# The Impacts of Open Data and eXplainable AI on Real Estate Price Predictions in Smart Cities
## 3 Results
### Model Evaluation  
After training and hyperparameter optimization, we evaluate the performance of the Baseline and Open Data Models using various metrics for a holistic assessment--MAE, MAPE, RMSE, and R\({}^{2}\), computed for the test set. Subsequently, a comparative analysis of the model's performance and reliability is made by juxtaposing both models' performance metrics. We calculate and discuss the Reduction/Improvement (%) in predictive precision, clearly showing how open data contributes to our model. Via this dual approach of an individual (standalone) performance evaluation and comparative analysis, we offer a comprehensive overview of the models' capabilities and quantitatively assess the value added by the open data features. The calculated performance metrics for the Baseline and Open Data Models in Table 5 provide quantitative insights into their predictive capabilities for real estate prices. The performance metrics calculated on the test set estimate how the models will likely perform in real-world scenarios, thereby providing an understanding of the model's value. The Baseline Model's performance on the test set, with an MAE of 51,733.88 and a MAPE of 19.58%, indicates that while it can capture some of the trends in real estate pricing, its predictions may not be reliable enough for precise financial decision making. The RMSE value of 72,488.87 further suggests that the model is particularly challenged by properties with higher prices or unique features not well-represented in the testing data. An R\({}^{2}\) of 0.79 shows that the model has a fair predictive capacity but also indicates that about a fifth of the variance in housing prices is left unexplained. In contrast, the Open Data Model's performance metrics show notable improvements. The reductions in MAE and MAPE by 8.24% and 9.36%, respectively, mean that the predictions are closer to the actual selling prices with fewer outliers, which can be particularly valuable for investors and realtors seeking to price properties or forecast market trends. The RMSE reduction of 5.71% indicates fewer significant errors, which is essential for evaluating high-value properties. The increase in R\({}^{2}\) by 2.96% reflects a better fit to the data, suggesting that incorporating open data has provided the model with additional explanatory power to account for the price variability. The performance gains from the Open Data Model suggest that the features derived from open data sources provide a more nuanced view of the real estate market. These improvements in the model's predictive capabilities are due to the additional data giving more detailed neighborhood characteristics, such as enhanced mobility, comprehensive quality of life, well-being assessments, and improved governance, macroeconomic, and financial indicators not accounted for in the Baseline Model. To summarize, the Open Data Model's improved accuracy makes it a more robust tool for stakeholders in the real estate market, offering more reliable predictions and insights. The gains in precision and reduced errors highlight the value of incorporating open data into predictive models. These results underscore the significance of using open data in real estate AI-driven solutions, particularly for smart cities. Integrating diverse open data sources can provide superior models with more accurate and insightful predictions. These model improvements can lead to better-informed decisions in real estate investments, policymaking, and market analysis.