International evidence on the predictability of returns to securitized real estate assets: econometric models versus neural networks

The performance of various statistical models and commonly used financial indicators for forecasting securitised real estate returns are examined for five European countries: the UK, Belgium, the Netherlands, France and Italy. Within a VAR framework, it is demonstrated that the gilt-equity yield ratio is in most cases a better predictor of securitized returns than the term structure or the dividend yield. In particular, investors should consider in their real estate return models the predictability of the gilt-equity yield ratio in Belgium, the Netherlands and France, and the term structure of interest rates in France. Predictions obtained from the VAR and univariate time-series models are compared with the predictions of an artificial neural network model. It is found that, whilst no single model is universally superior across all series, accuracy measures and horizons considered, the neural network model is generally able to offer the most accurate predictions for 1-month horizons. For quarterly and half-yearly forecasts, the random walk with a drift is the most successful for the UK, Belgian and Dutch returns and the neural network for French and Italian returns. Although this study underscores market context and forecast horizon as parameters relevant to the choice of the forecast model, it strongly indicates that analysts should exploit the potential of neural networks and assess more fully their forecast performance against more traditional models.

Towards a method for the economic evaluation of environmental indicators in UK Integrated Arable Farming Systems

Integrated Arable Farming Systems (IAFS), which involve a reduction in the use of off-farm inputs, are attracting considerable research interest in the UK. The objectives of these systems experiments are to compare their financial performance with that from conventional or current farming practices. To date, this comparison has taken little account of any environmental benefits (or disbenefits) of the two systems. The objective of this paper is to review the assessment methodologies available for the analysis of environmental impacts. To illustrate the results of this exercise, the methodology and environmental indicators chosen are then applied to data from one of the LINK - Integrated Farming Systems experimental sites. Data from the Pathhead site in Southern Scotland are used to evaluate the use of invertebrates and nitrate loss as environmental indicators within IAFS. The results suggest that between 1992 and 1995 the biomass of earthworms fell by 28 kg per hectare on the integrated rotation and rose by 31 kg per hectare on the conventional system. This led to environmental costs ranging between £2.24 and £13.44 per hectare for the integrated system and gains of between £2.48 and £14.88 for the conventional system. In terms of nitrate, the integrated system had an estimated loss of £72.21 per hectare in comparison to £149.40 per hectare on the conventional system. Conclusions are drawn about the advantages and disadvantages of this type of analytical framework. Keywords: Farming systems; IAFS; Environmental valuation; Economics; Earthworms; Nitrates; Soil fauna

International office investment in global cities: the production of financial space and systemic risk

The paper explores the relationships between UK commercial real estate and regional economic development as a foundation for the analysis of the role of real estate investment in local economic development. Linkages between economic growth, development, real estate performance and investment allocations are documented. Long-run regional property performance is not the product of long-run economic growth, and weakly related to indicators of long-run supply and demand. Changes in regional portfolio weights seem driven by neither market performance nor underlying fundamentals. In the short run, regional investment shifts show no clear leads or lags with market performance.

The financial effects of uniform and mixed corporate social performance

Firms typically present a mixed picture of corporate social performance (CSP), with positive and negative indicators exhibited by the same firm. Thus, stakeholders’ judgements of corporate social responsibility (CSR) typically evaluate positives in the context of negatives, and vice versa. Building on social judgement theory, we present two alternative accounts of how stakeholders respond to such complexity, which provide differing implications for the financial effects of CSP: reciprocal dampening and rewarding uniformity. Echoing notable findings on strategic consistency, our US panel study finds that firms that exhibit uniformly positive or uniformly negative indicators in particular dimensions of CSP outperform firms that exhibit a mixed picture of positives and negatives, which supports the notion that stakeholders’ judgements of CSR reward uniformity.