Targeting a Housing Subsidy: A Data-Driven Approach

Project Overview

This project was completed as part of the Public Policy Analytics course at UPENN. Emil City is considering a more proactive approach for targeting homeowners who qualify for a home repair tax credit program. This analysis aimed to train the best classifier to predict which homeowners are most likely to accept the credit and use the results to inform a cost/benefit analysis. By leveraging historical campaign data, we developed models to predict homeowners’ likelihood of accepting the subsidy and conducted a comprehensive cost-benefit analysis. The goal was to optimize resource allocation and increase participation in the credit program.

Click any image to read the full report on RPubs, detailing our methodologies and findings.