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It is conventional to assume that property investments in the UK are priced on the basis that investors require a total return approximately 2 per cent above the current redemption yield on long dated gilts. Some yield premium seems intuitively appropriate due to certain apparent disadvantages of property relative to gilts, eg higher risk, poorer liquidity and greater transfer and management costs. However, the purpose of this paper is to illustrate that such apparent demerits are largely illusory, and to promote the view that investors in growth freeholds need require no yield premium, and indeed may justifiably accept a discount on yields available from long dated gilts valued around par.

AbstractPurchased materials often account for more than 50% of a manufacturer's product nonconformance cost. A common strategy for reducing such costs is to allocate periodic quality improvement targets to suppliers of such materials. Improvement target allocations are often accomplished via ad hoc methods such as prescribing a fixed, across‐the‐board percentage improvement for all suppliers, which, however, may not be the most effective or efficient approach for allocating improvement targets. We propose a formal modeling and optimization approach for assessing quality improvement targets for suppliers, based on process variance reduction. In our models, a manufacturer has multiple product performance measures that are linear functions of a common set of design variables (factors), each of which is an output from an independent supplier's process. We assume that a manufacturer's quality improvement is a result of reductions in supplier process variances, obtained through learning and experience, which require appropriate investments by both the manufacturer and suppliers. Three learning investment (cost) models for achieving a given learning rate are used to determine the allocations that minimize expected costs for both the supplier and manufacturer and to assess the sensitivity of investment in learning on the allocation of quality improvement targets. Solutions for determining optimal learning rates, and concomitant quality improvement targets are derived for each learning investment function. We also account for the risk that a supplier may not achieve a targeted learning rate for quality improvements. An extensive computational study is conducted to investigate the differences between optimal variance allocations and a fixed percentage allocation. These differences are examined with respect to (i) variance improvement targets and (ii) total expected cost. For certain types of learning investment models, the results suggest that orders of magnitude differences in variance allocations and expected total costs occur between optimal allocations and those arrived at via the commonly used rule of fixed percentage allocations. However, for learning investments characterized by a quadratic function, there is surprisingly close agreement with an "across‐the‐board" allocation of 20% quality improvement targets. © John Wiley & Sons, Inc. Naval Research Logistics 48: 684–709, 2001

Childhood stunting, being short for one's age, has life-long consequences for health, human capital and economic growth. Being stunted in early childhood is associated with slower cognitive development, reduced schooling attainment and adult incomes decreased by 5–53%. The World Health Assembly has endorsed global nutrition targets including one to reduce the number of stunted children under five by 40% by 2025. The target has been included in the Sustainable Development Goals (SDG target 2.2). This paper estimates the cost of achieving this target and develops scenarios for generating the necessary financing. We focus on a key intervention package for stunting (KIPS) with strong evidence of effectiveness. Annual scale-up costs for the period of 2016–25 were estimated for a sample of 37 high burden countries and extrapolated to all low and middle income countries. The Lives Saved Tool was used to model the impact of the scale-up on stunting prevalence. We analysed data on KIPS budget allocations and expenditure by governments, donors and households to derive a global baseline financing estimate. We modelled two financing scenarios, a 'business as usual', which extends the current trends in domestic and international financing for nutrition through 2025, and another that proposes increases in financing from all sources under a set of burden-sharing rules. The 10-year financial need to scale up KIPS is US$49.5 billion. Under 'business as usual', this financial need is not met and the global stunting target is not reached. To reach the target, current financing will have to increase from US$2.6 billion to US$7.4 billion a year on average. Reaching the stunting target is feasible but will require large coordinated investments in KIPS and a supportive enabling environment. The example of HIV scale-up over 2001–11 is instructive in identifying the factors that could drive such a global response to childhood stunting.