Yun Fah Chang, Wei Cheng Choong, Sing Yan Looi, Wei Yeing Pan and Hong Lip Goh
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…
Abstract
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.
Details
Keywords
Behrooz Nazemi and Mohsen Rafiean
The purpose of this paper is to use Group Method of Data Handling (GMDH)-type artificial neural network to model the affecting factors of housing price in Isfahan city housing…
Abstract
Purpose
The purpose of this paper is to use Group Method of Data Handling (GMDH)-type artificial neural network to model the affecting factors of housing price in Isfahan city housing market.
Design/methodology/approach
This paper presents an accurate model based on GMDH approach to describing connection between housing price and considered affecting factors in case study of Isfahan city based on trusted data that have been collected from 1995 to 2017 for every six months. The accuracy of the model has been evaluated by mean absolute percentage error (MAPE), root mean square error (RMSE) and mean absolute error (MAE) in this case.
Findings
Due to the obtained value of MAPE, RMSE and MAE and also their interpretation, accuracy of modelling the factors affecting housing price in Isfahan city housing market using GMDH-type artificial neural network that has been conducted in this paper, is acceptable.
Research limitations/implications
Due to limitation of reliable data availability about affecting factors, selected period is from 1995 to 2017. Accessing to longer periods of reliable data can improve the accuracy of the model.
Originality/value
The key point of this research is reaching to a mathematical formula that accurately shows the relationships between housing price in Isfahan city and effective factors. The simplified formula can help users to use it easily for analysing and describing the status of housing market in Isfahan city of Iran.
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Nayomi Kankanamge, Tan Yigitcanlar, Ashantha Goonetilleke and Md. Kamruzzaman
The purpose of this paper is to investigate the role of gamification as a novel technique in motivating community engagement in disaster-related activities in order to address the…
Abstract
Purpose
The purpose of this paper is to investigate the role of gamification as a novel technique in motivating community engagement in disaster-related activities in order to address the question of how gamification can be incorporated into disaster emergency planning.
Design/methodology/approach
This study conducts a systematic literature review and explores available gamified applications for disaster emergency planning and their purpose of use. In total, 51 scholarly articles on the topic and 35 disaster-related gamified applications are reviewed.
Findings
The findings reveal the following: (a) gamified applications (n = 35) are used for education, research and intervention purposes; (b) gamified applications create new opportunities for community engagement and raise disaster awareness among the community in virtual environments; and (c) gamified applications help shape a new culture – i.e. gamified culture – that supports smart disaster emergency planning practice.
Originality/value
During the recent years, utilisation of game elements in non-game contexts – i.e., gamification – has become a popular approach in motivating people in various actions. Increasing research highlighted the benefits of gamification in enhancing community engagement, creating interactive environments, providing better behavioural outcomes and influencing democratic processes. Despite some of the applications indicating the potential of gamification in disaster emergency planning, the use of gamification technique in this discipline is an understudied area. This study reveals gamification can be incorporated into disaster emergency planning.