Predicting Housing Prices with Machine Learning
In real estate, location used to be everything. Today, it’s data.
Introduction: Data Meets Real Estate
Predicting housing prices isn’t just a numbers game—it's a high-stakes challenge that impacts buyers, sellers, and market analysts. With the real estate market being so dynamic, price estimation requires more than just intuition. It demands data.
In this project, I explored how advanced machine learning models can make those predictions more accurate and explainable. Specifically, I worked with XGBoost, Gradient Boosting, and Random Forest to forecast home sale prices using property-level data from Ames, Iowa.
Instead of just building a predictive model, I aimed to understand the why behind the numbers. In other words, which features truly drive housing values—and how can we explain these results in ways that make sense to real estate professionals and everyday buyers?
Research Goals
This project centered around three primary questions:
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How accurately can machine learning models predict housing prices?
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Which features have the most influence on a home’s valuation?
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Which model balances performance, interpretability, and efficiency?
Data Overview: Where It All Begins
I used two datasets: Ames_Housing_Price_Data.csv and Ames_Real_Estate_Data.csv. Together, they contained detailed information on nearly 3,000 residential properties sold in Ames, Iowa.
To organize the data effectively, I grouped the features into three categories:
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Property-Related:
GrLivArea,AgeOfHouse -
Condition-Related:
OverallQual,OverallCond -
Utility-Based:
GarageCars,TotalOutdoorSpace
Data Cleaning and Feature Engineering
To get the most from the data, I performed several key preprocessing steps:
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Resolved column mismatches
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Imputed missing values (excluding meaningful zeros)
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Created engineered features like:
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Living Space Efficiency =
GrLivArea / LotArea -
LotFrontage Ratio =
LotFrontage / LotArea -
Age of House =
YrSold - YearBuilt -
Time Since Last Remodel =
YrSold - YearRemodAdd
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These new features helped the model learn meaningful relationships—especially when raw values didn’t tell the whole story.
Exploring the Data: What Affects Home Prices?
Before jumping into modeling, I ran exploratory data analysis (EDA) to better understand relationships between features and SalePrice.
Strong Correlation Between Size and Price
As expected, homes with more living area tend to sell for higher prices.
Digging Deeper with Correlation
The correlation heatmap below shows how strongly different features relate to sale price. OverallQual and GrLivArea stand out as top predictors.
Interaction Effects Between Features
Looking at the pairplot below, you’ll notice patterns between features. For example, homes with higher GrLivArea and larger garages often command premium prices.
Neighborhood Pricing Trends
Location matters. Certain neighborhoods consistently outperform others—even when properties have similar characteristics.
Modeling Approach: Finding the Best Fit
I started with a baseline linear regression model to set performance expectations. However, it quickly became clear that linear models weren’t enough. They assume a straight-line relationship and can’t capture complex feature interactions.
To improve on this, I trained three advanced ensemble models:
- Random Forest
- Gradient Boosting
- XGBoost (which ultimately performed the best)
These models are ideal for real estate prediction because they capture non-linearities and allow feature interactions to naturally emerge.
Evaluation Metrics: How We Measure Success
To compare model performance, I used:
- Mean Squared Error (MSE): Measures average squared error between predicted and actual sale prices.
- R² Score: Measures how well the model explains variability in sale prices.
Lower MSE and higher R² values indicate stronger predictive performance.
Model Results: Why XGBoost Wins
Among the models tested, XGBoost clearly outperformed the rest. It achieved:
- Lowest MSE: 5.35e+08
- Highest R² Score: 0.90
Top Predictors According to XGBoost
GrLivAreaOverallQualGarageCarsTotalBsmtSFAgeOfHouse
These results align with both domain knowledge and earlier EDA findings.
Residual Analysis
A residual plot helps visualize whether the model makes unbiased predictions across the full range of housing prices.
The residuals are scattered randomly around zero—a good sign of a well-fitted model.
Explainability with SHAP: Understanding Predictions
While predictive performance is important, stakeholders also need to trust and understand model decisions. That’s where SHAP (SHapley Additive Explanations) comes in.
SHAP values help interpret both global feature importance and individual predictions. They answer questions like: Why did the model predict this price for this house?
Global SHAP Summary
This plot shows how much each feature (and its value) pushed predictions up or down.
SHAP Feature Importance
This ranked chart gives a clean summary of which features consistently impact predictions the most.
XGBoost Internal Importance (for comparison)
Stakeholder Recommendations
For Buyers:
- Use feature-based insights to evaluate whether a listing price is justified.
- Focus on properties with good fundamentals: high
OverallQual, solidGrLivArea, and functional garages.
For Sellers:
- Renovate where it counts. Improvements to quality and layout can yield higher sale prices.
- Price listings competitively by comparing with similar homes in the same neighborhood.
For Real Estate Platforms:
- Incorporate ML models like XGBoost into pricing engines.
- Use SHAP plots to deliver transparency and trust in automated pricing suggestions.
Key Takeaways and Future Directions
- Best Model: XGBoost achieved the strongest overall performance.
- Interpretability Matters: SHAP helped translate predictions into real-world explanations.
- Feature Engineering Was Critical: Derived metrics like
LivingSpaceEfficiencyandAgeOfHouseimproved results.
Future Work:
- Introduce macroeconomic variables like interest rates and inflation
- Experiment with time-series modeling to predict price trends
- Add new data sources, including energy scores or property imagery
Final Thoughts
Machine learning is reshaping how we understand and predict housing prices. With the right models and the right features, we can move beyond guesswork and into decision-making that is explainable, reliable, and data-driven.
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