diff --git a/pm/tasks.org b/pm/tasks.org
index c928339..588a7c4 100644
--- a/pm/tasks.org
+++ b/pm/tasks.org
@@ -803,26 +803,19 @@ correct and document deterministic normalized quantity fields so unit-cost analy
 - The missing purchases fields were a carry-through bug: normalization had `normalized_quantity` and `normalized_quantity_unit`, but `build_purchases.py` never wrote them into `data/review/purchases.csv`.
 - Normalized quantity now prefers explicit package basis over `each`, so rows like `PEPSI 6PK 7.5Z` resolve to `90 oz` and `KS ALMND BAR US 1.74QTS` purchased twice resolves to `3.48 qt`.
 - The derivation stays conservative and does not convert units during normalization; parsed units such as `oz`, `lb`, `qt`, and `count` are preserved as-is.
-* [ ] t1.18: add regression tests for known quantity/price failures (1-2 commits)
+* [x] t1.18: add regression tests for known quantity/price failures (1-2 commits)
 capture the currently broken comparison cases before changing normalization or purchases logic
 
 ** acceptance criteria
-1. when generating `data/purchases.csv`, add `effective_price` = `effective_total` / `normalized_quantity`
-2. define `effective_price` behavior explicitly from the covered cases:
-   - use `net_line_total` when present and non-zero, else use `line_total`
-   - divide by `normalized_quantity` when `normalized_quantity > 0`
-   - leave blank when no valid denominator exists
-   - never emit `0` or divide-by-zero for missing-basis cases
-   - `effective_price` only comparable within same `normalized_quantity_unit` unless later analysis converts the units
-3. ensure the new tests assert the intended `effective_price` behavior for the known banana, ice, and beef patty examples
-4. add tests covering known broken cases:
+1. ensure the new tests assert the intended `effective_price` behavior for the known banana, ice, and beef patty examples
+2. add tests covering known broken cases:
    - giant bananas produce non-blank effective price
    - giant bagged ice produces non-zero effective price
    - costco bananas retain correct effective price
    - beef patty comparison rows preserve expected quantity basis behavior
-5. tests fail against current broken behavior and document the expected outcome
-6. include at least one assertion that effective_price is blank rather than `0` or divide-by-zero when no denominator exists
-7. pm note: this task should only add tests/fixtures and not change business logic
+3. tests fail against current broken behavior and document the expected outcome
+4. include at least one assertion that effective_price is blank rather than `0` or divide-by-zero when no denominator exists
+- pm note: this task should only add tests/fixtures and not change business logic
 ** pm identified problems
 we have a few problems to scope. looks like:
 1. normalize_giant_web not always propagating weight data to price_per
@@ -862,37 +855,45 @@ purchase_date	retailer	normalized_item_name	catalog_name	category	product_type	q
 10/10/2025	giant	BAGGED ICE	bagged ice cubes	frozen	ice	1	EA	20	lb		20	lb	weight	4.99	4.99		4.99	line_total_over_qty			0.2495	parsed_size_lb	0.0156	parsed_size_lb_to_oz	0
 ```
 ** evidence
-- commit:
-- tests:
-- datetime:
+- commit: `605c944`
+- tests: `./venv/bin/python -m unittest tests.test_purchases` (fails as expected before implementation: missing `effective_price` in purchases rows)
+- datetime: 2026-03-23 12:52:32 EDT
 
 ** notes
+- Added purchases-level regression coverage for the known comparison cases before implementation: Giant banana, Costco banana, Giant bagged ice, Costco beef patties, and a blank-denominator case.
+- The current failure mode is the intended one for this task: `build_purchase_rows()` does not yet emit `effective_price`, so the tests document the missing behavior before `t1.18.1`.
 
-* [ ] t1.18.1: fix effective price calculation precedence and blank handling (1-3 commits)
+* [x] t1.18.1: fix effective price calculation precedence and blank handling (1-3 commits)
 correct purchases/effective price logic for the known broken cases using existing normalized fields
 
 ** acceptance criteria
-1. effective_price uses explicit numerator precedence:
+1. when generating `data/purchases.csv`, add `effective_price` = `effective_total` / `normalized_quantity`
+2. effective_price uses explicit numerator precedence:
    - prefer `net_line_total`
    - fallback to `line_total`
-2. effective_price uses `normalized_quantity` when present and > 0
-3. effective_price is blank when no valid denominator exists
-4. effective_price is never written as `0` or divide-by-zero for missing-basis cases
-5. existing regression tests for bananas and ice pass
+3. effective_price uses `normalized_quantity` if not blank
+4. effective_price is blank when no valid denominator exists
+5. effective_price is never written as `0` or divide-by-zero for missing-basis cases
+6. effective_price is only comparable within same `normalized_quantity_unit` unless later analysis converts the units
+7. existing regression tests for bananas and ice pass
 - pm note: keep this limited to calculation logic; do not broaden into catalog or review changes
 
 ** evidence
-- commit:
-- tests:
-- datetime:
+- commit: `dc0d061`
+- tests: `./venv/bin/python -m unittest discover -s tests`; `./venv/bin/python build_purchases.py`
+- datetime: 2026-03-23 12:53:34 EDT
 
 ** notes
+- `effective_price` is now a downstream purchases field only. It does not replace `price_per_lb` / `price_per_each`; it gives one deterministic comparison value based on the existing normalized quantity basis.
+- The implemented precedence is: use non-zero `net_line_total` when present, otherwise `line_total`; divide by `normalized_quantity` when that denominator is > 0; otherwise leave blank.
+- This keeps the calculation conservative for mixed-quality data: Costco bananas and ice now compute correctly, while rows like Giant patties with no quantity basis stay blank instead of producing `0` or a divide-by-zero artifact.
 
 
 * [ ] t1.18.2: fix giant normalization quantity carry-through for weight-based items (1-3 commits)
 ensure giant normalization emits usable normalized quantity for known weight-based cases
 
 ** acceptance criteria
+
 1. giant bananas populate normalized quantity and unit from deterministic weight basis
 2. giant weight-based items that already produce `price_per_lb` also carry enough quantity basis for effective price calculation where supported
 3. existing regression tests pass without changing normalized_item_id behavior