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The abundant of Waqf land (WL) administrated by State Islamic Religion Council (SIRC) are growing over the years unsolved. The location of these WL are strategic and capable for commercial benefits through property development investment project. Yet, the development funds’ constraint restraints SIRC to develop the WL’s potential. This study examine the structure of management of indirect real estate investment (IREI) instrument and carried out a benchmarking comparison analysis to suggest a workable IREI to unlock WL potential. But, concept of Waqf prevents sub-sale of Waqf ownership exchange transaction. While, private property trust fund (PPTF) is a close fund and distribute dividend. This study suggest a conceptual idea Waqf private property trust fund (WPPTF) as mechanism for WL unlock initiative. The structure of management of WPPTF is different since Waqf unit holders whom cash donor would not receive any investment return, instead SIRC as a caretaker of Waqf properties benefit all investment return. The acceleration of Waqf property potential would be useful for investment project development of more frozen WL.

PurposeThere are a plethora of putative influencing variables available in the literature for modelling real estate prices using AI. Their choice tends to differ from one researcher to the other, consequently leading to subjectivity in the selection process. Thus, there is a need to seek the viewpoint of practitioners on the applicability and level of significance of these academically established variables.Design/methodology/approachUsing the Delphi technique, this study collated and structured the 35 underlying micro- and macroeconomic parameters derived from literature and eight variables suggested by 11 selected real estate experts. The experts ranked these variables in order of influence using a seven-point Likert scale with a reasonable consensus during the fourth round (Kendall's W = 0.7418).FindingsThe study discovered that 16 variables are very influential with seven being extremely influential. These extremely influential variables include flexibility, adaptability of design, accessibility to the building, the size of office spaces, quality of construction, state of repairs, expected capital growth and proximity to volatile areas.Practical implicationsThe results of this study improve the quality of data available to valuers towards a fortified price prediction for investors, and thereby, restoring the valuers' credibility and integrity.Originality/valueThe “volatility level of an area”, which was revealed as a distinct factor in the survey is used to add to current knowledge concerning office price. Hence, this study offers real estate practitioners and researchers valuable knowledge on the critical variables that must be considered in AI-based price modelling.

Effective maintenance management requires proper data management for decision-making purposes. Big Data (BD) and Business Intelligence’s (BI) growing trend has created many challenges for government data management in particular. The government finds difficulties in integrating the massive volume of data with high-speed processing due to incapable database management in the current system, and the issues are not appropriately addressed. This paper contributes significantly, which focuses on an intelligent system that lets the government make an integral part of decision-making and can be applied horizontally to solve the problems in practice. Accordingly, an efficient data repository system with real-time analysis is proposed in this paper and it looks at a real case study highlighting the need for proper data management in government.

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Maintenance data for government buildings in Putrajaya, Malaysia, consists of a vast volume of data that is divided into different classes based on the functions of the maintenance tasks. As a result, multiple interactions from stakeholders and customers are required. This necessitates the collection of data that is specific to the stakeholders and customers. Big data can also forecast for predictive maintenance purposes in maintenance management. The current data practise relies solely on well-structured statistical data, resulting in static analysis and findings. Predictive maintenance under the Big Data idea will also use non-visible data such as social media and web search queries, which is a novel way to use Big Data analytics. The metamodel technique will be used in this study to evaluate the predictive maintenance model and faulty events in order to verify that the asset, facilities, and buildings are in excellent working order utilising systematic maintenance analytics. The metamodel method proposed a predictive maintenance procedure in Putrajaya by utilising the big data idea for maintenance management data.

The demand for green buildings in the property market is substantially increasing. The motivation for the investment on green buildings ranges from environmental concerns and social benefits to financial savings during the operational stage. However, these perceived benefits have been argued to be mostly theoretical and yet to be empirically proven. There is often a performance gap between the expected and the actual measured performance of green buildings once operational. Green buildings simply fail to perform as to what it was intended despite the thorough design and technological considerations put at the initial stage of their development. Hence, by reviewing various literatures, this paper targets to indicate and discuss the factors that hinder green buildings from achieving their fullest performance potential. Six theoretical factors namely miscommunication, technologies used, modeling tools, construction process and handover, occupant behavior and management and control were identified from various literatures. The findings in this paper will be a commencement for further studies pertaining to non-performance of green buildings.

Investment in REITs has become significant in recent years due to the stability and sustainable performance of the investment. A study on the management perspective is very important but this perspective is very limited. Asset management will derive from the profit optimization of the investment. Therefore, it is important to assess asset management strategies to ensure the sustainable performance of the assets. This paper aims to assess asset management strategies among matured REIT companies in developed countries in comparison with Malaysian REIT companies from the perspective of the managers. This research employed qualitative analyses by using content analysis techniques. A total of 41 REIT companies from the United States (US), Japan, Singapore, Australia and Malaysia were assessed. The analyses focused on the similarities and differences between the strategy framework identified in the literature review and the strategies adopted by global REITs and Malaysian REITs under review. The study will enable all REIT stakeholders to become well-informed on global REIT asset management that will derive the maximum profit from the investment. The success of developed countries’ REITs will provide guidelines for Malaysian REITs to adopt the best practice of strategic asset management from REITs in mature markets. Furthermore, this study is one of few papers that have discussed the issue of strategic property investment, particularly focusing on REITs.