ArticleScientific data2026
Large-scale modeling for housing condition prediction using machine learning algorithms.
Article in Scientific data, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
0 citing papers in PubMed.
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Authors and funding
4 authors.
Funding
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 km
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.