## 4 Multi-Source Data Fusion Technology and Algorithm
### Application of Multi-Source Data Fusion Algorithm in Real Estate Management  
In the construction of smart cities for real estate management, the application of multi-source data fusion algorithms is crucial. Among them, the fusion of sensor data and GIS data provides real-time environmental monitoring and urban spatial analysis. Integrating real-time monitoring information and geospatial data promotes application scenarios such as urban planning, disaster warning, and ecological monitoring and improves decision-making efficiency [35]. However, the fusion process faces challenges such as data quality and privacy security. At the same time, the combination of social media data and citizen feedback data enables managers to have a more comprehensive understanding of the use of urban real estate and make scientific decisions. Through data preprocessing and fusion algorithms, the real estate market can be analyzed, urban facilities can be optimized, and community governance and public services can be improved. These two data fusion schemes provide strong support for real estate management, but in practical applications, attention should still be paid to data quality and compliance to achieve the sustainable development of smart cities. The comparison of application cases of sensor data and GIS data, social media data, and citizen feedback data in real estate management is shown in Table 3.  
In the construction of smart cities, multi-source data fusion algorithms are crucial for real estate management and urban optimization but face many challenges in actual operations [38]. For example, heterogeneous data sources (such as sensors, social media, citizen feedback, and GIS data) have significant differences in format, scale, semantics, and quality, which hinders effective integration. To this end, data preprocessing techniques (such as cleaning and standardization) need to be used to improve data quality and consistency and lay a good foundation for subsequent analysis. When processing massive data, the computational complexity and storage requirements are high, which may lead to performance bottlenecks. Therefore, distributed and cloud computing technologies can be used to achieve parallel processing and improve efficiency while reducing storage requirements through data compression and indexing [39]. In the context of smart city operations, the protection of privacy and data security is paramount [1]. We must be vigilant against topical security threats such as cyber-attacks, data breaches, and unauthorized \begin{table}
\begin{tabular}{c c} \hline \hline
**Data Fusion Type** & **Application Cases** \\ \hline \multirow{4}{*}{Fusion of sensor data and GIS data [36]} & 1. Real-time monitoring of urban area environment to support decision-making and urban planning. \\ \cline{2-3}  & 2. Extract and analyze geographic features, such as land use and traffic conditions, to assess real estate value. \\ \cline{2-3}  & 3. Optimize traffic management, adjust traffic signals through data fusion, and improve circulation efficiency. \\ \hline \multirow{4}{*}{Fusion of social media data and citizen feedback data [37]} & 1. Real estate market analysis, combined with housing supply and residents’ satisfaction, provides a basis for policy-making. \\ \cline{2-3}  & 2. Optimize urban supporting facilities and analyze the service and transportation convenience of different areas. \\ \cline{2-3}  & 3. Community governance: timely addressing of environmental and safety issues and improving the quality of life of residents. \\ \cline{2-3}  & 4. Optimize public services, analyze citizen feedback, and improve the pertinence and effectiveness of government services. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of application cases of sensor data and GIS data, social media data, and citizen feedback data in real estate management. access, which can compromise sensitive information and disrupt urban services [2].