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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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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.

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|March 11, 2026
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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.