# Contract (P0)
**Question.** Summarise the property-sales export `data/sales.csv` and reconcile it to provider control totals in `data/metadata.json`.
**Estimands.** (1) number of records readable unambiguously (n_valid_records = n_analysed); (2) sum and mean of price_cad over them; (3) median of price_cad/area_m2 over them; (4) reconciliation: control count vs valid count (n_unreadable = control - valid) and control price sum vs valid sum (implied unreadable sum). Meaningful assuming control totals describe the same full table before export, and that readable records are exported faithfully.
**Outputs.** out/results.json (task_id, n_records_control, n_valid_records, n_unreadable_records, n_analysed, sum_price_control, sum_price_valid, sum_price_unreadable_implied, mean_price_valid, median_price_per_m2_valid, feasibility, feasibility_reason, data_issues, n_input_rows{"sales.csv"}, random_seed); out/analytic.csv (sale_id, municipality, sale_date, price_cad numeric, area_m2; n_analysed rows); out/exclusions.csv (step, rule, n_before, n_after, n_removed, rationale; chained); out/report.md (6 sections); out/analysis.py; out/claims.json.
**Information rules.** Only the two data files. No reconstruction/imputation/guessing of unreadable records. feasibility = complete only if all control records are analysed; else partial with exact count and reason.
**Invalidity.** Imputing garbled records; counting unreadable lines as valid; using outside info. Task would be infeasible if no records were readable.