Abstract
While housing price prediction is well-studied, the prediction of large-scale housing conditions remains underexplored due to data limitations. This paper addresses this gap by developing a machine-learning model to predict housing conditions across the United States. We integrated property-level data from the Warren Group with neighborhood characteristics from the U.S. Census Bureau’s American Community Survey and trained three gradient-boosting algorithms: CatBoost, LightGBM, and XGBoost. Despite XGBoost’s slightly higher balanced accuracy, CatBoost was selected as the best model due to its superior resistance to overfitting. The final model’s predictions were aggregated to census tracts, ZIP code tabulation areas, and a 36.13 km2 resolution hexagonal grid for national-scale spatial analysis. The resulting comprehensive dataset can serve as a valuable resource for researchers and practitioners to analyze the geography of housing quality with applications in urban planning, disaster management, community resilience, public health, and more.
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Data availability
The Housing Condition Scores dataset is available at the Figshare repository https://doi.org/10.6084/m9.figshare.29606177.v1.
Code availability
The codes utilized to create the dataset are available on GitHub (https://github.com/kim-kyusik/housing_condition_scores) and are compatible with Python 3.12.4.
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Acknowledgements
The research reported in this publication was supported by the Gulf Research Program of the National Academies of Sciences, Engineering, and Medicine under award number SCON-10000677 and the Centers for Disease Control and Prevention Climate Ready Cities and States Initiative (NUE1EH001496-02-00).
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K. Kim and C. Uejio designed and conceptualized the research; K. Kim processed data; K. Kim performed the ML algorithms for prediction; K. Kim wrote the original draft and visualizations; T. Holmes, E. Powell, and C. Uejio reviewed and edited the manuscript.
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Kim, K., Holmes, T., Powell, E. et al. Large-scale modeling for housing condition prediction using machine learning algorithms. Sci Data (2026). https://doi.org/10.1038/s41597-026-07012-w
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DOI: https://doi.org/10.1038/s41597-026-07012-w
