# The Impacts of Open Data and eXplainable AI on Real Estate Price Predictions in Smart Cities
## 4 Discussion
### Synergy of Proprietary and Open Data in Real Estate Price Prediction  
The SHAP heatmap plot from Figure 7 allows us to see which features are most important across all instances and how each feature's value affects the model's predictions for each individual instance. It highlights the multifaceted nature of real estate pricing, where many factors interact in complex ways to influence the final property valuation. The SHAP heatmap visually represents the dual impact of proprietary and open data on real estate price predictions. Proprietary data, which include specific details about a property like its size (PGA), condition, and exact location (Latitude, Longitude, and ZIP7), directly and significantly impact price predictions due to the close relation to the property's inherent qualities. For example, PGA's color intensity and spread underscore its role as a primary valuation factor, reflecting the market's emphasis on property size. These factors are primary considerations for buyers and sellers, reflected in their strong influence as indicated by SHAP values in the predictive model. Figure 7: SHAP value heatmap for feature impact analysis in real estate price Prediction: This heatmap displays the distribution of SHAP values for each feature across 4508 instances in the test dataset. Each row represents a feature such as PGA, Longitude, or EPC, and each column corresponds to an instance. The color intensity reflects the impact of the feature on the model’s prediction for that instance, with red indicating a higher positive impact and blue indicating a negative impact. The black line graph at the top illustrates the distribution of prediction outputs (\(f(x)\)) across all instances. Features are ordered vertically by their overall impact on model output. This visual analysis helps identify which features are most influential and how they vary across different property instances, providing insights into the predictive behavior of the model. Open data, conversely, offers essential contextual information encompassing socioeconomic, environmental, and infrastructural aspects of a property's surroundings and displays a more varied influence across the heatmap. This information includes accessibility to cultural amenities, public transportation, and economic indicators. The shades and distribution of colors for features like BankEval and proximity to transportation reveal their conditional importance. These features may not always exhibit a strong, direct impact like the proprietary data, but they are critical for capturing the nuanced interplay of external factors contributing to a property's market value. For instance, the impact of proximity to cultural amenities varies widely across the dataset, as seen in the SHAP heatmap, suggesting that cultural access is a significant value driver in this market. The SHAP analysis also highlights the synergy between these two data categories. While proprietary data points to the inherent value based on property specifics, open data encapsulates external factors that can either amplify or diminish this inherent value. Stakeholders such as investors and urban planners can leverage this insight for targeted interventions; for example, improving public transportation links might increase property values in a given area, an insight that can be inferred from the model's sensitivity to these features. Furthermore, the heatmap's patterns reveal how integrating proprietary and open data in the model enables a more detailed real estate market segmentation. The varying SHAP values across properties for open data features suggest that the importance of specific amenities can differ significantly depending on the property's location and target demographic. The model achieves a more holistic understanding of property valuation by integrating these data types. Proprietary data provide detailed insights into the property, while open data adds breadth, reflecting the property's broader context and external influences.