Optimal portfolio choice in real terms: Measuring the benefits of TIPS

In this paper we solve an optimal portfolio choice problem to measure the benefits of Treasury Inflation Indexed Securities (TIPS) to investors concerned with maximizing real wealth. We show how the introduction of a real riskless asset completes the investor asset space, by contrasting optimal portfolio allocations with and without such assets. We use historical data to quantify gains from availability of TIPS in the presence of other asset classes such as equities, commodities, and real estate. We draw a distinction between buy-and-hold long-term investors for whom TIPS fully displace nominal risk-free assets and short-term investors for whom TIPS improve the investment opportunity set of real returns. Finally, we show how gains from TIPS are tempered by the availability of alternative assets that covary with inflation, such as gold and real estate.     

Is there a real estate factor premium?

In this paper, we study the variation of expected returns on five different asset portfolios in a multi-factor model. We found the presence of a real estate factor, in addition to both a stock factor and a bond factor in asset pricing. This suggests that mutual fund managers should seriously consider including real estate assets in their portfolios, since one cannot capture the real estate factor premium without having some kind of real estate exposure. Another result is that the market segmentation found in previous studies disappears in a more general model of asset pricing in which we allow for multi-factors other than the market factor to affect asset returns. This implies that real estate assets can be treated just like other assets as far as mean-variance efficient asset allocations are concerned. We also have some preliminary evidence that equity REITs and the Russell-NCREIF index are driven by the same underlying real estate factor.     

Findings from a Cross-Sectional Housing Risk-Factor Model

Housing data from the last 25 years show that returns to residential real estate in the U.S. can be volatile and vary significantly among locations. The variations in returns are driven by economically as well as geographically and psychologically motivated factors, but so far, no asset pricing model that adequately explains systematic risks in cross-sectional housing returns is widely accepted. This paper proposes an asset pricing model for housing returns that includes a market-wide return factor, an economically motivated factor derived from income growth, a geographically based factor derived from land supply elasticity and a momentum factor, which is psychological in nature. The model explains well the systematic risks in housing returns and is robust to different portfolio segmentations. Moreover, the model illustrates that local risk factors indirectly capture the risk previously attributed to market-wide price changes. While housing is not actively traded when compared to other financial assets, understanding the risk-factors that explain housing return in cross-section provides important insight for real estate investors, builders, real estate future traders, homeowners, banks and other mortgage lenders.     

Unexpected Inflation,Capital Structure,and Real Risk‐adjusted Firm Performance

Managers can improve real risk‐adjusted firm performance by matching nominal assets with nominal liabilities, thereby reducing the sensitivity of real risk‐adjusted returns to unexpected inflation. The net asset value of US equity real estate investment trusts (REITs) serves as a good proxy for nominal assets and, accordingly, we use a sample of US REITs to test our hypothesis. We find that for the firms in our sample: (i) their real risk‐adjusted performance, and (ii) their inflation‐hedging qualities are inversely related to deviations from this ‘matching‐nominals’ argument. In addition to providing managers with a vehicle to maximize real risk‐adjusted performance, our findings also provide investors with the tools to infer inflation‐hedging qualities of equity investments.     

The predictability of real estate returns and market timing

Recent evidence suggests that all asset returns are predictable to some extent with excess returns on real estate relatively easier to forecast. This raises the issue of whether we can successfully exploit this level of predictability using various market timing strategies to realize superior performance over a buy-and-hold strategy. We find that the level of predicability associated with real estate leads to moderate success in market timing, although this is not necessarily the case for the other asset classes examined in general. Besides this, real estate stocks typically have higher trading profits and higher mean risk-adjusted excess returns when compared to small stocks as well as large stocks and bonds even though most real estate stocks are small stocks.     

Statistical properties of returns: Financial assets versus commercial real estate

This study examines the distribution of commercial real estate returns by region (east, midwest, south, and west), by property type (office, retail, R&D office, and warehouse) and in the aggregate, and compares their distributions to those of financial assets. Nominal and real returns are examined for quarterly, semiannual, and annual periods. The quarterly nominal returns on the financial assets are mostly normal with very little indication of autocorrelation. In contrast, non-normality and autocorrelation are present in most of the nominal quarterly real estate series. The non-normality is greatly reduced when semiannual or annual returns are considered or when the quarterly series are corrected for autocorrelation. The non-normality is also lower for real returns than it is for nominal returns.     

Time-varying correlation between stock market returns and real estate returns

Direct investment in commercial or residential real estate is found to provide valuable diversification benefits for Australian investors though this is not so evident for indirect real estate investment vehicles like listed Australian real estate investment trusts (A-REIT). Further, multivariate analysis of Australian real estate and share market quarterly returns, spanning the period from the 3rd quarter 1986 to the 3rd quarter 2009, suggest that the correlation between real estate returns and share market returns is time-varying. Finally, while all of the asset class correlation coefficients increased with the Global Financial Crisis period this broad movement in asset class correlation is not evident in during the Wall Street Crash of 1987.     

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.     

Excess Volatility and UK Investment Trusts

Abstract:   The issue of whether or not asset prices are more volatile than the underlying fundamentals is an empirical question with implications for market efficiency. Recent research suggests that the volatility of closed end fund returns in the USA is significantly higher than the returns on assets held by the funds. This has been attributed to noise trading as closed‐end fund shares are predominantly held by individual investors. This study demonstrates that UK investment trust returns exhibit similar excess volatility in spite of the prevalence of institutional investors. However, big investment trusts in terms of market capitalisation show greater excess volatility than small trusts. Although most of the excess volatility appears to be idiosyncratic, investor sentiment index is the most important variable associated with residual returns.     

Owner-occupied homes,income-producing properties,and REITs as inflation hedges: Empirical findings

New evidence on the correlation patterns of various real estate returns with inflation is presented. Returns on a wide array of real estate, nonresidential as well as residential, are investigated. Stock and bond returns are also analyzed for comparison purposes. Extensive heterogeneity is found in real estate return correlations with inflation. Nonresidential property returns are most strongly positively correlated with inflation, although the appreciation in owner-occupied homes is also positively associated with inflation. However, REIT returns tend to be strongly negatively correlated with inflation. In this respect, they look more like traditional stocks and bonds than any other type of real estate. Finally, new evidence on return correlations with energy prices is also presented. Nonresidential real estate performs best here, too, although no real estate asset fully compensates investors for adverse energy price shocks.     

Feedback Effects and Asset Prices

Feedback effects from asset prices to firm cash flows have been empirically documented. This finding raises a question for asset pricing: How are asset prices determined if price affects fundamental value, which in turn affects price? In this environment, by buying assets that others are buying, investors ensure high future cash flows for the firm and subsequent high returns for themselves. Hence, investors have an incentive to coordinate, which may generate self‐fulfilling beliefs and multiple equilibria. Using insights from global games, we pin down investors' beliefs, analyze equilibrium prices, and show that strong feedback leads to higher excess volatility.     

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.     

The Relationships between Real Estate Price and Expected Financial Asset Risk and Return: Theory and Empirical Evidence

In pricing real estate with indifference pricing approach, market incompleteness is shown to significantly alter the conventional pricing relationships between real estate and financial asset. Specifically, we focus on the pricing implication of market comovement because comovement tends to be stronger in financial crisis when investors are especially sensitive to price declines. We find that real estate price increases with expected financial asset return but only in weak market comovement (i.e., a normal market environment) when investors enjoy diversification benefit. When market comovement is strong, real estate price strictly declines with expected financial asset return. More importantly, contrary to the conventional positive relationship from real option studies, real estate price generally declines with expected financial asset risk. With realistic market parameters, we show that there is a nonlinear relationship between real estate price and financial risk. When the market comovement is strong, real estate price only increases with financial asset risk when the risk is low but eventually declines with the risk when it becomes high. Our cross-country empirical results also show that the relationship between financial market risk and real estate price is non-monotonic, conditional on the degree of market comovement.     

Corporate real estate's impact on the takeover market

This article explores the issues and problems associated with corporate real estate ownership as viewed through the takeover market. The perception held by managers is that corporate real estate assets are unique, specialized assets. This perception conflicts with financial theory which states that the market values all corporate assets based only on their expected future cash flows. Thus corporate real estate assets are priced according to their cash flows and are like other corporate assets. This study tests the hypothesis that corporate real estate is a specialized asset by examining the impact real estate assets have on the takeover market. The study uses a logit regression model in order to attempt to predict which firms become takeover targets. If corporate real estate in general is a specialized asset, then real estate is expected to be an important variable in predicting takeover targets. Although the logit model has little predictive accuracy, results from the prediction model suggest that corporate real estate plays a significant part in determining the likelihood of a firm's becoming a takeover target. The greater the real estate holdings, the greater the likelihood of a firm's becoming a takeover target.     

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.     

On the exclusion of assets from tests of the two-parameter model: A sensitivity analysis

This study investigates the sensitivity of tests of the CAPM to different sets of asset returns. Tests are conducted with market portfolios that include returns for bonds, real estate, and consumer durables in addition to common stocks. Even when stocks represent only 10% of the portfolio's value, inferences about the CAPM are virtually identical to those obtained with a stocks-only portfolio. In contrast, inferences are sensitive to the set of assets used in the tests.     

Asymmetric Information and the Predictability of Real Estate Returns

This article examines the relation between systematic price changes and the heterogeneity of investors information sets in real estate asset markets. The empirical implications rely on a theoretical economy in which information asymmetry alters the dynamic relation between returns and trading volume. We employ a filter-rule methodology to determine predictability in returns and augment the return-based conditioning set with trading volume. The additional conditioning information is necessary since the model is underspecified when predictability is based on returns alone. Our results provide new insight into the coexistence of informational and noninformational exchange in the speculative markets for real estate assets. Specifically, we find that the predictability of real estate returns is generally more indicative of portfolio rebalancing effects than an adverse-selection problem. These results are unique in addressing the time-variation in information asymmetry.     

How Do Institutional Factors Affect International Real Estate Returns?

Prior international real estate studies recognize the importance of country-specific factors for explaining real estate security returns. Using firm level observations from the FTSE NAREIT/EPRA Index for 2004?C2006, we construct a set of multifactor multivariate statistical regression models to identify and pin-point country-specific institutional features that determine differences for excess real estate security returns. Our analyses indicate that the excess real estate returns (i.e., required risk premiums) are, in part, determined by the quality of a country??s legal system and the corporate governance environment, controlling for various country-specific macro-economic variables and firm-level characteristics. We further find that the impact of institutional factors on international real estate returns is more prominent in the Asia-Pacific Region, and recent development of the REIT structure across the world does not alter the importance of corporate governance and legal system quality for determining real estate returns.     

Trading patterns of big versus small players in an emerging market: An empirical analysis

This study uses a Vector Autoregressive (VAR) model to examine interdependencies among institutional investors, big individual investors, and small individual investors, and the effects of their trading on stock returns on the Taiwan Stock Exchange (TSE). The results imply that, during the sample period, big individual investors are the most well informed players; their trading affects not only stock returns but also small individual investors. Small individual investors are not well informed and are slow learners. Their orders to trade tend to provide liquidity to institutional and big individual investors, but there is no compensation for their liquidity services. We find that institutional investors follow neither positive-feedback nor negative-feedback trading strategies. Overall, the responses to shocks, except for those of small individual investors, decay quickly, indicating that the TSE can absorb shocks quickly and efficiently. Our analysis implies that small individual investors would be better off institutionalizing their investment decisions (e.g., by investing in mutual funds).     

What’s wrong with Pittsburgh? Delegated investors and liquidity concentration

What makes an asset institutional quality? This paper proposes that one reason is the existing concentration of delegated investors in a market through a liquidity channel. Consistent with this intuition, it documents differences in investor composition across US cities and shows that delegated investors concentrate their investments in cities with higher turnover. It then estimates a search model showing how heterogeneity in liquidity preferences makes some markets more liquid, even when assets have identical cash flows. The paper provides evidence for clientele equilibria arising in frictional asset markets and suggests that a liquidity channel may explain divergent paths in city development.