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  3. Large-scale Modeling For Housing Condition Prediction Using Machine Learning Algorithms.
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  3. Large-scale Modeling For Housing Condition Prediction Using Machine Learning Algorithms.

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Large-scale modeling for housing condition prediction using machine learning algorithms.

Kyusik Kim1,2, Tisha Holmes3, Emily Powell4

  • 1Florida State University, Department of Geography, Tallahassee, FL, USA. kkim84@kennesaw.edu.

Scientific Data
|March 11, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study developed a machine-learning model to predict national housing conditions, addressing data limitations. CatBoost was chosen for its resistance to overfitting, providing a valuable resource for spatial analysis.

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Area of Science:

  • Environmental Science
  • Urban Planning
  • Data Science

Background:

  • Housing price prediction is common, but large-scale housing condition prediction is limited by data availability.
  • Existing research has not fully explored the spatial variations in housing quality across the United States.
  • Understanding housing conditions is crucial for various societal applications.

Purpose of the Study:

  • To develop and validate a machine-learning model for predicting housing conditions at a national scale.
  • To overcome data limitations in assessing large-scale housing quality.
  • To create a comprehensive dataset for spatial analysis of housing conditions.

Main Methods:

  • Integrated property-level data (Warren Group) with U.S. Census Bureau neighborhood data.
  • Trained and compared three gradient-boosting algorithms: CatBoost, LightGBM, and XGBoost.
  • Selected CatBoost as the best model due to its superior resistance to overfitting.

    • Main Results:

    • The CatBoost model demonstrated strong predictive performance for housing conditions.
    • Predictions were aggregated to census tracts, ZIP code tabulation areas, and a hexagonal grid for spatial analysis.
    • A comprehensive dataset for national-scale housing quality analysis was generated.

    Conclusions:

    • The developed machine-learning model effectively predicts national housing conditions.
    • The resulting dataset is a valuable resource for analyzing the geography of housing quality.
    • Applications include urban planning, disaster management, community resilience, and public health.