Predicting Home Prices with Machine Learning
Predicting Home Prices with Machine Learning
Introduction
Buying or selling a home is one of the most significant financial decisions people make. Accurately predicting home prices is essential for buyers, sellers, and real estate professionals to make informed choices. Machine learning provides a powerful tool to enhance accuracy in home price estimation. In this blog, we will walk through the process of building a ML model to predict home sale prices using the Ames Housing dataset.
The Ames Housing Dataset
The Ames Housing dataset, introduced by Dean De Cock in 2011, is widely used dataset for predictive modeling. It contains 2,580 records of home sales in Ames, Iowa, from 2006 to 2010, with 82 explanatory variables detailing property attributes. These variables describe features such as lot size, street access, and zoning classifications. The dataset also includes structural characteristics like the number of stories, construction materials, and quality ratings. Additional aspects such as basement size, garage type, and the number of bedrooms and bathrooms further define the properties. Outdoor features, including porches, fencing, and pools, as well as sale-related details such as sale type and final price, make this dataset comprehensive for real estate analysis.
Exploratory Data Analysis (EDA)
A thorough exploratory data analysis was conducted to identify patterns, distributions, and potential data issues.
Data Types
The dataset consists of 39 numerical features, such as square footage and the total number of rooms, 29 categorical features, including neighborhood and foundation type, and 14 ordinal features, which represent ranked attributes such as material and condition quality ratings.
Handling Missing Data
Several features exhibited substantial missing values, primarily due to their non-applicability to certain homes. For example, PoolQC is missing in 99.7% of it's information, which is expected since most homes do not have a pool. Similarly, MiscFeature, which accounts for additional property attributes like sheds or tennis courts, is absent in 96% of the records. Alley access is missing in 93% of cases, indicating that most properties lack rear alleyways. Fence data is unavailable for 80% of homes, and FireplaceQu is missing in 48%, likely due to the absence of fireplaces in those homes. To address missing values, we dropped features with excessive missing data.
Sale Price Analysis
Most homes in the dataset are sold for prices ranging between $100,000 and $250,000, with a few properties exceeding $500,000, which are considered potential outliers. The sale price distribution is right-skewed, requiring a log transformation to improve normality.
Feature Correlations with Sale Price
Several features strongly correlate with sale price. The highest correlation is with OverallQual (79%), meaning better-quality homes tend to sell for more. GrLivArea (72%) also plays a major role, as larger living spaces increase home values. TotalBsmtSF (65%) suggests that bigger basements add value, while GarageCars (64%) and GarageArea (63%) indicate that garage size contributes positively to home price. Additionally, newer and recently renovated homes tend to have higher valuations, as seen with YearBuilt (54%) and YearRemodAdd (51%). Lastly, 1stFlrSF (64%) reinforces the idea that larger first-floor areas increase home value.
Categorical Feature Analysis
Categorical variables in the dataset, such as neighborhood, house style, and foundation type, provide essential context for home pricing. Neighborhood influences sell prices significantly, as certain areas tend to have higher property values due to location advantages, school districts, or community amenities. House style plays a role in pricing as well, with two-story homes generally commanding higher median prices than one-story homes due to increased living space. Foundation type also impacts home value, with poured concrete foundations being associated with higher property prices compared to cinder block or stone foundations. Understanding these categorical variables is crucial, as they help reveal structural and location-based pricing trends.
Model Selection
To evaluate predictive performance, I tested three models. Simple Linear Regression was used as a baseline model to establish fundamental relationships between features and sale price. It was also an extension of our exploratory data analysis (EDA), helping us understand how individual features impact home prices before implementing more complex models. However, it lacks the flexibility needed to handle feature interactions and nonlinearity.
Ridge Regression was applied to address multicollinearity, a common issue when features are highly correlated. By incorporating an L2 regularization penalty, Ridge Regression shrinks the coefficients of less significant features, preventing overfitting and improving generalization. This model performed significantly better than simple linear regression, particularly when handling correlated features like living area and basement size.
Finally, we implemented XGBoost Regression, an advanced tree-based model that builds upon gradient boosting. XGBoost iteratively trains small decision trees, learning from the residuals of previous trees to minimize errors. It includes regularization techniques to prevent overfitting, handles missing data efficiently, and optimizes feature selection through built-in tree pruning. Unlike linear models, XGBoost can effectively capture complex relationships between variables, making it the most powerful model for this dataset. Hyperparameter tuning, including learning rate adjustments and cross-validation, further optimized its performance, allowing it to outperform both Ridge Regression and Simple Linear Regression.
Feature Engineering & Preprocessing
To optimize model performance, several preprocessing techniques were applied. Since the sale price distribution was skewed, a log transformation was performed to normalize it. Categorical variables were encoded differently depending on the model. One-hot encoding was used for Ridge Regression, as this method works best for linear models by converting categorical variables into binary indicators without introducing an ordinal relationship. On the other hand, ordinal encoding was applied to categorical features in XGBoost since tree-based models can naturally handle ranked values and benefit from ordered numeric representations.
Additionally, feature engineering was conducted to enhance model performance. Several new features were created to capture key aspects of the dataset. TotalBath was derived by summing all full and half bathrooms in the house, providing a more comprehensive representation of bathroom availability. HouseAge was calculated as the difference between the year the house was built and the year of sale, offering insight into how the age of the home affects its value. YearSinceRemodel was created to measure the time elapsed since the last remodel, highlighting the impact of renovations. TotalSF was computed by combining above-ground living area, basement square footage, and garage area, providing a holistic measure of total usable space. Additionally, Porch was introduced as a new feature by aggregating all porch-related variables, simplifying the dataset while retaining the influence of outdoor spaces on home prices.
Missing values were handled by dropping features with excessive data gaps, replacing categorical missing values with "None," and imputing numerical missing values using the median. Outliers were addressed by removing homes with living areas exceeding 4,000 square feet to prevent bias. Numerical features were standardized using StandardScaler for Ridge Regression. Additionally, hyperparameter tuning was conducted through Grid Search to optimize model parameters.
Model Performance and Summary
Among the models tested, XGBoost demonstrated the highest predictive performance by effectively capturing feature interactions while minimizing overfitting
Conclusion and Future Directions
XGBoost emerged as the most effective model for predicting home prices due to its ability to handle missing data, model nonlinear relationships, and manage multicollinearity. However, there are opportunities to further improve the model. Enhanced outlier detection techniques, such as Isolation Forests, could refine data quality. Feature selection methods like Principal Component Analysis (PCA) or Recursive Feature Elimination (RFE) could improve model efficiency by focusing on the most relevant predictors. Additionally, exploring alternative ensemble methods, such as LightGBM or deep learning approaches, may yield further improvements in predictive accuracy. Incorporating macroeconomic indicators, such as interest rates and inflation trends, could provide greater contextual insights into home price fluctuations. Finally, deploying the model as a web-based application would make real-time price predictions accessible to real estate professionals and consumers.
Final Thoughts
Machine learning is revolutionizing real estate analytics by offering objective, data-driven insights into home valuations. As predictive models continue to evolve, integrating additional data sources and refining feature selection techniques will further enhance accuracy and market transparency.
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