# The Impacts of Open Data and eXplainable AI on Real Estate Price Predictions in Smart Cities
AI plays a fundamental role in sectors like engineering and construction, helping to address housing challenges and infrastructure development, thereby improving the quality and efficiency of urban services [1]. The development of smart cities significantly impacts the housing market, affecting factors such as housing supply, prices, and quality. Studies have shown that the availability of smart cities' housing infrastructure and quality are generally higher than in the surrounding regions [17]. Smart cities prioritize the development of physical infrastructure and digital technologies for urban management, resulting in improved housing resources [18]. Therefore, these cities offer more extensive and better housing infrastructure than the provinces they are located in, indicating a higher standard of living for their residents [19]. Additionally, smart cities focus on convenience and sustainability in housing, incorporating smart systems for heat supply, water supply, sewage networks, and the use of energy-efficient building materials [15; 20]. The housing market dynamics in smart cities are influenced by various factors such as accessibility, diversity of amenities, and sustainability [15; 21]. The concept of smart cities emphasizes the importance of walkability and accessibility to different services in determining the price of a property [22; 23]. Factors like distance to key locations and the number of commercial establishments within a certain radius can significantly impact a house's price per meter value [19]. Additionally, the housing market in major cities tends to experience more volatility and higher price fluctuations than in other regions [24]. Differences in housing market characteristics, regulations, and cultural preferences also contribute to variations in housing prices and supply across regions [25]. Overall, the housing market in smart cities is influenced by a combination of physical attributes, location-specific factors, and market dynamics, making it a complex and evolving sector. On the other hand, the housing supply in smart cities is influenced by a complex interplay of various factors, including financing costs, construction costs, vacancy rates, and regulatory constraints [26]. Studies have shown that financing costs, such as interest rates, have a negative effect on housing starts--a crucial economic indicator, as it reflects the health and direction of the housing market and, by extension, provides insights into broader economic conditions [12]. Similarly, construction costs, including material and wage costs, can also affect the housing supply, although the results are inconclusive in some cases [19]. Vacancy rates and sales delays have been found to have a negative impact on housing starts, indicating that longer selling times can lead to fewer housing starts [20]. Regulatory constraints, such as land regulation and planning controls, can influence the housing supply, with more restrictive regulations leading to fewer housing starts [24]. The housing market in smart cities is not always perfectly competitive, and policy interventions may be necessary to maximize social welfare and ensure affordability and inclusion [19; 24]. Other factors also play crucial roles in measuring and understanding housing supply, such as housing stock, demolitions and renovations, market availability, and geographical distribution. In essence, these factors highlight the complex dynamics involved in the housing supply of smart cities, and understanding these dynamics is crucial for promoting a sustainable housing market and achieving the goals of smart city initiatives. The real estate market, characterized by its complexity and dynamism, necessitates using advanced predictive tools capable of deciphering the interplay of numerous variables. In recent years, tree-based machine learning models and artificial neural networks (ANN) have emerged as front-runners to enhance prediction accuracy for real estate prices [27; 28; 29; 30; 31].