Many studies have been conducted for modeling the underlying non-linear relationship between pricing attributes and price of property to forecast the housing sales prices. In recent years, more advanced non-linear modeling techniques such as Artificial Neural Networks (ANN) and Fuzzy Inference Systems (FIS) have emerged as effective techniques to predict the house prices. In this paper, we propose a fuzzy least-squares regression-based (FLSR) model to predict the prices of real estates. A comprehensive comparison studies in terms of prediction accuracy and computational complexity of ANN, Adaptive Neuro Fuzzy Inference System (ANFIS) and FLSR has been carried out. ANN has been widely used to forecast the price of real estates for many years while ANFIS has been introduced recently. On the other hand, FLSR is comparatively new. To the best of our knowledge, no property prices prediction using FLSR was developed until recently. Besides, a detailed comparative evaluation on the performance of FLSR with other modeling approaches on property price prediction could not be found in the existing literature. Simulation results show that FLSR provides a superior prediction function as compared to ANN and FIS in capturing the functional relationship between dependent and independent real estate variables and has the lowest computational complexity.

Purpose With the impending development of green buildings in Kuala Lumpur, the capital city of Malaysia, managing the criteria within the requirements of the rating tool’s certification shall be the responsibility of the respective parties when these buildings are in operations. In tenanted buildings, a lease agreement spells out the responsibilities of the owner/landlord and the tenants. The paper aims to discuss these issues. Design/methodology/approach The main aim of this study is to gauge the implementation of green lease among office buildings in Kuala Lumpur. This is made through an initial review of the adoption of green building criteria as well as the determination of the drivers and barriers perceived by office building managers in implementing green lease. Findings A survey amongst the managers of top grade office buildings in Kuala Lumpur revealed that the majority practiced some of the green criteria outlined in the Green Building Index. It also revealed that the most significant green lease terms commonly acknowledged by the respondents were energy and water-saving consumption as well as recycled material usage. Originality/value Through the identification of the barriers to implement green lease, the most significant barriers identified were related to cost and financing. Having identified these obstacles, appropriate action can be taken to bring forward green lease awareness amongst the various stakeholders in the office building sector in ensuring the successful operation of green buildings.

Smart Cities have grown in prominence due to advancement in ICT and the new paradigm of sustainable city management and development. Whilst many authors have proposed guidelines and framework for Smart City implementation, less attention has been given to the assessment of Smart City performance. The mainstream Smart City assessment framework generally entails the quantitative assessment of factors, elements and initiatives categorised under the Smart City dimensions. However, this approach is problematic and impractical because it requires a large amount of different baseline data that is often at times unavailable due to various reasons. This paper describes an alternative framework for smart city assessment, one that is based on the modification of Giffmger’s to make it amenable to leaner data. The proposed assessment framework was adopted to assess the smart city performances of Seoul, Singapore, and Iskandar Malaysia which were then compared. With the use of the framework for the performance assessment, the city that has performed better than the others is able to be identified.

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The aim of this study is to determine the trend of escalation for both primary and secondary housing units. This research applies the desk study approach which uses secondary data from statistical format, which is the property market report (PMR) from the year of 2004 to 2014. Data from the PMR was analysed by using descriptive statistics method which provides a general overview of the house price movement. The observation only focuses the house price movement from the (9) districts in Selangor state. Results show that even though the volume of transactions decreases, the prices of residential properties are steadily increasing which also indirectly escalates the secondary market price.