Grey Spectrum Analysis of Air Quality Index and Housing Price in Handan

Zhang, Kai; Chen, Yan; Wu, Lifeng

doi:10.1155/2019/8710138

Cited by 3 publications

(3 citation statements)

References 13 publications

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“…By speculating on the wind speed rather than the distance from the landfill, they used a big data analysis to understand the relationship between the negative externalities generated by the landfill and the housing prices. Furthermore, Zhang et al. (2019) analysed the relationship between the Handan air quality index and house prices using the grey system theory.…”

Section: Resultsmentioning

confidence: 99%

Factors affect the housing prices in China: a systematic review of papers indexed in Chinese Science Citation Database

Jotikasthira

2022

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PurposeThis paper aims to explore the factors that affect housing prices as per Chinese articles indexed in the Chinese Science Citation Database (CSCD). There were different foci regarding what drove housing prices in China in Chinese articles, and international journal articles in English. As most previous English articles only threw light on international research, it motivated the researchers to systematically review Chinese literature’s factors that affected housing prices in China.Design/methodology/approachThis paper reviewed housing price research articles indexed in the two largest Chinese academic research databases: the CSCD and China Knowledge Infrastructure Engineering Database (CNKI.NET). It systematically collected the data and adopted descriptive analysis techniques and synthesis.FindingsThis research reviewed the literature published from 2015 to 2020 and revealed some unique factors affecting China's housing prices. For example, research focused on administrative aspects such as macroeconomic regulation and control (often known as macro control). Authors of Chinese articles suggested that the two-child policy affected housing prices, which differed from that in the English journal articles. The research results implied that researchers should read top Chinese journals on top of good international journals when they study China's real estate market in the future.Research limitations/implicationsBecause the domestic real estate market started late, domestic real estate transaction data and real estate-related statistics are more difficult to obtain. The research is mostly based on the relationship between supply and demand, government policy and individual consumer factors, and the sample has a short time span.Practical implicationsAs China is a planned economy country, administrative factors are one main factor that affects the housing price. There were a significant number of articles in Chinese that considered this factor to be the main driver of the real estate price. It included government investment and macro-control, i.e. direct government intervention to cool down the overheated economy. Yet, there are few English articles that threw light on this factor including the commodity housing supply and government behaviour that affect housing price. The second-child policy, which is unique in China, also played an important role in the determination of the housing price. In the articles indexed in CNKI, the second-child rate, willingness to have a second child or having a second child were mentioned in the Chinese articles but not the English ones.Social implicationsIn this paper, the economic, social, administrative and environmental factors were summarised, which basically covered all the factors affecting housing prices. The administrative factors were a special group of factors that affect the housing price because of the country's planned economic system. Secondly, it provided useful information to real estate development enterprises in China. To make a correct investment and management decision, real estate development enterprises must understand the actual situation and possible problems of the industry. In this study, we analysed the research literature on the real estate industry in China for the period from 2015 to 2020 one by one and determined the influencing factors of the housing price, which provided references for effective cost control. Thirdly, it allows the public to understand and grasp the real estate industry. As the housing price has been continuously increasing, the public pays increasing attention to the real estate industry. Through the literature analysis of the impact of real estate prices, this paper revealed the elements of house price expenses, which makes it convenient for ordinary people to understand the real estate industry.Originality/valueThis study allows foreigners who do not know Chinese to know more about factors that drove housing prices from the Chinese perspective. It also provides insights to overseas developers who wish to enter the property market in China. The results can be generalised to other non-English-speaking real estate research.

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Section: Resultsmentioning

confidence: 99%

Factors affect the housing prices in China: a systematic review of papers indexed in Chinese Science Citation Database

Jotikasthira

2022

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“…e correlation degree of each factor is sorted depending on the numerical value and describes the relative changes of the reference sequence and comparison sequence. Generally, if the correlation degree between two factors is large, the changes of construction water and the influencing factors are essentially the same [25,26].…”

Section: Identification Of Key Factors Based On Grey Relationalmentioning

confidence: 99%

Research on the Prediction of the Water Demand of Construction Engineering Based on the BP Neural Network

Peng

Wang

et al. 2020

Advances in Civil Engineering

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The scientific and effective prediction of the water consumption of construction engineering is of great significance to the management of construction costs. To address the large water consumption and high uncertainty of water demand in project construction, a prediction model based on the back propagation (BP) neural network improved by particle swarm optimization (PSO) was proposed in the present work. To reduce the complexity of redundant input variables, this model determined the main influencing factors of water demand by grey relational analysis. The BP neural network optimized by PSO was used to obtain the predicted value of the output interval, which effectively solved the shortcomings of the BP neural network model, including its slow convergence speed and easy to fall into local optimum problems. In addition, the water consumption interval data of the Taiyangchen Project located in Xinyang, Henan Province, China, were simulated. According to the results of the case study, there were four main factors that affected the construction water consumption of the Taiyangchen Project, namely, the intraday amount of pouring concrete, the intraday weather, the number of workers, and the intraday amount of wood used. The predicted data were basically consistent with the actual data, the relative error was less than 5%, and the average error was only 2.66%. However, the errors of the BP neural network model, the BP neural network improved by genetic algorithm, and the pluralistic return were larger. Three conventional error analysis tools in machine learning (the coefficient of determination, the root mean squared error, and the mean absolute error) also highlight the feasibility and advancement of the proposed method.

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“…With rapid urbanization and a continuous increase in population density, the UHI effect has become an important adverse factor affecting the quality of urban life [1][2][3][4][5]. UHI effects caused by highdensity urban buildings have been reported around the world; it has changed the urban thermal environment [6][7][8][9][10]. As a result of its effects on regional climates, urban hydrology, air quality, urban biological distribution and behavior, and many urban ecological processes, the UHI effect has raised a number of ecological and environmental issues [11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

A Least Cumulative Ventilation Cost Method for Urban Ventilation Environment Analysis

Xie

Liu

2020

Complexity

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The correct urban building layout is an important influencing factor in urban ventilation, and the heat island effect has become an important factor affecting the quality of urban life. Optimization of the urban building layout can play a role in mitigating the heat island effect. The traditional ventilation corridor analysis method, based on a least-cost path analysis, can only generate a few main ventilation corridors. It is difficult to obtain global ventilation results covering the whole study area using this method of analysis. On the basis of urban morphology and a least-cost path analysis, this study proposes a “least cumulative ventilation cost” method for analyzing urban ventilation. Taking Wuhan downtown as a research area, the urban ventilation environment under different wind directions and seasons was analyzed. This method can effectively express the ventilation conditions throughout the whole study area and can simultaneously express the quality of the generated corridors effectively. The results show that Wuhan has three levels of ventilation corridor. Moreover, the ventilation conditions in Wuchang (Wuchang, Qingshan, and Hongshan) are better than those in Hankou (Qiaokou, Jianghan, and Jiang’an).

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Grey Spectrum Analysis of Air Quality Index and Housing Price in Handan

Cited by 3 publications

References 13 publications

Factors affect the housing prices in China: a systematic review of papers indexed in Chinese Science Citation Database

Factors affect the housing prices in China: a systematic review of papers indexed in Chinese Science Citation Database

Research on the Prediction of the Water Demand of Construction Engineering Based on the BP Neural Network

A Least Cumulative Ventilation Cost Method for Urban Ventilation Environment Analysis

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