Alberto Plazzi

Forecasting Real Estate Prices

This chapter reviews the evidence of predictability in U.S. residential and commercial real estate markets. First, we highlight the main methodologies used in the construction of real estate indices, their underlying assumptions and their impact on the stochastic properties of the resultant series. We then survey the key empirical findings in the academic literature, including short-run persistence and long-run reversals in the log changes of real estate prices. Next, we summarize the ability of local as well as aggregate variables to forecast real estate returns. We illustrate a number of these results by relying on six aggregate indexes of the prices of unsecuritized (residential and commercial) real estate and REITs. The effect of leverage and monetary policy is also discussed.

The Cross-Sectional Dispersion of Commercial Real Estate Returns and Rent Growth: Time Variation and Economic Fluctuations

We estimate the cross-sectional dispersions of returns and growth in rents for commercial real estate using data on U.S. metropolitan areas over the sample period 1986 to 2002. The cross-sectional dispersion of returns is a measure of the risk faced by commercial real estate investors. We document that, for apartments, offices, industrial and retail properties, the cross-sectional dispersions are time varying. Interestingly, their time-series fluctuations can be explained by macroeconomic variables such as the term and credit spreads, inflation and the short rate of interest. The cross-sectional dispersions also exhibit an asymmetrically larger response to negative economics shocks, which may be attributable to credit channel effects impacting the availability of external debt financing to commercial real estate investments. Finally, we find a statistically reliable positive relation between commercial real estate returns and their cross-sectional dispersion, suggesting that idiosyncratic fluctuations are priced in the commercial real estate market.

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.

Exploiting Property Characteristics in Commercial Real Estate Portfolio Allocation

We use a parametric portfolio approach to estimate optimal commercial real estate portfolio policies. We do so using the NCREIF data set of commercial properties over the sample period 1984:Q2 to 2009:Q1. The richness of this extensive data set and the flexibility of the parametric portfolio approach allow us to consider: (i) a large cross-section of individual properties across various regions and property types; (ii) several property-specific conditioning variables, such as cap rates, leverage, value, and vacancy rates; and (iii) various macro-economic factors. Property-specific conditioning information is found to be economically important even for portfolios that are well-diversified across geographical regions and property types.

Why Invest in Emerging Markets? The Role of Conditional Return Asymmetry

We use a quantile-based measure of conditional skewness or asymmetry of asset returns that is robust to outliers and therefore particularly suited for re calcitrant series such as emerging market returns. We study the following portfolio returns: developed markets, emerging markets, the world, and separately 73 countries. We find that the conditional asy mmetry of returns varies significantly over time. This is true even after taking into account condit ional volatility effects (GARCH) and unconditional skewness effects (TARCH) in returns. Interestingly, we find that the conditional asymmetry in developing countries is negatively correlated with that in emerging markets. This finding has implications for portfolio allocation, given th e fact that the correlation of the returns themselves has been historically high and is increasing. In contrast to conditional volatility fluctuations, which are hard to explain with macroeconomic fundamentals, we find a strong relationship between the conditional skewness and macroeconomic variables. Moreover, the negative relationship between conditional asymmetry across developed and emerging markets can be explained by macroeconomic fundamental factors in the cross-section, as both markets feature opposite responses to those fundamentals. The economic significance of the conditional asymmetry is also demonstrated in an international portfolio allocation set ting.

The Risk-Return Relationship and Financial Crises

The risk-return trade-off implies that a riskier investment should demand a higher expected return relative to the risk-free return. The approach of Ghysels, Santa-Clara, and Valkanov (2005) consisted of estimating the risk-return trade-off with a mixed frequency - or MIDAS - approach. MIDAS strikes a compromise between on the one hand the need for longer horizons to model expected returns and on the other hand to use high frequency data to model the conditional volatility required to estimate expected returns. Using the approach of Ghysels, Santa-Clara, and Valkanov (2005), after correcting a coding error pointed out to us, we find that the Merton model holds over samples that exclude financial crises, in particular the Great Depression and/or the subprime mortgage financial crisis and the resulting Great Recession. We find that a simple flight to safety indicator separates the traditional risk-return relationship from financial crises which amount to fundamental changes in that relationship. For those months or quarters we characterize as flight to safety we find there is no (i.e. neither negative nor positive) risk-return trade-off.

Financial Market Misconduct and Public Enforcement: The Case of Libor Manipulation

Using comprehensive data on London Interbank Offer Rate (Libor) submissions from 2001 through 2012, we document systematic evidence consistent with banks manipulating Libor to profit from Libor related positions and, to a degree, to signal their creditworthiness during the distressed times for banks. The evidence is initially stronger for banks that were eventually sanctioned by the regulators and disappears for all banks post-2010 in the aftermath of Libor investigations. Our findings suggest that public enforcement, with the threat of large penalties and the loss of reputation, can be effective in deterring financial market misconduct.Using data on Libor submissions from 1999 to 2012, wend weak support for the hypothesis that banks manipulate submissions to appear less risky and strong support for the hypothesis that banks manipulate Libor to generate higher cash ows. Our results are stronger for the manipulation period as identied by regulators (January 2005 to May 2009), for currencies and maturities with substantial notional amounts of interest-rate derivatives outstanding, for European banks, and for banks that have already paidnes related to manipulation. We calculate the cumulative gains in bank market capitalization due to manipulation to be

Birds of a Feather – Do Hedge Fund Managers Flock Together?

16 to

What Constrains Liquidity Provision? Evidence From Hedge Fund Trades

19 billion.

Inflation Risk Premia, Yield Volatility and Macro Factors

Using a novel data set, we construct a network of hedge fund managers based on past employment. Employment in the same industry and employment in the same firm lead to more similar investment behavior in terms of systematic risk (beta), abnormal performance (alpha), and residual returns, explaining about a quarter of the differences. Employment at the same firm at the same time affects only residual returns. Results are robust to fund and manager level controls, as well as to endogeneity concerns. More connected funds perform better, and prior experience in pension funds and banks aids performance.