Generate Housing Price ML Training Data

Generate production-ready housing price regression datasets with correlated features (sqft/bedrooms/bathrooms), continuous price targets, and mixed feature types, built for price prediction models.

Regression3,000 rows6 features$50K-$1.5M rangeCorrelated featuresNoise 0.1

Generate Housing Data

Housing Price template configuration

Here's the pre-built template configuration. Customize everything after loading.

housing-price.json

{
  "templateName": "Housing Price",
  "taskType": "regression",
  "numSamples": 3000,
  "features": [
    { "name": "square_feet", "type": "numeric", "distribution": "normal", "mean": 1800, "std": 600 },
    { "name": "bedrooms", "type": "numeric", "distribution": "poisson", "mean": 3 },
    { "name": "bathrooms", "type": "numeric", "distribution": "poisson", "mean": 2 },
    { "name": "lot_size", "type": "numeric", "distribution": "log-normal", "mean": 8000, "std": 4000 },
    { "name": "property_type", "type": "categorical", "categories": ["single_family", "condo", "townhouse", "multi_family"] },
    { "name": "has_garage", "type": "boolean", "trueRatio": 0.65 }
  ],
  "correlations": [
    { "feature1": "square_feet", "feature2": "bedrooms", "coefficient": 0.7 },
    { "feature1": "square_feet", "feature2": "bathrooms", "coefficient": 0.6 }
  ],
  "target": { "type": "continuous", "min": 50000, "max": 1500000 },
  "noise": 0.1
}

Built for Housing

Every feature is configured with domain-appropriate distributions and realistic parameters.

Correlated Property Features

Square footage correlates with bedrooms (0.7) and bathrooms (0.6), reflecting real property relationships. Larger homes have more rooms. The quality report verifies these correlations.

Continuous Price Target

Regression target ranging from $50K to $1.5M. Unlike classification, the model predicts a continuous value, ideal for training linear regression, random forests, and gradient boosting models.

Mixed Feature Types

Numeric (square feet, lot size), discrete (bedrooms, bathrooms), categorical (property type), and boolean (has garage) features, covering the full range of real estate data types.

Property Type Categories

Four property types (single family, condo, townhouse, multi-family) with weighted distributions. Model type-specific pricing patterns and test category encoding strategies.

Who uses Housing Price training data?

Real Estate Tech Teams

Build and benchmark automated valuation models (AVMs) with realistic property data. Test pricing algorithms with known ground truth before deploying on real listings.

Data Science Students

Learn regression fundamentals with a classic housing price dataset. Practice feature engineering, model selection, and evaluation with realistic correlated features.

Fintech Mortgage Teams

Prototype property valuation and risk assessment models with synthetic housing data. No real property records needed for development and testing.

Regression Data Quality

SynthForge IO generates regression datasets with realistic feature correlations and continuous targets, not just classification.

Continuous Target Variable

Housing price is a continuous value ($50K-$1.5M), not a category. The data is purpose-built for regression models: linear regression, random forests, XGBoost, and neural networks.

Feature Correlations

Square footage correlates with both bedrooms (0.7) and bathrooms (0.6), ensuring the generated data reflects real property relationships rather than independent noise.

Log-Normal Lot Sizes

Lot size follows log-normal distribution. Most lots are moderate with a realistic long tail of large properties, matching real estate market distributions.

R-squared and RMSE Evaluation

The baseline model evaluation reports R-squared, RMSE, and MAE for regression datasets, appropriate metrics instead of classification accuracy.

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Frequently asked questions

How is this different from classification datasets?

Housing price is a regression task. The target is a continuous value ($50K-$1.5M), not a category. The model predicts a number, and evaluation uses R-squared, RMSE, and MAE instead of accuracy, precision, and recall.

How do the feature correlations work?

Square footage correlates with bedrooms (0.7) and bathrooms (0.6) using Cholesky decomposition. This means larger homes tend to have more rooms, matching real property data. The quality report verifies actual correlations match your configuration.

What price range does this template use?

The default range is $50,000 to $1,500,000, covering a broad residential real estate market. You can adjust the range to match specific markets, e.g., $200K-$5M for luxury markets or $50K-$300K for affordable housing.

What export formats are available?

Datasets export as a ZIP containing CSV and Parquet files with separate train/test/validation splits, a data quality report, and an auto-generated Jupyter notebook for immediate exploration and modeling.

Can I add location features?

Yes. The template is a starting point. You can add features like zip_code (categorical), latitude/longitude (numeric), school_rating (numeric), or neighborhood (categorical) to increase geographic realism.

Start Generating Housing Price Training Data

Load the Housing Price template, customize features and parameters, and export publication-ready datasets in seconds.