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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 and market integration: From the perspective of city circle
Vol 5, Issue 1, 2024
Issue release: 31 December 2024
VIEWS - 4954 (Abstract)
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Abstract
China, as a transitional economy, faces a high level of market segmentation among administrative regions, which lowers the efficiency of resource allocation and the total factor productivity (TFP) rate. The literature has focused on the negative effects of local protectionism and administrative division on the formation of market integration in the economic growth process. However, considering that administrative districts such as prefectures usually overlap with cultural regions in China, the effects of local protectionism and administrative division on market segmentation may be overestimated because cultural diversity may also be negatively related to market integration. More importantly, diversity of dialect tends to increase the cost of communication, making it a barrier to labor migration and decreasing the level of generalized trust among people. As a result, it may have adverse effects on the market integration process. Recently, more empirical works have explored the relationship between cultural diversity, which they usually measure as the number of dialects and amount of economic growth in the region, and have generally shown consistent results. For example, a study has shown that dialect diversity has adverse effects on GDP per capita. Another finds that dialect diversity and not genetic differences can explain regional disparities in China to a large extent. Similarly, Scholars indicates that dialect diversity has adverse effects on the productivity of neighboring counties. Nevertheless, to the best of our knowledge, few works reveal the impact of dialect diversity on the level of market segmentation among regions in China. Taking a somewhat different approach, we directly focus on the effects of dialect diversity on market segmentation. Empirically, to estimate the causal effects of dialect diversity on market segmentation, we randomly build the synthetic metropolitan area as the fundamental analysis unit in which a core prefecture borders several other governorates. Consequently, within the artificial metropolitan area, the number of dialects and amount of market segmentation can be measured. Given that the synthetic metropolitan area does not belong to any particular administrative district, the differences in market segmentation between synthetic metropolitan areas are attributed to variations in dialect and other economic or geographic factors rather than the administrative division between areas. Based on the method developed, this paper uses the seven categories of retail prices in prefectures in 2016 to calculate the market segmentation index of each synthetic metropolitan area, which it takes as the dependent variable. Furthermore, this paper constructs a dialect diversity index for each synthetic metropolitan area, which it takes as the key independent variable. The results show that diversity of dialect is a critical factor in lowering the amount of market integration in China. The findings are robust to various checks. Furthermore, this paper takes the number of local theatrical genres as an instrumental variable of dialect diversity. The instrumented estimations show that a one-dialect increase in the synthetic metropolitan area increases the amount of market segmentation by about 2.42%. The amount of market segmentation in the synthetic metropolitan area, which has the average number of dialects, is 8.23% higher than in areas with only one dialect. The empirical results imply that it is essential to weaken local protectionism and enhance cultural integration between regions to decrease market segmentation. This paper makes three contributions to the literature. First, it enriches the broad interpretations of the causes of market segmentation from the dialect diversity viewpoint. Second, it directly estimates the effects of dialect diversity on market segmentation and determines the long-term effects of cultural factors, providing new cultural economics evidence from China. Third, this paper contributes to the literature analyzing the underlying mechanisms behind dialect diversity and growth, suggesting that market segmentation is another mechanism used to understand this causal relationship.
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Prof. Mehmet Cetin
Kastamonu University,
Turkey