ML PROJECT / RESEARCH
Comprehensive machine learning project using Neural Networks, Random Forest, and Gradient Boosting to predict housing prices with 92% R-squared accuracy.
92%
R-squared score on test data
50K+
Property records analyzed
15%
Over traditional models
This comprehensive machine learning project analyzes housing market trends across 20 metropolitan areas, incorporating economic indicators, demographic factors, and historical pricing data to predict future housing prices with exceptional accuracy.
The project employs ensemble methods combining Neural Networks, Random Forest, and XGBoost models, achieving a 92% R-squared accuracy score and outperforming traditional prediction models by 15%. The system processes over 50,000 property records and integrates real-time economic indicators for dynamic predictions.
Enter property details and click Predict Price to see results
| Model | RMSE (%) | R-Squared | Adjusted R² | MAE ($) | Top 3 Features |
|---|---|---|---|---|---|
| Neural Network | 3.1 | 0.92 | 0.91 | $8,900 | Location, Square Footage, Interest Rate |
| Random Forest | 3.9 | 0.88 | 0.87 | $10,200 | Location, Square Footage, Interest Rate |
| Gradient Boosting | 4.3 | 0.85 | 0.84 | $12,300 | Location, Square Footage, Unemployment Rate |
| Linear Regression (Baseline) | 5.2 | 0.74 | 0.73 | $15,000 | Location, Square Footage, Year Built |
Key Findings:
Analysis: Location shows the strongest positive correlation (+0.75) with property prices, confirming that prime real estate areas command higher values. Interest rates exhibit negative correlation (-0.45), indicating that rising rates lead to lower property prices—consistent with broader economic trends.
Feature importance calculated using permutation importance method on the Neural Network model. Location accounts for 31.2% of predictive power.
5-Fold CV R² Scores
Learning Rate
0.05
Tested: [0.01, 0.05, 0.1, 0.2]
Max Depth
7
Tested: [3, 5, 7, 9, 11]
N Estimators
500
Tested: [100, 300, 500, 1000]
Min Child Weight
3
Tested: [1, 3, 5, 7]
Subsample
0.8
Tested: [0.6, 0.7, 0.8, 0.9, 1.0]
Colsample Bytree
0.8
Tested: [0.6, 0.7, 0.8, 0.9, 1.0]
Grid Search Results: 480 parameter combinations tested over 12.3 hours using 8-core CPU. Best parameters selected based on 5-fold cross-validation R² score.
Residual plots were generated to assess prediction accuracy. The Neural Network model exhibited the smallest residuals, indicating excellent fit with the data.
Smallest residuals, normally distributed
Low residual variance across price ranges
Slight heteroscedasticity detected
Key Observations:
52,847
Total Records
42
Features
20
Metro Areas
2015-2024
Time Period
The application of machine learning in real estate price prediction demonstrates significant advantages over traditional methods. The Neural Network model achieved an R² value of 0.92, indicating it explains 92% of the variance in property prices—a substantial improvement over the Linear Regression baseline (R² = 0.74).
Feature importance analysis revealed that location, square footage, andeconomic indicators (particularly interest rates) play the most significant roles in determining property prices. The high correlation between location and price (+0.75) aligns with industry knowledge that prime real estate areas command premium values.
The negative correlation with interest rates (-0.45) confirms that rising rates lead to lower property prices, consistent with broader economic trends. This relationship is particularly valuable for real estate investors and analysts seeking to time market entries and exits.
Machine learning provides valuable insights for real estate stakeholders, enabling data-driven decisions and reducing investment risks. Future research can incorporate more granular data such as neighborhood-level attributes, transaction histories, and social factors to further improve model accuracy.