The accuracy of housing price predictions can vary widely based on several factors, including the model used, the quality of the data, and the specific market conditions. Common metrics for evaluating prediction accuracy include:
- Mean Absolute Error (MAE): Measures the average magnitude of errors in predictions, without considering their direction.
- Median Absolute Error (MedAE): Similar to MAE but focuses on the median, which can be more robust to outliers.
- R-squared (R²): Indicates how well the model explains the variability of the target variable.
In practice, models can achieve reasonable accuracy, but results can differ significantly across different datasets and regions. It's essential to validate models with appropriate metrics and cross-validation techniques to assess their predictive performance.
