PurposeThis paper aims to gain exposure to Australian real estate investment trusts (A‐REITs). Many institutional investors make use of securitised property funds as they employ experienced property professionals with specialist knowledge of underlying property fundamentals, direct property markets and the 30‐plus A‐REITs. As securitised property funds operate in a competitive environment, investment performance benchmarks are important.Design/methodology/approachTo add to the familiar risk and return benchmarks, the risk adjusted performance (RAP) measure first outlined by Modigliani and Modigliani provides an additional and valuable return measure to a definite level of risk. This research selected 16 wholesale securitised property funds each with seven years of continuous quarterly total return data.FindingsOverall a large proportion of the selected funds (14 out of 16), on average, outperformed the market benchmark return (14.53 per cent) with the worst fund marginally under‐performing the index by 0.54 per cent. In contrast, the annualised RAP measure highlighted the differences in the securitised property fund returns for a given level of risk, with a wide 12.90‐16.66 per cent range. To achieve this uniform level of risk, five securities property funds had to replace up to 21 per cent of their property portfolio with a risk‐free asset (90 day bank bills). The RAP measure also decomposes the excess returns above the benchmark. In this instance, the securitised property funds outperformance was from a mixture of active portfolio selection and simply taking on additional risk exposure.Originality/valueThe research demonstrated the benefits of analysing securitised property funds beyond the standard return and risk measures. The RAP approach provides a measure of return for a definite level of risk with the benchmark excess attributed to portfolio selection and additional risk. This performance information can provide valuable additional information for an astute investor.

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Purpose -In Australia, the A$2.2 trillion managed funds industry including the large pension funds (known locally as superannuation funds) are the dominant institutional property investors. While statistical information on the level of Australian managed fund investments in property assets is widely available, comprehensive practical evidence on property asset allocation decision-making process is underdeveloped. The purpose of this research is to identify Australian fund manager's property asset allocation strategies and decision-making frameworks at strategic level. Design/methodology/approach -The research was undertaken in May-August 2011 using an in-depth semi-structured questionnaire administered by mail. The survey was targeted at 130 leading managed funds and asset consultants within Australia. Findings -The evaluation of the 79 survey respondents indicated that Australian fund manager's property allocation decision-making process is an interactive, sequential and continuous process involving multiple decision-makers (internal and external) complete with feedback loops. It involves a combination of quantitative analysis (mainly mean-variance analysis) and qualitative overlay (mainly judgement, or "gut-feeling", and experience). In addition, the research provided evidence that the property allocation decision-making process varies depending on the size and type of managed fund. Practical implications -This research makes important contributions to both practical and academic fields. Information on strategic property allocation models and variables is not widely available, and there is little guiding theory related to the subject. Therefore, the conceptual frameworks developed from the research will help enhance academic theory and understanding in the area of property allocation decision making. Furthermore, the research provides small fund managers and industry practitioners with a platform from which to improve their own property allocation processes. Originality/value -In contrast to previous property decision-making research in Australia which has mainly focused on strategies at the property fund investment level, this research investigates the institutional property allocation decision-making process from a strategic position involving all major groups in the Australian managed funds industry.

ABSTRACT

Purpose – Modern property investment allocation techniques are typically based on recognised measures of return and risk. Whilst these models work well in theory under stable conditions, they can fail when stable assumptions cease to hold and extreme volatility occurs. This is evident in commercial property markets which can experience extended stable periods followed by large concentrated negative price fluctuations as a result of major unpredictable events. This extreme volatility may not be fully reflected in traditional risk calculations and can lead to ruin. The paper aims to discuss these issues. Design/methodology/approach – This research studies 28 years of quarterly Australian direct commercial property market performance data for normal distribution features and signs of extreme downside risk. For the extreme values, Power Law distribution models were examined as to provide a better probability measure of large negative price fluctuations. Findings – The results show that the normal bell curve distribution underestimated actual extreme values both by frequency and extent, being by at least 30 per cent for the outermost data point. For the statistical outliers beyond 2 SD, a Power Law distribution can overcome many of the shortcomings of the standard deviation approach and therefore better measure the probability of ruin, being extreme downside risk. Practical implications – In highlighting the challenges to measuring property market performance, analysis of extreme downside risk should be separated from traditional standard deviation risk calculations. In recognising these two different types of risk, extreme downside risk has a magnified domino effect with the tendency of bad news to come in crowds. Big price changes can lead to market crashes and financial ruin which is well beyond the standard deviation risk measure. This needs to be recognised and developed as there is evidence that extreme downside risk determinants are increasing by magnitude, frequency and impact. Originality/value – Analysis of extreme downside risk should form a key part of the property decision process and be included in the property investment manager’s toolkit. Modelling techniques for estimating measures of tail risk provide challenges and have shown to be beyond traditional risk management practices, being too narrow and constraining a definition. Measuring extreme risk and the likelihood of ruin is the first step in analysing and dealing with risk in both an asset class and portfolio context.