Risk management and the corporate real estate portfolio

In a global business economy, firms have a broad range of corporate real estate needs. During the past decade, multiple strategies and tactics have emerged in the corporate real estate community for meeting those needs. We propose here a framework for analysing and prioritising the various types of risk inherent in corporate real estate decisions. From a business strategy perspective, corporate real estate must serve needs beyond the simple one of shelter for the workforce and production process. Certain uses are strategic in that they allow access to externalities, embody the business strategy, or provide entrée to new markets. Other uses may be tactical, in that they arise from business activities of relatively short duration or provide an opportunity to pre-empt competitors. Still other corporate real estate uses can be considered “core” to the existence of the business enterprise. These might be special use properties or may be generic buildings that have become embodiments of the organisation’s culture. We argue that a multi-dimensional matrix approach organised around three broad themes and nine sub-categories allow the decision-maker to organise and evaluate choices with an acceptable degree of rigor and thoroughness. The three broad themes are Use (divided into Core, Cyclical or Casual) – Asset Type (which can be Strategic, Specialty or Generic) and Market Environment (which ranges from Mature Domestic to Emerging Economy). Proper understanding of each of these groupings brings critical variables to the fore and allows for efficient resource allocation and enhanced risk management.

Correlation shifts and real estate portfolio management

The success of any diversification strategy depends upon the quality of the estimated correlation between assets. It is well known, however, that there is a tendency for the average correlation among assets to increase when the market falls and vice-versa. Thus, assuming that the correlation between assets is a constant over time seems unrealistic. Nonetheless, these changes in the correlation structure as a consequence of changes in the market’s return suggests that correlation shifts can be modelled as a function of the market return. This is the idea behind the model of Spurgin et al (2000), which models the beta or systematic risk, of the asset as a function of the returns in the market. This is an approach that offers particular attractions to fund managers as it suggest ways by which they can adjust their portfolios to benefit from changes in overall market conditions. In this paper the Spurgin et al (2000) model is applied to 31 real estate market segments in the UK using monthly data over the period 1987:1 to 2000:12. The results show that a number of market segments display significant negative correlation shifts, while others show significantly positive correlation shifts. Using this information fund managers can make strategic and tactical portfolio allocation decisions based on expectations of market volatility alone and so help them achieve greater portfolio performance overall and especially during different phases of the real estate cycle.

Time weighted portfolio optimisation

In estimating the inputs into the Modern Portfolio Theory (MPT) portfolio optimisation problem, it is usual to use equal weighted historic data. Equal weighting of the data, however, does not take account of the current state of the market. Consequently this approach is unlikely to perform well in any subsequent period as the data is still reflecting market conditions that are no longer valid. The need for some return-weighting scheme that gives greater weight to the most recent data would seem desirable. Therefore, this study uses returns data which are weighted to give greater weight to the most recent observations to see if such a weighting scheme can offer improved ex-ante performance over that based on un-weighted data.

Real estate portfolio construction and estimation risk

The use of MPT in the construction real estate portfolios has two serious limitations when used in an ex-ante framework: (1) the intertemporal instability of the portfolio weights and (2) the sharp deterioration in performance of the optimal portfolios outside the sample period used to estimate asset mean returns. Both problems can be traced to wide fluctuations in sample means Jorion (1985). Thus the use of a procedure that ignores the estimation risk due to the uncertain in mean returns is likely to produce sub-optimal results in subsequent periods. This suggests that the consideration of the issue of estimation risk is crucial in the use of MPT in developing a successful real estate portfolio strategy. Therefore, following Eun & Resnick (1988), this study extends previous ex-ante based studies by evaluating optimal portfolio allocations in subsequent test periods by using methods that have been proposed to reduce the effect of measurement error on optimal portfolio allocations.

Change & flexibility: the role of serviced office space in corporate real estate portfolios and office markets

With the increasing pace of change, organisations have sought new real estate solutions which provide greater flexibility. What appears to be required is not flexibility for all uses but appropriate flexibility for the volatile, risky and temporal part of a business. This is the essence of the idea behind the split between the core and periphery portfolio. The serviced office has emerged to fill the need for absolute flexibility. This market is very diverse in terms of the product, services and target market. It has grown and gained credibility with occupiers and more recently with the property investment market. Occupiers similarly use this space in a variety of ways. Some solely occupy serviced space while others use it to complement their more permanent space. It therefore appears that the market is fulfilling the role of providing periphery space for at least some of the occupiers. In all instances the key to this space is a focus on financial and tenurial flexibility which is not provided by other types of business space offered.

Portfolio size and the reduction of dispersion: the case of the United Kingdom commercial real estate market

This paper investigates the potential benefits and limitations of equal and value-weighted diversification using as the example the UK institutional property market. To achieve this it uses the largest sample (392) of actual property returns that is currently available, over the period 1981 to 1996. To evaluate these issues two approaches are adopted; first, an analysis of the correlations within the sectors and regions and secondly simulations of property portfolios of increasing size constructed both naively and with value-weighting. Using these methods it is shown that the extent of possible risk reduction is limited because of the high positive correlations between assets in any portfolio, even when naively diversified. It is also shown that portfolios exhibit high levels of variability around the average risk, suggesting that previous work seriously understates the number of properties needed to achieve a satisfactory level of diversification. The results have implications for the development and maintenance of a property portfolio because they indicate that the achievable level of risk reduction depends upon the availability of assets, the weighting system used and the investor’s risk tolerance.

Risk reduction and real estate portfolio size

Despite a number of papers that discuss the advantages of increased size on risk levels in real estate portfolios there is remarkably little empirical evidence based on actual portfolios. The objective of this paper is to remedy this deficiency by examining the portfolio risk of a large sample of actual property data over the period 1981 to 1996. The results show that all that can be said is that portfolios of properties of a large size, on the average, tend to have lower risks than small sized portfolios. More importantly portfolios of a few properties can have very high or very low risk.

Is there a place for property in the multi-asset portfolio?

The case for property has typically rested on the application of modern portfolio theory (MPT), in that property has been shown to offer increased diversification benefits within a multi asset portfolio without hurting portfolio returns especially for lower risk portfolios. However this view is based upon the use of historic, usually appraisal based, data for property. Recent research suggests strongly that such data significantly underestimates the risk characteristics of property, because appraisals explicitly or implicitly smooth out much of the real volatility in property returns. This paper examines the portfolio diversification effects of including property in a multi-asset portfolio, using UK appraisal based (smoothed) data and several derived de-smoothed series. Having considered the effects of de-smoothing, we then consider the inclusion of a further low risk asset (cash) in order to investigate further whether property's place in a low risk portfolio is maintained. The conclusions of this study are that the previous supposed benefits of including property have been overstated. Although property may still have a place in a 'balanced' institutional portfolio, the case for property needs to be reassessed and not be based simplistically on the application of MPT.

A review of the spatial economics of non-timber forest product extraction: implications for policy

Patterns of forest cover and forest degradation determine the size and types of ecosystem services forests provide. Particularly in low-income countries, nontimber forest product (NTFP) extraction by rural people, which provides important resources and income to the rural poor, contributes to the level and pattern of forest degradation. Although recent policy, particularly in Africa, emphasizes forest degradation, relatively little research describes the spatial aspects of NTFP collection that lead to spatial degradation patterns. This paper reviews both the spatial empirical work on NTFP extraction and related forest degradation patterns, and spatial models of behavior of rural people who extract NTFPs from forest. Despite the impact of rural people's behavior on resulting quantities and patterns of forest resources, spatial–temporal models/patterns rarely inform park siting and sizing decisions, econometric assessments of park effectiveness, development projects to support conservation, or REDD protocols. Using the literature review as a lens, we discuss the models' implications for these policies with particular emphasis on effective conservation spending and leakage.

An assessment of the MODIS collection 5 leaf area index product for a region of mixed coniferous forest

Canopy leaf area index (LAI), defined as the single-sided leaf area per unit ground area, is a quantitative measure of canopy foliar area. LAI is a controlling biophysical property of vegetation function, and quantifying LAI is thus vital for understanding energy, carbon and water fluxes between the land surface and the atmosphere. LAI is routinely available from Earth Observation (EO) instruments such as MODIS. However EO-derived estimates of LAI require validation before they are utilised by the ecosystem modelling community. Previous validation work on the MODIS collection 4 (c4) product suggested considerable error especially in forested biomes, and as a result significant modification of the MODIS LAI algorithm has been made for the most recent collection 5 (c5). As a result of these changes the current MODIS LAI product has not been widely validated. We present a validation of the MODIS c5 LAI product over a 121 km2 area of mixed coniferous forest in Oregon, USA, based on detailed ground measurements which we have upscaled using high resolution EO data. Our analysis suggests that c5 shows a much more realistic temporal LAI dynamic over c4 values for the site we examined. We find improved spatial consistency between the MODIS c5 LAI product and upscaled in situ measurements. However results also suggest that the c5 LAI product underestimates the upper range of upscaled in situ LAI measurements.