# The Impacts of Open Data and eXplainable AI on Real Estate Price Predictions in Smart Cities
## 5 Conclusions  
This study made significant strides in applying AI for real estate price prediction within smart cities, particularly by leveraging proprietary and open data synergy. Our findings reveal that incorporating open data about socio-economic indicators and infrastructure,such as public transport links and cultural facilities, plays a pivotal role in enhancing the accuracy of an XGBoost model, as evidenced by the 8.24% improvement in the MAE. The application of SHapley Additive exPlanations (SHAP) has provided transparency into the model's decision making, elucidating the weighted significance of each variable. Proprietary data on a property's size and exact location have emerged as critical predictors of real estate values. Meanwhile, open data variables like accessibility to amenities and economic health indicators have been instrumental in capturing the contextual nuances of the property's environment. The practical implications of these results are far-reaching for urban planners and policymakers. Integrating diverse data sources can refine urban development strategies, ensuring they are grounded in a comprehensive understanding of the factors driving real estate values. This approach can support informed decision making that promotes sustainable and equitable growth, allowing for more targeted and effective urban planning policies that cope with the evolving needs and trends of the real estate market. Furthermore, our conclusions emphasize the importance of transparency in AI-driven analytics. Tools such as SHAP heatmaps increase model accountability and serve as a bridge in communication with non-technical stakeholders, making complex AI assessments more accessible and understandable. However, the study acknowledges certain limitations, including the model's reliance on the quality and availability of open data, which can vary significantly across different contexts. Those data limitations could be subject to speculation about potential biases inherent in both our proprietary real estate data and the open data about points of interest (POIs) and macroeconomic indicators. For instance, proprietary real estate data may be biased toward properties listed on a specific platform and might not reflect the market as a whole. Open data on POIs could exhibit a geographical bias, being more detailed in urban areas than peri-urban ones, potentially skewing the model's performance across different regions. Also, the absence of detailed socioeconomic and demographic data might limit the model's ability to accurately predict prices in diverse communities. For example, the model might not account for the impact of gentrification, changes in neighborhood demographics, or residents' economic mobility, which can significantly affect real estate values and might affect the generalizability of our findings. Furthermore, changes in housing policy, zoning laws, and real estate regulations could affect market prices. Such changes can have significant, sometimes rapid, impacts on real estate values and market dynamics, aspects that our model may not entirely capture. Therefore, further refinement of our model may be necessary to reflect these impacts accurately. The study also has temporal limitations. The data gathered from 2018 to 2021 provide a snapshot of the market during this period. Longer-term trends and cyclical market behaviors may not be fully captured within this timeframe. Additionally, this timeframe may not reflect the impact of rapid technological advancements and changing market dynamics on real estate prices. For instance, emerging trends such as remote work could alter the attractiveness of different locations after 2021, potentially shifting demand and prices in ways our model might not accurately foresee. By advancing the field of XAI and highlighting the value of open data, our study contributes to the responsible and informed use of AI in developing smart cities.