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.     

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."     

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

This comment points out a flaw in Gau and Wang's recent empirical analysis of appraisal return bias and clarifies a point in Geltner's previous article that may have misled Gau and Wang as well as others. In examining appraisal bias in returns it is important to distinguish between cross-sectional versus time-series moments. In dealing with time-series data, both the arithmetic mean and the geometric mean returns offer useful information and can complement each other in correcting for appraisal bias in the first moment of returns series.     

Commercial Real Estate Leasing, Asymmetric Information, and Monopolistic Competition

We model the choice of lease type, gross lease versus net lease, in an environment in which lessees have private information with respect to their expected intensity of utilization of the leased space, and in which lessors have market power with respect to the pricing of the lease. Unless the lessor can provide operating services at lower cost than the lessee, there exists a lemons problem. We examine a market in which lessors can provide operating services at lower cost. Given asymmetric information with respect to expected lessee utilization and/or damage to the leased space, the lessor offers both a gross and net lease, where the higher expected utilization lessees select the gross lease and the lower expected utilization lessees select a net lease. Lease pricing depends on both the lessor's beliefs with respect to lessee utilization of the space and the lessor's market power. In a monopolistic market, relative to a competitive market, a lessor charges higher rent for a gross lease relative to a net lease in order to extract a portion of the gain from shifting operating services to the lessor. Given the higher rent for a gross lease, a smaller proportion of lessees (only very high utilization lessees) selects a gross lease in a monopolistic market. Therefore, the expected cost savings associated with shifting operating services/provision of maintenance to the lessor are smallest in a monopolistic market.     

Institutional Capital Flows and Return Dynamics in Private Commercial Real Estate Markets

This article examines the short- and long-run dynamics among institutional capital flows and returns in private real estate markets. At the aggregate U.S. level, we find evidence that lagged institutional flows significantly influence subsequent returns. When disaggregating by property type at the national level, we find that capital flows predict subsequent returns in the apartment and office sectors, but not in the retail and industrial markets. At the metropolitan level, we find that the flows help explain subsequent returns in a limited number of core business statistical areas (CBSAs), although these CBSAs collectively represent about 30% of institutional capital. We find no evidence that institutional returns are predictive of future capital flows at the national or CBSA level, suggesting that institutional investors are not chasing returns.     

Risk and Return on Real Estate: Evidence from Equity REITs

We analyze monthly returns on an equally weighted index of eighteen to twenty-three equity (real property) real estate investment trusts (REITs) that were traded on major stock exchanges over the 1973–87 period. We employ a multifactor Arbitrage Pricing Model using prespecified macroeconomic factors. We also test whether equity REIT returns are related to changes in the discount on closed-end stock funds, which seems plausible given the closed-end nature of REITs.
Three factors, and the percentage change in the discount on closed-end stock funds, consistently drive equity REIT returns: unexpected inflation and changes in the risk and term structures of interest rates. The impacts of these variables on equity REIT returns is around 60% of the impacts on corporate stock returns generally. As expected, the impacts are greater for more heavily levered REITs than for less levered REITs. Real estate, at least as measured by the return performance of equity REITs, is less risky than stocks generally, but does not offer a superior risk-adjusted return and is not a hedge against unexpected inflation.     

基于地球信息系统(GIS)的房产管理系统

地理信息系统(GIS)是融地理学、几何学、计算机科学及各类应用对象为一体的综合性高新技术，通过GIS可以把房产管理的各种信息与反映地理位置的图形有机地结合在一起，并可以根据用户的需求对信息进行分析，从而为房产管理的现代化管理摸索出一条捷径。章就房产管理系统需求、系统目标、系统功能、系统选型和结构等方面进行探讨。     

Public Information and Abnormal Returns in Real Estate Investment

This study performs empirical tests of the semistrong form efficiency of a real estate investment market. An asset pricing model is utilized to estimate the abnormal returns resulting from two types of public information, major changes in government tax shelter and rent control policies as well as unanticipated changes in interest rates. In both cases the results find an absence of significant abnormal returns and no evidence to suggest that real estate investors can utilize information concerning government policy changes or interest rate movements to earn higher returns on a risk-adjusted basis. In general the findings of this study conform to the semistrong form version of the efficient markets hypothesis.     

The Risk and Return of Commercial Real Estate: A Property Level Analysis

I compare the performance of the index‐based time series approach and the cross‐sectional approach in estimating factor loadings of nontraded assets, and show that the latter likely provides less biased and more efficient estimates. I then use the cross‐sectional approach to estimate the loadings of privately owned commercial real estate on the Fama and French (1993) factors, the Pastor and Stambaugh (2003) liquidity factor, and two bond market factors, using a sample of 14,115 properties in the 1977–2012 period. I find statistically significant loadings, of which the signs seem consistent across property types, but the magnitude varies. Using the time series approach on the same data, I find insignificant loadings on virtually all factors. To investigate the sources of the weak results from the time series approach, I conduct a Monte Carlo simulation in which both approaches are correctly specified and indices can be estimated perfectly. Simulation results suggest that the cross‐sectional approach provides more accurate estimates under reasonable market conditions.     

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.     

Contagion Channels between Real Estate and Financial Markets

The recent crisis has demonstrated the linkages between asset classes within a country as well as the association between assets internationally. We provide for a better understanding of some of these linkages by conducting an empirical investigation of the channels underlying the risk of contagion between real estate investment trusts (REITs) and stocks in the United States. We test for three financial mechanisms potentially driving contagion. A behavioral dimension in the crisis propagation is also examined by considering investor sentiment and panic risk. We find that contagion prevails between REITs and stocks and that this phenomenon is driven by behavioral and liquidity mechanisms.     

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.     

Temporal and Spatial Information Diffusion in Real Estate Price Changes and Variances

This article examines patterns of temporal and spatial diffusion of real estate price changes. In addition to means, changes in volatility are tracked in reaction to substantial new information, estimated with GARCH-M methods. The data covers towns in Connecticut and near San Francisco. There is evidence of negative feedback at short lags, contrary to previous research on housing and other assets. There is also evidence of a moving average error process which tends to reverse recent shocks. Significantly positive spatial information diffusion is found from neighboring towns in Connecticut but none in control tests on nonneighboring towns. The results also include evidence of a risk-reward tradeoff in housing price changes in the San Francisco area.     

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.     

Influential Journals, Institutions and Researchers in Real Estate

A threshold citation approach is used to measure the research influence of academic real estate journals, institutions and individual researchers. Real Estate Economics followed by The Journal of Real Estate Finance and Economics and Journal of Real Estate Research are the most influential real estate journals. Almost 63% of heavily cited works in core real estate journals are published in real estate journals. Twenty-one percent of the heavily cited works are published in Real Estate Economics . An overwhelming 80% of the citations of the 21 most heavily cited papers in real estate come from articles published in real estate journals. Even when real estate articles are published in top-tier finance and economics journals, the majority of the citations associated with these articles come from top real estate journals. This provides strong evidence of the existence of a distinct real estate research discipline. As compared to prior studies, an expanded universe of institutions is found to influence real estate research. Research-extensive universities generating high-quality economics, statistics and finance research influence the real estate discipline. The individuals that are most influential, however, are generally those with substantial real estate discipline specific research.     

Determinants of the Rate of Return for Nonresidential Real Estate: Inflation Expectations and Market Adjustment Lags

This paper analyzes the economic forces that determine the real rate of return for nonresidential real estate. Our analysis shows that the intermarket variation in the real rate of return is statistically significant, and the rate of return differs by land use and market area, as well as over time in response to changes in macro-economic conditions. We use inflation variables as surrogates for changes in macroeconomic conditions over time. In contrast to earlier studies, we find that nonresidential real estate may not outperform expected inflation. We believe that the impact of expected inflation (and other macroeconomic variables) on real estate rates of return depends upon the interaction of the macro-environment and specific local real estate market conditions. Finally, our empirical evidence suggests that the effects of a given shock dissipate rather quickly in most markets when we take into account locational and property use differences.     

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.