## 5 Conclusions
For example, if the analysis shows that the vacancy rate in a certain area is mainly affected by the lack of nearby employment opportunities, urban planners can formulate policies to attract enterprises to invest in this area, thereby increasing employment and reducing the vacancy rate. In addition, algorithms have also shown their irreplaceable role in predicting future urban vacancy trends and evaluating the effects of policy interventions. The application of algorithms in urban residential vacancy rate observation not only improves the accuracy and efficiency of observation, but also provides strong support for coping with the challenges of complex environments, diverse data sources and urban development needs. It is important to recognize that traditional statistical methods, despite their limitations, retain unique value in scenarios requiring transparency, computational efficiency or handling of small datasets. Future research should explore hybrid approaches that combine the interpretability of traditional methods with the predictive power of advanced algorithms, ensuring robust and actionable solutions for urban housing management. The scientific contribution of this study lies in an innovative multi-source data fusion algorithm framework, which effectively improves the accuracy of vacancy rate observation; finds some new factors affecting the vacancy rate, such as the influence of community cultural activity richness on the vacancy rate; and constructs a more adaptive vacancy rate observation model to better cope with different urban environments and data characteristics. Looking ahead, the study will continue to deepen the application of algorithms in data processing, model building and decision support, and further optimize the algorithm performance, reduce the computational complexity, enhance the interpretability of the model and make it more suitable for the practical application needs. Emerging technologies such as blockchain and quantum computing also hold promise. For example, [PERSON] et al. proposed a blockchain-based framework for secure sharing of vacancy rate data across government agencies, addressing privacy concerns in multi-source fusion [114]. [PERSON] et al. explored quantum computing's potential to accelerate deep learning algorithms for large-scale vacancy rate analysis [115]. Interdisciplinary cooperation should be strengthened and geography, computer science, economics and other multidisciplinary knowledge should be integrated, to provide a more solid theoretical foundation for algorithm innovation. At the same time, it pays close attention to the trend of technological innovation, and actively explores the application of emerging technologies such as blockchain in data security and credibility improvement, as well as the potential impact of quantum computing on the efficiency of algorithms. From the international perspective, the present research results have an important reference significance for the observation of the global urban residential vacancy rate. Cities in different countries and regions are faced with the problem of residential vacancy in the development process. The algorithm application experience and multi-source data fusion method in this study can be extended to other cities to help them accurately observe the vacancy rate and optimize the allocation of housing resources. Based on this study, other researchers can further expand the application of the algorithm in different cultural backgrounds and policy environments, explore more universal observation models of vacancy rate, and promote the observation research of urban residential vacancy rate to a deeper and broader field. While this study highlights the potential of multi-source data fusion and adaptive algorithms, empirical cases are still concentrated in a few megacities (e.g., Shenzhen, Xiong'an). There is a lack of universal validation for smaller cities or regions with diverse cultural and policy contexts.