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.
Foreign human capital, cultural diversity and urban innovation in China
Vol 5, Issue 1, 2024
VIEWS - 3937 (Abstract)
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Abstract
From the perspective of cross regional flow of human capital, this paper studies the impact and mechanism of foreign human capital on urban innovation in China, and reveals the innovation effect of foreign human capital. The theoretical research shows that the efficient allocation of innovation elements brought by foreign human capital and the diversified externalities associated with it are the source for cities to maintain innovation vitality and competitiveness. The empirical study matching the individual micro data of Chinese census with the urban patent data found that foreign human capital has a significant role in promoting urban innovation, especially in the innovation of invention patents with the highest technological content. Further research on the mechanism reveals that the externality of cultural diversity brought by foreign human capital is an important channel to promote urban innovation. Therefore, the absorption and integration of foreign labor is the key to the city full of innovation and vitality.
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Prof. Mehmet Cetin
Kastamonu University,
Turkey
