# The Impacts of Open Data and eXplainable AI on Real Estate Price Predictions in Smart Cities
## 3 Results
### Comparative Analysis of Predictive Models  
Our comparative analysis aims to discern the most performant model for predicting real estate prices, a crucial step toward enhancing the accuracy and reliability of real estate analytics, particularly in the context of this study's dataset and regression task. To this end, we systematically evaluated a selection of commonly employed predictive models in real estate analytics--XGBoost [11], Random Forest Regression (RFR) [92], AdaBoost [32], and artificial neural networks (ANN) [30]--using a comprehensive suite of evaluation metrics. These metrics, including Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), and the R\({}^{2}\) coefficient, were applied across training and testing datasets to measure each model's performance in terms of accuracy, consistency, and ability to generalize to new data.  
Our analysis highlighted XGBoost as the standout model, particularly excelling in test dataset generalization, a key indicator of its robustness against overfitting and superior predictive accuracy. While Random Forest showed promise in training performance, XGBoost's consistent superiority across training and testing underscored its balanced approach to error reduction and precision in prediction. Conversely, despite showing potential, AdaBoost and ANN fell short of XGBoost's benchmarks, with ANN notably underperforming in predictive accuracy despite its theoretical capacity to model complex, non-linear relationships, suggesting a misalignment with the specific characteristics of our real estate dataset.  
Table 3 succinctly presents our comparative findings, underlining XGBoost's optimal balance between training performance and testing applicability, particularly its lower error rates and higher R\({}^{2}\) values on unseen data. These results advocate for XGBoost's selection and underscore its potential in real-world applications, where generalizing from historical to future data is crucial.  
Choosing XGBoost aligns with our broader research objectives, specifically enhancing model transparency and interpretability via Explainable AI (XAI) techniques. This approach  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Metric** & **XGBoost** & **RFR** & **AdaBoost** & **ANN** \\ \hline \multirow{4}{*}{Train} & MAE & 40,821.22 & 36,166.76 & 73,917.53 & 63,803.96 \\  & MAPE & 16.66 & 14.34 & 31.27 & 58.37 \\  & RMSE & 55,568.64 & 51,184.51 & 95,210.09 & 91,295.80 \\  & R\({}^{2}\) & 0.87 & 0.89 & 0.62 & 0.65 \\ \hline \multirow{4}{*}{Test} & MAE & 51,733.88 & 52,678.57 & 74,764.45 & 65,366.37 \\  & MAPE & 19.58 & 19.74 & 30.43 & 58.31 \\ \cline{1-1}  & RMSE & 72,488.87 & 74,482.71 & 97,492.31 & 94,369.38 \\ \cline{1-1}  & R\({}^{2}\) & 0.79 & 0.78 & 0.62 & 0.64 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparative performance of predictive models on real estate proprietary data.  
increases the utility of XGBoost and provides stakeholders in the real estate sector with clear, understandable insights into the factors influencing price predictions. The selected model's robustness and XAI integration significantly strengthen the credibility and trustworthiness of our study's findings.  
In conclusion, the systematic evaluation of predictive models supports the selection of XGBoost for its adept handling of real estate data complexities. By achieving an ideal equilibrium between minimizing overfitting risks and maximizing predictive accuracy, XGBoost stands out as the benchmark, meeting our analysis criteria. The implications of this choice extend far beyond this study, setting a foundation for innovative approaches to predictive modeling in real estate analytics.