## 1 Introduction  
In the context of the rapid development of smart cities, multi-source data fusion and analysis algorithms play a vital role in optimizing real estate management, which encompasses activities such as property valuation, market analysis, investment strategy, regulatory compliance, and urban efficiency. The urbanization process brings challenges such as population growth, resource shortages, and environmental pollution, and smart city construction is considered to be an effective way to solve these problems [1]. At present, urban real estate management is restricted by information asymmetry, low resource utilization efficiency, and unreasonable decision-making, which affects its sustainable development [2]. Multi-source data fusion technology can integrate different types of data from sensors, social media, public feedback, and GIS to achieve comprehensive and real-time information perception, thereby providing a scientific decision-making basis for real estate management [3]. Through deep mining of data, analysis algorithms can effectively provide urban managers with support for evaluation, planning, and supervision, improve the efficiency of real estate management, optimize urban spatial layout, and promote the development of cities towards sustainability and intelligence [4]. A large number of studies have shown that multi-source data fusion and analysis algorithms have many key meanings in smart cities. First, multi-source data fusion technology enables governments and enterprises to better control real estate information and improve the efficiency of management and decision-making. Secondly, through data analysis, urban planners can deeply explore the laws of urban space utilization, thereby achieving more efficient resource allocation [5]. Furthermore, these technologies are capable of identifying a range of potential issues in urban development, such as traffic congestion, inadequate housing, and inadequate public services. For instance, in the case of traffic congestion, smart data analysis can help in optimizing traffic flow by predicting peak hours and suggesting alternative routes. In terms of housing, multi-source data can inform policymakers about the demand and supply dynamics, enabling them to develop strategies to address housing shortages. Additionally, these technologies can provide insights into the distribution and quality of public services, aiding in the equitable allocation of resources. By uncovering such challenges, these technologies offer policymakers targeted solutions, thereby fostering the green and sustainable development of cities [6]. The quality of life of residents is also improved by these technologies, and public service facilities can be optimized by better understanding residents' needs [7]. In addition, research and development of multi-source data fusion and analysis algorithms will help promote the innovation of related technologies and provide continuous technical support for the construction of smart cities [8]. This study provides a theoretical basis and practical guidance for solving the challenges of urban real estate management, optimizing spatial layout, and enhancing sustainable development capabilities. In this literature review, we review the application of multi-source data fusion and analysis algorithms in smart city construction to assist real estate management and urban optimization. In Section 2, we discuss in detail the multi-source data in smart city construction, including various data sources such as sensors, social media, citizen feedback, and GIS data. This section focuses on analyzing the sources, characteristics, and challenges faced in practical applications of these data, which lays the foundation for data fusion and analysis in subsequent sections. In Section 3, we discuss the principles and methods of different data fusion technologies, including data preprocessing, data matching and association, and various data fusion algorithms.