What Moves Retail Property Returns at the Metropolitan Level?

In this article the determinants of metropolitan-level appraisal-based retail property returns are examined by estimating a six-equation model of retail construction starts, retail sales, stock-market returns, commercial mortgage rates, inflation, and the logarithm of stock-market volatility. Residuals from these equations are then used to explain actual movements in retail real estate returns. Our empirical procedure looks at both unadjusted and unsmoothed appraisal-based retail real estate returns. The general finding is that unsmoothed appraisal-based retail real estate returns lag significantly behind market conditions. Furthermore, the results suggest that very little of the variation in metropolitan-level appraisal-based retail real estate returns can be explained by macroeconomic news events.     

The Relative Effect of Property Type and Country Factors in Reduction of Risk of Internationally Diversified Real Estate Portfolios

We examine and test the merits of diversifying portfolios of real estate securities internationally and across property types. Our analysis covers the period January 1990 through July 2005. Using data from the Global Property Research GPR 250 Property Securities Index, which has monthly prices for five property type indexes in 21 countries, we decompose country and property type sources of variation in real estate security returns. We find that property type effects are smaller than country effects. Property type specialization explains only 6% of the variance of national real estate securities index returns. Because property type effects are so small, country diversification is a more effective tool for achieving risk reduction than property type diversification. In addition, we find evidence that the relative importance of country effects is decreasing while that of industry effects is increasing. However, country effects continue to dominate property type effects.     

Smoothing in appraisal-based returns

This article presents a conceptual analysis of smoothing in the second moments of appraisal-based returns series in commercial real estate. The intent of the article is to lay the groundwork necessary for the more scientific use of appraisal-based returns time series for the purpose of inferring the true second moments. Formal smoothing models are presented together with their theoretical implications for smoothing in various second moments of interest to investment analysts. Empirical estimators for inferring true moments from appraisal-based data are described. Limited empirical findings from previous literature are also briefly discussed in the light of the theoretical findings of this study. The overall conclusion is that appraisal-based returns can be very useful in studying the risk characteristics of commercial real estate assets, provided that this type of data is corrected for smoothing as discussed in the article.     

The Global Determinants of Direct Office Real Estate Returns

We investigate the determinants of direct office real estate returns by analyzing rents, capital appraisals, and total returns. A recently compiled global database of major cities in Asia, Europe, and the United States provides a unique opportunity to give a macro-view on the effects of economic growth and supply and demand factors on nominal real estate returns. The global database provides quarterly observations from 1986 to 1999. To address the smoothness problem of appraisal-based price data and regulated rents, we employ the Generalized Method of Moments to estimate a dynamic panel-data model. The model allows us to combine the cross-sectional and time-series dimension in our quarterly data. We find that gross domestic product, inflation, unemployment, vacancy rate, and the available stock all have an effect on real estate returns.     

Recovery of Real Estate Returns for Portfolio Allocation

Appraisal-based return indexes may not approximate the true real estate return distributions because of understated return volatility. Recovery of returns from reported, appraisal-based returns may be possible by evoking models to correct for appraisal-based smoothing of the second moment. Because recovery intentionally alters the volatility of the reported return distribution and the correlations among assets in the portfolio, the weights to real estate are likely affected. Our examination of the portfolio implications of altering the return distribution indicates that weights may be quite sensitive to the effects of recovery across a reasonable range of correlation regimes. A comparative analysis of several recovery models reveals that all models achieve the objective of inflating the volatility of reported returns. However, the models also change the mean of the return distribution, which either counteracts or magnifies the effect of the volatility change on allocations. These findings bring into question the applicability of recovery models in their current form.     

Sticky Valuations,Aggregation Effects,and Property Indices

Previous studies on real estate smoothing have generally focused on the second moment of returns for individual properties. Although this body of research has developed plausible reasons for explaining the observed lower risk associated with real estate, no explanation has, however, been offered to account for the large difference in serial correlation at the individual property level compared with the index level. This article addresses this issue and also offers an explanation for the difference in serial correlation observed with different frequency real estate indices. Employing the framework developed by Holbrook Working (1960), we argue that the high levels of serial correlation typically observed in real estate indices results from a combination of random and sticky appraisals that induce cross-correlations between the component returns. Using the concept of sticky values we question the results of Lai and Wang (1998) in which they argue that the variance of appraisal-based returns should always be greater than true returns. We argue that a pragmatic conclusion regarding volatility should be conditioned on the underlying stochastic processes. We draw a distinction between serial cross-sectional and temporal sticky appraisal processes that influence smoothing at the index and individual property levels. Our results indicate that smoothing does not appear to be a serious issue at the individual property level. However, when different appraisal processes are aggregated into an index the underlying cross-correlation between those processes can induce high levels of smoothing.     

The Short-Term Inflation-Hedging Characteristics of U.K. Real Estate

This study investigates the short-term inflation-hedging characteristics of U.K. real estate compared to other U.K. investments. It considers not only total returns but also changes in income and changes in capital values. The analyses are undertaken using annual and quarterly data. Stocks, bonds, appraisal-based real estate (including the three property types, separately), and real estate stocks are considered. Real estate series, constructed from the original appraisal series to take account of autocorrelation, also are used. The methodology is based on that devised by Fama and Schwert (1977) and tests are undertaken for stationarity and structural breaks. Hypotheses are established about the coefficients on expected and unexpected inflation in the model, and these are tested. It is concluded that real estate has poorer short-term hedging characteristics for total return, change in capital value, and change in income than stocks but better characteristics than bonds. However, there is evidence to suggest that the relationships change under different economic environments.     

Interactions Between Property and Equity Markets: An Investigation of Linkages in the United Kingdom 1972–1992

Two strands of real estate research—that concerned with the relationships between securitized real estate and the underlying market and that dealing with the role of property in the wider economy—rarely are considered together. The paper utilizes the U.K. equity market and property company share data to explore the relationships between real estate and the rest of the economy, using a two-sector analytic model. Causality analysis suggests that the wider economy leads the real estate market in the short term but that, with a longer lag structure, positive real estate returns may point to negative future returns in the rest of the economy. This provides weak confirmatory evidence for theories of capital switching between sectors.     

Corporate Real Estate Ownership Implications: International Performance Evidence

In this paper we explore corporate real estate ownership internationally. Based on a sample of 4,636 companies from 18 industries and 9 countries we document distinct patterns and trends in the corporate ownership level of real estate. Real estate ownership appears to be driven by industrial rather than national differences, with corporate real estate ratios ranging between 0.13 for Business Services and 0.63 for the Mining sector. Overall, real estate ownership appears to be decreasing over time, which may be due to the gaining popularity of lease alternatives. When analyzing the stock performance of the companies in our sample, we discover a significantly negative relationship between real estate ownership and a firms systematic risk. Idiosyncratic risk bears no significant relationship with real estate ownership. With respect to stock returns our results show that returns are lowest among firms with the highest real estate ownership levels in each industry. After controlling for the variation in risks, the remaining return patterns differ strongly across industries, with a significantly negative relationship between stock outperformance and real estate ownership for Communications and Business Services and a positive but not significant relationship for Transportation.     

On the Hybrid Nature of REITs

The consensus that emerges from the current research on the linkage between securitized and direct investment in real estate is that direct (private) real estate returns play a relatively minor role in the real estate investment trust (REIT) return generating process. However, this result may at least partially be due to the coarseness of the measures of direct real estate returns or the relatively short return horizons used in previous studies. This study takes a different and unique perspective. Unlike earlier studies we do not use aggregated, average appraisal based returns on direct real estate investment. Instead, we use the MIT TBI indexes, which are transaction based price indexes, available both on the aggregate and sub-index levels. We find that the relation between REIT and direct real estate returns appears to be stronger at longer horizons. More specifically, using a cointegration framework, we find robust evidence that REITs and the underlying real estate are related and that they share a long run equilibrium. Interestingly, we find that both REITs and direct real estate returns adjust towards this long run relationship. When we examine property type level data we find similar results.     

Are Securitized Real Estate Returns more Predictable than Stock Returns?

This paper examines whether the predictability of securitized real estate returns differs from that of stock returns. It also provides a cross-country comparison of securitized real estate return predictability. In contrast to most of the literature on this issue, the analysis is not based on a multifactor asset pricing framework as such analyses may bias the results. We use a time series approach and thus create a level playing field to compare the predictability of the two asset classes. Forecasts are performed with ARMA and ARMA–EGARCH models and evaluated by comparing the entire empirical distributions of prediction errors, as well as with a trading strategy. The results, based on daily data for the 1990–2007 period, show that securitized real estate returns are generally more predictable than stock returns in countries with mature and well established REIT regimes. ARMA–EGARCH models are found to have portfolio outperformance potential even in the presence of transaction costs, with generally better results for securitized real estate than for stocks.     

Are REITs real estate? Evidence from international sector level data

The aim of this study is to examine whether securitized real estate returns reflect direct real estate returns or general stock market returns using international data for the U.S., U.K., and Australia. In contrast to previous research, which has generally relied on overall real estate market indices and neglected the potential long-term dynamics, our econometric evaluation is based on sector level data and caters for both the short-term and long-term dynamics of the assets as well as for the lack of leverage in the direct real estate indices. In addition to the real estate and stock market indices, the analysis includes a number of fundamental variables that are expected to influence real estate and stock returns significantly. We estimate vector error-correction models and investigate the forecast error variance decompositions and impulse responses of the assets. Both the variance decompositions and impulse responses suggest that the long-run REIT market performance is much more closely related to the direct real estate market than to the general stock market. Consequently, REITs and direct real estate should be relatively good substitutes in a long-horizon investment portfolio. The results are of relevance regarding the relationship between public and private markets in general, as the ‘duality’ of the real estate markets offers an opportunity to test whether and how closely securitized asset returns reflect the performance of underlying private assets. The study also includes implications concerning the recent financial crisis.     

Frothy Housing Markets and Local Stock-Price Movements

Prior empirical research finds habitat effects manifest in stock pricing among firms that share headquarters cities. We empirically investigate whether trends in residential real estate prices affect headquarters-city stock pricing phenomena for companies across U.S. metro areas for 1989?C2004. Specifically, we hypothesize that stocks of firms headquartered in ??hot?? residential real estate markets experience higher returns compared to stocks of firms from ??cold?? markets. We also hypothesize that stocks of firms headquartered in hot real estate markets display stronger return comovement with same-city stocks. We find support for these hypotheses during the 1999?C2004 sample period which coincides with the start of the housing bubble of the 2000?s; we find mixed results in earlier periods. Our findings indicate that city-specific home price patterns conditionally affect stock pricing of local firms, suggesting that investor behavior is influenced by localized shocks to household real estate wealth.     

The sensitivity of bank stock returns to real estate

According to the Federal Reserve Board, banking firms have recently been shifting significantly larger portions of their loan portfolios into real estate. This increase in real estate lending has caused concern about the continuing economic health of banks on the part of state and federal regulators, since changes in real estate returns, evidenced by changes in property value, can potentially have a significant impact on bank default risk and profit-ability. However, concerned parties do not seem to have explicitly considered the relationship between mortgage default risk and the specific characteristics of real estate investments.This study examines the sensitivities of stock returns for different bank groups, based on the percentage of total loans in real estate and the percentage of loans in five different mortgage categories (construction and development loans, farmland loans, one- to four-family residential loans, multifamily residential loans, and nonresidential and nonfarm loans), to changes in real estate market returns. This is done by developing and using a three-index model.The results of this study indicate that bank stocks, overall, are very sensitive to changes in real estae returns. Banks, with a larger portion of their total loans invested in all types of real estate loans, except farmland loans, are most sensitive to changes in real estate returns.     

Book Assets,Real Estate,and Returns on Common Stock

Since real estate is common to most firms, this study examines whether there is a real estate factor in common stock returns that is not completely captured by existing asset pricing models. The three-factor model of Fama and French (1993), hereafter FF, is extended to incorporate a unique real estate factor. Using his extended-FF model, we examine the returns on 53 industry portfolios of common stocks over the 1972 through 1995 time period. The results indicate that a significant 19 percent of the industries are systematically related to the real estate factor. Most interestingly, we show that the loading of the real estate factor in common stock return is related to the loading of the book-to-market equity factor in these returns. We also construct decile portfolios of common stocks based on historical sensitivities of common stock returns to the real estate factor. The coefficients on the real estate factor vary systematically across the decile portfolios. The results of our analysis suggest that portfolio managers should manage their exposure to real estate.     

International Diversification Strategies for Direct Real Estate

This paper will disentangle the performance of international real estate into property type performance and region selection. This helps to create an international diversification strategy for direct real estate. We use constrained cross-section regression with dummy variables for regions and property types to measure the best risk reducer. We analyze the impact of currency changes on total returns by looking at a hedged and un-hedged portfolio, both stock and equally weighted. The findings show that geographic factors have the largest influence on the volatility of international real estate returns. The average variance of the regional effects is higher than the property type effects and therefore the regional effects have a higher influence on the variation of the total portfolio. However, the regional effects are less stable through time, compared with the variance and correlation of the property type effects. Also the property type effect seems to become a more important factor for the return over time, especially when the return is expressed in local currency.     

Do Alternative Real Estate Investment Vehicles Add Value to REITs? Evidence from German Open-ended Property Funds

Besides the more commonly used REITs, German investors can also invest in a lesser-known real estate vehicle, Open-ended Property Funds. OPFs are considered a compromise between listed and direct real estate investments. OPF fund managers generally provide daily (perfect) liquidity. However, if liquidity falls below 5%, share redemptions in these funds can be temporarily suspended for a period of up to two years. During this time, investors will only be able to sell shares on the secondary market (exchange), and are thus subject to significant liquidity risk. The objective of this paper is to analyze whether OPFs add value to investor portfolios above that provided by REITs. We show that OPFs have a diversification advantage over REITs in low-risk portfolios, despite their larger potential liquidity risk. REIT liquidity is comparable to that of ordinary common stock, but OPFs exhibit an average initial discount to funds’ NAV of about 6% when share redemptions are temporarily suspended. However, in the long-run, this potential redemption suspension does not negatively influence OPF performance (in case OPFs reopen again). This makes OPFs an attractive investment alternative to REITs for investors who have a high level of risk aversion and a long-term investment horizon, such as endowments, insurance companies, and pension funds.     

The Anatomy of Public and Private Real Estate Return Premia

Market-wide, stock market specific, and real estate market specific risk – what kind of risk and to which extent drives the returns of listed real estate? Based on a structural asset pricing model calibrated to the empirical data in the U.S., we show that at least two thirds of the risk premium of listed real estate are driven by the same factors as direct real estate. Our results shed new light on the risk-characteristics of listed real estate returns and are of high interest for academics, regulators, and portfolio managers alike.     

Alpha and Persistence in Real Estate Fund Performance

This paper investigates whether fund managers investing in the direct real estate market can systematically and persistently deliver superior risk-adjusted returns. The research that has been published has typically focused on the performance of managers trading public real estate securities. Our study draws on a unique data set of commercial real estate funds collated by the Investment Property Databank (IPD) in the United Kingdom, covering up to 280 funds over the period 1981 to 2006. The widespread finding is that very few managers appear to be able to generate excess risk-adjusted returns. Furthermore, there is little evidence of performance persistence in either fund returns or risk-adjusted fund returns.     

Market States and the Effect on Equity REIT Returns due to Changes in Monetary Policy Stance

This study investigates the effect of changes in monetary policy on US equity real estate investment trust (EREIT) returns in lower and higher return ranges during bull, bear, and volatile stock market states using quantile regression. Results show that EREIT returns are sensitive to changes in monetary policy at different EREIT return ranges in different market states. During bull markets, changes in monetary policy have a significant negative impact on EREIT when investors have lower expectations of real estate price increases, but are not effective when investors have higher expectations of real estate price increases. During volatile and bear markets, EREIT returns are not sensitive to changes in monetary policy stance. Results also show that EREIT returns respond positively to stock returns in various states and conditions.