Privately Versus Publicly Held Asset Investment Performance

Classic asset pricing is problematic as a method to assess privately held asset investment performance. We propose an alternative approach that involves adjusting the characteristics of assets constituting an index or portfolio to match the asset characteristics of a reference index or portfolio. This approach is applied to commercial real estate, where we create an index of REIT returns to compare to the NCREIF index. To enhance comparability, return indices are adjusted for partial-year financial data, leverage, asset mix and fees. Adjusted results over a 1980–1998 sample period show general convergence between the indices, although an annual return difference of over three percentage points remains in favor of public market asset ownership. Possible causes of the investment performance gap include liquidity and geography as missing risk factor adjustments, an unrepresentative sample period, and the form in which commercial real estate assets are held.     

A Different Look at Commercial Real Estate Returns

Commercial real estate makes up a relatively small percentage of most institutional portfolios, even though the existing literature has consistently reported attractive risk-return characteristics that would suggest much larger allocations. This discrepancy has been explained by a perceived lack of comparability between return series calculated for real estate and those calculated for other asset classes. Just as investors actively involved in the futures markets do not consider individual common stocks to be traded continuously, those active in the stock market do not consider real estate to be traded continuously. In both cases, adjustments to reported returns are necessary to achieve a degree of comparability. This study makes such adjustments, using sales data from properties that help comprise the National Council of Real Estate Investment Fiduciaries / Frank Russell Company (NCREIF/FRC) Index to generate a "transaction-driven" commercial real estate return series. Examination of the risk-return characteristics of this series shows that it is quite different from traditionally reported real estate return series and far more consistent with risk-return characteristics that have been reported for other asset classes.     

The Substitutability of Real Estate Assets

This paper investigates the degree of substitutability between securitized real estate assets and real estate assets whose prices are appraisal-based. Given the insensitivity of unsecuritized asset's returns to the returns on stock market indices, equilibrium asset pricing models cannot be used to compare these two avenues of investment. Two assets are deemed substitutable if the information sets underlying unbiased, minimum error variance estimates of their pricing parameters are identical. The empirical evidence shows that the prices of the transactions-based assets—real estate investment trusts and the stock price index of the home building industry—follow a random walk while the prices of the appraisal-based assets—FRC/NCREIF indices—do not. The variance decompositions of the vector autoregressions also show that the level of economic activity helps predict the price indices of appraisal-based assets while the stock market index and the term structure of interest rates are better predictors of the prices of transactions-based assets     

Transactions-Driven Commercial Real Estate Returns: The Panacea to Asset Allocation Models?

A transactions-driven commercial real estate return series is generated in this study to determine whether the reliance on appraised values in the estimation of real estate returns is the source of the reported underpricing of real estate relative to stocks, bonds, and bills when analyzed in a traditional mean-variance setting. The reported underpricing of commercial real estate would be rational if transactions-driven returns exhibit more variance than appraisal-driven returns. While we find that transactions-driven real estate returns have greater variance than appraisal-driven returns for individual properties, most of the individual property risk is idiosyncratic and diversified away at the portfolio level. Real estate continues to be a dominate asset class in mean-variance allocation models even when represented with transactions-driven indices.¹     

Commercial Real Estate Rental Index: A Dynamic Panel Data Model Estimation

Using the actual quarterly rental income generated in the years between 2001 and 2010 by over 9,000 NCREIF commercial properties, we construct a commercial real estate rental index and estimate the time series properties (e.g., mean‐reversion speed and volatility) of market‐wide rental growth using a dynamic panel data model. The dynamic panel data model has several advantages over a standard hedonic regression. In addition, we incorporate age effects into our panel data model, and by doing so we correct the age bias in the repeated sales method and in the simple average method. Our estimates show that rental growth is cyclical but it generally lags behind broader economic growth. Surprisingly, the long‐term average rental growth is significantly lower than what is usually perceived, and the volatility of rental growth can be significantly under estimated when the conventional methods are adopted. We also find significant cross‐property type and cross‐region variations in the rental adjustment process. In contrast to the existing literature, we find a strong negative relation between rental growth and cap rate, and that this relation is significantly stronger than that between NOI growth and cap rate. Finally, we establish an empirical relation between price return and rental growth in the commercial real estate market.     

What Does the Stock Market Tell Us About Real Estate Returns?

This paper analyzes the risks and returns of different types of real estate-related firms traded on the New York and American stock exchanges (NYSE and AMEX). We examine the relation between real estate stock portfolio returns and returns on a standard appraisal-based index, and find that lagged values of traded real estate portfolio returns can predict returns on the appraisal-based index after controlling for persistence in the appraisal series. The stock market reflects information about real estate markets that is later imbedded in infrequent property appraisals. Additional analysis suggests that the differences in the return and risk characteristics across different types of traded real estate firms can be explained in part by appealing to real estate market fundamentals relating to the degree of dependence of the real estate firm upon rental cash flows from existing buildings. These findings highlight the heterogeneity of securitized real estate-related firms.     

Return Risk and Cash Flow Risk with Long-term Riskless Leases in Commercial Real Estate

This paper presents a conceptual analysis of some of the key fundamentals that underlie the risk characteristics of commercial real estate returns. In particular, the relationship between the property's return risk and its cash flow risk is explored. This relationship is important because it is the return risk that should matter most to investors, yet it is the cash flow risk or market risk about which we may have the most objective information and the most intuition. This is because real estate assets are generally unsecuritized and trade too infrequently to observe time series of returns (including appreciation) that could be used to directly study the risk characteristics of the returns. By explicitly incorporating the possibility of cash flows governed by riskless long-term leases, this paper also explores the relationship between lease term and both cash flow risk and return risk.     

Random Walks and the Cointegration of the ACLI and NCREIF

Do NCREIF returns influence commercial mortgage underwriters when they adjust capitalization rates? Are the ACLI capitalization series and the NCREIF return series cointegrated at the national and the smaller geographic sub‐division levels? This research uses a two‐step procedure to test for cointegration. First, the Phillips–Perron unit‐root procedure must show that each series is a unit‐root random walk. Previous research usually has assumed that these series are random walks, with the implication that the commercial mortgage market is efficient. Second, the Phillips–Ouliaris test of the residuals of a function of the two series determines the possibility of cointegration. At the national level and for the Northeast and Pacific regions the two series are random walks and cointegrated. In other geographic sub‐divisions, neither or only one series is a random walk and therefore the data does not support a relationship. The lack of functional relationships in four of the six smaller geographic regions suggests that underwriters are not obtaining the NCREIF information or are ignoring it. The lack of random walks with the implication about capital‐market efficiency invites further research.     

Temporal Aggregation in Real Estate Return Indices

Temporal aggregation is defined as the use of spot valuations of properties occuring over an interval of time to impute the spot value of a property or of a real estate value index as of a single point in time. Temporal aggregation may characterize not only appraisal-based indices but also indices based directly on transaction prices, such as the National Real Estate Index (NREI) and regression-based indices such as hedonic or repeat-sales indices. This paper analyzes the effect of temporal aggregation on the smoothing of the time series second moments in the resulting real estate return index. Assuming true spot returns are uncorrelated, temporal aggregation-induced smoothing will cause the empirically observed real estate index to understate the own-variance by one-third and the beta by one-half. This amount of bias in the second moments can have major implications for the real estate share in an optimal portfolio. Thus, empirical-based investment analysis could be led astray by smoothing even if the real estate return index is "transaction-based" rather than "appraisal-based."     

The Effect of Alternative Return Measures on Restricted Mixed-Asset Portfolios

A restricted portfolio is constructed which includes NYSE common stocks, corporate bonds, government bonds, small capitalization common stocks, residential real estate and farmland and returns for each of four different tax brackets (0%, 15%, 30%, 45%). Next, three alternative measures of rates of return for residential real estate and farmland are used. Finally, since some researchers believe that standard risk measures (variance and standard deviation) do not capture the total risk in real estate, the risk for the real estate returns is increased five times while the returns are held constant. The twenty–four optimal portfolios (four tax brackets with two measures of risk and three measure of return for residential real estate and farmland) are then derived. These results are then compared and contrasted to each other to ascertain the change in sensitivity of the optimal portfolios due to different tax rates, different rates–of–return estimates and different risk estimates.     

Errors in Variables, Links between Variables and Recovery of Volatility Information in Appraisal-Based Real Estate Return Indexes

The present article proposes a multivariate approach to unsmoothing appraisal-based real estate return indexes to recover the true market volatility information in real estate returns. It scrutinizes the role played by errors in variables, in conjunction with an analysis of other economic activities relevant to real estate returns, to exploit the functional relationship and the mechanism of interactions between real estate returns and these economic activities. Appraisal smoothing can therefore be detected and corrected properly and efficiently, without presuming a weakly efficient real estate market. The approach is then applied to U.K. real estate indexes as empirical examples. The results suggest a reasonable volatility in U.K. real estate investment that is close to reality. It is found that the volatility of the true market return on real estate is 1.5404–1.9282 times that of the return on the appraisal-based indexes, in contrast to figures of 2.4862–5.8720 produced by the fully unsmoothing procedure.     

Geographic Portfolio Allocations,Property Selection and Performance Attribution in Public and Private Real Estate Markets

This article examines the effects of geographic portfolio concentrations on the return performance of U.S. public real estate investment trusts versus private commercial real estate over the 1996–2013 time period. Adjusting private market returns for differences in geographic concentrations with public markets, we find that core private market performance falls. Using return performance attribution analysis, we find that the geographic allocation effect constitutes only a small portion of the total return difference between listed and private market returns, whereas individual property selection within geographic locations explains, in part, the documented outperformance of listed versus private real estate market returns.     

International Real Estate Returns: A Multifactor, Multicountry Approach

We examine the risk and return characteristics of publicly traded real estate companies from 14 countries over the period 1990 to 2001. Our data are monthly country-level commercial real estate indexes constructed by the European Public Real Estate Association (EPRA). We find substantial variation in mean real estate returns and standard deviations across countries. Using various global- and country-level factor models, we find that there is evidence of a strong global market risk component, measured relative to the Morgan Stanley Capital International world index, in most countries. However, even after controlling for the effects of global market risk, an orthogonalized country-specific market risk factor is highly significant, especially for real estate indexes in Asia–Pacific markets. We find that a country-specific value risk factor has some explanatory power in addition to the country-specific market factor, but U.S.-based market, value and size risk factors do not provide any additional explanatory power. These findings imply that the international diversification opportunities with real estate companies are more complex than previously thought.     

Alternative Benchmarks for Evaluating Mutual Fund Performance

While real estate investment trusts (REITs) have experienced very high growth rates over the past 15 years, the growth in mutual funds that invest in REITs has been even more dramatic. REIT mutual fund returns are typically presented relative to the return on a simple value-weighted REIT index. We ask whether including additional factors when benchmarking funds' returns can improve the explanatory power of the models and offer more precise estimates of alpha. We investigate three sets of REIT-based benchmarks, plus an index of returns derived from non-REIT real estate firms, namely homebuilders and real estate operating companies. The REIT-based factors are a set of characteristic factors, a set of property-type factors and a set of statistical factors. Using traditional single-index benchmarks, we find that about 6% of the REIT funds exhibit significant positive performance using traditional significance levels, which is more than twice what random chance would predict. However, with the multiple-index benchmarks that we prefer, this falls considerably to only 0.7%. In addition, we find that these sets of factors and the non-REIT indices better explain the month-to-month returns of the REIT mutual funds. This suggests that investors or researchers evaluating REIT mutual fund performance may benefit from a multiple-benchmark approach.     

Market Efficiency and Return Statistics: Evidence from Real Estate and Stock Markets Using a Present-Value Approach

This paper develops a methodology to identify asset price response to news in the framework of the Campbell–Shiller log-linear present-value equation. We further show that a slow price adjustment in real estate markets not only induces a high serial autocorrelation in excess returns, but also dampens the return volatility and the correlation with excess returns in other asset markets. Using Hong Kong real estate and stock market data, we find that the quarterly real estate price assimilates only about half the effect of market news, whereas the quarterly stock price incorporates the news fully. Our analysis identifies a cumulative price adjustment that recovers lost information in real estate returns due to market inefficiency and thereby restores the real estate return volatility and the correlation between real estate and stock markets.     

Twenty Years of the NCREIF Property Index

This study overviews the performance of the NCREIF Property Index, by property type, over the twenty-year period ended in 1998. More exactly, performance is analyzed from the perspective of the fundamental sources of return: initial earnings yield, dividend payout ratios, earnings growth, shifts in capitalization rates and other (less significant) effects. (While this approach is here applied to private real estate equities, nothing precludes its application to a variety of other investment classes.) Our results indicate the fundamental sources that have contributed to the Index's considerable cross-sectional variation as well as its time-series variation. Therefore, this study should be viewed as a useful historical account for those interested in understanding the ex post return-generating process of the Index and its property-type components as well as those who wish to model the ex ante return-generating process for a variety of applications in both the equity and debt markets—regardless of whether the securities are publicly or privately traded.     

The Integration of Commercial Real Estate Markets and Stock Markets

This paper tests whether commercial real estate markets (both exchange-traded and non-exchange-traded) are integrated with stock markets using multifactor asset pricing models. The results support the hypothesis that the market for exchange-traded real estate companies, including REITs, is integrated with the market for exchange-traded (non-real-estate) stocks. Moreover, the degree of integration has significantly increased during the 1990s. However, when appraisal-based returns (adjusted for smoothing) are used to construct real estate portfolio returns, the results fail to support the integration hypothesis, although this may reflect the inability of these estimated private market returns to accurately proxy for commercial real estate returns. Interestingly, the growth rate in real per capita consumption is consistently priced in both commercial real estate markets and stock markets, whereas previous studies have found mixed evidence on the role of consumption in explaining ex ante stock returns.     

House Prices and Inflation

The present paper examines the long-run impact of inflation on homeowner equity by investigating the relationship between house prices and the prices of nonhousing goods and services, rather than return series and inflation rates as in previous empirical studies on the inflation hedging ability of real estate. There are two reasons for this methodological departure from prior practice: (1) while the total return on housing cannot be accurately measured, the total return on housing is fully reflected in housing prices, and (2) given that using returns or differencing a time series leads to a loss of long-run information contained in the series, valuable long-run information can be captured by using prices. Also, unlike previous related studies, we exclude housing costs from goods and services prices to avoid potential bias in estimating how inflation affects housing prices. Monthly data series are collected for existing and for new house prices as well as the consumer price index excluding housing costs for the period 1968–2000. Based on both autoregressive distributed lag (ARDL) models and recursive regressions, the empirical results yield estimated Fisher coefficients that are consistently greater than one over the sample period. Thus, we infer that house prices are a stable inflation hedge in the long run.     

Returns and Information Transmission Dynamics in Public and Private Real Estate Markets

This paper examines U.S. public and private commercial real estate returns at the aggregate level and by the four major property types over the 1994–2012 time period. Returns are carefully adjusted for differences between public and private markets in financial leverage, property type focus and management fees. Unconditionally, we find that passive portfolios of unlevered core real estate investment trusts (REITs) outperformed their private market benchmark by 49 basis points (annualized) over the 1994–2012 sample period. Our baseline vector autoregression results suggest that REIT returns do not embed additional commercial real‐estate‐specific information useful in predicting private market returns. These results strongly suggest that equity REIT returns react to fundamental (latent) asset pricing information more quickly than private market returns given their greater liquidity and price revelation. REITs therefore serve as a fundamental information transmission channel to private market returns when asset pricing variables are omitted.     

Valuation of venture capital investments: empirical evidence

Using the valuation data of 421 US venture capital transactions and 176 initial public offerings, we test a simple binomial valuation model in modelling the risk‐return profiles of venture capital investments. We find that the model is consistent with the previous knowledge on the risk‐return profile of venture capital investments. The results also confirm the hypotheses that early‐stage ventures have higher implied risk and implied volatility of the returns than more established ones.
Additionally, we analyse the predictive power of the binomial pricing model and compare it to corresponding 'traditional' models that utilize risk‐adjusted rates of return. We construct one‐step ex post return forecasts for the sample ventures and compare the results to the actually realized returns. The findings indicate that the fit of the binomial model is better than the fit of the corresponding 'traditional' models.
The results imply that option‐based methods have empirical relevance in the pricing analysis of privately held companies and projects. Furthermore, practitioners can benefit from using these methods when analysing the risk‐return structure of private companies and R&D projects.