Search results

Real estate investment has been generally accepted as a value-adding proposition for a portfolio investor. Such an impression is not only shared by investment professionals and financial advisors but also appears to be supported by an overwhelming amount of research in the academic literature. The benefits of adding real estate as an asset class to a well-diversified portfolio are usually attributed to the respectable risk-return profile of real estate investment together with the relatively low correlation between its returns and the returns of other financial assets. By using the regime-switching technique on an extensive historical dataset, we attempt to look for the statistical evidence for such a claim. Unfortunately, the empirical support for the claim is neither strong nor universal. We find that any statistically significant improvement in risk-adjusted return is very much limited to the bullish environment of the real estate market. In general, the diversification benefit is not found to be statistically significant unless investors are relatively risk averse. We also document a regime-switching behavior of real estate returns similar to those found in other financial assets. There are two distinct states of the real estate market. The low-return (high-return) state is characterized by its high (low) volatility and its high (low) correlations with the stock market returns. We find this kind of dynamic risk characteristics to play a crucial role in dictating the diversification benefit from real estate investment.

We empirically investigate the hypothesis that the less transparent (more difficult to value) the target’s assets are the more likely it is that the acquiring firm can obtain higher short- and long-term returns. We analyze a sample of 1,538 friendly acquisitions partitioned in two separate dimensions: acquisitions of public versus private firms, and acquisitions of a firm’s assets versus acquisitions of a firm’s assets and its management. Using a sample of (nondiversifying) real estate transactions with a public REIT as the acquirer, we find that acquisitions of public firms have insignificant short-term abnormal returns. Acquisitions of private targets have positive and significant short-term abnormal returns. The acquirer’s abnormal returns are higher in both cases when the transactions involve acquisition of the target firm’s management. We find parallel results when analyzing the acquirer’s Q over the merger year and the three following years. Our conclusions are robust to the type of financing (cash, stock, or a combination) used in the acquisition.

A systemic risk measure is proposed accounting for links and mutual dependencies between financial institutions utilizing tail event information. Financial Risk Meter (FRM) is based on least absolute shrinkage and selection operator quantile regression designed to capture tail event co-movements. The FRM focus lies on understanding active set data characteristics and the presentation of interdependencies in a network topology. Two FRM indices are presented, namely, FRM@Americas and FRM@Europe. The FRM indices detect systemic risk at selected areas and identify risk factors. In practice, FRM is applied to the return time series of selected financial institutions and macroeconomic risk factors. The authors identify companies exhibiting extreme “co-stress” as well as “activators” of stress. With the SRM@EuroArea, the authors extend to the government bond asset class, and to credit default swaps with FRM@iTraxx. FRM is a good predictor for recession probabilities, constituting the FRM-implied recession probabilities. Thereby, FRM indicates tail event behavior in a network of financial risk factors.

Real estate investment trusts (REITs) provide a mechanism through which investors can participate in the real estate market with liquidity and transparency. In this study, we benchmark the performance of 11 residential REITs for the period 2009–2013. The study tracks the performance of residential REITs through the economic crisis period. The data envelopment analysis (DEA) model uses well-performing units (efficiency of 1% or 100%) that are closest to the underperforming unit on the efficiency frontier as a “role model” (peer units) for the underperforming unit. In addition, the DEA model also calculates by how much a nonperforming unit should increase the output level or decrease the inputs level to be on the efficiency frontier (100%) (slack values). Thus, the DEA model identifies the underperforming units and the most feasible path to move to efficiency frontier. The DEA model identifies the peer units that are closely related to these units and calculates the value of the slack variables required to achieve the same efficiency level as their peers.