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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.

The purpose of this paper is to provide a better understanding of the performance implications for UK DC pension fund investors who choose to combine global listed and UK unlisted real estate in a blended allocation relative to a pure unlisted solution.

Blended listed and unlisted real estate portfolios are constructed. Investor risk and returns are then studied over the full 15 year sample horizon and distinct cyclical phases over this period using a number of risk-return metrics. Performance is then contrasted with that of a pure unlisted solution, as well as UK equity market and bond total returns over the same period.

A UK DC pension fund investor choosing to construct a blended global listed and UK unlisted real estate portfolio would have experienced material return enhancement relative to a pure unlisted solution. The “price” of this enhanced performance and improved liquidity profile is, unsurprisingly, higher portfolio volatility. However, because of the improved returns, the impact upon measured risk adjusted returns is less significant.

Relatively liquid blended listed and unlisted real estate portfolios create efficient risk and return outcomes for investors.

This study uses actual fund rather than index data (i.e. measures delivered returns to investors), has chosen a global rather than single country listed real estate allocation and is focused on providing clarity around the real estate exposure for a specific investment requirement, the UK DC pension fund market.

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.

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.

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.

The purpose of this paper is to analyse the performance of UK property funds using the dual sources of active management, Active Share and tracking error, to distinguish between the types of active management styles used by funds.

The authors use data on 38 UK real estate funds and classify them into five active management categories using the dual sources of active management, Active Share and tracking error. Then, the authors compare their return performance against Active Share, tracking error, fund size and leverage. Therefore the paper is able to answer two of the fundamental questions of investment: does active management add value and what form of active management, stock selection or factor risk, is better at adding value to the fund?

There are three main conclusions. First, the approach of Cremers and Petajisto (2009) and Petajisto (2010) is able to classify real estate funds in the UK on their management activity into categories that makes intuitive sense and seem stable over time. Second, balanced funds show relatively low Active Shares and particularly low tracking errors, due to the benefits of property-type diversification. In contrast, specialists funds display higher Active Shares and both low and high tracking errors depending on their stock-picking approach; diversified or concentrated. Third, an analysis over different time periods confirmed that funds in the sample essentially remained in the same categories within the sample period, even during markedly different market return periods. This implies that investors need to constantly monitor changes in the market and switch between fund management styles, if at all possible.

The analysis was only based on 38 funds with complete data over the sample period and the relationship between fees and active management was not examined, even though ultimately investors are concerned with returns after management fee. It would be instructive therefore if the number of funds and time period was expanded to see if the results are robust and to see whether management fees outweigh the benefits of active manager.

The findings should enable investors to make a more informed investment decisions in the future.

To the best of the author’s knowledge this is the first paper to apply the dual sources of active management, Active Share and tracking error, in the UK real estate market.

The aim of this paper is to improve the understanding of what drives the performance of non-listed real estate funds.

The authors performed a panel regression analysis on the basis of an extensive sample from Investment Property Databank (IPD) covering returns and selected characteristics of German Spezialfonds over a period of five years from 2006 until 2010. The analysis was performed for the whole sample as well as separately for three distinctively different subperiods: the boom of 2006-2007, the downturn of 2008-2009 and the recovery of 2010.

The analysis uncovered significant differences in the drivers across the cyclical phases. During the boom phase, leverage and global portfolio allocation positively affected returns, while allocation to Germany had a negative effect. In contrast, fund volume, management costs and allocation to offices led to underperformance. Finally, in the recovery of 2010, leverage, allocation to Germany and diversification across property types improved performance, while higher liquidity and focus on retail had a negative impact.

In addition to providing extensive and unique insights into the determinants of performance of the German Spezialfonds, the results should be of interest to fund managers looking for advice on the optimal positioning of their funds in response to changing economic environments.

Despite the significant practical importance of the topic for the real estate fund industry, it has been addressed by few researchers so far.

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.