The Case for Risk Parity as an Alternative Strategy for Asset Allocation in Real Estate Portfolios

Following the recent financial crisis, the need for diversification cannot be over emphasized. This notwithstanding, some concerns have been raised regarding the efficacy of the conventional asset allocation methods. These traditional strategies encompass equal weighting, minimum variance, and mean- variance optimization based on modern portfolio theory (MPT). MPT remains the most widely used method despite its inherent estimation error in the determination of expected returns and correlations. Portfolio construction using MPT in real estate presents similar challenges where naive diversification is predominantly used to diversify portfolios. Thus, these conventional strategies have been questioned especially in the wake of the financial crisis due to poor performance of portfolios.This paper introduces an alternative strategy of asset allocation called 'risk parity' to real estate portfolios by using the framework from mainstream finance. In contrast with the traditional strategies, the aim of risk parity is equal risk allocation across asset classes in a portfolio. To achieve the broad aim, the paper relies heavily on a systematic analysis of listed as well as direct real estate to ascertain whether risk parity provides a better alternative to the traditional allocation strategies employed in the real estate market. The paper argues that compared to the traditional allocation strategies, a risk parity based portfolio is likely to produce superior risk-reward tradeoff. Utilizing risk parity strategy in constructing a portfolio does not require the creation of expected return assumptions as only the asset class covariances are needed. These covariances can be more accurately estimated from historical data than expected returns. In lower risk portfolios, leverage can be employed in order to increase return expectation while derivatives can be used as a way to attain desired market exposures more safely and inexpensively.