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The purpose of this paper is to investigate the property investment decision-making process of Australian unlisted property funds.

Drawing on previous research into property investment decision making by Australian REITs, a normative model of the unlisted property fund investment decision-making process is proposed. Based on exploratory investigation through semi-structured interviews with senior Australian unlisted property fund decision makers, a descriptive model of the property investment decision-making process by Australian unlisted property funds is developed. The normative model and descriptive model are compared and a prescriptive model of the Australian unlisted property fund investment decision-making process proposed.

A four-stage, 20-step process proposed in the normative model was found to be generally supported by the descriptive model developed, potentially comprising a possible prescriptive model for the Australian unlisted property fund investment decision-making process.

Further research is required to investigate risk-return issues, whether the prescriptive model is generalisable across other property investment decision-making groups or over time and whether it may lead to “good” decisions.

The prescriptive model proposed may contribute consistency and transparency to the decision-making process, if adopted by Australian unlisted property funds, potentially leading to better decisions.

Greater consistency and transparency in property investment decision making by Australian unlisted property funds may lead to the optimal allocation of capital and greater investor confidence in the sector.

The findings comprise the first possible prescriptive model of the Australian unlisted property fund investment decision-making process, forming a basis for comparative investigation of that process adopted by other property investment decision-making groups such as Australian REITs and Australian retail property funds.

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.

This paper investigates the relationship between the sustainable finance disclosure regulation (SFDR) and the performance of unlisted real estate funds.

While existing literature has primarily focused on the impact of voluntary sustainability disclosure, such as certifications or reporting standards, this study addresses a significant research gap by constructing and analyzing the financial J-Curve of 40 funds under the SFDR. The authors employ a panel regression analysis to examine the effects of different SFDR categories on fund performance.

The findings reveal that funds categorized under Article 8 of the SFDR do not exhibit significantly poorer performance compared to funds categorized under Article 6 during the initial phase after launch. On average, Article 8 funds even demonstrate positive returns earlier than their peers. However, the panel regression analysis suggests that Article 8 funds slightly underperform when compared to Article 6 funds over time.

While investors may not anticipate lower initial returns when opting for higher SFDR categories, they should nevertheless be aware of the limitations inherent in the existing SFDR labeling system within the unlisted real estate sector.

To the best of our knowledge, this study represents the first quantitative examination of unlisted real estate fund performance under the SFDR. By providing unique insights into the J-Curves of funds, our research contributes to the existing body of knowledge on the impact of sustainability regulations in the financial sector.

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.

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.

The aim of the paper is threefold: to provide an overview of gearing in the Australian real estate investment market; formally examine the relationship between property returns, risk and gearing; and provide some guidance in evaluating “optimal” gearing levels for real estate investment.

A mathematical modelling framework is presented for evaluating the relationship between investment returns, risk and gearing levels.

The study highlights that risk rises with rising gearing levels and that risk‐adjusted returns fall with rising gearing. Furthermore, it is shown that the gearing‐risk relationship is influenced not only by the cost of debt structure but also the interdependency between ungeared returns and interest rates.

The study suggests that current gearing levels from Australian listed property trusts and unlisted wholesale property funds are relatively conservative. This implies that current gearing could be increased, in particular for wholesale funds, without taking on substantially more risk whilst enhancing returns.

The paper is of value to industry and academia as it offers an extended framework for evaluating the relationship between risk and gearing. In particular, the framework provides insight for gauging gearing levels when constructing real estate investment strategies.

Importantly, the paper shows that the interdependency between ungeared returns and interest rates significantly impacts the relationship between risk and gearing.

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.

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.

The purpose of this paper is to close the transparency gap by comparing ex ante and ex post performance disclosure, thus providing important conclusions regarding the transparency of this important German market segment.

Closed-ended real estate funds (CEREFs) are one of the biggest segments of unlisted private equity funds in Germany. CEREFs have a central “profitability promise” that is based on ex ante forecasts given in the prospectus. Typically, equity is tied to these investments for up to 20-30 years, leaving investors highly insecure whether their expectations will be fulfilled and fund managers actually achieve prospected performances ex post.

The performance variance analysis of all German CEREFs outstanding during the global financial crisis reveals that prospect-performance disclosures as well as prospect-performance variances cause substantial problems in Germany due to overestimation biases of many fund managers.

As typical for the recent scholarly debate, also the past disclosure practice in Germany prohibits a long-term performance analysis, unless researchers apply instruments of modern investment analysis like comprehensive financial plans (“Visualisation of Financial Implications)”.

The transparency developments in CEREF-reporting of the last decade deliver precise recommendations regarding the internal and external performance variance analysis, risk-profiles and stress tests for the future fund management.

The introduced methodology would increase transparency in the segment of CEREF and, thus, improve investor protection. Since private households in Germany mainly acquire these funds, this is a contribution to sustainability in private asset management.

The paper develops a new methodological framework for performance measurement of unlisted funds. It then assesses for the first time the impact of transparency and trust on fund performances by applying a performance variance analysis.

The purpose of this paper is to examine the effectiveness of housing as a property investment vehicle. In this analysis, the performance and diversification benefits of housing over 1996‐2007 are investigated.

Sharpe and Sortino ratios were employed to assess the risk‐adjusted performance of housing and major financial and real estate assets. Correlation analysis was also employed to examine the portfolio diversification benefits of housing.

The study found that housing is an effective property investment vehicle in which it delivers the highest risk‐adjusted returns and reveals negative correlation with major assets. The enhancement of these attractive features is also evident in recent years.

This study has implications for investor who seek to include housing as part of their portfolio. The analysis and results are limited by the quality of the data.

This study is one of the few studies in housing investment, particularly the housing market in Australia. Additionally, this study is probably the first attempt to assess the downside risk of housing.