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  3. CME (Part 2): Forecasting Asset Class Returns

Forecasting Real Estate Returns

Summary

Forecasting Real Estate Returns

  • Learning Outcome

    • Understanding the impact of economic and competitive factors on expectations for real estate investment markets and sector returns.

  • Characteristics of Real Estate Investment

    • Physical asset, not a financial asset.

    • Heterogeneous, indivisible, immobile.

    • Produces returns through services; illiquid and costly to transfer.

    • Focus primarily on directly held, unlevered, income-producing real estate.

  • Historical Real Estate Returns

    • Challenges due to infrequent and erratic transactions.

    • Reliance on appraisals over transactions, leading to smoothing issues in return calculations.

    • Impact on volatility and correlation estimates.

  • Real Estate Cycles

    • Tied to business cycles, influenced by economic activity.

    • Cyclical patterns in property values, rents, and occupancy rates.

    • Boom-bust cycles characterized by overbuilding and eventual market correction.

  • Capitalization Rates (Cap Rates)

    • Key valuation metric for commercial real estate.

    • Influenced by economic conditions, interest rates, and credit availability.

    • Variations in cap rates across different property types and locations.

  • Risk Premium Perspective on Real Estate Expected Return

    • Sensitivity to long-term interest rates.

    • Exposure to credit risk of tenants and property value fluctuations.

    • Importance of a significant liquidity risk premium.

  • Real Estate in Equilibrium

    • Incorporation into equilibrium frameworks like the Singer–Terhaar model.

    • Adjustments for liquidity and other unique real estate characteristics.

  • Public vs. Private Real Estate

    • Differences in investment characteristics and performance.

    • Comparison of direct real estate and REITs (Real Estate Investment Trusts).

    • Analysis of diversification benefits and correlations with other asset classes.

  • Long-Term Housing Returns

    • Global perspective on the performance of residential real estate compared to other asset classes.

    • Variations in performance across different periods and countries.

  • Example: Assessing Real Estate Investments

    • Case studies of different real estate firms: Office Growth Partners, Mega-Box Properties, and Exclusive Elegance Inc.

    • Analysis of risk and required return for each investment scenario.

Learning Outcome

  • explain how economic and competitive factors can affect expectations for real estate investment markets and sector returns

Real estate is inherently quite different from equities, bonds, and cash. It is a physical asset rather than a financial asset. It is heterogeneous, indivisible, and immobile. It is a factor of production, like capital equipment and labor, and as such, it directly produces a return in the form of services. Its services can be sold but can be used/consumed only in one location. Owning and operating real estate involves operating and maintenance costs. All these factors contribute to making real estate illiquid and costly to transfer. The characteristics just described apply to direct investment in real estate (raw land, which does not produce income, is an exception). We will address the investment characteristics of equity REITs versus direct real estate, but unless otherwise stated, the focus is on directly held, unlevered, income-producing real estate.

Historical Real Estate Returns

The heterogeneity, indivisibility, immobility, and illiquidity of real estate pose a severe problem for historical analysis. Individual properties trade infrequently and erratically in time, so there is little chance of getting a sequence of simultaneous, periodic (say, quarterly) transaction prices for a cross section of properties. Even in mor e developed real estate markets, there is a tendence for market transactions to occur predominantly in properties with lower to moderate historical price growth. As a result, real estate owners/investors must rely heavily on appraisals, rather than transactions, in valuing properties. Owing to infrequent transactions and the heterogeneity of properties, these appraisals tend to reflect slowly moving averages of past market conditions. As a result, returns calculated from appraisals represent weighted averages of (unobservable) “true” returns—returns that would have been observed if there had been transaction prices—in previous periods. This averaging does not, in general, bias the mean return. It does, however, significantly distort estimates of volatility and correlations. The published return series is too smooth; that is, the usual sample volatility substantially understates the true volatility of returns. Meanwhile, by disguising the timing of response to market information, the smoothing tends to understate the strength of contemporaneous correlation with other market variables and spuriously induce a lead/lag structure of correlations.

In order to undertake any meaningful analysis of real estate as an asset class, the analyst must first deal with this data issue. It has become standard to “unsmooth” appraisal-based returns using a time-series model. Such techniques, which also apply to private equity funds, private debt funds, and hedge funds, are briefly described in a later section.

Real Estate Cycles

Real estate is subject to cycles that both drive and are driven by the business cycle. Real estate is a major factor of production in the economy. Virtually every business requires it. Every household consumes “housing services.” Demand for the services provided by real estate rises and falls with the pace of economic activity. The supply of real estate is vast but essentially fixed at any point in time.17 As a result, there is a strong cyclical pattern to property values, rents, and occupancy rates. The extent to which this pattern is observable depends on the type of real estate. As emphasized previously, changes in property values are obscured by the appraisal process, although indications can be gleaned from transactions as they occur. The extent to which actual rents and occupancy rates fully reflect the balance of supply and demand depends primarily on the type of property and the quality of the property. High-quality properties with long leases will tend to have little turnover, so fluctuations in actual rents and occupancy rates are likely to be relatively small. In contrast, demand for low-quality properties is likely to be more sensitive to the economy, leading to more substantial swings in occupancy and possibly rents as well. Properties with short leases will see rents adjust more completely to current supply/demand imbalances. Room rates and occupancy at low-quality hotels will tend to be the most volatile.

Fluctuations in the balance of supply and demand set up a classic boom–bust cycle in real estate. First, the boom: Perceptions of rising demand, property values, lease rates, and occupancy induce development of new properties. This investment spending helps drive and/or sustain economic activity, which, in turn, reinforces the perceived profitability of building new capacity. Then, the bust: Inevitably, optimistic projections lead to overbuilding and declining property values, lease rates, and occupancy. Since property has a very long life and is immobile, leases are typically for multiple years and staggered across tenants. In addition, since moving is costly for tenants, it may take many months or years for the excess supply to be absorbed.

A study by Clayton, Fabozzi, Gilberto, Gordon, Hudson-Wilson, Hughes, Liang, MacKinnon, and Mansour (2011) suggested that the US commercial real estate crash following the global financial crisis was the first to have been driven by the capital markets rather than by a boom–bust cycle in real estate fundamentals.18 The catalyst was not overbuilding, Clayton et al. argued, but rather excess leverage and investment in more speculative types of properties. Consistent with that hypothesis, both the collapse in property prices and the subsequent recovery were unusually rapid. The authors attributed the accelerated response to underlying conditions to appraisers responding more vigorously to signals from the REIT and commercial mortgage-backed security markets. It remains to be seen whether this phenomenon will persist in less extreme circumstances.

Capitalization Rates

The capitalization (cap) rate, defined as net operating income (NOI) in the current period divided by the property value, is the standard valuation metric for commercial real estate. It is analogous to EBITDA as a percentage of EV (reciprocal of EV/EBITDA valuation multiple) for a typical corporate issuer. It is not, strictly speaking, a cash flow yield because a portion of operating income may be reinvested in the property.19 As with any equity, an estimate of the long-run expected/required rate of return can be derived from this ratio by assuming a constant growth rate for NOI—that is, by applying the Gordon growth model.

E(Rre) = Cap rate + NOI growth rate .7

The long-run, steady-state NOI growth rate for commercial real estate as a whole should be reasonably close to the growth rate of GDP. The observation that over a 30-year period UK nominal rental income grew about 6.5% per annum, roughly 2.5% in real terms,20 is consistent with this relationship.

Over finite horizons, it is appropriate to adjust this equation to reflect the anticipated rate of change in the cap rate.

E(Rre) = Cap rate + NOI growth rate − %ΔCap rate .8

This equation is analogous to the Grinold–Kroner model for equities, except there is no term for share buybacks. The growth rate of NOI could, of course, be split into a real component and inflation.

Exhibit 6 shows private market cap rates as of 30 June 2021 for US commercial properties differentiated by type, location, and quality. The rates range from 34.7% for industrial properties to 6.8% for retail. The relatively high cap rate for retail reflects the investors’ perception that of short-term risks related to in-person shopping during the COVID-19 pandemic and longer-term risks related to ecommerce continuing to take market share from in-store retail.

Exhibit 6:

Private Market Cap Rates (%) as of 30 June 2021

Property Type

Average

Higher Risk

Lower Risk

Hotels

53.0

Limited Service 7.7

Full Service 7.1

Health Care

4.86

Skilled Nursing 9.5

Medical Office 5.7

Retail Malls

6.8

Low Productivity 8.8

High Productivity 5.0

Industrial

3.74

Office

5.0

Secondary Cities 6.6

Gateway Cities 4.7

Apartments

4.55

Source: CenterSquare Investment Management (2018).

The private market cap rate for hotels should be something around 5.0% as per a report from the cited source (CenterSquare Investment Management, see attached image below extracted from the report) Link to the report: https://www.centersquare.com/wp-content/uploads/2022/09/CenterSquare-REIT-Cap-Rate-Perspective-Q2-2021.pdf Also, the number in the description might be 3.74% for industrial properties instead of 34.7%. Both 53% and 34.7% seem to be quite off. No way for such a high return, unless there was a bubble.

In-store share losses to ecommerce is especially intense for lower-productivity (less profitable) locations. Cap rates for high- and low-productivity shopping malls began to diverge even before the global financial crisis. In 2006, the difference in cap rates was 1.2 percentage points; by 2018, it was 3.2 percentage points.21

Cap rates reflect long-term discount rates. As such, we should expect them to rise and fall with the general level of long-term interest rates, which tends to make them pro-cyclical. However, they are also sensitive to credit spreads and the availability of credit. Peyton (2009) found that the spread between cap rates and the 10-year Treasury yield is positively related to the option-adjusted spread on three- to five-year B-rated corporate bonds and negatively related to ratios of household and non-financial-sector debt to GDP. The countercyclical nature of credit spreads mitigates the cyclicality of cap rates. The debt ratios are effectively proxies for the availability of debt financing for leveraged investment in real estate. Since real estate transactions typically involve substantial leverage, greater availability of debt financing is likely to translate into a lower required liquidity premium component of expected real estate returns. Not surprisingly, higher vacancy rates induce higher cap rates.

The Risk Premium Perspective on Real Estate Expected Return

As a very long-lived asset, real estate is quite sensitive to the level of long-term rates; that is, it has a high effective duration. Indeed, this is often the one and only characteristic mentioned in broad assessments of the likely performance of real estate as an asset class. Hence, real estate must earn a significant term premium. Income-earning properties are exposed to the credit risk of the tenants. In essence, a fixed-term lease with a stable stream of payments is like a corporate bond issued by the tenant secured with physical assets. The landlord must, therefore, demand a credit premium commensurate with what his or her average tenant would have to pay to issue such debt. Real estate must also earn a significant equity risk premium (relative to corporate debt) since the owner bears the full brunt of fluctuations in property values as well as uncertainty with respect to rent growth, lease rollover/termination, and vacancies. The most volatile component of return arises, of course, from changes in property values. As noted previously, these values are strongly pro-cyclical, which implies the need for a significant equity risk premium. Combining the bond-like components (term premium plus credit premium) with a stock-like component implies a risk premium somewhere between those of corporate bonds and equities.

Liquidity is an especially important risk for direct real estate ownership. There are two main ways to view illiquidity. For publicly traded equities and bonds, the question is not whether one can sell the security quickly but, rather, at what price. For real estate, however, it may be better to think of illiquidity as a total inability to sell the asset except at randomly spaced points in time. From this perspective, the degree of liquidity depends on the average frequency of these trading opportunities. By adopting this perspective, one can ask how large the liquidity premium must be to induce investors to hold an asset with a given level of liquidity. Ang, Papanikolaou, and Westerfield (2014) analyzed this question. Their results suggest liquidity premiums on the order of 0.60% for quarterly average liquidity, 0.90% for annual liquidity, and 2%, 4%, and 6% for liquidity on average every 2, 5, and 10 years, respectively.22 All things considered, a liquidity premium of 2%–4% would seem reasonable for commercial real estate.

Real Estate in Equilibrium

Real estate can be incorporated into an equilibrium framework (such as the Singer–Terhaar model). Indeed, doing so might be deemed a necessity given the importance of real estate in global wealth. There are, however, a few important considerations. First, the impact of smoothing must have been removed from the risk/return data and metrics used for real estate. Otherwise, inclusion of real estate will distort the results for all asset classes. Second, it is important to recognize the implicit assumption of fully liquid assets in equilibrium models. Adjusting the equilibrium for illiquidity—that is, adding a liquidity premium—is especially important for real estate and other private assets. Third, although real estate investors increasingly venture outside their home markets, real estate is still location specific and may, therefore, be more closely related to local, as opposed to global, economic/market factors than are financial claims.

Public vs. Private Real Estate

Many institutional investors and some ultra-wealthy individuals are able to assemble diversified portfolios of direct real estate holdings. Investors with smaller portfolios must typically choose between limited, undiversified direct real estate holdings or obtaining real estate exposure through financial instruments, such as REIT shares. Assessing whether these alternatives—direct real estate and REITs—have similar investment characteristics is difficult because of return smoothing, heterogeneity of properties, and variations in leverage.

A careful analysis of this issue requires (1) transaction-based returns for unlevered direct real estate holdings, (2) firm-by-firm deleveraging of REIT returns based on their individual balance sheets over time, and (3) carefully constructing direct real estate and REIT portfolios with matching property characteristics. Exhibit 7 shows the results of such an analysis.

Exhibit 7:

Direct Real Estate vs. REITs: Four Property Types, 1994–2012

Mean Return (%)

Standard Deviation (%)

Direct Real Estate

REITs

Direct Real Estate

REITs

Unlevered

Levered

Unlevered

Levered

Aggregate

8.80

9.29

11.09

9.71

Apartment

9.49

9.08

11.77

11.42

9.50

20.69

Office

8.43

9.37

10.49

10.97

10.58

23.78

Industrial

9.00

9.02

9.57

11.14

11.65

23.46

Retail

8.96

9.90

12.04

11.54

10.03

23.73

Source: Ling and Naranjo (2015, Table 1).

Deleveraging the REITs substantially reduces both their mean returns and their volatilities. The volatilities are roughly cut in half. Clearly, the deleveraged REIT returns are much more similar to the direct real estate returns than are the levered REIT returns. In the aggregate, REITs outperformed direct real estate by 49 bps per year with lower volatility. Looking at specific property types, REITs had higher returns and lower volatility in two categories—office and retail. Industrial REITs had essentially the same return as directly owned industrial properties but with higher volatility. Apartment REITs lagged the direct market but with significantly lower volatility.

Exhibit 7 certainly shows some interesting differences. The pattern of unlevered REIT returns by property type is not the same as for direct real estate. Retail REITs had the highest return, and industrial REITs had the lowest. Among directly owned properties, apartments had the highest return and offices the lowest. A similar mismatch appears with respect to volatilities.

Overall, this study tends to support the general conclusion reached by most comparisons: Public and private commercial real estate are different. The extent of the difference is less clear. It does appear that once we account for differences in leverage, REIT investors are not sacrificing performance to obtain the liquidity afforded by publicly traded shares. Perhaps REIT investors are able to capture a significant portion of the liquidity risk premium garnered by direct investors (because the REIT is a direct investor) as well as benefit from professional management.

What about the diversification benefits of real estate as an asset class? REITs are traded securities, and that fact shows up in their much higher short-term correlation with equities. In contrast, direct real estate is often touted as a good diversifier based on the notion that it is not very highly correlated with equities. As noted previously, the smoothed nature of most published real estate returns is a major contributor to the appearance of low correlation with financial assets, including with REITs. Once that is corrected, however, the correlation is higher, even over reasonably short horizons, such as a quarter or a year. Importantly, REITs are more highly correlated with direct real estate and less highly correlated with equities over multi-year horizons.23 Thus, although REITs tend to act like “stocks” in the short run, they act like “real estate” in the longer run. From a strategic asset allocation perspective, REITs and direct real estate are more comparable than conventional metrics suggest.

Long-Term Housing Returns

Savills World Research (2016) estimated that residential real estate accounts for 75% of the total value of developed properties globally. Most individuals’ homes are their primary, perhaps only, real estate investment. A relatively new database provides a global perspective on the long-term performance of residential real estate (housing), equities, and bonds.24 The database covers 145 years (1870–2015) and 16 countries.

Jordà, Knoll, Kuvshinov, Schularick, and Taylor (2017) found that residential real estate was the best performing asset class over the entire sample period, with a higher real return and much lower volatility than equities. However, performance characteristics differed before and after World War II:

  • Residential real estate had a higher (lower) real return than equities before (after) World War II.

  • Residential real estate had a higher real return than equities in every country except Switzerland, the United Kingdom, and the United States over 1950–1980 but a lower return than equities in every country for 1980–2015.

  • Residential real estate and equities had similar patterns—that is, a strong correlation—prior to the war but a low correlation after the war.

  • Equity returns became increasingly correlated across countries after the war, but residential real estate returns are essentially uncorrelated across countries.

Exhibit 8 shows the real returns for equities and residential real estate in each country since 1950.

Exhibit 8:

Real Equity and Housing Returns by Country, 1950–2015

Note: Annual percentage returns are shown.

Source: Jordà et al. (2017).

EXAMPLE 7

Assessing Real Estate Investments

Tammi Sinclair, an analyst at a large retirement fund, recently attended investor presentations by three private real estate firms looking to fund new projects. Office Growth Partners specializes in building and owning low-cost, standardized office space for firms seeking to place sales representatives in the most rapidly growing small population areas across the region. Mega-Box Properties builds and owns large, custom-designed distribution facilities for multinational makers of brand-name products. The facilities are strategically located near major global transportation hubs. Exclusive Elegance Inc. develops and then manages some of the world’s most luxurious, sought-after residential buildings in prime locations. It never breaks ground on a new property until at least 85% of the units have been sold and, to date, has never failed to sell out before construction is complete.

Identify important characteristics of each business that Sinclair will need to consider in establishing a required rate of return for each potential investment.

Guideline answer:

Office Growth Partners (OGP) is likely to be a very high-risk investment. It essentially chases hot markets, it builds generic office space, and its typical tenants (opportunistic sales forces) are apt to opt out as soon as the market cools. All these aspects suggest that its business is very exposed to a boom-and-bust cycle. It is likely to end up owning properties with persistently high vacancy rates and high turnover. Hence, Sinclair will likely require a rather high expected return on an investment in OGP. (higher risk premium)

Mega-Box’s business should be fairly stable. The distribution centers are strategically located and designed to meet the needs of the tenant, which suggests long-term leases and low turnover will benefit both Mega-Box and the tenant firms. The average credit quality of the tenants—multinational makers of brand-name products—is likely to be solid and disciplined by the public bond and loan markets. All things considered, Sinclair should probably require a significantly lower expected return on an investment in Mega-Box than in OGP.

Exclusive Elegance appears to be even lower risk.

First, it deals only in the very highest-quality, most sought-after properties in prime locations. These should be relatively immune to cyclical fluctuations.

Second, it does not retain ownership of the properties, so it does not bear the equity/ownership risks.

Third, it is fairly conservative in the riskiest portion of its business—developing new properties. However, Sinclair will need to investigate its record with respect to completing development projects within budget, maintaining properties, and delivering top-quality service to residents.

ChatGPT Summary

Forecasting Real Estate Returns

  • Learning Outcome

    • Understanding the impact of economic and competitive factors on expectations for real estate investment markets and sector returns.

  • Characteristics of Real Estate Investment

    • Physical asset, not a financial asset.

    • Heterogeneous, indivisible, immobile.

    • Produces returns through services; illiquid and costly to transfer.

    • Focus primarily on directly held, unlevered, income-producing real estate.

  • Historical Real Estate Returns

    • Challenges due to infrequent and erratic transactions.

    • Reliance on appraisals over transactions, leading to smoothing issues in return calculations.

    • Impact on volatility and correlation estimates.

  • Real Estate Cycles

    • Tied to business cycles, influenced by economic activity.

    • Cyclical patterns in property values, rents, and occupancy rates.

    • Boom-bust cycles characterized by overbuilding and eventual market correction.

  • Capitalization Rates (Cap Rates)

    • Key valuation metric for commercial real estate.

    • Influenced by economic conditions, interest rates, and credit availability.

    • Variations in cap rates across different property types and locations.

  • Risk Premium Perspective on Real Estate Expected Return

    • Sensitivity to long-term interest rates.

    • Exposure to credit risk of tenants and property value fluctuations.

    • Importance of a significant liquidity risk premium.

  • Real Estate in Equilibrium

    • Incorporation into equilibrium frameworks like the Singer–Terhaar model.

    • Adjustments for liquidity and other unique real estate characteristics.

  • Public vs. Private Real Estate

    • Differences in investment characteristics and performance.

    • Comparison of direct real estate and REITs (Real Estate Investment Trusts).

    • Analysis of diversification benefits and correlations with other asset classes.

  • Long-Term Housing Returns

    • Global perspective on the performance of residential real estate compared to other asset classes.

    • Variations in performance across different periods and countries.

  • Example: Assessing Real Estate Investments

    • Case studies of different real estate firms: Office Growth Partners, Mega-Box Properties, and Exclusive Elegance Inc.

    • Analysis of risk and required return for each investment scenario.

ChatGPT Link: https://chat.openai.com/c/00ab7431-d4c5-4c3c-b3f1-d1d997795ceb

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Last updated 1 year ago