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House price fluctuations send vital signals to many parts of the economy, and long-term predictions of house prices are of great interest to governments and property developers. Although predictive models based on economic fundamentals are widely used, the common requirement for such studies is that underlying data are stationary. This paper aims to demonstrate the usefulness of alternative filtering methods for forecasting house prices.

We specifically focus on exponential smoothing with trend adjustment and multiplicative decomposition using median house prices for Sydney from Q3 1994 to Q1 2017. The model performance is evaluated using out-of-sample forecasting techniques and a robustness check against secondary data sources.

Multiplicative decomposition outperforms exponential smoothing at forecasting accuracy. The superior decomposition model suggests that seasonal and cyclical components provide important additional information for predicting house prices. The forecasts for 2017–2028 suggest that prices will slowly increase, going past 2016 levels by 2020 in the apartment market and by 2022/2023 in the detached housing market.

We demonstrate that filtering models are simple (univariate models that only require historical house prices), easy to implement (with no condition of stationarity) and widely used in financial trading, sports betting and other fields where producing accurate forecasts is more important than explaining the drivers of change. The paper puts forward a case for the inclusion of filtering models within the forecasting toolkit as a useful reference point for comparing forecasts from alternative models.

To the best of the authors’ knowledge, this paper undertakes the first systematic comparison of two filtering models for the Sydney housing market.

This paper aims to develop a house price forecasting model to investigate the impact of neighbourhood effect on property value.

Multi-level modelling (MLM) method is used to develop the house price forecasting models. The neighbourhood effects, that is, socio-economic conditions that exist in various locations, are included in this study. Data from the local government area in Greater Sydney, Australia, has been collected to test the developed model.

Results show that the multi-level models can account for the neighbourhood effects and provide accurate forecasting results.

It is believed that the impacts on specific households may be different because of the price differences in various geographic areas. The “neighbourhood” is an important consideration in housing purchase decisions.

While increasing housing supply provisions to match the housing demand, governments may consider improving the quality of neighbourhood conditions such as transportation, surrounding environment and public space security.

The demand and supply of housing in different locations have not behaved uniformly over time, that is, they demonstrate spatial heterogeneity. The use of MLM extends the standard hedonic model to incorporate physical characteristics and socio-economic variables to estimate dwelling prices.

Sensing- and warning-based technologies are widely used in the construction industry for occupational health and safety (OHS) monitoring and management. A comprehensive understanding of the different types and specific research topics related to the application of sensing- and warning-based technologies is essential to improve OHS in the construction industry. The purpose of this paper is to examine the current trends, different types and research topics related to the applications of sensing- and warning-based technology for improving OHS through the analysis of articles published between 1996 and 2017 (years inclusive).

A standardized three-step screening and data extraction method was used. A total of 87 articles met the inclusion criteria.

The annual publication trends and relative contributions of individual journals were discussed. Additionally, this review discusses the current trends of different types of sensing- and warning-based technology applications for improving OHS in the industry, six relevant research topics, four major research gaps and future research directions.

Overall, this review may serve as a spur for researchers and practitioners to extend sensing- and warning-based technology applications to improve OHS in the construction industry.

Automated technologies have been applied to facility management (FM) practices to address labour demands of, and time consumed by, inputting and processing manual data. Less attention has been focussed on automation of visual information, such as images, when improving timely maintenance decisions. This study aims to develop image classification algorithms to improve information flow in the inspection-repair process through building information modelling (BIM).

To improve and automate the inspection-repair process, image classification algorithms were used to connect images with a corresponding image database in a BIM knowledge repository. Quick response (QR) code decoding and Bag of Words were chosen to classify images in the system. Graphical user interfaces (GUIs) were developed to facilitate activity collaboration and communication. A pilot case study in an inspection-repair process was applied to demonstrate the applications of this system.

The system developed in this study associates the inspection-repair process with a digital three-dimensional (3D) model, GUIs, a BIM knowledge repository and image classification algorithms. By implementing the proposed application in a case study, the authors found that improvement of the inspection-repair process and automated image classification with a BIM knowledge repository (such as the one developed in this study) can enhance FM practices by increasing productivity and reducing time and costs associated with ecision-making.

This study introduces an innovative approach that applies image classification and leverages a BIM knowledge repository to enhance the inspection-repair process in FM practice. The system designed provides automated image-classifying data from a smart phone, eliminates time required to input image data manually and improves communication and collaboration between FM personnel for maintenance in the decision-making process.

This paper aims to investigate the factors that contribute to the changes of house prices including ethnic factors. Australia is a multicultural country with diversified ethnicities. The median price of established houses (unstratified) in Sydney has reached a new record high of $910,000 in December 2015, increasing around 58.2 per cent from March 2011 [Australian Bureau of Statistics (ABS), 2015a]. However, the prices of some suburbs have increased more than prices of others.

Six suburbs that represent ethnic majority originally including White, India and China will be selected as pilot studies. Hedonic regression analysis will be applied for the analysis based on 2001, 2006 and 2011 census data.

It is found that the main drivers of house prices are the dwelling physical characteristics and accessibility to convenient transportation. The level of household income also plays an important role. However, the impact of changes of ethnic on changes of prices is not significant.

The study adds to the growing literature on the ethnicity changes on dwelling prices and is important for understanding whether some of the clusters of ethnic concentration or segregation effects property markets. This study is significant in its understanding of the main characteristics of ethnic changes of suburbs in Sydney.

An implication is that policy makers can attract different ethnic groups and encourage multicultural communities when they formulate housing and planning policies.

The relationship between ethnicity and house price appreciation is not extensively studied in Australia. This research contributes to the literature on the effects of ethnic changes on house prices and implications of policy formulation to encourage multicultural communities.

The purpose of this study is to examine employee imagination and implications for entrepreneurs of China. In 2015, the European Group of Organization Studies released a call for papers highlighting poor knowledge of employee imagination in organizations. To address this need, the current study hypothesizes employee imagination consisting of seven conditions common to the organizational experience of Chinese Entrepreneurs.

The current paper reviews the Chinese enterprising context. Cases from China are used to illustrate the effects of proposed conditions and their value for entrepreneurs and innovators in businesses undergoing change.

Employee imagination underpins and conditions how Chinese employees make sense of their organizations and better understand the process of organizational change. From the viewpoint of human resource management, emphasis on coaching and developing imagination enables businesses to stay competitive and adapt to environmental demands such as lack of information, too much information or the need for new information.

The proposed conditions apply to the Chinese context; however, their application to wider contexts is suggested and requires attention.

Employee imagination was found to be a powerful tool, which facilitates the process of organizational change management.

Theoretically, the research adds new insights to knowledge of a poorly understood organizational behavior topic – employee imagination. Practically, the research findings provide mangers with knowledge of conditions, which could be adopted as powerful tools in facilitating organizational change management.

The purpose of this paper is to review the existing literature on Lean Six Sigma (LSS) for services, construct a morphological analysis (MA) framework and identify research gaps to point to future research possibilities and priorities.

The MA framework is based on literature review of 175 papers published from 2003 to 2015, across 67 journals recognised by Scopus or ABS Academic Journal Quality Guide 2015. A three-phased methodology is used by the authors, with Phase1 featuring a five-stage systematic review protocol to identify relevant journal papers for review; Phase2 presenting a framework for classifying the reviewed papers in terms of their fundamental, methodological, chronological and sector-wise orientations; and Phase3 constructing an MA framework on the classified papers and identifying the research gaps.

The MA framework constructed based on six dimensions, namely, organizational context of applications, desired outcomes, implementation systems, LSS tools and techniques, integration with other management philosophies and evaluation methods, involving 40 focused themes, has revealed 355 distinct research gaps as opportunities for future research.

This paper confirms the existence of substantial scope and points to specific topics for further research in the area of LSS for services. The findings demonstrate the gaps in academic research on the subject. In addition, the study also helps organisational leaders and practitioners to look at LSS from a holistic perspective in the services context.

The MA framework of the existing literature on LSS for services presents a unique, systematic effort to identify research opportunities. In addition, a five-stage systematic review protocol is proposed in this paper. This could be valuable to researchers and practitioners in enabling them to systematically review the literature on research subjects of interest to them.