# Homelessness prediction models in high-income countries: a scoping review

## TL;DR

This study reviews prediction models for future homelessness in high-income countries, finding many models but limited validation.

## Contribution

The study provides the first comprehensive scoping review of homelessness prediction models in high-income countries.

## Key findings

- 15 studies met inclusion criteria, with most conducted in the US and using traditional regression methods.
- Most models used demographics, health status, and employment data, but only two were externally validated.
- The study highlights a need for more rigorous model evaluation and calibration metrics.

## Abstract

This scoping review aims to provide an overview of prediction models for future homelessness in high-income countries.

Several risk prediction models for homelessness have been developed and are being used in practice. However, no comprehensive review has captured the full scope of these models in regard to their target populations, data and variables used, type of model, and model validation.

We searched MEDLINE, Web of Science, the Cochrane Library, and Bielefeld BASE without language or time restraints. Following the JBI guidelines, screening was performed by two independent reviewers; the data were extracted by one, of which 20% was checked by a second one. Studies that reported on the development or validation of prediction models for becoming homeless in high-income countries, and whose study population included individuals residing in these countries who were not homeless at the time of recruitment, were included.

Our search resulted in 9,371 deduplicated records across databases. 15 studies met the inclusion criteria, of which 14 were model development studies and one was a validation study. 13 studies (87%) were conducted in the US, six of them in New York City (NYC). One study was conducted in Canada and one in Australia. Regarding the target population, three studies developed models for veterans and six studies targeted welfare applicants or recipients. One study focused on both youth emerging from public assistance and unemployed workers. Three studies developed models for the general population, while two were conducted in emergency departments. Of the 15 studies, 14 used traditional regression, seven employed other machine learning algorithms and six used both methods. The most common predictor types were: demographics, age, previous experiences of homelessness, human capital such as employment status or total debt and clinical variables such as physical or mental health status. Three studies combined geographical-level and individual-level data. In total, 25 models were identified, two of which were externally validated.

We found a broad spectrum of heterogeneity of models and population studies, an increase in model development over time, and limited use of calibration metrics. Prediction models for future homelessness have the potential to improve risk targeting and the effectiveness of preventive programs. As only two models were externally validated, we recommend that future research focuses on model evaluation.

The online version contains supplementary material available at 10.1186/s12889-025-24855-x.