# The Impacts of Open Data and eXplainable AI on Real Estate Price Predictions in Smart Cities
Currently, XAI technologies are being developed and applied in smart city projects, focusing on traffic volume prediction, population estimation, and urban analytics [7; 46] by leveraging the benefits of AI while ensuring responsible and accountable use in urban governance [38]. In this study, we use a cutting-edge technique within XAI--the SHapley Additive exPlanations (SHAP) by [PERSON] and [PERSON] [47]. The SHAP framework is grounded and derived from the Shapley values [48], a concept rooted in cooperative game theory that offers a consistent and locally accurate method to interpret model predictions. In machine learning, SHAP provides a unified measure of feature importance by assigning each feature an importance value for a particular prediction. SHAP values are the SHAP method's output, quantifying each feature's contribution to a particular prediction. These values give an intuitive understanding of feature contributions toward individual predictions, thereby lifting the veil on machine learning models. The SHAP value for a feature represents the average contribution of that feature to every possible prediction. These model-agnostic values are applicable for interpreting the results from any machine learning model, and they are trendy for tree-based models, such as the eXtreme Gradient Boosting (XGBoost) we use in this study. These values can explain why a model made a specific decision and offer a consistent and locally accurate way to explain the outputs of ML models [40; 49]. This study's objective is to leverage open data effectively for improving real estate price prediction. Concurrently, it aims to enhance the predictive model's transparency and interpretability by applying XAI, understanding which features are most influential, and measuring their impact on the model's price prediction for the property listings in our dataset. This dual approach seeks to bridge the gap between advanced model performance and the ability to interpret this model. Open data provides broader and more diverse data for precision analysis, while XAI will offer insights into how model predictions are made, thereby fostering trust and accountability in real estate analytics. We demonstrate that increased model interpretability can coexist with high prediction accuracy via systematic experimentation and detailed evaluations and offer practical benefits for urban planning and smart city development. In our three-phase approach, the proprietary data are the starting point for the analysis, which are then augmented by open data. We first train an XGBoost model with features from a proprietary dataset for predicting real estate prices and provide the rationale behind selecting XGBoost over other considered models. The second phase entails retraining this model by incorporating open data features. This method provides the ability to evaluate the consequential impact on the model's predictive accuracy and identify which features significantly affect real estate prices. We also explore the integration of Optuna--a hyperparameter optimization framework, with our XGBoost machine learning model and fine-tune it. In the third phase, we leverage SHAP values analysis to explain our model predictions effectively. This step facilitates a profound exploration of feature interactions and quantifies their relative importance. This approach is intended not only to advance the field of real estate analytics but also to contribute to more informed decision making in urban development, aligning with the needs of rapidly evolving housing markets. Our results reveal that integrating open data features significantly enhances model predictive power, with open data features such as bank evaluations, culture, and subway distance displaying the most substantial impact on real estate prices. The findings of this study add to the understanding of the symbiotic relationship between the real estate market and urban dynamics.