# The Impacts of Open Data and eXplainable AI on Real Estate Price Predictions in Smart Cities
## 3 Results
### Model Training and Optimization
Overall, the Open Data Model is engineered for precision and adaptability, aligning with the dynamic nature of real estate markets. It deftly combines detailed pattern detection with strategies to ensure reliability and generalizability, making it suitable for high-stakes, varied market predictions. Figure 2 depicts the price prediction error plots for the Baseline and Open Data Models. It shows how well the predicted values (on the y-axis) match the true values (on the x-axis). A perfect prediction would mean all points lie precisely on the dashed line, where the predicted value equals the true value. Several insights emerge by examining the price prediction error plots for both models. The Baseline Model plot shows a broad dispersion of predictions as the true values increase, indicating increased difficulty in predicting higher-priced properties. There are distinct outliers, especially for higher true values, suggesting the model may not capture all the factors influencing higher real estate prices. In the Open Data Model plot, the tighter clustering of points around the dashed line indicates that the model predictions are generally more accurate across the full range of prices. Its test points do not exhibit as much scatter relative to the training points as in the Baseline Model, which might indicate better generalization. The improved performance in the Open Data Model suggests that incorporating open data features enhanced the model's precision. Its consistent performance across different price ranges implies a better understanding of the underlying relationships in the data. The prediction errors concerning the predicted values can be assessed by analyzing the residual plots of the Baseline and Open Data Models (Figure 3). The price prediction error plots and the residual plots provide a comprehensive picture of each model's performance. Figure 2: Comparative analysis of price prediction accuracy between XGBoost models: This figure presents two scatter plots comparing predicted versus true values of real estate prices, with the train data in blue and the test data in green. Plot (**a**) shows the performance of the Baseline Model, while plot (**b**) depicts the Open Data Model. Each plot contains points representing individual predictions. The closer the points are to the dashed line of perfect fit (where predicted values equal true values), the more accurate the predictions. The Baseline Model shows a broader dispersion of points, indicating less accuracy, while the Open Data Model’s points are more concentrated around the identity line, suggesting higher accuracy in predictions. The residual plot for the Baseline Model shows a noticeable spread in residuals as the predicted property values increase, which could suggest a heteroscedasticity pattern, where the errors' variability increases with the prediction's magnitude. This pattern might indicate that the model's predictive accuracy is not consistent across the range of property values. The presence of residuals with large magnitudes, particularly on the higher end of the predicted values, highlights the model's limitations in accurately predicting more expensive properties. For the Open Data Model, the residuals are more tightly clustered around the zero line, suggesting more consistent prediction errors across different values. This homoscedastic pattern indicates that the model's performance is more uniform, regardless of the property's value. The residuals are distributed more symmetrically around the horizontal axis, implying fewer systematic biases in prediction. When comparing the two models, it is evident that the Open Data Model provides a more uniform performance with minor errors across the entire range of predicted values. These facts suggest that the Open Data Model is more robust, potentially due to the inclusion of the additional features of open data.