Real Estate Finance Economics

New evidence on the correlation patterns of various real estate returns with inflation is presented. Returns on a wide array of real estate, nonresidential as well as residential, are investigated. Stock and bond returns are also analyzed for comparison purposes. Extensive heterogeneity is found in real estate return correlations with inflation. Nonresidential property returns are most strongly positively correlated with inflation, although the appreciation in owner-occupied homes is also positively associated with inflation. However, REIT returns tend to be strongly negatively correlated with inflation. In this respect, they look more like traditional stocks and bonds than any other type of real estate. Finally, new evidence on return correlations with energy prices is also presented. Nonresidential real estate performs best here, too, although no real estate asset fully compensates investors for adverse energy price shocks.

Institutional investors typically allocate under 5 per cent of their portfolios to income-producing real estate, yet the results of most academic studies imply that far larger allocations are optimal. One widespread argument is that the conflict between observed behaviour and scholarly prediction is spurious in the sense that it arises due to the use of faulty appraisal-based data. While lagged appraisals undoubtedly do smooth real estate return data, we argue that two other factors help explain the low measured variance of income-producing property returns and the low covariances of appraisal-based index returns with the stock market. The first is that most appraisal-based series are unlevered while the Standard and Poor 500 firms are approximately 50 per cent levered. A synthetic leveraging (i.e. gearing) of the Frank Russell Company real property series almost doubles the variance in its returns. The second factor flows from real estate market fundamentals. Because rents basically are a fixed cost to tenant firms, rental flows on existing buildings should be more stable than are the firms’ cash flows. This implies that real property returns would not co-vary strongly with the stock market.

This paper analyzes the risks and returns of different types of real estate-related firms traded on the New York and American stock exchanges (NYSE and AMEX). We examine the relation between real estate stock portfolio returns and returns on a standard appraisal-based index, and find that lagged values of traded real estate portfolio returns can predict returns on the appraisal-based index after controlling for persistence in the appraisal series. The stock market reflects information about real estate markets that is later imbedded in infrequent property appraisals. Additional analysis suggests that the differences in the return and risk characteristics across different types of traded real estate firms can be explained in part by appealing to real estate market fundamentals relating to the degree of dependence of the real estate firm upon rental cash flows from existing buildings. These findings highlight the heterogeneity of securitized real estate-related firms.

An expanded inventory demand framework is developed that focuses on the impacts of changes in the local leasing environment. We model the lease-up process as a lottery in which changes in turnover or absorption affect the probability of winning the leasing lottery. In this context, builders rationally respond to transitory, not just permanent, changes in the local market. Hence, factors such as temporary shocks to tenant turnover affect the decision to build. The magnitude of turnover-induced cycles can vary across markets depending upon the vintage of the existing building stock, the local absorption rate and the rent elasticity of demand for space. This framework, which refocuses attention on the local determinants of the developer’s decision to build, hopefully will prove fruitful in future empirical efforts to explain development and vacancy behavior during the past decade.

This paper employs stock market-based data to examine the systematic risk and diversification properties of publicly traded equity real estate investment trusts (REITs). A unique data sample is created by combining firm return data with information on their property type holdings and the location of their investments. The systematic risk of equity REITs appears to vary by the type of property in which they invest, with beta being significantly higher for retail-oriented REITs than for REITs owning industrial and warehouse properties. In addition, the stock market data provides no evidence that REIT diversification across property types or broad geographic regions actually results in meaningful diversification as reflected in a standard market-based measure – the R^2 from a simple market model regression.

This study examines the predictability of monthly returns on equity real estate investment trusts (EREITs) over the period 1975-95 and compares it with that for small- and mid-cap firms. Using the time series approach of Jegadeesh (1990), evidence is found that monthly EREIT returns are predictable based on past performance. However, the predictability is not substantial enough to cover typical transactions costs, so that there is no evidence of unexploited arbitrage opportunities. The magnitude of EREIT predictability also is examined over different time periods, with the greatest amount found in the most recent data since 1992, which marks the emergence of the new wave of EREITs. Finally, persistence in individual REIT return performance is examined using a nonparametric technique. Limited evidence of persistence in performance is found, with retail-oriented REITs tending to exhibit the most persistence.

The real estate investment trust (REIT) structure has come under increasing scrutiny given the problems the structure poses for firms wishing to retain earnings in depressed real estate equity and debt markets. We estimate the net benefits of the structure to be no more than 2%–5% of industry equity market capitalization, although the benefits are larger for firms with lower payout ratios. In addition, the value of the format doubles as the share of tax exempt/ deferred investment in REITs increases to 40%, the fraction obtaining in the broader equity market. Educating this investor clientele on the benefits of the REIT structure is an important goal for REIT management.

We provide new evidence on the asset price incidence of corporate-level investment subsidies by examining the relative stock price performance of publicly traded companies in the real estate industry that should have been differentially affected by the capital gains tax rate reduction enacted in the Taxpayer Relief Act of 1997. By comparing real estate firms that have an organizational structure that allows entities who sell property to it to defer capital gains taxes and that plan to use the structure to acquire property with those that do not, we isolate the effect of the tax cut from industry trends and firm-level heterogeneity. When we examine the time period surrounding the reduction in the capital gains tax rate, our results suggest the tax change was substantially capitalized into lower share prices for these firms and the benefit of the seller’s capital gains tax deferral accrued mainly to the buyer of an appreciated property.

On the one hand, urban economics suggests that commercial real estate and owner-occupied housing are driven by common fundamentals, which should make them perform similarly. On the other hand, stronger limits to arbitrage in housing suggest that wider swings in prices, which are unrelated to fundamentals, are feasible in that property sector. And wider swings in fundamentals could be expected in the commercial sector due to the longer lead times for commercial buildings to be permitted and delivered, leading to a greater possibility of a temporal mismatch between supply and demand. Yet, the author finds many more similarities than differences across the two real estate sectors. The simple correlation between appreciation rates on owner-occupied housing and commercial real estate is between 40% and 60%, depending on the data source. Both sectors also exhibit similar time-series patterns in their price appreciation, with persistence across individual years and mean reversion over longer periods. In this article, Gyourko explores the various linkages between these two huge sectors, including the cost of land and materials, cost of capital, lender optimism, and access to the productivity and amenities of a given labor market area. With the recent dramatic declines in house prices, the strong similarities between the two sectors are foreboding given that the commercial real estate capital structure looks weak and a strong mean-reversion process in prices may still be underway.