 -9 9q 95 4 q4 -0 0q 96 q3 3 -0 1q 97 2 q2 -0 2q 98 q1 1 -0 2q 98 4 q4 -0 3q 99 q3 3 -0 4q 00 2 q2 -0 5q 1

Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.

400.00

Period Industrial Sector

Office Sector

Exhibit.1.

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Retail Sector

Value Versus Growth Real Estate Investment Strategy

43

 

Exhibit 2: Value-Growth Spread (10-Year Holding Period) 1200

Cumulative Spread

1000 800 600 400 200 0

85 Q1 -94 Q4 85 Q3 -95 Q2 86 Q1 -95 Q4 86 Q3 -96 Q2 87 Q1 -96 Q4 87 Q3 -97 Q2 88 Q1 -97 Q4 88 Q3 -98 Q2 89 Q1 -98 Q4 89 Q3 -99 Q2 90 Q1 -99 Q4 90 Q3 -00 Q2 91 Q1 -00 Q 91 4 Q3 -01 Q2 92 Q1 -01 Q4 92 Q3 -02 Q2 93 Q1 -02 Q4 93 Q3 -03 Q2 94 Q1 -03 Q4 94 Q3 -04 Q2 95 Q1 -04 Q4 95 Q3 -05 Q2

-200

Period Industrial Sector

Office Sector

Retail Sector

Exhibit 2.  

Exhibit 3: Value-Growth Spread (15-Year Holding Period) 1400

Cumulative Spread

1000 800 600 400 200

Industrial Sector

Exhibit 3.

Real Estate Investment Market, Nova Science Publishers, Incorporated, 2009. ProQuest Ebook Central,

Office Sector

30

5Q 2

4Q 4 90 Q

10

4Q 2

Period

90 Q

30

3Q 4 89 Q

10

3Q 2 89 Q

30

2Q 4 88 Q

30

10 88 Q

10

30

2Q 2

1Q 4 87 Q

1Q 2 87 Q

10

30

0Q 4 86 Q

86 Q

85 Q

19

9Q 4

0Q 2

0

85 Q

Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.

1200

44

Kwame Addae-Dapaah, Hin/David Kim Ho, Yan Fen Tan  

Exhibit 4a: Value-Growth Spread (Holding Period Exceeding 15 Years) 4500

Cumulative Spread

4000 3500 3000 2500 2000 1500 1000 500 85 Q1 85 04Q4 Q2 -04 Q4 85 Q3 -04 85 Q4 Q4 86 04Q4 Q1 -04 Q4 86 Q2 86 04Q4 Q3 -04 Q4 86 Q4 -04 87 Q 1 Q4 87 04Q4 Q2 -04 Q4 87 Q3 87 04Q4 Q4 -04 Q4 88 Q1 -04 88 Q 2 Q4 88 04Q4 Q3 -04 Q4 88 Q4 -04 89 Q4 Q1 -04 Q4 89 Q2 -04 89 Q 3 Q4 89 04Q4 Q4 -04 Q4

0

Period Industrial Sector

 

Office Sector

1400 1200 1000 800 600 400 200 0 92 Q1 -04 Q4 92 Q2 -04 Q4 92 Q3 -04 Q 4 92 Q4 -04 Q4 93 Q1 -04 Q4 93 Q2 -04 Q4 93 Q3 -04 Q4 93 Q4 -04 Q4 94 Q1 -04 Q4 94 Q2 -04 Q4 94 Q3 -04 Q4 94 Q4 -04 Q4 95 Q1 -04 Q4

Cumulative Spread

Exhibit 4b: Retail Sector Value-Growth Spread (Holding Period Exceeding 10 Years)

Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.

Period Retail

Exhibit 4.

Glamour office property portfolio did better than its industrial and retail counterparts. However, the better performance was nothing compared to the value office property portfolio. The value office property portfolio outperformed the growth office property portfolio in 39 out of the 61 five-year holding periods. In other words, the growth portfolio outperformed the value portfolio in 22 (out of the 61) investment formation periods between 1994Q1 and 1999Q2 (Exhibit 1 – full details are obtainable from authors). However, the superiority of the contrarian strategy is evident over the longer investment horizons (Exhibits 2-4). Over the 10year investment horizon, the value office portfolio outperformed its growth counterpart in 36 of the 41 formation periods (Exhibit 2). Furthermore, the superior performance of the contrarian strategy is attested by the 100% positive value-growth spread for the 15 and more than 15 years formation periods (Exhibits 3 and 4). The mean return value/growth office property portfolio returns for 5, 10, 15 and more than 15 years holding period are 102.6%/35.29%, 275.12%/66.05%, 944.65%/96.26% and 1929.81%/125.75% respectively (Exhibit 5). Thus, a dollar invested in value office property portfolio over the entire investment horizon, would have earned, on average, 1804.06% more than a dollar invested in

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Value Versus Growth Real Estate Investment Strategy

45

growth office property portfolio. It is worth noting that the differences between the mean returns for both portfolios (i.e. the value premium) are statistically significant at both the 0.01 and 0.05 levels (Exhibit 6a). Exhibits 7-9 clearly demonstrates that VpD1Gp for all the holding periods under consideration – i.e. the value portfolios are the most efficient (and therefore the optimal) choice. This implies that value portfolios stochastically dominate growth portfolios in the first, second and third order. In other words, the value portfolios statistically prognosticated a higher probability of success than the growth portfolios. For example, Exhibit 8b shows that there was about 95% and 60% probability that the 10-year holding period return for value and growth office portfolios respectively was greater than or equal to 40%. Thus, value portfolio investment should have been preferable to both risk averters and risk lovers (Kjetsaa and Kieff, 2003; Levy and Sarnat, 1972). Exhibit 5. Exhibit 5: Descriptive Return Statistics. Mean Return (%) Value Growth Office Quarterly 5.88 0.65 5 Years 102.6 35.29 10 Years 275.12 66.05 15 Years 944.65 96.26 > 15 Years 1929.81 125.75 Industrial Quarterly 6.03 0.98 5 Years 163.59 40.77 10 Years 405.55 107.46 15 Years 1023.36 187.18 > 15 Years 1992.29 258.69 Retail Quarterly 4.05 1.88 5 Years 201.54 65.62 10 Years 810.85 143.7 > 10 Years 980.84 203.76

Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.

Sectors

Holding Period

Standard Deviation Value Growth 7.62 3.67 109.72 58.12 191.53 65.39 327.07 37.55 1267.04 45.51 5.27 2.29 119.34 33.88 178.5 61.13 233.14 43.55 964.00 88.99 4.96 2.00 82.76 34.16 419.92 53.41 268.42 20.51

Coefficient of Variation Value Growth 1.3 5.60 1.07 1.65 0.7 0.99 0.35 0.39 0.66 0.36 0.87 2.32 0.73 0.83 0.44 0.57 0.22 0.23 0.48 0.34 1.23 1.06 0.41 0.52 0.52 0.37 0.27 0.10

Exhibit 6a: Equality of Means Test. Sectors

Holding Period

ValueGrowth Spread (%)

Office Quarterly

5 Years

10 Years

15 Years

5.23

67.32

209.08

848.4

t-test Pooledvariance Separatevariance Pooledvariance Separatevariance Pooledvariance Separatevariance Pooledvariance Separatevariance

Test statistic t

p-value

 

 

5.63

0.000

Reject

Reject

5.63

0.000

Reject

Reject

4.18

0.000

Reject

Reject

4.18

0.000

Reject

Reject

6.53

0.000

Reject

Reject

6.53

0.000

Reject

Reject

11.54

0.000

Reject

Reject

11.54

0.000

Reject

Reject

Real Estate Investment Market, Nova Science Publishers, Incorporated, 2009. ProQuest Ebook Central,

46

Kwame Addae-Dapaah, Hin/David Kim Ho, Yan Fen Tan

> 15 Years

1804.06

Industrial Quarterly

5 Years

10 Years

15 Years

> 15 Years

5.04

122.82

298.1

836.18

1733.60

Retail Quarterly

5 Years

Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.

10 Years

> 10 Years

2.16

135.91

667.15

777.08

Exhibit 6a (Continued) Pooled6.52 variance Separate6.52 variance Pooled8.00 variance Separate8.00 variance Pooled7.92 variance Separate7.92 variance Pooled10.48 variance Separate10.48 variance Pooled17.27 variance Separate17.27 variance Pooled8.21 variance Separate8.21 variance Pooled3.00 variance Separate3.00 variance Pooled9.11 variance Separate9.11 variance Pooled6.30 variance Separate6.30 variance Pooled10.41 variance Separate10.41 variance

Exhibit 6b: Equality of Variance Test Standard Deviation Holding Sectors Period Value Growth Office Quarterly 7.62 3.67 5 Years 109.72 58.12 10 Years 191.53 65.39 15 Years 327.07 37.55 > 15 Years 1267.04 45.51 Industrial Quarterly 5.27 2.29 5 Years 119.34 33.88 10 Years 178.50 61.13 15 Years 233.14 43.55 > 15 Years 964.00 88.99 Retail Quarterly 4.96 2.00 5 Years 82.76 34.16 10 Years 419.92 53.41 > 10 Years 268.42 20.51

0.000

Reject

Reject

0.000

Reject

Reject

0.000

Reject

Reject

0.000

Reject

Reject

0.000

Reject

Reject

0.000

Reject

Reject

0.000

Reject

Reject

0.000

Reject

Reject

0.000

Reject

Reject

0.000

Reject

Reject

0.000

Reject

Reject

0.000

Reject

Reject

0.002

Reject

Reject

0.002

Reject

Reject

0.000

Reject

Reject

0.000

Reject

Reject

0.000

Reject

Reject

0.000

Reject

Reject

0.000

Reject

Reject

0.000

Reject

Reject

F-test p-value statistics 4.33 3.56 8.58 101.39 775.15 5.30 0.08 8.53 28.65 117.35 6.144 5.87 61.81 171.34

Real Estate Investment Market, Nova Science Publishers, Incorporated, 2009. ProQuest Ebook Central,

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

 

 

Reject Reject Reject Reject Reject Reject Reject Reject Reject Reject Reject Reject Reject Reject

Reject Reject Reject Reject Reject Reject Reject Reject Reject Reject Reject Reject Reject Reject

Value Versus Growth Real Estate Investment Strategy

47

The relative superiority of the value portfolios is confirmed by the results of stochastic dominance test presented in Exhibits 7-9   Cumulative Probability

Exhibit7a: Stochastic Dominance Analysis for (Industrial Sector) 5Year Holding Period 1.00 0.80 0.60 0.40 0.20 0.00 -8 2. 5. 12 30 42 57 61 68 76 87 10 14 19 21 35 .0 46 88 .8 . . . . . . . 5 4 5 7 0 0 8 95 77 65 33 59 75 67 .04 .27 .00 .62 .02

Return (%) Value

 

Cumulative Probability

Exhibit 7b: Stochastic Dominance Analysis for (Industrial Sector) 10-Year Holding Period 1.00 0.80 0.60 0.40 0.20 0.00 -1

.3 1

44 .

25

73

7 4 3 3 3 2 2 2 1 1 8 1 .5 3.3 05. 36. 66. 10. 60. 80. 08. 37. 90. 85. 46. 6 10 26 19 80 58 14 91 51 77 50 13 2

Return (%) Value

 

Growth

Exhibit 7c: Stochastic Dominance Analysis (Industrial Sector) for 15-Year Holding Period Cumulative Probability

Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.

Growth

1.00 0.80 0.60 0.40 0.20 0.00 10 14 17 18 19 19 22 24 32 82 88 91 10 11 12 12 4. 4. 0. 3. 0. 4. 1. 6. 3. 7. 7. 7. 80 88 46 78 57 37 09 01 08 31 61 90 47 23 31 63 .2 .4 .4 .5 1 0 6 4

Return (%) Value

Growth

Exhibit 7. Contiuned Real Estate Investment Market, Nova Science Publishers, Incorporated, 2009. ProQuest Ebook Central,

48

Kwame Addae-Dapaah, Hin/David Kim Ho, Yan Fen Tan

 

Cumulative Probability

Exhibit 7d: Stochastic Dominance Analysis (Industrial Sector) for Investment Horizon Exceeding 15 Years

1.00 0.80 0.60 0.40 0.20 0.00 18 20 21 23 29 50 97 14 18 27 34 4 2 9 0 2 2 1 3 6 4. 6 20 .88 .10 .90 .56 .99 .69 3.0 0.3 9.3 9.3 0 3 2 9

Return (%) Value

Growth

Exhibit 7.

  C u m u lative Prob ab ility

Exhibit 8a: Stochastic Dominance Analysis (Office Sector) for 5-Year Holding Period 1.000 0.800 0.600 0.400 0.200 0.000 -3

4 .0

8

-2

1.5

5

-1

1 .0

3

7 .6

8

22

.81

39

.8 8

48

57

.6 5

.60

87

.8 2

10

5.9

8

12

2 .1

Value

 

Growth

Exhibit 8b: Stochastic Dominance Analysis (Office Sector) for 10-Year Holding Period 1.000

Cumulative Probability

Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.

Return (%)

0.800 0.600 0.400 0.200 0.000 57 47 30 19 13 11 94 80 51 40 -3 4. 5.6 6. 7. 4. 7. 8. 3. .2 .7 .5 .4 1 65 41 24 16 49 03 80 3 2 7 7

Return (%) Value

Growth

Exhibit 8. Continued Real Estate Investment Market, Nova Science Publishers, Incorporated, 2009. ProQuest Ebook Central,

0

15

8 .9

3

Value Versus Growth Real Estate Investment Strategy

 

49

Cumulative Probability

Exhibit 8c: Stochastic Dominance Analysis (Office Sector) for 15-Year Holding Period 1.000 0.800 0.600 0.400 0.200 0.000 10 11 12 13 35 64 72 10 11 12 12 50 58 94 0 1 9 6 .0 . . 1. 1 1 5 6 4 2 2 45 15 07 .11 .42 .33 .43 .77 .81 6.6 2.5 3.3 6.6 7 7 2 6

Return (%) Value

 

Growth

Exhibit 8d: Stochastic Dominance Analysis (Office Sector) for Investment Horizon Exceeding 15 Years 1.000

Cumulative Probability

0.900 0.800 0.700 0.600 0.500 0.400 0.300 0.200 0.100 0.000

Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.

52 .05

11 8 .4

0

14 4 .5

58 2 .5

5

7

17 81 .62

43 15 .95

Return (%)

Value

Growth

Exhibit 8.

Is The Superior Performance of Contrarian Strategy a Compensation for Higher Risk? According to the traditional school of thought (see literature review), the superiority of the contrarian strategy is a compensation for higher systematic risk (i.e. higher return is a reward for higher risk). If the value strategy is fundamentally riskier, it should under-perform the growth strategy during undesirable/bad states of the world – i.e. times of severe market decline when the marginal utility of consumption is high (Lakonishok et al., 1994). This section is therefore aimed at ascertaining if there is any synchrony between “value” underperformance and “bad” state of the world. Furthermore, traditional measures of risk (i.e. standard deviation) and risk-adjusted performance indicator (i.e. coefficient of variation) are used to compare “value” and growth strategies.

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50

Kwame Addae-Dapaah, Hin/David Kim Ho, Yan Fen Tan

 

Exhibit 9a: Stochastic Dominance Analysis (Retail Sector) for 5-Year Holding Period

Cumulative Probability

1.000 0.800 0.600 0.400 0.200 0.000 -1 -4 -1 0. 1. 1. 1.6 1.7 1.9 2.3 2.8 3.2 3.5 3.7 3.8 3. .7 .5 18 12 48 3 8 8 6 2 4 0 8 0 98 5 4

Return (%) Value

 

Growth

Cumulative Probability

Exhibit 9b: Stochastic Dom inance Analysis (Retail Sector) for 10Year Holding Period 1.000 0.800 0.600 0.400 0.200 0.000 72 94 11 25 17 40 15 37 1 11 10 .8 .7 8. 9. 4. 7. 3. 0. 77 295 9 5 09 42 2 1 90 . 30 51 20 . .8 53 10 8

Return (%)

 

Grow th

Exhibit 9c: Stochastic Dominance Analysis (Retail Sector) for Investment Horizon Exceeding 10 Years 1.000 0.800 0.600 0.400 0.200 0.000 17 7.4 2 18 6.3 4 18 9.2 9 19 5.8 9 21 9.1 9 22 5.4 5 23 7.9 2 74 2.4 1 79 7.3 7 12 08 .03 89 1.1 5 12 76 .47 13 83 .57

Cumulative Probability

Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.

Value

Return (%) Value

Growth

Exhibit 9.

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Value Versus Growth Real Estate Investment Strategy

51

 

Exhibit 10: Pacific Basin Real Estate Stock Market 2500

Index (1973=100)

2000 1500 1000 500 0 19 85 Q1

19 87 Q1

19 89 Q1

19 91 Q1

19 93 Q1

19 95 Q1

19 97 Q1

19 99 Q1

20 01 Q1

20 03 Q1

20 05 Q1

Period

Exhibit 10

Exhibit 11. Performance of Portfolios in Different States of the World. Tests for equality of Means Office

Mean value

Worst period 9.70

Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.

Next worst Period

Next best Period

Best Period Industrial

9.61

2.52

2.05

Worst period 4.41

Next worst Period

Next best Period

Best Period

5.69

6.17

8.05

Mean Mean Growth Spread

-0.74

-1.59

2.44

2.80

-0.23

-0.32

2.14

2.33

10.44

11.21

0.08

-0.75

4.64

6.01

4.04

5.72

t-test Pooledvariance Separatevariance Pooledvariance Separatevariance Pooledvariance Separatevariance Pooledvariance Separatevariance Pooledvariance Separatevariance Pooledvariance Separatevariance Pooledvariance Separatevariance Pooledvariance Separatevariance

Test statistic t

p-value

5.38

0.000

Reject

5.38

0.000

Reject

5.24

0.000

Reject

5.24

0.000

0.06

0.475

0.06

0.475

-0.83

0.206

-0.83

0.207

Reject Do not Reject Do not Reject Do not Reject Do not Reject

4.31

0.000

Reject

4.31

0.000

Reject

4.55

0.000

Reject

4.55

0.000

Reject

4.84

0.000

Reject

4.84

0.000

Reject

3.68

0.000

Reject

3.68

0.001

Reject

Real Estate Investment Market, Nova Science Publishers, Incorporated, 2009. ProQuest Ebook Central,

 

52

Kwame Addae-Dapaah, Hin/David Kim Ho, Yan Fen Tan Exhibit 11 (Continued) Retail

Worst period 5.37

Next worst Period

Next best Period

Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.

Best Period

3.44

6.38

0.77

0.96

2.50

1.03

3.14

4.41

0.94

5.35

-2.37

Pooledvariance Separatevariance Pooledvariance Separatevariance Pooledvariance Separatevariance Pooledvariance Separatevariance

3.43

0.001

Reject

3.43

0.001

0.56

0.290

0.56

0.290

Reject Do not Reject Do not Reject

8.28

0.000

Reject

8.28

0.000

-1.55

0.067

-1.55

0.072

Reject Do not Reject Do not Reject

Exhibits 1-4 show that the value strategy (industrial and retail sectors) virtually never under-performed the growth strategy in any holding period. It is the value office portfolio that underperformed “growth” between 1994Q1 and 1999Q2 (5-year holding period), and 1991Q1and 1992Q3 (10-year holding period). Apart from 1997-1999 (the period of SouthEast Asian economic crisis), the periods of “value” underperformance do not coincide with severe market declines. As far as the industrial and retail sectors are concerned, there is no underperformance of the value portfolios to be associated with severe market declines as defined by some pay-off relevant factor. The performance of the value and growth properties in four states of the world (i.e. Worst, Next Worst, Next Best, and Best 20 quarters) based on Datastream Indices for the Pacific Basin Real Estate Stock Market from 1985Q1 to 2005Q3 (Exhibit 10) is presented in Exhibit 11. After matching the quarterly returns for the growth and value portfolios with the changes in the real estate stock market return, the mean value-growth spread in each state is reported together with the corresponding t-statistics for the test that the difference in returns is equal to zero (Exhibit 11), i.e.

H o : μvalue − μ growth = 0 H o : μvalue − μ growth ≠ 0 Exhibits 10 & 11 Exhibit 11 shows that the value strategy did notably better than the growth strategy in all the 4 states of the world (industrial sector) except the best state of the world (office and retail sectors). However, these “value” underperformances are not statistically significant. The null hypothesis is rejected for all 4 states of the world (industrial sector), the “Worst” and “Next Worst” (office), and “Worst” and “Next Best” (Retail) states of the world to conclude that there is statistical difference between the means of the two populations. It is evident from Exhibit 11 that the superior performance of the value strategy was skewed towards negative market return months rather than positive market return months. The evidence indicates that there are no significant traces of a conventional asset pricing equilibrium in which the higher returns on the value strategy are compensation for higher systematic risk.

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Value Versus Growth Real Estate Investment Strategy

53

The volatility of the portfolios’ returns during the period of study is presented in Exhibit 5. The results show that value portfolios recorded higher standard deviation of returns than growth portfolios for all the holding periods and for the three property sectors. The results presented in Exhibit 6b indicate that the higher value portfolio standard deviations are significantly different, at the 0.01 level, from those of the growth properties. However, since the mean returns and variances of the two portfolios are different, the coefficient of variation (CV) is a more appropriate risk measure for comparison. The CVs in Exhibit 5 imply that the industrial and office sectors value portfolios were safer than the growth portfolios for all the holding periods except the more than 15-year holding period. Furthermore, the retail value portfolio was safer (based on CV) than its growth counterpart in only the 5-year holding periods – It was riskier than the retail growth portfolio in the remaining two holding periods (Exhibit 5). However, since value portfolios stochastically dominate growth portfolios in all the holding periods (exhibits 7-9), the latter is riskier than the former (Biswas, 1997). Hence, a risk model based on differences in standard deviation alone may not be a credible explanation for the superior performance of value properties.

Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.

POST-MODEL ESTIMATION – A TEST OF THE EXTRAPOLATION MODEL The paper provides empirical evidence to verify whether excessive extrapolation and expectational errors characterize growth and value strategies. First, the study period is divided into two: past (pre-portfolio formation) and future (post-formation) performances (see Panels B and C respectively of Exhibits 12-14). Exhibits 12-14 present some descriptive characteristics of the growth and value portfolios with respect to their initial yields, past growth rates, and future growth rates. Panel A of Exhibits 12-14 reveals that the value portfolios had higher initial yields than growth portfolios. This is supposed to portend lower expected growth rates for value properties. Panel B shows that, using several measures of past growth, including rental income and capital value, the growth portfolio performance for each sector (in relation to rental income) and for the industrial and retail sectors (relative to capital value) grew faster than the value portfolios over the pre- portfolio formulation period. Exhibit 12. Initial Yields, Past and Future Performances of Value and Growth Properties (Industrial Sector). Panel A: Initial Yields 1994 Q3

Initial Yield

1994 Q4

Portfolio Composition

Value 5.16 Ford Lauderdale Orlando Tampa

Real Estate Investment Market, Nova Science Publishers, Incorporated, 2009. ProQuest Ebook Central,

Growth 2.05 Memphis Sydney Brisbane AuckLand

54

Kwame Addae-Dapaah, Hin/David Kim Ho, Yan Fen Tan

Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.

Year 1985Q1 1985Q2 1985Q3 1985Q4 1986Q1 1986Q2 1986Q3 1986Q4 1987Q1 1987Q2 1987Q3 1987Q4 1988Q1 1988Q2 1988Q3 1988Q4 1989Q1 1989Q2 1989Q3 1989Q4 1990Q1 1990Q2 1990Q3 1990Q4 1991Q1 1991Q2 1991Q3 1991Q4 1992Q1 1992Q2 1992Q3 1992Q4 1993Q1 1993Q2 1993Q3 1993Q4

Panel B: Past Performances of Industrial Properties Value Growth Capital Growth Rental Growth Capital Growth (%) (%) (%) 3.49 -10.04 0.33 9.78 -2.77 -0.30 10.42 -10.74 1.11 8.07 -13.11 2.33 14.48 34.09 -0.10 8.26 14.43 4.34 7.47 -13.38 -3.22 -2.62 -8.56 -2.13 -3.56 -0.41 -1.16 5.50 3.74 -1.80 4.99 -1.61 -1.68 19.25 -6.02 -5.43 9.96 -12.62 -0.30 2.03 -28.59 0.55 3.50 -10.86 -0.31 0.71 -13.76 -1.30 -4.77 -14.75 0.07 -7.09 -9.42 0.01 -7.01 -2.32 0.23 -15.02 -8.35 -1.73 -23.97 -3.15 -4.31 -79.20 -17.69 -0.23 -81.46 39.75 -2.43 19.05 26.34 -4.04 13.41 21.41 -1.19 6.68 -9.09 -2.47 8.19 9.93 -2.51 -5.01 7.03 -7.32 -6.58 1.42 -0.89 -10.22 0.80 -3.10 -17.40 -16.83 -2.54 5.87 2.57 -4.30 1.43 16.89 -3.06 -5.41 -1.78 -2.42 -5.58 -18.94 -2.46 -19.91 -1.10 -3.14

Rental Growth (%) 28.09 17.08 53.13 75.05 1.96 133.92 57.14 25.23 25.14 12.58 -2.32 -2.21 10.18 2.81 14.89 10.39 44.75 6.44 -9.88 85.05 -0.15 0.91 46.27 15.10 49.70 1.62 6.49 43.55 4.04 20.29 -0.33 20.04 6.91 -0.14 7.45 33.85

1994Q1

-45.04

-8.95

-2.73

20.38

1994Q2

73.58

50.95

8.40

8.40

1994Q3

19.71

1.65

2.02

15.55

1994Q4

-6.31

3.17

2.06

32.13

Geometric Average Growth Rate

-7.45

-1.47

-1.21

20.43

Real Estate Investment Market, Nova Science Publishers, Incorporated, 2009. ProQuest Ebook Central,

Value Versus Growth Real Estate Investment Strategy Panel C: Future Performances of Industrial Properties

Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.

Value

Growth

Year

Capital Growth (%)

Rental Growth (%)

Capital Growth (%)

Rental Growth (%)

1995Q1

-9.86

12.95

1.30

6.14

1995Q2

3.55

1.24

1.16

-15.97

1995Q3

-2.22

10.48

-0.04

-14.62

1995Q4

0.49

-1.63

-0.72

35.19

1996Q1

3.43

6.28

2.83

49.37

1996Q2

-0.68

-1.06

0.36

-7.04

1996Q3

-3.21

-0.52

0.55

-11.06

1996Q4

11.81

5.63

0.56

35.99

1997Q1

-1.85

0.48

2.57

8.30

1997Q2

-1.07

-0.81

0.78

-10.67

1997Q3

9.08

1.93

1.59

4.66

1997Q4

1.92

-10.39

2.17

-5.20

1998Q1

-5.03

15.17

0.57

2.19

1998Q2

-1.20

-4.76

4.46

-4.61

1998Q3

-17.24

4.44

1.97

-22.83

1998Q4

-9.80

2.70

0.26

-17.75

1999Q1

-12.91

8.69

0.38

-5.93

1999Q2

15.41

0.36

1.01

0.30

1999Q3

7.36

-1.30

0.80

-8.00

1999Q4

-8.96

5.52

2.19

21.93

2000Q1

-23.09

-1.66

1.29

1.93

2000Q2

-18.13

2.17

1.10

6.85

2000Q3

-109.70

3.38

0.14

-19.98

2000Q4

-51.56

1.09

0.27

19.42

2001Q1

-167.77

2.84

1.02

14.03

2001Q2

401.62

0.71

0.78

6.18

2001Q3

23.38

0.94

1.34

7.47

2001Q4

22.95

-4.79

-0.58

72.33

2002Q1

-21.36

-0.31

-0.63

-2.24

2002Q2

22.64

0.25

-0.29

-4.13

2002Q3

60.71

-4.14

0.84

-10.59

2002Q4

-1.06

8.12

0.40

-6.92

2003Q1

37.16

-0.14

-9.98

23.37

2003Q2

-0.20

-4.44

-0.18

-0.63

2003Q3

12.40

-1.43

-5.64

-21.03

2003Q4

6.83

2.50

-1.56

-3.94

2004Q1

-6.19

-4.01

-11.51

13.44

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55

56

Kwame Addae-Dapaah, Hin/David Kim Ho, Yan Fen Tan Panel C (Continued)

.

2004Q2

-0.26

2.82

2.71

5.39

2004 Q3

30.22

1.22

1.56

-10.12

Geometric Average Growth Rate

4.88

1.43

0.10

1.76

Panel C shows that over the subsequent post-formulation years, the relative growth of rental income and capital value for growth properties was generally quite below expectation. The figures in Panels B and C represent the incremental growth in performances between the returns for the preceding and successive quarters’ portfolios, since the analysis is based on the assumption that portfolios are reformulated at the beginning of each quarter. Thus, the 401.62% capital growth for the value industrial portfolio in 2001Q2 reflects the growth in the performance of the 2001Q2 portfolio, relative to that of the 2001Q1 portfolio. These assumptions, which are in consonance with the finance literature, are merely to test the plausibility of naïve extrapolation being a credible explanation for the value superiority. They certainly are not intended in any way to imply/suggest that real estate investors do/should reformulate their portfolio quarterly. Exhibit 13. Initial Yields, Past and Future Performances of Value and Growth Properties (Office Sector).

Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.

Panel A: Initial Yields 1994 Q2

Initial Yield

1994 Q3

Portfolio Composition

Value 8.57 Austin Bethesda Houston San Francisco

Panel B: Past Performances of Office Properties Value Capital Rental Year Growth (%) Growth (%) 1985Q1 0.43 -13.02 1985Q2 12.86 -0.39 1985Q3 24.85 0.02 1985Q4 8.57 -0.26 1986Q1 11.04 -0.40 1986Q2 -6.44 -0.83 1986Q3 -7.64 -0.84 1986Q4 5.82 0.32 1987Q1 5.06 0.32 1987Q2 117.96 0.20 1987Q3 24.25 0.19

Growth 1.69 Melbourne (non-CBD) Auckland (non-CBD) Hong Kong (Central) Hong Kong (Wan Chai) Shanghai (Puxi)

Growth Capital Growth (%) -0.91 0.39 1.49 0.49 -0.17 -0.59 -2.51 -4.44 -2.38 -18.20 -1.43

Real Estate Investment Market, Nova Science Publishers, Incorporated, 2009. ProQuest Ebook Central,

Rental Growth (%) 17.37 35.80 25.45 11.44 14.22 -11.69 4.50 6.86 16.24 7.55 76.78

Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.

Value Versus Growth Real Estate Investment Strategy 1987Q4 1988Q1 1988Q2 1988Q3 1988Q4 1989Q1 1989Q2 1989Q3 1989Q4 1990Q1 1990Q2 1990Q3 1990Q4 1991Q1 1991Q2 1991Q3 1991Q4 1992Q1 1992Q2 1992Q3 1992Q4 1993Q1 1993Q2 1993Q3 1993Q4 1994Q1 1994Q2 1994Q3 Geometric Average Growth Rate

22.63 15.33 -4.93 -5.23 -0.84 -2.51 11.53 10.11 -3.00 -6.81 -32.37 -123.71 -5.34 -23.60 -30.87 -365.00 24.41 6.29 5.71 6.27 -3.58 4.94 24.32 23.81 12.86 14.16 -26.68 171.69

-0.95 -0.98 -2.75 -2.83 -1.01 0.21 0.98 0.96 0.18 -2.00 -1.69 -1.75 0.28 -1.88 -1.04 -1.20 -2.22 -0.66 -1.04 1.78 -14.67 8.56 5.00 4.60 4.41 4.47 3.04 2.87

-6.17 1.55 -0.87 -0.75 -2.06 0.80 -1.67 1.58 1.45 0.78 -1.84 -0.51 -3.65 -3.08 -0.30 -5.48 -6.24 -2.92 -3.59 -2.09 -5.93 -1.13 -7.79 1.60 -6.90 -0.71 2.66 1.10

7.09 54.31 -2.50 27.26 30.32 3.07 -4.66 8.70 26.72 30.64 43.05 27.99 15.76 16.45 0.12 6.68 32.65 -51.90 0.97 6.47 21.52 -1.54 64.61 21.86 -6.94 23.80 22.93 16.13

6.99

-0.56

-2.14

14.36

57

Recall that the Gordon’s formula (Gordon and Shapiro (1956)) can be rewritten as ⎛ I⎞ k p ⎜ ≡ ⎟ = R N − g p = d , where ⎝ P⎠

k p is the initial yield for property, I is the current rental

income, P is the market price, R N is the required nominal return, and

(gp −d )

is the

rental growth for actual, depreciating properties. These formulae literally imply that, holding discount rates constant, the differences in expected rental growth rates can be directly calculated from differences in initial yields. Since the assumptions behind these simple formulae are restrictive (e.g. constant growth rates, etc.), the paper does not calculate exact estimates of the differences in expected rental growth rates between value and growth portfolios. Instead, the paper seeks to ascertain whether the large differences in initial yields between value and growth properties can be justified by the differences in future rental growth rates.

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58

Kwame Addae-Dapaah, Hin/David Kim Ho, Yan Fen Tan Panel C: Future Performances of Office Properties

Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.

Value

Growth

Year

Capital Growth (%)

Rental Growth (%)

Capital Growth (%)

Rental Growth (%)

1994Q4

-0.13

9.23

0.00

1.67

1995Q1

1.32

8.39

-6.71

-4.71

1995Q2

1.15

27.02

1.89

-4.24

1995Q3

-0.52

-0.01

0.21

-4.54

1995Q4

0.24

-15.71

-3.50

-4.16

1996Q1

1.09

40.04

2.46

-0.36

1996Q2

1.04

10.40

4.33

53.02

1996Q3

1.40

-10.90

204.54

-0.55

1996Q4

4.02

15.21

16.62

-0.67

1997Q1

0.74

8.91

-7.23

-0.38

1997Q2

2.20

-22.28

-1.03

-0.91

1997Q3

2.75

2.37

-27.62

-0.94

1997Q4

10.79

10.91

38.40

-1.64

1998Q1

6.89

18.48

35.21

-7.33

1998Q2

5.21

6.91

-0.13

-2.97

1998Q3

1.62

17.80

-66.39

-2.73

1998Q4

4.24

1.15

-3.95

-11.83

1999Q1

1.44

34.66

-0.71

-4.29

1999Q2

0.85

2.82

-16.90

-5.25

1999Q3

2.38

-2.91

-27.68

-6.32

1999Q4

1.74

26.21

20.91

0.08

2000Q1

1.74

15.40

-10.90

6.48

2000Q2

3.82

2.66

100.67

6.58

2000Q3

1.88

-7.32

-69.73

7.74

2000Q4

3.37

28.93

9.54

6.16

2001Q1

0.86

38.04

-0.64

-0.41

2001Q2

2.31

-7.03

3.90

-0.62

2001Q3

0.41

1.56

-2.02

-1.44

2001Q4

-1.66

5.59

-56.00

-1.79

2002Q1

-0.94

-0.17

3.76

-3.20

2002Q2

-1.26

-4.00

-11.25

-2.72

2002Q3

-2.25

-5.37

-27.10

-3.20

2002Q4

-5.48

9.78

3.36

-4.41

2003Q1

0.63

4.22

26.10

1.19

2003Q2

-1.05

3.39

-13.64

-5.74

2003Q3

-1.64

-7.55

-1.12

-2.51

Real Estate Investment Market, Nova Science Publishers, Incorporated, 2009. ProQuest Ebook Central,

Value Versus Growth Real Estate Investment Strategy 2003Q4

-1.36

0.78

14.43

1.98

2004Q1

-0.65

9.32

80.07

-0.03

2004Q2

-0.47

-9.12

-5.76

2.80

Geometric A

-2.41

-0.51

1.21

5.96

Exhibit 14. Initial Yields, Past and Future Performances of Value and Growth Properties (Retail Sector). Panel A: Initial Yields 1997 Q4

Initial Yield

1998 Q1

Portfolio Composition

Value

Growth

12.22

1.75

Phoenix

Bangkok

San Diego

Jakarta

New South Wales

Shanghai

Panel B: Past Performances of Retail Properties

Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.

Value

Growth

Year

Capital Growth (%)

Rental Growth (%)

Capital Growth (%)

Rental Growth (%)

1992Q1

-32.98

-1.00

-0.20

-10.84

1992Q2

26.62

-1.03

0.45

42.40

1992Q3

15.59

-1.14

-3.76

7.81

1992Q4

11.56

-1.17

-5.35

0.04

1993Q1

0.09

-1.35

-0.26

18.47

1993Q2

4.91

-0.79

-0.07

51.52

1993Q3

0.06

-1.94

0.60

5.63

1993Q4

0.03

-1.39

-1.24

34.80

1994Q1

16.87

0.95

0.34

63.78

1994Q2

8.27

1.20

-1.04

6.09

1994Q3

8.22

1.17

0.31

11.37

1994Q4

8.17

1.14

-2.35

25.66

1995Q1

6.64

1.34

-1.47

26.05

1995Q2

-2.90

1.00

0.11

14.02

1995Q3

-3.53

0.97

-0.80

9.27

1995Q4

0.23

0.81

-3.78

28.87

1996Q1

-1.89

-1.10

0.16

12.70

1996Q2

-1.96

-1.26

9.71

39.45

1996Q3

-2.05

-1.42

1.32

4.26

1996Q4

-2.14

-1.60

0.94

11.56

1997Q1

-5.09

-5.75

2.19

18.03

1997Q2

-4.78

-5.26

91.16

0.64

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59

60

Kwame Addae-Dapaah, Hin/David Kim Ho, Yan Fen Tan Panel B (Continued) 1997Q3

-7.28

-7.75

2.00

34.45

1997Q4

-11.81

-11.25

-2.32

5.89

1998Q1

-9.30

-8.63

4.84

3.55

Geometric A

0.21

-1.87

2.60

17.40

Panel B of Exhibits 12-14 reveal that the average quarterly growth rate for rental income for the glamour portfolio was 20.43% compared to -1.47% (industrial), 14.36% compared to 0.56% (office) and 17.40% compared to -1.87% (retail) for the value portfolio over the preportfolio formation period. Panel C: Future Performances of Retail Properties

1998Q2

Value Capital Growth (%) -11.01

Rental Growth (%) -10.10

Growth Capital Growth (%) 12.39

Rental Growth (%) -0.48

1998Q3

-13.75

-12.35

-3.69

8.97

1998Q4 1999Q1

-18.94 0.74

-16.31 3.02

5.96 4.75

45.16 1.64

1999Q2

0.60

2.62

-2.98

3.60

1999Q3 1999Q4

0.47 0.34

2.27 1.95

0.13 12.45

6.54 9.49

2000Q1

2.60

1.83

7.93

-5.70

2000Q2 2000Q3

2.43 2.29

1.73 1.64

4.21 4.16

5.26 3.07

2000Q4

2.16

1.56

-2.67

0.37

2001Q1 2001Q2

2.47 3.22

1.93 2.97

-8.35 -4.43

2.09 -9.58

2001Q3

3.54

3.49

-0.27

4.73

2001Q4 2002Q1

3.13 1.77

2.82 2.24

-7.78 -8.66

3.40 13.37

2002Q2

2.75

3.24

-2.16

-26.49

2002Q3 2002Q4

0.11 1.94

1.01 2.36

6.88 25.46

37.22 1.19

2003Q1

0.69

0.68

28.96

-0.37

2003Q2 2003Q3

3.81 3.66

3.89 2.67

16.96 1.62

3.13 4.92

2003Q4

0.73

0.79

-0.08

1.55

2004Q1 2004Q2 Geometric

4.09 0.15

1.00 -3.24

-6.79 -7.08

-1.23 -1.35

0.71

0.01

2.27

3.65

Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.

Year

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Value Versus Growth Real Estate Investment Strategy

61

Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.

Every dollar invested in the value portfolio in 1994Q2 (office), 1994Q3 (industrial) and 1997Q4 (retail) had a claim to 8.57, 5.16 and 12.22 cents of the then existing corresponding rental income while a dollar invested in the growth portfolio was a claim to 1.69, 2.05 and 1.75 cents of the rental income (Panel A of Exhibits 12-14). Ignoring any difference in required rates of return, the large differences in initial yields have to be justified by an expectation of higher rental growth rates for glamour than value portfolios over a period of time. Thus, the expected rental income for the growth portfolio must be higher than the value portfolio at some future date. Accordingly, investors would like to know the number of quarters it would take for the rental income per dollar invested in the growth portfolios (0.0169, 0.0205 and 0.0175) to equate the rental income of the value portfolio (0.0857, 0.0516 and 0.1222), assuming that the differences in past rental income growth rates would persist. It would take approximately 26 quarters (office), 5 quarters (industrial) and 21 quarters (retail) for such equalization to occur (see Exhibit 15). Note that this equality is based on a flow basis, not on a present-value basis which would require an even longer time period over which glamour properties should experience superior growth. Unfortunately, a comparison of Panels B and C (Exhibits 12-14) show that the relatively higher expected future growth (implied by the higher growth rate in the pre-formation period) in the glamour portfolios during the post-formation period was a far cry from reality. The actual post-formation rental growth rate for glamour portfolios plummeted by 58.49% from 14.36% to 5.96% (office), 91.39% from 20.43% to 1.76% (industrial), and 79.02% from 17.4% to 3.65% (retail) per quarter. Alternatively, the post-formation rental growth rate for the value portfolios increased by 8.93% from -0.56% to -0.51% (office), 197.28% from 1.47% to 1.43% (industrial) and 100.53% from -1.87% to 0.01%. These results are consistent with the extrapolation model. Contrarian/glamour investors were pleasantly/unpleasantly surprised by the post formation portfolio results. Rental is, however, a portion of portfolio performance. Capital value is an important portion of a portfolios performance and thus, must be analyzed in relation to the extrapolation model. Exhibit 15. a: Growth of Industrial Sector’s Rental Income Per Dollar Invested (4th Quarter 1994) Quarter 0 1 2 3

Value Portfolio 5.16 5.08 5.01 4.94

Growth Portfolio 2.05 2.47 2.97 3.58

Quarter 4 5

Value Portfolio 4.86 4.78

Growth Portfolio 4.31 5.19

During the pre-formation period, the capital value growth rates for the glamour portfolios, -1.21% (industrial) and 2.6% (retail) were higher than those for value portfolios, 7.45% (industrial) and 0.21% (retail). The capital value growth rate for the office glamour portfolio (-2.14%), in contradistinction, was lower than office value portfolio (6.99%) during the pre-formation period (Exhibits 12-14). Exhibit 15b: Growth of Office Sector’s Rental Income Per Dollar Invested (3rd Quarter 1994)

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Kwame Addae-Dapaah, Hin/David Kim Ho, Yan Fen Tan Quarter 0 1 2 3 4 5 6 7 8 9 10 11 12 13

Value Portfolio 8.57 8.52 8.47 8.43 8.43 8.38 8.33 8.28 8.23 8.19 8.14 8.09 8.04 7.99

Growth Portfolio 1.69 1.93 2.18 2.42 2.66 2.90 3.15 3.39 3.63 3.87 4.12 4.12 4.12 4.36

Quarter 14 15 16 17 18 19 20 21 22 23 24 25 26

Value Portfolio 7.95 7.90 7.85 7.80 7.75 7.71 7.66 7.61 7.56 7.51 7.47 7.42 7.37

Growth Portfolio 4.60 4.84 5.09 5.33 5.57 5.81 6.06 6.30 6.54 6.78 7.03 7.27 7.51

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The results in Exhibits 12-14 reveal that while the capital value growth rate for the glamour industrial portfolio increased by 108.26% from -1.21% to 0.10%, that for the value industrial portfolio increased by 165.5% from -7.45% to 4.88% per quarter during the postformation period. Moreover, the capital value growth rate for the retail glamour portfolio declined by 12.69% from 2.6% to 2.27% while that for the value portfolio increased by 238.1% from 0.21% to 0.71% per quarter in the post-formation period. Once again, the results are consistent with the extrapolation model. Exhibit 15c: Growth of Retail Sector’s Rental Income Per Dollar Invested (3rd Quarter 1994) Quarter 0 1 2 3 4 5 6 7 8 9 10

Value Portfolio 12.22 11.99 11.76 11.53 11.31 11.08 10.85 10.62 10.39 10.16 10.16

Growth Portfolio 1.75 2.05 2.36 2.66 2.97 3.27 3.58 3.88 4.18 4.49 4.49

Quarter 11 12 13 14 15 16 17 18 19 20 21

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Value Portfolio 9.93 9.71 9.48 9.25 9.02 8.79 8.56 8.34 8.11 7.88 7.65

Growth Portfolio 4.79 5.10 5.40 5.71 6.01 6.31 6.62 6.92 7.23 7.53 7.84

Value Versus Growth Real Estate Investment Strategy

63

Exhibit 16: Variance Ratio Test. Sector Office

Investment Horizon

Value Portfolio

Growth Portfolio

4 Quarters 20 Quarters

34.975 0.183

0.806 4.832

40 Quarters

0.190

1.671

60 Quarters

0.144 0.080

0.945 0.586

4 Quarters 20 Quarters 40 Quarters

5.896

0.712

0.298 0.523

0.712 0.659

60 Quarters

0.254

0.479

80 Quarters 4 Quarters 20 Quarters

0.050 2.481

0.354 4.207

0.979

0.165

40 Quarters

1.331 0.722

0.095 0.069

80 Quarters Industrial

Retail

55 Quarters

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Variance Ratio

However, the results for the office portfolio are inconsistent with the extrapolation model. The capital value growth rate for the glamour office portfolio increased by 156.54% from 2.14% to 1.21% while that of the corresponding value portfolio declined by 134.48% from 6.99% to -2.41% per quarter. The pertinent question that needs to be addressed at this juncture is whether, given the post-formation performance of capital value growth rates for the industrial and office portfolios, the glamour portfolios can outperform the value portfolios at some time in the future. This is addressed via a mean reversion analysis.

Variance Ratio Test The results of the variance ratio tests are presented in Exhibit 16. The returns for both glamour and value portfolios for the three property sectors display mean reversion at long horizons. However, the office glamour portfolio returns exhibit positive serial correlation over investment horizons of up to 10 years (40 quarters) while the office value portfolio display negative serial correlation virtually over all the holding periods. This explains why the office glamour portfolio outperformed its value counterpart in 22 of the 61 5-year holding periods (Exhibit 1) as well as 5 of the 41 10-year holding periods (Exhibit 2). On the average, however, the value strategy outperformed the glamour strategy over the 5 and 10-year holding periods when the glamour portfolio displayed return inertia. As far as the industrial sector is concerned, both portfolios displayed mean reversion in all the holding periods under consideration. This is also true of the retail sector except that the retail value portfolio exhibited positive serial correlation for holding periods between 5 and

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Kwame Addae-Dapaah, Hin/David Kim Ho, Yan Fen Tan

10 years. These results imply that the superior performance of the contrarian strategy is not a flash in the pan – It will persist in future years.

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CONCLUSION The paper set out to investigate the comparative advantage(s) of the value and growth investment strategies to ascertain the sustainability of the superior performance (if any) of the contrarian strategy. The results of the study indicate that value portfolios for all three property sectors out-performed (in both absolute, and in most cases, risk-adjusted bases) growth portfolios over all the holding periods under consideration. A dollar invested in the value portfolio over 10 years, on the average, earned 209.07% (office), 298.09% (industrial) and 647.15% (retail) more than a dollar invested in the corresponding growth portfolios. Similarly, a dollar invested in the value portfolio over the entire period of study earned, on average, 1804.06% (office), 1733.61% (industrial) and 771.08% (retail) more than a similar investment in the growth portfolio. The difference between the performances of the value and the growth portfolios are statistically significant at the 0.01 level. Thus, the null hypothesis that there is no difference between the mean returns for the two portfolios is rejected. Furthermore, the superior performances of value portfolios occurred in almost all the four “states of the world”. The superior performance is not a compensation for higher risk as measured by the coefficient of variation (CV) for investment horizons of up to 5 years (retail) and 15 years (office and industrial). These findings are consistent with the contrarian strategy in finance. It must be noted, however, that the superior performance of the contrarian strategy for investment horizons of more than 5 years (retail) and 15 years (office and industrial) could be a compensation for higher risk as measured by the CV. Notwithstanding this caveat, the relative superiority of the value portfolio for each sector and holding period is confirmed by stochastic dominance test, which indicates that the value strategy is the optimal choice for both risk averters and risk lovers. In addition, the variance ratio test reveals that returns for both value and growth property portfolios exhibit mean reversion at long horizons. This means that the superior performance of the contrarian strategy is sustainable. The above results are consistent with the finance literature. This consistency cannot be attributed to data snooping as the studies in the finance literature are based on different data. The findings imply that high initial yield office, industrial and retail portfolios in the sample outperformed their low yield counterparts during the period under investigation. If the results can be generalized in any way, one may safely conclude that property investors should seriously consider contrarian real estate investment if they want to improve the performance of their portfolios.

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65

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Appendix A: Countries in the Three Sectors’ Portfolios Appendix A-1a: Countries in the Office Portfolio Code Country Code Country 1 Altanta 38 San Jose 2 Austin 39 Santa Ana 3 Baltimore 40 Seattle 4 Bethesda 41 Tampa 5 Birmingham 42 Warren 6 Boston 43 Wasington 7 Bridgeport 44 West Palm Beach 8 Cambridge 45 Sydney (CBD) 9 Charlotte 46 Melbourne (CBD) 10 Chicago 47 Brisbane (CBD) 11 Cincinnati 48 Perth (CBD) 12 Columbas 49 Canberra Region Office 13 Dallas 50 Sydney (non-CBD) 14 Denver 51 Melbourne (non-CBD) 15 Edison 52 Auckland (CBD) 16 Ford Lauder 53 Wellington(CBD) 17 Houston 54 Auckland (non-CBD) 18 Kansas City 55 Beijing 19 Lake County 56 Bangkok 20 Los Angeles 57 Hong kong(central)

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Value Versus Growth Real Estate Investment Strategy 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37

Miami Milwankee Minneapolis Newark new york Oakland Orlando Philadelphia Phoenix Pittsburgh Portland Raleigh Sacramento St Louis San Antonio San Diego San Francisco

58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73

hong kong(wan chai) hong kong(tst) hong kong east Jarkarta KL(KLCC) KL (DC) Makati Singapore (Raffles Place) Singapore (Shenton) Singapore (Marina) Shanghai (Puxi) Shanghai (Pudong) Seoul(Yoido) Seoul (Gangnam) seoul (CBD) Tokyo

Appendix A-1b: Composition of Value and Growth office Portfolios.

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Year

1985Q1 1985Q2 1985Q3 1985Q4 1986Q1 1986Q2 1986Q3 1986Q4 1987Q1 1987Q2 1987Q3 1987Q4 1988Q1 1988Q2 1988Q3 1988Q4 1989Q1 1989Q2 1989Q3 1989Q4 1990Q1 1990Q2 1990Q3 1990Q4 1991Q1 1991Q2 1991Q3 1991Q4

Country Code Growth Value Properties Properties 13 26 13,17,39 49,50 13,43 45,49,50 13,14,17 45,49,50 13,17,37 49,51 13,17,37 49,51 8,13,17 49,51 13,17,26 49,51 13,17,18 49,51 13,17,26 49,51 1,13,18 49,51 13,17,18 49,51 10,13,18 49,51 13,17,37 49,51 10,13,37 49,51 6,34,37 49,51 13,34,37 49,51,57 13,17,26,37 49,51,57 17,22,37 49,51,57 6,17,37 49,51,57 6,17,20,37 49,51,57 17,20,22,23 49,51,57 1,17,22,37 49,51,57 1,6,17,20 49,51,57 1,20,28,37 49,57,65 10,20,26,28 49,57,65 1,6,20,28 49,57,65 6,17,26,28 49,57,65

Year

1995Q3 1995Q4 1996Q1 1996Q2 1996Q3 1996Q4 1997Q1 1997Q2 1997Q3 1997Q4 1998Q1 1998Q2 1998Q3 1998Q4 1999Q1 1999Q2 1999Q3 1999Q4 2000Q1 2000Q2 2000Q3 2000Q4 2001Q1 2001Q2 2001Q3 2001Q4 2002Q1 2002Q2

Country Code Growth Properties 4,16,45,47,48 23,41,45,47,48 2,17,35,45,47 17,45,46,47,48 11,18,45,47,48 6,12,28,45,48 18,28,45,47,48 6,10,37,45,46,47 11,29,45,46,47,48 24,29,35,45,47,48 2,37,38,39,45,47 2,12,25,29,37,45,47 2,12,18,37,45,47,48 11,13,18,24,37,48 6,11,12,18,25,37,45 8,12,24,26,37,47,48 1,8,11,21,24,26,45 1,12,21,24,26,48,51 12,24,25,26,32,37,46 5,6,10,45,46,47,48 5,24,25,45,46,47,48 4,37,39,42,45,46,47,48, 6,19,45,46,47,48,52 26,27,39,45,46,47,48 5,16,39,42,45,46,48 16,24,26,39,45,47,48 6,11,16,45,46,47,48 5,7,39,45,46,47,48

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Value Properties 54,56,57,61,68 54,56,57,61,68 56,57,61,68,69 56,57,61,68,69 56,57,61,68,69 53,56,61,68,69 56,61,64,68,69 54,56,61,64,68,69 54,56,61,64,68,69 56,57,61,64,68,69 56,61,62,64,68,69 55,56,61,64,68,69 55,56,61,64,68,69 55,56,61,62,64,68 55,56,61,62,64,68 55,56,61,62,64,68 56,61,62,64,70,71,72 56,61,62,64,70,71,72 56,61,62,64,70,71,72 56,61,62,64,70,71,72 55,56,61,64,70,71,72 55,56,61,64,70,71,72 55,56,61,64,70,71,72 55,56,61,64,70,71,72 55,56,61,64,70,71,72 55,56,61,64,70,71,72 55,56,61,64,70,71,72 55,56,61,69,70,71,72

69

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Kwame Addae-Dapaah, Hin/David Kim Ho, Yan Fen Tan

1992Q1 1992Q2 1992Q3 1992Q4 1993Q1 1993Q2 1993Q3 1993Q4 1994Q1 1994Q2 1994Q3

1994Q4 1995Q1 1995Q2

6,14,20,28 1,7,20,28 14,20,28,37 20,23,28,37 10.14,20,28 14,20,28,37 2,18,28,38 17,20,28,40 2,20,38,40 2,17,28,40 2,4,17,37 Growth Properties 2,17,38,40 2,17,25,33 2,25,38,42

Appendix (Continued) 57,58,59,65 2002Q3 18,21,22,34,39,45,48 57,58,59,65 2002Q4 5,16,21,29,40,45,47,48 49,57,58,59 2003Q1 5,12,17,24,34,35,45,48 7,57,58,59 2003Q2 5,12,21,35,39,45,48 51,57,58,59 2003Q3 4,20,25,34,35,39,45,48 51,54,57,59 2003Q4 9,14,20,29,35,37,39,45 51,54,57,59 2004Q1 4,7,10,34,35,39,44,45 51,54,57,59 2004Q2 4,10,20,21,39,41,44,51 51,54,57,58,68 2004Q3 15,20,25,37,39,44,45,46 51,54,57,58,68 2004Q4 15,16,20,37,39,44 51,54,57,58,68 2005Q1 12,15,28,35,40 Value Growth Properties Properties 49,51,54,57,68 2005Q2 10,12,15,20,37 49,56,57,61,68 2005Q3 1,12,25,27,37 49,56,57,61,68

55,56,61,69,70,71,72 55,56,61,69,70,71,72 55,56,61,69,70,71,72 55,56,61,69,70,71,72 55,56,61,69,70,71,72 55,56,61,69,70,71,72 55,61,68,69,70,71,72 55,61,68,69,70,71,72 55,61,68,69,70,71,72 2,5,17,49,53 22,38,53,54 Value Properties 3,52,53,54 3,5,53,32

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Appendix B-1: Countries in the Industrial Portfolio. Code 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26

Country Altanta Austin Baltimore Boston Cambridge Camden Charlotte Chicago Cincinnati Columbus Dallas Denver Edison Fort Lauderdale Fort Worth Houston Indianapolis Kansas City Lake County Los Angeles Louisville Memphis Miami Minneapolis New York Oakland

Code 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52

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Country Oklahoma City Orlando Oxnard Philadelphia Phoenix Portland Reno Riverside Sacramento St. Louis Salt Lake City San Diego San Francisco San Jose Santa Ana Seattle Tacoma Tampa Warren Washington Wilmington Worcester sydney Melbourne Brisbane Auckland(nz)

Value Versus Growth Real Estate Investment Strategy Appendix A-2b: Composition of Value and Growth Industrial Portfolio.

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Year

Country Code Growth Properties

Value Properties

1985Q1

16,31

3

1985Q2

16,40

1985Q3

3,36

1985Q4

Year

Country Code Growth Properties

Value Properties

1995Q3

12,15,44

22,31,51,52

15,49

1995Q4

13,14,34

10,22,50,52

22,49

1996Q1

3,44,51

2,42,50,52

3,31

22,49

1996Q2

2,12,46

32,40,44,52

1986Q1

16,31

46,49

1996Q3

3,14,23

6,25,44,52

1986Q2

3,31

15,49

1996Q4

14,17,23

32,44,50,52

1986Q3

16,36

3,49

1997Q1

15,34,35

17,32,46,52

1986Q4

16,28,31

22,49

1997Q2

14,17,35

16,24,44,52

1987Q1

11,16,28

22,49

1997Q3

14,34,35

2,17,49,52

1987Q2

16,31,38

15,22,49

1997Q4

17,26,34

24,37,50,52

1987Q3

16,26,40

10,22,49

1998Q1

3,14,34

16,25,50,52

1987Q4

11,16,40

22,49

1998Q2

17,26,42

12,25,50,52

1988Q1

6,11,16

3,49

1998Q3

3,13,26

14,16,28,52

1988Q2

6,16,28

15,49

1998Q4

3,26,40,44

8,9,27,52

1988Q3

6,16,28

22,49

1999Q1

10.17,26,37

18,25,27,52

1988Q4

14,16,28

3,49

1999Q2

12,14,26,37

27,33,51,52

1989Q1

6,16,20

1,49

1999Q3

10,26,40,44

9,25,30,52

1989Q2

6,16,34

22,49

1999Q4

14,26,34,44

5,30,35,52

1989Q3

15,28,31

9,49

2000Q1

1,17,26,35

5,9,13,52

1989Q4

14,15,28

8,49

2000Q2

14,17,29,48

6,15,24,52

1990Q1

9,20,28

44,49

2000Q3

13,17,31,48

9,40,44,52

1990Q2

6,20,31

16,49

2000Q4

13,14,29,31

18,27,35,52

1990Q3

10,15,28

22,49

2001Q1

13,17,25,33

7,15,27,51,52

1990Q4

1,6,10

17,49

2001Q2

5,13,25,39,42

2,15,30,33,51

1991Q1

10,20,46

15,49

2001Q3

13,25,27,33,44

2,3,16,29,30

1991Q2

10,20,44

4,22,49

2001Q4

1,13,14,39,44

6,29,33,51,52

1991Q3

9,20,46

36,49

2002Q1

15,16,25,33,39

2,30,36,44,52

1991Q4

4,17,44

22,49

2002Q2

8,23,25,33,39

30,36,45,48,50

1992Q1

4,34,35

20,44,49

2002Q3

6,13,25,33,39

24,29,36,45,48

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72

Kwame Addae-Dapaah, Hin/David Kim Ho, Yan Fen Tan Appendix A-2b. (Continued) Country Code

Year

Growth Properties

Value Properties

1992Q2

4,22,35

13,14,49

1992Q3

4,35,41

13,16,49

1992Q4

4,28,35

1993Q1

13,35

1993Q2

17,34,35

22,49,52

1993Q3

4,35,41

22,49,52

1993Q4

22,28,35

44,49,52

1994Q1

4,6,35

1994Q2 1994Q3

Year

Country Code Growth Properties

Value Properties

2002Q4

13,14,25,43,44

2,12,18,29,52

2003Q1

12,14,16,27,33

2,37,45,50,51

22,49

2003Q2

10,12,16,33,38

30,43,45,50,52

22,28,49

2003Q3

23,33,36,38,43

7,10,26,30,45

2003Q4

6,27,33,35,44

19,23,30,45,52

2004Q1

6,23,35,36,44

10,26,30,45,52

2004Q2

5,14,23,41,43

10,30,33,45,52

44,49,52

2004Q3

6,14,23,36,47

10,30,45,51,52

3,4,35

22,49,51,52

2004Q4

4,14,23,28,50

2,30,33,51,52

8,14,35

22,49,51,52

2005Q1

4,5,6,23,47

44,45,46,52

1994Q4

14,28,44

22,49,51,52

2005Q2

5,15,30,32,35

10,18,,27,44

1995Q1

14,28,46

22,49,51,52

2005Q3

5,25,30,34,40

21,22,37,52

1995Q2

6,14,46

49,50,51,52

Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.

Appendix A-3a: Countries in the Retail Portfolio Code 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Country Altanta Austin Baltimore Bethesda Chicago Columbus Dallas Denver Fort Lauderdale Fort Worth Houston Jacksonville Los Angeles Miami Minneapolis New York Oakland Orlando Philadelphia Phoenix Portland Raleigh Sacramento San Antonio

Code 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48

Country San Diego San Francisco San Jose Santa Ana Seattle Washington West Palm Beach New South Wales Retail Victorian Retail Queensland Retail Western Australian Retail new zealand retail Beijing Bangkok Hong kong(prime) hong kong (suburban) Jarkarta KL(CC) KL(suburban) Makati Singapore (prime) Singapore (Suburban) Singapore (Marina) Shanghai

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73

Appendix A-3b: Composition of Value and Growth Retail Portfolio.

Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.

Year

1992Q1 1992Q2 1992Q3 1992Q4 1993Q1 1993Q2 1993Q3 1993Q4 1994Q1 1994Q2 1994Q3 1994Q4 1995Q1 1995Q2 1995Q3 1995Q4 1996Q1 1996Q2 1996Q3 1996Q4 1997Q1 1997Q2 1997Q3 1997Q4 1998Q1 1998Q2 1998Q3 1998Q4

Country Code Growth Properties 18,20,30 20,28,30 7,28,30 18,28,30 18,20,30 18,19,30 17,18,30 7,18,30 7,20,30 7,18,30 18,29,30 2,9,30 9,18,30 18,23,30 19,25,30 13,16,25,30 10,15,28,30 1,15,30,33 13,15,25,30 15,29,30,33 15,20,25 30,32,33 9,20,25,27 20,25,34 20,25,32 19,25,33,34 15,19,20,26 14,19,25

Year Value Properties 35,45 35,45 35,45 34,45 35,45 34,35,45 35,45 32,34,45 34,41,45 36,41,45 36,41,45 36,41,45 36,41,45 36,41,45 36,41,45 36,41,45 38,41,48 38,41,48 38,41,48 38,41,48 38,41,45,48 38,41,45,48 38,41,45,48 38,41,48 38,41,48 38,41,48 38,41,48 38,41,48

1999Q1 1999Q2 1999Q3 1999Q4 2000Q1 2000Q2 2000Q3 2000Q4 2001Q1 2001Q2 2001Q3 2001Q4 2002Q1 2002Q2 2002Q3 2002Q4 2003Q1 2003Q2 2003Q3 2003Q4 2004Q1 2004Q2 2004Q3 2004Q4 2005Q1 2005Q2 2005Q3

Country Code Growth Properties 9,17,19,26 14,18,19,33 10,16,18,19 8,10,14,25 8,10,14,18,19 3,8,18,27,32 8,18,19,27,35 14,24,27,29,35 5,14,27,33,34 14,26,27,33,34 19,27,31,34,35 5,26,31,34,35 5,10,31,34,35 18,31,32,34,35 14,32,33,34,35 14,31,33,34,35 11,14,16,31,32 14,16,25,26,31 13,14,31,32,33 7,14,19,26,33 8,11,14,15,27 14,16,22,26,27 4,14,17,26,27 12,14,17,22 6,9,16,22 14,18,24,27 14,17,18,27

Value Properties 37,38,41,48 37,38,41,48 37,38,41,48 37,38,41,48 37,38,41,48 37,38,41,48 37,38,41,48 37,38,41,48 37,38,41,43,48 37,38,41,43,48 37,38,41,43,48 37,38,41,43,48 37,38,41,43,48 37,38,41,43,48 37,38,41,43,48 37,38,41,43,48 37,38,41,43,48 37,38,41,43,48 37,38,41,48 37,38,41,43,48 37,38,41,48 37,38,41,48 37,38,41,48 6,9,36 19,21,36 6,19,36 7,21,36

Appendix B: States of the World. Office and Industrial Portfolios Year State Year 1985Q1 W 1990Q2 1985Q2 W 1990Q3 1985Q3 W 1990Q4 1985Q4 W 1991Q1 1986Q1 W 1991Q2 1986Q2 W 1991Q3 1986Q3 W 1991Q4 1986Q4 W 1992Q1 1987Q1 W 1992Q2 1987Q2 NW 1992Q3

State W NW W W W W NW NW W W

Year 1995Q3 1995Q4 1996Q1 1996Q2 1996Q3 1996Q4 1997Q1 1997Q2 1997Q3 1997Q4

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State NB B B B B B B B B B

Year 2000Q4 2001Q1 2001Q2 2001Q3 2001Q4 2002Q1 2002Q2 2002Q3 2002Q4 2003Q1

State NB NB NB NB NW NB NB NB NW NW

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Kwame Addae-Dapaah, Hin/David Kim Ho, Yan Fen Tan

1987Q3 1987Q4 1988Q1 1988Q2 1988Q3 1988Q4 1989Q1 1989Q2 1989Q3 1989Q4

NW NW W NW Worst NW NW NW NW NW

Appendix B (Continued) 1992Q4 W 1998Q1 NB 1993Q1 W 1998Q2 NB 1993Q2 NW 1998Q3 NW 1993Q3 NW 1998Q4 W 1993Q4 NB 1999Q1 NB 1994Q1 B 1999Q2 NB 1994Q2 NB 1999Q3 B 1994Q3 NB 1999Q4 NB 1994Q4 B 2000Q1 B 1995Q1 NB 2000Q2 NB

1990Q1

NW

1995Q2

Retail Portfolio Year State 1992Q1 W 1992Q2 W 1992Q3 W 1992Q4 W 1993Q1 W 1993Q2 W 1993Q3 W 1993Q4 NW 1994Q1 NB 1994Q2 NB 1994Q3 NB 1994Q4 NB 1995Q1 NW 1995Q2 NW

Year 1995Q3 1995Q4 1996Q1 1996Q2 1996Q3 1996Q4 1997Q1 1997Q2 1997Q3 1997Q4 1998Q1 1998Q2 1998Q3 1998Q4

NB

2000Q3

State NB NB NB B B B B B B B NW NW Worst Worst

Year 1999Q1 1999Q2 1999Q3 1999Q4 2000Q1 2000Q2 2000Q3 2000Q4 2001Q1 2001Q2 2001Q3 2001Q4 2002Q1 2002Q2

W=Worst , NW=Next Worst , NB= Next Best , B=Best

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2003Q2 2003Q3 2003Q4 2004Q1 2004Q2 2004Q3 2004Q4 2005Q1 2005Q2 2005Q3

NW NW NB B B B B B B B

Year 2002Q3 2002Q4 2003Q1 2003Q2 2003Q3 2003Q4 2004Q1 2004Q2 2004Q3 2004Q4 2005Q1 2005Q2 2005Q3

State NW W W W W NB NB B B B B B Best

NB State NW NW NB NW NB NB NW NB NB NW NW W NW NW

In: Real Estate Investment Market Editors: Sofia M. Lombardi, pp. 75-103

ISBN: 978-1-61668-395-5 © 2010 Nova Science Publishers, Inc.

Chapter 3

RESTRUCTURING REAL ESTATE MARKET INFORMATION MANAGEMENT TO FACILITATE LANDBASED INVESTMENT ACTIVITIES IN GHANA Raymond T. Abdulai* and Felix N. Hammond ABSTRACT

Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.

Real estate is so important a subject that it cannot be left out any serious macroeconomic deliberation and the collective quest for investment, wealth creation, poverty alleviation and economic development. This is amply demonstrated by the negative effects that the current real estate market downturn is having on every facet of the economies of rich nations. The role played by, especially, private real estate in the economic development of the advanced world is well documented. The importance of well established real estate markets that operate efficiently cannot, therefore, be overemphasised. One area that has and continues to dominate discussions relates to how real estate market information should be organized and managed to guide participants in the markets to make efficient purchase, sale and investment decisions. It is often the responsibility of the state to organize and manage real estate market information through implementation of land registration programmes. In Ghana, despite 126 years of unbroken history of implementing land registration programmes, it is estimated that only 8% of real estate ownership has been registered. It is important to properly comprehend this problem and its fundamental causes in order to proffer the appropriate remedies. Using the quantitative research methodology, this study seeks to offer explanations of the large lag in land registration in Ghana. It has been established that the fundamental root cause of the problem is the fact that the operation of Ghanaian state agencies that are responsible for the organization, management and dissemination of real estate market information is not based on clear economic principles. As a starting point, it is recommended that a nationwide timed-bound real estate ownership census akin to the survey conducted in Britain that resulted in the Domesday Book of 1086 be carried out *

Corresponding author: Email: R. [email protected] Tel: +44 (0)151 321 2573.

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Raymond T. Abdulai and Felix N. Hammond and it should be financed by the government. From then onwards, it should be in the interest of the state to ensure that every real estate ownership or transaction is recorded by instituting an incentive package that would attract people to register; after all, such information would be sold to the public at a price. In this way a viable real estate ownership information system would be created, which would enable the real estate market to operate efficiently.

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INTRODUCTION That real estate is so important a subject to be left out of consideration in any serious macroeconomic deliberation and in the collective quest for investment, wealth creation, poverty alleviation and economic development is amply attested to by the current downturn in real estate markets across the developed world (Abdulai and Hammond, 2008). Economic historians like North and Thomas (1973), Rosenberg and Birdzell (1986), Torstensson (1994) and Goldsmith (1995) have documented the role played by, particularly, private real estate in the economic development of advanced nations. Indeed, shelter and for that matter real estate, has been and continues to be one of the major global economic drivers. Housing, for instance, has played a major role in shaping the business cycles of countries like USA, Britain, New Zealand and Canada (Hale, 2008). According Hale (2008), from 2000-2005, for instance, house prices rose by 78% in Australia, 65% in New Zealand, 50% in Canada, 102% in Britain and 50% in the USA and this housing boom within the period produced negative savings rates and higher consumer spending in such countries. Hale (2008) further observes that the current weakness in house prices caused by the credit crunch has produced a downturn in consumer spending especially in Britain and the USA. In Britain alone, currently, there are 11.8 million residential mortgages, with loans worth over £1.2 trillion (CML, 2008) and the commercial real estate markets alone contribute some 6% to its annual Gross Domestic Product (GDP) (BPF, 2006). The figure is bound to be considerably more if the contributions of residential and other segments of the real estate market are taken into account in Britain. The above shows that there is a connection between real estate and economic development. It, therefore, implies that the performance of any economy is, to a large extent, contingent upon the performance of its underlying real estate markets. This point is selfevident across the rich countries of the world but as aptly noted by Hammond (2006), it is less so in developing nations, particularly in sub-Saharan Africa. It follows that, if correctly organised, real estate markets in developing countries, particularly, in sub-Saharan Africa, could spur greater economic growth and possibly facilitate the lifting of the four-fifth of the regions population that currently survive on less than US$2.50 a day (Chen and Ravallion, 2008). In order to attain these ends, real estate markets must facilitate large volumes of year-onyear first time and repeat voluntary real estate trades. It must also interact constructively with the financial system to facilitate large volumes of year-on-year real estate based credit transactions and indirect investments in real estate. By fostering large volumes of first time and repeat transactions in real estate, especially, between well informed voluntary buyers, sellers, borrowers and creditors, real estate markets are able to continually push real estate resources from lower productive uses to higher productive uses. By so doing, they ensure that

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real estate in a given society continually improves upon its contributions to the economic development processes. The trouble is real estate markets are typically beset with fundamental restraints, which prevent or slow down the conduct of large volume of first time and repeat real estate trades. These restraints generally consist of ill-defined property rights (Alchian, 1965; Demsetz, 1967; Pejovich, 1990; Feder and Feeney, 1991), high transaction costs (Coase, 1960; Williamson, 1981; Rao, 2003), monopolisation of segments of the real estate markets, uncontrolled negative side effects of libertarian competition and the possible non-provision of public goods (Smith, 1776; Hicks, 1939; Harberger, 1959; Friedman, 2002). Whilst all of these restraints are important and need urgent attention, costly access to full and perfect information is lately being recognised as the most enduring restraint (Stigler, 1961; Williamson, 1991; Wyatt & Fisher, 1998; Rose, 2002), especially, in less developed economies. To appreciate the reasons why costly and imperfect information is critical, it is important to commence with the nature and purpose of the real estate markets. Most real estate trades involve the reciprocal interchange of property rights on one hand and money on the other. In effect, through trade, the parties interchange positions – the original owner of the real estate takes the place of the original owner of the price money and vice versa. Thereafter, the new owner of the real estate gains the backing of the law to make whatever uses and profits from the real estate, subject of course to the provisions of land use planning and other relevant laws of the society. The new owner may use the real estate as collateral to raise capital for investments to earn better returns. At the same time, the law disables all others from making any use or decisions regarding the real estate unless where the law so permits. The economic view is that buyers pay a price to interchange positions with original owners because, in their estimation, the original owners have undervalued or underpriced the economic potentials of the real estate in so far as its future rents/benefits are concerned (Smith, 1776). Likewise, the owners of real estate may also feel that purchasers have undervalued the real worth or benefits of the money they intend to use in purchasing it and that they (the owners of the real estate) can generate more returns from the money than anticipated by the purchasers. Consequently, by interchanging positions, buyers are able to make such uses of the real estate involved or rearrange or modify it in a manner that will enable it to generate the anticipated full benefits. Buyers who are proven right by the coming market events reap better benefits/returns from the real estate than what the original owners would have reaped. By buying, improving and putting real estate to those uses that generate higher benefits/returns, purchasers, through the interchange, push the real estate from lower productive uses to higher productive uses. If real estate that was the subject matter of a previous transaction is subsequently sold again, all things being equal, it would be pushed further into a higher productive use and so on. By so doing, real estate continually increases its contributions to the economic development processes. Real estate markets function well when buyers, creditors, investors, sellers, borrowers and issuers make informed decisions and choices relating to sales, purchases and pricing. The ability of actors to make informed decisions depends on easy and cheap access to high quality and clear information on which they make their decisions. To aid this, there is the need for real estate market participants to possess upfront, clear and quality information on all available real estate stock on the market at any given time. They must also know the true

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Raymond T. Abdulai and Felix N. Hammond

owners of each available stock. Finally, they must have information relating to recent transactions involving comparable real estate. Without such upfront information, transacting parties will suffer from information deficit with severe consequences. Firstly, purchasers or investors suffering from information deficit may end up paying more for real estate than the total income that it is capable of generating. Likewise, creditors may end up advancing more credit than the economic worth of the real estate in question. Secondly, since the essence of the transaction is for the parties to interchange positions, purchasers or creditors may end up paying the price money or advancing credit to phony owners. This is very crucial, because, without buying the property rights from the actual owner, the law will disable the buyer, creditor or investor from taking the place of the true owner. Thus, if the purported seller is not actually the owner, the buyer, by paying the seller, misallocates his or her financial resources. As always, the market will punish such misallocations with economic loses. Such buyers, creditors and investors will, as a result, lose their money and gain nothing in return. It is a rational expectation that buyers, creditors and investors would seek keenly to avoid or minimise such punishments. They will, thus, strive to feel sure that the purported sellers they intend to deal with are, indeed, the undisputed owners, before embarking on the transactions. However, without a workable system to provide the key information at the right quality, with the right contents, in the right form and at reasonable cost, there is a real risk that real estate market participants may make their most serious decisions based on suspicious data and many would end up as recipients of the punishments of the market. The cost of information deficit can be huge, though less readily obvious. These costs include loss of money through payments to phony parties, ownership litigation expenses, loss of income earning opportunities and so on. The prospects of such cost would lower the confidence of market participants, thereby slowing down the rate of transactions, which will in turn slow down the performance of the market and hence deny the development process of the invaluable contributions that the real estate market could have made to it. Fortunately, real estate is an example of search goods (Hammond, 2006; Abdulai, 2007) and hence distinct from experience goods such as a meal. Unlike experience goods whose potential benefits can only be ascertained after consumption, it is possible to search and discover sufficient information about real estate far before they are purchased or admitted as collateral in credit transactions (Weiner and Vining, 1999; Hammond, 2006). It is possible, for instance, to discover through inspections the relevant information regarding its essential physical (for example size, location and neighbourhood quality), legal (property rights and restrictions) and economic (price and rental values) attributes that determine its utilities before arriving at a final purchase or price decisions. The implications are three-fold. Firstly, in an economic system in which the requisite information is readily available at zero search cost, owners (borrowers) or sellers (creditors) would base their purchase, sale or price decisions on concrete and reliable information or market data. This will expectedly lead to optimal decisions on price and quantities that “will satisfy preference to the greatest extent possible” (Nell, 1984). Secondly, where, on the other hand, because of its technical nature, the data is not intelligible to the market participants above, they may seek advice from those equipped with the skills and expertise in analysing such data. These specialists may include valuers, lawyers and estate agents. Thirdly, where the required information is non-existent, severely disorganised, out of date or considerably costly to obtain, market participants as well

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as market intermediaries such as valuers, estate agents and lawyers may end up basing purchase, sale or pricing decisions on suspicious or severely inadequate information. By its very nature, however, most of the information that buyers could base their decisions on are held by the owners of the real estate. Meanwhile because real estate transactions are not contracts of good faith (Uberrimae Fidei), owners are not under legal obligations to disclose any information known to them, which could help buyers form their views on the terms of trade. Besides, the prospects of opportunistic behaviours suggest that buyers cannot fully trust the information volunteered by owners as they could be inaccurate or intended to mislead them. The alternative will be for buyers to conduct their own private real estate research and interviews with neighbours to procure the relevant information needed for their decisions. This, however, is time consuming and a costly process. As Stigler (1961), Vickrey (1961) and Mirrlees (1971) argue, because of the cost involved in information gathering, actors are inevitably less than fully informed when they make their most optimal market decisions. Traditionally, buyers rely on publicly accessible information systems such as land registration and cadastral systems to develop their views as to the validity of the claims of ownership made by the parties purporting to sell or use the real estate as security in credit transactions. In the case of Ghana, however, the World Bank (2007) estimates that these publicly available systems hold just about 8% of information on real estate transactions. Thus, there are about nine in ten chances that information on real estate that a buyer is interested in would not be held in these systems. This makes buyers to incur high costs to investigate the true ownership position privately through market inquiries and interviews with neighbours. This heightens the cost of real estate transactions in Ghana, which may be slowing down the development of its real estate market. It is the job of government to alleviate these costs to get the market operating at efficient levels. To do this, there is the need, first, to understand the causes of the considerable backlog of real estate ownership information capture and through the causation proffer workable solutions. It is this aim the chapter seeks to pursue. The rest of the chapter is organised as follows. The section that follows this introduction describes the land registration systems that exist globally after which section three gives an overview of the Ghanaian land registration systems. The research methodology adopted for the study is described in section four whilst section five presents and discusses the date collected from a field survey. The penultimate section deals with implications and the last section concludes the chapter.

TYPES OF LAND REGISTRATION SYSTEMS Globally there are two types of land registration systems, which are deed registration and title registration. The fundamental principles that underpin the two systems are explicated as follows. In a deed registration system, legally recognized and protected real estate ownership arises upon conclusion of an agreement or contract between the real estate grantor and grantee (Deininger, 2003). According to Deininger (2003), the entry of the agreement and its key contents into the public registry is to provide notice to the world of the existence of the real estate ownership and challenges to it will be handled through civil litigation. However, in a title registration system, it is the entry into the registry that gives the ownership legal validity,

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Raymond T. Abdulai and Felix N. Hammond

guaranteed by the State; all entries in the register are prima facie evidence of the legal status of the real estate (Deininger, 2003). Even though in the title registration system, legal validity of real estate ownership arises from the fact of registration, it is important to note that it does not emanate from only registration. In the deed registration system, for example, legal validity arises from the contract or agreement between the grantor and grantee and not from registration. Furthermore, legal validity and recognition of real estate ownership can emanate from other legislation apart from the title registration system depending on the legal system that operates in a particular country. In Ghana, for example, the customary or traditional system of real estate ownership is not based on registration but it is recognized by the legal system. Under Ghana’s customary law, proof of real estate ownership is not by any form of documentation; it is rather by physical occupation and possession and the recognition of this fact by members of the society, particularly, adjoining owners of real estate (Antwi, 2000; Abdulai, 2006 and 2007). However, under the 1992 Constitution of Ghana (the supreme law of the country) and the Conveyancing Decree of 1973 (NRCD 175), the customary system of real estate ownership is recognized by the common law courts. Indeed, legal recognition of Ghana’s customary system of real estate ownership dates back to 1925 when the British colonial administrators introduced the Native Courts (Crook, 2002). In Nigeria, despite the nationalization of land under the Land Use Decree of 1978, customary real estate transactions (which are not based on any form of documentation) are recognized by the state sponsored court system in so far as they are consistent with existing State law and are also not opposed to public policy (Ikejiofor, 2006). Other countries in the developing world where the customary system of real estate ownership is legally recognised (whether it is registered or not) include Mozambique, Lesotho, Malawi, Mali, Namibia, Niger, South Africa, Swaziland, Tanzania, Uganda, Zambia and Zimbabwe (Alden-Wiley, 2002; cited in Deininger, 2003). Unregistered real estate ownership is legally recognised in the United Kingdom; however, in unregistered real estate sales, the law requires the root of title to be investigated 15 years back (which used to be 30 years per-dating 1970) before the trading takes place (Abdulai, 2006; MacKenzie and Phillips, 2008). In comparative terms, the gap between the percentage of real estate ownership that is registered in the developing and developed world is very huge. Available data from de Soto (2000), Deininger (2003), World Bank (2007), Abdulai (2007) and HM Land Registry (2009) bespeak that in the developing world, the percentage of registered real estate ownership is 15 whilst in the advanced world, it ranges from 65% to 100%. In Africa, it is 2% - 10% while in East Asia it is less than 30%. Having distinguished between the types of land registration systems that exist globally, it is expedient at this stage to explain how real estate and its registration affect the economic development of a nation. It is real estate that is normally accepted by financial institutions as collateral for advancing loans for investment. Real estate is regarded as a suitable collateral asset because it is a commodity that is consumed in situ; that is, it is an immovable asset. The importance of mortgaging one’s real estate asset to financial institutions cannot be overemphasised from the perspective of both the mortgagor (borrower) and the mortgagee (lender). This is because collateral arrangements partly or fully shift the risks of loan loss from the mortgagee to the mortgagor. In the event of default on the part of the mortgagor, it will trigger the loss of his or her mortgaged real estate. Thus, for the mortgagee, the collateral provides a form of protection or insurance against the loss of the loan since there are normally

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enough legal arrangements to foreclose the mortgage transaction where the mortgagor defaults. At the same time, the prospect of losing one’s real estate is an incentive for the mortgagor to be committed regarding the repayment of the loan granted. The pledging of real estate, accompanied by the registration of ownership and mortgage transactions tremendously overcomes the problems of asymmetrical information and moral hazard. Land registration is a record keeping system and, therefore, creates a real estate ownership database, which is very important in every economy. When information is recorded in a central system, which is accessible to the public, it makes it easy for such data to be accessed for various purposes. The availability of a real estate ownership database facilitates real estate transactions and real estate taxation, which reduces transactions costs. De Soto (2000) alludes to this purpose when he emphasises the role of land registration in facilitating communication, information sharing, networking and transactions. In the absence of land registration, legal experts are often commissioned to conduct searches to trace the root of title in real estate transactions and verify that the real estate is not subject to any undisclosed obligations, which is normally a long process, time consuming and expensive. This point is amply demonstrated by what obtains in the UK and France. As earlier, indicated, in the UK where real estate is not registered and it is to be traded, the law requires that the root of title be investigated 15 years back. In the case of France, it is 30 years. Thus, land registration significantly enhances the smooth operation of real estate markets. Regarding the registration of mortgages, the common law position is that if a mortgage transaction is not registered and the mortgagor clandestinely transfers the mortgaged real state via sale to a bona fide purchaser for value who is unaware of the existence of the mortgage transaction, the mortgage as an encumbrance will not be binding on such a purchaser (MacKenzie and Phillips, 2008). However, if the mortgage transaction is registered, the purchaser cannot defend himself or herself on the basis that he or she is a bona fide purchaser for value without notice since the existence of the mortgage will be a public record and based on the principle of caveat emptor, it will be his or her responsibility to check the public records. Thus, the registration of mortgage transactions protects lenders/financial institutions from the activities of unscrupulous mortgagors as it makes it impossible for any purchaser of mortgaged real estate to plead bona fide purchaser for value without notice. From the preceding discussion, it is obvious that real estate and land and mortgage registration play a critical role in the economic development of a nation; they greatly help to improve access to formal capital for investment and wealth creation.

REAL ESTATE OWNERSHIP INFORMATION SYSTEMS IN GHANA Albeit traditionally, deeds and land title registration systems are the two main systems that have been employed by many nations to overcome real estate ownership information asymmetry, in Ghana, various parallel models of real estate ownership registration systems manned by six separate and independent public real estate sector institutions have emerged

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for the task. These are Lands Commission, Deed Registry, Land Title Registry, Survey Department, Land Valuation, Board, and Office of the Administrator of Stool Land1 (OASL). The earliest official real estate ownership registration system in the country is the deed registration system (manned by the Deed Registry which, is practically under Lands Commission), a legacy of the colonial order established largely to supposedly protect the interests of expatriate merchants of the colonial era (Meek, 1949). This commenced with the enactment of the Land Registry Ordinance (No. 8) in 1883, which was re-enacted in 1895 (cap 133) and revised in 1951. The first postcolonial administration, in 1962, replaced these laws with the Land Registry Act (Act 122), which made registration of instruments accompanied by a survey plan of the land, compulsory (Section 24 Act 122). The second official though not juridical real estate ownership registration system is the Plotting System, which is manned by Lands Commission. This came into being when in 1945 the Stamp Ordinance (Cap 168) was amended (No. 29 of 1945) to introduce an internal administrative requirement to attach a “Particulars Delivered Form” (PDF) to all real estate contract documents submitted to the erstwhile Lands Department (now Lands Commission) for the assessment and payment of stamp duty on the purchase or leasing of real estate. The PDF made provision for a short description of land and details of the transaction together with an annexed plan of the land and any building thereon. The extracts from these data together with the site plan was then plotted onto survey maps and referenced. This became an administrative parcel-based database in which data is organised around the proprietary land unit for valuation and stamp duty assessment purposes. Considering its purpose as an administrative database, which was not meant for public consumption, the scientific accuracy of the survey plans attached was not insisted on. Also as Brobbey (1991), a former chairman of Land Valuation Board (the original custodian of the system), states, consideration and prices stipulated in the transaction documents, which formed the basis and main inputs for this database were severely understated to evade higher stamp duty or tax liabilities. Additionally, according to Antwi (2000), Hammond (2006) and Abdulai (2007), the plotting system admits irreconcilable data in respect of the same proprietary land unit, as that did not adversely affect the objective of the database. Yet, owing to the difficulties in obtaining search reports from the deed registration system as against the relative ease with which information could be obtained from the plotting system (because it was parcel-based), many real estate market participants relied more on unofficial searches from the plotting system as the basis for their real estate market decisions; at present about 90% of all real estate information searches are conducted from the plotting system instead of the juridical deed and land title registration systems even though the plotting registers still lack legal recognition (Hammond, 2006). Information obtained from this plotting system by the public confers no rights or obligations against the government regarding its accuracy. Records held in the plotting system are most of the time so inaccurate that invariably an inspection has to be conducted to confirm doubts in a majority of the cases submitted for registration; in some 1

In Ghana, there are primarily two types of landownership, which are State and private landownership. Private land composes of traditional land (land vested in communities represented by chiefs/kings and families/clans) and individual land whilst State land refers to land that has been acquired by the State from the private land sector. In the southern part of Ghana, chiefs/kings as traditional rulers sit in state on specially designed stools or chairs. In the olden days, they sat in state on stools, which were regarded as the symbols of authority. However, as time progressed and with modernization, they started using specially designed chairs. Thus today, most of them use chairs but the stool remains the symbol of authority. In southern Ghana where land is vested in communities represented by chiefs/kings, it is call stool land whilst in northern Ghana, it is referred to as skin land.

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cases site visits have established that the real estate intended to be registered does not even exist on the geographical maps at all (Hammond, 2006). The third model is the concurrence system, which is also handled by the Lands Commission. Practically the distinction between this system and the plotting system is only one of convenience. In 1962, the enactment of the Administration of Lands Act (Act 123) produced the curious outcome that non-commercial stool land transactions were not to be formalised and hence need not be captured within the prevailing official information machinery. Transacting parties could, thus, dispense with the formalities of seeking the public recording of those transactions that are made “without payment of valuable consideration or in exchange for nominal consideration” (Act 123). Such transactions need not be evidenced in writing and need not be registered under the deed registry model as well as the plotting system. Under the same law, however, commercial stool land transactions that “involves the payment of any valuable consideration or which would, by reason of it being to a person not entitled by customary law to the free use of land involved the payment of any such consideration” (Section 8 of Act, 123) were to be compulsorily formalised. Since the deeds, plotting and concurrence systems are practically unified, the chapter will group them together in the ensuing analysis and discussion. The fourth real estate ownership information system is the land title registration system, which was introduced in 1986 under the Land Title Registration Law of 1986 (PNDCL, 152). The law is currently operational in Greater Accra region and Kumasi in the Ashanti region only2. The agency responsible for title registration is the Land Title Registry. The system was introduced to replace the deeds registration model and by implication the plotting model as well. The implementation of the law had false starts with serious implications for the asymmetry problems. Firstly, an entirely new government department (the Land Title Registry) was established to carry out the implementation of the law. This engendered fierce clash of departmental interests as managers of the existing system felt their powers and influence were threatened by the new agency. The import of section 13 of the law was to ensure that within 90 days of declaring an area a registration district, all previously recorded transactions under the deeds system considered genuine and accurate were to be transferred directly onto the new land title register under PNDCL 152. Thereafter, deeds and plotting will cease to operate in the declared areas. However, to date (after almost 23 years) not a single document has been transferred this way. The practical consequences are that the land title system operates in parallel with the deeds and plotting systems. As aptly noted by Antwi (2000) and Hammond (2006), the harsh reality is that the information in these respective systems is not harmonised in any comprehensive way; indeed, often contradictory. From the preceding discourse, it is obvious that government interventions in the area of real estate market information management in Ghana have had 126 years of unbroken history. Put roughly, for the registration systems to have been able to capture all existing real estate ownership or transactions information by now, assuming all information captured is accurate, the systems should have been capturing information at a rate of at least 0.8 percent (less then 1 %) annually; this figure is calculated by simply dividing 100% coverage by the 126 years of land registration practice in Ghana. Yet according to AMCAD (1998), only about 30% of 2

Ghana is divided into ten regions for political and administrative purposes and each region has a capital, which is the administrative centre. Greater Accra and Ashanti regions are some of the regions in Ghana. The capital of Greater Accra region is Accra, which is also the capital of Ghana and that of Ashanti region is Kumasi.

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existing information on urban real estate has been captured as at 1998. This represents an information capture rate of about 0.26% per annum in urban areas of Ghana, which is about a third as fast as it should be. The World Bank finding referred to earlier that only 8% of information on real estate transactions across the country are recorded in the registration systems also indicates that a much slower national rate of capture than the 0.26% per annum. This implies that, even if all the information captured by these parallel registration systems is accurate and consistent (and they are not) only about 8% of the asymmetric information problems together with their economic repercussions would have been dealt with by these systems. The challenge to this chapter is to provide a convincing and empirically validated explanation to the large lag in real estate market information capture in Ghana and to offer practical solutions.

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RESEARCH METHODOLOGY The quantitative, qualitative and mixed methodologies research approaches were examined as to their appropriateness for the study and the quantitative research methodology was finally adopted. Empirical data was collected in Ghana between June 2007 and January 2008 using field survey data collection procedures. The survey participants fell into two broad groups. The first group consisted of the government agencies in charge of the administration of the State real estate ownership information systems. The second group consisted of purchasers of real estate who had sought or were seeking to register their real estate ownership with the relevant systems. With regard to the first group of survey participants, Lands Commission, Deeds and Land Title Registries represent the end points of all the activities directed at registration of real estate ownership in the country and were, therefore, the agencies surveyed. As earlier noted, the deeds, plotting and concurrence systems are practically unified and are, thus, grouped together under Lands Commission. Due to time and cost considerations, the study concentrated on Accra, which is the capital of Ghana; Accra also has the most active real estate market in the country. Data were obtained from these agencies through systematic records investigation and participant observation. Regarding participant observation, firstly, the chain link of activities involved in registering real estate ownership were meticulously observed individually, timed and costed. Each activity was observed and timed on three separate occasions without the prior notice of the observee. The average time for the three observations was then adopted as the typical time span for each activity. Admittedly, the accuracy could have be improved if the number of observations had been increased, but prevailing time and cost constraints restricted the number of observations that could possibly be taken. Even so, this study is aimed at providing indications of the order of things and not to actually estimate pinpoint accurate figures. The average time per activity was then valued. The best way of valuing the time spent is to apply the time preference rate; that is, the going risk free interest rate (government bond rates), applicable at the time. However, government bond markets in Ghana are well known to be highly underdeveloped and overly laden with political influences to the extent that on occasions, the risk-free rate surpasses the risk adjusted rates obtainable from the country’s stock market. It is, therefore, quite unreasonable to rely on the prevailing bond rates as accurate reflection of the time value

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of money in that country. Even if an accurate risk-free rate were obtainable, it has to be adjusted to reflect the risk of the activity by adding a risk premium to it. Being a public service, determining the applicable risk premium to apply is problematic and can be fraught with errors. As a result, the simplified way around this, was to adopt the average wage rate for the official performing the activity as the minimum time value of money rate. This is valid because, it at least, reflects the government’s own (of course not the market) valuation of the time of the official. The time spent on an activity was, thus, multiplied by the wage rate of the official to arrive at the minimum estimate of the value of the activity. Additionally, 303 real estate purchasers were randomly selected and surveyed using purposely-designed questionnaire administered face-to-face. No questionnaire was posted or left with respondents for later collection or administration. Generally, the questionnaire sought for data on how much it cost them in terms of time spent and out of pocket expenses to have their real estate ownership recorded in the public registers. Also, similar data on how much it cost to conduct a search from the registration systems was obtained from the respondents.

PRESENTATION OF DATA AND DISCUSSION

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Delays To ascertain whether the plotting/concurrence system is competent in capturing the relevant information early, fully and accurately, data on 235 randomly sampled documents supplied to the system and processed for concurrence under the concurrence and plotting systems were obtained. Table 1 shows the descriptive statistics on the times (in days) it took for the relevant property information to be captured into the concurrence information system. The study identified a total of 22 bureaucratic steps in the information capture process and take, according to Table 1, 600 days on the average to complete the recording of information on an average plot size of 2.27 acres. Most of the documents were processed in 258 days (Mode). The fastest processing time was 34 days while the slowest processing time was 3,530 days. The crucial point at issue, however, is given that real estate transactions are occurring regularly in Ghana, the average processing time of 600 days means that the system could be some 600 days out of date at any particular time. The sluggishness of the system is clearly contributing significantly to the lag in information capture and even leading to the data available being possibly outdated. The data used to analyze the speed of data capture under the land title registration system is the full data on applications made by real estate owners in the study area to the land title registry from the date of its inception in practice in 1988 to 2002 (15 years). The data, thus, represent the total return series for the 15-year period. The 2002 end point has been deliberately chosen because, in actual fact, the year 2002 marks a new era in land administration in Ghana. It is in reality the year that the ongoing Ghana Land Administration Project under the auspices of the World Bank took off in practice. The data and following analysis, therefore, provides an important baseline data against which the performance of the Ghana Land Administration project can be ascertained ex-post.

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Table 2 provides statistical description of the data. According to the Table, on the average 7,452 applications are made to the Land Title Registry annually. Out of this, about 1,849 constituting 25% come from areas where pre-prepared sectional survey maps exist. Averagely 15.8 % of this (that is, the 25%) is completed per year, representing a backlog of about 84% per year of all applications coming from areas with pre-prepared sectional plans. The cumulative effect of this 16% completion rate is extraordinary. Table 3 reports the data on the cumulative effects of annual backlogs imposed on succeeding years and then on the overall outstanding applications. The Table shows that over the period, the number of titles completed declined progressively to about 3% by 2002 of outstanding cases. The total backlog of cases as at 2002 stood at 94,106, representing about 97% of total requests for documentation, which could not be serviced. To be able to make rational predictions about the processing time required to reduce the backlog considerably, an Ordinary Least Square (OLS) model is derived based on the data obtained. An additional independent dataset, SECTPLAN is included in the data contained in Table 3 to help estimate the impacts of the prior availability of sectional map for particular applications on the processing time. The key variables relied on in deriving the regression model are, thus, YEAR representing the year of applications, APPLICATION, representing the cumulative applications for particular years, COMPLETED, representing the number of applications completed in a particular year and SECTPLAN representing the number of applications for which sectional plans existed. For applications coming from areas where preprepared sectional maps exist, it is expected that the physical details of the proprietary land units were already known and the processing will not entail a re-survey of the land concerned. The model summary from the regression analysis shows that the variables SECTPLAN, APPLICATION and YEAR as defined earlier explains about 65% (R Square = 0.646 in Table 4) of the number of titles completed in any particular year. Table 1. Descriptive Statistics on Processing Delays

Plot Size Days Valid N (listwise)

N

Minimum

Maximum

Statistic 235.00 235.00

Statistic 0.01 34.00

Statistic 61.42 3530.00

Mean Statistic 2.27 599.90

Mode

Std. Error 0.59 41.50

Statistic 0.32 258.00

Std. Deviation Statistic 9.00 636.19

235.00

Source: Field Survey (2008)

Table 2. Descriptive Statistics

Applications Sectplan Completed Valid N (Listwise)

N 15 15 15 15

Minimum 2,274.00 172.00 150.00

Maximum 23,409.00 24,59.00 2,663.00

Sum 11,1778.00 27,731.00 17,672.00

Source: Field Survey (2008)

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Mean 7,451.8667 1,848.7333 1,178.1333

Std. Deviation 5773.3847 664.1310 715.0598

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Table 3. Cumulative Delays in Land Title Registration Year 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002

Cumulative Pending Application 2,274.00 5,809.00 11,318.00 17,472.00 29,120.00 45,584.00 67,633.00 69,363.00 74,563.00 78,682.00 83,494.00 86,328.00 89,677.00 93,607.00 96,769.00

Completed 843.00 150.00 400.00 200.00 1,010.00 1,360.00 2,000.00 650.00 1,906.00 1,018.00 1,977.00 1,098.00 1,147.00 1,250.00 2,663.00

Backlog 1,431.00 5,659.00 10,918.00 17,272.00 28,110.00 44,224.00 65,633.00 68,713.00 72,657.00 77,664.00 81,517.00 85,230.00 88,530.00 92,357.00 94,106.00

% Completed 37.07 2.58 3.53 1.14 3.47 2.98 2.96 0.94 2.56 1.29 2.37 1.27 1.28 1.34 2.75

Source: Field Survey, 2008

Table 5. Reports the regression coefficients of the model. The results show that the functional relationship between the number of completed applications and the explanatory variables is: Table 4. Correlation Coefficient Model 1

R .804

R Square .646

Adjusted R Square .549

Std. Error of the Estimate 480.1509

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a Predictors: (Constant), SECTPLAN, APPLICATIONS, YEAR

Table 5. Regression Coefficients

Model 1

a

Unstandardized Coefficients B (Constant) -300404.10 Year 151.31 Applications 0.08 Sectplan -0.46 Dependent Variable: Completed

Std. Error 75629.66 38.03 0.03 0.29

Standardized Coefficients Beta 0.95 0.63 -0.43

t -3.97 3.98 2.68 -1.60

Sig. 0.00 0.00 0.02 0.14

COMPLETED= −300404.10 + 151.31YEAR+ 0.08APPLICATIO N − 0.46SECTIONPLA N

This indicates that the backlog situation increases or deteriorates by some 151 documents every year and the pre-existence of sectional maps helps improve the backlog situation only marginally by less than a day. The number of applications per particular year also contributes marginal increases to the existing backlog situation. What is perhaps more interesting is that, the number of completed applications in any given year is affected by the total number of applications submitted in the same year plus the hangover of incomplete applications from

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previous years. This is explained by the fact that, the backlog cases are not sitting unattended. Resources are allocated to work on them at various stages. But the limitation of resources means that the existing resources are so thinly spread out to cover all documents in the processing line that in the end only few are completed. The model predicts that, if things remain substantially unchanged, it would not be until the year 2070 that the backlogs could be cleared (61 years from 2009).

The Policy Induced Asymmetry The land title registration and concurrence/plotting systems, as already noted, operate in parallel. Table 6 below reports the data on the documentation requests made to the respective systems between 1997 and 2002. Table 6. Comparison of Documents Presented for Land Title Registration and Concurrence/Plotting Between 1997 and 2002

Applications for plotting Applications for land title registration Difference

2002 6,861

2001 14,371

2000 9,229

1999 6,610

1998 4,858

1997 4,267

4,412.00

5,077.00

4,447.00

4,811.00

5,830.00

6,025.00

2,449.00

9,294.00

4,782.00

1,799.00

-972.00

-1,758.00

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Source: Data on the applications for plotting was obtained from the records of the Lands Commission, Accra while the data on applications for land title registration was obtained from the records of the Land Title Registry, Accra.

According to the data, with the exception of 1997 and 1998, documentation requests made to the plotting system consistently outstripped those to the land title registration system. It is known in practice that some real estate owners who can afford prefer to direct their documentation requests to both systems. But not many do that, as it is costly. This has led, expectedly, to inconsistent dataset and even the prevalence of irreconcilable information on the same real estate held in the two systems. This is ironic given that the plotting system lacked legal recognition while the title registration system is legally compulsory, at least according to the enabling law. Clearly, there is a market signal in favour of the plotting system even though the signal is not strong enough to eliminate land title registration altogether. The question that arises is why will more people prefer a system that has no legal backing and fraught with inaccurate dataset to one that is legally compulsory based on scientifically accurate surveys and also claims to offer indefeasible title? The answer to this could dictate policy direction and reforms. Since costs are widely accepted as important disincentive to patronage, the ensuing section examines the respective cost implications of the two systems.

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The Costs of Real Estate Documentation Requests The official charges for one documentation under the plotting/concurrence system stood at about a third (¢300,000.00) of the cost of the land title system, which is ¢950,000.00. The study found that almost as a convention, in addition to the official charges, actors who request for documentation make extra official payments to staff of the responsible agencies to get their requests serviced as early as possible. Table 7 shows the detail extra-official costs incurred under both systems. Column 1 of the Table indicates the respective milestone activities available under both the land title and plotting/concurrence systems. To compute the costs, the costs per milestone activity were first estimated from the data obtained from the field survey. The costs per documentation procedure were then estimated by accumulating the costs of all the respective activities required to service particular requests for documentation under the respective systems. Thus, columns 3-10 are the costs of the different strands of processes used to service requests for the different forms of documentation under the registration systems. Column 10 reports the unit costs incurred to have the documentation request serviced under the land title registration system. The data show that the extra official costs incurred in having documentation requests serviced under the respective strands of procedures under the plotting/concurrence system range from a minimum of about £3,837.00 to a maximum of about £3,900.00 with an average costs of about £3,870.00. This is practically similar to the £3,860.00 under the land title system (see Column 10, row 19). Thus, the extra-official payments do not appear to have provided any significant incentive for the market to prefer one system to the other. Perhaps the differences in the official charges rather could explain the differences in the patronage between the two systems. This by itself does not tell much about the costliness of the system. Indeed it could even be cheap if the benefits from the documentation to the individual actors exceed these costs. Economic theory suggests, however, that if that is the case, utility maximising actors would be willing, subject to affordability, to bear the costs in order to receive the benefits. This means that overall requests for documentation will be very high if the benefits exceed these costs. However, given the reality that only 8% of potential requests for documentation have been made, it leaves no one in doubt that these costs hardly commensurate with the benefits that actors are gaining from the documentation. But how much is it costing the system to capture this information?

The Costs of Servicing Documentation Requests The cost estimates for the respective processes are based on the labour hours spent per processing tasks as obtained from the field survey. Available direct labour hours are used as the allocation base to apportion the overhead costs (see Table 8 below).

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Table 7. Non-Official Compliance Costs Grouped by Policy Processes

1 2 3 4 5 6 7 8 9 10 11 12 13 14

Compliance Activity Transaction Documents Site plan Concurrence Stool Consent Plotting State Consent Land Title Oath of Proof Tax Clearance Certificate Stamp Duty Travel Time Transportation Waiting Time Delays Total (¢) Total ($) Total (£)

Source: Field Survey (2008)

State Lease Renewal Distributive

Concurrence

Stool Consent

Plotting

Allocation

State Consent

Regularization

Land Title

Regulative

Regulative

Regulative

Distributive

Distributive

Redistributive

Regulative

-

123,000.00

123,000.00

123,000.00

-

-

123,000.00

123,000.00

2,800,000.00 158,529.41

128,000.00 920,000.00 2,800,000.00 107,425.13 158,529.41

128,000.00 541,000.00 2,800,000.00 107,425.13 158,529.41

128,000.00 558,783.78 2,800,000.00 107,425.13 158,529.41

2,800,000.00 158,529.41

687,704.92 2,800,000.00 107,425.13 158,529.41

128,000.00 2,800,000.00 107,425.13 158,529.41

128,000.00 2,800,000.00 107,425.13 158,529.41

142,196.65 142,196.65 142,196.65 142,196.65 142,196.65 142,196.65 9,509,120.00 9,509,120.00 9,509,120.00 9,509,120.00 9,509,120.00 9,509,120.00 18,899,120.00 18,899,120.00 18,899,120.00 18,899,120.00 18,899,120.00 18,899,120.00 1,790,780.00 1,790,780.00 1,790,780.00 1,790,780.00 1,790,780.00 1,790,780.00 28,600,824.67 28,600,824.67 28,600,824.67 28,600,824.67 28,600,824.67 28,600,824.67 61,900,570.73 63,178,995.86 62,799,995.86 62,817,779.64 61,900,570.73 62,695,700.78 6,796.68 6,937.05 6,895.44 6,897.39 6,796.68 6,883.99 3,837.22 3,916.47 3,892.97 3,894.07 3,837.22 3,886.51

142,196.65 142,196.65 9,509,120.00 9,509,120.00 18,899,120.00 18,899,120.00 1,790,780.00 1,790,780.00 28,600,824.67 28,600,824.67 62,258,995.86 62,258,995.86 6,836.04 6,836.04 3,859.44 3,859.44

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Table 8. Overhead Labour Rate Overheads Administrative Expenses Service Expenses Investment Expense Total Overheads Labour Force Available Labour Hours/Day Number Of Days Per Year Total Direct Labour Hours Overhead Rate Per Direct Labour Hour Overhead Rate Per Direct Labour Hour ($) Overhead Rate Per Direct Labour Hour (£)

Source: Field Survey (2008)

Lands Commission

Land Title Registry

Land Valuation Board

Survey Department

¢6,133,170,000.00

¢2,000,000,000.00

¢1,800,000,000.00

¢5,431,000,000.00

¢2,531,200,000.00

¢1,442,170,000.00 ¢12,676,170,000.00 ¢20,251,510,000.00 264

¢2,400,000,000.00 ¢3,200,000,000.00 ¢7,600,000,000.00 82

¢2,538,400,000.00 ¢1,100,000,000.00 ¢5,438,400,000.00 818

¢89,488,000,000.00 ¢60,826,000,000.00 ¢155,745,000,000.00 251

¢1,949,400,000.00 ¢4,689,400,000.00 ¢9,170,000,000.00 539

8

8

8

8

8

260

260

260

260

260

549,120.00

170,560.00

1,701,440.00

522,080.00

1,121,120.00

¢36,879.94

¢44,559.10

¢3,196.35

¢298,316.35

¢8,179.32

$4.01

$4.84

$0.35

$32.43

$0.89

GBP 2.11

GBP 2.55

GBP 0.18

GBP 17.07

GBP 0.47

OASL

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Table 9. Summary Costing of Information Capture

1 2 3 4 5 6 7 8 9 10 11 12 13 14

Information Capture Process Concurrence Stool Land Consent State Land Consent Plotting State Land Allocation Stamp Duty Tax Clearance Certificate Land Title Lodgement Certificate Preparation Title Certification Regularization Parcel Plan Cadastral Renewal Lease

Source: Field Survey (2008)

Direct Labour Costs 226,064.86 183,207.80 458,004.23 139,559.77 1,246,417.74 51,896.08

Overheads 522,440.00 461,390.00 723,386.53 251,970.00 1,222,850.00 11,168.00

Total 748,504.86 644,597.80 1,181,390.76 391,529.77 2,469,267.74 63,064.08

15 % MarkUp Margin 112,275.73 96,689.67 177,208.61 58,729.47 370,390.16 9,459.61

Grand Total 860,780.59 741,287.47 1,358,599.38 450,259.23 2,839,657.90 72,523.70

Grand Total ($) 93.56 80.57 147.67 48.94 308.66 7.88

Grand Total (£) 49.24 42.41 77.72 25.76 162.45 4.15

44,308.57 15,503.58 38,298.23 52,033.87 2,057,974.10 377,991.64 1,895,021.73 1,199,528.89

102,900.00 142,720.00 347,434.00 164,574.00 1,993,190.00 140,958.00 517,092.00 935,730.00

147,208.57 158,223.58 385,732.23 216,607.87 4,051,164.10 518,949.64 2,412,113.73 2,135,258.89

22,081.29 23,733.54 57,859.83 32,491.18 607,674.62 77,842.45 361,817.06 320,288.83

169,289.85 181,957.12 443,592.06 249,099.05 4,658,838.72 596,792.08 2,773,930.79 2,455,547.72

18.40 19.78 48.22 27.08 506.40 64.87 301.51 266.91

9.68 10.41 25.38 14.25 266.52 34.14 158.69 140.48

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In this analysis the total overheads are assumed to be the total budgets of the respective agencies less their personnel expenses. This approach assumes that the greater the direct labour hours for a task, the greater will be the overhead expenditure incurred. This may not necessarily be the case but offers a reasonable basis for apportioning the overheads and sufficient for the purpose of this study. To accomplish this, a single overhead rate for the respective agencies based on their staff numbers and approved budgets for 2008 is estimated. The estimates are reported in Table 10. The overhead rates are assigned to all processes handled by the corresponding agencies. Based on these, the costs for the respective task are established. Column 1 of Table 9 above is a break down of the policy processes into the major identifiable activities. The estimated labour and overhead costs for each of the activities together with their total costs have also been estimated in columns 3, 4, and 5 respectively. It could be observed that a conservative mark-up of 15 percent (see column 6) has been applied as provision for the incidents of costs that could not be established. The last two columns provide the equivalent costs in US Dollars and Pounds for ease of international comparison. Table 10 shows basically that the overall mean activity costs is ¢1,275,153 (Min = 72,523 and Max = 4,658,838, St. Deviation = ¢1,379,787). To appreciate the full scale of the costs incurred by both the government and market dealers to have real estate market information captured in Ghana, the costs computed from both ends have been put together. This is presented in Table 11. The Table reports at the top row, the major individual policy activities and the corresponding policy category under which they fall. Details of their corresponding compliance and administrative costs are then computed and reported accordingly.

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Table 10. Descriptive Statistics on the Costs of Processing Activities

Mean Min Max St. Deviation

Direct Labour Costs 570,415.08 15,503.58 2,057,974.10 719,477.83

Overheads

Total

538,414.47 11,168.00 1,993,190.00 542,361.56

1,108,829.54 63,064.08 4,051,164.10 1,199,815.38

15% Margin 166,324.43 9,459.61 607,674.62 179,972.31

Grand Total 1,275,153.98 72,523.70 4,658,838.72 1,379,787.69

Source: Field Survey (2008)

The Budget Share To examine the adequacy of the resources devoted to these systems, the budgets of the responsible organisations are examined. But in the absence of any universally agreed standard for ascertaining the optimality of organisation’s budgets, a comparative approach is adopted by which the budgets of these systems are compared with those of similar organisations. Rows 2 to 5 of Table 8 above show the average annual budgets of these agencies.

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Table 11. The Aggregate Marginal Costs of Real Estate Policy in Ghana Policy Process Policy Category Compliance Costs Administration Costs Total (¢) Total ($) Total (£)

Source: Field Survey (2008).

State Lease Concurrence Renewal Distributive Regulative 61,900,570.73 75,178,995.86 5,235,478.15 3,960,999.84 67,136,048.88 79,139,995.70 7,297.40 8,602.17 1,533.09 2,219.82

Stool Consent

Plotting

Allocation

State Consent

Regularization

Land Title

Regulative Regulative Distributive Distributive Redistributive Regulative 74,799,995.86 62,817,779.64 61,900,570.73 62,695,700.78 62,258,995.86 62,258,995.86 3,841,506.73 4,000,737.73 5,939,877.16 4,458,818.64 7,759,057.98 3,100,219.26 78,641,502.59 66,818,517.37 67,840,447.89 67,154,519.42 70,018,053.84 65,359,215.12 8,547.99 7,262.88 7,373.96 7,299.40 7,610.66 7,104.26 2,191.30 1,514.93 1,573.39 1,534.15 1,697.97 1,431.44

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Table 12 below presents an analysis of the share of these budgets on national internally generated revenue, tax and non-tax revenues. The computed proportion of the budgets on the total government revenue (column 6) is an index of the respective year’s budgets’ share of national revenue. As reported in Table 12, the land sector’s budget share of the preceding year’s government revenue oscillates between a minimum of 0.25 percent in 2001 to a maximum of 0.65 percent attained in 2000. This works out to an overall average of less than 0.5 percent (Mean = 0.49%) share of government internally generated revenue in the corresponding preceding years. This diverges starkly from for, example, the forestry sector’s average share of 1.5 percent (minimum = 0.04% and maximum = 2.42%). This is so even though the two agencies perform similar functions, the land sector focusing on the land side, which constitutes about 62.9 percent of the total land area and the forestry sector focusing on forestlands, savannah land transactions, shrub and thickets, which constitutes just about 26.6 percent of the total land area of Ghana (FAO, 2004). It does not, therefore, appear that the land sector is at least heavily resourced. Table 12. Comparison with Government Revenue (Million of Cedis) Year

National Non-Tax Revenue

National Tax Revenue

Total Internally Generated Revenue

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1999 310,400.00 3,089,100.00 3,399,500.00 2000 961,600.00 3,731,700.00 4,693,300.00 2001 3,731,700.00 6,556,900.00 10,288,600.00 2002 252,400.00 8,547,500.00 8,799,900.00 2003 298,600.00 12,556,300.00 12,854,900.00 2004 1,136,300.00 17,403,000.00 18,539,300.00 Mean 1,115,166.67 8,647,416.67 9,762,583.33

Subsequent Year's Budget Share of Budget for the sampled Government land sector agencies Revenue (%) Land Forestry Land Forestry Sector Sector Sector Sector 20,762.90 1,343.00 0.61 0.04 30,596.10 74,883.00 0.65 1.60 25,998.50 141,861.00 0.25 1.38 42,029.10 212,585.00 0.48 2.42 66,240.40 286,510.00 0.52 2.23 76,652.60 251,122.00 0.41 1.35 43,713.27 161,384.00 0.49 1.50

Source: Data on non-tax and tax revenue were extracted from the budget statements of Ghana (1999 – 2005).

The Size of the Personnel Roster In general, wages and salaries in the public services in Ghana are very low, indeed lowest in West Africa (United Nations, 2002, p.9). According to the World Bank (2005, p.2) public service wage bill as a proportion of Ghana’s GDP is lower than the average of low and middle-income countries. That said, a look at the empirical evidence from a comparative perspective shows that the land sector in practice deploys very small workforce in real estate market information capture in Ghana. The evidence as analyzed in Table 13 shows actually that the workforce sizes of the responsible bureaucracies are shrinking in real terms. Overall, the agencies involved in real estate market information capture together employ a total of 1,954 staff across the 10 regions of the country, which is less than 0.3 percent (0.24 %) of the total public service workforce of 800,000.00 (United Nations, 2005). The distribution of this total staff level among the individual agencies is even more revealing. Currently the Lands Commission, the agency responsible for the plotting/concurrence and deed registration systems has a total workforce of 264, an average of 26.4 staff per region and

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0.03% of the public service. The Survey Department, which also services the land title system with all its survey map needs, has a workforce of 539, an average of 53.9 staff per regional office and 0.07% of the public service. The Land Title Registry, the agency responsible for the land title system has 82 staff (representing 0.01% of the public service). If it is taken that each staff works for 269 days in a year and 7 hours in a day then the land title registry deploys a total of 154,406 man-hours per annum. Given that the Land Title registry receives on the average 7,452 applications per year (see above), it follows that 21 man-hours is required to complete an application (approx 7 man hours for 0.33 documentation service). Thus, with the existing strength, the registry should be servicing documentation requests at a rate of 28 documents per day, which appears reasonable. Yet the current average rate of 1178 per year (Table 2) implies that the registry is on the contrary delivering at about 4 documents per day. This explains a bulk of the delays. This does not appear to be the result of inadequate staff but inefficient or under employment of available staff. The absolute manual processing system employed in servicing requests worsens this. The situation is practically the same with the plotting/concurrence and deed registration systems under the Lands Commission.

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IMPLICATIONS Figure 1 illustrates the implications of the preceding analyses. In Fig 1, take the vertical axis to represent the price of particular category of real estate; say real estate class X, while the horizontal axis indicates the likely quantities associated with particular prices. Take D0 to denote the social optimal demand function for class X real estate under conditions of costless, perfect and equally distributed information. At this position, the equilibrium or optimal quantities of class X property expected to be purchased will be Q0 at unit price P0. This price and quantity arrangements represent informed purchasers decision points and it will be the point at which the real estate market will be said to be performing most efficiently. Typically, few if any, real estate markets, like other specialists markets operate, as mentioned earlier, at this level owing to general paucity of information in pristine real estate markets. As Senior (1854; cited in Rose, 2002) argues “the detail of commerce are so numerous, the difficulty of obtaining early and accurate information is so great, and the facts themselves are so constantly changing that the most cautious merchants are often forced to act upon very doubtful premises”. This is even more so in the developing world where as Geertz (1978, p.29) asserts: “information is poor, scarce, mal-distributed, inefficiently communicated and intensely valued”. This is not different from what actually pertains in Ghana, with only 8%real estate market information capture, huge backlogs of documentation requests to clear and inordinate inaccuracies in the records system.

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Table 13. Real Estate Sector Workforce.

Agency

2001 Others Total

Professionals

Subprofessional

52

24

211

33

29

23 7

Lands Commission Survey Department OASL Land Title Registry Land Valuation Board Total Percentage

Source: Field Survey (2008).

2005 Others Total

Professionals

Subprofessional

287

39

16

209

264

Inter-census Percentage Change -8.01

459

521

33

35

471

539

3.45

21 3

205 56

249 66

23 23

21 36

301 23

251 82

0.80 24.24

36

28

773

837

35

655

128

818

-2.27

151 7.70

105 5.36

1,704 86.94

1960 100.00

153 7.83

763 39.05

1,132 57.93

1,954 100.00

-0.31

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Raymond T. Abdulai and Felix N. Hammond

Price

P1 P0

S

c b a

D1

e

P2 d

D0 D2

Q2

Q0

Q1 Quantity

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Figure 1. The Dynamics of Asymmetric Property Information

The implications for Ghana can be daunting. As Akerlof (1970), Spence (1973), Rothschild & Stiglitz (1976) and Stiglitz (2001) have found, in such a system because real estate owners tend to know more about their properties including the quality of their titles than potential buyers or lenders, if purchasers proceed on the basis of their less than full and accurate information, multiple equilibriums could result. These may comprise one in which purchasers/lenders overvalue the intrinsic qualities of the real estate and the other in which they under value the intrinsic qualities. Much of the traditional literature on information asymmetry cited above focused almost exclusively on the scenario in which purchasers under-value the intrinsic qualities of commodities. The deeper implications of the scenario in which purchasers overvalue the commodity have has little attention in the literature and this is, thus, explored in this chapter. If the intrinsic qualities of properties are overvalued, purchasers could bid higher or lenders could grant higher credit more than their actual optimal price. This means that instead of operating along D0, such purchasers or lenders could, in fact, operate on a sub-optimal level D1. At this level, Q1 instead of Q0 quantities of say class X real estate will be demanded at unit price of P1. The practical consequences of the D1 scenario are that real estate or land resources in this category will more likely be allocated to uses above their optimal capacity. This will lead to an efficiency loss as real estate is presumably taken out of its current use to a new use that will produce returns that are less than the price (P1) offered for the real estate. For example, if residential real estate is overpriced to the levels of commercial real estate, the optimal returns produced by the particular real estate will match the optimal (P0) for residential real estate (not for commercial real estate) and will most likely fall below those of commercial real estate say P1. This premature allocation to commercial use may not be sustainable and could result in under utilization or inefficient use of the real estate at the expense of its actual optimal use, which is residential. From the credit market standpoint, real estate owners whose real estate is overvalued by credit institutions will have less incentive to pay up their loans as the amount of loan granted is likely to be much higher than the true price of the real estate. They, therefore, stand to gain more if they fail to pay up their loans. These situations will most likely impose on society a social cost or deadweight loss represented by the triangle bce in Figure 1 on each unit of class X real estate acquired at price

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P1. Motivated by the higher windfall returns or rent associated with overpricing, real estate owners are likely to firstly ensure that the rent associated with overpricing persists and secondly that where possible they grow by hyping the utilities of their real estate and downplaying the defects in their real estate to compel purchasers to overprice their bid and operate along D1. Essentially, this is accomplished by withholding information, for instance, regarding defects in their real estate. This requires the expenditure of real resources to provide such information blockade through, for instance, the preparation and dissemination of false or misleading boundary maps and documents, the employment of personnel to peddle misinformation and even to maintain some form of physical presence on the land or real estate in the form of land guards among others. These expenditures are incurred by diverting resources away from other productive sectors or uses just to prevent accurate information from getting to purchasers with the sole aim of maintaining a sub-optimal condition D1. Indeed, if real estate owners do not protect this rent and, hence, overpricing, they can be sure that other suppliers (erstwhile purchasers who have now gained the true position of the information and, thus, realise they can gain by operating as suppliers) will attempt to enter the market and increase supply thereby forcing prices downward towards P0. This shows that under conditions that lead to D1 scenario the market does not provide appropriate incentives for information disclosure by real estate owners as their profits are maximised by the asymmetry and hence without any exogenous interventions, the asymmetry will prevail. Conversely, if purchasers or lenders undervalue the intrinsic qualities, they do so because they perceive real estate in general as potentially problematic either as a result of title defects or otherwise. The result is that even high quality real estate in terms of title perfections, for instance, will also be priced the same as low quality ones by purchasers and creditors. This will lead to D2 scenario at lower quantity Q2 and price P2. As Akerlof (1970) demonstrates, when this happens, high quality real estate owners will have no incentive to put their real estate on the market and hence most high quality real estate will be withdrawn leaving only problematic real estate on the market. Nonetheless, on occasions, circumstances such as urgent need for money and other emergencies could force owners of high quality real estate to put their real estate on the market for sale. But then, real estate owners in bringing their real estate onto the market will prefer to supply their real estate between Q2 and Q0 in their existing uses because the returns from that use (discounted over time) as given by the area under the supply curve (abQ2Q0) exceed the price that is available in the market. This leads automatically to efficiency losses as these uses fall short of the optimal uses and hence the real estate is denied its highest and best uses. If, however, purchasers fail to pay this price, then hard-pressed real estate owners will be compelled to sell their real estate at the price of a low quality real estate. In that case, it is this high quality real estate owner who eventually bears most of the inefficiency costs of the asymmetric information since all they may get for their real estate is the price of a low quality or defective registered real estate. The end result is that the real estate is more likely to be allocated to uses below their current capacity and would generate less returns than they should at the expense of the realisation of the social optimal for the society. The only actors who stand to gain in this scenario are the purchasers as they make savings on price but this again imposes social costs or deadweight losses represented on Figure 1 as triangle abd for each unit of real estate purchase on society. As a result, in this scenario, good quality real

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estate owners have the incentive to disclose their information to purchasers or lenders so that they could get better price for their real estate. What emerges from this analysis is that: (a) real estate information asymmetry fundamentally leads to market distortions; and (b) all forms of market distortions result in social costs or efficiency losses. These social costs or efficiency losses arise out of a loss of producer surplus (the area between the supply curve and P0 because some real estate owners would be willing to sell some quantity of real estate at a price between P2 and P0, and of consumer surplus (the area between P0 and the demand curve D1) as some buyers would be willing to pay more than P0 for some of the real estate. In the long run, unless prevented by artificial barriers such as government interventions and legal impediments, economic theory suggests that the market will devise its own solutions to overcome the asymmetries. Spence (1973) drawing on insights from Akerlof (1971) shows that as a market solution, purchasers and lenders will most likely employ signalling to overcome the asymmetries by devising proxies or signals that will enable them approximate the key qualities of real estate they intend to deal in. In Ghana, the pragmatic market signals used by purchasers in particular to ascertain the general title quality of real estate includes: (1) the ability of the owner to physically be in possession of the real estate as evidenced by the construction of permanent fencing, substantial development of the land and the owners’ ability to deposit building materials on the site without fear of confrontation; (2) the regular presence of an onsite caretaker; and (3) positive testimonial from neighbours among others. Some purchasers and lenders rely on these signals to sort out or screen real estate owners. Particular purchasers or lenders would have their own predetermined threshold signal levels they require to be sufficiently confident to progress with the transaction and would sort out real estate sellers or borrowers as either being above or below the threshold level. However, these have limited appeal to official purchasers or lenders, as they tend not to provide all the details that such transactions require including title defects. Yet equipped with the knowledge that some purchasers and informal lenders would rely on these signals, real estate owners have the incentive to incur signalling and other costs by again diverting resources from other productive sectors to acquire or put the relevant signals in place to attract purchasers. Rothschild & Stiglitz (1976) also show that in addition to signalling or as an alternative to it, purchasers and lenders may employ screening by offering a menu of questions or terms and relying on the choices made by real estate owners or borrowers to screen and categorize them into high quality or low quality real estate. In Ghana, screening is used more in addition to than as an alternative to signalling in real estate transactions. Beyond the signalling and screening, private economic agents are also expected to emerge to take advantage of the accompanying arbitrage opportunity created by the asymmetry. Indeed, the work of real estate brokers, valuers, agents and solicitors among others is precisely to specialise in the acquisition and supply of this withheld accurate information to get the market going not at D1 or D2 but at D0. Where in particular society’s private actors are sluggish in stepping in to supply the requisite information, which is what tends to be the case in the developing world, then government intervention may have to be invoked to bridge the information gaps and get the market going at D0.

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CONCLUSION The huge backlog of outstanding real estate ownership documentation in Ghana is perhaps the most crucial bane on the development of that country's real estate market. Any attempts at relieving that market of its inefficiency must necessary involve measures that will speed up the rate of information capture and remedy the inaccuracies in the system. The current arrangement as well as the proposed arrangements under the Land Administration Project (costing $53 million and funded by international donor agencies like the World Bank), does not hold the capacity to achieve this. The proposed arrangements are not different in essence from the system that has led to this situation. The most likely way forward would be a systematic real estate ownership census throughout the country to be financed by the state.

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REFERENCES Abdulai, R. T. (2007). The Operation of Urban Traditional Landholding Institutions in subSaharan Africa: A Ghana Study, Unpublished PhD Thesis, University of Wolverhampton, UK.. Abdulai, R. T. (2006). Is Land title regsitartion the answer to insecure and uncertain property rights in sub-Saharan Africa? RICS Research Paper Series, 6(6). Abdulai, R. T. & Hammond, F. N. (2008). Foreword, Real Estate and Development Economics Research Journal, 1(1), pp.vi-ix. Akerlof, G. A. (1970). The Market for 'Lemons': Quality Uncertainty and the Market, Mechanism, Quarterly Journal of Economics, 3, 488-500. Alchian, A. A. (1965). Some Economics of Property Rights, Politico, 30, 816-829. Alden-Wiley, L. (2002). Comments on the Legal Basis of for Land Administration in an African Context, Paper presented at the World Bank Regional Land Policy Workshop, Kampala, Uganda, 29 April - 2 May AMCAD, (1998). Land Records Storage and Management Study, Accra: Ministry of Lands and Forestry, Government of Ghana and The American Cadastre Inc. Antwi, A. Y. (2000). Urban Land Markets in sub-Saharan Africa: A Quantitative Study of Accra, Ghana, Unpublished PhD Thesis, Napier University, UK. British Property Federation (BPF), (2006). Property Development and the Community: What are the Hidden Benefits of Development? Publication of BPF. Brobbey, W. K. (1991). Improving Land Delivery Systems for Shelter, Final Report of Land Policy Consultancy, Accra: Government of Ghana, UNDP and HABITAT. Chen, S. & Ravallion, M. (2008). The Developing World is Poorer than We Thought, But No Less Successful in the Fight Against Poverty, World Bank Policy Research Working Paper No. 4703. Coase, R. H. (1960). The Problem of Social Cost, Journal of Law and Economics, 3, 1-44. Cook, R. C. (2002). Alternative dispute resolution systems: what kind of alternative to the courts? Paper presented at a Workshop on Land Rights and Legal Institutions in Kumasi - Ghana, 28 Feb. Council of Mortgage Lenders (CML), (2008). CML News and Views, Available at: www.cml.org.uk.

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Deininger, K. (2003). Land Policies for Growth and Poverty Reduction, World Bank Policy Research Report, Washington DC and Oxford: World Bank and Oxford University Press. Demsetz, H. (1967). Towards a Theory of Property Rights, American Economic Review, 57, 347-359. De Soto, H. (2000). The Mystery of Capital: Why Capitalism Triumphs in the West and fails everywhere else. London: Bantam Press. Feder, G. & Feeney, D. (1991). Land Tenure and Property Rights: Theory and Implications for Development Policy, The World Bank Economic Review, 5, 135-153. Friedman, L. S. (2002). The Microeconomics of Public Policy Analysis. Oxforshire: Princeton University Press. Geertz, C. (1978). The Bazaar Economy: Information and Search in Peasant Marketing, American Economic Review, 68, 28-32. Goldsmith, A. A. (1995). Democracy, Property Rights and Economic Growth, Journal of Development Studies, 32(2), 157-174. Hale, D. (2008). The Global Driver: How Housing is Drving the World Economy, Gale: Cengage Learning. Hammond, F. N. (2006). The Economic Impacts of sub-Saharan Africa Urban Real Estate Policies, Unpublished PhD Thesis, University of Wolverhampton, UK. Harberger, A. C. (1959). Monopoly and Resource Allocation, American Economic Review, 49, 134-146. Hicks, J. R. (1939). The Foundations of Welfare Economics, The Economic Journal, 49(196), 696-712. HM Land Registry (2009). ‘Know where you stand – register your land’, available at: www1. landregistry. gov. uk/ register_ dev/voluntary/ Ikejiofor, U. (2006). Equity in informal land delivery: Insights from Enugu, Nigeria, Land Use Policy, 23(4), 448-459. MacKenzie, J. A. & Phillips, M. (2008). Textbook on land law. Oxford: Oxford University Press. Mirrlees, J. A. (1971). An Exploration in the Theory of Optimum Income Taxation, Review of Economic Studies, 38(114), 175-208. Nell, E. J. (1984). Free Market Conservatism: A Critique of Theory and Practice. Australia: George Allen and Unwin Publishers Ltd. North, D. & Thomas, R. (1973). The Rise of the Western World. Cambridge: Cambridge University Press. Pejovich, S. (1990). The Economics of Property Rights: Towards a Theory of Comparative Systems. Netherlands: Kluwer Academic Publishers. Rao, P. K. (2003). The Economics of Transaction Costs: Theory, Methods and Application. New York: Palgrave Macmillan. Rothschild, M. & Stiglitz, J. E. (1976). Equilibrium in Competitive Insurance Markets: An Essay on the Economics of Imperfect Information, Quarterly Journal of Economics, 90, 626-649. Rose, M. (2002). Implications of Costly Information. Library of Economics and Liberty. Available at http:// econlib. org/ library/ columns/Teachers/ Information.html. Rosenberg, N. & Birdszell, L. E. (1986). How the West Grew Rich: The Economic Transformation of the Western World, London: I. B. Tauris and Co Ltd. Publishers. Senior, N. W. (1854). Political Economy. London: Richard Griffin and Co.

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Smith, A. (1776). An Inquiry into the Nature and Causes of the Wealth of Nations. In E. Cannan, (Ed.), Library of Economics and Liberty. London: Methuen and Co., Ltd. Spence, M. (1973). Job Market Signalling, Quarterly Journal of Economics, 87(3), 355-374. Stigler, G. J. (1961). Economics of Information, Journal of Political Economy, 69, 213-225. Stiglitz, J. E. (2001). Information and the Change in the Paradigm in Economics, Prize Lecture delivered at Columbia Business School, Columbia University, 8 December. Torstensson, J. (1994). Property Rights and Economic Growth - An Empirical Study, Kyklos, 47(2), 231-247. United Nations (UN) Department of Economic and Social Affairs (DESA) The Division for Public Economics and Public Administration (DPEPA), (2002). Report on the National Dissemination Workshop on Public Service Ethics in Africa: The Ghana Country Report. New York: United Nations. United Nations, (2005). World Population Prospects: The 2004 Revision, Highlights, New York: United Nations. Vickrey, W. (1961). Counterspeculation, Auctions and Competitive Sealed Tenders, J. Finance, 16, 8-37. Weiner, D. L. & Vining, A. R. (1999). Policy Analysis: Concepts and Practice (3 ed.). New Jersey: Prentice-Hall, Inc. Williamson, I. (1991). Land Information Management at the World Bank, The Australian Surveyor, 36. Williamson, O. E. (1981). Contract Analysis: The Transaction Costs Approach. In P. Burrows, & C. G. Veljanovski, (Eds.), The Economic Approach to Law. London: Butterworths. Word Bank, (2007). Doing Business in 2007, How to Reform. Washington DC: World Bank and International Finance Corporation. Wyatt, P. & Fisher, P. (1998). Property Information Today: Geographic and Land Information Management. In P. Wyatt and P. Fisher, (Eds.), Property information today: Geographic and land information management. London: Butterworths.

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Chapter 4

INVESTMENT CHARACTERISTICS OF HOUSING MARKET: FOCUSING ON THE STICKINESS OF HOUSING RENT Chihiro Shimizu

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ABSTRACT The turmoil in the international financial market since the subprime loan crisis has had a significant effect on the real-estate investment market in Japan, particularly the Japan real-estate investment trust (J-REIT) market. This suggests that the real-estate investment market is becoming part of the financial market. It is necessary to precisely understand the mechanism of risk generation and cash flow in the real-estate market to understand the characteristics of the real-estate investment market. The purpose of this study is to statistically clarify the characteristics of the five problems that have been recently pointed out as risk factors in the real-estate investment market for housing. Specifically, we have attempted to clarify the following five intrinsic problems, which are considered to be characteristics of the housing market: 1) the return problem, 2) the small-scale investment problem, 3) the risk associated with the adjustment of rent, 4) the key tenant problem, and 5) the inflation problem, all of which have been pointed out to be problems in the housing and commercial property markets. Regarding the risk associated with the adjustment of rent, we investigated the actual situation in the housing market by considering the decrease in housing rent with the age of the building and the adjustment of housing rent when a new contract is concluded between a landlord and a new tenant. The results indicated that the yearly rate of decrease in housing rent for nontimbered houses is as high as approximately 6% over the first five years after construction, but decreases to 2.6% over the 5th to 10th years and 2.5% over the 10th to 20th years, indicating that the long-term rate of decrease in housing rent is small. The probability of no change in rent was converted to a yearly value of 0.6585, which means that the revenue from the housing rent of 65% of leasehold properties does not change. This result revealed that housing rent in the Japanese market is extremely sticky compared with that in the US. Regarding the risk associated with the adjustment of rent, the probability of downward adjustment of the housing rent should be considered; however, in most cases, the housing rent is left unchanged. Even when the housing rent is

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Chihiro Shimizu adjusted downward, decreases of more than 10% comprised only 11.2% of all the adjustments. Also note that the occurrence of rent adjustment is random with respect to time; the housing rent market is not strongly affected by the economic environment, in contrast to the market for office buildings; a turnover of residents occurs because of events such as marriage, childbirth, and relocation, regardless of the economic cycle, causing the housing rent to change.

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1. BACKGROUND TO REAL-ESTATE INVESTMENT MARKET Owing to the turmoil in the financial market since the subprime loan crisis, which started in summer 2007, the structure of the real-estate investment market has drastically changed in Japan and overseas. Not only has the cost of capital procurement increased owing to the turmoil in the international financial market, but also the ideal risk control method of the entire financial system is changing; under such circumstances, the mobilization of capital into the real-estate market has slowed, and only limited capital has flowed into specific areas. There is clear evidence of macroscopic slackness in the market and differentiation in the allocation of funds (?). In particular, Japan experienced a substantial decline of stock prices, particularly in the JREIT market. In October 2008, a house-related REIT was driven to bankruptcy. In the J-REIT market, the decrease in stock prices of REITs of houses and commercial properties has been significant since the middle of 2007, even before the start of the turmoil in the international financial market. The reasons behind this are as follows. In the case of house REITs, 1) an upward return cannot be expected when the real-estate investment market is active (return problem), and 2) many properties should be involved in REITs to ensure a certain scale of investment because the amount of investment per property is small; as a result, the cost required to examine all of the properties (due diligence cost) is high (smallscale investment problem). In the case of commercial-property REITs, 3) large downward adjustment of housing rents was implemented in Nagoya City (Narumi) in Aichi Prefecture and Narashino City in Chiba Prefecture, indicating the uncertainty of profitability in a market that has been considered to be stable (risk associated with the adjustment of rent), 4) related to 3), because the share of revenue from key tenants in each commercial property is large, adjustment of the rent of key tenants will significantly affect the total revenue from the investment (key tenant problem), and 5) the trend of long-term cost-push inflation has become apparent, increasing the probability of a decrease in profitability (inflation problem). These problems will remain even when the condition of the financial market recovers, because they are structural problems existing in each real-estate market. Therefore, unless these problems are resolved, it will be difficult for these markets to properly attract investment funds. However, there are some misunderstandings associated with the above five problems, as explained below. Also, these problems can be seen to be attractive when considered from a different viewpoint or when they are interpreted in terms of an investment fund having different characteristics. The purpose of this study is to clarify the nature and structure of these five problems and the characteristics of the housing market through a positive analysis.

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2. CHARACTERISTICS OF HOUSING MARKET

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2.1. Investment Characteristics of Housing Market For the return problem, two background issues exist: one is that the adjustment of housing rent seldom occurs, that is, the housing rent is sticky, and the other is that the value of houses decreases with age, causing the house price and housing rent to decrease. However, the first issue can be interpreted as providing the investors with stable revenue. The housing rent does not increase even during an economic upturn, which in turn means that revenue does not significantly decrease when the market stagnates. This is very attractive for investors who seek stable revenue. Regarding the second issue, i.e., the decrease of both the revenue and house price with increasing age of the building, it is necessary to consider this issue as a risk factor. It is also necessary to precisely understand the rate of decrease in the housing rent, particularly with increasing age of the building, and to develop a portfolio that can compensate the decrease in the housing rent. The second problem, the small-scale investment problem, is closely related to the third and fourth problems. The scale of housing investment per apartment block (?) is extremely small compared with that of other types of real estate. When this fact is considered paradoxically, the investment can be regarded as diversified. Furthermore, because the scale of each property is small and the investment is also diversified for one apartment block, the key tenant problem observed in commercial property funds can be avoided in the case of the housing market. However, regarding the risk associated with the adjustment of rent, which is closely related to the return problem, it has been pointed out that the housing rent is considered sticky only on the basis of the prediction of specialists; no experimental studies on the mechanism of the adjustment of housing rent have been carried out. Considering the fact that in some cases rent in the commercial property market, which had been considered to be stable, was adjusted markedly downward, this problem should be discussed on the basis of objective analytical results. If the stickiness is demonstrated and the mechanism underlying the adjustment of housing rent is revealed, we can manage the risks in accordance with the mechanism. The final problem is the inflation problem. An increase in the prices of commodities decreases consumers’ willingness to spend, which has an adverse effect on sales at commercial properties. Demand for commodities with high price elasticity is strongly affected by an increase in prices. In contrast, food and residential services have low price elasticity, in the sense that demand for them does not change significantly when their prices change, i.e., an increase in prices does not have a significant effect on demand. For long-term funds such as pensions, the major goal of which is to act as an inflation hedge, the management of assets, the performance of which is above the rate of increase of the consumer price index (CPI), is required. Focusing on the constituents of the CPI basket, housing rent made up 26.3% or approximately one-quarter of the CPI as of 2005. Therefore, it is possible to consider investment in housing rent as being synonymous with investment in the CPI. Assuming that the target of long-term funds is a stable CPI rather than a high upward return, this target can be realized by investing funds in the long-term housing rent market.

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In addition, the link between the CPI and economic and financial policies has been strengthening. The central banks of various countries have increased interest rates with increases in CPI, using the CPI as a policy target; this is known as inflation targeting. In this sense, for long-term funds investing in the CPI or in housing rent, which is a major constituent of the CPI, is considered to be an important strategy.

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2.2. Statistical Characteristics of Housing Market When the characteristics of the housing market are considered from the viewpoint of the investment market on the basis of the above discussion, the problems in the housing market can be summarized into the following two problems: 1) housing rent depreciation with the age of buildings and 2) the adjustment of housing rent. The first problem is demonstrated by analyses such as regression analysis1. In this study, the relationship between the age of buildings and housing rent depreciation in the housing rent market is clarified using a similar method. Next, we focus on the stickiness of housing rent. This topic is very important in the field of macroeconomics and has been reported by many researchers. For example, housing rent is adjusted at the time of contract renewal in the US. Including new contracts between a new tenant and landlord, and rollover contracts, which are completed when the tenant decides to remain in the same property after the initial contract has ended, an average of 29% cases of housing rent (in terms of the number of transactions) remains unchanged annually in the US2. In particular, it is reported that the percentage of transactions in which the housing rent remains unchanged at a rollover contract is 36%; the housing rent in a rollover contract is more sticky than that in a new contract. It is expected that a similar tendency can also be observed in Japan. In particular, the housing rent for a rollover contract is not altered during the term of the contract and is rarely altered when the contract is renewed as long as the same resident continues to stay in the property, because the adjustment of housing rent is restricted by the Land Lease and House Lease Law and other factors. We start by observing macroscale changes in the housing market (3.1), then estimate housing rent depreciation on the basis of the hedonic function formulated in this study (3.2). We then clarify the mechanism behind the adjustment of housing rent (3.3). Finally, the characteristics of the housing market are reevaluated on the basis of the results obtained in this analysis.

1

For example, refer to the studies by Housing Research and Advancement Foundation of Japan (2008), and Shimizu, Nishimura, and Karato (2007). 2 In previous reports, US researchers analyzed the stickiness of housing rent by classifying housing rent into two types of contract, i.e., new and rollover contracts, on the basis of individual data from an American Housing Survey and a questionnaire-based follow-up survey

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3. MACROSCALE CHANGES IN HOUSING RENT AND HOUSE PRICES AND THE STICKINESS OF HOUSING RENT

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3.1. Macroscale Changes in House Prices, Housing Rent, and CPI Housing Rent Macroscale changes in housing rent are observed in terms of the changes in house prices and housing rent in the 23 wards of Tokyo. First, it is necessary to estimate price indices to analyze macroscale changes in housing rent and house prices. Each house is different in terms of specifications and facilities, and it is not possible to find two identical properties. Even if the specifications and facilities are the same, the extent of deterioration will differ if the age of the building differs. In other words, the housing market has a unique feature that no identical properties exist. In addition to this uniqueness, the advance of technologies related to houses (particularly condominiums) is relatively fast, and the quality of new properties increases with the progress of time. Such characteristics have already been revealed in many previous studies. To deal with the uniqueness of properties and rapid changes in their quality, the hedonic price method and repeat sales method can be used to estimate house price indices. For example, both the Halifax house price index, which is a typical house price index in the UK, and the Recruit house price index in Japan are estimated by the hedonic price method; while the Case-Shiller house price index, which is a typical house price index in the US, is estimated by the repeat sales method. In this study, we used the same estimation method as that adopted to estimate the Recruit house price index and the data of the weekly housing advertisement magazines (for housing rent) published by Recruit Co., Ltd. A housing rent index (hereafter, hedonic rent index) and a house price index are estimated from the housing rent at the time of new contracts and house price information, respectively, both of which are provided by Recruit Co., Ltd. The estimated indices and the CPI rent index (Japanese CPI for rent) are compared. In the series of analyses in this study, the following eight indices are analyzed and compared: 1) nontimbered house price index, 2) timbered house price index (both of which are estimated using the house price data prepared by Recruit Co., Ltd.), 3) hedonic rent index (nontimbered + timbered), 4) nontimbered house hedonic rent index, 5) timbered house hedonic rent index, 6) CPI rent index, 7) timbered house CPI rent index, and 8) nontimbered house CPI rent index (the last two indices are obtained from the breakdown of the CPI rent index). The reason for comparing estimated indices with the CPI rent indices is that the CPI rent index is estimated on the basis of continuously paid housing rent. The revenue from housing rent, which predominantly determines the performance of real-estate investments, is calculated using the housing rent actually paid by a tenant, rather than the housing rent in the new contract. It is considered that the CPI rent index appropriately reflects these considerations. The hedonic price method is a method of estimating the price index by formulating the structure of house prices and housing rent using a generalized regression analysis method. Table 1 summarizes the results of regression analysis to estimate the housing rent for both nontimbered and timbered houses, the nontimbered house price, and the timbered house

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price. The adjusted values of R2 for the housing rent function, the nontimbered house price function, and the timbered house price function are 0.657, 0.833, and 0.691, respectively. All three models can estimate the price with a relatively high power of explanation. The nontimbered house hedonic rent index, the timbered house hedonic rent index, and the nontimbered house hedonic rent index for the central business district (CBD), which includes Chiyoda, Chuo, and Minato wards, were estimated using a similar method to that used for the house price index (Table 2). Table 1. Estimation results of hedonic rent/price: 1986-2006 Property Characteristics (in log) Constant

Non-timbered HP (house price) Coefficient t-value 4.335 555.10

Timbered HP (house price) Coefficient t-value 5.118 562.46

－

－

－

－

-0.185

-194.36

FS: Floor space

-0.230

-601.32

0.007

5.97

－

－

RW:Road Width

－

－

－

－

0.179

144.57

-0.037

-281.15

-0.184

-333.49

-0.070

-165.22

-0.039

-134.03

-0.061

-93.28

-0.137

-146.88

-0.036

-85.81

-0.034

-36.40

-0.058

-50.27

BD:Bus Dummy

-0.020

-1.61

－

－

-0.201

-8.59

BD×WT

-0.050

-10.15

-0.056

-38.85

0.009

1.07

－

－

0.020

37.85

－

－

0.009

38.66

0.016

34.15

0.010

21.72

-0.043

-93.52

－

－

－

－

-0.049

-102.25

－

－

－

－

－

－

－

－

0.010

12.21

－

－

－

－

0.035

7.82

LA:Lot Area

Age: Age of building WT: Walk Time to the nearest station TT: Travel Time to CBD

TU: Total Units

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HR (house rent) Coefficient t-value 9.009 2193.65

RT: Market reservation time FF: First Floor Dummy THD:Timbered house dummy SD:South Dummy LD:Land Dummy Ward (city) Dummy RDi (i=0,…,I) Railway/Subway Line Dummy LDj (j=0,…,J) Time Dummy TDi (i=0,…,I) Adjusted R square= Number of Observations=

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

0.657

0.833

0.691

718,811

218,768

338,222

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Table 2. Estimation results of hedonic rent: 1990-2006 Property Characteristics (in log)

Non-timbered HR(house rent) Coefficient t-value 9.223 3371.72 -0.220 -529.75

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Constant FS: Floor space Age: Age of -0.041 building WT: Walk Time to the nearest -0.036 station TT: Travel Time -0.029 to CBD BD:Bus Dummy -0.036 BD×WT -0.048 RT: Market 0.008 reservation time FF: First Floor -0.041 Dummy Ward (city) Dummy Yes RDi (i=0,…,I) Railway/Subway Line Dummy Yes LDj (j=0,…,J) Time Dummy Yes TDi (i=0,…,I) Adjusted R square= 0.680 Number of Observations= 532,149

Non-timbered HR(house rent:CBD) Coefficient t-value Coefficient t-value 9.596 1918.49 8.859 1000.02 -0.377 -405.27 -0.053 -37.86

Timbered HR(house rent)

-269.91

-0.050

-154.18

-0.039

-101.65

-113.60

-0.041

-68.20

-0.048

-39.80

-63.59

-0.056

-63.69

0.022

14.00

-2.29 -7.90

0.031 -0.049

1.47 -6.06

-0.046 -0.021

-0.39 -0.43

28.33

0.013

28.69

0.007

7.05

-70.28

-0.034

-52.51

-0.035

-10.17

Yes

Yes

Yes

Yes

Yes

Yes 0.695

0.695

153,625

153,625

The changes in the hedonic rent index, nontimbered house price index, and timbered house price index over time are shown in Figure 1. Both the nontimbered and timbered house price indices rapidly increased from the first quarter of 1986 to the fourth quarter of 1987; assuming that the index in the first quarter of 1986 is 1, its value in the fourth quarter of 1987 increased to 2.3 for the nontimbered house price index and 2.5 for the timbered house price index. Subsequently, the indices decreased slightly then increased again, and in the fourth quarter of 1990, the nontimbered house price index increased to 3.2 and the timbered house price index increased to 2.6. In similar studies conducted over the same period, the indices were estimated using the actual transaction data of houses in a residential district, and similar results in terms of the rate of increase and the timing of the peak were obtained3. On the other hand, the hedonic rent index gradually increased from 1986 to 1992; in the second quarter of 1992 it reached its maximum value of 1.39, after which it decreased.

3

According to the studies by Shimizu and Nishimura (2006)(2007), the long-term land price index is calculated using data from actual transactions of land. The long-term land price index increased by a factor of 2.8 from the first quarter of 1986 to the fourth quarter of 1987. After that, the index decreased and then increased again until the fourth quarter of 1990. Similar tendencies in terms of the degree of increase and the timing of the peak are observed in analyses using different data sources, indicating the robustness of the result.

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To elucidate the relationship between the hedonic rent and the prices of owned houses, average houses are considered and the rate of return (ratio of estimated housing rent/house price (%), hereafter rent/price ratio) was calculated (Figure 2). The rent/price ratio exceeded 6% in 1986; after that, because of the increase in house prices, it decreased to less than 3% in 1990. However, with the subsequent decrease in house prices, the ratio increased again and surpassed 6.5% in 2001. With the recent increase in house prices, the ratio again decreased to approximately 5.5% by the end of 2006.   3.5

Non-timbered house price

Index:1986/1st quarter=1

3

2.5

Timbered house price

2

Hedonic house rent

1.5

1

QT2006/4

QT2005/4

QT2004/4

QT2003/4

QT2002/4

QT2001/4

QT2000/4

QT1999/4

QT1998/4

QT1997/4

QT1996/4

QT1995/4

QT1994/4

QT1993/4

QT1992/4

QT1991/4

QT1990/4

QT1989/4

QT1988/4

QT1987/4

QT1986/4

0.5

Figure 1. Trend of house price/rent : 1986/1st quarter～2006/4th quarter  

8

6 5 4 3

3.5

2

Index:1986/1st quarter=1

3

Non-timbered house price 2.5

2

Non-timbered house rent 1.5

QT2006/4

QT2005/4

QT2004/4

QT2003/4

QT2002/4

QT2001/4

QT2000/4

QT1999/4

QT1998/4

QT1997/4

QT1996/4

QT1995/4

QT1994/4

QT1993/4

QT1992/4

QT1991/4

QT1990/4

QT1989/4

QT1988/4

QT1987/4

1

QT1986/4

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Rent/Price Ratio

Rent / Price Ratio(%)

7

Figure 2. Trend of hedonic house rent index, price index and rent / price ratio (%) : 1986/1st quarter～2006/4th quarter

Next, the hedonic rent index, estimated using the housing rents for new contracts, and the CPI rent index are compared (Figure 3). The hedonic rent index increased by 40% from 1986 to the second quarter of 1992; however, the CPI rent index increased by only 15%. After that, the hedonic rent index decreased but the CPI rent index continued to increase, although the trend in the hedonic rent index has been roughly in agreement with that of the CPI rent index since the fourth quarter of 1994.

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1.4 1.35

Hedonic rent

Index:1986/1st quarter=1

1.3 1.25 1.2 1.15 1.1

CPI rent 1.05 1

2006/4

2005/4

2004/4

2003/4

2002/4

2001/4

2000/4

1999/4

1998/4

1997/4

1996/4

1995/4

1994/4

1993/4

1992/4

1991/4

1990/4

1989/4

1988/4

1987/4

1986/4

0.95

To observe the recent trends of these indices, separate estimations are carried out using the timbered and nontimbered house hedonic rent indices. The trend in the nontimbered house hedonic rent index, focusing on central Tokyo, was also analyzed to take into consideration the variations between areas (the results of the regression analysis are summarized in Table 2). Figure 4 shows the nontimbered house hedonic rent index, timbered house hedonic rent index, nontimbered house CPI rent index, and timbered house CPI rent index, using their values in the first quarter of 2000 as a baseline. The nontimbered house hedonic rent index in central Tokyo (for CBD), the nontimbered house hedonic rent index for the 23 wards of Tokyo, and the timbered house hedonic rent index for the 23 wards of Tokyo decreased by 40, 20, and 10%, respectively, from their peaks to their values in 2000. However, both the nontimbered and timbered house CPI rent indices continuously increased during the period when the hedonic rent index was decreasing. The trends of the nontimbered and timbered house CPI rent indices are similar to those of the hedonic rent indices between 1994 and 2000. In particular, after 2000, the timbered house CPI rent index decreased significantly. Table 3 summarizes the average annual changes (%) in various indices for different periods as a summary of the above findings. In 1987-1990, the hedonic rent index increased by 5.2%, but the increases in the nontimbered and timbered house CPI rent indices were far smaller, 2.93% and 1.7%, respectively. 1.4

1.3 Non-timbered hedonic rent:CBD

Index:1986/1st quarter=1

Non-timbered hedonic rent

1.2

1.1

Timbered hedonic rent

1

0.9

Non-timbered house CPI rent Timbered house CPI rent

QT2006/4

QT2005/4

QT2004/4

QT2003/4

QT2002/4

QT2001/4

QT2000/4

QT1999/4

QT1998/4

QT1997/4

QT1996/4

QT1995/4

QT1994/4

QT1993/4

QT1992/4

QT1991/4

0.8

QT1990/4

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Figure 3. Trend of house hedonic rent and CPI : 1986/1st quarter～2006/4th quarter

Figure 4. Compare of Hedonic rent index and CPI : 1990/1st quarter～2006/4th quarter Real Estate Investment Market, Nova Science Publishers, Incorporated, 2009. ProQuest Ebook Central,

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Table 3. Annual Change of House Price/Rent Index Nontimbered HP (house price)

Timbered HP (house price)

1987-1990 27.45% 1991-1993 -12.34% 1994-1996 -12.82% 1997-1999 -4.69% 2000-2002 -1.89% 2003-2005 1.55% *Average Rate of Annual Change (%)

19.51% -14.62% -9.55% -5.34% -2.13% 2.23%

HR (house rent)

Nontimbered HR (house rent)

5.20% 0.46% -3.37% 0.02% 0.39% -0.49%

-0.11% -3.48% -0.10% 0.42% -0.34%

Nontimbered HR (house rent): CBD -4.59% -4.80% -0.46% 0.64% -0.23%

Timbered HR (house rent)

CPI: HR

CPI: Nontimbered HR

CPI: Timbered HR

2.55% -2.81% 0.37% 0.38% -1.03%

2.31% 2.93% 0.33% 0.15% -0.77% -0.37%

2.93% 3.79% 1.05% 1.08% -1.84% -0.21%

1.70% 2.68% 0.03% 0.00% -0.52% -0.46%

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Furthermore, between 1991 and 1993, the hedonic rent index decreased (表３によりますと、マイナスは非木造のみです。ご確認ください。); however, the nontimbered and timbered house CPI rent indices increased; these increases continued up to 1996. As discussed above, the housing rent for new contracts and the hedonic rent index experienced significant increases and decreases during the bubble period and the subsequent collapse of the bubble economy, respectively. However, the CPI rent index gradually continued increasing during the bubble period and did not decrease significantly after the collapse of the bubble economy. This gradual increase is related to the return problem; however, viewing it from a different perspective, it can be considered that a stable return can be realized by investing in the housing market. When looking at reports on the housing investment market, indices using the housing rents for new contracts are frequently referred to1. It is considered that the CPI rent index, which is based on actual payments, is more appropriate as an index because the performance of housing investments is not calculated on the basis of the housing rents for new contracts, rather it is calculated as the sum of housing rents of properties in which tenants actually reside. Why does the CPI rent index markedly deviate from the hedonic rent index, which is based on the housing rent for new contracts? Specialists in the US have commented that this phenomenon is strange for investors in the US. To understand the background behind this, it is important to observe the mechanism of the adjustment of housing rents. Discussing this problem is also synonymous with examining the problem of the adjustment of rent, which has been mentioned for the commercial property market. In the next section, the problem of the adjustment of housing rent, along with the change in housing rent due to depreciation with the age of the building is examined by positive analysis.

3.2. Mechanism of Changes in Housing Rent In observing the mechanism of changes in housing rent, change in housing rent caused by depreciation with the age of the building, in addition to the macroscale changes discussed in section 3.1, should be considered. On the basis of the results of regression analysis, the change in housing rent caused by the deterioration of the building with age is observed (Figure 5). The yearly rate of decrease in the housing rent for nontimbered houses is lower than that for timbered houses. Furthermore, the yearly rate of decrease in housing rent in the CBD is small. The yearly decrease in nontimbered house rent was found to be as large as approximately 6% over the first five years after construction, but to decrease to 2.6% over the 5th to 10th years and 2.5% over the 10th to 20th years, indicating that the rate of decrease in housing rent 1

In the housing rent indices prepared by Recruit Co., Ltd., and the condominium rent index prepared by STB Research Institute Co., Ltd. (http://www.athome.co.jp/news/m_index/imagessample02.pdf), the indices are calculated using housing rents for new contracts.

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is low. This finding indicates that only the initial decrease in the housing rent, which is observed over the first several years after the construction of the building, should be controlled. In other words, the decrease in the housing rent does not have a significant effect on the investment in properties in which tenants actually reside. 1

0.95

Index:First Age=1

CBD_Non

0.9 Non-timbered

Timbered 0.85

0.8

1 2 3 4 5 6 7 8 9 1011121314151617181920212223242526272829303132333435 Age of Building(Year)

Figure 5. Property Age and Rent Level.  

h

30

7

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35

Figure 6. Changes in Housing Rent with Time.

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The above finding indicates the average trend for housing rents in the 23 wards of Tokyo. Figure 6 shows the changes in housing rent with time for seven selected properties as samples. As shown in Figure 6, adjustment of the housing rent occurs only at the time of contract renewal; in other words, the housing rent does not change continuously. The important points here are the timing and monetary range of the adjustment of housing rent, for example, the occurrence and the range of risks associated with the adjustment of rent, such as those observed in the commercial property market

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3.3. Frequency of Adjustments and Stickiness of Housing Rent It was found that housing rent, which is determined in the goods and services market, changes very slowly compared with price changes in the asset market. Furthermore, the mechanism of changes in the hedonic rent index, which is determined on the basis of housing rent in new contracts alone, is different from that of the CPI rent index, which is determined on the basis of housing rent for both new and rollover contracts. The period of lease contracts in the Tokyo metropolitan area is generally two years, during which the probability of rent adjustment is low. In addition, adjustments of housing rents are markedly affected by the institutional constraints imposed by laws such as the Land Lease and House Lease Law; the adjustment of housing rent, particularly an increase in housing rent, rarely occurs, even at the renewal of a contract, as long as the same tenant lives in the same property (Yamazaki (2000)). Next, the degree of stickiness of housing rent clarified in the previous study is explained. The weekly change in the housing rent for a certain housing unit i in period t (Rit) with respect to the housing rent for the same property in the previous week (Rit-1) was observed for data obtained from the database prepared by Recruit Co., Ltd. We can obtain the dates at which the former tenant leaves and a new tenant arrives and the range of adjustment of the housing rent from the database. However, it is not possible to observe the adjustments of housing rent at the time of agreement of rollover contracts. According to the analysis of data provided by major property management companies, the adjustment of housing rent can occur at the time of agreement of a rollover contract, although this only happened in approximately 3% of cases. In this sense, in the analysis of data obtained from the database prepared by Recruit Co., Ltd., we should be aware that a constant level of error may be involved in the estimation of Rit/Rit-1, although the error level is thought to be minute. Figure 7 shows the distribution of weekly rent changes calculated under the assumptions described above (n=18,582,863). The probability of a housing unit having no change in rent was 0.992, indicating the high potential stickiness of the housing rent. This was converted to a yearly value of 65% (0.99252). It has been reported that the yearly value for the US is 29%. The stickiness in the housing rent market in Japan is extremely high.

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0.0006

0.0005

0.0004

0.0003

0.0002

0.0001

99,2]

9,1.9]

9,1.8]

9,1.7]

9,1.6]

9,1.5]

9,1.4]

9,1.3]

9,1.2]

1.00

9,1.1]

0.91]

0.81]

0.71]

0.61]

0.51]

0.41]

0.31]

0.21]

0.11]

0

Figure 7. Weekly rent change distribution 0.0012

1989-1991 1986-1988

0.001

1989-1991 1992-1994

0.0008

1995-1997 1998-2000

0.0006

1986-1988

2001-2003

2001-2003 0.0004 1986-1988 0.0002

(1.95,1.96]

(1.84,1.85]

(1.73,1.74]

(1.62,1.63]

(1.51,1.52]

(1.4,1.41]

(1.29,1.3]

(1.18,1.19]

(1.07,1.08]

(0.97,0.98]

(0.86,0.87]

(0.75,0.76]

(0.64,0.65]

(0.53,0.54]

(0.42,0.43]

(0.31,0.32]

(0.2,0.21]

0

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Figure 8. Weekly rent change distribution by Year 1

Regarding the risk associated with the adjustment of rent, the probability of downward rent adjustment is considered. Considering the fact that the adjustment of housing rent is observed in only 35% of properties, this risk is extremely small. In most cases, housing rents remained unchanged. Also, when the housing rent was adjusted downward, a decrease in rent exceeding 10% accounted for only 11.2% of cases. The distribution of weekly changes in rent for different time periods is shown in Figure 8. As shown in the figure, the percentage of adjustment and its distribution have varied markedly over time. In particular, between 1989 and 1991, i.e., a period of rising housing rents, a large peak exists to the right of 1, and the distribution is skewed to the right. For other periods, the distributions are similar, and the frequency of adjustment toward a decrease in housing rent is large. Figure 9 shows weekly price stickiness in terms of Rt/Rt-1 over time. Except during the bubble period, the weekly price stickiness of housing rent has remained almost constant with an average of approximately 0.992 from 1992 to 2006. This finding suggests that the stickiness of the housing rent estimated in Figure 7 remains similar, except during exceptional periods such as the bubble period.

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1 0.998 0.996 0.994 0.992 0.99

Rt / Rt-1

0.988 0.986 0.984 0.982 0.98 0.978 0.976 0.974 0.972

2006

2005

2004

2003

2002

2001

2000

1999

1998

1997

1996

1995

1994

1993

1992

1991

1990

1989

1988

1987

1986

0.97

Year

Figure 9. Weekly rent change distribution by Year2

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Although the above tendency is observed, in the actual management of real estate, factors such as how long tenants continuously reside in a certain property or the reasons for tenants to move in and out are important. The lease contract is generally renewed every two years in Japan. Therefore, it is expected that the adjustment of housing rent is dependent on time (time-dependent pricing).

No. of observations Average Median Mode SD Skewness Kurtosis Min Max

Figure 10. Histogram of completed price spells: duration time

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157,815 112 177 108 155 1.507 2.402 53 1,144

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Chihiro Shimizu  

Figure 11. CDF of Price Duration  

Nelson-Aalen cumulative hazard estimate

.5

.0 0 2

0

.0 0 1 0

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1

.0 0 3

1 .5

.0 0 4

2

.0 0 5

Smoothed hazard estimate

0 0

200 95% CI

400 analysis time

600

800

Smoothed hazard function

200

400 analysis time 95% CI Cumulative hazard

600

800

95% CI y

Figure 12. Estimate Result of Hazard Function

According to the results of previous studies, the weekly probability of tenant turnover (the former tenant of a property expressing the intention to vacate the property so that it becomes available for lease) is almost constant at 0.0025 for residential periods of 100 weeks to approximately 400 weeks. Viewing this from a different perspective, this figure is converted into a value of stickiness; the weekly probability that a tenant continues to reside in the same property is 0.9975. This figure corresponds reasonably closely to the stickiness of a housing unit having no change in rent (0.992) in Figure 7. The stickiness of no rent change can be converted to a monthly value of 0.9900 (0.9924), indicating that the probability of tenant turnover in a given month is approximately 1%.

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These results indicate that the occurrence of tenant turnover, which is when the rent is most likely to be adjusted, is independent of time; adjustments usually occur because of events such as marriage, childbirth, and relocation. This means that the housing market is not strongly affected by the economic environment, in contrast to the market for office buildings; rather, tenant turnover is triggered by the above events, which are independent of the business cycle, and the housing rent is adjusted at these times.

4. IMPLICATIONS FOR HOUSING INVESTMENT MARKET In this study, the characteristics of the housing market are discussed in terms of the stickiness of housing rent; the degree of stickiness and the mechanism of the adjustment of housing rent are clarified by statistical analysis. It is considered that the results of the study have the following implications regarding the housing investment market, and that the close link between housing investments and the CPI has several important implications for the management of investments.

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Conclusion 1. Inflation Hedge Function For long-term funds, such as pensions, the major goal of which is to act as an inflation hedge, the management of assets, the performance of which is above the rate of increase of the CPI, is required. Focusing on the constituents of the CPI basket, housing rent made up 26.3% or approximately one-quarter of the CPI as of 2005. Therefore, it is possible to consider investment in housing rent as being synonymous with investment in the CPI. (The correlation coefficient between the general CPI rent index in the 23 wards of Tokyo and the CPI rent index is 0.998 and that between the former and the CPI rent for leased houses under private management is 0.990.) Assuming that the target of long-term funds is a stable CPI rather than a high upward return, this target can be realized by investing in long-term housing rent. However, the actual investment return is considered as net income, which is calculated by subtracting several costs such as long-term repair expenses from the revenue obtained from housing rent; we should pay attention to changes in these costs. The analysis of several costs associated with investments in property will be discussed in future studies.

Conclusion 2. Relationship with Interest Rate Risk Recently, the link between the CPI and economic and financial policies has been strengthening. The central banks of different countries have increased interest rates whenever the CPI has increased, using the CPI as a policy target; this is known as inflation targeting. In this sense, for long-term funds, investing in the CPI or in housing rent, which is a major constituent of the CPI, is considered to be an important strategy.

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Conclusion 3. Stability of Housing Rent The hedonic rent index, which is determined on the basis of housing rent in new contracts alone, is compared with the CPI rent index for the period of 1986 to 2006, including the bubble period. First, the hedonic rent index increased from 1986 to 1992; assuming that the index in the first quarter of 1986 was 1, in the second quarter of 1992 it reached its maximum value of 1.39, after which it decreased. Next, when compared with the CPI rent index, the hedonic rent index increased by 40% from 1986 to the second quarter of 1992. However, the CPI rent index increased by only 15%. After that, the hedonic rent index decreased but the CPI rent index continued to increase, although the trend in the hedonic rent index has been roughly in agreement with that of the CPI rent index since the fourth quarter of 1994. Namely, the CPI rent index gradually increased even during the bubble period and experienced no significant decrease after the collapse of the bubble economy. It is considered that the CPI rent index, which is determined on the basis of actual payments, is more appropriate as an index because the performance of housing investments is not calculated from the housing rents for new contracts, rather it is calculated as the sum of housing rents of properties in which tenants actually reside. In this sense, the gradual increase in the CPI rent index is considered as a return problem. Viewing this from a different perspective, it can be concluded that a stable return can be realized owing to this gradual increase and that the risk of a macroscopic decrease in the housing rent is extremely limited.

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Conclusion 4. Problem Associated with Depreciation with age of Building Although the risk of a macroscopic decrease in the housing rent is limited, the risk of the housing rent decreasing with the age of the building is considered for each property. We examined this by a hedonic approach, and the results indicated that the yearly rate of depreciation of the housing rent of nontimbered houses is as large as approximately 6% over the first five years after construction, but decreases to 2.6% over the 5th to 10th years and 2.5% over the 10th to 20th years, indicating that the long-term rate of decrease in housing rate is small. This finding indicates that only the initial decrease in the housing rent, which is observed in the first several years after the construction of the building, should be controlled as a risk factor. In other words, the decrease in the housing rent owing to the increasing age of the building has a negligible effect on the investment in properties in which tenants actually reside. However, it is known that the lifetime of residential houses is shorter than that of buildings used for offices. Therefore, it is expected that the long-term costs of repair, maintenance, and operation, which are generated during the period of investment, may significantly vary. The risk associated with this factor should be discussed in future studies.

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Conclusion 5. Problem Associated with Adjustment of Rent In the commercial facility market, the significant downward adjustment of the rent of key tenants has a significant impact on the total revenue from the investment; therefore, the problem associated with the adjustment of rent has attracted attention. However, in the case of housing, this problem is less likely to occur because the percentage of the revenue from each property is small. The weekly probability of a housing unit having no change in rent was 0.992. This was converted to a yearly value of 0.6585 (0.99252). This figure indicates that the yearly adjustment of housing rent is observed in only 35% of properties; conversely, revenue from the housing rent of 65% of leasehold properties does not change each year. A previous study reported that the corresponding figure for the US is 29%, demonstrating that the stickiness in the housing rent market in Japan is extremely high. Regarding the risk associated with the adjustment of rent, the probability of downward rent adjustment should be considered. Considering the fact that the adjustment of housing rent is observed in only 35% of properties each year, this risk is extremely small. In most cases, housing rents remained unchanged. It was also found that when a downward adjustment of housing rent occurred, the percentage of cases in which the decrease in rent exceeded 10% of the original rent was only 33.2% (p.6の日本語には11.2%とありました。)

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Conclusion 6. Independence of Economic Environment Whether the adjustment of housing rent is correlated with or independent of the economic environment is very important in composing a portfolio in combination with other assets such as stocks. The probability of tenant turnover with respect to the residence period was calculated by formulating a hazard function for the residence period. When the probability of tenant turnover was converted to a monthly value for stickiness, it was 0.9900 (0.9924), indicating that the monthly probability of tenant turnover is approximately 1%. This value is converted to a yearly value of slightly below 12%. The above results indicate that the occurrence of tenant turnover, which is when the rent is most likely to be adjusted, is independent of time; adjustments of housing rent occur because of events such as marriage, childbirth, and relocation. In other words, the housing market is not strongly affected by the economic environment, in contrast to the market for office buildings. The characteristics of the housing investment market were statistically clarified through the series of analyses explained above. As a result of the recent turmoil in the international financial market, it is necessary to reconstruct the real-estate investment market from a comprehensive perspective as a target of investment and management in the future. In concrete terms, we should put importance on converting real estate into financial investment products by regarding it as a core asset among a diverse portfolio by fully extracting the investment characteristics of real estate, focusing on the housing market. For example, in deciding the constituents of a portfolio and the share of each constituent, various factors must be considered: whether to hedge the risk by not choosing investment

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products that are linked with the financial market should be pursued; whether products that hedge the risk of inflation should be pursued; whether higher return than other operating assets is pursued; or whether high stability is pursued. There are no investment products that satisfy all of the above factors, and investors should clarify their goals when they invest in real estate. Once the characteristics of a source that induces a flow of revenue are fully understood, the design and development of financial products that extract the advantages of the source using financial techniques are required. We should note that the characteristics that induce a flow of revenue depend on the type of real estate. We hope that a real-estate financial market in which the attractive properties of the investment market are further enhanced will evolve via the reconstruction of the conventional financial system.

REFERENCES

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[1]

Abe, Naohito & Akiyuki Tonogi (2008). “Micro and Macro Price Dynamics over Twenty Years in Japan--A Large Scale Study Using Daily Scanner Data”, Research Center for Price Dynamics Working Paper, No. 18, January. [2] Baldman, Andrew & Nakamura, Alice (2006). “An Empirical Analysis of the Fifferent Concepts for Owned Accomodation in the Canadian CPI: The Case of Otawa,19962005”, OECD-IMF Workshop Real Estate Price Indexes (Paris, 6-7 November 2006) Paper19. [3] Benigno, Pierpaolo. (2004). “Optimal Monetary Policy in a Currency Area”, Journal of International Economics, 63, 293-320. [4] Caballero, Ricardo, J. & Eduardo Engel, (1993). “Microeconomic Rigidities and Aggregate Price Dynamics”, European Economic Review, Vol.37, 697-717. [5] Calvo, Guillermo (1983). “Staggered Prices in a Utility-Maximizing Framework”, Journal of Monetary Economics, Vol.12, 383-398. [6] Crone, Theodore, M., Nakamura, Leonard & Voith, Richard (2004). “Hedonic Estimates of the cpst of housing services: Rental and owner-occupied units”, Price Federal Reserve of Bank of Philadelphia Working, Papers, No. 04-22. [7] Crone, Theodore, M., Nakamura, Leonard & Voith, Richard (2006). “The CPI for Rents: A Case of Understated Inflation”, Price Federal Reserve of Bank of Philadelphia Working, Papers, No. 06-7. [8] Diewert, Erwin, (2007). “The Paris OECD-IMF Workshop on Real Estate Price Indexes: Conclusions and Future Directions”, University of British Columbia Discussion Paper 07-1. [9] Diewert, W., Erwin & Nakamura, Alice, O. (2008). “Accounting for Housing in a CPI”, Price and Productivity Measurement, Volume 1, Housing, Chapter 2, 13-48. [10] Gali. Jordi & Gertler, Mark (1999). “Inflation Dynamics: A Structural Econometric Analysis”, Journal of Monetary Economics, Vol.44, 195-222. [11] Gali, Jordi, Gertler, Mark & Lopez-Salido, David J. (2001). “European Inflation Dynamics”, European Economic Review, Vol.45, 1237-1270. [12] Genesove, David 2003). “The Norminal Regility of Apartment Rents”, The Review of

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[13] [14] [15] [16] [17] [18] [19]

[20]

[21]

[22]

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[23]

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Economics and Statisticss, Vol.85(4), 844-853. Goodhart, Charles, (2001). “What Weight Shuold be Given to Asset Prices in Measurement of Inflation?” The Economic Journal, Vol.111, (No.472), 335-356. Goodman, A. C. & Thibodeau, T. G. (2003). “Housing market segmentation and hedonic prediction accuracy”, Journal of Housing Economics, Vol.12, 181-201. Gordon, Robert, J. & Todd van Goethem, (2005). “A Century of Housing Shelter Prices: Is there a downward bias in the CPI”, NBER Working Paper, No. 11776. Grenadier, Steven, R. (1995). “Valuing lease contracts: A real-options approach”, Journal of Financial Economics, Vol.38, 297-331. Ito, Takatoshi & Keiko Nosse Hirono, (1993). “Efficiency of the Tokyo Housing Market”, NBER Working Paper, No. 4832. Saito, Yukiko & Tsutomu Watanabe, (2008). “Menu Costs and Price Change Distributions: Evidence from Japanese Scanner Data”.(Mimeo) Shimizu, C. & Nishimura, K. G. (2006). “Biases in Appraisal Land Price Information: The Case of Japan”, Journal of Property Investment and Finance, Vol.26, No.2, 150175. Shimizu, C. & Nishimura, K. G. (2007). “Pricing structure in Tokyo metropolitan land markets and its structural changes: pre-bubble, bubble, and post-bubble periods”, Journal of Real EstateFinance and Economics, Vol.35 (4), 475-496. Shimizu, C., Nishimura, K. G. & Asami, Y. (2004). “Search and Vacancy Costs in the Tokyo Housing Market: An Attempt to Measure Social Costs of Imperfect Information”, Review of Urban &Regional Development Studies, Vol.16, No.3, 210230. Yamazaki, F. (2000). “Economic Analysis of Land and Housing Market” (in Japanese, “Tochi to Jutakusijou no keizaibunseki”), University of Tokyo Press. House Price Index Research Group, (2008). “Research on Improvement of Housing Market in Japan –the Possibility of Introducing Nonrecourse Loans and the Structure of House Prices-,” Housing Research and Advancement Foundation of Japan.

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In: Real Estate Investment Market Editors: Sofia M. Lombardi, pp. 125-137

ISBN: 978-1-61668-395-5 © 2010 Nova Science Publishers, Inc.

Chapter 5

FANNIE MAE AND FREDDIE MAC: CHANGES TO THE REGULATION OF THEIR MORTGAGE PORTFOLIOS N. Eric Weiss

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SUMMARY This chapter analyzes the costs and benefits of the Fannie Mae’s and Freddie Mac’s retained portfolios while they remain under conservatorship. Increasing numbers of homeowners are threatened with foreclosure because of interest rate resets on subprime mortgages, combined with stagnant or falling home prices. Congress responded to this situation by passing the Housing and Economic Recovery Act of 2008 (H.R. 3221, P.L. 110-289), which uses the congressionally chartered, stockholder-owned government-sponsored enterprises (GSEs), Fannie Mae and Freddie Mac, to lead the market in providing more affordable mortgages. The GSEs have retained mortgage portfolios with a combined value of more than $1.4 trillion. The size of the portfolios, past management problems, risks to the financial system, and potential cost to the taxpayer led, in part, to provisions of the Housing and Economic Recovery Act that changed the rules governing the activities and regulation of Fannie Mae and Freddie Mac. The bill created the Federal Housing Finance Agency (FHFA) and authorized it to regulate the size of the GSEs’ retained mortgage portfolios; it also raised the conforming loan limit in certain high-cost areas, thereby allowing the GSEs to purchase larger mortgages in these areas. Previous regulatory actions have affected the GSEs’ portfolios. In 2006, following discovery of accounting and management problems, the GSEs agreed to restrictions on their retained portfolios. In 2007, the Office of Federal Housing Enterprise Oversight (OFHEO), now the Federal Housing Finance Agency (FHFA), denied requests from both Fannie and Freddie to raise or eliminate the caps, but these restrictions were relaxed shortly afterwards. On September 6, 2008, the GSEs were placed in conservatorship (government management).

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One condition of the conservatorship set the portfolio limit to $850 billion as of December 2009, with a 10% yearly decline until the portfolios reach $250 billion. The GSEs’ portfolios include mortgages and mortgage-backed securities (MBS) that are subject to financial risks. When these risks are not managed properly, or if market movements turn dramatically against the GSEs, the government faces two unsatisfactoryalternatives: eitherlet the GSEs go into default and work to control the financial repercussions, or step in and assume payments on the GSEs’ debt at a significant cost to taxpayers. The GSEs and their supporters argue that the profits generated by the investment portfolios enhanced the GSEs’ ability to support affordable housing programs and reduce mortgage interest rates.

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BACKGROUND Increasing numbers of homeowners are threatened with foreclosure because of interest rate resets on mortgages in the subprime and Alt-A mortgage markets, and falling home prices in formerly rapidly appreciating markets. The Economic Stimulus Act of 2008 (P.L. 110-185) temporarily increased the conforming loan limit, which established the maximum size of a mortgage that Fannie Mae and Freddie Mac — two congressionally chartered, stockholder-owned businesses — can purchase.1 The GSEs, which are prohibited by law from directly making mortgage loans to homeowners, purchase mortgages from the original lenders, who can then make more loans. Fannie Mae and Freddie Mac add their guarantee of timely payment of the mortgages and bundle them into mortgage-backed securities (MBS), which they either keep in their portfolios or sell to investors. The Housing and Economic Recovery Act of 2008 (P.L. 110-289) created a new regulator (the Federal Housing Finance Agency or FHFA), and gave it broad authority to regulate the GSEs’ assets including their retained mortgage portfolios. The legislation could help homeowners by making affordable refinancing more available and by increasing the conforming loan limit. On September 7, 2007, regulators placed Fannie Mae and Freddie Mac under conservatorship, which gives FHFA control over their operations. FHFA increased the limit for GSEs’ portfolios to $850 billion each until December 31, 2009, and then requires the GSE to reduce their portfolios by at least 10% annually until they reach $250 billion each. Absent conservatorship, the Housing and Economic Recovery Act could encourage the GSEs to purchase mortgages that refinance homeowners out of subprime and other troubled mortgages by adding new funds to support mortgages for distressed homeowners. The high cost exception to the conforming loan limit could allow certain homeowners in these high 1

The nationwide conforming loan limit, the maximum size mortgage that Fannie Mae and Freddie Mac can purchase, was modified by the Economic Stimulus Act of 2008, P.L. 110-185, from $417,000 to add a $729,720 limit in high cost areas; this increase expires December 31, 2008. The Housing and Economic Recovery Act of 2008, P.L. 110-289, makes the high cost exception permanent, but revises downward the maximum mortgage size to $625,500. These limits are revised annually based on house prices. Reform of the regulator of Fannie Mae, Freddie Mac, and the Federal Home Loan Banks is contained in Title I of the Housingand Economic Recovery Act of 2008, signed by the president July 30, 2008. Unless stated otherwise, all bills in this chapter were introduced in the 110th Congress. Fannie Mae and Freddie Mac are known as government-sponsored enterprises (GSEs). This chapter will refer to them as GSEs. There is a third housing GSE, the Federal Home Loan Banks (FHLBanks) that have not created large portfolios and are owned by members, not the public. This chapter does not discuss the FHLBs; for additional information on them, see CRS Report RL32815, Federal Home Loan Bank System: Policy Issues, by Edward V. Murphy.

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cost areas to benefit from the lower interest rate that conforming mortgages have compared to jumbo mortgages.2 The FHFA, with financial support from Treasury, established a conservatorship and, as part of the conservatorship agreement, temporarily raised portfolio limits to $850 billion. Portfolio limits are then gradually reduced by at least 10% annually until each portfolio is less than $250 billion. The temporary increase could allow the GSEs to provide more liquidity to mortgage markets during the current financial turmoil, but the gradual reduction could address concerns about systemic risk.3 Treasury’s financial support allows the GSEs to buy more mortgages than they would otherwise be able to in turbulent financial markets. If the GSEs respond by acquiring more mortgages, then the GSEs would assume the risk of default by the homeowner. At the time that the GSE conservatorship was announced, Treasury announced that it had signed contracts to provide financial support for Fannie Mae and Freddie Mac. Treasury agreed to •

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• •

make short-term, collateralized loans to the GSEs with interest rates set at the London Inter Bank Offer Rate (LIBOR) plus 50 basis points (0.5%), purchase new GSE MBS on the open market, and purchase senior preferred stock from the GSEs if their liabilities exceed their assets.

In return, Treasury received from each GSE $1 billion in new senior preferred stock and warrants to purchase 80% of the common stock at a nominal price. Prior to conservatorship, accounting and management problems at the GSEs led FHFA’s predecessor, the Office of Federal Housing Enterprise Oversight (OFHEO), to restrict the GSEs’ activities by limiting the size of their mortgage portfolios. These problems at both of the GSEs came to light after they agreed to register one class of stock with the Securities and Exchange Commission (SEC). By law, the GSEs were exempt from filing financial statements with the SEC. Nevertheless, both agreed to register one class of common stock.4 This irrevocable decision made them subject to requirements to file reports with the SEC on their finances and on changes in insider stock holdings. While preparing to register its stock, Freddie Mac announced in January 2003 that it had understated its earnings, and it began to revise its financial statements and to install management controls to ensure accurate financial reporting in the future.5 In a restatement issued November 2003, Freddie Mac increased its net income for 2002 and earlier years by a total of $5.0 billion. Freddie Mac paid $125 million in civil fines, and $50 million to settle SEC charges that it fraudulently misstated earnings. In addition, Freddie Mac has paid more 2

Jumbo mortgages traditionally have been defined as mortgages that are larger than the conforming loan limit. With the combination of a national conforming loan limit (currently $417,000) and a high-cost area exception ($729,750 until December 31, 2008), different people who use the term“jumbo” either refer to loans above $417,000 or above $729,750. In any case, mortgages not eligible for GSE purchase are typically more expensive than those that the GSEs can purchase. 3 Systemic risk is the risk that problems in one area (or one company) could spread throughout the system in potentially catastrophic ways. 4 12 U.S.C. 1717(c)(1) exempted Fannie Mae from registering with the SEC, and 12 U.S.C. 155(g) exempted Freddie Mac. Section 1112 of P.L. 110-289 ended that exemption. 5 CRS Report RS21567, Accounting and Management Problems at Freddie Mac, by Mark Jickling contains more details.

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than $410 million to settle investor lawsuits. Unable to file required financial statements with the SEC until its accounting problems were resolved, Freddie Mac filed its first timelyquarterly report (10-Q) with the SEC on July 18, 2008. Fannie Mae registered its common stock with the SEC on March 31, 2003, and thus became subject to SEC reporting requirements. In September 2004, OFHEO charged that Fannie Mae had failed to follow Generally Accepted Accounting Principles (GAAP).6 Fannie Mae responded that its disagreement with OFHEO involved differences in interpretation of very technical rules, rather than improprieties. After investigating, the SEC announced that Fannie Mae’s financial reports and managementwere inadequateand directed the GSE to restate itsearnings for the previous five years. Fannie Mae was unable to file required financial statements with the SEC until its accounting problems were resolved. In December 2006, Fannie Mae released restated financials for 2001-2005 that reduced its earnings by $6.3 billion, and Fannie Mae subsequently paid $400 million in civil penalties. Fannie Mae resumed timely SEC filings on November 9, 2007. Because of concerns over the GSE’s management and controls, OFHEO proposed in 2006 that Fannie Mae should not increase its retained mortgage-related portfolio to more than the amount held on December 31, 2005 ($727 billion). Fannie Mae agreed. Separately, Freddie Mac agreed in a letter to OFHEO to limit its annual portfolio growth to 2%, or approximately $28 billion. Without these agreements, the GSEs would have been able to increase their retained portfolios as desired. On August 11, 2007, OFHEO denied requests from both GSEs to relax the limitations on their portfolios. OFHEO stated that sufficient progress had not been made to resume timely financial reporting (including annual 10-K and quarterly 10-Q filings with the SEC) and that management controls were not adequate for more growth. Approximately one month later (on September 19, 2007), OFHEO announced that it was making several changes that would have the effect of allowing the GSEs to increase their retained mortgage holdings to $735 billion each and to grow beyond this.7 First, it gave each GSE the same portfolio cap as of July 1, 2007.8 Second, it agreed that Fannie Mae could increase its portfolio at the same rate as Freddie Mac — not more than 2% per year and not more than 0.5% per quarter. This would allow each GSE to increase its portfolio by$14.7 billion annually, or $3.7 billion quarterly. Third, for the fourth quarter of 2007 (OctoberDecember 2007), each GSE’s portfolio could grow by up to 1%, but the 2% annual cap would still apply. This would allow each GSE to increase its portfolio size by $7.4 billion in the last quarter of 2007. Fourth, OFHEO imposed additional reporting requirements on both GSEs. The GSEs have lost money every quarter starting in the third quarter of 2007. FHFA placed the GSEs in conservatorship on September 7, 2008. In reaching its decision, the FHFA cited continuing troubles in the mortgage credit environment in general, and the inability of the GSEs to raise significant capital in particular.9 As part of the conservatorship, the GSEs 6

CRS Report RS21949, Accounting Problems at Fannie Mae, by Mark Jickling. Office of Federal Housing Enterprise Oversight, “OFHEO Provides Flexibility on Fannie Mae, Freddie Mac Mortgage Portfolios” September 19, 2007, available at [http://www.ofheo.gov/ newsroom.aspx?ID=388&q1=0&q2=0]. 8 Historically, Fannie Mae’s retained mortgageportfolio has been larger than Freddie Mac’s. The difference has narrowed since the agreements on portfolio size with OFHEO. 9 U.S. FHFA, “Statement of James Lockart,” press release, September 7, 2008, p. 3-5, available at [http://www.treas.gov/press/releases/reports/fhfa_statement_ 090708hp1128.pdf]. The press release discusses 7

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agreed to new rules for their portfolios. Initially, the GSEs would be allowed to expand their retained portfolios without additional capital requirements to $850 billion each until December 31, 2009. After that, the conservatorship agreements call for portfolios to decline 10% per year until they reach $250 billion each.10 The GSEs can create and sell an unlimited amount of MBS without additional capital.

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GSE RISKS Although lenders had been informed that the GSEs’ bonds were not backed by the U.S. government, many thought that there was an implied guarantee that the federal government would back the GSEs, if necessary. There was some basis for this belief, because tax laws were revised in 1982 to help Fannie Mae avoid becoming insolvent.11 The conservatorships with their continued bond payments and Treasury financial support add to this justification, as does testimony by FHFA Director James B. Lockhart before the Senate Committee on Banking, Housing, and Urban Affairs on October 23, 2008.12 This section discusses potential financial risks that the reorganized GSEs are likely to confront during and after the conservatorship. The conservatorship and the agreements with Treasury have placed an all but explicit guarantee behind the GSEs’ bonds, although stockholders were not protected. Under the agreements signed with Treasury, the GSEs’ risks are effectively transferred to the federal government. Treasury has agreed to purchase $100 billion of new preferred stock on an as needed basis from each GSE.13 In other words, if a GSE were to become insolvent, the government would invest up to $100 billion in the GSE. The government will receive warrants to purchase common stock for a nominal cost if it purchases the preferred stock. Treasury can increase one or both ceilings with a new agreement with conservator(s). If the GSEs are unable to sell new MBS, the Treasury has agreed to purchase them using the Federal Reserve Bank of New York as its fiscal agent. The only limit on the amount of MBS purchased is the debt ceiling. Treasury announced that it has begun to purchase MBS, but it has not announced the volume of these purchases.14 Treasury has attempted to minimize the risk by requiring collateral for loans and obtaining first claim on any funds available for dividends.

financial markets troubles from February 2008 onward, especially a market indicator of lack of confidence in the GSEs, the spread between GSE debt yields and yields on U.S. Treasuries. 10 U.S. Treasury, Fact Sheet: Treasury Senior Preferred Stock Purchase Agreement, press release, September 7, 2008, p. 2, available at [http://www.treas.gov/press/releases/reports/ pspa_factsheet_090708%20hp1128.pdf]. 11 P.L. 97-372, 96 Stat.1726 et seq., “The Miscellaneous Revenue Act of 1982.” See Section 102, titled “Adjustment to Net Operating Loss Carryback and Carryforward Rules for Federal National Mortgage Association.” 12 Testimony of FHFA Director James B. Lockhart before U.S. Senate Committee on Banking, Housing and Urban Affairs, “Turmoil in the U.S. Credit Markets: Examining Recent Regulatory Responses,” 110th Cong., 2nd sess., October 23, 2008, available at [http://banking.senate.gov/public/_files/LOCKHARTTestimony1023.pdf]. A clarification is available at [http://www.ofheo.gov/newsroom.aspx?ID=478&q1=1&q2=None]. 13 U.S. Treasury, Fact Sheet: Government Sponsored Enterprise Credit Facility, press release, September 7, 2008, available at [http://www.treas.gov/press/releases/reports/ gsecf_factsheet_090708.pdf]. 14 “US Treasury began buying Fannie, Freddie MBS in September,” Reuters, available at [http://www.reuters.com/article/rbssFinancialServicesAndRealEstateNews/idUSN03340 78720081003].

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To conserve GSE funds, the conservators have suspended dividends on common and preferred stock. After this announcement, the price of the GSEs’ common and preferred stocks declined. If conservatorship ends or dividend payments resume, the prices of the various types of stock are likely to increase. Conservatorship may affect the GSEs’ portfolios because it gives them access to a new source of funds, the Government-Sponsored Enterprise Credit Facility (GSECF), and allows Treasury to purchase new GSE mortgage-backed securities. This assures Fannie Mae and Freddie Mac access to relatively inexpensive funds to finance their portfolios and a ready market for MBS if they decide to sell them. Following standard financial risk analysis, GSE risks are broken down into credit risk, prepayment risk, interest rate risk, and operational risk. These risks are discussed as they apply to the GSE. How various legislative options would affect these risks is discussed in the analysis section, which follows.

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Credit Risk. Credit risk is the risk that the borrowers (mortgagors) will not repay their loans on time. When Fannie and Freddie buy mortgages and combine them into MBS, they guarantee that the loans will be repaid on time. In 2005, according to media reports, Standard & Poor’s and most other major observers concluded that because of the different maturity dates, loan-to-value ratios, private mortgage insurance, and geographic diversification, credit risk was not a serious problem.15 In hindsight, default rates on loans increased in many places in the country at the same time, for many classes of mortgages, so geographic diversification proved to be less of a protection for the GSEs than many assumed it would be.16 Prepayment Risk. Prepayment risk is the risk to an investor that a mortgage will be paid before its full term is concluded, leaving the investor to find another investment — perhaps when interest rates have decreased. Prior to the current housing cycle, prepayment risk was considered more likely to be serious than credit risk. Homeowners prepay for two major reasons: moving and to obtain more favorable terms. Many subprime borrowers took out their mortgages anticipating prepaying. Prepayment risk falls on the ultimate holder of a mortgage or MBS. Since 1986, the GSEs have offered multiclass MBS, which divide prepayment risk among the different classes. They are customized for investors to match their tolerance and preference for prepayment risk versus anticipated yield. When GSEs keep the MBS, they also keep this risk. Interest Rate Risk. Interest rate risk comes from financing the MBS portfolios by borrowing money (issuing bonds), and is related to prepayment risk. The GSEs face much higher interest rate risk for mortgages held in portfolio than for mortgages that they issue as MBS. To finance the long-term loans held in their portfolios, the GSEs use short-term bonds and financial derivatives. When interest rates increase, the GSEs must roll over their bonds with higher-rate ones. When interest rates decrease, homeowners prepay their mortgages, and 15

James R. Haggerty, “Mortgage-Securities Drop Will Depend on Economy,” Wall Street Journal, September 17, 2005, p. B7. For a typical Standard and Poor’s analysis see Victoria Wagner, “Freddie Mac,” Standard & Poor’s Raging Direct, November 30, 2005. Available at [http://www.freddiemac.com/investors/pdffiles/s-andp2005.pdf]. 16 See U.S. FHFA, “Statement of James Lockart,” press release, September 7, 2008, p. 4, citingthe “alarming levels” of mortgagedelinquency rates as a contributing factor to placing the GSEs in conservatorship.

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the GSEs buy new ones at lower rates. Between July 2007 and July 2008, Fannie Mae’s gross mortgage portfolio rose from $730 billion to $758 billion. Fannie Mae’s mortgage guarantee business through MBS was much larger, rising from $2.2 trillion to $2.6 trillion during the same period.17 Interest rate risk can be very serious. Many savings and loan associations became insolvent in the early 1980s because of it. During that time, Fannie Mae’s portfolio was poorly hedged. While he was Treasury Secretary, John W. Snow testified that “Fannie Mae became insolvent on a mark-to-market basis. Only a combination of legislative tax relief, regulatory forbearance, and a decline in interest rates allowed Fannie Mae to grow out of its problem.”18 Despite state-of-the-art hedging with financial derivatives, some believe that the GSEs’ portfolios continue to have significant interest rate risk. If the GSEs have to make large adjustments to their portfolios, only very large financial institutions will beableto handlethe other side of the financial transactions. If these financial institutions are unwilling or unable to take the other side of the financial transaction, the GSEs could be unable to refinance or adjust their retained mortgage portfolios.19 Operational Risk. Operational risk is the risk of loss due to inadequate or failed internal procedures and systems. Fannie Mae’s and Freddie Mac’s accounting and management problems have raised questions about internal controls. Accounting systems provide the basis for portfolio adjustment decisions. If the accounting system is providing inaccurate information, the resulting portfolio adjustment decisions are likely to be incorrect.

THE ROLE OF PORTFOLIOS UNDER CONSERVATORSHIP

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FHFA’s conservatorship announcement cited five reasons for the action: • • • • •

Safety and soundness issues including capitalization, Current market conditions, Financial performance and condition of each company, Funding difficulties, and The critical importance each company has in supporting the residential mortgage market in this country.20

17

Fannie Mae, Monthly Summary Highlights: July 2008, July 2008, available at [http://www.fanniemae.com/ir/pdf/monthly/2008/073108.pdf]. 18 U.S. Department of Treasury, Testimony of Secretary John W. Snow Before the U.S. Senate Committee on Banking, Housing and Urban Affairs, “Proposals for Housing GSE Reform, ” press release, April 7, 2005, p. 4, available at [http://www.treas.gov/press/releases/ js2362.htm]. 19 In a letter from Alan Greenspan, then-Chairman of the Federal Reserve, to the Honorable Robert F. Bennett, U.S. Senate, September 2, 2005, p. 1, available at [http://online.wsj.com/public/resources/d ocuments/Greenspan091505.pdf]. Greenspan wrote: “Moreover, the success of interest-rate-risk management, especially the exceptionally rapid timing necessitated by dynamic risk adjustments, requires that the ultimate counterparties to the GSEs’ transactions provide sufficient liquidity to finance an interest-rate-risk transfer that counters the risk. Otherwise, large and destabilizing adjustments will result in sharp changes in the interest rates required to rebalance and hedge a portfolio.” 20 U.S. FHFA, “Statement of FHFA Director James B. Lockhart,” press release, September 7, 2008, p. 5, available at [http://www.treas.gov/press/releases/reports/fhfa_statement_ 090708hp1128.pdf].

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The issues of current capitalization, financial performance and condition, and the inability of each GSE to fund itself directly arguably relate to problems created by financing long-term mortgages with short-term borrowing. Arguably with smaller portfolios, their need to raise capital would have been less and their capitalization Fannie Mae reports that as of the end of August 2008, approximately one week before being placed under conservatorship, it had a portfolio of $760 billion, and Freddie Mac reports that its portfolio at the end of August 2008 was $761 billion.21 Fannie Mae’s portfolio grew at a relatively slow 4.4% annualized rate in the month of August, but Freddie Mac’s portfolio decreased at an annualized 56.2% rate. Both GSEs appear to have been slowing their portfolio growth rates since February 2008, but this has not been a smooth month-to-month decline. Delinquency rates on mortgages steadily increased between July 2007 and August 2008. Some might conclude from this that, in response to financial market conditions, the GSEs were both trying to limit or reduce their portfolio sizes. One advantage of reducing portfolio size is that it both raises capital and reduces the need for capital as a cushion against delinquency and losses. The government’s financial support and the elimination of capital requirements allow each of the GSEs to increase its mortgage portfolio by approximately $90 billion very inexpensively. It can also sell new MBS without any reserve against losses. This could increase profitability.

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GSE MORTGAGE PORTFOLIOS This section analyzes the benefits and costs of proposals to alter the limits on the GSEs’ portfolios. As discussed above, the conservatorshipagreements with GSE have temporarily increased GSE portfolio limits to $850 billion each, with this amount declining gradually to $250 billion each. Furthermore, the terms of the conservatorship do not require the GSE to hold capital against increases in their portfolios or new MBS sold. Absent the conservatorship, the recently enacted GSE reform bill delegated authority to the FHFA to regulate the GSEs’ portfolios. This section discusses the issues involved in either increasing or decreasing those limits.

Linking Limits to Subprime Refinances During the legislative debate on GSE regulation, some proposals to increase the GSEs’ mortgage portfolios contained a requirement that a large percentage (which varied depending on the proposal) would be devoted to providing subprime borrowers with a way to refinance out of their high interest rate mortgages into more affordable ones.22 The homeowners would benefit because they would keep their homes and refinance into a mortgage with lower 21

Fannie Mae, Monthly Summary, August 2008, available at [http://www.fanniemae.com/ ir/pdf/monthly/ 2008/083108.pdf]. Freddie Mac, Monthly Volume Summary, August 2008, available at [http://www.freddiemac.com/investors/volsum/pdf/0808mvs.pdf]. 22 CRS reports on subprime mortgages include CRS Report RL33930, Subprime Mortgages: Primer on Current Lending and Foreclosure Issues, by Edward Vincent Murphy; and CRSReport RL33775, Alternative Mortgages: Causes and Policy Implications of TroubledMortgage Resets in the Subprime and Alt-A Markets, by Edward Vincent Murphy.

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monthly payments. Some investors holding the subprime mortgages could benefit as they get out of subprime mortgages that have a higher probability of defaulting and causing losses. Other investors, such as those expecting interestpaymentsinlater years, would suffer losses because of the prepayments. The GSEs could benefit because the new mortgages might be profitable, and the increase in their mortgage portfolios could provide additional profit. A subprime mortgage can have a fixed rate or an adjustable rate. A fixed rate subprime can have an introductory reduced payment before becoming fully amortizing at the agreed upon fixed rate. An adjustable-rate subprime mortgage also can have an introductory “teaser” period (typically two or three years), before becoming fully amortizing and adjusting based on some interest rate on a stated schedule. A news story highlighted the case of a subprime borrower whose mortgage interest rate will increase in 2008 from 8.2% to 14%; the monthly payment will increase from $3,700 to $8,000.23 The idea is that many subprime homeowners who cannot afford the subprime mortgage after the reset could afford the monthly payments of a traditional 30-year mortgage. For calendar year 2007, even before changes to OFHEO’s policy, the GSEs could purchase and retain in portfolio approximately $320 billion in mortgages to replace those being paid off by borrowers. Fannie Mae could purchase and retain in portfolio $124 billion in mortgages and MBS.24 Likewise, for calendar year 2007, Freddie Mac could purchase and retain in portfolio $196 billion of mortgages and MBS; $168 billion would replace those being paid off by borrowers, and $28 billion would be allowed by the 2% growth.25 In addition, Fannie Mae and Freddie Mac can purchase without limit mortgages that they assemble in mortgage-backed securities (MBS), add their guarantees of timely payment of principal and interest, and sell to investors.

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Loans Related to a Public Policy Goal The portfolio limits could be tied to purchases of loans that are related to the GSEs’ public mission. Examples of other policy goals might include mortgages for higher-risk, lowincome borrowers, jumbo mortgages, energy efficient mortgages, elderly reverse mortgages, or mortgages targeted to other populations. Often, these policy goals involve a mortgage instrument without a long track record or with which the GSEs, or the investors who buy the GSEs’ MBS, have little experience. The GSEs historically have kept some types of nontraditional loans in their portfolios because they apparently are hard to package and to sell in MBS at a price that the GSEs find attractive. The GSEs, with their experience, have found them more profitable to retain than to sell. Allowing the GSEs to retain loans related to

23

Rick Brooks and Constance Mitchell Ford, “The United States of Subprime,” Wall Street Journal, October 11, 2007, p. A1, A16. 24 In the first half of 2007, Fannie Mae’s retained mortgage portfolio experienced nearly $62 billion in liquidations. Fannie Mae’s annualized liquidation rate was 17%. See Fannie Mae, Monthly Summary, July 28, 2007, available at [http://www.fanniemae.com/ir/pdf/monthly/ 2007/063007.pdf]. 25 Freddie Mac experienced almost $84 million inliquidations and its annualized liquidation rate was 24% in the first half of 2007. See Freddie Mac, Monthly Volume Summary: June 2007, available at [http://ww w.freddiemac.com/investors/volsum/pdf/0607mvs.pdf].

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another policy goal in their portfolios would then result in lower interest rates to borrowers who meet the policy’s criteria.26 Also, the GSEs might be willing to purchase nontraditional mortgages related to another policy goal if there were other provisions that would make the overall change profitable after adjusting for risk and increased goodwill. For example, a statistical analysis of combined enterprise profitability reveals that between 1983 and 2001, each $1 million of MBS outstanding added $2,200 to net income (profit), but each $1 million in retained mortgages or MBS added $5,300 to net income.27 In other words, a dollar in their retained portfolios generated more than twice as much profit as a dollar of MBS sold to other investors. Arguably, this increased profit from retaining a mortgage in portfolio might be sufficient to induce the GSEs to buy nontraditional mortgages, but only if the nontraditional mortgages could be retained in portfolio. Allowing the GSEs to retain these mortgages would benefit nontraditional borrowers. The GSEs would either expand existing lending programs, such as nontraditional mortgages targeted to fulfil their housing goals, or create new programs. The interest rates on these loans would be higher than on prime mortgages — the higher rate would compensate for the higher risk of default — but the rates would be less than on mortgages financed outside the GSEs’ structure. Even so, not every nontraditional borrower would qualifyunder the GSEs’ underwriting standards. In light of FHFA’s statements detailing the reasons for placing Fannie Mae and Freddie Mac under conservatorship, the future of the GSEs’ policy oriented mortgage purchases — housing goals, and contributions to the housing trust fund and capital magnet fund — is unclear. With the need to conserve capital to survive, one could argue that these programs should be suspended. One could also argue, however, that with the federal government’s backing the need for capital is reduced and that the amount of capital that would be expended for these programs is relatively insignificant.

Risk Elements Prior to conservatorship, the costs of increasing the GSE portfolio caps were mainly the costs of increased risk to the financial system.28 It is difficult to compare potential costs against concrete benefits of increasing portfolio caps. The GSEs manage many risks common to many businesses in the financial sector. These risks can affect the companies, stockholders, employees, bondholders, and business partners, and because of their size, the GSEs’ risks can also affect the nation’s financial system and the economy. These risks can be analyzed using the four categories discussed previously.

26

In the secondary market, investors bid on mortgages taking the contracted interest rates as given. If investors want a higher yield, they offer a lower price for mortgages. Investors might demand a higher yield because the interest rates on alternative investments have increased, or because risk has increased. 27 This relationship breaks down after 2001. The reason appears to be in part due to the restatement of earnings by the GSEs, and in part to net interest income almost doubling between 2001 and 2002. Data source: Office of Federal Housing Enterprise Oversight, Mortgage Markets and The Enterprises in 2006. 28 CRS Report RS22307, Limiting Fannie Mae’s and Freddie Mac’s Portfolio Size, by N. Eric Weiss covers the risks from the GSEs’ portfolios in more detail.

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Under conservatorship, any losses in excess of the GSEs’ capital will be adirect cost to the Treasury. While Treasury states that it anticipates that the short-term GSE credit facility loans and MBS purchases will be profitable, there is no way to guarantee this. Suspension of dividends has saved funds for the GSEs at the cost of the stockholders who would have received them.

CONCLUSION

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The GSEs’ portfolios include mortgages and mortgage-backed securities that are subject to credit risk, prepayment risk, interest rate risk, and operational risk. If these risks are mismanaged, or if market movements turn unexpectedly against the GSEs, the government faces two unsatisfactory alternatives: either let the GSEs go into default and try to control the financial repercussions, or step in and assume payments on the GSEs’ debt at taxpayer expense. On September 7, 2008, the government chose to assume GSE obligations at taxpayer expense. The issue of portfolio size will likely continue to be debated as policymakers consider what form the GSEs should take when they emerge from conservatorship.

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In: Real Estate Investment Market Editors: Sofia M. Lombardi, pp. 139-149

ISBN: 978-1-61668-395-5 © 2010 Nova Science Publishers, Inc.

Chapter 6

OVERVIEW OF THE SECURITIES ACT OF 1933 AS APPLIED TO PRIVATE LABEL MORTGAGE-BACKED SECURITIES Kathleen Ann Ruane*

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SUMMARY Mortgage-backed securities that are packaged and issued by private industry participants are required to comply with the Securities Act of 1933. Issuers of so-called private label mortgage-backed securities must either register these securities pursuant to the rules the Securities and Exchange Commission has set forth, or obtain an exemption from registration. Failure to register or fall under an exemption could result in liability for the issuer and other parties involved in the offering. Furthermore, material misstatements or omissions in the offering materials may also result in liability under the Securities Act. This chapter will provide an overview of the Securities Act of 1933 as it may be applied to mortgage-backed securities issued by private industry participants.

INTRODUCTION Generally speaking, there are two types of mortgage-backed securities (MBSs). The first are those securities that are packaged and issued by government sponsored entities (GSEs) — the Federal National Mortgage Association (“Fannie Mae”) and the Federal Home Loan Mortgage Corporation (“Freddie Mac”) — and a wholly owned government corporation, the Government National Mortgage Association (“Ginnie Mae”). The second are those MBSs that are packaged and issued by private market participants (i.e., mortgage companies, savings and loans, and commercial banks), known as private label MBSs. *

Email: [email protected], 7-9135

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The laws governing the issuance of these two types of MBSs are different. MBSs offered by the GSEs and Ginnie Mae are exempt from the registration requirements and ongoing disclosure obligations contained in the federal securities laws.1 Private label MBSs do not enjoy a blanket exemption from the federal securities laws and are classified by the Securities and Exchange Commission as a type of “asset-backed security” (ABS) that must register under the Securities Act of 1933 (‘33 Act or Securities Act) or obtain an exemption and provide continuing disclosures required by the Securities Exchange Act of 1934 (‘34 Act or Exchange Act).2 This chapter will provide an overview of the registration requirements for private label MBSs under the Securities Act. It also highlights the most frequently used exemptions for private label MBSs. It outlines potential liability for fraud and/or material misstatements in the required disclosures and the consequences for failure to register when required by federal securities laws. This chapter will not discuss reporting requirements or liability for MBSs under the Exchange Act of 1934.3

SECURITIES ACT REGISTRATION FOR PRIVATE LABEL MORTGAGEBACKED SECURITIES

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The Securities Act requires issuers of all types of securities to register the offering with the Securities and Exchange Commission (SEC) or to qualify for an exemption from the registration requirements.4 A registration statement consists of two parts: a prospectus, which must be delivered with every offer to sell the securities and contain the information outlined in Section 10 of the Securities Act,5 and other information which need not be provided to potential purchasers but must be on file with the SEC and available for public inspection.6

1

Ginnie Mae is a wholly owned corporation of the U.S. government and the securities it guarantees are exempt securities under Section 3(a)(2) of the Securities Act of 1933 (15 U.S.C. § 77c(a)(2)) and Section 3(a)(12) of the Securities Exchange Act of 1934 (15 U.S.C. § 78c(a)(12)). The Federal National Mortgage Association Charter Act provides that securities guaranteed by Fannie Mae will be exempt securities in the same manner as those guaranteed by Ginnie Mae. 12 U.S.C. § 1723c. A similar exemption for Freddie Mac-guaranteed securities is contained in the Federal Home Loan Mortgage Corporation Act. 12 U.S.C. § 1455g. It is worth noting that these exemptions do not exempt these securities from all of the antifraud provisions. For instance, Section 10(b) of the Exchange Act and Rule 10b-5 apply to all issuers of securities whether or not the security was registered. 15 U.S.C. §78j; 17 C.F.R. § 10b-5. 2 Securities Act Release No. 33-8518; 34-5095, 70 Fed. Reg. 1506 (Jan. 7, 2005) (“Final Rule in Regulation AB”). Codified at 17 C.F.R. Parts 210, 228, 229 et al. 3 Many MBS issuers are able to suspend their reporting obligations under the Exchange Act, because the offerings typically have such a small number of record holders. See Final Rule in Regulation AB, supra note 2, §III D. Section 15(d) of the Exchange Act suspends reporting requirements each year so long as there are fewer than 300 record holders at the beginning of the year. 15 U.S.C. §78o(d). This assumes that the MBS is not trading on a national securities exchange or automated quotation system (which MBSs, again typically, do not), because such trading would trigger registration requirements under Section 12 of the Exchange Act. 15 U.S.C. §78l. Suspension of reporting requirements does not mean exemption from liability under other portions of the Exchange Act. See e.g., 15 U.S.C. §78j. 4 See Sections 3(b), 4, 5, 7 of Securities Act, 15 U.S.C. §77b-g. 5 15 U.S.C. § 77j. 6 Section 7 of Securities Act, 15 U.S.C. §77g.

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Failure to file a registration statement when one is required results in a violation of Section 5 of the ‘33 Act and strict liability under Section 12(a)(1).7

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The Registration Statement for Private Label MBSs Sections 4 and 5 of the Securities Act require issuers of securities to register the offerings and provide prospectuses for sales that are not exempt.8 Sections 7 and 10 of the Securities Act prescribe the information required in the registration statements and prospectuses that are issued pursuant to offerings under Sections 4 and 5.9 Section 7 requires the registration statement to contain the information and documents outlined in Schedule A (15 U.S.C. §77aa), which is the Schedule under which all issuers that are not foreign governments must file.10 Section 7 grants the Commission the power to prescribe rules and regulations describing the information and documents to be contained in registration statements if the Commission deems them to be “necessary or appropriate in the public interest or for the protection of investors.”11 Pursuant to this authority, the Commission has designed registration statements, which correspond to the various types of securities and types of issuers of securities. For private label MBSs, issuers must use either registration statement Form S-1 or Form S-3.12 Form S-3 is the preferable registration statement type for most issuers because it is considered to be less burdensome than other types of registration statements. In order to be eligible for Form S-3, in most cases, the registrant must already have a class of securities registered pursuant to Sections 12(b) or 12(g) of the Exchange Act (15 U.S.C. §78l), or be required to file reports pursuant to Section 15(d) of the Exchange Act for at least the preceding 12 months (15 U.S.C. §78o).13 The Commission included this requirement under the theory that information contained in the disclosures required by these sections could be incorporated by reference into the new MBS registration, thereby reducing the work required to prepare a new MBS registration statement.14 The registrant must also have filed all reports required in a timely manner within the previous 12 months.15 If the MBS offering qualifies as an offering of investment grade securities, however, the requirements for use of Form S-3 are slightly different. A non-convertible security (such as an MBS) may qualify as an investment grade security if, at the time of sale, “at least one nationally recognized statistical rating organization ... has rated the security in one of its 7

Section 12(a)(1) of the Securities Act states that any person who sells a security in violation of Section 5 (15 U.S.C.§77e) is liable to the person purchasing the securities from him for the purchase price with interest, less any incomereceived from the security or for damages if the purchaser no longer owns the security. 8 15 U.S.C. §§ 77d – 77e. 9 15 U.S.C. §§ 77g, 77j. 10 Section 7 of Securities Act, 15 U.S.C. §77g. A Section 10 prospectus is required to contain much of the information contained in the registration statement, unless the prospectus is of a type permitted by the Commission that summarizesor omits information contained in the base prospectus. Furthermore, the Commission may require more information tobe provided in prospectuses by rule or regulation. 15 U.S.C. §77j. 11 Id. 12 Final Rule in Regulation AB, supra note 2, §III B. 13 17 C.F.R. § 239.13. 14 See Item 12, Form S-3, 17 C.F.R. § 239.13. 15 17 C.F.R. § 239.13.

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generic rating categories which signifies investment grade; typically, the four highest rating categories (within which there may be sub-categories or gradations indicating relative standing) signify investment grade.”16 An offering of investment grade MBSs occurs when MBSs that qualify as investment grade are offered for cash and delinquent assets within the asset pool do not constitute 20% or more of the pool (measured in dollar volume).17 If the offering is an offering of investment grade MBSs, the registrant is not required to have securities registered pursuant to Sections 12(b) or 12(g) of the Exchange Act (15 U.S.C. §78l) or be subject to the reporting requirements of Section 15(d) of the Exchange Act (15 U.S.C. §78o) in order to register using Form S-3.18 The issuer of an offering of investment grade MBSs still must have filed all reports required in the previous 12 calendar months in a timely fashion to qualify to use Form S-3. If the MBS offering does not qualify to use Form S-3, then the offering must be registered on Form S-1, which is the form all registrants must use if they do not qualify to register on another form.19

Shelf-Registration Shelf-registration allows an issuer to file a registration statement and, instead of selling the securities immediately following the effective date, place the securities on a “shelf” to be sold when the issuer believes the time to be right.20 This is a popular method of registration for private label MBSs. Mortgage related securities, a subset of MBSs, automatically qualify for “shelf-registration.”21 Even if the private label MBS offering in question is not a mortgage related security, the private label MBS offering may qualify for shelf-registration nonetheless.22 For private label MBS offerings, the securities may remain on the “shelf” for up to three years from the initial effective date.23 Once the company “takes down” the securities for sale, if there has been a change involving the structural features of the MBSs, credit enhancement or other aspects of the MBSs that were not described in the base prospectus, a new registration statement, or post-effective amendment may be required.24 Some changes do not warrant such labor intensive disclosure, however, and the changes may be described in the final prospectus filed with the SEC.25 If the securities have not been sold by the end of the original three-year period, another registration statement may be filed.26

16

17 C.F.R. § 239.13 (b)(2). A non-convertible security is a security that cannot be converted into some other security. 17 17 C.F.R. § 239.13 (b)(5). 18 17 C.F.R. § 239.13 (a)(4). 19 17 C.F.R. § 239.11. 20 See Wilmarth, Jr., Arthur E., The Transformation of the U.S. Financial Services Industry, 1975-2000: Competition Consolidation and Increased Risk, 2002 U. Ill. L. Rev. 215, 410 (“A shelf registration permits a qualified issuer to sell securities at any time during an extended offering [period].”). 21 Rule 415 under the Securities Act, 17 C.F.R. §230.415 (a)(vii). In order to be considered a “mortgage related security” the security must be rated in one of the top two ratings by at least one nationally recognized statistical rating organization. 15 U.S.C. § 78c(a)(41). 22 17 C.F.R. §230.415 (a). The rule allows for shelf-registration of securities that are registered on form S-3. 23 Rule 415(b), 17 C.F.R. §230.415. 24 See Final Rule in Regulation AB, supra note 2, §III. 25 Securities Act Rule 424, 17 C.F.R. §230.424. 26 Rule 415(b), 17 C.F.R. §230.415.

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Regulation AB Private label MBSs are required to file registration statements that comply with Regulation AB.27 Regulation AB is tailored specifically to various types of asset-backed securities (like MBSs).28 The Commission realized that disclosures required by other SEC regulations were not properly tailored to elicit useful information for MBS investors.29 Other regulations required too much information irrelevant to MBSs and little or no information about other aspects of MBSs that investors needed in order to make informed investment decisions. Therefore, Regulation AB requires more information about the assets in a particular securitized pool, delinquent assets in the pool, the structure of the transaction, the experience of the servicer of the asset pool as well as other parties involved in administering the particular asset pool at issue, and other information unique to offerings of asset-backed securities (like credit enhancements on the asset pool).30 Information with respect to the registrant (management of the registrant company, performance of the registrant company’s stock) may be omitted for MBS offerings because this information does not necessarily inform the investor about the potential performance of the asset pool.31

Exemptions

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Certain offerings of private label MBSs may be exempt from registration under the Securities Act. The most common exemptions for MBS offerings are described below.

Private Placement Offerings (Regulation D) The most common exemption from registration for MBSs is the exemption for so-called “private placement offerings.” Section 4(2) of the Securities Act exempts “transactions by an issuer not involving any public offering.”32 Section 3(b) allows the Commission to exempt certain offerings, not in excess of a specified dollar amount, from registration by rule or regulation. Pursuant to its authority in these two sections, the Commission adopted Regulation D.33 Regulation D, found in Rules 501 through 508 under the Securities Act, provides guidance to issuers regarding which offerings would not be considered “public offerings.”34 The issuer must file notice with the SEC of any sales pursuant to Regulation D.35

27

See 17 CFR §§ 229.110 – 229.1123. “Asset-backed security means a security that is primarily serviced by the cash flows of a discrete pool of receivables or other financial assets, either fixed or revolving, that by their terms convert into cash within a finite time period, plus any rights or other assets designed to assure the servicing or timely distributions of proceeds to the security holders ... ” 17 C.F.R. § 229.1101(c). The definition is intentionally broad, because the Commission intended to create a principals based approach to disclosure relating to these types of assets rather than a set of rigid rules for each different type ofABS. See Final Rule in Regulation AB, supra note 2. 29 See Final Rule in Regulation AB, supra note 2. 30 See 17 CFR §§ 229.110 – 229.1123. 31 See Final Rule in Regulation AB, supra note 2. 32 15 U.S.C. §77d. 33 15 U.S.C. §77c (b). 34 17 C.F.R. §§ 230.501-508. 35 17 C.F.R. § 230.503. 28

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Rule 504 Under Rule 504, an issuer (except an issuer that is an investment company) may sell up to $1 million worth of private label MBSs in any 12-month period to any number of purchasers, regardless of their accreditation.36 No information is required to be provided to investors purchasing securities pursuant to this exemption.37 Rule 505 An issuer may sell up $5 million worth of private label MBSs in a 12 month period to any number of accredited investors and up to 35 other purchasers.38 Accredited investors are defined to include large, frequent market participants that are presumed to have the ability to independently obtain the information that they need.39 If the securities are offered to unaccredited investors, some disclosure is required under Rule 502, but a full registration statement is not required.40

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Rule 506 Rule 506 is likely the most common exemption from registration for MBSs. Under this rule, an issuer may sell any amount of securities to any number of accredited investors41 and up to 35 so called “sophisticated investors.”42 In order for the unaccredited investors to be considered “sophisticated,” the issuer must reasonably believe that those investors (or their representatives) are capable of evaluating the merits and risks of the securities offered.43 If the securities are offered to unaccredited investors, some disclosure is required under Rule 502, but a full registration statement is not required.44 Section 4(6) of the Securities Act This section exempts sales of up to $5 million from registration if the sales are made to accredited investors.45 To qualify for this exemption, the issuer may not publicly advertise the sale of the securities, nor may the issuer publicly solicit buyers. The issuer must also file notice with the Commission of the sale, a requirement similar to that of Regulation D.

36

17 C.F.R. § 230.504. Id. 38 17 C.F.R. § 230.505. However, in order to calculate the number of purchasers of securities, one must refer to Rule 501 (e), which exempts accredited investors from the total number of purchasers when calculating that number for purpose of exemptions under Rules 505(b) and 506(b). 17 C.F.R. § 230.501(e). 39 They are, for example, banks, insurance companies, investment companies, employee benefit plans, business development companies, large charitable and educational institutions, directors, executive officers, and general partners of the issuer, persons with a net worth about $1 million, persons with an annual income of more than $200,000, and any trust valued over $5 million that is run by a sophisticated person. 17 C.F.R. § 230.501(a). 40 17 C.F.R. § 230.502(b). 41 See 17 C.F.R. § 230.501(a). 42 17 C.F.R. § 230.506. Rule 506 actually says that there may be no more than 35 purchasers for an offering to qualify under this section. See 17 C.F.R. § 230.506(2)(i). However, in order to calculate the number of purchasers of securities, one must refer to Rule 501 (e), which exempts accredited investors from the total number of purchasers when calculating that number for purpose of exemptions under Rules 505(b) and 506(b). 17 C.F.R. § 230.501(e). 43 17 C.F.R. § 230.506 (b)(2)(ii). 44 17 C.F.R. § 230.502(b). 45 15 U.S.C. § 77d(6). 37

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Rule 144A Rule 144A allows the unlimited resale of securities that were never registered pursuant to the Securities Act so long as the purchaser is a “qualified institutional buyer” (QIB).46 QIBs are defined as enumerated types of institutional investors (i.e., insurance companies or employee benefit plans) that own over $100 million in securities unaffiliated with the entity making the offering.47 Because the market for private label MBSs consists primarily of QIBs, Rule 144A is commonly used.48 Section 28 Section 28 of the Securities Act gives the Commission the authority to, conditionally or unconditionally, “exempt any person, security, or transaction, or any class of persons, securities, or transactions, from any provision or provisions of this title or of any rule or regulation issued under this title, to the extent that such exemption is necessary or appropriate in the public interest, and is consistent with the protection of investors.”49 The Commission, therefore, has wide discretion to create exemptions from the registration requirements of the Securities Act.

PRIVATE RIGHTS OF ACTION UNDER THE SECURITIES ACT

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Sections 11 and 12 of the Securities Act provide private causes of actions for material misstatements or omissions contained in the registration of private label MBS securities.50 Section 15 of the act creates liability for controlling persons. These causes of action are described in this section.

Section 11 Civil Liability for a False Registration Statement Section 11 creates a private right of action for purchasers of securities issued pursuant to a false or materially misleading registration statement.51 To establish liability, a plaintiff must show that the registration statement, at the time it became effective, contained a material misstatement or omission.52 A statement is material if “an average prudent investor ought

46

17 C.F.R. § 230.144A. 17 C.F.R. § 230.144A(a). 48 See Final Rule in Regulation AB, supra note 2. 49 15 U.S.C § 77z-3. 50 It is important to note that these causes of action apply only to public offerings (Section 12) and offerings made pursuant to an effective registration statement (Section 11). They do not apply to many offerings that are exempted from registration requirements. As a result, in order to recover for fraud and other deceptive practices related to the sale of exempted securities, investors likely would need to allege violations of Section 10b of the Exchange Act and Rule 10b-5 (15 U.S.C. §78j; 17 C.F.R. § 10b-5). 51 15 U.S.C. § 77k. 52 Id. Unlike claims for violations of Rule 10b-5 under the Exchange Act, plaintiffs do not need to allege or provescienter (i.e., knowledge on the part of the defendant) for violations of Section 11 of the Securities Act. See AlaskaElec. Pension Fund v. Pharmacia Corp., 554 F.3d 342, 348 n.4 (3d. Cir. 2009); J&R Mktg. v. GMC, 549 F.3d 384, 392(6th Cir. 2008). Because claims for violations of Section 11 “sound in fraud,” plaintiffs must comply with Rule 9(b) ofthe Federal Rules of Civil Procedure. Rule 9(b) requires a plaintiff to “state with 47

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reasonably to be informed [of the information] before purchasing the security registered.”53 For the purposes of Section 11, a statement is material if, had it been stated correctly or disclosed, it “would have deterred or tended to deter the average prudent investor from purchasing the securities in question.”54 Because Section 11 requires an effective registration statement in order to apply, securities that are sold pursuant to an exemption from registration are not subject to liability for violations of Section 11.55 Liability for violations may include • • •

the difference between the amount paid for the security and the value at the timethe suit is brought, or the difference between the amount paid for the security and the price at which the security was sold in the market before the suit, or the difference between the amount paid for the security and the price at which it was sold after suit, but before judgment is entered, if that amount is less than the damages representing the difference between the amount paid for the security and the value at the time the suit was brought.56

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MBS Suits A number of lawsuits have been filed by investors against issuers of MBSs alleging violations of Section 11 of the Securities Act. Alleged violations include failure to comply with the underwriting standards described in the offering documents, failure to disclose true risks of default on loans, and misrepresentations that the assets backed by the securities were, in fact, “investment grade.”57 As these cases move through the courts, issues facing the causes of action will become more clear. Defenses A claim of liability under Section 11 may always be defeated by proof that the purchaser knew of the untruth or omission at the time the security was acquired.58 Furthermore, if a defendant can prove that “any portion or all [of the damages suffered by the plaintiff] represents other than the depreciation in value of such security resulting from” the misstatement in the registration statement, that portion of the damages is not recoverable.59 In other words, if a defendant can show that it was not the misstatement or omission in the registration statement that caused the value of the shares to fall, but some other market force, the plaintiff cannot recover the loss of value represented by the extraneous influence. particularity the circumstancesconstituting fraud or mistake. Malice, intent, knowledge, and other conditions of a person's mind may be allegedgenerally.” Fed. R. Civ. Pro. 9(b). 53 Rule 405 under the Securities Act, 17 C.F.R. § 230.405. 54 Escott v. BarChris Constr Corp., 283 F. Supp. 643, 681 (S.D.N.Y. 1968). 55 15 U.S.C. § 77k. 56 15 U.S.C. § 77k(a). 57 See, Public Employees Retirement System of Mississippi v. Goldman Sachs Group, Inc., Case No. 09-CV-1110 (S.D.N.Y. February, 2009); Public Employees Retirement System of Mississippi v. Morgan Stanley, Case No. ________ (Cal. Sup. Ct. December, 2008); Boilermaker-Blacksmith National Pension Trust v. Wells Fargo Mortgage-Backed Securities 2006-AR1 Trust, Case No. 09-CV-833 (S.D.N.Y. January, 2009); BoilermakerBlacksmith National Pension Trust v. WAMU Mortgage Pass Through Certificates, Series 2006-AR1, Case No. C-09-0037 (W.D. WA January, 2009). 58 15 U.S.C. § 77k(a). 59 15 U.S.C. § 77k(e).

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The issuer has absolute liability under Section 11.60 Section 11 allows other individuals, besides the issuer, to be sued for violations, including corporate executives and others who signed the registration statement.61 These defendants may assert the “due diligence” defense.62 For the purposes of this defense, there are two portions of a registration statement: the “expert” portions and the “unexpert” portions. For example, in MBS offerings, the portion describing the pooling and servicing agreement for the underlying asset pool is prepared and signed by experts in accounting and auditing.63 Defendants, other than the expert that prepared the “expert” section at issue, may assert a due diligence defense to the preparation of the expert portions if the defendants can show that, after a reasonable investigation, they “had no reasonable grounds to believe and did not believe” there to be any material misstatements or omissions in the expert portion of the registration statement.64 “Reasonable investigation” means that which is required of a reasonable man in the care of his own property.65 In other words, those who sign the registration statement are entitled to trust the experts paid to prepare the expert portions, absent any red flags.66 With respect to the unexpert portions (and to the expert portions for the expert charged with preparing and signing those portions), defendants may assert the due diligence defense if they can show that, after a reasonable investigation the defendants had reasonable grounds to believe and did believe that there was no material misstatement or omission.67 This is a higher standard than the standard described in the preceding paragraph.68 Those signing the registration statement are not entitled to assume all information in it is correct because they trust those who prepared the statement. The defendants must, at the least, have read the registration statement and taken into account all knowledge available to them to gauge the statements accuracy.69

Section 12 Civil Liability Arising in Connection with Prospectuses and Communications Section 12 applies to two different scenarios, each of which may apply to the issuance of private label MBSs. Both are briefly described below.

60

15 U.S.C. § 77k(b). 15 U.S.C. § 77k(a). These persons or entities are principal executive officers, principal financial officers, controllers or principal accounting officers, directors, persons that are about to become directors, those who prepared and signed the expert portions of the registration statement, and underwriters. 62 See 15 U.S.C. § 77k(b)(3). 63 See, e.g., Form S-3, Sun Real Estate Trust (August 29, 2007), available at http://www.sec.gov/ Archives/edgar/data/ 1407749/000137468007000006/forms3.txt. 64 15 U.S.C. § 77k(b)(3)(A). 65 15 U.S.C. § 77k(c). 66 BarChris Constr Corp., 283 F. Supp. at 687. 67 15 U.S.C. § 77k(b)(3)(B)-(C). 68 See BarChris Constr Corp., 283 F. Supp. at 688. 69 Id. 61

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Section 12(a)(1) Under Section 12(a)(1), a seller70 is strictly liable for selling securities in violation of Section 5. To establish a claim under this subsection, a plaintiff need only show that he bought securities and that the securities were not registered.71 The burden is on the defendant to show that there was an exemption for the offering. Section 12(a)(2) Section 12(a)(2) creates liability for any person who sells securities pursuant to a prospectus or oral communication that contains a material misstatement or omission.72 Liability under this section is not strict liability, however. A defendant who can prove that “he did not know, and in the exercise of reasonable care could not have known of such untruth or omission” will not be held liable.73 A defendant may reduce his liability under 12(a)(2) to the extent that he can show the decrease in the securities’ value was caused by factors other than the alleged misstatement or omission in the prospectus or oral communication.74 Furthermore, this section only applies to public offerings; private placements, such as those accomplished under Rules 506, are not covered.75

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MBS Suits Many of the suits filed alleging violations of Section 11 in the registration and sale of MBSs, also allege violations of Section 12(a)(2).76 As these cases move through the courts, issues facing the causes of action will become more clear. Section 15 Liability of Controlling Persons Section 15 makes those persons or entities that, through stock ownership or other arrangement, control the persons or entities that are liable under Sections 11 and 12 jointly and severably liable for violations of those sections.77 This provision could become important for the purposes of private label MBS liability. Issuers of MBSs are typically specially created for the purposes of a specific offering.78 Therefore, in order to recover for violations of 70

More persons than the issuer may be included in the definition of “seller.” “Sellers” may include individuals such as solicitors and others who may be financially motivated to sell a security. See Pinter v. Dahl, 486 U.S. 622 (1988). 71 15 U.S.C. § 77l(a)(1). 72 15 U.S.C. § 77l(a)(2). 73 Id. 74 15 U.S.C. § 77l(b). 75 Gustafson v. Alloyd Co., 513 U.S. 561 (1995). 76 See, Public Employees Retirement System of Mississippi, v. Goldman Sachs Group, Inc., Case No. 09-CV-1110 (S.D.N.Y. February, 2009); Public Employees Retirement System of Mississippi v. Morgan Stanley, Case No. ________ (Cal. Sup. Ct. December, 2008); Boilermaker-Blacksmith National Pension Trust v. Wells Fargo Mortgage-Backed Securities 2006-AR1 Trust, Case No. 09-CV-833 (S.D.N.Y. January, 2009); BoilermakerBlacksmith National Pension Trust v. WAMU Mortgage Pass Through Certificates, Series 2006-AR1, Case No. C-09-0037 (W.D.WA January, 2009). 77 15 U.S.C § 77o. 78 See Final Rule in Regulation AB, supra note 2, §III B. See, e.g., Public Employees Retirement System of Mississippi, v. Goldman Sachs Group, Inc., Case No. 09-CV-1110 (S.D.N.Y. February, 2009); Public Employees Retirement System of Mississippi v. Morgan Stanley, Case No. ________ (Cal. Sup. Ct. December, 2008); Boilermaker-Blacksmith National Pension Trust v. Wells Fargo Mortgage-Backed Securities 2006-AR1 Trust, Case No. 09-CV-833 (S.D.N.Y. January, 2009); Boilermaker-Blacksmith National Pension Trust v. WAMU Mortgage Pass Through Certificates, Series 2006-AR1, Case No. C-09-0037 (WD.WA. January, 2009).

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Section 11 and 12 in MBS offerings, it may be necessary to sue the persons controlling the entities making the offering.

SEC ENFORCEMENT OF THE SECURITIES ACT

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The Commission has the statutory authority to bring an action for violation of the Securities Act, as well as any violation of the rules and regulations issued by the Commission pursuant to the act.79 Whenever the Commission believes a person has violated or is about to violate the provisions of the Securities Act, the Commission has the power to issue a cease and desist order.80 Pursuant to any cease and desist order, the Commission has the authority to order accounting and disgorgement.81 The Commission may also bring civil or criminal actions for violations of the act.82 In conjunction with the enforcement described above, the Commission may bring an action for violation of Section 17 of the Securities Act. Section 17 is a general antifraud provision. It prohibits any individual, in the offer or sale of securities, from employing various means or devices of fraud.83 Some courts have held that there is an implied private right of action under Section 17 (similar to that of Rule 10b-5 of the Exchange Act), but the Supreme Court has yet to rule on this question.84

79

Sections 19, 20(a) of the Securities Act, 15 U.S.C. §§77s, 77t. 15 U.S.C § 77h-1. 81 Id. 82 15 U.S.C. §77t. 83 15 U.S.C. § 77q. 84 Herman & Maclean v. Huddleston, 459 U.S. 375, 378 n. 2 (1983). 80

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In: Real Estate Investment Market Editors: Sofia M. Lombardi, pp. 151-159

ISBN: 978-1-61668-395-5 © 2010 Nova Science Publishers, Inc.

Chapter 7

EXAMINING THE CONTINUING CRISIS IN RESIDENTIAL FORECLOSURES AND THE EMERGING COMMERCIAL REAL ESTATE CRISIS: PERSPECTIVES FROM ATLANTA

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Jon D. Greenlee Chairman Kucinich, Ranking Member Jordan, and members of the Subcommittee, I appreciate the opportunity to appear before you today to examine several issues related to the condition of the banking system. First, I will discuss credit conditions and bank underwriting standards, with a particular focus on commercial real estate (CRE), and I will briefly address conditions in the state of Georgia. I will then describe Federal Reserve activities to enhance liquidity and improve conditions in financial markets. Finally, I will discuss the ongoing efforts of the Federal Reserve to ensure the overall safety and soundness of the banking system, as well as actions taken to promote credit availability.

BACKGROUND The Federal Reserve has supervisory and regulatory authority for bank holding companies, state-chartered banks that are members of the Federal Reserve System (state member banks), and certain other financial institutions and activities. We work with other federal and state supervisory authorities to ensure safety and soundness of the banking industry, foster stability of the financial system, and provide for the fair and equitable treatment of consumers in financial transactions. The Federal Reserve is not the primary federal supervisor for the majority of commercial banks. Rather, it is the consolidated supervisor of bank holding companies, including financial holding companies, and conducts inspections of those institutions. The primary purpose of inspections is to ensure that the holding company and its nonbank subsidiaries do not pose a threat to the soundness of the company’s depository

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institutions. In fulfilling this role, the Federal Reserve is required to rely to the fullest extent possible on information and analysis provided by the appropriate supervisory authority of the company’s bank, securities, or insurance subsidiaries. The Federal Reserve is also the primary federal supervisor of state member banks, sharing supervisory responsibilities with state agencies. In this role, Federal Reserve supervisory staff regularly conduct on-site examinations and off-site monitoring to ensure the safety and soundness of supervised state member banks. A key aspect of the supervisory process is evaluating risk-management practices. The Federal Reserve is involved in both regulation--establishing the rules within which banking organizations must operate--and supervision--ensuring that banking organizations abide by those rules and remain, overall, in safe and sound condition. Because rules and regulations in many cases cannot reasonably prescribe the exact practices each individual bank should use for risk management, supervisors design policies and guidance that expand upon requirements set in rules and regulations and establish expectations for the range of acceptable practices. Supervisors rely extensively on these policies and guidance as they conduct examinations and assign supervisory ratings. Beginning in the summer of 2007, the U.S. and global economies entered a period of intense financial turmoil that has presented significant challenges for the financial services industry. These challenges intensified in the latter part of 2008 as the global economic environment weakened further. As a result, parts of the U.S. banking system have come under severe strain, with some banking institutions suffering sizable losses. The number of bank failures has also risen this year.

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CONDITIONS IN FINANCIAL MARKETS AND THE ECONOMY Although conditions and sentiment in financial markets have improved in recent months, significant stress and weaknesses persist. Corporate bond spreads remain high by historical standards as both expected losses and risk premiums remain elevated. Encouragingly, economic growth moved back into positive territory last quarter, in part reflecting a pickup in consumer spending and an increase in residential investment. However, the unemployment rate has continued to rise, reaching 9.8 percent in September. In this environment, borrowing by businesses and households has remained weak. The available data suggest that household and nonfinancial business debt likely decreased in the third quarter after having contracted in the first half of the year. For households, residential mortgage debt and consumer credit fell sharply in the first half of the year, and the decline in consumer credit continued in July and August. Nonfinancial business debt also decreased modestly in the first half of 2009 and appears to have contracted further in the third quarter as net decreases in commercial paper outstanding and bank loans more than offset solid net issuance of corporate bonds. Loans outstanding at depository institutions fell in the second quarter of 2009. In addition, the Federal Reserve’s weekly bank credit data suggest that bank loans to households and to nonfinancial businesses contracted sharply in the third quarter as well. These declines reflect the fact that weak economic growth can both dampen demand for credit and lead to tighter credit supply conditions. Tighter credit conditions are especially challenging for small

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businesses, which tend to rely more heavily on despository institutions for credit. There are more than 27 million small businesses nationally that employ about half of the nation’s private-sector workforce and these businesses have approximately $1 trillion in debt. In a recent National Federation of Independent Business survey, small businesses reported that credit conditions were about as tight as in previous recessions; at the same time, their main economic concern was lower sales. Results from the Federal Reserve’s Senior Loan Officer Opinion Survey on Bank Lending Practices in July indicate that both the availability and demand for bank loans are well below pre-crisis levels. In July, more banks reported tightening their lending standards on consumer and business loans than reported easing, although the degree of net tightening was well below levels reported last year. Almost all of the banks that tightened standards indicated concerns about a weaker or more uncertain economic outlook, and about one-third of banks surveyed cited concerns about deterioration in their own current or future capital positions. The survey also indicated that demand for consumer and business loans had weakened further. Indeed, decreased loan demand from creditworthy borrowers was the most common explanation given by respondents for the contraction of business loans this year. Loan quality deteriorated significantly for both large and small institutions during the second quarter of this year. At the largest 50 bank holding companies, nonperforming assets climbed more than 20 percent, raising the ratio of nonperforming assets to 4.3 percent of loans and other real estate owned. Most of the deterioration was concentrated in residential mortgage and construction loans, but commercial, CRE, and credit card loans also experienced rising delinquency rates. Results of the banking agencies’ Shared National Credit review, released in September, also document significant deterioration in large syndicated loans, signaling likely further deterioration in commercial loans.1 At community and small regional banks, nonperforming assets increased to 4.4 percent of loans at the end of the second quarter, more than six times the level for this ratio at year-end 2006, before the financial crisis began. Home mortgages and CRE loans accounted for most of the increase, but commercial loans have also shown marked deterioration during recent quarters. As a result, credit losses at banking organizations continued to rise, and banks face risks of sizable additional credit losses given the outlook for production and employment. In addition, while the year-on-year decline in housing prices slowed in the second quarter, continued adjustments in the housing market suggest that foreclosures and mortgage loss severities are likely to remain elevated. Moreover, the value of both existing commercial properties and land, which collateralize commercial and residential development loans, have declined sharply in the first half of this year, suggesting that banks are vulnerable to significant further deterioration in their CRE loans. In sum, banking organizations continue to face significant challenges, and credit markets are far from fully healed.

1

See Board of Governors of the Federal Reserve System, Federal Deposit Insurance Corporation, Office of the Comptroller of the Currency, and Office of Thrift Supervision (2009), “Credit Quality Declines in Annual Shared National Credits Review ,” joint press release, September 24.

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PERFORMANCE OF THE BANKING SYSTEM Despite these challenges, the stability of the banking system has improved since last year. Many financial institutions have raised significant amounts of capital and have achieved greater access to funding. Importantly, through the rigorous Supervisory Capital Assessment Program (SCAP) stress test conducted by the banking agencies earlier this year, some institutions demonstrated that they have the capacity to withstand more-adverse macroeconomic conditions than are expected to develop and have repaid the government’s Troubled Asset Relief Program (TARP) investments.2 Depositors’ concerns about the safety of their funds during the immediate crisis last year have also largely abated. As a result, financial institutions have seen their access to core deposit funding improve. However, the condition of the banking system is far from robust. Two years into a substantial economic downturn, loan quality is poor across many asset classes and, as noted earlier, continues to deteriorate as weakness in housing markets affects the performance of residential mortgages and construction loans. Higher loan losses are depleting loan loss reserves at many banking organizations, necessitating large new provisions that are producing net losses or low earnings. In addition, although capital ratios are considerably higher than they were at the start of the crisis for many banking organizations, poor loan quality, subpar earnings, and uncertainty about future conditions raise questions about capital adequacy for some institutions. Diminished loan demand, more-conservative underwriting standards in the wake of the crisis, recessionary economic conditions, and a focus on working out problem loans have also limited the degree to which banks have added high-quality loans to their portfolios, an essential step to expanding profitable assets and thus restoring earnings performance. In Georgia, the performance of banking organizations has deteriorated significantly over the past several quarters as the region’s real estate expansion reversed course. Like their counterparts nationally, Georgia banks have seen a steady rise in non-current loans and provisions for loan losses, which have weighed on bank earnings and capital. Since the turmoil in financial markets emerged more than two years ago, 25 banks in Georgia have failed. Notably, almost all of the banks that have failed in Georgia thus far were located in the metro- Atlanta market and had a high percentage of total loans in land acquisition, development, and construction. Most of the lending activity at these failed banks was related to the region’s housing boom in the first half of this decade. Also of note, many of the failed banks relied heavily on brokered deposit funding to support what had been very strong asset growth. At the end of 2007, the average ratio of brokered deposit funds was 13 percent at banks in the state of Georgia, compared to just 7 percent at the national level. It is clear that substantial financial challenges remain for banking institutions, both in Georgia and across the United States. In particular, some large regional and community banking firms that have built up unprecedented concentrations in CRE loans will be particularly affected by emerging conditions in real estate markets.

2

For more information about the SCAP, see Ben S. Bernanke (2009), “The Supervisory Capital Assessment Program ,” speech delivered at the Federal Reserve Bank of Atlanta 2009 Financial Markets Conference, held in Jekyll Island, Ga., May 11, www.federalreserve.gov/newsevents/speech/bernanke20090511a.htm.

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CURRENT CONDITIONS IN COMMERCIAL REAL ESTATE MARKETS The Federal Reserve has been focused on CRE exposures at supervised institutions for some time. As part of our supervision of banking organizations in the early part of this decade, we observed rising CRE concentrations, especially in some large regional and community banking firms. Given the central role that CRE lending played in the banking problems of the late 1980s and early 1990s, we led an interagency effort to develop supervisory guidance on CRE concentrations. The guidance was finalized in 2006 and published in the Federal Register in early 2007. In that guidance, we emphasized our concern that some institutions’ strategic-and capital-planning processes did not adequately recognize the risks arising from their CRE concentrations. We stated that institutions actively involved in CRE lending should perform ongoing assessments to identify and manage concentrations through stress testing and similar exercises were needed to identify the potential impact of adverse market conditions on earnings and capital. As weaker housing markets and deteriorating economic conditions have impaired the quality of CRE loans at supervised banking organizations, the Federal Reserve has devoted significantly more resources to assessing the quality of regulated institutions’ CRE portfolios. These efforts include monitoring the impact of declining cash flows and collateral values, as well as assessing the extent to which banks have been complying with the CRE guidance. Reserve Banks that are located in more adversely affected geographic areas have been particularly focused on evaluating exposures arising from CRE lending. We have found, through horizontal reviews and other examination activities, that many institutions would benefit from portfolio-level stress testing, improved management information systems, and more robust appraisal practices. Additionally, some institutions need to improve their understanding of how single-name, sectoral and geographic concentrations can impact capital levels during downturns. Prices of existing commercial properties have already declined substantially from the peak in 2007 and will likely decline further. As job losses have accelerated, demand for commercial property has declined and vacancy rates have increased. The higher vacancy levels and significant decline in the value of existing properties have placed particularly heavy pressure on construction and development projects that do not generate income until after completion. Developers typically depend on the sales of completed projects to repay their outstanding loans, and with prices depressed amid sluggish sales, many developers are finding their ability to service existing construction loans strained. As a result, Federal Reserve examiners are reporting a sharp deterioration in the credit performance of loans in banks’ portfolios and loans in commercial mortgage-backed securities (CMBS). At the end of the second quarter of 2009, approximately $3.5 trillion of outstanding debt was associated with CRE, including loans for multifamily housing developments. Of this, $1.7 trillion was held on the books of banks and thrifts, and an additional $900 billion represented collateral for CMBS, with other investors holding the remaining balance of $900 billion. Also at the end of the second quarter, about 9 percent of CRE loans in bank portfolios were considered delinquent, almost double the level of a year

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earlier.3 Loan performance problems were the most striking for construction and development loans, especially for those that financed residential development. More than 16 percent of all construction and development loans were considered delinquent at the end of the second quarter. Of particular concern, almost $500 billion of CRE loans will mature during each of the next few years. In addition to losses caused by declining property cash flows and deteriorating conditions for construction loans, losses will also be boosted by the depreciating collateral value underlying those maturing loans. The losses will place continued pressure on banks’ earnings, especially those of smaller regional and community banks that have high concentrations of CRE loans. The current fundamental weakness in CRE markets is exacerbated by the fact that the CMBS market, which previously had financed about 30 percent of originations and completed construction projects, has remained closed since the start of the crisis. Delinquencies of mortgages backing CMBS have increased markedly in recent months. Market participants anticipate these rates will climb higher by the end of this year, driven not only by negative fundamentals but also by borrowers’ difficulty in rolling over maturing debt. In addition, the decline in CMBS prices has generated significant stresses on the balance sheets of financial institutions that must mark these securities to market, further limiting their appetite for taking on new CRE exposure.

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FEDERAL RESERVE ACTIVITIES TO HELP REVITALIZE CREDIT MARKETS The Federal Reserve, along with other government agencies, has taken a number of actions to strengthen the financial sector and to promote the availability of credit to businesses and households. In addition to aggressively easing monetary policy, the Federal Reserve has established a number of facilities to improve liquidity in financial markets. One such program is the Term Asset-Backed Securities Loan Facility (TALF), which was announced in November 2008 to facilitate the extension of credit to households and small businesses. Before the crisis, securitization markets were an important conduit of credit to the household and business sectors; some have referred to these markets as the “shadow banking system.” Securitization markets (other than those for mortgages guaranteed by the government) closed in mid-2008, with most of the issuance since that time importantly dependent on government support. Under the TALF, eligible investors may borrow to finance purchases of the AAA-rated tranches of various classes of asset-backed securities. The program originally focused on credit for households and small businesses, including auto loans, credit card loans, student loans, and loans guaranteed by the Small Business Administration. More recently, investors have also been able to use the TALF to purchase both existing and newly issued CMBS, which were included to help mitigate the refinancing problem in that sector. 3

The CRE loans considered delinquent on banks’ books were non-owner-occupied CRE loans that were 30 days or more past due.

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The TALF has had some success in restarting securitization markets. Rate spreads for asset-backed securities have declined substantially, and there is some new issuance that does not depend on the facility. By improving credit market functioning and adding liquidity to the system, the TALF and other programs have provided critical support to the financial system and the economy.

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AVAILABILITY OF CREDIT The Federal Reserve has long-standing policies in place to support sound lending and credit intermediation. Guidance issued during the CRE downturn in 1991 and in effect today instructs examiners to ensure that regulatory policies and actions do not inadvertently curtail the availability of credit to sound borrowers.4 This guidance also states that examiners should ensure that loans are being reviewed in a consistent, prudent, and balanced fashion to prevent inappropriate downgrades of credits. It is consistent with guidance published in early 2007 that addressed risk management of CRE concentrations. The 2007 guidance states that institutions that have experienced losses, hold less capital, and are operating in a more risksensitive environment are expected to employ appropriate risk-management practices to ensure their viability.5 We are currently in the final stages of developing interagency guidance on CRE loan restructurings and workouts. Banks have raised concerns that Federal Reserve examiners are not always taking a balanced approach to the assessment of CRE loan restructurings. At the same time, our examiners have observed incidents where banks have been slow to acknowledge declines in CRE project cash flows and collateral values in their assessment of potential loan repayment. This new guidance supports balanced and prudent decisionmaking with respect to loan restructuring, accurate and timely recognition of losses, and appropriate loan classification. The guidance reiterates that classification of a loan should not be based solely on a decline in collateral value, in the absence of other adverse factors, and that loan restructurings are often in the best interest of both the financial institution and the borrower. The expectation is that banks should restructure CRE loans in a prudent manner, recognizing the associated credit risk, and not simply renew a loan in an effort to delay loss recognition. Prudent real estate lending depends upon reliable and timely information on the market value of the real estate collateral. This has been a cornerstone of the regulatory requirements for real estate lending and is reflected in the agencies’ appraisal regulations. In that regard, the Federal Reserve requires its regulated institution to have real estate appraisals that meet minimum appraisal standards, including the Uniform Standards of Professional Appraisal Practice, and contain sufficient information to support the institution’s credit decision. Over 4

See Board of Governors of the Federal Reserve System, Division of Banking Supervision and Regulation (1991), “Interagency Examination Guidance on Commercial Real Estate Loans,” Supervision and Regulation Letter SR 91-24 (November 7), www.federalreserve.gov/BoardDocs/SRLetters/1991/SR9124.htm; and Office of the Comptroller of the Currency, Federal Deposit Insurance Corporation, Federal Reserve Board, and Office of Thrift Supervision (1991), “Interagency Policy Statement on the Review and Classification of Commercial Real Estate Loans,” joint policy statement, November 7,www.federalreserve.gov/ BoardDocs/SRLetters/1991/SR9124a1.pdf. 5 See Board of Governors of the Federal Reserve System, Division of Banking Supervision and Regulation (2007), “Interagency Guidance on Concentrations in Commercial Real Estate,” Supervision and Regulation Letter SR 07-1 (January 4), www.federalreserve.gov/boarddocs/srletters/2007/SR0701.htm.

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the past several years, the Federal Reserve has issued several appraisal-related guidance documents to emphasize the importance of a bank’s appraisal function and the need for independent and reliable appraisals. More recently, the Federal Reserve and the other federal agencies issued a proposal to revise the Interagency Appraisal and Evaluation Guidelines, which is expected to be finalized in the coming months. These guidelines reinforce the importance of sound appraisal practices. Given the lack of market sales in many markets and the predominant number of distressed sales in the current environment, regulated institutions face significant challenges today in assessing the value of real estate. We expect institutions to have policies and procedures for obtaining new or updated appraisals as part of their ongoing credit review. An institution should have appraisals or other market information that provide appropriate analysis of the market value of the real estate collateral and reflect relevant market conditions, the property’s current “as is” condition, and reasonable assumptions and conclusions. Bank examiners generally will not challenge an institution’s appraisal and other collateral valuation information that are based on well-supported analysis. Guidance issued in November 2008 by the Federal Reserve and the other federal banking agencies also encouraged banks to meet the needs of creditworthy borrowers in a manner consistent with the principles of safety and soundness while taking a balanced approach in assessing borrowers’ ability to repay and making realistic assessments of collateral valuations.6 In addition, the Federal Reserve has directed examiners to be mindful of the effects of excessive credit tightening in the broader economy, and we have implemented training for examiners and outreach to the banking industry to underscore these intentions. We are aware that bankers may become overly conservative in an attempt to ameliorate past weaknesses in lending practices, and we are working to emphasize that it is in all parties’ best interests to continue making loans to creditworthy borrowers.

CONCLUSION While financial market conditions in the United States have improved notably over the past year, the overall environment continues to be somewhat strained, and some geographic areas like the Southeast are experiencing more difficultly than others. The Federal Reserve, working with the other banking agencies has acted--and will continue to act--to ensure that the banking system remains safe and sound and is able to meet the credit needs of our economy. We have aggressively pursued monetary policy actions and have provided liquidity to help repair the financial system. In our supervisory efforts, we are mindful of the riskmanagement deficiencies at banking institutions revealed by the financial crisis and are ensuring that institutions develop appropriate corrective actions. It will take some time for the banking industry to work through this current set of challenges and for the financial markets to fully recover. In this environment, the economy will need a strong and stable financial system that can make credit available. We want banks 6

See Board of Governors of the Federal Reserve System, FDIC, Office of the Comptroller of the Currency, and Office of Thrift Supervision (2008), “Interagency Statement on Meeting the Needs of Creditworthy Borrowers,” joint press release, November 12, www.federalreserve.gov/newsevents /press/bcreg/20081112a.htm.

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to deploy capital and liquidity, but in a responsible way that avoids past mistakes and does not create new ones. The Federal Reserve is committed to working with other banking agencies and the Congress to promote the concurrent goals of fostering credit availability and a safe and sound banking system. Thank you again for your invitation to discuss these important issues at today’s hearing. I would be happy to answer any questions that you may have.

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In: Real Estate Investment Market Editor: Sofia M. Lombardi, pp. 161-168

ISBN: 978-1-61668-395-5 © 2010 Nova Science Publishers, Inc.

Chapter 8

SHORT COMMUNICATION: DIVERSIFICATION IN LISTED REAL ESTATE INVESTMENT FUND REPORTING IN SOUTH AFRICA Valmond Ghyoot*

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ABSTRACT The study set out to determine the extent to which diversification, as promoted in the financial literature, is actually implemented by institutional investors in South Africa. Diversification theory is encapsulated in a conceptual model of potential diversification strategies. The universe of listed real estate investment trusts in South Africa (Property Unit Trusts and Property Loan Stock Companies) was evaluated in 2004 and the study was updated in 2009. Content analysis was used to compare the conceptual model of potential diversification strategies with the annual reports of the listed real estate investment funds. The study finds that in 2004 few of the available diversification strategies were reported on. By 2009, reporting was more comprehensive. The study also explores focused strategies as an alternative to diversification.

INTRODUCTION Real estate portfolio diversification is a popular research area, judging by the regularity of scientific articles on the topic. It is also one of the most urgent issues identified in surveys about real estate investment research priorities among pension funds in the U.S.A. and in Australia (Worzala, Gilliand and Gordon, 2002; Newell, Acheompong and Worzala, 2002).

*

Corresponding author: Email: [email protected].

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Several within-real estate diversification categories have been identified and shown to be effective, based on such criteria as economic base, international differences, industry type, tenant type and ownership vehicle. Yet, examination of the annual reports of Property Unit Trust (PUT) or Property Loan Stock (PLS) companies1 will reveal that diversification is usually reported on mainly in two categories: property sector (property type) and geographic region. This narrow focus has been questioned before (Ori, 1995:27). More than two decades ago, Hartzell, Heckman and Miles (1986:252) warned that “... current industry practice represents little more than naive diversification. Due to the low levels of systematic risk, current distinctions by region and property type make little sense in a world of costly diversification.” They recommend that more diversification categories should be used in real estate portfolio management. This study examines the apparent discrepancy between real estate diversification strategies in research and in practice, using the annual reports of listed real estate trusts as a basis. Alternatives to diversification are also explored.2

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DIVERSIFICATION LOGIC Within a real estate portfolio, diversification is especially important. Real estate assets are not homogeneous and do not move as a group. Portfolios therefore have proportionally lower systematic risk and higher unsystematic risk than stocks. This makes real estate diversification more effective (Miles and McCue, 1984:66; Hartzell, Heckman and Miles, 1986:246). If a real estate portfolio is not diversified efficiently, the manager is accepting unnecessary unsystematic risk. Simply spreading an investment over many properties will remove some unsystematic risk. Average portfolio variance, a common risk measure for real estate investment, decreases rapidly as the number of properties increases from one to ten (Grissom, Kuhle and Walther, 1996:201). Accordingly, simply adding more properties will diversify a portfolio. This is naive diversification (Markowitz, 1952). Beyond the initial gains, however, no more benefit is derived simply by adding additional properties (Francis, 1993:595). The portfolio manager has to use a more efficient approach. Much like analysis of variance in statistics, markets have to be broken down into homogeneous segments that have high internal correlation and low correlation with other segments (Francis, 1993:598-599; Lieblich, 1995:1021). The efficient frontier of a portfolio of properties is defined by yield and risk. Any characteristic of real estate that affects these parameters could thus be a source of diversification. The bundle of rights theory is useful here, because an almost unlimited number of partial interests (limited real rights) may be created in a parcel of real estate—for example, a lease, or a financial interest such as a mortgage bond or a derivative. Every partial interest could be a basis of diversification.

1 2

Property Unit Trusts and Property Loan Stock Companies are similar, but not identical to REITS. Negotiations are under way to implement a REIT structure in South Africa. This is an update of a previous study (Ghyoot, 2004 and 2006), which is available from the author ([email protected]).

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Short Communication

DIVERSIFICATION CATEGORIES IN ANNUAL REPORTS To determine which diversification categories are actually reported on by listed real estate investment funds in South Africa, the annual reports of the universe of 21 listed funds were examined for the initial study in 2004. This was updated by examining a systematic sample of 11 out of the 18 listed fund reports in 2009. Exhibit 1 lists all the funds that existed in 2004 and in 2009, and their type. For those funds that were included in the study, the date of the annual report that was evaluated is given. Exhibit 1. The universe of listed real estate funds in 2004 and 2009

Fund name

2004 study Type

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Acucap Alan Gray

PLS PUT

Report analysed 2003 2003

ApexHi Atlas Capital Emira

PLS PLS PUT PUT

2003 2003 2003 2003

Growthpoint

PLS

2003

Hyprop Ifour

PLS PLS

2003 2003

Martprop Metboard Octodec Pangbourne Paramount Premium Prima Redefine Resilient

PUT PLS PLS PLS PLS PLS PUT PLS PLS

2003 2003 2003 2004 2003 2003 2003 2003 2003

SA Retail Spear head Sycom

PLS PLS PUT

2003 2003 2004

Fund name

2009 study Type

Acucap

PLS

Report analysed 2008

Ambit Apexhi

PLS PLS

Not analysed 2008

Capital Emira Fountainhead Growthpoint Hospitality Hyprop

PUT PUT PUT PLS PLS PLS

2008 2008 2008 Not analysed 2008 Not analysed

Madison

PLS

Asset managers

Octodec Pangbourne

PLS PLS

Not analysed 2008

Premium

PLS

Not analysed

Redefine Resilient SA Corporate

PLS PLS PUT

2008 2008 2008

Sycom Vukile

PUT PLS

Not analysed 2009

Source: Author

The results of both analyses are given in Exhibit 2. The left column lists seventeen potential real estate portfolio diversification categories identified by researchers.3 The middle 3

The list is based on Anon (2003); Cheng and Liang (2000); De Witt (1997); Del Casino (1995); Dohrmann (1995:87); Eicholtz and Hoesli (1995); Grissom, Kuhle and Walther (1996); Hartzell, Heckman and Miles (1986:230, 240, 250); Lee and Devaney (2004); Lieblich (1995:1021); Louargand (1992); Mueller and Laposa

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column identifies those categories that were actually mentioned in annual reports in the 2004 study, or were implied by the context. The rightmost column lists the categories mentioned or implied in the annual reports for the 2009 study. Exhibit 2. Real estate portfolio diversification categories in research and as reported in South Africa Potential diversification Category Asset quantity 1. Number of properties Location 2. Geographic region 3. Urban v suburban 4. International 5. Economic region Asset type 6. Property sector (-type)

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7. Industry type Property characteristics 8. Life cycle 9. Property size 10. Building quality 11. Building type Investment and finance Investment vehicle (ownership form) 13. Financing structure 14. Investment period Tenants 15. Tenant mix 16. Lease expiry profile 17. Lease types

Mentioned or implied in annual report? 2004 study 2009 study Implicit in all portfolios

Implicit in all portfolios

Standard category, always mentioned

Standard category, always mentioned

Implied in a few cases

Sometimes mentioned Implied in a few cases

Standard category, always mentioned

Standard category, always mentioned

Often mentioned Implied by institutional investment

Always mentioned Often mentioned

Mostly implied by nature of the fund

Sometimes mentioned Often mentioned

Sometimes mentioned or implied Often mentioned

Always mentioned Always mentioned Sometimes mentioned

Source: Author.

The table reveals that in 2004 few diversification categories were reported on, or implied in annual reports. From the sample of reports analysed in 2009, it is clear that the number of categories reported on has increased substantially. In 2004, two standard categories were always mentioned: geographic region and property type. By 2009 these standard categories are joined by three additional categories, which are always mentioned directly or implied by the data provided: property size, tenant mix and lease expiry profile. International investments

(1995); Mueller and Ziering (1992); Newell and Keng (2003); Pagliari (1990); Peng, Hudson-Wilson and Capps (2000:137); Pyhrr et al (1989:131,266); Seiler, Webb and Myer (1999); Viezer (2000:75); Wellner and Thomas (2004:2); Ziering and Hess (1995) and Ziering and McIntosh (1999). Real Estate Investment Market, Nova Science Publishers, Incorporated, 2009. ProQuest Ebook Central,

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are mentioned for the first time in 2009. Under the Collective Schemes Investment Control Act, 2002, such investment is now permitted.

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FOCUS AS AN ALTERNATIVE TO DIVERSIFICATION Diversification is expensive and difficult to implement. For every category invested in, the fund needs specialist consultants and specialised information (Hartzell, Heckman and Miles, 1986:246). Diversification is sometimes difficult to implement because a diversification category may be targeted, but suitable property in that category may not be available, or capital market conditions at the time may be unsuitable (Hudson-Wilson, 2000:212). Specialisation per property type allows concentration of resources. John Rainier, CEO of the now defunct Allan Gray Property Trust, holds the opinion that focused funds are more predictable than diversified funds, a fact that helps investors. He states that a listed fund may prefer to invest where there is profit to be made, rather than follow a restrictive strategy (Anon 2001). This is supported by Hedander (2005: 87), who states that the potential cost of management of a diversified firm is higher than a focused firm. Focus could increase unsystematic risk, but in real estate this is also the reason why excess profits are possible. A portfolio manager who understands the asset class and the specific submarket would be unwise not to capitalise on market inefficiencies, even at some increased risk. King and Young (1994: 6) suggest that Modern Portfolio Theory, on which the diversification principle is based, does not apply in real estate markets. It is better for an investor to apply underwriting principles (fundamental analysis) and investigate each individual investment thoroughly. Such an approach would also favour a focused investment strategy. Within the listed real estate fund industry in the United States, Capozza and Seguin (1999) found that a focus on property type is associated with an increase in wealth. Similar results were obtained in a Swedish study (Cronqvist, Högfeldt and Nilsson, 2001), who found that diversified real estate companies have lower values than focused companies. Hedander (2005: 88, 89) finds that diversified real estate investment firms are, on average, less profitable than more focused firms. Australian Listed Property Trusts shifted from a diversified to a more focused strategy between 1987 and 2004. This followed a general trend in other industries to decrease diversification (Hedander, 2005: 85, 107). Finally, the large funds often prefer that smaller funds be focused, to simplify their own portfolio balancing.

CONCLUSION There has been a marked improvement in the number of diversification categories reported on over the past five years. Judging by their annual reports, the portfolio managers of listed real estate investment funds in South Africa are doing what is possible in terms of diversification. Evaluation of the annual reports has also provided an opportunity to observe other matters. The reporting by most South African listed real estate funds at the time of the first study was described as being of a poor standard (Van Rooyen, 2005:5). Standards have

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improved dramatically between 2004 and 2009 and transparency has increased. For example, apart from detailed statements on future strategy, reports typically reflect the vacancy levels and average rentals achieved per sector and even per property. Geographic region is still mentioned as diversification category in the annual reports of listed companies. Effective diversification is essentially economic in nature and politically defined geographic regions are not necessarily meaningful. Several authors stress economic diversification and Mueller (1993:61) recommends completely dropping geographic region as a category. The persistent use of geographic region in listed real estate annual reports is questionable and should be modified. Goetzmann and Wachter (1995:271,299) warn that even investing in two widely separated cities such as New York and Los Angeles will not necessarily have a significant effect on diversification. The cities could be economic twins. On the other hand, some cities that are located close together, differ economically. Investing in these cities simplifies diversification and lessens the cost. More research on focus as an investment strategy by portfolio managers is needed. A combination of focus and diversification is evident in the strategy of the Hospitality Property Fund. This fund invests only in the hospitality industry, yet its annual report for 2008 has one of the best ratings for reporting on diversification. The fund’s managers seem to have captured the best of both worlds. Hopefully this study will stimulate even more detail about diversification strategies in annual reports, and stimulate more research in this field.

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REFERENCES Acucap Properties Limited. (2008). Annual report. Cape Town: Acucap. Anon. (2001). Diversification vs. focused portfolios. SAPOA Online 6/13/2001. www.sapoa.org.za. Anon. (2003). International diversification in property securities by Japanese investors 19732001. www.econ.mq.edu.au/cjes/research/Weston_2003_3. ApexHi Properties Limited. (2008). Annual report. Johannesburg: ApexHi. Capital Property Fund. (2008). Annual report. Rivonia:Capital. Capozza, D. & Seguin, P. (1999). Focus, transparency and value: the REIT evidence. Real Estate Economics, 27(4), 54-62, quoted in Hedander (2005, 90). Cheng, P. & Liang, Y. (2000). Optimal diversification: is it really worthwhile? Journal of Real Estate Portfolio Management, 6(1), 7-16. Cronqvist, H., Högfeldt, P. & Nilsson, M. (2001). Why agency costs explain diversification discounts. Real Estate Economics, 29(1), 85-126, quoted in Hedander (2005, 90). De Witt, P. M. (1997). Real estate diversification benefits. Journal of Real Estate Research, 14(1/2), 117-135. Del Casino, J. D. (1995). Portfolio diversification considerations. In J. L. Pagliari, (ed.), The Handbook of Real Estate Portfolio Management. (912-966). Chicago: Irwin. Dohrmann, G. (1995). The evolution of institutional investment in real estate. In J. L. Pagliari, (ed.), The Handbook of Real Estate Portfolio Management, (3-116). Chicago: Irwin.

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Eicholtz, P. M. A. & Hoesli, M. (1995). Real estate portfolio diversification by property type and region. Journal of Property Finance, 6(3), 39-59. Emira Property Fund. (2008). Annual report. Sandton:Emira. Fountainhead Property Trust. (2008). Annual report. Johannesburg: Fountainhead. Francis, J. C. (1993). Management of Investments. 3rd ed. New York: McGraw-Hill. Ghyoot, V. (2004). Diversification in the direct and listed property industry in South Africa. Paper presented at the Asian Real Estate Society Conference, Delhi, 9-11 August 2004. Ghyoot, V. (2006). Diversification information in annual reports. SA Business Review, 10(2), 111-129. Goetzmann, W. N. & Wachter, S. M. (1995). Clustering methods for real estate portfolios. Real Estate Economics, 23(3), 271-310. Grissom, T. V., Kuhle, J. L. & Walther, C. H. (1996). Diversification works in real estate, too. In J. B. Major, & F. Pan, (Eds.). Contemporary Real Estate Finance. (198-206). Upper Saddle River, New Jersey: Prentice Hall. Hartzell, D., Heckman, J. & Miles, M. (1986). Diversification categories in investment real estate. AREUEA Journal, 14(2), 230-254. Hedander, J. (2005). Focus, liquidity and firm value. Pacific Rim Property Research Journal, 11(1: March), 84-112. Hospitality Property Fund. (2008). Annual report. Johannesburg: Hospitality. Hudson-Wilson, S. (2000). Modern portfolio theory applied to real estate. In S. HudsonWilson, (ed.), Modern Real Estate Portfolio Management. (209-218). New Hope, Pennsylvania: Frank J Fabozzi Associates. King, D. A. & Young, M. S. (1994). Why diversification doesn’t work. Real Estate Review, (Summer), 6-12. Lee, S. & Devaney, S. (2004). Country, sector and regional factors in European property returns. Paper presented at the annual meeting of the European Real Estate Society, Milan, Italy, 2-5 June 2004. Lieblich, F. (1995). The real estate portfolio management process. In J. L. Pagliari, (ed.), The Handbook of Real Estate Portfolio Management. (998-1058). Chicago: Irwin. Louargand, M. A. (1992). A survey of pension fund real estate portfolio risk management practices. Journal of Real Estate Research, 7(4), 361-373. Markowitz, H. (1952). Portfolio selection. Journal of Finance, 7(1, March), 77-91. Miles, M. & McCue, T. (1984). Diversification in the real estate portfolio. The Journal of Financial Research, 7(1), 57-68. Mueller, G. R. (1993). Refining economic diversification strategies for real estate portfolios. Journal of Real Estate Research, 8(1), 55-68. Mueller, G. R. & Laposa, S. P. (1995). Property-type diversification in real estate portfolios: a size and return perspective. Journal of Real Estate Portfolio Management, 1(1), 39-50. Mueller, G. R. & Ziering, B. A. (1992). Real estate portfolio diversification using economic diversification. Journal of Real Estate Research, 7(4), 375-386. Newell, G., Acheompong, P. & Worzala, E. (2002). Property research priorities in Australia. Paper delivered at the annual conference of the Pacific Rim Real Estate Society, Christchurch, 21-23 January 2002. Newell, G. & Keng, T. Y. (2003). The significance of property sector and geographic diversification in Australian institutional portfolios. Pacific Rim Property Research Journal, 9(3), 248-264.

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Ori, J. J. (1995). A seven-step portfolio diversification strategy. Real Estate Review, (Summer), 27-. Pagliari, J. L. (1990). Real estate in 3-D: see it now!. Real Estate Issues, Fall-Winter, 16-19. Pangbourne Properties Limited. (2008). Annual report. Rivonia:Pangbourne. Peng, R., Hudson-Wilson, S. & Capps, O. (2000). Modelling office returns at the regional level. In S. Hudson-Wilson, (ed.), Modern Real Estate Portfolio Management. (123-138). New Hope, Pennsylvania:Frank J Fabozzi Associates. Pyhrr, S. A., Cooper, J. R., Wofford, L. E., Kaplin, S. D. & Lapides, P. D. (1989). Real Estate Investment: Strategy, Analysis, Decisions. 2nd ed. New York:Wiley. Redefine Income Fund. (2008). Annual report. Johannesburg:Redefine. Resilient Property Income Fund. (2008). Annual report. Rivonia:Resilient. SA Corporate Real Estate Fund. (2008). Annual report. Durban: SA Corporate. Seiler, M. J., Webb, J. R. & Myer, F. C. N. (1999). Diversification issues in real estate investment. Journal of Real Estate Literature, 7, 163-179. South Africa (Republic). (2002). Collective Investment Schemes Control Act, Act 45 of 2002. Van Rooyen, D. (2005). Genoteerde eiendom kort King-pil, Sake-Rapport, (22 May), 5. Viezer, T. W. (2000). Evaluating ‘within real estate’ diversification strategies. Journal of Real Estate Portfolio Management, 6(1), 75-95. Vukile Property Fund Limited. (2009). Annual report. Constantia Kloof: Vukile. Wellner, K. & Thomas, M. (2004). Diversification benefits from European direct real estate investments with a special focus on the German market. Paper delivered at the European real estate society conference, Milan, Italy, 2-5 June 2004. Worzala, E. M., Gilliand, D. & Gordon, J. (2002). The real estate research needs of the plan sponsor community. Journal of Real Estate Portfolio Management, 8(1), 65-78. Ziering, B. & Hess, R. (1995). A further note on economic versus geographic diversification. Real Estate Finance, 12(3), 53-60. Ziering, B. & McIntosh, W. (1999). Property size and risk: why bigger is not always better. Journal of Real Estate Portfolio Management, 5(2),105-112.

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Chapter 9

SHOULD BANKING POWERS EXPAND INTO REAL ESTATE BROKERAGE AND MANAGEMENT? Walter W. Eubanks*

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ABSTRACT In late 2000, the Federal Reserve and the Treasury proposed to increase banking powers. They proposed allowing banking companies to engage in real estate brokerage and management, as activities that are financial in nature. The substantiative issues are the respective nature of banking and of real estate activities and the potential impact on consumers. Procedural questions involve the intent of Congress in P.L. 106-102, which delegated authority to both agencies to issue new regulations. The reintroduced Community Choice in Real Estate Act, H.R. 111/S. 413, 110th Congress, would permanently remove these real estate activities from consideration under the marketadaptive powers of the regulators. In the mean time, Treasury spending bills have forestalled any such regulations for six fiscal years, most recently in P.L. 110-5.

SUMMARY In late 2000, the Federal Reserve and the Treasury proposed to increase banking powers. They proposed allowing banking companies to engage in real estate brokerage and management, as activities that are financial in nature. The substantiative issues are the respective nature of banking and of real estate activities and the potential impact on consumers. Procedural questions involve the intent of Congress in P.L. 106-102, which delegated authority to both agencies to issue new regulations. The reintroduced Community Choice in Real Estate Act, H.R. 111/S. 413, 110th Congress, would permanently remove these real estate activities from consideration under the market-adaptive powers of the regulators. *

Email: [email protected], 7-7840

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In the mean time, Treasury spending bills have forestalled any such regulations for six fiscal years, most recently in P.L. 110-5.

FRAMEWORK OF LEGISLATION AND REGULATION The Gramm-Leach-Bliley Act (GLBA, P.L. 106-102)1 was landmark legislation that allowed banking, securities, and insurance companies to operate in affiliation with each other under the organizational form of financial holding companies (FHCs). GLBA also permitted FHCs, like financial subsidiaries of banks (FSs), to engage in a variety of activities not previously allowed to banks or companies owning banks.2 Under GLBA, the Federal Reserve (Fed) and the Treasury Department, which contains the Office of the Comptroller of the Currency (OCC), have authority to issue regulations expanding activities for FHCs and FSs, respectively. In GLBA, §103 requires that the Fed find that new activities for FHCs are financial in nature, incidental to a financial activity, or, both “complementary” to a financial activity and not posing a substantial risk to safety and soundness. §121 repeats the standard for the OCC governing FSs. Congress crafted GLBA as a compromise to allow financial affiliations while avoiding a general mixing of “banking” with “commerce.” It specifically excluded bank FSs from underwriting insurance and from real estate investment and development, except as may already have been authorized by other law.3

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PROPOSED BROKERAGE AND MANAGEMENT REGULATION In December 2000, the Fed and the Treasury released a proposal to allow banking companies into new real estate businesses, under §§103 and 121.4 Their proposal would allow FHCs and FSs to enter real estate brokerage and property management, if these activities could be considered financial in nature or incidental to a financial activity (not the less exacting “complementary” test). “Brokerage” includes acting as an intermediary between parties to a real estate transaction, listing and advertising real estate, soliciting sales, negotiating terms, and handling closings. It is not purchase or sale of property as an owner, and it requires state licensing and regulation. “Property management” includes soliciting tenants, negotiating leases, servicing rents, maintaining security deposits, making operating payments, and overseeing upkeep. Managers thus need not be owners, and banking firms could not become owners of real estate through this proposal. 1

113 Stat. 1338-1481. FHCs hold controlling stock interests in separately incorporated or chartered businesses, such as banks, mortgagecompanies, stockbrokers and dealers, etc. The Federal Reserve supervises all FHCs, which are not federally insured.FSs are businesses that banks themselves own. The bank regulators supervise FSs, which, while not necessarilyfederally insured, are owned directly by insured banks. These structural differences are important because GLBAallows more latitude for uninsured FHCs to operate in nontraditional lines of business. FHCs are considered less likely than banks and bank subsidiaries to cause difficulties for the federal support mechanisms for banks, especially deposit insurance funds, should they encounter losses. 3 113 Stat. 1373, 12 U.S.C. 24a. 4 Board of Governors of the Federal Reserve System and Department of the Treasury, “Bank Holding Companies and Change in Bank Control,” Federal Register, vol. 66, no. 2, January 3, 2001, pp. 307-314. 2

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The Fed and the OCC historically disallowed real estate brokerage and property management activities for their regulated institutions. The Office of Thrift Supervision (also within the Treasury) does allow subsidiaries of federal savings associations to provide real estate brokerage and property management services. About half the states seem to allow these activities for the financial institutions that they charter and regulate; however, actual practice of bank realty powers appears very rare.5 Conversely, real estate brokers and managers cannot offer essential banking services—accepting deposits and making commercial loans—and are not seeking to become bank-like. They do not want to form financial holding companies or obtain bank charters, and especially seek to avoid becoming regulated by the Fed or other banking agency. Bankers (American Bankers Association, Financial Services Roundtable, and New York Clearing House Association) requested this authority. In their view, it would allow financial institutions to offer a fuller range of financial service, using many skills that banks already have. They argue that these activities are financial in nature and would lower the costs of realty transactions. Other supporters are the America’s Community Bankers, Consumer Bankers Association, Independent Community Bankers of America, Realty Alliance, and Real Estate Services Providers Council. The National Association of Realtors (NAR) opposes the proposal, arguing that no law, including GLBA, authorizes banking firms to provide real estate brokerage and property management, which it argues are nonfinancial in nature. From its perspective, the proposal would create anticompetitive and anticonsumer concentrations of power dominating the realty industry and increasing costs to consumers. Other opposing entities are the Building Owners and Managers Association, Consumers Union, Institute of Real Estate Management, International Council of Shopping Centers, National Affordable Housing Management Association, and National Association of Homebuilders.

Arguments Concerning the Nature of the Industries Favoring the Proposal (1) Banks, FHCs, and FSs already engage in a variety of other real estate activities: financing, appraising, leasing, settling, escrowing, and investment advising. (2) Agency services that FHCs and FSs provide in securities and insurance are similar to those of real estate brokers and property managers. (3) FHCs may act as “finders,” bringing together buyers and sellers of non-real-estate assets generally. (Found parties must negotiate terms, including prices, for themselves.)6 (4) Bankers already act as intermediaries in arranging commercial real estate equity financing (transfer of title, control, and risk arrangements for projects) and often finance the underlying projects.

5

Conference of State Bank Supervisors, “Real Estate Brokerage Chart,” available at http://www.csbs.org/ government/ legislative/realestate/re_chart.htm. 6 12 CFR 225.86(d). Real Estate Investment Market, Nova Science Publishers, Incorporated, 2009. ProQuest Ebook Central,

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Walter W. Eubanks (5) Several diversified financial companies provide realty services beyond their more traditional banking, securities, and insurance services. Some realty-based companies offer bank-like services, most visibly mortgages. (6) Some savings associations and state-chartered banks already provide these real estate services. Twenty-seven states and the federal Office of Thrift Supervision appear to allow the activities at issue for deposit-based financial institutions, at least statutorily.

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Opposing the Proposal (1) GLBA specifically prohibits FSs from engaging in real estate development and investment. Thus, its intent may have been to restrain new realty powers of bankers. (2) Real estate brokerage and property management are commercial activities. Their necessary hands-on sales skills are far different from lending. When bankers sponsored Real Estate Investment Trusts in the 1970s, most collapsed with large losses. (3) Real estate brokerage and property management involve negotiation of realty transactions. That role has been forbidden to FHC s as “finders.” FHC finders may not engage in any activity requiring registration or licensing as a realty agent or broker. (4) One study states that the real estate industry is highly competitive and efficient, much more productive than financial services generally.7 If so, bankers would presumably bring almost no net benefit to real estate brokerage and property management. (5) Entry of deep-pocket banking companies, which benefit from federal assistance including deposit insurance, might drive out brokers and property managers, which typically operate on a much smaller scale. (6) Competition for lending could decline if buyers believe that one-stop realty transacting and financing would ease credit approval. Mortgage lenders not involved with the brokerage part of realty transactions might lose business.

Arguments Concerning Customers (Consumers/Businesses) Favoring the Proposal (1) Customers could benefit from lower costs and greater convenience if one organization provided most realty services bundled together. Transaction details (paperwork) often overwhelm buyers and sellers of property. Consumers, including buyers of these services, generally prefer more competitors in a field to fewer.8 (2) Clients of banks need not face complications of start-from-scratch checking of creditworthiness, which their bankers already know. The credit approval/

7 8

A conclusion of a study by Leonard Zampano of the University of Alabama presented at the NAR Midyear Legislative Meetings and Trade Expo, Washington, DC, May 17, 2001. American Bankers Association, “Consumers Want More Real Estate Competition, New Survey Reveals,” athttp://www.aba.com/Press+Room/051501realestate.htm.

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underwriting process is the stage of real estate purchase that is usually the most delayed. (3) Laws against forcing customers to obtain both nonlending services and loans from banking companies (which observers call “tying”) would still restrain market power of companies providing banking and realty services jointly. Meanwhile, many real estate brokers seem to have close ties with favorite mortgage lenders, title companies, etc., making it easy for customers to deal with almost one-stop financial shopping.

Opposing the Proposal (1) Customers might believe that obtaining realty brokerage or property management services from bankers would ease credit approval for their financing. Better, unbundled deals may be available from competition among multiple providers. (2) Customer service could suffer with fewer specialized providers. Bank credit standards might not be appropriate for realty transactions requiring flexibility, especially when tightening credit quality concerns (“credit crunches”) cut back bank lending. (3) Low- and moderate-income households lacking bank relationships might not benefit from bundled realty services designed for bank clients of greater resources.

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DEVELOPMENTS AND LEGISLATION The House Subcommittee on Commercial and Administrative Law held its Oversight Hearing on Proposed Federal Reserve/Treasury Department Real Estate Brokerage and Management Rule. The Senate Subcommittee on Financial Institutions held its hearing, Bank and Financial Holding Company Engagement in Real Estate Brokerage and Property Management, the House Subcommittee on Financial Institutions and Consumer Credit held a hearing on H.R. 3424. The Community Choice in Real Estate Act of 2001 (which had a Senate version, S. 1839) would continue to keep banks out of real estate management.

2003 Representative Calvert and Senator Allard reintroduced the Community Choice in Real Estate Act, now numbered H.R. 111 and S. 98, to prohibit FHCs and national banks from engaging, directly or indirectly, in real estate brokerage or real estate management activities. Both measures were identical to their predecessors. The 108th Congress passed the basic federal spending package, P.L. 108-7. It retained the prohibition amendment, disallowing any funds for Treasury Department issuance of the bankers’ real estate regulation in FY2003.

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2004 Representative Northup reintroduced the amendment into the Transportation Appropriations bill H.R. 2989. The measure prohibited FY2004 funds from being used to implement the proposed rule. The House approved that measure.9 Senate approval resulted in P.L. 108-199, continuing no-spending language. For the next fiscal year (FY2005), no-spending language reappeared as Section 523 of H.R. 5025, the Transportation, Treasury, and Independent Agencies Appropriations Act. Its ban on Treasury regulatory issuance via a spending cutoff was in the original measure, which cleared the subcommittee. Stronger, permanent, prohibitory language was included in Section 217 of the counterpart S. 2806, which, if Congress had approved it, would have had the force of law to prevent the proposed activity in the future. Following conference approval, the FY2005 omnibus spending measure, P.L. 108-447, adopted the House version.10 The final version of the Treasury appropriations language thus included the third moratorium, until the end of FY2005.

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2005 Representative Calvert reintroduced the Community Choice in Real Estate Act, H.R. 111. Senator Allard reintroduced its companion bill, S. 98. Conversely, Representative Oxley introduced H.R. 2660, the Fair Choice and Competition in Real Estate Act of 2005, on May 26. It would amend the Bank Holding Company Act of 1956 (the foundation for GLBA) to allow real estate brokerage activities and real estate management activities for financial holding companies and financial subsidiaries of national banks.11 The House Committee on Financial Services held a hearing, Protecting Consumers and Promoting Competition in Real Estate, on June 15.12 In its first report on real estate brokerage, the Government Accountability Office found that state-chartered bank activity (where permitted) had little effect on competition or consumers.13 On December 5, the OCC relaxed prohibitions on bank investments in real estate development projects. The agency wrote two interpretive letters allowing national banks to develop a hotel and a mixed-use project. The Bank of America proposed to invest in a 150room hotel, and PNC sought to develop a facility with a hotel, retail office space, offices, and condominiums. A third interpretive letter was written dated December 21, 2005, allowing Union Bank of California to invest in a wind energy project in which the bank would own 70% of the project, including the land and wind turbines. The OCC defended its approval of the December 5 interpretive letters, citing 12 U.S.C. § 29 that allows banks to invest in bank premises. Among the justifications for approval of the wind project was that 12 U.S.C. § 29 provides that national banks may purchase, hold, and convey real estate and that this acquisition of interests in real estate is not speculative. Those developments would appear 9

Division F, Title II, Section 538. Congressional Record, November 25, 2003, p. H12415. Division H, Section 519, Congressional Record, November 20, 2004, p. H10358. 11 Karen L. Werner, “Reps. Oxley, Frank Introduce Measure To Allow Real Estate Brokerage for Banks,” Daily Report for Executives, May 31, 2005 , p. A-11. 12 See http://financialservices.house.gov/hearings.asp?formmode=detail&hearing=395. 13 Real Estate Brokerage: Factors That May Affect Price Competition, GAO-05-947. 10

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essentially to end the stricture against national bank ownership and leasing of real estate, thereby moving further toward allowing bankers into real estate brokerage, etc.14

2006 In the FY2006 appropriations process for H.R. 3058, covering the Treasury, conferees adopted House language prohibiting the Treasury from finalizing the contentious rule in FY2006 (Section 718). Conferees rejected stronger language in the Senate version (introduced in the form of an amendment)of the measure (Section 723) that might have permanently prevented a decision on the issue, therefore issuance of any permissive regulation. President Bush signed this measure into law (P.L. 109-115) on November 30, 2005.

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2007 The Community Choice in Real Estate Act of 2007 was reintroduced in both houses of the 110th Congress as S. 413 by Senator Hillary Clinton and as H.R. 111 by Representative Paul Kanjorski. Like the previous versions of these bills, these new proposals would amend the Bank Holding Company Act of the United States to prohibit financial holding companies and national banks from engaging, directly or indirectly, in real estate brokerage or real estate management activities. In the mean time, the rule in FY2006 (Section 718) was continued under the Revised Continuing Appropriations Resolution, 2007 (P.L. 110-5). In short, the House appropriations bill, which for the past six years has included a one year prohibition on funding the Treasury to complete the rulemaking that was authorized by the 1999 GrammLeach-Bliley Act was extended another year.

14

R. Christian Bruce, “OCC Defends Letter on Real Estate Powers While Realtors Call for Action From Congress,” Daily Report for Executives, February 2, 2006, p. A-31.

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Chapter 10

EMERGING ECONOMIES AND SECONDARY MORTGAGE MARKETS Raymond T. Abdulai* and Frank Gyamfi-Yeboa

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ABSTRACT Access to long-term credit remains one of the major obstacles to solving the perennial housing problems in many emerging economies. These countries have been making serious attempts at developing their mortgage markets in recent times. There is a general consensus on the need for emerging economies to develop housing finance systems that would ensure easy, affordable and sustainable accessibility to credit. The exact nature and the elements of such a system are still subject to debate. In this commentary, we argue for the institution of secondary mortgage markets but recommend the use of mortgage credit institutions in the short to medium term.

INTRODUCTION The use of mortgage debt in financing home purchases is a common feature of most advanced economies. The capital outlay required in home purchases is usually beyond the amount a typical household can accumulate in savings over a period when the need to own a home becomes most pressing. Most advanced economies have developed housing finance systems that allow households to have access to a reliable and sustainable source of funding for housing. One of such systems is the operation of a secondary mortgage market (SMM), where existing mortgages are bought and sold. This market provides liquidity to banks and other mortgage originators by allowing them to replenish funds and to help solve the maturity mismatch problem that many originators face. The secondary mortgage market has its origins in the US and has been in operation since 1938 when the Federal National Mortgage Association (FNMA or “Fannie Mae”) was *

Corresponding author: Email: [email protected] , Tel: +44 (0)151 321 2573

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established by an Act of the US Congress. The market has since evolved over the years in the US and is currently dominated by Fannie Mae and Freddie Mac, the two major government sponsored agencies (GSEs). The studies of Kolari, Fraser and Anari (1998), Todd (2000), Ambrose, LaCour-Little and Sanders (2002) and Passmore et al. (2002) have identified various benefits of a secondary mortgage market; these include reduction in mortgage costs and easy access to credit. Jaffe and Renaud (1997) note that there are various forms of secondary mortgage market systems, which are differentiated by the instrument used for the mortgage sale and the type of investors or institutions who buy the mortgages. The two prominent SMM systems are the use of mortgage credit institutions (MCIs) to provide longterm loans to depository institutions that hold mortgages and the use of mortgage securitization. The MCIs raise funds from the capital markets and distribute the proceeds to participating mortgage lenders. The services and products offered by MCIs provide liquidity and help mortgage lenders to manage their assets and liabilities. An example of an MCI is the Federal Home Loan Bank System in the US. Mortgage securitization, on the other hand, involves the issuance of securities against a pool of mortgages. The securitization process starts with the purchase of mortgage loans by either the GSEs or private market conduits such as investment banks. The interest and principal receivable from the mortgage loans in a pool are then packaged and sold to investors in the form of mortgage backed securities. As at the end of 2008, about 60% of all home mortgages outstanding had been securitized in the US (The Federal Reserve System, 2008). Thus, the use of mortgage securitization dominates the US secondary mortgage market. The recent turmoil in the financial markets occasioned by the housing crisis in the US raises concerns about the dark side of an SMM, particularly, using mortgage securitization as an instrument. It has been argued that low underwriting standards prevalent in subprime lending, where loans are made to borrowers with weak credit, was facilitated by mortgage securitization. Since mortgage originators did not have to keep mortgages on their books, they had little incentive to carefully scrutinize borrowers. Passmore and Sparks (2000), for instance, show that the use of automated underwriting and mortgage securitization tends to lead to mortgage originators cherry picking and keeping mortgages of high credit quality whilst passing on those with low quality to securitizers. The surge in subprime lending activity in the early part of this decade was largely driven by the ease with which such mortgages could be sold in the secondary mortgage market. It is now well established that the recent downturn in the global economy was precipitated by increased delinquency on mortgages, especially, subprime loans in the US. Given the near collapse of the financial system and the global recession that resulted from the excesses in the mortgage market, it is likely that policy makers and governments in developing countries would be skeptical about the need to institute secondary mortgage markets in their countries. The critical issues that most policy makers in developing economies need to consider are: whether or not it is prudent to develop a secondary mortgage market for a developing country in the first place, given the damage that excesses in this market can bring to even the most advanced economies like the US; and the parameters on which a good housing finance system should be based. These issues are important because most emerging economies have been making serious attempts at developing their mortgage markets over the past few decades and the decision to institute a secondary mortgage market as part their housing finance system might be influenced by the recent happenings in the US mortgage market.

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PARAMETERS OF A GOOD HOUSING FINANCE SYSTEM A good housing finance system should address three key issues. Firstly, it should integrate the mortgage market and the broader financial market. The competition for capital among different investments and users is often keen in most emerging economies. In any fairly efficient and liberalized economic environment, capital will typically be allocated to uses that promise the highest return on a risk-adjusted basis. It is important to stress that mortgage debt is only one of many assets available to banks and other investors in debt instruments. One way to ensure that the housing sector attracts adequate amount of capital is to make mortgages part of the mainstream assets on the capital market by integrating the mortgage and capital markets. A mortgage market that is segmented from the broader capital markets could potentially constrain the supply of credit for home purchases. According to Devaney and Pickerill (1990), McGarvey and Meador (1991) and Goebel and Ma (1993) the introduction of a secondary mortgage market and deregulation of financial markets are catalysts for integration; the authors have identified deregulation of financial markets to be the dominant cause for integration. One could, therefore, argue that if the aim is to integrate mortgage and capital markets, then emerging economies would only need to deregulate their financial markets. Instituting a secondary mortgage market might only be considered as an unnecessary complication. However, as we argue below, albeit deregulation of financial markets is an important step towards integration, it is not sufficient to reduce or eliminate the constraints on the supply of mortgage credit. The second issue that a good housing finance system should address is to ensure that any constraints on mortgage credit are eliminated or reduced. This issue is somewhat related to the first except that as shown by Gyamfi-Yeboah and Ziobrowski (2009), the constraints on mortgage credit could remain significant even when the mortgage and capital markets are integrated. Using data from South Africa, the authors show that even though the South African mortgage market was well integrated into the capital markets prior to the year 2000, the constraints on mortgage credit persisted until the introduction of the secondary mortgage market in 2001 when the constraints began to ease significantly. The implication of this finding is that a formal mechanism is required to help channel funds to the housing sector. A mechanism that could be used is mortgage securitization but given the problems it has created for even an advanced economy like the US, alternative mechanisms such as the use of MCIs may better suit the peculiar situations of emerging economies. Lastly, a good housing finance system should ensure that there is a reduction in mortgage costs to borrowers. The ultimate objective of any good housing finance system should not only be to make funds available to households, but the costs of such funds must be reasonable. Any efficiency that results from the institution of a formal housing finance system must benefit households.

CONCLUSION In this commentary, the possibility of instituting SMMs in developing economies is examined. The excesses in the US mortgage market in the recent past caused in part by mortgage securitization, raises legitimate questions on whether SMMs should play a

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significant role in the development of mortgage markets in emerging economies. In spite of the role played by the secondary mortgage market in the recent housing crisis in the US, it still remains a very essential tool that can be used to channel long-term funds to the housing market. We argue that emerging economies would need to institute SMMs as part of a formal housing finance system in order to ensure affordable and sustainable access to long-term capital. The nature and form of the SMM systems that such economies institute should be dictated by the peculiar needs of the country, the depth of its capital market and the lessons from the experiences of other countries notably the US. It appears that the use of MCIs would be the more feasible option for most developing countries given the complex nature of mortgage securitization and the underdeveloped nature of the capital markets in these economies.

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REFERENCES Ambrose, B. W., LaCour-Little, M. & Sanders, A. B. (2004). The Effect of Conforming Loan Status on Mortgage Yield Spreads: A Loan Level Analysis. Real Estate Economics, 32(4), 541-69. Devaney, M. & Pickerill, K. (1990). The Integration of Mortgage and Capital Markets. The Appraisal Journal, January, 109-113. Goebel, P. R. & Ma, C. K. (1993). The Integration of Mortgage Markets and Capital Markets. Journal of the American Real Estate and Urban Economics Association, 21, 511-538. Gyamfi-Yeboah, F. & Ziobrowski, A. J. (2009). The Integration of Mortgage and Capital Markets in Emerging Economies – Evidence from South Africa. Journal of Real Estate Finance and Economics, DOI 10.1007/s11146-009-9166-2. Jaffe, D. M. & Renaud, B. (1997). Strategies to Develop Mortgage Markets in Transition Economies. In J. Doukas, V. Murinde and C. Wihlborg (Eds.). Financial Sector Reform and Privatization in Transition Economies, Amsterdam, Elsevier Science Publication. Kolari, J. W., Fraser, D. R. & Anari, A. (1998). The Effect of Securitization on Mortgage Market Yields: A Cointegration Analysis. Real Estate Economics 26(4), 677-93. McGarvey, M., & Meador, M. (1991). Mortgage Credit Availability, Housing Starts and the Integration of Mortgage and Capital Markets: New Evidence Using Linear Feedback Journal of the American Real Estate and Urban Economics Association, 19, 25-40. Passmore, W. & Sparks, R., (2000). Automated Underwriting and the Profitability of Mortgage Securitization. Real Estate Economics, 28(2), 285-305 Passmore, W., Sparks, R. & Ingpen, J. (2002). GSEs, Mortgage Rates, and the Long-Run Effects of Mortgage Securitization. Journal of Real Estate Finance and Economics, 25(3), 215-42 Todd, S. (2001). The Effects of Securitization on Consumer Mortgage Costs. Real Estate Economics, 29(1), 29-54

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CHAPTER SOURCES

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The following chapters have been previously published: Chapter 1 – This is an edited, excerpted and augmented edition of a United States Congressional Budget Office testimony, given by Douglas W. Elmendorf, dated January 28, 2009. Chapter 2 – This is an edited, excerpted and augmented edition of a National University of Singapore Department of Real Estate Publication. Chapter 3 – This is an edited, excerpted and augmented edition of a Liverpool John Moores University and Wolverhampton University publication. Chapter 4 – This is an edited, excerpted and augmented edition of a publication written by Chihiro Shimizu, on July 31, 2009. Chapter 5 – This is an edited, excerpted and augmented edition of a United States Congressional Research Service publication, Report #RL34236, dated October 28, 2008. Chapter 6 – This is an edited, excerpted and augmented edition of a United States Congressional Research Service publication, Report #R40498, dated April 8, 2009. Chapter 7 – This is an edited, excerpted and augmented edition of a testimony given before Oversight and Government Reform Committee, dated November 2, 2009. Chapter 8 – This is an edited, excerpted and augmented edition of a publication written by Valmond Ghyoot, for the FPD Business School in South Africa. Chapter 9 – This is an edited, excerpted and augmented edition of a United States Congressional Research Service publication, Report #RS21104, dated April 24, 2007. Chapter 10 –This is an edited, excerpted and augmented edition of a publication written by Raymond T. Abduali and Frank Gyamfi-Yeboa.

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INDEX

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A  accessibility, xi, 177 accounting, ix, 8, 9, 11, 20, 27, 33, 34, 127, 129, 130, 133, 147, 149 accounting standards, 9, 33 adjustment, ix, 21, 105, 106, 107, 108, 115, 117, 118, 119, 121, 123, 133 Africa, x, 76, 80, 103, 161 age, ix, 105, 107, 108, 109, 115, 122 appraisals, 157, 158 arbitrage, 35, 100 Asia, viii, 31, 36 assessment, 11, 22, 82, 157 assets, vii, 1, 2, 5, 7, 8, 9, 11, 12, 13, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 33, 37, 107, 121, 123, 124, 128, 129, 142, 143, 146, 153, 154, 162, 171, 178, 179 assumptions, 56, 57, 117, 158 asymmetric information, 84, 99 asymmetry, 81, 83, 98, 99, 100 Australia, 36, 37, 76, 102, 161, 167 authority, x, 21, 25, 26, 27, 82, 128, 134, 141, 143, 145, 149, 151, 152, 169, 170, 171 authors, 42, 44, 166, 179 availability, vii, x, 1, 2, 6, 7, 13, 14, 18, 81, 86, 151, 153, 156, 157, 159

B  background, 107, 115 balance sheet, 6, 8, 9, 11, 12, 13, 17, 18, 24, 156 bank failure, 9, 152 bank ownership, 175 bankers, 158, 172, 173, 175 banking, x, 3, 8, 9, 10, 131, 151, 152, 153, 154, 155, 156, 158, 169, 170, 171, 172, 173 banking industry, 151, 158

bankruptcy, 8, 12, 18, 106 banks, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 21, 22, 23, 24, 29, 144, 151, 152, 153, 154, 155, 156, 157, 158, 170, 171, 172, 173, 174, 175, 177, 179 basis points, 28, 129 behavior, 12, 34, 36 Beijing, 37, 68, 72 bond market, 84 bondholders, 136 bonds, 4, 5, 24, 131, 132, 152 borrowers, vii, 1, 2, 4, 5, 8, 9, 13, 15, 16, 17, 18, 76, 77, 78, 100, 132, 134, 135, 136, 153, 156, 157, 158, 178, 179 borrowing, vii, 1, 2, 4, 10, 14, 18, 24, 28, 30, 132, 134, 152 Britain, viii, 75, 76 business cycle, 76, 121 buyer, 14, 24, 25, 78, 79, 145

C  Canada, 76 capital markets, 14, 178, 179, 180 cash flow, ix, 21, 22, 32, 33, 34, 36, 105, 143, 155, 156, 157 causation, 79 central bank, 2, 9, 19, 27, 108, 121 certificates of deposit, 25 City, 68, 70, 106 civil servants, 22 classes, 132, 154, 156 classification, 34, 37, 157 coefficient of variation, 38, 49, 53, 64 collateral, 12, 20, 23, 24, 25, 29, 77, 78, 80, 131, 155, 156, 157, 158 commerce, 96, 170 commercial bank, 3, 5, 139, 151 commodity, 80, 98 common law, 80, 81

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communication, 81, 148 community, 38, 153, 154, 155, 156, 168 comparative advantage, 32, 64 compensation, 17, 49, 52, 64 competing interests, 13 competition, 77, 173, 174, 179 complications, 172 concentration, 165 conceptual model, x, 161 concrete, 78, 123, 136 confidence, 3, 4, 5, 9, 78 Congress, 22, 127, 128, 159, 169, 170, 173, 174, 175, 178 Congressional Budget Office, 8, 20, 27, 28, 30, 181 consensus, xi, 37, 177 Constitution, 80 construction, ix, 2, 100, 105, 115, 122, 153, 154, 155, 156 consumer price index, 107 consumer surplus, 100 consumers, vii, x, 1, 13, 19, 23, 107, 151, 169, 171, 174 consumption, vii, 1, 49, 78, 82 control, x, 3, 22, 24, 106, 128, 137, 148, 171 correlation, 63, 121, 162 correlation coefficient, 121 costs, ix, 2, 8, 9, 14, 16, 17, 19, 20, 21, 22, 78, 79, 81, 88, 89, 93, 99, 100, 121, 122, 127, 134, 136, 166, 171, 172, 178, 179 covering, 175 credit, vii, x, xi, 1, 2, 3, 4, 5, 6, 7, 8, 13, 14, 16, 20, 21, 22, 23, 24, 26, 27, 28, 30, 76, 78, 79, 98, 130, 132, 137, 142, 143, 151, 152, 153, 155, 156, 157, 158, 172, 173, 177, 178, 179 credit market, vii, 1, 2, 14, 98, 153, 157 credit rating, vii, 1, 4 creditors, 7, 12, 13, 18, 19, 76, 77, 78, 99 cumulative distribution function, 39 currency, 2, 9, 19, 27

D  database, 35, 81, 82, 117 debt, x, 3, 4, 6, 11, 12, 13, 14, 18, 20, 21, 24, 26, 29, 30, 128, 131, 137, 152, 153, 155, 156, 177, 179 debts, 3, 18, 23 decision-making process, 32 decisions, viii, 13, 75, 77, 78, 79, 82, 133, 143 defects, 99, 100 deficit, 20, 78 definition, 143, 148 delinquency, 2, 134, 153, 178 delivery, 102 demand curve, 100

deposit accounts, 3 deposits, 8, 19, 21, 23, 29, 170, 171 depreciation, 33, 108, 115, 122, 146 deregulation, 179 derivatives, 132, 133 developing countries, 76, 178, 180 developing nations, 76 differentiation, 106 directors, 144, 147 disclosure, 99, 140, 142, 143, 144 discounting, 22 discourse, 83 distortions, 33, 100 distribution, 39, 66, 95, 117, 118, 119 diversification, x, 132, 161, 162, 163, 164, 165, 166, 167, 168 dominance, viii, 31, 36, 38, 39, 47, 64 duration, 41, 119

E  earnings, 8, 19, 21, 29, 32, 33, 34, 35, 129, 130, 136, 154, 155, 156 East Asia, 52, 80 economic activity, vii, 1, 2, 6, 7 economic boom, 8 economic crisis, 52 economic cycle, ix, 106 economic development, viii, 75, 76, 77, 80, 81 economic downturn, 15, 154 economic efficiency, 15 economic growth, vii, 1, 8, 10, 76, 152 economic theory, 100 economics, 39 Education, 14, 65 employees, 22, 136 employment, 5, 96, 99, 153 energy, 12, 135, 174 environment, ix, 9, 106, 121, 123, 130, 152, 157, 158, 179 equality, 38, 51, 61 equilibrium, 52, 96 equity, 3, 6, 10, 11, 13, 16, 17, 28, 29, 33, 171 exercise, 36, 148 expenditures, 21, 99 expertise, 78 exposure, 19, 156 extrapolation, 32, 40, 53, 56, 61, 62, 63

F  failure, 4, 15, 140, 146 family income, 15 federal funds, 2, 3, 4, 23

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Federal Reserve Board, 157 finance, vii, viii, xi, 1, 12, 14, 15, 25, 31, 32, 34, 35, 37, 39, 56, 64, 132, 133, 156, 164, 171, 177, 178, 179, 180 financial crisis, 2, 4, 9, 21, 153, 158 financial distress, 2 financial institutions, vii, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 19, 23, 25, 80, 81, 133, 151, 154, 156, 171, 172 financial markets, vii, x, 1, 2, 3, 4, 6, 7, 9, 10, 19, 24, 129, 131, 151, 152, 154, 156, 158, 178, 179 financial performance, 134 financial resources, 30, 78 financial sector, vii, 1, 6, 8, 9, 15, 16, 19, 136, 156 financial support, 4, 129, 131, 134 financial system, vii, ix, 1, 2, 7, 9, 10, 12, 16, 76, 106, 124, 127, 136, 151, 157, 158, 178 financing, 14, 132, 134, 171, 172, 173, 177 firms, 4, 6, 8, 11, 13, 15, 36, 154, 155, 165, 170, 171 focusing, 95, 113, 123 Ford, 53, 68, 135 forecasting, 37 foreclosure, ix, 2, 5, 15, 16, 18, 127, 128 fraud, 140, 145, 149 funding, 6, 13, 14, 15, 18, 19, 24, 25, 27, 29, 30, 154, 175, 177 funds, vii, x, 1, 2, 3, 4, 5, 8, 9, 10, 11, 12, 15, 18, 19, 25, 28, 106, 107, 108, 121, 128, 131, 132, 137, 154, 161, 163, 165, 170, 173, 174, 177, 178, 179, 180

G  GDP, 8, 9, 76, 95 General Motors, 15 Generally Accepted Accounting Principles, 130 generation, ix, 105 Georgia, x, 151, 154 Germany, 9 global economy, 178 goals, 124, 135, 136, 159 goods and services, vii, 1, 117 government, iv, viii, ix, x, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 19, 20, 21, 22, 26, 29, 76, 79, 82, 83, 84, 93, 95, 100, 127, 128, 131, 134, 136, 137, 139, 140, 141, 154, 156, 171, 178 government intervention, 83, 100 government securities, 13 Great Depression, 9 gross domestic product, 8 gross national product, 9 growth, vii, viii, 5, 6, 9, 10, 31, 32, 33, 34, 35, 36, 37, 38, 40, 42, 44, 45, 49, 52, 53, 56, 57, 60, 61, 62, 63, 64, 130, 134, 135, 154

growth rate, 32, 34, 40, 53, 57, 60, 61, 62, 63, 134 guidance, 7, 143, 152, 155, 157, 158 guidelines, 158

H  holding company, 151 homeowners, ix, 13, 18, 127, 128, 132, 134, 135 Hong Kong, 37, 56 House, 16, 17, 66, 108, 109, 114, 117, 125, 171, 173, 174, 175 households, vii, 1, 2, 13, 152, 156, 173, 177, 179 housing, vii, ix, x, xi, 1, 2, 14, 15, 17, 20, 21, 26, 28, 76, 105, 106, 107, 108, 109, 112, 115, 117, 118, 119, 120, 121, 122, 123, 124, 128, 132, 136, 153, 154, 155, 177, 178, 179, 180 hypothesis, 33, 35, 36

I  ideal, 33, 106 images, 115 IMF, 9, 124 impairments, 5 implementation, viii, 75, 83 incentives, 7, 16, 18, 36, 99 income, 2, 17, 19, 30, 32, 36, 40, 53, 56, 57, 60, 61, 78, 95, 121, 129, 135, 136, 144, 155, 173 indicators, 33, 34 indices, 109, 111, 113, 115 induced bias, 35 industrial sectors, 37 industry, x, 139, 152, 162, 165, 166, 167, 171, 172 inefficiencies, 165 inefficiency, 33, 99, 101 inertia, viii, 31, 32, 63 inflation, ix, 105, 106, 107, 108, 121, 124 inflation target, 108, 121 information sharing, 81 inspections, 78, 151 institutions, vii, xi, 1, 3, 6, 7, 8, 11, 12, 19, 21, 23, 25, 29, 30, 80, 81, 98, 133, 144, 151, 152, 153, 154, 155, 157, 158, 171, 177, 178 instruments, 19, 24, 82, 179 insurance, 3, 11, 12, 21, 24, 29, 80, 132, 144, 145, 152, 170, 171, 172 integration, 179 intentions, 158 interbank market, 3 interest rates, x, 3, 4, 5, 8, 9, 17, 18, 108, 121, 128, 129, 132, 133, 136 intermediaries, 79, 171 internal controls, 133 International Monetary Fund, 9

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investment bank, 3, 4, 35, 178 investors, x, 6, 11, 12, 16, 17, 25, 28, 32, 33, 35, 36, 37, 38, 39, 40, 56, 61, 64, 77, 78, 107, 115, 124, 128, 132, 134, 135, 136, 141, 143, 144, 145, 146, 155, 156, 161, 165, 166, 178, 179 Italy, 167, 168

J  Japan, viii, 8, 9, 65, 105, 106, 108, 109, 117, 119, 123, 124, 125 Jordan, x, 151 judges, 18 judgment, 22, 146 justification, 131

K  Korea, 9

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L  labour, 89, 93 lack of confidence, 8, 131 land, viii, 75, 77, 79, 80, 81, 82, 83, 85, 86, 88, 89, 95, 96, 98, 99, 100, 102, 103, 111, 125, 153, 154, 174 Land Use Policy, 102 language, 174, 175 laws, 18, 77, 82, 117, 131, 140 lawyers, 13, 78 legislation, 13, 21, 80, 128, 170 lending, 2, 3, 4, 5, 6, 8, 10, 11, 12, 13, 14, 19, 20, 26, 27, 30, 136, 153, 154, 155, 157, 158, 172, 173, 178 likelihood, 7, 18, 24 limitation, 88 limited liability, 3, 23 line, 3, 88 liquid assets, 3 liquidity, x, 2, 3, 9, 13, 19, 21, 25, 129, 133, 151, 156, 157, 158, 159, 167, 177, 178 litigation, 78, 79 loans, iv, vii, 1, 2, 3, 4, 5, 6, 7, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 22, 23, 24, 25, 26, 30, 76, 80, 98, 128, 129, 131, 132, 135, 136, 137, 139, 146, 152, 153, 154, 155, 156, 157, 158, 171, 173, 178 local government, 30

M  management, viii, ix, x, 8, 9, 11, 12, 22, 32, 75, 83, 103, 107, 117, 119, 121, 123, 127, 129, 130, 133, 143, 152, 155, 157, 158, 165, 169, 170, 171, 172, 173, 174, 175

marginal utility, 38, 49 market segment, 125 markets, vii, viii, ix, xi, 1, 2, 3, 4, 6, 9, 13, 15, 18, 19, 20, 21, 32, 36, 37, 38, 75, 76, 77, 81, 96, 105, 106, 125, 128, 129, 154, 155, 156, 157, 158, 162, 165, 177, 178, 179, 180 marriage, ix, 106, 121, 123 measures, 8, 9, 33, 34, 38, 41, 49, 53, 101, 173 media, 32, 35, 132 Miami, 69, 70, 72 mixing, 170 model, x, 33, 39, 40, 53, 61, 62, 63, 83, 86, 87, 88, 161 models, 34, 66, 67, 81, 110 modernization, 82 monetary policy, 4, 156, 158 money, vii, 1, 2, 4, 7, 10, 11, 12, 17, 25, 29, 35, 77, 78, 85, 99, 130, 132 money markets, 25 moral hazard, 7, 81 moratorium, 174 mortgage-backed securities, vii, x, 3, 14, 16, 17, 19, 20, 24, 26, 28, 128, 132, 135, 137, 139, 155 Mozambique, 80 multiples, 34 multiplier, 32

N  Namibia, 80 nation, 1, 80, 81, 136, 153 negative equity, 18 Netherlands, 102 New South Wales, 59, 72 New Zealand, 36, 37, 76 Nigeria, 80, 102 null hypothesis, 38, 52, 64

O  observations, 41, 84 OECD, 124 Office of Management and Budget, 21, 22 Oklahoma, 70 omission, 145, 146, 147, 148 order, viii, 45, 75, 76, 82, 84, 89, 141, 142, 143, 144, 145, 146, 148, 149, 180 ownership, viii, 3, 6, 10, 12, 75, 78, 79, 80, 81, 82, 83, 84, 85, 101, 148, 162, 164

P  Pacific, viii, 31, 52, 65, 167 parameters, 162, 178 participant observation, 84

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Index pensions, 107, 121 performance indicator, 49 Perth, 68 planning, 77, 155 plausibility, 56 PLS, 162, 163 policy makers, 178 policy responses, 19 poor, 34, 96, 154, 165 portfolio, x, 3, 24, 34, 37, 38, 39, 42, 44, 45, 52, 53, 56, 60, 61, 62, 63, 64, 107, 123, 128, 129, 130, 132, 133, 134, 135, 136, 137, 155, 161, 162, 163, 164, 165, 166, 167, 168 portfolio investment, 45 portfolio management, 162, 167 portfolios, ix, x, 12, 32, 34, 37, 38, 40, 45, 47, 52, 53, 56, 57, 61, 63, 64, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 154, 155, 164, 166, 167 positive relation, 33 positive relationship, 33 poverty, viii, 75, 76 poverty alleviation, viii, 75, 76 power, 110, 141, 149, 171, 173 prediction, 107, 125 preference, 78, 84, 132 present value, 21, 29 pressure, 155, 156 price changes, 117 price elasticity, 107 price index, 109, 110, 111, 112 prices, ix, 2, 5, 12, 13, 14, 15, 16, 17, 18, 32, 33, 34, 76, 82, 96, 99, 107, 109, 112, 127, 128, 132, 153, 155, 156, 171 private sector, 7, 15, 18 probability, ix, 39, 45, 105, 106, 117, 118, 120, 123, 135 probability density function, 39 production, 153 productivity, 15 profit, 7, 19, 135, 136, 165 profitability, 32, 34, 106, 134, 136 profits, x, 8, 12, 13, 15, 23, 34, 77, 99, 128, 165 program, 3, 10, 13, 14, 17, 18, 21, 24, 25, 27, 29, 30, 156 property rights, 77, 78, 101 public capital, 11 public goods, 77 public interest, 141, 145 public policy, 80 public service, 85, 95

Q  quantitative research, viii, 75, 84

R  random walk, 41 range, 2, 16, 19, 89, 117, 152, 171 ratings, 4, 142, 152, 166 real estate, vii, viii, x, 5, 8, 9, 12, 31, 32, 36, 37, 52, 56, 64, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 88, 93, 95, 96, 98, 99, 100, 101, 107, 119, 123, 124, 151, 153, 154, 157, 158, 161, 162, 163, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175 real terms, 95 reality, 61, 83, 85, 89 reason, 10, 83, 109, 136, 165 recession, 2, 4, 5, 9, 10, 11, 15, 178 recognition, 80, 82, 88, 157 reconstruction, 124 recovery, 2, 6, 7, 8, 9, 10 region, 83, 95, 154, 162, 164, 166, 167 Registry, 80, 82, 83, 86, 88, 91, 96, 97, 102 regression, 34, 86, 87, 108, 109, 113, 115 regression analysis, 86, 108, 109, 113, 115 regression method, 34 regulation, vii, ix, 8, 127, 134, 141, 143, 145, 152, 170, 173, 175 regulations, x, 141, 143, 149, 152, 157, 169, 170 regulators, xi, 8, 128, 169, 170 regulatory requirements, 157 relationship, 34, 40, 87, 108, 112, 136 rent, ix, 99, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 117, 118, 119, 120, 121, 122, 123 repair, 121, 122, 158 reserves, 2, 19, 154 resources, 7, 15, 76, 88, 93, 98, 99, 100, 133, 155, 165, 173 retail, viii, 31, 36, 37, 42, 44, 52, 53, 60, 61, 62, 63, 64, 72, 174 returns, viii, 16, 17, 31, 33, 34, 35, 36, 37, 38, 41, 42, 44, 52, 53, 56, 63, 64, 77, 98, 99, 167, 168 revenue, ix, 95, 105, 106, 107, 109, 121, 123, 124 risk, vii, ix, 1, 2, 3, 5, 11, 12, 18, 20, 21, 22, 27, 34, 35, 38, 39, 40, 45, 49, 52, 53, 64, 78, 84, 105, 106, 107, 118, 122, 123, 129, 131, 132, 133, 135, 136, 137, 152, 157, 158, 162, 165, 167, 168, 170, 171, 179 risk management, 133, 152, 157, 167

S  safety, x, 151, 152, 154, 158, 170

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Index

sales, 2, 34, 77, 80, 107, 109, 141, 143, 144, 153, 155, 158, 170, 172 savannah, 95 savings, 8, 9, 76, 99, 133, 139, 171, 172, 177 savings rate, 76 search, 78, 82, 85 securities, x, 3, 5, 8, 13, 14, 16, 18, 19, 20, 22, 24, 25, 26, 28, 29, 33, 139, 140, 141, 142, 143, 144, 145, 146, 148, 149, 152, 156, 157, 166, 170, 171, 172, 178 Securities Exchange Act, 140 security, 28, 33, 79, 140, 141, 142, 143, 145, 146, 148, 170 selecting, 37 self-interest, 35 seller, 25, 78, 148 Senate, 131, 133, 173, 174, 175 Senate approval, 174 shape, 10 shaping, 76 shareholders, 6, 11 shares, 3, 10, 28, 29, 146 side effects, 77 Singapore, 31, 37, 69, 72, 181 skills, 78, 171, 172 skin, 82 social costs, 99, 100 sole proprietor, 23 solvency, 3, 6, 8, 11, 16 South Africa, v, x, 80, 161, 162, 163, 164, 165, 167, 168, 179, 180, 181 space, 110, 111, 174 speed, 85, 101 spillover effects, 16 Spring, 65 stability, 124, 151, 154 standard deviation, 38, 49, 53 standards, x, 4, 5, 136, 146, 151, 152, 153, 154, 157, 173, 178 statistics, 39, 52, 85, 162 stock, 3, 6, 8, 22, 28, 29, 32, 33, 34, 36, 38, 52, 77, 84, 106, 129, 130, 131, 132, 143, 148, 170 stock price, 22, 106 strategies, x, 7, 16, 32, 33, 34, 35, 38, 49, 53, 64, 66, 161, 162, 166, 167, 168 stress, 152, 154, 155, 166, 179 structural changes, 125 subprime loans, 5, 178 sub-Saharan Africa, vii, 76, 101, 102 subsidy, 11, 12, 18, 20, 21, 22, 27, 28 superiority, vii, viii, 31, 32, 35, 37, 38, 40, 42, 44, 47, 49, 56, 64 supervision, 152, 155

supervisor, 151, 152 supply, 2, 6, 7, 11, 14, 99, 100, 152, 179 supply curve, 99, 100 Supreme Court, 18, 149 surplus, 100 sustainability, 32, 64 systemic risk, 129

T  takeover, 20, 23 Tanzania, 80 targets, 16 tax credit, 27 taxation, 81 tenants, 106, 115, 116, 119, 122, 123, 170 threat, vii, 1, 151 threshold, 100 threshold level, 100 thrifts, 8, 155 time periods, 118 time series, 41 timing, 111, 117, 133 Title I, 128, 174 Title II, 174 total costs, 93 total revenue, 106, 123 trade, 13, 25, 77, 79 trading, 35, 80, 140 transaction costs, 77 transactions, 19, 20, 22, 37, 76, 78, 79, 80, 81, 83, 85, 95, 100, 108, 111, 133, 143, 145, 151, 171, 172, 173 Treasury bills, 4 trust, viii, 79, 105, 136, 144, 147 turnover, ix, 106, 120, 121, 123

U  U.S. Treasury, 131 UK, 66, 81, 101, 102, 109 UN, 103 uncertainty, 6, 7, 12, 106, 154 unemployment, 152 unemployment rate, 152 United Kingdom, 80 United Nations, 95, 103 United States, 8, 9, 10, 135, 154, 158, 165, 175, 181 universe, x, 161, 163 urban areas, 84

V  Valencia, 9 variables, 33, 86, 87

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Index variance, 38, 39, 40, 41, 45, 46, 51, 52, 63, 64, 162 volatility, 22, 53

W 

Z  Zimbabwe, 80

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war, 34, 109, 110, 113, 117, 121 warrants, 22, 28, 129, 131 weakness, 76, 154, 156 wealth, vii, viii, 1, 6, 38, 39, 75, 76, 81, 165

West Africa, 95 wind turbines, 174 workers, vii, 1, 7, 15 World Bank, 79, 80, 84, 85, 95, 101, 102, 103

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