Purpose The purpose of this paper is to analyse and predict the housing prices in Petaling district, Malaysia and its six sub-regions with a set of housing attributes using functional relationship model. Design/methodology/approach A new multiple unreplicated linear functional relationship model with both the response and explanatory variables are subject to errors is proposed. A total of 41,750 housing transacted records from November 2008 to February 2016 were used in this study. These data were divided into 70% training and 30% testing sets for each of the selected sub-regions. Individual housing price was regressed on nine housing attributes. Findings The results showed the proposed model has better fitting ability and prediction accuracy as compared to the hedonic model or multiple linear regression. The proposed model achieved at least 20% and 40% of predictions that have less than 5% and 10% deviations from the actual transacted housing prices, respectively. House buyers in these sub-regions showed similar preferences on most of the housing attributes, except for residents in Shah Alam who preferred to stay far away from shopping malls, and leasehold houses in Sri Kembangan are more valuable. From the h-nearest houses indicator, it is concluded that the housing market in Sungai Buloh is the most volatile in Petaling District. Research limitations/implications As the data used are the actual housing transaction records in Petaling District, it represents only a segment of Malaysian urban population. The result will not be generalized to the entire Malaysian population. Practical implications This study is expected to provide insights to policymakers, property developers and investors to understand the volatility of the housing market and the influence of determinants in different sub-regions. The potential house buyers could also use the model to determine if a house is overpriced. Originality/value This study introduces measurement errors into the housing attributes to provide a more reliable analysis tool for the housing market. This study is the first housing research in Malaysia that used a large number of actual housing transaction records. Previous studies relied on small survey samples.

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E-hailing, which is also known as ride-hailing or ride-sourcing, has been on the rise across the globe. However, it is known as a disruptive innovation as unregulated services have triggered major disruptions and led to certain setbacks in the transportation industry. It has been blamed for the rise in privacy and safety issues, unfair business competition, and a decline in demand in the traditional taxi industry. In response to these issues, certain countries have started to regulate the e-hailing industry. The objective of this study is to identify, analyze, and contrast on the form and enforcement of Malaysian-Indonesian’s e-hailing regulation framework by using qualitative-comparative research. In this study, literature has been gathered from credible online sources. Based on the results of the study, there are commonalities and differences between the Malaysian and Indonesian’s e-hailing regulation framework. Furthermore, the implications of the study have been discussed. For instance, the current state of the e-hailing industry is underregulated rather than unregulated. Policy makers should seek to improve existing regulation framework rather than starting new regulations from ground up to minimize the risk of overregulation that might eventually diminish the industry.

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