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Accurate prediction of urban residential rents is of great importance for landlords, tenants, and investors. However, existing rents prediction models face challenges in meeting practical demands due to their limited perspectives and inadequate prediction performance. The existing individual prediction models often lack satisfactory accuracy, while ensemble learning models that combine multiple individual models to improve prediction results often overlook the impact of spatial heterogeneity on residential rents. To address these issues, this paper proposes a novel prediction model called GERPM, which stands for Geographically Weighted Stacking Ensemble Learning-Based Urban Residential Rents Prediction Model. GERPM comprehensively analyzes the influencing factors of residential rents from multiple perspectives and leverages a geographically weighted stacking ensemble learning approach. The model combines multiple machine learning and deep learning models, optimizes parameters to achieve optimal predictions, and incorporates the geographically weighted regression (GWR) model to consider spatial heterogeneity. By combining the strengths of deep learning and machine learning models and taking into account geographical factors, GERPM aims to improve prediction accuracy and provide robust predictions for urban residential rents. The model is evaluated using housing data from Nanjing, a major city in China, and compared with representative individual prediction models, the equal weight combination model, and the ensemble learning model. The experimental results demonstrate that GERPM outperforms other models in terms of prediction performance. Furthermore, the model's effectiveness and robustness are validated by applying it to other major cities in China, such as Shanghai and Hangzhou. Overall, GERPM shows promising potential in accurately predicting urban residential rents and contributing to the advancement of the rental market. [ABSTRACT FROM AUTHOR]

With the rise of a new generation of technology and industrial changes, the service-oriented manufacturing industry has become the direction of future development. With the background of new manufacturing, this paper constructs an economic development threshold model of employment density of consumer goods industry based on data from Shanghai and Tokyo from 2007 to 2016, and empirically analyzes the impact of the employment density of the consumer goods industry on urban economic development under different population densities. At the same time, by comparing the experience of Tokyo, the development status and prospects of Shanghai's consumer goods industry are explored. The study found that the threshold of Tokyo's consumer goods industry is 0.608. When population density is lower than this threshold, the consumer goods industry continues to promote the economic development of Tokyo; however, when the population density is higher than this threshold, the consumer goods industry begins to inhibit the economic development of Tokyo. The Shanghai consumer goods industry threshold is 0.329. Under the threshold, most of the consumer goods industry contributions to the economy are negative, but above the threshold, they begin to show a positive trend. The inflection point of the effect curve of Tokyo's consumer goods industry on economic development has appeared, but the inflection point of Shanghai's consumer goods industry has not yet appeared. Compared with Tokyo, the economic vitality of Shanghai's consumer goods industry has not yet been fully released. With the continued increase of population density in Shanghai, the growth potential of the consumer goods industry is huge, and it is expected to reshape the flourishing age of Shanghai's light industry brand. [ABSTRACT FROM AUTHOR]