Asia Pacific Academy of Science Pte. Ltd. (APACSCI) specializes in international journal publishing. APACSCI adopts the open access publishing model and provides an important communication bridge for academic groups whose interest fields include engineering, technology, medicine, computer, mathematics, agriculture and forestry, and environment.
As China's pillar industry, the property market has suffered a considerable impact in recent years, with a decline in turnover and many developers at risk of bankruptcy. As one of the most concerned factors for stakeholders, housing prices need to be predicted more objectively and accurately to minimize decision-making errors by developers and consumers. Many prediction models in recent years have been unfriendly to consumers due to technical difficulties, high data demand, and varying factors affecting house prices in different regions. A uniform model across the country cannot capture local differences accurately, so this study compares and analyses the fitting effects of multiple machine learning models using February 2024 new building data in Changsha as an example, aiming to provide consumers with a simple and practical reference for prediction methods. The modeling exploration applies several regression techniques based on machine learning algorithms, such as Stepwise regression, Robust regression, Lasso regression, Ridge regression, Ordinary Least Squares (OLS) regression, Extreme Gradient Boosted regression (XGBoost), and Random Forest (RF) regression. These algorithms are used to construct forecasting models, and the best-performing model is selected by conducting a comparative analysis of the forecasting errors obtained between these models. The research found that machine learning is a practical approach to property price prediction, with least squares regression and Lasso regression providing relatively more convincing results.
Occupational diversity in Chinese cities: facts, evolution and policy implications
Vol 5, Issue 1, 2024
VIEWS - 151 (Abstract)
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Abstract
Measuring the occupational diversity and trend of Chinese cities from the perspective of the distribution of urban occupational types, and discussing the impact of urban characteristics on urban occupational diversity can provide reference for guiding urban full and high-quality employment. By using the improved Herfindahl index, panel data fixed effect model and Oaxaca blinder decomposition, it is found that the occupational diversity in Chinese cities showed a trend of first decreasing and then increasing from 2002 to 2016, which was due to the adjustment of urban industrial structure and the emergence of new economy after the financial crisis; at the same time, the larger the city, the higher the administrative level and the more developed the economy, the higher the degree of occupational diversity. The conclusion of the study is helpful to intuitively understand the occupational distribution, industrial structure and division of labor in different cities, evaluate the human resource structure and economic development potential of cities, and then provide policy suggestions for different cities to formulate industrial development and human resource planning.
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Prof. Mehmet Cetin
Kastamonu University,
Turkey