The Role of Real Estate in the Portfolio Allocation Process

This study explores the role of direct real estate investment in a portfolio context incorporating the real estate imperfections of indivisible assets and no short sales. Mean-variance efficient portfolios are calculated using Treasury-bills, bond and equity indices together with cash flows and appraised values from a set of twenty-two properties having an aggregate appraised value of $336 million. Real estate diversification benefits are shown to be the greatest with smaller properties and are most advantageous at higher target levels of return. The study suggests that a 9% allocation to real estate is optimal, rather than the 20% figure suggested in other studies. Copyright American Real Estate and Urban Economics Association.

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. Copyright American Real Estate and Urban Economics Association.

Analyzing Real Estate Data Problems Using the Gibbs Sampler

Real estate data are often characterized by data irregularities: missing data, censoring or truncation, measurement error, etc. Practitioners often discard missing- or censored-data cases and ignore measurement error. We argue here that an attractive remedy for these irregularity problems is simulation-based model fitting using the Gibbs sampler. The style of the paper is primarily pedagogic, employing a simple illustration to convey the essential ideas, unobscured by implementation complications. Focusing on the missing-data problem, we show dramatic improvement in inference by retaining rather than deleting cases of partially observed data. We also detail Gibbs-sampler usage for other data problems. Copyright American Real Estate and Urban Economics Association.