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The Chinese housing market has gone through rapid growth during the past decade, and house price forecasting has evolved to be a significant issue that draws enormous attention from investors, policy makers and researchers. This study investigates neural networks for composite property price index forecasting from ten major Chinese cities for the period of July 2005–April 2021.

The goal is to build simple and accurate neural network models that contribute to pure technical forecasts of composite property prices. To facilitate the analysis, the authors consider different model settings across algorithms, delays, hidden neurons and data spitting ratios.

The authors arrive at a pretty simple neural network with six delays and three hidden neurons, which generates rather stable performance of average relative root mean square errors across the ten cities below 1% for the training, validation and testing phases.

Results here could be utilized on a standalone basis or combined with fundamental forecasts to help form perspectives of composite property price trends and conduct policy analysis.

This study aims to investigate dynamic relations among office property price indices of 10 major cities in China for the years 2005–2021.

Using monthly data, the authors adopt vector error correction modeling and the directed acyclic graph for the characterization of contemporaneous causality among the 10 indices.

The PC algorithm identifies the causal pattern, and the linear non-Gaussian acyclic model algorithm further determines the causal path from which we perform innovation accounting analysis. Sophisticated price dynamics are found in price adjustment processes following price shocks, which are generally dominated by the top tier of cities.

This suggests that policies on office property prices, in the long run, might need to be planned with particular attention paid to the top tier of cities.

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 main purpose of this study is to detect homogeneous housing market areas among 196 districts of 5 major cities of Turkey in terms of house sale price indices. The second purpose is to forecast these 196 house sale price indices.

In this paper, the authors use the monthly house sale price indices of 196 districts of 5 major cities of Turkey. The authors propose an autoregressive (AR) model-based fuzzy clustering approach to detect homogeneous housing market areas and to forecast house price indices.

The AR model-based fuzzy clustering approach detects three numbers of homogenous property market areas among 196 districts of 5 major cities of Turkey where house sale price moves together (or with similar house sales dynamic). This approach also provides better forecasting results compared to standard AR models by higher data efficiency and lower model validation and maintenance effort.

In this study, the authors could not use any district-based socioeconomic and consumption behavioral indicators and any discrete geographical and property characteristics because of the data limitation.

The finding of this study would help property investors for establishing more effective property management strategies by taking different geographical location conditions into account.

From the government side, knowing future rises, falls and turning points of property prices in different locations can allow the government to monitor the property price changes and control the speculation activities that cause a dramatic change in the market.

There is no previous research paper focusing on neighborhood-based clusters and forecasting house sale price indices in Turkey. At this point, it is the first academic study.

Polymeric particulate composites with thermoplastics, especially polypropylene (PP) matrix with mineral fillers, are of great practical importance due to their simple possibility of modifying mechanical properties and reducing the price/volume ratio of the resulting material. Both filler properties and interface properties have a great effect on the mechanical properties, primarily on stiffness and toughness, of the resulting composite material. Good final dispersion of the filler particles also plays a very important role. To reach the best adhesion and distribution of the particles, various procedures are carried out for activation of the particles. Therefore, the purpose of this paper is to investigate and discuss the effect of using plasma as a tool for treating commercially available CaCO₃ nanoparticles in PP matrix.

The effect of the composite structure on its mechanical properties was studied from an experimental as well as a theoretical point of view. For an experimental study, four PP matrix were chosen. For use as filler, the commercially available precipitated surface-treated calcium carbonate was chosen. The composites were prepared with 5, 10, and 15 wt% of fillers. The sequence of expositions of plasma was chosen to verify the optimal treatment duration. The filler particles were characterized by several structure analytical methods. The composite mechanical properties were characterized by tensile, bending, impact, and creep tests. The deformation behavior of the three-phase composite with homogeneously distributed coated particles was numerically simulated on a microscopic scale.

The main conclusions of this work can be summarized as follows: with the use of plasma to the precipitated calcium carbonate, composites with well-dispersed particles can be prepared; the surface modification using plasma is done mainly by grafting –OH groups onto the particles’ surface; a synergetic effect of modifier enhancing the performance was observed; performance modifier increases the resistance against viscoelastic strain; and the size of the particles and their volume content generally lead to increase in the macro modulus of the composite.

Plasma, as a tool for treating the inorganic fillers, enables to destroy the agglomerates in composite, which is the basic way on how to optimally utilize the synergetic effect of composite with PP matrix.

The purpose of this paper is to evaluate consumers’ willingness to pay (WTP) for composite flour bread produced with a blend of 15-40 per cent cassava flour blended with wheat flour in Ghana.

The analysis is based on interviews with 350 consumers in the Ashanti and Eastern Regions of Ghana to assess their awareness, perceptions and WTP for cassava-wheat composite bread. From these consumer interviews, a hedonic regression model was applied to evaluate consumers’ WTP for various attributes of composite flour bread. Price-related and health-related perceptions of consumers on cassava-wheat composite bread were investigated with perception indices. Multi-attribute preference-based contingent ratings that rate product attributes in terms of importance to consumers was employed. The implicit prices of the product attributes representing the contribution of the product attributes to the WTP amount were also computed.

The paper finds that consumers who are aware of cassava-blended flour bread and who like its taste and texture are willing to pay more than consumers who are unaware. This leads to a policy recommendation advocating increased advertising of the economic and nutritional benefits of cassava-wheat blended composite flour bread.

Future studies should explore the choice experiments to examine preferences for the food product.

This paper evaluates consumers’ WTP for composite flour bread produced with a blend of 15-40 per cent cassava flour and wheat flour. Given widespread reliance on imported wheat flour and the simultaneously large volumes of locally available cassava, it is important to consider opportunities for import substitution (and possible cost reduction for consumers) of blended flour products such as cassava-wheat composite flours. Nigeria has imposed a 10 per cent blending requirement for this reason. Ghana has taken important measures recently for the development of high-quality cassava flour, and so research on its potential and actual uptake is welcomed and highly relevant to food security and agribusiness development.

This study uses an event study approach which is the development of the efficient market hypothesis theory. First, the random walk test was conducted on the Jakarta Composite Index (JCI) to test the efficiency in the weak form. Furthermore, event study analysis was carried out on JCI and nine sectoral indices to determine the impact of COVID-19 related events on price movements. The study found that JCI prices follow a random walk pattern so that the stock market in Indonesia is efficient, at least in a weak form. In the event study testing, only events related to the first confirmed case of COVID-19 and the implementation of large-scale social restriction in Indonesia affected the composite index. From a sectoral point of view, only the event of Jakarta’s call center had no impact on price changes in the sectoral index. Thus, each index had a different effect throughout the event. The reaction seen from the movement of prices for the composite and sectoral index to the public information explains that the condition of the Indonesian capital market is efficient, at least in semi-strong form.

Examines the fifthteenth published year of the ITCRR. Runs the whole gamut of textile innovation, research and testing, some of which investigates hitherto untouched aspects. Subjects discussed include cotton fabric processing, asbestos substitutes, textile adjuncts to cardiovascular surgery, wet textile processes, hand evaluation, nanotechnology, thermoplastic composites, robotic ironing, protective clothing (agricultural and industrial), ecological aspects of fibre properties – to name but a few! There would appear to be no limit to the future potential for textile applications.

Examines the sixteenth published year of the ITCRR. Runs the whole gamut of textile innovation, research and testing, some of which investigates hitherto untouched aspects. Subjects discussed include cotton fabric processing, asbestos substitutes, textile adjuncts to cardiovascular surgery, wet textile processes, hand evaluation, nanotechnology, thermoplastic composites, robotic ironing, protective clothing (agricultural and industrial), ecological aspects of fibre properties – to name but a few! There would appear to be no limit to the future potential for textile applications.