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The paper seeks to examine whether selectivity and timing performance of fund manager is sensitive to the choice of market benchmarks. The two benchmarks used are the Kuala Lumpur Composite Index (KLCI) and the Exchange Main Board All‐Share (EMAS) Index.

The paper seeks to employed Jensen's model to estimate the overall fund performance and Henriksson and Merton's model to separate the fund manager's investment performance into the selectivity and market‐timing components.

The findings indicate that, on average, the funds display negative overall performance with either the KLCI or the EMAS Index. In addition, there is little variation in the manager's market‐timing and selectivity performance across alternative market benchmarks. It is also reported that a manager's poor timing ability contributes significantly to the fund's negative overall performance.

The paper employed just two market benchmarks. Inclusion of more market benchmarks in future research may provide further support for the existing findings.

Regardless of the market benchmarks used, the results imply that fund managers should seriously reassess their market timing efforts, given that their predictions are very often in the wrong direction than in the right direction. Such findings suggest that no economic benefit accrues to the average fund manager involved in market‐timing activities.

The paper provides first evidence on the sensitivity of a fund manager's separate investment components (timing and selectivity) to different specification of the market benchmarks.

In 1986, a mid‐sized manufacturer of building materials (“Company A”) was just completing a major new renovation of an old production facility. As the new facility came on line, company executives needed to establish concrete goals for minimizing costs in the hotly competitive and price‐sensitive market for its product. To meet this objective, the company benchmarked itself against eight of the most profitable competitors in the industry.

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.

This paper aims to examine the performance and benchmark asset allocation policy of 70 KiwiSaver funds catergorised as growth, balanced or conservative over the period October 2007-June 2016. The study focuses on the sources for returns variability across time and returns variation among funds.

Each fund is benchmarked against a portfolio of eight indices representing eight invested asset classes. Three measures were used to examine the after-fee benchmark-adjusted performance of each fund: excess return, cumulative abnormal return and holding period returns difference. Tracking error and active share were used to capture manager’s benchmark deviation.

On average, funds underperform their respective benchmarks, with the mean quarterly excess return (after management fees) of −0.15 per cent (growth), −0.63 per cent (balanced) and −0.83 per cent (conservative). Benchmark returns variability, on average, explains 43-78 per cent of fund’s across-time returns variability, and this is primarily driven by fund’s exposures to global capital markets. Differences in benchmark policies, on average, account for 18.8-39.3 per cent of among-fund returns variation, while differences in fees and security selection may explain the rest. About 61 per cent of balanced and 47 per cent of Growth funds’ managers make selection bets against their benchmarks. There is no consistent evidence that more actively managed funds deliver higher after-fee risk-adjusted performance. Superior performance is often due to randomness.

This study makes use of a unique data set gathered directly from KiwiSaver managers and captures the long-term strategic asset allocation target which underlines the investment management process in reality. The study represents the first attempt to examine the impact of benchmark asset allocation policy on KiwiSaver fund’s returns variability across time and returns variation among funds.

Compiled by K.G.B. Bakewell covering the following journals published by MCB University Press: Facilities Volumes 8‐18; Journal of Property Investment & Finance Volumes 8‐18; Property Management Volumes 8‐18; Structural Survey Volumes 8‐18.

Index by subjects, compiled by K.G.B. Bakewell covering the following journals: Facilities Volumes 8‐18; Journal of Property Investment & Finance Volumes 8‐18; Property Management Volumes 8‐18; Structural Survey Volumes 8‐18.

Compiled by K.G.B. Bakewell covering the following journals published by MCB University Press: Facilities Volumes 8‐18; Journal of Property Investment & Finance Volumes 8‐18; Property Management Volumes 8‐18; Structural Survey Volumes 8‐18.

Compiled by K.G.B. Bakewell covering the following journals published by MCB University Press: Facilities Volumes 8‐18; Journal of Property Investment & Finance Volumes 8‐18; Property Management Volumes 8‐18; Structural Survey Volumes 8‐18.

Environment, social, governance (ESG) has taken on increased importance in real estate investment in recent years, with benchmarking ESG being critically important for more informed real estate investment decision-making. Using 60 stakeholder interviews with senior real estate executives, this paper examines the strategic issues regarding benchmarking ESG in real estate investment; specifically, identifying areas going forward where ESG benchmarks need to be improved. This includes the issues of granularity, climate resilience and climate risk, as well as an increased focus on outcomes and performance, and using best practice procedures in delivering ESG in real estate investment.

In total, 60 stakeholder interviews were conducted with key real estate players globally to assess the use of ESG benchmarking in real estate investment at various levels (asset/fund-level, listed real estate, delivery, reporting and internal benchmarking), across regions and across different types of real estate investment players (real estate fund manager, real estate investment trust (REIT), institutional investor and real estate advisor). This enabled key strategic insights to be identified for improved ESG benchmarking practices in real estate investment going forward.

There was clear evidence of the need for improved benchmarks for ESG in real estate investment. More focus was needed on performance, outcomes and impacts, with a stronger focus on granularity around the issues of climate resilience and climate risk. Improvements in Global Real Estate Sustainability Benchmark (GRESB), as well as increased attention to Task Force for Climate-Related Financial Disclosures (TCFD) were seen as important initiatives. Clear differences were also seen in the use of these ESG benchmarks on a regional basis; with Australia and Europe seen as the world leaders. These strategic stakeholder insights regarding ESG saw the development of best practice guidelines for the more effective delivery of ESG benchmarks for more informed real estate investment decision-making, as well as a series of recommendations for improving ESG benchmarking in real estate investment.

ESG benchmarking is a critical area of real estate investment decision-making today. By utilising stakeholder interviews, the strategic insights from key players in the real estate investment space are identified. In particular, this paper identifies how the current ESG benchmarks used in real estate investment need to be improved for a more critical assessment of climate resilience and climate risk issues at a more granular level. This enables the identification and delivery of more effective ESG best practice procedures and recommendations for improving ESG benchmarking in real estate investment going forward. These issues have clear impacts on ongoing capital raisings by investors, where benchmarking ESG is an increasingly important factor for real estate investors, tenants and real estate asset managers.

Based on the stakeholder interview responses, this paper has identified key areas for improvement in the current benchmarks for ESG in real estate investment. It is anticipated that an increased focus on technology and the availability of more granular data, coupled with user demand, will see more focus on assessing performance, outcomes and impacts at a real estate asset-specific level and produce a fuller range of ESG metrics, more focused on climate resilience and climate risk. This will see a more effective range of ESG benchmarks for more informed real estate investment decision-making.