F. C. Neil Myer

Diversification Issues in Real Estate Investment

Real estate asset management has been and will continue to be a topic of great interest. Specifically, does real estate warrant inclusion in an efficient portfolio? And if so, how much should be invested in real estate? This article reviews the extant literature in the area of real estate diversification and helps identify the reasons that there exists so much divergence in the answer to the question, “How much in real estate?” Moreover, diversification as it relates to real estate is discussed in reference to both mixed-asset portfolios and for diversification within the real estate asset class. Directions for future research are also discussed.

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.

The Bootstrap Efficient Frontier for Mixed-Asset Portfolios

How much in real estate? To answer this question, uncertainty needs to be introduced into the efficient frontier, so that a confidence interval can be estimated for the real estate weight in a mixed‐asset portfolio. Instead of focusing on a single optimal portfolio, this study examines the entire efficient frontier using the traditional point estimate method and the bootstrap simulation. The bootstrap distributions of the estimated weight vectors indicate that their confidence intervals are large enough to render them effectively useless. Once uncertainty is introduced, the efficient frontier becomes fuzzy and the weight vectors become even fuzzier.

The Relationship between Healthcare REITs and Healthcare Stocks

A two-factor regression model was used to examine the relationship between returns on healthcare equity REITs (EREITs) and healthcare stocks from 1985 to 1992. General stock indices were incorporated in the model to account for the influence of the market. Multiple positive contemporaneous relationships were found between six of the seven REITs studied and portfolios of other healthcare stocks. Furthermore, in four of the six REITs with positive results, significant correlations were evident between individual REIT portfolios and the SIC indices with which they showed a significant relationship. These results are consistent with a common factor or factors affecting the returns of both healthcare EREITs and stocks.

Cointegration in return series and its effect on short‐term prediction

Refers to previous research on the relationship between returns for different asset classes and on cointegration; and applies Johansen’s (1988) methodology to develop a prediction model. Uses 1978‐1995 data on five US asset classes (treasury bills, long‐term bonds, large capitalization common stocks, unsecuritized real estate and securitized real estate equity) to investigate cointegration between them. Shows that the index of unsecuritized real estate is positively related to treasury bills and negatively related to long‐term bonds and securitized real estate; and that returns for it can be forecast more accurately by using VECM models rather than unrestricted VAR models. Considers the implications for portfolio allocation, compares the results with other research fundings and calls for further research.