A Comparative Anatomy of REITs and Residential Real Estate Indexes: Returns, Risks and Distributional Characteristics

Real Estate Investment Trusts (REITs) are the only truly liquid assets related to real estate investments. We study the behavior of U.S. REITs over the past three decades and document their return characteristics. REITs have somewhat less market risk than equity; their betas against a broad market index average about .65. Decomposing their covariances into principal components reveals several strong factors. REIT characteristics differ to some extent from those of the S&P/Case-Shiller (SCS) residential real estate indexes. This is partly attributable to methods of index construction. Our examination of REITs suggests that investment in real estate is far more risky than what might be inferred from the widely-followed SCS series.

On computing mean returns and the small firm premium

The mean return computational method has a substantial effect on the estimated small firm premium. The buy-and-hold method, which best mimics actual investment experience, produces an estimated small-firm premium only one-half as large as the arithmetic and re-balanced methods which are often used in empirical studies. Similar biases can be expected in mean returns when securities are classified by any variable related to trading volume

Orange Juice and Weather

Frozen concentrated orange juice is an unusual commodity. It is concentrated not only hydrologically, but also geographically; more than 98 percent of U.S. production takes place in the central Florida region around Orlando.' Weather is a major influence on orange juice production and un- like commodities such as corn and oats, which are produced over wide geographical areas, orange juice output is influenced primarily by the weather at a single location. This suggests that frozen concentrated orange juice is a relatively good candidate for a study of the interaction between prices and a truly exogenous determinant of value, the weather

Integration and contagion in US housing markets

This paper explores integration and contagion among US metropolitan housing markets. The analysis applies Federal Housing Finance Agency (FHFA) house price repeat sales indexes from 384 metropolitan areas to estimate a multi-factor model of U.S. housing market integration. It then identifies statistical jumps in metropolitan house price returns as well as MSA contemporaneous and lagged jump correlations. Finally, the paper evaluates contagion in housing markets via parametric assessment of MSA house price spatial dynamics. A R-squared measure reveals an upward trend in MSA housing market integration over the 2000s to approximately .83 in 2010. Among California MSAs, the trend was especially pronounced, as average integration increased from about .55 in 1997 to close to .95 in 2008! The 2000s bubble period similarly was characterized by elevated incidence of statistical jumps in housing returns. Again, jump incidence and MSA jump correlations were especially high in California. Analysis of contagion among California markets indicates that house price returns in San Francisco often led those of surrounding communities; in contrast, southern California MSA house price returns appeared to move largely in lock step. The high levels of housing market integration evidenced in the analysis suggest limited investor opportunity to diversify away MSA-specific housing risk. Further, results suggest that macro and policy shocks propagate through a large number of MSA housing markets. Research findings are relevant to all market participants, including institutional investors in MBS as well as those who regulate housing, the housing GSEs, mortgage lenders, and related financial institutions.integration; correlation; contagion; house price returns

Integration and Contagion in US Housing Markets

This paper explores integration and contagion among US metropolitan housing markets. The analysis applies Federal Housing Finance Agency (FHFA) house price repeat sales indexes from 384 metropolitan areas to estimate a multi-factor model of U.S. housing market integration. It then identifies statistical jumps in metropolitan house price returns as well as MSA contemporaneous and lagged jump correlations. Finally, the paper evaluates contagion in housing markets via parametric assessment of MSA house price spatial dynamics. A R-squared measure reveals an upward trend in MSA housing market integration over the 2000s to approximately .83 in 2010. Among California MSAs, the trend was especially pronounced, as average integration increased from about .55 in 1997 to close to .95 in 2008! The 2000s bubble period similarly was characterized by elevated incidence of statistical jumps in housing returns. Again, jump incidence and MSA jump correlations were especially high in California. Analysis of contagion among California markets indicates that house price returns in San Francisco often led those of surrounding communities; in contrast, southern California MSA house price returns appeared to move largely in lock step. The high levels of housing market integration evidenced in the analysis suggest limited investor opportunity to diversify away MSA-specific housing risk. Further, results suggest that macro and policy shocks propagate through a large number of MSA housing markets. Research findings are relevant to all market participants, including institutional investors in MBS as well as those who regulate housing, the housing GSEs, mortgage lenders, and related financial institutions.Integration, correlation, contagion, house price returns

Corporate serial acquisitions: An empirical test of the learning hypothesis

Recent empirical papers report a declining trend in the cumulative abnormal return (CAR) of acquirers during an M&A program. Does this necessarily imply that acquiring CEOs are infected by hubris and are not learning from previous mistakes? We first confirm the existence of this declining trend on average. However, we find a positive CAR trend for CEOs likely to be infected by hubris, which is significantly different from the negative trend found for CEOs who are more likely to be rational. We also explore the time between successive deals and find empirical evidence to suggest that many CEOs learn substantially during acquisition programs.

Learning, hubris and corporate serial acquisitions

Recent empirical research has shown that, from deal to deal, serial acquirers' cumulative abnormal returns (CAR) are declining. This has been most often attributed to CEOs hubris. We question this interpretation. Our theoretical analysis shows that (i) a declining CAR from deal to deal is not sufficient to reveal the presence of hubris, (ii) if CEOs are learning, economically motivated and rational, a declining CAR from deal to deal should be observed, (iii) predictions can be derived about the impact of learning and hubris on the time between successive deals and, finally, (iv) predictions about the CAR and about the time between successive deal trends lead to testable empirical hypotheses.

The Effectiveness of State and Local Regulation of Handguns: A Statistical Analysis

One aspect of the continuing debate over weapons control, apart from Constitutional issues, is whether legislation is inherently capable of reducing crime and deaths by shooting. The opponents of increased control, tacitly admitting that empirical evidence is one means for measuring the effect of weapons regulation, have contended that [e]xpert opinion and compelling evidence seem to indicate that the amount or kind of crime in a community is not substantially affected by the relative ease with which a person can obtain a firearm. National Rifle Association of America, The Gun Law Problem 10. In the following study the authors employ data analysis techniques to examine the efficacy of state and municipal controls on handguns. They conclude that many lives would be saved if all states increased their level of control to that of New Jersey, the state having the most stringent gun control laws