Using Integrated City Data and Machine Learning to Identify and Intervene Early on Housing-Related Public Health Problems

Abstract

Context: Housing is more than a physical structure-it has a profound impact on health. Enforcing housing codes is a primary strategy for breaking the link between poor housing and poor health.

Objective: The objective of this study was to determine whether machine learning algorithms can identify properties with housing code violations at a higher rate than inspector-informed prioritization. We also show how city data can be used to describe the prevalence and location of housing-related health risks, which can inform public health policy and programs.

Setting: This study took place in Chelsea, Massachusetts, a demographically diverse, densely populated, low-income city near Boston.

Design: Using data from 1611 proactively inspected properties, representative of the city's housing stock, we developed machine learning models to predict the probability that a given property would have (1) any housing code violation, (2) a set of high-risk health violations, and (3) a specific violation with a high risk to health and safety (overcrowding). We generated predicted probabilities of each outcome for all residential properties in the city (N = 5989).

Results: Housing code violations were present in 54% of inspected properties, 85% of which were classified as high-risk health violations. We predict that if the city were to use integrated city data and machine learning to identify at-risk properties, it could achieve a 1.8-fold increase in the number of inspections that identify code violations as compared with current practices.

Conclusion: Given the strong connection between housing and health, reducing public health risk at more properties-without the need for additional inspection resources-represents an opportunity for significant public health gains. Integrated city data and machine learning can be used to describe the prevalence and location of housing-related health problems and make housing code enforcement more efficient, effective, and equitable in responding to public health threats.

FIGURE 1

Tradeoffs in Sensitivity and PPV (Top) and Test Characteristics (Bottom) for Best Performing Models for Each Outcome^a Abbreviation: PPV, positive predictive value. ^aSee Supplemental Material Figure 1 (available at http://links.lww.com/JPHMP/A770) for the relative importance of the top 20 variables for each model.

FIGURE 2

Spatial Distribution and Prevalence of Predicted Housing Code Violations in Chelsea, Massachusetts^{a a}Each circle represents a property and its color represents the predicted probability for each outcome. When the predicted probability is 50% or greater, properties are categorized as positive for the outcome. Circles are enlarged to protect privacy. Areas without color contain no rental properties.