DS-12 · A housing model that knows what it does not know

Build a home-value model for a Richmond-area ZIP set. Then do the part that matters: find the segments where it is systematically wrong, and decide whether it should be used at all.

Zillow Home Value Index research data (https://www.zillow.com/research/data/). Census ACS structure age, tenure, income (https://www.census.gov/data/developers/data-sets/acs-5year.html). Local assessment records (https://data-henrico.opendata.arcgis.com/).

ZHVI is already a smoothed index, not raw transactions — modeling it is modeling someone else's model. Aggregate-level fit hides neighborhood-level failure. Automated valuation models have a documented history of underperforming in majority-Black neighborhoods; a student who does not check for this has skipped the assignment.

A value model with error decomposed by neighborhood characteristics, an explicit fairness check across neighborhood demographics, and a written recommendation about deployment — including, if warranted, "do not deploy."

CT: understanding what modeling an index means. CR: designing a segment-level error audit. CO: adversarial peer review looking for hidden failure. CM: a model card with an intended-use and out-of-scope section. CZ: valuation models affect wealth; a recommendation not to deploy is a legitimate result.

An appraiser, housing-policy researcher, or fair-lending analyst reviews the audit design at Checkpoint 1 and the deployment recommendation at Checkpoint 2. School-supervised.

R1: the target is understood as an index. R2: sources and vintages aligned. R3: a simple baseline (median by ZIP). R4: error decomposed by segment, not just overall. R5: a real model card. R6: an explicit deployment stance with reasons.

(a) The student reports 8% median error and stops, never finding the 30% error in two neighborhoods. (b) The student runs a fairness check, finds disparity, notes it in one sentence, and recommends deployment anyway.