Commercial Real Estate Returns: An Anatomy of Smoothing in Asset and Index Returns

In this article, we investigate the commonly used autoregressive filter method of adjusting appraisal‐based real estate returns to correct for the perceived biases induced in the appraisal process. Many articles have been written on appraisal smoothing but remarkably few have considered the relationship between smoothing at the individual property level and the amount of persistence in the aggregate appraisal‐based index. To investigate this issue we analyze a large sample of appraisal data at the individual property level from the Investment Property Databank. We find that commonly used unsmoothing estimates at the index level overstate the extent of smoothing that takes place at the individual property level. There is also strong support for an ARFIMA representation of appraisal returns at the index level and an ARMA model at the individual property level.     

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.     

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 Dynamics of Appraisal Smoothing

We investigate the dynamics of appraisal smoothing in the National Council of Real Estate Investment Fiduciaries (NCREIF) index return using time‐varying asset pricing models. We find that smoothing is on average close to zero but varies substantially over time. From the inception of the NCREIF index in 1978 until the mid‐1990s, there was little evidence of smoothing. Smoothing has increased significantly since the mid‐1990s to the end of 2010. Smoothing increases when property prices or uncertainty increases. However, it decreases when sentiment in the property market is high or during recession periods. The substantial variation in the level of smoothing indicates that the volatility of unsmoothed appraisal‐based property returns would be significantly over‐ or under‐estimated for different periods if unsmoothed with a long‐run average smoothing.     

Estimating Real Estate's Systematic Risk from Aggregate Level Appraisal-Based Returns

This paper estimates the systematic risk (or "beta") of unsecuritized investment grade commercial real estate, as represented by the FRC and PRISA indices of institutional real estate holdings. Systematic risk defined with respect to national consumption is compared to systematic risk defined with respect to the stock market. Also, the risk estimates are explicitly adjusted to account for "smoothing" in appraisal-based aggregate level returns data. The systematic risk of these real estate indices appears to be virtually zero with respect to the stock market, even after correcting for smoothing, but substantially positive with respect to national consumption.     

Estimating Returns on Commercial Real Estate: A New Methodology Using Latent-Variable Models

Despite their widespreao use as benchmarks of U.S. commercial real estate returns, indexes produced by the National Council of Real Estate Investment Fiduciaries (NCREIF) are subject to measurement problems that severely impair their ability to capture the true risk–return characteristics–especially volatility–of privately held commercial real estate. We utilize latent-variable statistical methods to estimate an alternative index of privately held (unsecuritized) commercial real estate returns. Latent-variable methods have been extensively applied in the behavioral sciences and, more recently, in finance and economics. Unlike factor analysis or other unconditional statistical approaches, latent variable models allow us to extract interpretable common information about unobserved private real estate returns using the information contained in various competing measures of returns that are measured with error. We find that our latent-variable real estate return series is approximately twice as volatile as the aggregate NCREIF total return index, but less than half as volatile as the NAREIT equity index. Overall, our results strongly support the use of latent-variable statistical models in the construction of return series for commercial real estate.     

Estimating the Lagging Error in Real Estate Price Indices

Real estate indices based on appraisals or sale prices of properties are known for their slow response to market news. These indices can therefore be represented (in logarithm) as the sum of a latent "true" price index and a lagging error. We show that the latent appreciation return and the lagging error can be jointly estimated in a state–space model, which has two key features. First, it employs exogenous variables known to predict asset returns to predict the latent appreciation return. Second, it incorporates known sources of the lagging error, such as the partial adjustment in observed index to the latent appreciation return and the seasonality in reappraisal quality. We find that, after the estimated lagging errors are removed, the appraisal–based National Council of Real Estate Investment Fiduciaries returns become more informative and hence exhibit (i) greater variance, (ii) weaker auto correlation, (iii) higher correlation with the returns of the securitized real estate and (iv) more timely response to market news.     

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.     

Unsmoothing Real Estate Returns: A Regime‐Switching Approach

We propose newly developed unsmoothing techniques for appraisal‐based real estate returns based on a regime‐switching threshold autoregressive (TAR) model. We show that when true returns follow a TAR process, conventional linear autoregressive techniques are misspecified and underestimate true variance. Two exogenous variables, equity returns and gross domestic product growth, outperform other variables as regime indicators and appear to capture risks of downturns in real estate. We extend the model to the smoothing equation, allowing for switching behavior by appraisers, using two new techniques: the TAR‐AR and TAR‐TAR approaches. The “co‐switching” specification opens up a new frontier of empirical research. We estimate the TAR‐TAR using FT returns as the regime indicator, and we find results that outperform conventional smoothing models and have plausible economic explanations.     

Gains From Diversifying Into Real Estate: Three Decades of Portfolio Returns Based on the Dynamic Investment Model

This paper compares the investment policies and returns for portfolios of stocks and bonds with and without up to three categories of real estate. Both domestic and global settings are examined, with and without the possibility of leverage. The portfolios were generated via the dynamic investment model based on the empirical probability assessment approach applied to past (joint) realizations of returns, both with and without correction for "smoothing" in the real estate data series. Our principal findings are: (1) the gains from adding real estate, on a semi-passive (equal-weighted) basis, to portfolios of either U.S. or global financial assets were relatively modest; in contrast, (2) the gains from adding real estate to the universe of U.S. financial assets under an active strategy were rather large (in some cases highly statistically significant), especially for the very risk-averse strategies; (3) the gains from adding U.S. real estate to a universe of global financial assets under an active strategy were mixed, although generally favorable for the highly risk-averse strategies; (4) correcting for second-moment smoothing in the real estate returns series had a relatively small impact for the more risk-tolerant strategies; and (5) there was some evidence that desmoothing resulted in improved probability estimates.     

Real Estate Returns: A Comparison with Other Investments

Real estate returns, measured unleveraged, have been between those of stocks and bonds over 1960–1982. Due to appraisal smoothing and imperfect marketability, one must be careful about directly comparing measured real estate returns with those on other assets. It is likely, however, that low correlations with stocks and bonds make real estate a diversification opportunity for traditional portfolio managers. In addition, the issue of how various assets are priced is addressed. While stocks are priced primarily on market or beta risk, and bonds are priced primarily on interest rate and default risk, the real estate pricing mechanism includes residual risk and non-risk factors such as taxes, marketability costs and information costs.     

The Consequences of REIT Index Membership for Return Patterns

The impact of stock market index membership on Real Estate Investment Trust (REIT) stock returns is unclear. Returns may become more like those of other indexed stocks and less like those of their underlying properties. Taking advantage of the inclusion of REITs in major S&P indexes starting in 2001, we find that shared index membership significantly increases the correlation between REIT returns. However, index membership also enhances the link between REIT returns and the underlying real estate, consistent with improved pricing efficiency.     

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     

Appraisal-Based Real Estate Returns under Alternative Market Regimes

In this article we use Monte Carlo simulation to study the statistical properties of real estate returns. We set up a model where transactions prices are noisy signals of true prices. We then consider a number of appraisal rules, derived from Bayesian and non-Bayesian theory, to estimate the current true price and rate of return. The class of exponential smoothing and Kalman filter rules perform well at both the disaggregate (returns on an individual property) and aggregate (returns on a real property portfolio) levels. A special case of exponential smoothing (α= 1.0) places all weight on current market data. Since this case eliminates smoothing, our results suggest that appraisers should place all weight on current data (no weight on past data) provided that they want to estimate returns rather than values. However, these results should be used with caution if sales prices are very noisy.     

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     

Value Creation in REIT Property Sell-Offs

We examine major sales of real property by public U.S. Real Estate Investment Trusts (REITs) 1992–2002. We find that abnormal shareholder returns are significantly positive, a result that is consistent with findings for conventional firms that sell off real estate. Because REITs do not pay taxes, this finding supports the view that abnormal returns in real estate sell-offs by all types of firms are derived largely from asset allocation efficiencies and do not result exclusively from tax benefits. Shareholder returns are lower in sell-offs motivated by a desire to reduce long-term debt, as is consistent with financial theory regarding the information content of leverage decisions. Returns are inversely related to the firm's operating performance prior to the sell-off announcement, further supporting the case that improved asset efficiencies create value in real estate sell-offs.     

Risk and the Performance of Real Estate Investment Trusts: A Multiple Index Approach

This paper analyzes the returns of a sample of Real Estate Investment Trusts and examines their risk-adjusted performance using both single index (i.e., CAPM) and multiple index (i.e., APT) models. It is shown that while the performance rankings of the investment trusts are not very sensitive to the risk-adjustment model, the actual performance measures do sometimes differ substantially. Unfortunately, because of the high volatility of these real estate investments, the differences in investment performance across trusts generally are not statistically significant.     

Short Sales and Price Discovery in the Hong Kong Real Estate Market

Indirect real estate (IRE) returns are often shown to lead direct real estate (DRE) returns. Apart from differences in liquidity, transaction costs, and management skills, the DRE market is also less complete than the IRE market—when negative shocks arrive, one can only short IRE (e.g., real estate stocks or REITs), but not DRE. This study investigates if short sales in the IRE market convey any information to the DRE market. Based on high‐frequency (weekly) property price data in Hong Kong from 2000 to 2012, we find that short sales in the IRE market led DRE returns, even after controlling for the lagged IRE returns in a VAR model. This supports an information spillover mechanism in which the DRE market learns private information that is not reflected in IRE returns. The spillover effect, however, weakened after the recent global financial crisis because the increased uncertainty over the credibility of individual firms made short sales more reflective of firm‐specific information than real estate market fundamentals.     

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.     

A Further Examination of Appraisal Data and the Potential Bias in Real Estate Return Indexes

Recent articles by Giliberto [2] and Geltner [1] examine the biases inherent in the use of appraisal data in real estate performance measurement. This note takes another look at the direction and magnitude of any bias in holding period returns. Using appraisal data from a commingled real estate fund, we show that in actual application the size of the holding period return bias can be quite small and this bias may have no appreciable effect on real estate return indexes.