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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.
Cultural diversity and enterprise innovation: A study from the perspective of dialect
Vol 5, Issue 1, 2024
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Abstract
China has a vast territory. The rich and different regional culture formed over the past 5000 years is an ideal scene for the study of "culture and finance". This paper uses the number of urban dialects and dialect differentiation index to measure regional cultural diversity, and empirically studies its impact on enterprise innovation. The results show that in areas with more diverse cultures, private high-tech enterprises will obtain more innovative output. After using instrumental variables to solve endogenous problems and excluding the impact of educational development, the conclusion of this paper is still robust. Further research also found that the greater the difference between different dialects in the city where the company is located, the greater the population inflow, the better the inclusiveness and the higher the level of intellectual property protection, the more significant the impact of dialect diversity on innovation. The conclusion of this paper will help the academic community to understand the non institutional reasons behind the unbalanced economic development of Chinese cities from a new perspective, and also provide empirical evidence from non immigrant countries with deep cultural heritage for the current international academic research on "culture and finance".
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Copyright (c) 2024 Yue Pan, Jinli Xiao, Yiyi Dai
This work is licensed under a Creative Commons Attribution 4.0 International License.
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Prof. Mehmet Cetin
Kastamonu University,
Turkey
