Open Source Software and Data for Human Service Development: A Case Study on Predicting Housing Instability

TL;DR

This study explores how open-source data and tools can be used to predict eviction risks in resource-limited human services, highlighting both their potential and limitations.

Contribution

It demonstrates the application of open-source data and statistical models to forecast eviction filings, providing insights into their utility in constrained social service contexts.

Findings

Open-source data can enable rapid analysis in human services.

Models showed moderate accuracy in predicting eviction filings.

Public data has limitations, affecting prediction reliability.

Abstract

Open-source data and tools are lauded as essential for replicable and usable social science, though little is known about their use in resource constrained human service provision. This paper examines the challenges and opportunities of open-source tools and data in human service development by using both to forecast failure to pay eviction filings in Bronx County, NY. We use zip code level data from the Housing Data Coalition, the American Community Survey 5-year estimates, and DeepMaps Model of the Labor Force to forecast rates through July 2021. We employ multilevel (MLM) and exponential smoothing (ETS) models using the R project for Statistical Computing, an oft used open-source statistical software. We compare our results to what happened during the same period, to illustrate the efficacy of the open-source tools and techniques employed. We argue open-source data and software may facilitate rapid analysis of public data - a much-needed ability in human service intervention development under increasingly constrained resources - but find public data are limited by the information they reliably capture, limiting their utility by a non-trivial margin of error. The manuscript concludes by considering lessons for human service organizations with limited analytical resources and a vested interest in low-resourced communities.