In this project, the core model used was a regression model, aimed at predicting future home values for various regions based on historical data. Here's a more detailed breakdown of the model and its outcomes:
Model Selection and Tuning: The model chosen for the task was likely a Random Forest Regressor or similar ensemble method due to its robustness in handling non-linear relationships and its ability to avoid overfitting. The model was trained using features like historical home values, regional data (Zip codes, Metro areas, etc.), and economic indicators. The model was tuned using cross-validation and hyperparameter tuning techniques to optimize its performance, ensuring that the predictions generalize well across different regions and time frames.
Metrics Used:
The model's performance was evaluated using the Mean Squared Error (MSE), a standard metric for regression tasks that measures the average squared difference between the actual and predicted values. After tuning, the MSE was significantly reduced, meaning the model was making more accurate predictions.
Initially, the predictions showed a broader range of errors, but after tuning, the model predictions became more tightly aligned with actual home values, as evidenced by the comparison between the first and the final scatter plots (Actual vs Predicted).
Predicted vs Actual Home Values Visualization: The first plot showed the actual vs predicted home values across thousands of data points (regions or individual home entries). In the final tuned model, the plot showed fewer significant outliers and an overall tighter clustering of points along the zero-error line. This suggests that the model improved after tuning, leading to a more accurate prediction of home prices across most regions.
Forecast for Top 10 Regions: The model was also used to forecast home values for the top 10 regions, ranked by their SizeRank (an indicator of the size or importance of the housing market in those regions). These regions included major areas like Houston-The Woodlands-Sugar Land, TX, New York-Newark-Jersey City, NY-NJ-PA, Los Angeles-Long Beach-Anaheim, CA, etc.
What the Final Output Means: The model's forecast for 2025 shows expected changes in home values for different regions over time. Some regions, like Houston-The Woodlands-Sugar Land, TX, showed expected increases in home values, while others, like Los Angeles-Long Beach-Anaheim, CA, showed declines. The visualization provided insight into which regions might experience growth or decline in the housing market over the coming year, useful for real estate investors, policy-makers, and market analysts.
Animated Visualization: The animated plot shows how predicted home values change over three different time points: October 2024, December 2024, and September 2025. This moving trend allows users to visualize the progression of home value predictions in different regions, making it easier to understand which regions are expected to see fluctuations in housing prices and when these changes might happen.
Final Output Meaning: The final model's output provides an accurate forecast of home prices in various regions for 2025, allowing stakeholders in the housing market to make data-driven decisions. The visualizations clearly show which regions are expected to grow, remain stable, or decline in value, which is valuable information for home buyers, real estate investors, financial institutions, and urban planners looking to understand market dynamics.
