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This 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.

To 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.

Overall 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.

The 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.

The first authorized property unit trusts in the UK were created in September 1991, and have operated unspectacularly since then, as the current property climate has not, in general, been encouraging. Similar Canadian investment vehicles first appeared in the early 1980s, and experienced significant growth through the property boom of the mid‐1980s. Since then, with decreasing property values many of these funds have been forced to reorganize. Yet, the structure of the Canadian funds would have complied with the most significant aspects of the UK regulations. The consideration of the nature of the manifold difficulties of the Canadian funds suggests that the regulations established in the UK may not prevent similar problems. Accordingly, the specifics of the Canadian funds and the manner in which their structure added further levels of risk to that inherent in their underlying assets should be considered relative to the possible future performance of similar vehicles in the UK.

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.

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.

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.

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.

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.

Long lease real estate funds (over £15bn in Q3 2020) have emerged as an increasingly important part of UK pension fund real estate portfolios. This paper explores the reasons for their dramatic growth, their characteristics and performance.

This study uses data for the period 2004–2020 collected directly from fund managers and from AREF/MSCI and empirical analysis to explore their characteristics and performance.

Pension fund de-risking and regulatory guidance have supported the dramatic growth of long lease real estate funds. Long lease real estate funds have delivered strong risk-adjusted returns relative to both balanced property funds (with shorter lease terms) and the wider property market. This relative performance has been particularly strong when wider property market performance has been weak. Long lease funds have objectives aligned with liability matching and their performance suggests they are lower risk (more bond-like) investments. In addition, our analysis highlights they are far less responsive to the wider property market than balanced funds. However, they are not significantly different from balanced property funds in terms of their short-term relationship with gilt yield movements.

For pension funds and other investors the paper highlights that long lease real estate funds offer a different exposure than balanced property funds. Long lease funds have objectives more closely aligned to the overall objectives for pension fund investment but are not significantly more reliable than balanced property funds in the short-term as a liability hedge. For real estate fund managers, occupiers, developers and others active in the real estate market, the paper highlights why these funds have been (and are likely to remain) attractive to investors leading to substantial demand for long lease real estate investments.

This is the first study to review this increasingly important part of the UK real estate fund universe.

Property investment vehicles are reviewed from a literature perspective drawing upon the experience of real estate investment trusts in the USA and contrasting this with European examples. The primary focus of the paper is upon German funds, using survey evidence to evaluate their structural characteristics. The paper forwards from a theoretical perspective an assessment framework indicating how different types of fund can be matched to product opportunities on the basis of risk, appreciation potential, nature of contract, location and use.

In a bid to broaden the understanding of the real estate investment decision-making framework, the purpose of this paper is to examine the real estate asset allocation decision-making practices of real estate funds in Nigeria, a developing economy. This is with a view to providing information toward enhancing real estate investment decisions.

A mixed-methods approach comprising a combination of literature review, expert interviews and semi-structured questionnaire survey is adopted for this study. Through literature review and expert interviews, the asset allocation decision-making process of institutional real estate funds was identified. Based on the literature review and expert discussions, a semi-structured questionnaire was developed and self-administered on fund/portfolio managers of 59 institutional real estate funds in Nigeria to investigate their asset allocation decision-making practice. Data were analyzed using descriptive and inferential statistics for the closed-ended questions while the open-ended questions were content analyzed.

The findings revealed that the asset allocation decision-making process utilized by public and private real estate funds follows an opportunistic asset accumulation approach. The decision-making process also varies depending on the nature of the fund. Further findings showed that government policies, political uncertainties and regulatory mechanism motivate asset allocation decisions. Moreover, majority of the sampled real estate funds employed a combination of in-house personnel and external consultants (hybrid), while mean/standard deviation and cash flow analysis (DCF, NPV) were mostly utilized by the funds in making property investment decisions.

The findings implied that the real estate asset allocation decision-making process of institutional property investors in Nigeria deviates from the normative model of the asset allocation process prescribed in the literature and varies depending on the nature of the real estate funds. As such, familiarization of institutional investors with government policies, political climate and other regulatory mechanism (barriers to entry) guiding the ownership and operation of real estate assets in the country could improve their real estate investment decisions.

The study complements and extends existing literature on real estate asset allocation decision-making process of institutional investors from the viewpoint of the actors involved in a developing African economy.

Looks at the nature of property unit trusts and their attractiveness to different types of investors. Considers the prospects for a successful launch of property funds. Outlines the main features of authorized property funds (APFs) and implications of these proposals for the property market and operators in the market with particular reference to the Australian experience. Concludes on the likely impact of APFs and highlights the lessons to learned by the UK from the Australian experience.

Since the mid‐1990s, in a generally strongly performing property market, there has been huge growth in the aggregate size and number of global property funds in both listed and unlisted formats. Managers have been able to raise significant capital, which potentially rewards them with performance fees without necessarily being able to provide clear evidence of out‐performance against defined market benchmarks or performance targets. In a more challenging, mature and increasingly transparent market this is unlikely to continue to be the case as it will be increasingly possible to assemble performance records. The purpose of this paper is to describe the sources of risk and return within property funds and set out a more holistic performance attribution framework encompassing the concepts of alpha (out‐performance) and beta (risk), which traditional attribution frameworks in property fund management do not.

A four component risk and return attribution framework is put forward. The first two components are portfolio structure which measures the impact of allocations to more or less risky markets, and stock selection which considers more or less risky assets. Fund structure, measures the impact of financial leverage and fees and finally the return impact of timing is attributed to the movement of capital into and out of the fund.

A case study of a single unlisted fund has been used to compare traditional attribution results with an examination of alpha and beta return attribution. In this instance fund structure, which is largely the financial leverage impact, is found to be significant. This simply reflects extra risk taking and there is no clear evidence of manager out‐performance, yet significant performance fees are paid to the manager.

The paper provides a complete framework for the performance measurement and attribution of property funds, which enables investors to gain a fuller understanding of these increasingly used investment conduits.

Property is a key investment asset class that offers considerable benefits in a mixed-asset portfolio. Previous studies have concluded that property allocation should be within the 10-30 per cent range. However, there seems to be wide variation in theory and practice. Historical Australian superannuation data shows that the level of allocation to property asset class in institutional portfolios has remained constant in recent decades, restricted at 10 per cent or lower. This is seen by many in the property profession as a subjective measure and needs further investigation. The purpose of this paper is to compare the performance of the AU$431 billion industry superannuation funds’ strategic balanced portfolio against ten different passive and active investment strategies.

The analysis used 20 years (1995-2015) of quarterly data covering seven benchmark asset classes, namely: Australian equities, international equities, Australian fixed income, international fixed income, property, cash and alternatives. The 11 different asset allocation models are constructed within the modern portfolio theory framework utilising Australian ten-year bonds as the risk free rate. The Sharpe ratio is used as the key risk-adjusted return performance measure.

The ten different asset allocation models perform as well as the industry fund strategic approach. The empirical results show that there is scope to increase the property allocation level from its current 10-23 per cent. Upon excluding unconstrained strategies, the recommended allocation to property for industry funds is 19 per cent (12 per cent direct and 7 per cent listed). This high allocation is backed by improved risk-adjusted return performance.

The constrained optimal, tactical and dynamic models are limited to asset weight, no short selling and turnover parameters. Other institutional constraints that can be added to the portfolio optimisation problem include transaction costs, taxation, liquidity and tracking error constraints.

The 11 different asset allocation models developed to evaluate the property allocation component in industry superannuation funds portfolio will attract fund managers to explore alternative strategies (passive and active) where risk-adjusted returns can be improved, compared to the common strategic approach with increased allocation to property assets.

The research presents a unique perspective of investigating the optimal allocation to property assets within the context of active investment strategies, such as tactical and dynamic models, whereas previous studies have focused mainly on passive investment strategies. The investigation of these models effectively contributes to the transfer of broader finance and investment market theories and practice to the property discipline.

To achieve long‐term performance, superannuation balanced funds typically invest in a range of defined asset classes based on a strategic asset allocation approach. In an Australian context, the purpose of this paper is to examine the performance of the balanced investment option against eight different investment strategies and how the property allocation changes with different asset allocation models.

The analysis is based on ex post data covering 17 years (1995 to 2011). The selected passive and active allocation models are set within the modern portfolio theory framework utilising Australian ten year bonds as the risk free rate. The Sharpe ratio is used as the key risk‐adjusted return performance measure.

Property provided the second highest risk adjusted return profile behind the alternative asset class. The different asset allocation models perform as well as the conventional strategic approach and in many instances property allocation is found to be under‐allocated on a return optimisation basis. Depending on the asset allocation model, property when included within a multi‐asset portfolio improves the portfolio risk‐adjusted return profile by 2 per cent to 28 per cent.

For an Australian superannuation balanced fund, the empirical results show that there is scope to increase the property allocation level from current 10 per cent to 23 per cent. This knowledge will be beneficial for funds currently re‐profiling investment portfolios to achieve stable risk‐adjusted returns.

The research contributes to both practical and academic fields, as it offers a methodological approach on how allocation to property assets can be improved using a series of passive and active asset allocation strategies.