Stationarity and Cointegration in Systems with Real Estate and Financial Assets

This study examines the properties of wealth indices for investments in several asset classes (real estate, stocks, bonds, and Treasury bills), for several types of real estate (office, retail, research and development office, and warehouse), and by region (East, Midwest, South, and West). The series representing the value of investments in real estate and financial assets are not stationary; therefore, ordinary statistical procedures cannot be applied. Since many of the properties that are included in the real estate series have outside appraisals on an annual basis, especially in the fourth quarter, the real estate series may show seasonal influences. Hence, the appropriate test for cointegration is the Johansen's test, which is formulated in such a way as to allow for deterministic seasonality by the inclusion of seasonal dummy variables. The finding of cointegration implies that there is a long-run relationship between the series in the cointegrated system. When the CPI (or a proxy for inflation) is included in the three systems, the number of common factors increase to two, implying that inflation plays an important role in creating a linkage between these time series. These findings also have implications for developing portfolios comprising financial assets and real estate. The findings also have implications for developing a model to forecast real estate prices.     

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.     

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.     

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.     

The Role of Real Estate in an Institutional Investor's Portfolio Revisited

Many papers have recently pointed out that institutional investors allocate only a very small fraction of their portfolio to real estate, much smaller than theory would dictate. This raises the question, are institutional investors underinvested in real estate equities? Or do we simply have the wrong priors? This paper is an attempt to provide some new insights into this asset allocation paradox. The key conclusions of the paper are several: First, unlike other assets, it would appear that real estate, and real estate diversification, pays off at the very time when the benefits are most needed, that is, when consumption growth opportunities are low. Second, real estate returns are predictable. In fact, the amount of predictability in real estate returns appears to be about the same as in stock returns. Third, real estate performs well in an asset-liability framework. Fourth, the chance of experiencing a large loss on real estate over a long horizon is quite small. We also report here that private sector commercial real estate investments represent between 6 and 12 percent of investable wealth in the United States. Thus, it follows (if one believes the capital asset pricing model) that if institutional investors were to invest more in real estate (up to 12 percent of their assets), they should be able to eliminate nonmarket or unique risk. All of this leaves us a bit dumbfounded as to why institutional investors hold only between 2 and 3 percent of their assets in real estate.     

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.     

Determinants of US investment in real estate abroad

The purpose of this paper is to analyse and discuss those factors which are contributing to the expansion of US FDI in real estate. The empirical results of this model of FDI in real estate show that as US foreign financial liabilities increase, there is an accompanying increase in its FDI in real estate. This result is consistent with the study by Russekh, F., Ruffin, R., 1986. The role of foreign direct investment in US capital flows. Am. Econ. Rev. 76, 1127–1130, who showed that US FDI abroad is a substitute for US financial assets. Furthermore, the empirical results indicate that as returns from the US stock market decline, there are more incentives for US investors to invest in foreign real estate. The empirical results also show that US financial wealth, US FDI in manufacturing and banking and US bilateral trade contribute positively to the expansion of US FDI in real estate.     

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.     

Harmful diversification: Evidence from alternative investments

Alternative assets have become as important as equities and fixed income in the portfolios of major investors, and so their diversification properties are also important. However, adding five alternative assets (real estate, commodities, hedge funds, emerging markets and private equity) to equity and bond portfolios is shown to be harmful for US investors. We use 19 portfolio models, in conjunction with dummy variable regression, to demonstrate this harm over the 1997–2015 period. This finding is robust to different estimation periods, risk aversion levels, and the use of two regimes. Harmful diversification into alternatives is not primarily due to transactions costs or non-normality, but to estimation risk. This is larger for alternative assets, particularly during the credit crisis which accounts for the harmful diversification of real estate, private equity and emerging markets. Diversification into commodities, and to a lesser extent hedge funds, remains harmful even when the credit crisis is excluded.     

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.     

Valuation in US Commercial Real Estate

We consider a log‐linearized version of a discounted rents model to price commercial real estate as an alternative to traditional hedonic models. First, we verify a key implication of the model, namely, that cap rates forecast commercial real estate returns. We do this using two different methodologies: time series regressions of 21 US metropolitan areas and mixed data sampling (MIDAS) regressions with aggregate REIT returns. Both approaches confirm that the cap rate is related to fluctuations in future returns. We also investigate the provenance of the predictability. Based on the model, we decompose fluctuations in the cap rate into three parts: (i) local state variables (demographic and local economic variables); (ii) growth in rents; and (iii) an orthogonal part. About 30% of the fluctuation in the cap rate is explained by the local state variables and the growth in rents. We use the cap rate decomposition into our predictive regression and find a positive relation between fluctuations in economic conditions and future returns. However, a larger and significant part of the cap rate predictability is due to the orthogonal part, which is unrelated to fundamentals. This implies that economic conditions, which are also used in hedonic pricing of real estate, cannot fully account for future movements in returns. We conclude that commercial real estate prices are better modelled as financial assets and that the discounted rent model might be more suitable than traditional hedonic models, at least at an aggregate level.     

Investing for the Long-run in European Real Estate

We calculate optimal portfolio choices for a long-horizon, risk-averse investor who diversifies among European stocks, bonds, real estate, and cash, when excess asset returns are predictable. Simulations are performed for scenarios involving different risk aversion levels, horizons, and statistical models capturing predictability in risk premia. Importantly, under one of the scenarios, the investor takes into account the parameter uncertainty implied by the use of estimated coefficients to characterize predictability. We find that real estate ought to play a significant role in optimal portfolio choices, with weights between 12 and 44%. Under plausible assumptions, the welfare costs of either ignoring predictability or restricting portfolio choices to traditional financial assets only are found to be in the order of 150–300 basis points per year. These results are robust to changes in the benchmarks and in the statistical framework.      

Asset market linkages: Evidence from financial, commodity and real estate assets

We use a general Markov switching model to examine the relationships between returns over three different asset classes: financial assets (US stocks and Treasury bonds), commodities (oil and gold) and real estate assets (US Case-Shiller index). We confirm the existence of two distinct regimes: a “tranquil” regime with periods of economic expansion and a “crisis” regime with periods of economic decline. The tranquil regime is characterized by lower volatility and significantly positive stock returns. During these periods, there is also evidence of a flight from quality - from gold to stocks. By contrast, the crisis regime is characterized by higher volatility and sharply negative stock returns, along with evidence of contagion between stocks, oil and real estate. Furthermore, during these periods, there is strong evidence of a flight to quality - from stocks to Treasury bonds.     

Dynamics of Private and Public Real Estate Markets

Using five assets (T-bills, bonds, stocks, and both public and private real estate), this study investigates how cointegration of capital markets affects the dynamics of public and private real estate markets. The results show that the price indices of the five assets are nonstationary and cointegrated. Some implications for the long-term equilibrium relationship for portfolio diversification, price discovery and prediction are discussed. In a Granger causality framework, error-correction augmented VAR models (VECM) and unrestricted VAR models are compared with respect to the conclusion regarding the interaction between public and private real estate returns. VECM is also shown to improve the prediction of private real estate returns relative to an unrestricted VAR model. These results raise questions about previous research studies regarding the dynamics between public and private real estate returns. It is shown that the long-term equilibrium relationship establishes a feedback between the two real estate markets, but the private market seems to informationally lead the public one. Possible explanations are also explored.     

基于房地产市场的我国系统性金融风险测度与预警研究

本文阐释了基于房地产市场的系统性金融风险形成机制,据此建立了分阶段、跨部门的房地产市场的系统性金融风险网络模型,并运用2006-2017年16家上市银行数据,分析和测度了我国房价大幅下跌所引发的系统性金融风险水平和结构,构建了基于房地产市场的系统性金融风险预警指标并进行测算。研究发现:在房价下跌30%的压力情景下,我国金融体系的潜在总损失总体呈级数式上升,年均增长22.70%;基于房地产市场的系统性金融风险值(SR)呈现先上升后波动下降的总体趋势;系统性金融风险(SR)的脆弱性指标(FLI)整体呈现波浪式振荡变化,且与房地产贷款/权益整体呈反向变动,系统性金融风险(SR)的传染性指标(CTI)在2012-2017年呈持续下降趋势,且与金融市场压力指数、金融机构间资产占总资产比重呈现出高度的一致性变化趋势。最后,基于房地产市场的系统性金融风险预警指标(SRWI)值呈收敛式振荡走势,表明基于房地产市场的系统性金融风险总体可控且呈收敛式下降。     

Determinants of Foreign Versus Domestic Real Estate Investment: Property Level Evidence from Listed Real Estate Investment Firms

We examine the determinants of foreign real estate investment relative to the domestic case using the portfolios of a large sample of publicly traded real estate investment companies; where foreign investment is defined as the property owner headquarters being located in a different country than a given asset. The cross-sectional results provide strong evidence that real estate firms are more likely to take a smaller stake in larger assets when investing abroad. The penchant for large assets holds when controlling for economic activity, real estate investment opportunities, depth and sophistication of the capital markets, investor protection and the legal framework, administrative burdens and regulatory limitations, and the socio-cultural and political environment at both the property nation and headquarter nation levels. In general, foreign ownership is less likely with industrial, office, retail, and self-storage properties. Capital market development is consistently negatively related to foreign investment.     

Performance Chasing,Fund Flows and Fund Size in Real Estate Mutual Funds

Real estate mutual funds have grown dramatically in number, size, scope and assets under management over the last 15 years, but little assessment is evident. The present study addresses this limitation. Better prior period performance is associated with greater shares of fund inflows for a period. Returns, however, are negatively associated with increased fund flows and fund size. Investors chase past performance limiting fund managers’ ability to optimize investments. Under normal market conditions, but departing from typical mutual fund performance, real estate mutual fund returns generally exceed relevant benchmarks on a before expenses basis and match benchmark returns after expenses. The ability to meet and exceed benchmark returns, however, does not hold during the financial crisis period. Overall, more established funds are shown to have higher returns while fund turnover is not a determinant of returns.     

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.     

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.