Real estate 'value' stocks and international diversification

Abstract

In recent years there has been an increased interest in the extent to which managers can improve their property portfolio position through international diversification. Much of this interest has centred on the use of various statistical/econometric tests of time-varying correlations and long-run equilibrium positions using whole of country property indices. In this paper, a short-run tactical asset allocation approach to securitized property is adopted. Using neural network methodology, a neural network model that 'learns' well-established rules of portfolio investment is built. The model uses a set of individual property companies across three of the most highly securitized property markets in the world viz. the US, the UK and Australia. The standpoint of a UK investor is adopted and the model is asked to compare portfolios constructed purely from domestic assets with portfolios constructed from internationally held assets allowing for foreign exchange adjustments. When the foreign exchange risk is actively managed, the outcomes from the analysis suggest that the gains from hedging are conditional on both the return to the unhedged position and the volatility of the underlying currency being hedged.