Contrasting Real Estate with Comparable Investments ... - CiteSeerX

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Contrasting Real Estate with Comparable Investments, 1978 to 2008 JACK CLARK FRANCIS AND ROGER G. IBBOTSON

JACK CLARK FRANCIS is a professor in the department of economics and finance at Bernard Baruch College in New York, NY. [email protected]

ROGER G. IBBOTSON is a professor in practice at the Yale School of Management, Chairman and CIO of Zebra Capital Management, and founder and advisor to Ibbotson Associates, a Morningstar Company. [email protected]

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his article measures the returns from residential, commercial, and farm real estate in the U.S. over the period from 1978 to 2008. Both physical and financial real estate are included. The returns are contrasted with comparable stock, bond, and commodity investments, and with inflation. Recessions and other episodic events that are peculiar to each asset category are reviewed. Shortly before the subprime mortgage crisis began, a survey of institutional investors found that real estate investing was becoming increasingly popular (Dahr and Goetzmann [2006]). Several factors enhance the attractiveness of real estate investing, one of which is attractive tax advantages, in particular, for owner-occupied homes. The Real Estate Investment Trust Act of 1960 made pools of real estate easier to invest in and more liquid by securitizing physical real estate and mortgages. Fannie Mae, Freddie Mac, and Ginnie Mae helped homebuyers get mortgages under favorable terms. Fannie Mae issued its first mortgage-backed security in 1968, and Freddie Mac and Ginnie Mae began operating in a similar fashion soon afterward. And the Agricultural Credit Act of 1987 created the Farm Credit System Financial Assistance Corporation. The development of real estate investment indices, such as the S&P/Case–Shiller® Home Price Indices (HPI), made it easier to

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evaluate real estate as an asset class. In 2003, Dow Jones unveiled the iShares U.S. Real Estate Index Fund, an exchange-traded fund (ETF) that invests solely in real estate investment trusts (REITs) (Hudson-Wilson, Fabozzi, and Gordon [2003]). In 2006, Dow Jones launched a series of international REIT indices. In 2007, the Chicago Mercantile Exchange (CME) launched futures and options based on the S&P/Case–Shiller HPI. And, in 2009, an exchange-traded fund, MacroShares Major Metro Housing, began trading on the NYSE to enable day traders to speculate on and hedge their bets on U.S. real estate prices. Although the market for physical real estate is attractive in many respects, product heterogeneity causes real estate to be illiquid with high transaction and search costs. Furthermore, most real estate investments involve a lack of transparency that encourages the misuse of asymmetric information. When the market prices of real estate started downward in 2006, these liquidity problems, along with high leverage, accentuated the subprime mortgage crisis. The study presented in this article provides some investment guidelines to help asset allocators, investors, and speculators make decisions within this complex maze. In the study, we analyzed 31 years of empirical data from 1978 through 2008, and we tabulated timeseries returns to passive investors who buy-andhold real estate and collect rent. Our research

THE JOURNAL OF PORTFOLIO MANAGEMENT

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goal was to sample the four most recent recessions and other important events, and, also, as much as possible, to use one consistently defined data source for each asset category.1 Historical data is crucial for evaluating the role of real estate in the asset allocation process. Our investigation of residential, farm, and business real estate indicates that, although these categories are significantly positively correlated, their cycles experience peaks and troughs in different years. In addition, when measured over several complete business cycles, the categories earn different average returns. Comparing physical real estate with comparable stock, bond, and commodity investments also reveals correlations that are low enough to facilitate diversification benefits. Before analyzing the time series of investment returns, we estimated the significance of the real estate market by providing cross-sectional estimates of the value of the entire U.S. physical real estate market.

EXHIBIT 1 Estimated Values of Aggregate U.S. Real Estate

EXHIBIT 2 Business Real Estate in the U.S. by Property Type, Year-End 1999

HOW BIG IS THE U.S. REAL ESTATE MARKET?

The U.S. national aggregate value estimate of all privately owned real estate equals the sum of three major components: homes, business (commercial) real estate, and farmland. Residential Real Estate

Single-family residences are the largest real estate category in the U.S. with 59% of the single-family residences being owner-occupied (U.S. Census Bureau [2007]). Most individual investors typically own one home, which usually represents a large part of their total wealth. And individuals who own more than one house tend to invest in the same area, which means most residences are poorly diversified investments. Institutional investors do not like to invest in single-family residences, because homes require micromanagement of heterogeneous problems. The residential real estate data used in Exhibit 1 are estimates from the U.S. Department of Commerce Bureau of Economic Analysis (BEA). As shown in Exhibit 1, the BEA estimates the aggregate market value of U.S. residential real estate to be $17,870 billion ($17.87 trillion) at the end of 2007.

Source: See Table 5, Case, Glaeser, and Parker [2000, p. 135].

Business Real Estate

Exhibit 2 lists the subcategories that compose business (commercial) real estate based on the work of Case, Glaeser, and Parker [2000], which was the most recently published estimate we could find from a respected source. We chose Case, Glaeser, and Parker’s subcategories and estimates after consulting several data sources and after collaborating with real estate professionals. We found that most data sources revealed more dispersion, in terms of percentage, in the estimated values of business real estate than we found in the other two real estate categories. To obtain a 2007 estimate for aggregate U.S. business real estate to use in Exhibit 1 we increased the 1999 total of $5,951 billion ($5.951 trillion) from Exhibit 2 by the average annual business returns for 2000 through 2007. This gives the ($5.951 × 2.6905 =) $16.01 trillion estimate for the 2007 aggregate value of all U.S. business real estate shown in Exhibit 1. Farm Real Estate

The estimates of aggregate U.S. real estate values are for 2007, the year for which the most recent data are 2

CONTRASTING REAL ESTATE WITH COMPARABLE INVESTMENTS, 1978 TO 2008

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available (USDA [2009]). The farm data are based on the value per acre of farmland and buildings in the continental U.S. The U.S. Agriculture Department’s 2007 Census of Agriculture reports 921,460,000 acres of U.S. farmland in 2007 with an average market value of $2,160 per acre. Based on these figures, we arrived at an estimate for the total value of all U.S. farmland of $1.99 trillion at the end of 2007, as shown in Exhibit 1. Aggregates

Exhibit 1 aggregates the market values of three types of physical real estate—residential, business, and farms—to a total national value of $35,870 billion ($35.87 trillion) in 2007. By 2008, this total had declined to $31.01 trillion. Data not shown here reveal that business real estate appreciated more rapidly than the other categories of real estate during the years before the subprime mortgage crisis. As a result of differing rates of price change, the aggregate market value proportions of home, business, and farm real estate continually change through time. National Acreage

Exhibit 3 shows that the total land area of the U.S. was 2.264 billion acres in 2002 with slightly over 60% of that privately owned. Exhibit 3 breaks the total 2,264 million acres into four categories of land ownership and four categories of land use. Much of the nonprivate land is used for public parks, reservations for Native Americans, and other

purposes that render it unavailable to private investors. These omitted categories are rarely purchased or sold and, therefore, are not included in our analysis of investment opportunities. Exhibit 3 shows that (19.5% + 25.9% =) 45.4% of the total acreage in the continental U.S. is used agriculturally. It is noteworthy that these millions of acres of farmland are worth only 5.6% of the aggregate total value of U.S. real estate. The low average price per acre of farmland suggests that in the years ahead it will provide the least-cost land for the expansion of residential suburbs, second homes, and new commercial facilities. Because 45.4% of total U.S. acreage is relatively inexpensive farmland, the country has ample real estate for years of continued expansion. TOTAL RETURNS FROM PHYSICAL AND FINANCIAL REAL ESTATE

The remainder of this study contrasts the time series of holding period returns from comparable investments. All the returns are total returns that include both price-change income (appreciation) and cash income, measured annually, ⎛ Change in value⎞ ⎛ Cash income ⎞ ⎟ ⎟ + ⎜ during the ⎜ during the ⎟ ⎟ ⎜ ⎜ ⎝ holding period ⎠ ⎝ holding period ⎠

⎛ Total rate of ⎞ ⎜ return during ⎟ = ⎜ ⎟ ( Value at beginning of the holding period ) ⎝ holding period ⎠

EXHIBIT 3 Ownership and Use of U.S. Land by Major Categories (Millions of Acres), 2002

Notes: 1Includes reserved forest land in parks and other special purposes; 2Excludes an estimated 98 million acres in special uses that have forest cover and therefore are included with forest land in this exhibit; 3Managed in trust by the Bureau of Indian Affairs, Alaskan Native tribes, and various individuals. Source: Economic Research Service, U.S. Department of Agriculture, and other government sources.

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Physical Real Estate

The residential real estate time series on capital appreciation is based on data from S&P/Case–Shiller HPI and the Office of Federal Housing Enterprise Oversight (OFHEO), as explained in the appendix. The returns from business real estate are computed from a transactions-based index posted on the commercial real estate website at MIT. Older data come from the National Council of Real Estate Investment Fiduciaries (NCREIF) Property Index. The time series of farm data is from the U.S. Department of Agriculture. The farm income used to compute total returns is primarily rent received from farms. Further detail about farm, home, and business real estate data are in the appendix. Financial Real Estate

Real estate investment trusts (REITs) securitize illiquid physical real estate by comingling the titles to diversified real estate holdings in a pool and then selling shares in the pool that can be listed and traded on liquid stock exchanges. Although the market for REITs is growing rapidly, REITs represent only a small proportion of physical real estate. Equity REITs are defined as having at least 75% of their assets invested directly in real property, and mortgage REITs as having at least 75% of their assets invested in financial assets, such as mortgages and short-term real estate loans. Hybrid REITs invest in both real property and mortgages. Our source of REIT data is the National Association of Real Estate Investment Trusts (NAREIT). These return data are aggregated from actively traded REITs, so that the REIT indices used in our analysis are not subject to appraisal bias. Exhibit 4 shows the compound (geometric mean) annual returns of the three types of physical real estate indices. Residential real estate’s total return was 5.68% compounded over the 31-year sample period, which was the lowest return of the three physical real estate categories. Farm real estate had a compounded return of 8.76%, and business real estate had a compounded return of 9.99%, the highest of the three categories. Fisher and Goetzmann [2005] explained why these index returns are upward biased.2 We considered three types of mortgage returns—a Citigroup 30-year GNMA index, a U.S. governmentbacked mortgage index, and the Merrill Lynch Mortgage Master Index. Not surprisingly, the government-insured

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mortgages had the lowest returns, but all three data series yielded similar results.3 Financial Comparables

The comparable financial investments we used in our analysis include the following. The Standard & Poor’s (S&P) 500 Index, which is a well-known domestic common stock price index composed of large and medium-sized stocks that are almost all listed on the New York Stock Exchange (NYSE). The Small Company Stocks Index, which is a domestic index that is included to capture returns from the smallest quintile of NYSE stocks, and the MSCI index of world stocks measured in U.S. dollars to provide a multinational view. Noncallable U.S. Treasury bond returns of two different maturities were sampled: a time series of 1-year constant-maturity Treasury bonds and a time series of long-term (roughly 20-year) Treasury bonds. In addition, total returns from a long-term corporate bond index, composed primarily of AAA- and AA-grade bonds were analyzed to include bond yields with bankruptcy premiums.4 The S&P GSCI® (formerly the Goldman Sachs Commodity Index) is a well-known measure of raw material prices that is correlated (+0.31) with the U.S. Bureau of Labor and Statistics Consumer Price Index (CPI). The U.S. inflation rate is measured as the percentage change in CPI. The CPI is based on a continually changing basket of 300 goods and services used by most U.S. urban consumers. Cost-of-living allowances in many U.S. labor union contracts, Social Security payments, and the salaries of many U.S. government employees are tied to the percentage changes in the CPI (or changes in the inflation rate). Comparing Total Annual Returns

Columns B and C of Exhibit 4 present the rateof-return measures for U.S. real estate investments and comparable financial investments. Over the period 1978–2008, equity REITs had average rates of return that were comparable to the stock market indices. Equity REITs’ high returns were attributable to one or more of the following: 1) favorable financial leverage, 2) expertise of managers, over and above the fees charged, and 3) investor demand for these liquid real estate products exceeded the relatively small supply of available REITs.


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EXHIBIT 4 Statistics Describing Total Annual Returns, 1978–2008

Note: *Data was unavailable for 1978 and 1979.

The average return measures for most categories of physical real estate, presented in Panel A of Exhibit 4, rank significantly below the best investment alternatives in Panels B and D. We suggest that one reason investors are willing to accept relatively modest long-run average returns is the inflation hedge that physical real estate provides. Residences (+0.35), businesses (+0.35), and farms (+0.48) are significantly positively correlated with the CPI, as noted in many previous writings (see Samiei and Schinasi [1994], Just and Miranowski [1993], Fisher [1930], and, Liu, Hartzell, and Hoesli [1997]). Exhibit 5 illustrates time-series fluctuations in the annual returns from the three categories of physical real estate. Two observations can be drawn. First, during the 31-year sample period the nation’s residential and farm real estate indices experienced less variability of return and lower returns than business real estate; this is also documented in Exhibit 4. One reason for this is that home

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and farm owners receive government subsidies that are unavailable to businesses. Second, the three real estate indices seem to fluctuate somewhat independently. Three noteworthy independent events are worth reviewing in this regard. First, the Midwest farm crisis of the 1980s was caused by an unusual drop in demand for grain exports that occurred in conjunction with two back-to-back recessions. This large regional economic collapse barely shows up in the farm series. Second, the 1991–1992 crash in business real estate was worsened by overbuilding and unanticipated effects from federal financial legislation. Third, the price of farmland held up better than the prices of residential and business real estate as the subprime mortgage crisis began in 2006. But taking a longer-run statistical view of the 31-year sample period, the correlations between the three types of physical real estate are all high and positive.5 These large positive correlations provide evidence


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EXHIBIT 5 Average Annual Total Returns from Residences, Business, and Farms in the U.S.

of economic dependencies that can temporarily be obscured by episodes like the three just mentioned. U.S. capital is a mobile resource that permits the real estate in different sections of the nation and dissimilar types of real estate to serve as substitutes for each other. For instance, because most farmland can be readily converted to a business or residential purpose, significant positive national cross correlations will continue to exist. A period of particularly good or bad local returns for a specific category of real estate can obscure, but does not alter, the long-run convertibility of one type of real estate into another. Appraised Real Estate Values

High management costs, high information costs, high costs of implementing changes, site-specific risks, and large and inconvenient denominations prevent most physical real estate from being bought and sold frequently. Because abundant transaction data are unavailable, real estate analysts usually use appraised values. For the three categories of physical real estate that we analyzed, we used transactions-based sample data as much as possible. Nevertheless, to varying extents, appraised values were used instead of market-determined

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prices when satisfactory market price data were not available. For example, the 31 years of farm values used in our analysis were all based on appraisals, and the 9 years of OFHEO residential values that we used were also based on appraisals; OFHEO data relies on appraisals and some FNMA and GNMA market data. The 7 years of NCREIF returns we used are based on appraisals as well, but statistical techniques were used to unsmooth the NCREIF returns. 6 Appraisers look at comparable prices, which are frequently stale. In other words, most appraised real estate values go through a smoothing process. The positive serial correlations for partially or completely appraised physical residential real estate (+0.68), business real estate (+0.44), and farm real estate (+0.74) are shown in column E of Exhibit 4, Panel A, and are among the highest serial correlations shown in Exhibit 4, except for inflation and T-bill returns (T-bill returns are tied economically to inflation). The asset categories in Panels B, C, D, and E of Exhibit 4 are based on market-determined prices that behave more randomly through time. The smoothing process that results from appraisals also imparts a downward bias to the standard deviations of returns from the three categories of physical real estate in Panel A, column D, of Exhibit 4.


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Real estate appraisers attempt to follow widely accepted guidelines as they try to estimate what the price would have been if a transaction had taken place. Unintentionally, most real estate appraisers prepare smoothed appraisals reflecting prices that actually occurred in the past. Correlating instantaneous market prices from active markets with smoothed (non-instantaneous) appraisals biases both the correlation coefficients and the standard deviations downward.7 High and positive serial correlations, downwardbiased standard deviations, and downward-biased crosscorrelation coefficients are all byproducts of using appraised values of physical real estate. Using transactions-based indices, in which transactions are infrequent, can cause similar biases. Unless these econometric problems are taken into consideration, they can lead to naive discussions that tend to exaggerate the appeal of investing in physical real estate.8 ECONOMIC ANALYSIS OF PHYSICAL REAL ESTATE

In this section, we compare the economic analysis of residential real estate, business real estate, and farms and farmland. Residential Real Estate Values

Exhibit 6 illustrates a 31-year time series of residential housing returns, U.S. T-bill returns, inflation rates, official National Bureau of Economic Research (NBER) peaks and troughs in the business cycle, and other factors

that will be discussed later in the article. We can make the following observations: 1. The occurrence of a mini-recession in 1980 and four recessions in the years 1981–1982, 1990–1991, 2001, and beginning in December 2007. 2. Over the 31-year sample period, T-bill yields and inflation rates (depicted by the spikes in Exhibits 6 and 7) tended to decline. Long-term market interest rates (not shown) declined also. 3. The periods of elevated U.S. military outlays to support operations in the Gulf War of 1991, the peacekeeping mission in Kosovo in 1999, and the Iraq War beginning in 2003 did not seem to have a noticeable impact on real estate values. 4. Federal legislation—the Tax Reform Act of 1986 and the Financial Institutions Reform, Recovery, and Enforcement Act (FIRREA) in 1989—tended to have a negative impact on the prices of physical real estate. 5. The subprime mortgage crisis began in 2006 and continues to decimate real estate prices. The recent fall in home prices far surpasses earlier drops. In an effort to maintain and encourage stable family life, the U.S. Congress legislated subsidies to stimulate home buying. Cho [2007] provided a comprehensive review of the U.S. mortgage intermediation system and delineated the roles that FNMA, GNMA, and FHLMC played in financing home purchases. Homebuyers in the U.S. are able to obtain mortgages easier and at lower interest rates because of various home mortgage subsidies

EXHIBIT 6 Returns on Homes and Other Cyclical Indicators, Annual Rates, 1978–2008

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provided by these government sponsored enterprises (GSEs). As a result, in the U.S., the amount of home mortgage debt outstanding is highly positively related to homeownership. Farmers enjoy fewer subsidies from GSEs, such as the Farm Credit System and the Federal Agricultural Mortgage Corporation, than homebuyers enjoy. Buyers of U.S. business real estate do not benefit from these types of subsidy. Essentially, legislated programs provide a safer and more subsidized environment for the residential real estate industry than for either farm or business real estate.9

estate investments. The combined impact of the shrinking number of S&Ls making real estate loans, the collapse of FSLIC, FIRREA’s new 1989 restrictions on lending for business real estate, and the 1990–1991 recession pushed the returns from business real estate to low levels over the period 1986–1993.10 At the same time, construction companies and developers used the easy credit that S&Ls made available to finance overbuilding (Geltner and Goetzmann [2000] and Brown [2000]). This situation is summarized by Case, Glaeser, and Parker [2000] as follows: The late 1980s and early 1990s witnessed a boomand-bust cycle in commercial real estate markets of world-wide dimensions. From 1988 to 1992, as commercial real estate values were dropping sharply in the northeastern United States, the same thing was happening all over Europe and in many parts of Asia. The losses in value were at times extraordinary (p. 134).

Business Real Estate

Recessions negatively impact the market values of all types of physical real estate, but they have the biggest impact on business real estate. As shown in Exhibit 7, during the 1990–1991 recession, the returns from business real estate were pushed below zero. In 1987, the savings and loan (S&L) industry experienced a crisis, adding to the negative pressures resulting from the 1990–1991 recession. Between 1987 and 1993, hundreds of thrift institutions went bankrupt and the Federal Savings and Loan Insurance Corporation (FSLIC) collapsed. The FSLIC had to be taken over by the Federal Deposit Insurance Corporation (FDIC) in order to continue to provide deposit insurance (Saunders and Cornett [2006, p. 46], Freund and Seelig [1993], and FDIC [1997]). Legislative changes did not help the deteriorating real estate environment. To improve the failing thrift industry, the FIRREA of 1989 was enacted to restrain savings banks from making loans for nonresidential real

After observing this economic collapse, Shiller explained how even prudent experts can be caught up in the formation of an economic bubble (Shiller [2002]). After collapsing in 1991–1992, business real estate joined, somewhat belatedly, the economic recovery that started in 1992. Although the recession of 2001 lowered returns from business real estate, the sector was not badly hurt. Starting in 1993, business real estate earned positive annual returns until the subprime mortgage crisis pulled down commercial real estate values in 2008. In spite of several years with negative returns, business real estate earned an average annual return during 1978–2008

EXHIBIT 7 Business Real Estate Returns and Other Cyclical Indicators, Annual Rates, 1978–2008

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that was significantly higher than the average returns from the subsidized residential and farm sectors, as shown in Panel A of Exhibit 4. The Farm Credit System subsidizes farm products and several legislated programs provide a safer, more subsidized environment for the farming industry than for business real estate in the U.S. As a result, the standard deviation of returns from farm real estate is lower than the comparable business real estate risk statistic. The smaller number of years with negative returns and the smaller standard deviations of returns for residential real estate suggest that, at the aggregate (portfolio) level, this asset class is also less risky than both business and farm real estate. The higher average returns for business real estate in Exhibit 4 seem to include a risk premium that induces investors into this riskier, unsubsidized category of real estate. As a result of its riskiness, many institutional investors manage their exposure to business risk by holding shares of business investments in diversified portfolios. Farms and Farmland

Barlett [1993], Barnett [2000], and others document the Midwest farm crisis of the 1980s. Inflation and market interest rates in the U.S. rose during the late 1970s and peaked in the early 1980s. During the 1970s, strong export demand for wheat, corn, and soybeans powered a boom for grain farmers in the Corn Belt (see Crittenden [1981]). As a result of this unusually large export demand for grain, farmers in Missouri, Iowa, Illinois, Indiana, and Ohio became aggressive buyers of new farm machines and farmland during the late 1970s. Federal Reserve Regulation Q (interest rate ceilings) made it easy to borrow at low interest rates during the 1970s until in the early 1980s when the new higher interest rate ceilings began to be enforced. During the early 1980s, the Farm Credit System, a cooperatively owned lender, continued to make loans to farmers at very low interest rates. The Depository Institutions Deregulation and Monetary Control Act (DIDMCA) of 1980 legislated increased lending powers for federally chartered thrift institutions. The unintended consequence of these changes was that farmers, especially those in the Midwest, were being “set up for a fall.” A mini-recession in 1980 slowed the momentum of the 1970s farming boom in the Midwest. At the same time, export demand for agricultural goods—in particular, grains produced in the Midwest—dropped off sharply. Concurrently, the Federal Reserve was battling

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inflation by holding market interest rates at high levels. Higher interest rate ceilings were also being phased in annually pursuant to the DIDMCA of 1980—both nominal and inflation-adjusted interest rates skyrocketed. To make things tougher for the Midwest farmers, an official NBER recession slowed the entire nation during 1981–1982. As the Midwest farm crisis unfolded, thousands of leveraged Corn Belt farmers were bankrupted by a combination of high (nominal and real) interest rates and falling grain prices. During the early 1980s, thousands of farmers in the Midwest stopped being aggressive buyers of farmland and became griefstricken sellers of their family farms as they walked up the courthouse steps to face bankruptcy. Exhibit 5 shows that even while the Corn Belt farmers (19% of all U.S. farmers) were enduring the Midwest farm crisis in the early 1980s, the national returns on farm real estate remained high. CONTRASTING PHYSICAL REAL ESTATE WITH FINANCIAL INVESTMENTS

Inflation (deflation) typically conveys price gains (declines) to all types of physical real estate. This causes positive correlations between the inflation rate and the returns from all three categories of physical real estate, as shown in Exhibit 8.11 Investing in REITs

The three categories of REITs included in Exhibit 8 are very highly positively correlated. It is tempting to conclude that the low correlations between some REITs and the three types of physical real estate mean that economic conditions affect financial real estate instruments differently than they affect physical real estate—but that oversimplifies the relationships a bit. The statistics contain bias caused by the smoothing effects from using some appraised real estate values instead of market-determined prices. Smoothing biases downward the correlations of real estate and other asset categories shown in Exhibit 8. Unfortunately, a large number of research studies that have tried to explain the prices of REITs have not reached a consensus; see, for example, Ambrose, Highfield, and Linneman [2005], Chaudhry, Maheshwari, and Webb [2004], Gentry, Jones, and Mayer [2004], and Ling and Naranjo [2003]. Comparing Panels A and B of Exhibit 4 shows that, over the 31-year sample, REITs have standard deviations


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EXHIBIT 8 Correlations between Real Estate, REITs, Stocks, and Inflation

that are far larger than the standard deviations from the underlying physical real estate. Some of these large differences can be attributed to downward-biased standard deviations of the physical real estate that are caused by smoothed appraisal values. The divergent standard deviations may also result from the fact that REITs use leverage and, in addition, some REITs might have used derivatives to alter their returns. It should also be mentioned that the portfolio managers of some REITs are able to generate superior returns (Kallberg, Liu, and Trzcinka [2000]). These factors make it difficult to price REITs as well as contribute to the low correlations between REITs and physical real estate (Chen, Downs, and Patterson [2007] and Damodaran and Liu [1993]). Exhibit 4 shows that REIT indices have standard deviations that are as large as or larger than the standard

deviations from the equity indices. REITS are also significantly positively correlated with the domestic equity indices, especially the equity REITs. Equity REITS are correlated +0.51 with the S&P 500, +0.74 with the small stocks index, and +0.47 with the MSCI World, but are not correlated as highly with the underlying physical real estate. These statistics suggest that investing in equity REITs might feel more like investing in a portfolio of common stocks than investing in the physical real estate. Research to determine if REITs are integrated with the stock market in the U.S. is inconclusive (Ling and Naranjo [1999]); however, as mentioned previously, the possibility exists that REITs are also simply mispriced. The three common stock indices listed in Exhibit 8 are highly positively correlated with each other. In contrast, the much lower (sometimes negative) correlations

EXHIBIT 9 Correlations between Bonds, Bond-Like Instruments, and Inflation

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between the equity indices and the returns from physical real estate offer managers of equity portfolios valuable diversification and hedging possibilities (see Froot [1995]). Mortgage Returns, Bonds, and Inflation

The correlations in Exhibit 9 are based on marketdetermined (not appraised) data. The exhibit shows that the three types of mortgage securities are extremely positively correlated. Significant negative correlations (the statistics are not published) between all three types of mortgage securities and the values of all three categories of physical real estate result logically from three interrelated facts. First, physical real estate values are directly related to inflation. Second, Fisher [1930] taught us that market interest rates are directly related to inflation. And, third, the market prices of mortgages (collateralized bonds) vary inversely with market interest rates. In Exhibit 9, the mortgage REIT index is positively correlated with the GSE mortgage index, GNMA bonds, the Merrill Lynch Mortgage Master Index, long-term U.S. T-bonds, and long-term corporate bonds, because they are all either long-term bonds or are related to longterm bonds. Most of the differences between their positive correlations exist because of differences in maturities and/or quality ratings. CONCLUSIONS

People enjoy stories about real estate deals that quickly bestow great wealth on some lucky investor. While such incidents occur, Exhibit 4 presents aggregate U.S. data indicating that the average real estate investor accumulates wealth slowly from modest long-run rates of return that are usually positive. Real estate returns, on average, are less than stock market returns, but real estate is almost always purchased with a mortgage, which levers up the investor’s return of equity. Real estate investing provides additional benefits from federal mortgage subsidies, tax deductibility of mortgage interest, and low correlations between stock market returns and real estate returns that can be valuable diversification opportunities. In addition, prior to 2006, homeowners’ desires to “keep the home fires burning” motivated many U.S. homeowners to profit during the 11 years, on average, they owned their homes. In the U.S., financial real estate markets and new financial products have been growing more sophisticated

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over the past several decades. These developments have made it easier for the “average Joe” to get a mortgage, buy a home, refinance his mortgage, and move to a different home. These developments include the widespread availability of subsidized mortgages from government sponsored enterprises, 1960 legislation that created REITs, the securitization of mortgages, evolution of better real estate price indices, financial futures based on real estate price indices that traded on commodity exchanges, and exchange-traded funds, which made it possible to daytrade on and/or hedge expectations about real estate prices. If progress continues at this pace, the U.S. real estate market will become even more sophisticated. In addition to the widespread advantages available to most real estate investors, a few subcategories of real estate have offered historically attractive rates of return. If an investor is willing to gamble that history will repeat itself, it is worth noting that the average returns from equity REITs far surpassed the returns from physical real estate during our sample period. Investing in carefully selected private mortgages or government-insured mortgages can offer good returns at a more comfortable level of risk. Physical real estate is often called a passive investment because, in most cases, it is impractical to buy and sell frequently. However, physical real estate is not really a passive investment because it requires continual active maintenance and management. Dissimilarly, comparable financial investments are liquid and can be traded actively. Financial investments are passive investments in the physical sense, because they do not require the investor to perform maintenance work. Physical real estate is not only less liquid and requires more upkeep, but physical real estate is not a good substitute for comparable investments. Essentially, some investors find physical real estate to be a less convenient and more complicated investment than comparable financial instruments. To overcome these obstacles, the large financial services industry in the U.S. continually develops new products. The subprime mortgage crisis harmed the U.S. real estate market in ways that still are not clear. Real estate prices that continue to drop may offer buyers some good investment opportunities when the crisis ends. We hope this article will help homeowners, investors, speculators, and hedgers develop strategies, analyze trading schemes, and make insightful assessments of the risk–return tradeoffs that become available.


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APPENDIX Data Sources This appendix reviews the sources for the raw data. Much of the financial data were obtained from databanks purveyed by Morningstar/Ibbotson Associates in Chicago. Exhibit A1 summarizes key characteristics of the data on physical real estate investing.

An index from the U.S. Office of Federal Housing Enterprise Oversight (OFHEO) in Washington, DC, was used to obtain data going back to 1978. The total number of existing housing units is based on data from the U.S. Bureau of the Census Census of Housing, as reported in the Statistical Abstract of the United States. The home rental income data is from the National Income and Product Accounts (NIPA) tables Rental Income of Persons by Legal Form of Organization and by Type of Income.

Farm Real Estate Data

Business Real Estate Data We used a transactions-based index (TBI) posted on the commercial real estate (CRE) website at Massachusetts Institute of Technology (MIT) in Cambridge, MA. For data prior to 1985, we used National Council of Real Estate Investment Fiduciaries (NCREIF) business real estate data. Before 1995, NCREIF coproduced the Russell/NCREIF Property Index with the Frank Russell Corporation, but in 1995 NCREIF took sole responsibility for its Property Index and renamed it the NCREIF Property Index (NPI). The NPI data-contributing members must have at least $50 million of real estate under management. Only operating, not development, properties are included in the NPI, and only investment-grade, non-agricultural, and income-producing properties. Leveraged properties are included in the index on an unleveraged basis. Data on about 4,700 apartments, hotels, office buildings, manufacturing buildings, and retail stores with a total market value of about $135 billion from around the U.S. are tabulated quarterly. Each property’s market value is determined by consistently applied real estate appraisal methodology.

Residential Real Estate Data The S&P/Case–Shiller Home Price Index provided data from 1987 to 2008—the years for which its national data exists.

We used farm real estate data from the U.S. Department of Agriculture (USDA). The farm capital variable is measured as the average value per acre of land and buildings averaged across the U.S. For the period 1978–1992, the data were available from the USDA publication Farm Real Estate Market Developments—Outlook and Situation Report.12 For 1983, the data were published in the USDA publication Balance Sheet of the Farming Sector. The data on price per acre and income per acre, for the period 1978–1983, were from the USDA publication Economic Indicators of the Farm Sector. The data for the average gross cash rental income as a percent of value were from the USDA publication Cash Rents for Farms, Cropland, and Pasture, for the period 1984–1992; our focus was on the farm data. Income returns were calculated from gross cash rents; property taxes were not taken into consideration. For the years 1984–1992, the USDA data on price per acre and income data are from farm income returns and are measured as the value-weighted average of stateby-state average gross cash rental income as a percentage of the real estate value. Weights are the total value of farmland and buildings at the beginning of each year from the USDA publication Agricultural Resources: Agricultural Land Values and Markets Situation and Outlook Report. The data for 1993–2008 are from a USDA organization called National Agricultural Statistics Service (NASS), with data for the years 1993–2000 taken from their annual publication Agricultural Land values and

EXHIBIT A1 Sources of Data on Physical Real Estate

Notes: *The 1978 availability of NCREIF data determined the starting date of this study; **Because only annual USDA data was available, we used annual data throughout the study.

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Agricultural Cash Rents and for the years 2001–2008 from their annual publication Land Values and Cash Rents.

ENDNOTES 1

We used the S&P/Case–Shiller Home Price Index from 1987 to 2008—the years for which the national data exist. The index from the Office of Federal Housing Enterprise Oversight (OFHEO) was used for the period 1978–1986. For the years prior to 1987, we used data provided by Robert Shiller on his website in a file that contains data from both the S&P/Case–Shiller HPI and the OFHEO index. These same data were used in Shiller [2005]. We are indebted to Robert Shiller for these data. Rental income data for both homes and farms are available from the National Income and Product Accounts (NIPA) tables Rental Income of Persons by Legal Form of Organization and by Type of Income. For business (commercial) real estate, we used a transactions-based index (TBI) posted on the commercial real estate (CRE) website at MIT; this data begins in 1985. NCREIF data were used for the years 1978 –1984. We unsmoothed NCREIF price change returns and added rent returns to obtain total returns using a technique described in Fisher and Geltner [2000]. For an excellent discussion, see Chapter 25 and the appendix to Chapter 25 in Geltner, Miller, Clayton, and Eichholtz [2007]. We are indebted to David Geltner, Jeffrey D. Fisher, and William N. Goetzmann for their assistance with the business data. 2 The average rates of return in Exhibit 4 are upwardbiased estimates of the returns investors actually earn. When it is possible to compute the internal rate of return (IRR or dollarweighted rate of return), the IRR is usually less than the geometric mean (compounded average or time-weighted) return. Fisher and Goetzmann [2005, p. 8] provided IRR estimates from NCREIF commercial real estate data that are 185 basis points below those portfolios’ compounded average returns. IRRs are frequently not included in studies like this because the intermediate cash inflows and outflows needed to compute the IRR are detailed data not normally available to the public. The IRR measures investor returns after the cash inflows and outflows that typify real estate investments, not the return a continual investment earns (an index, for instance). 3 Data from subprime real estate mortgages and structured real estate mortgage products have returns that are difficult to measure because the returns involve unique yield levels, yield changes, and losses from defaults. 4 The S&P 500 Index, small stock index, long-term corporate bond index, and 20-year U.S. Treasury bond Index data are from the Ibbotson [2009]. 5 Exhibit 8 shows the high and positive correlation coefficients between the three types of physical real estate indices measured over the 31-year sample period. The correlations between real estate and other asset categories would be higher

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if these returns could be measured more accurately. Accurate real estate measurements are complicated by two unique factors. First, real estate appraisals smooth the returns from all types of real estate and, thereby, lower the cross correlations. Second, the USDA surveys farm prices on about June 30 of each year, but business and residential returns are usually measured over nonsynchronous calendar periods ending on December 31. 6 Unsmoothing techniques are suggested by Fisher, Geltner, and Webb [1994], Fisher and Geltner [2000], and Geltner et al. [2007] in the appendix to Chapter 25. 7 For a more detailed textbook discussion of the smoothing that arises from using appraised real estate values rather than market-determined prices see Geltner et al. [2007], Chapter 25 and the Appendix to Chapter 25, which is on a CD accompanying the book. For our entire 31-year sample, we used the transactions-based index for business real estate returns posted on the commercial real estate website at MIT as much as possible (it did not exist before 1985) rather than the appraisalbased NCREIF data. Even transactions-based methods usually contain artificial smoothing because the transactions occur during infrequent intervals. 8 For example, downward-biased standard deviations from physical real estate investments make the well-known Sharpe ratio a misleading performance measure for real estate (Sharpe [1966]). 9 The suggestion that residential real estate might be a less risky asset category than business and farm real estate is, of course, not true for single pieces of local real estate. Most residential real estate is held by individual homeowners as a single undiversified investment that is subject to idiosyncratic local occurrences. 10 To make matters worse, the Tax Reform Act of 1986 lengthened the depreciable lives of assets and removed some of the tax shelter characteristics for real estate investors. 11 Inflation and mortgage interest yields trended downward erratically from 1980 through 2009, throughout most of our sample period. The prices of all three categories of real estate trended upward during this period of decreasing mortgage interest yields. But, as illustrated by the annual returns for real estate in Exhibit 5, other influences sometimes overpowered the falling mortgage interest yields and caused significant independent declines in the market prices of all three categories of real estate from time to time. 12 Prior to 1986, values were reported as of April 1. From 1986 to 1989, values were reported as of February 1.

REFERENCES Ambrose, B.W., M.J. Highfield, and P.D. Linneman. “Real Estate and Economies of Scale: The Case of REITs.” Real Estate Economics, Vol. 33, No. 2 (2005), pp. 323–350.


13


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Barlett, P.F. American Dreams, Rural Realities: Family Farms in Crisis. Chapel Hill, NC: University of North Carolina Press, 1993. Barnett, B.J. “The U.S. Farm Financial Crisis of the 1980s.” Agricultural History, Vol. 74, No. 2 (2000), pp. 366–380. Brown, D.T. “Liquidity and Liquidation: Evidence from Real Estate Investment Trusts.” Journal of Finance, Vol. 55, No. 1 (2000), pp. 469–485. Case, K.E., E.L. Glaeser, and J.A. Parker. “Real Estate and the Macroeconomy.” Brookings Papers on Economic Activity, Vol. 2000, No. 2 (2000), pp. 119–162. Chan, S.-H., J. Erickson, and K. Wang. Real Estate Investment Trusts. Oxford University Press, 2003. Chaudhry, M.K., S. Maheshwari, and J.R. Webb. “REITs and Idiosyncratic Risk.” Journal of Real Estate Research, Vol. 26, No. 2 (2004), pp. 207–222. Chen, H., D.H. Downs, and G.A. Patterson. “The Short Interest Market for Publicly Traded Real Estate.” RERI Final Report, January 31, 2007. Cho, M. “180 Years’ Evolution of the U.S. Mortgage Banking System: Lessons for Emerging Mortgage Markets.” International Real Estate Review, Vol. 10, No. 1 (2007), pp. 171–212. Crittenden, A. “Outlook: Food Trade—Increasing Dependence.” New York Times, February 8, 1981. Dahr, R., and W. Goetzmann. “Institutional Perspectives on Real Estate Investing.” Journal of Portfolio Management, Vol. 32, No. 4 (2006), pp. 106–116. Damodaran, A., and C. Liu. “Insider Trading as a Signal of Private Information.” Review of Financial Studies, Vol. 6, No. 1 (1993), pp. 79–119. Economist. “The Game of Commercial Property Investment Has Gone Global,” November 25, 2006, p. 74. Federal Deposit Insurance Corporation. “Commercial Real Estate and the Banking Crisis of the 1980s and Early 1990s.” Chapter 3 in Vol. 1 of History of the Eighties—Lessons for the Future: An Examination of the Banking Crises of the 1980s and Early 1990s, 1997.

——. The Theory of Interest. New York, NY: The MacMillan Company, 1930. Fisher, J.D., and D.M. Geltner. “De-Lagging the NCREIF Index: Transactions Prices and Reverse-Engineering.” Real Estate Finance, Vol. 17, No. 1 (2000), pp. 7–22. Fisher, J.D., D.M. Geltner, and R.B. Webb. “Value Indices of Commercial Real Estate: A Comparison of Index Construction Methods.” Journal of Real Estate Finance and Economics, 9(1994), pp.137–164. Fisher, J.D., and W.N. Goetzmann. “Performance of Real Estate Portfolios.” Journal of Portfolio Management, Special Real Estate Issue, Vol. 31, No. 5 (2005), pp. 32–45. Freund, J.L., and S.A. Seelig. “Commercial Real Estate Problems: A Note on Changes in Collateral Values Backing Real Estate Loans Being Managed by the FDIC.” FDIC Banking Review, Vol. 6, No. 1 (1993), pp. 26–30. Froot, K.A. “Hedging Portfolios with Real Assets.” Journal of Portfolio Management, Vol. 21, No. 4 (1995), pp. 60–77. Geltner, D., and W. Goetzmann. “Two Decades of Commercial Property Returns: A Repeated-Regression-Based Version of the NCREIF Index.” Journal of Real Estate Finance and Economics, Vo. 21, Part 1 (2000), pp. 5–22. Geltner, D., N. Miller, J. Clayton, and P. Eichholtz. Commercial Real Estate, 2nd ed. Thomson South-Western, 2007. Gentry, W.M., C.M. Jones, and C.J. Mayer. “Do Stock Prices Really Reflect Fundamental Values? The Case of REITs.” Working Paper, NBER, 2004. Goetzmann, W.N., and R.G. Ibbotson. “The Performance of Real Estate as an Asset Class.” Journal of Applied Corporate Finance, Vol. 3, No. 1 (1990), pp. 65–75. Hudson-Wilson, S., F.J. Fabozzi, and J.N. Gordon. “Why Real Estate?” Journal of Portfolio Management, Special Real Estate Issue, Vol. 29, No. 5 (2003), pp. 12–27. Ibbotson® SBBI ® 2009 Classic Yearbook: Market Results for Stocks, Bonds, Bills, and Inflation, 1926–2008. Morningstar, Inc., 2009. Just, R.E., and J.A. Miranowski. “Understanding Farmland Price Changes.” American Journal of Agricultural Economics, Vol. 75, No. 1 (1993), pp. 156–168.

Fisher, I. The Purchasing Power of Money, rev. ed., 1922. New York, NY: Kelley Publishers, reprinted in 1971.

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Page 15

Kallberg, J.G., C.L. Liu, and C. Trzcinka. “The Value Added from Investment Managers: An Examination of Funds of REITs.” Journal of Financial and Quantitative Analysis, Vol. 35, No. 3 (2000), pp. 469–485.

Shiller, R.J. “Bubbles, Human Judgment, and Expert Opinion.” Financial Analysts Journal, Vol. 58, No. 3 (2002), pp. 18–26. Reprinted in The ICFAI Journal of Behavioral Finance (India), Vol. 1, No. 3 (2004), pp. 7–17.

Ling, D., and A. Naranjo. “The Integration of Commercial Real Estate Markets and Stock Markets.” Real Estate Economics, Vol. 27, No. 3 (1999), pp. 483–515.

——. Irrational Exuberance, 2nd ed. Princeton University Press, 2005.

——. “The Dynamics of REIT Capital Flows and Returns.” Real Estate Economics, Vol. 31, No. 3 (2003), pp. 405–434. Liu, C., D. Hartzell, and M. Hoesli. “International Evidence on Real Estate Securities as an Inflation Hedge.” Real Estate Economics, Vol. 25, No. 2 (1997), pp. 193–221. Rappaport, J. “A Guide to Aggregate House Price Measures.” Economic Review, 2nd Qtr. Reserve Bank of Kansas City (2007), pp. 41–77. Samiei, H., and G. Schinasi. “Real Estate Price Inflation, Monetary Policy, and Expectations in the United States and Japan.” IMF Working Paper No. 94/12, 1994. Available at http://ssrn.com/abstract = 883433. Saunders, A., and M. Cornett. Financial Institutions Management, 5th ed. New York, NY.: McGraw-Hill/Irwin, 2006.

U.S. Census Bureau, Housing & Household Economic Statistics Division. American Housing Survey for the U.S., 2003. U.S. Department of Agriculture (USDA). 2002 Census of Agriculture, Vol. 1, Part 51, issued April 2004. Available at http://www.agcensus.usda.gov/Publications/2002/USVolume1 04.pdf ——. Agricultural Baseline Projections, U.S. Agricultural Sector Measures, 2007–2016, February 2007, p. 65. ——. 2007 Census of Agriculture, Vol. 1, Part 51, issued February 2009. Available at http://www.agcensus.usda.gov/Publications/ 2007/Full_Report/index.asp.

To order reprints of this article, please contact Dewey Palmieri at [email protected] or 212-224-3675.

Sharpe, W. “Mutual Fund Performance.” Journal of Business, 39 (1966), pp. 119–138.

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