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.     

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."     

Price Discovery in American and British Property Markets

This paper examines the securitized (public) and unsecuritized (private) commercial property markets in the United States and the United Kingdom for evidence of price discovery. Appraisal-based returns are corrected for smoothing, without presupposing the true returns to be uncorrelated or unpredictable across time. Real Estate Investment Trusts (REITs) and property company returns are corrected for leverage. We find evidence that price discovery occurs in the securitized market structure in both countries, and that this price information does not fully transmit to the unsecuritized markets for a year or more. In Britain, the unsecuritized market appears to be more closely and immediately linked to the securitized market than is the case in the U.S.     

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.     

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.     

Forecasting Prices and Excess Returns in the Housing Market

The paper uses quarterly indexes of existing single-family home prices estimated with microdata on properties that sold more than once to estimate excess returns to investment in owner-occupied housing. Housing prices and excess returns are estimated over the period 1970:1 to 1986:3 for Atlanta, Chicago, Dallas, San Francisco. Using time-series cross-section regressions we test for the forecastability of prices and excess returns using a number of independent variables. Price changes in one year tend to continue for more than one year in the same direction. The ratio of construction costs to price, changes in adult population and increases in real per capita income all are positively related to excess returns or price changes over the subsequent year. The results add weight to the argument that the market for single-family homes is inefficient.     

Appraisal Smoothing: The Other Side of the Story

Appraisal smoothing has been widely accepted as an important factor to consider when analyzing real estate returns using appraisal-based data. In this paper, we demonstrate that the general applicability of the appraisal-smoothing arguments developed so far in the literature is limited by the assumptions upon which the arguments are based. We further show that the use of appraisal-based data can result in a higher (not lower) variance than that of true returns. Given this, it might be more fruitful to analyze the unique characteristics of real estate markets as possible explanations for the seemingly low variance observed in appraisal-based (or transaction-based) return indexes.     

Do Real Estate Prices and Stock Prices Move Together? An International Analysis

The relationship between stock prices and real estate prices has been the subject of substantial debate in both the academic and practitioner literatures. Existing studies have focused on the time series of stock and real estate returns using data from a single country, such as the U.S. By necessity, these studies examine return and price changes over short intervals, creating a bias when property values are smoothed from year to year. Using data from 17 different countries over 14 years, this paper examines the relation between stock returns and changes in property values and rents. Consistent with other country-specific studies, we find that, with the exception of Japan, the contemporaneous relation between yearly real estate price changes and stock returns is not statistically significant. However, when the data are pooled across countries and when we look at longer measurement intervals, a significant relation between stock returns and both rents and value changes becomes apparent. Real estate prices are also found to be significantly influenced by GDP growth rates and provide a good long-term hedge against inflation but a poor year-to-year hedge.     

Inferring an Investment Return Series for Real Estate from Observations on Sales

Accurate measurement of the returns to real estate investment are essential to sound analysis. This paper improves upon the traditionally employed method—collecting comparable sales data. A dynamic model of real estate appraisal is developed in which agents have incomplete information, heterogeneous search costs, and varying expectations. Various types of simulation analysis of the model indicate it performs best in the sense that the return estimate converges to the true value faster than other simpler rules.     

Real Estate for the Long Term: The Effect of Return Predictability on Long-Horizon Allocations

We examine how the predictability of real estate returns affects the risk of, and optimal allocations to, real estate for investors of differing investment horizons. Returns to direct real estate are mean reverting, and risk decreases with horizon. This is driven by a tendency for property transaction prices to overshoot inflation. Mean reversion in real estate returns is weaker than that of equities, resulting in real estate having similar risk to equities for long-term investors. However, optimal portfolios have large allocations to direct real estate at all horizons, and the allocation increases with horizon. Finally, we find that real estate investment trusts are a redundant asset class for investors with access to direct real estate as an asset class, but they do have a role in optimal allocations when direct property investment is not feasible.     

REAL OPTIONS VOLATILITY ESTIMATION WITH CORRELATED INPUTS

Real Options Analysis (ROA) provides a framework for valuing reactive and proactive managerial flexibility in investment decisions. Estimating the volatility parameter for a real options model is challenging because there are typically no historical returns for the underlying asset and no current market prices. A previously developed method of using simulation to estimate the volatility parameter for a real investment is demonstrated. The effects of serial price correlation and price-demand cross-correlation on volatility parameters developed with this method are explained. Finally, managerial implications of these findings are discussed.     

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.     

Asymmetric Volatility, Correlation and Returns Dynamics Between the U.S. and U.K. Securitized Real Estate Markets

We construct synchronously priced indices of securitized property listed on the New York Stock Exchange and London Stock Exchange. The indices are then utilized to examine dynamic information flows between the two markets. By analyzing returns behavior, asymmetric volatility spillover effects and exceedance correlations, this study shows that the real estate markets in these two countries experience significant interaction on a daily basis when synchronously priced data are utilized. These results are different from when close-to-close returns are examined, implying that the use of close-to-close data can misconstrue the true dynamics that exist between these markets. Results also show significant asymmetric effects on both the volatility and correlation dynamics between the markets. This has several implications for property portfolio managers, indicating that positive and negative news impact the markets differently. This is particularly true for the United Kindom, where daily foreign news from the United States can influence U.K. volatility.     

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.     

Repeat‐Sales Indexes: Estimation without Assuming that Errors in Asset Returns Are Independently Distributed

This article proposes an alternative specification for the second stage of the Case‐Shiller repeat‐sales method. This specification is based on serial correlation in the deviations from the mean one‐period returns on the underlying individual assets, whereas the original Case‐Shiller method assumes that the deviations from mean returns by the underlying individual assets are i.i.d. The methodology proposed in this article is easy to implement and provides more accurate estimates of the standard errors of returns under serial correlation. The repeat‐sales methodology is generally used to construct an index of prices or returns for unique, infrequently traded assets such as houses, art and musical instruments, which are likely to be prone to exhibit serial correlation in returns. We demonstrate our methodology on a data set of art prices and on a data set of real estate prices from the city of Amsterdam.     

Optimal Valuation of Claims on Noisy Real Assets: Theory and an Application

A theory for valuing claims on noisy real assets is developed and applied. Central to the theory is determination of the dynamics for the best estimate of real asset value. The dynamics of the value estimate are shown to differ from the dynamics of the true asset value only in the arrival rate of information. The rate of information arrival in the value estimate can be faster or slower than information arrival in the true asset value, which can lead to unexpected outcomes in the valuation and exercise of options on noisy real assets. The theory we develop is illustrated through an application. An imperfectly competitive market for real estate development is examined, in which agents compete over the timing of lead investment. Information spillover and free–rider incentives are shown to cause significant delay in lead investment. Delay together with a competitive response once lead investment has occurred explain observed patterns of development in gentrified urban land markets and multistage development projects.     

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.     

Optimal Valuation of Noisy Real Assets

We study the optimal valuation of real assets when true asset values are unobservable. In our model, the observed value cointegrates with the unobserved true asset value to cause serial correlation in the time series of observed values. Autocorrelation as well as total variance in the observed value are used to calculate an efficient unbiased estimate of the true asset value (the time–filtered value). The optimal value estimate is shown to have three time–weighted terms: a deterministic forward value, a comparison of observed values with previously determined time–filtered values, and a convexity correction for incomplete information. The residual variance measures the precision of the value estimate, which can increase or decrease monotonically over time as well as display a linear or nonlinear time trend. We also show how to revise time–filtered estimates based on the arrival of new information. Our results relate to work on illiquid asset markets, including appraisal smoothing, tests of market efficiency, and the valuation of options on real assets.     

Illiquidity and Pricing Biases in the Real Estate Market

This article addresses the micro-analytic foundations of illiquidity and price dynamics in the real estate market by integrating modern portfolio theory with models describing the real estate transaction process. Based on the notion that real estate is a heterogeneous good that is traded in decentralized markets and that transactions in these markets are often characterized by costly searches, we argue that the most important aspects defining real estate illiquidity in both residential and commercial markets are the time required for sale and the uncertainty of the marketing period. These aspects provide two sources of bias in the commonly adopted methods of real estate valuation, which are based solely on the prices of sold properties and implicitly assume immediate execution. We demonstrate that estimated returns must be biased upward and risks downward. These biases can be significant, especially when the marketing period is highly uncertain relative to the holding period. We also find that real estate risk is closely related to investors' time horizons, specifically that real estate risk decreases when the holding period increases. These results are consistent with the conventional wisdom that real estate is more favorable to long-term investors than to short-term investors. They also provide a theoretical foundation for the recent econometric literature, which finds evidence of smoothing of real estate returns. Our findings help explain the apparent risk-premium puzzle in real estate—that is, that ex post returns appear too high, given their apparent low volatility—and can lead to the formal derivation of adjustments that can define real estate's proper role in the mixed-asset portfolio.     

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.