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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.
Dialect diversity, element agglomeration and city size—Empirical Test Based on satellite lighting data
Vol 4, Issue 1, 2023
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Abstract
Since the reform and opening up, China's urbanization has developed rapidly Behind the rapid urbanization is the imbalance and insufficiency of urban development From the perspective of social and cultural diversity, this paper explores the impact of the diversity of dialect types on urban scale The diversity of dialects leads to the division of trust, hinders the cross regional flow of factors, affects the agglomeration effect of factors, and then affects the expansion of urban scale Using the diversity index of regional dialect types and 2016 nppviirs urban night light index, this paper empirically studies the impact of dialect diversity on urban scale The measurement results show that: Dialect diversity has a significant negative impact on urban scale On average, the addition of one dialect category will lead to a 4.5% decline in the size of the city measured by the night light index 55%. A series of robustness tests and causal identification show that the estimation result in this paper is the causal relationship of robustnessfurther empirical research shows that dialect diversity mainly affects the expansion of urban scale by hindering the flow and agglomeration of labor, capital and technological factorsthe Enlightenment of this study: To build a diversified and inclusive modern city, we need to weigh the costs and benefits of cultural diversity and unity, break cultural barriers, eliminate cultural prejudices, improve social trust, and give full play to the complementary effects brought by multiculturalism
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Copyright (c) 2023 Congming Ding, Xueyang Huang, Min Zhou
This work is licensed under a Creative Commons Attribution 4.0 International License.
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Prof. Mehmet Cetin
Kastamonu University,
Turkey
