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Are Manufactured Home Values Fair Across Race and Gender?
You're about to step into the shoes of a data detective. Imagine you have a stack of public Census data on manufactured homes across the Mountain West. Your mission: uncover whether these homes—often the most affordable option—are valued differently depending on the owner's race or gender. No fancy lab, just raw numbers and your curiosity. You'll sift through rows of data, looking for patterns that might hint at bias. Maybe you'll find that homes owned by women are appraised lower, or that certain racial groups consistently see lower values. This isn't just about numbers; it's about fairness in housing. Ready to see what the data reveals?
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Think of a time you or someone you know felt treated unfairly because of who they were. What happened, and how did it feel?
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Have you ever noticed that things like houses or cars seem to be priced differently depending on the neighborhood or the owner? Can you recall a specific example?
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If you were in charge of making sure home values are fair, what's the first thing you'd check in the data?
Applied MLMachine LearningData ScienceFairness AuditarXiv · 2026ap_statistics/exploring_two_variableap_statistics/inference$0 public datasetsSupplies: laptop onlyStandard laptop (CPU only, 8GB+ RAM; Windows/macOS/Linux)Intermediate
Research gap
Imagine you're a data scientist auditing whether manufactured homes—a key affordable housing option—are valued fairly across different racial and gender groups in the Mountain West. Using only public Census data, you can uncover hidden biases that might otherwise go unnoticed. How would you design an audit to ensure equity?
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