Search results

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 paper is to examine how consumers respond differently to “scratch and save (SAS)” promotions versus “tensile price claims (TPC).” SAS promotions provide a possible discount (determined probabilistically) but conceal the exact amount until purchase. Tensile price claims (e.g. “up to 25 percent off on items marked with a red tag”) make imprecise price promotional claims. In addition to making indefinite price claims, SAS promotions (e.g. scratch and save up to 25 percent off) include gambling elements; the exact discount is determined randomly for individual consumers by a scratch‐off card.

Two experimental studies are conducted.

Evidence from two experiments indicates that consumers perceive SAS promotions to be more ambiguous than tensile price claims. In addition, the results demonstrate consumer uncertainty towards SAS promotions but also consumer willingness to gamble: deep discount SAS promotions are perceived as more attractive than limited‐scope tensile price claims.

The findings suggest that consumers perceive SAS offers more enticing than limited tensile price claims as the proposed discount increases. Furthermore, establishing a minimum savings offer could be used to encourage consumers to shop at retailers offering SAS promotions.

Limited work has focused on examining how consumers respond to SAS promotions because SAS promotions are a relatively new store‐level promotional tool. Furthermore, no research effort has been extended to directly compare consumers' perceptions of SAS promotions with tensile price claims.

The paper aims to assess the impact of agricultural output changes on the general price level over time with China as an example.

A simple numerical global general equilibrium (GE) model of two regions (China and the rest of the world) and three commodities (agriculture, manufacturing goods, and services) is used to assess the impacts of agricultural output changes on the overall economy price changes. The numerical GE model of this paper consists of production, final consumption, and market clear conditions. The results are generated with the GE model calibrated to aggregated China's input‐output tables of 1987, 1997, and 2005.

The results suggest that China witnessed a declining influence of agricultural output changes on general price changes. The contribution of given agricultural output change on the general price change in 2005 was merely less than 60 percent of that in 1987, which in turn implies that macro policies targeting to curb general inflation via boosting agricultural output will be less effective as those of 20 years ago.

China's policy makers should rely less and less on promoting agricultural output policies to fight against general inflation and should resort to non‐agricultural policies.

The paper argues that the influence of agriculture on the China's general price indices has been weakening along with China's economic development with a numerical GE model calibrated to aggregated China's input‐output tables of 1987, 1997, and 2005.

This study aims to analyze the effect of interrupted rolling on microstructures and mechanical properties of Mg–8Li–xGr composite is investigated.

Graphene reinforced composite was developed by using stir casting route and rolled with different reduction in thickness such as 50, 75 and 90%. Microstructure, hardness and tensile characteristics of the rolled samples were evaluated.

Investigation on microstructures of rolled composite depicts that increase in rolling reduction % resulted in fine elongated grains and decreased aspect ratio. Further, it was also observed that increasing percentage of rolling reduction promotes the dissolution of ß Li phase and as a result the ductility of composite decreases. Interrupted rolled samples showcase higher hardness when compared with as-cast composite. Composite rolled with 90% reduction displays higher yield strength of 219 MPa. Hardening capacity of composites decreases with increase in reduction percentage due to the effective reduction in grain size.

Investigation on the influence of interrupted rolling on microstructures and mechanical properties of Mg graphene composite. The in-depth understanding of this will help to improve its wide spread application.

The Chinese housing market has witnessed rapid growth during the past decade and the significance of housing price forecasting has undoubtedly elevated, becoming an important issue to investors and policymakers. This study aims to examine neural networks (NNs) for office property price index forecasting from 10 major Chinese cities for July 2005–April 2021.

The authors aim at building simple and accurate NNs to contribute to pure technical forecasts of the Chinese office property market. To facilitate the analysis, the authors explore different model settings over algorithms, delays, hidden neurons and data-spitting ratios.

The authors reach a simple NN with three delays and three hidden neurons, which leads to stable performance of about 1.45% average relative root mean square error across the 10 cities for the training, validation and testing phases.

The results could be used on a standalone basis or combined with fundamental forecasts to form perspectives of office property price trends and conduct policy analysis.

The purpose of this study is to propose a new method for the end-to-end classification of steel surface defects.

This study proposes an AM-AoN-SNN algorithm, which combines an attention mechanism (AM) with an All-optical Neuron-based spiking neural network (AoN-SNN). The AM enhances network learning and extracts defective features, while the AoN-SNN predicts both the labels of the defects and the final labels of the images. Compared to the conventional Leaky-Integrated and Fire SNN, the AoN-SNN has improved the activation of neurons.

The experimental findings on Northeast University (NEU)-CLS demonstrate that the proposed neural network detection approach outperforms other methods. Furthermore, the network’s effectiveness was tested, and the results indicate that the proposed method can achieve high detection accuracy and strong anti-interference capabilities while maintaining a basic structure.

This study introduces a novel approach to classifying steel surface defects using a combination of a shallow AoN-SNN and a hybrid AM with different network architectures. The proposed method is the first study of SNN networks applied to this task.