diff --git a/etl/eligibility/ha_15_32/ha_analysis_batch_3.py b/etl/eligibility/ha_15_32/ha_analysis_batch_3.py
index 9462642f..a0b7e0bb 100644
--- a/etl/eligibility/ha_15_32/ha_analysis_batch_3.py
+++ b/etl/eligibility/ha_15_32/ha_analysis_batch_3.py
@@ -5210,7 +5210,7 @@ def fml_analysis(loader):
     ]
 
     # TODO: There will be some properties that are subject to CIGA that do not look like they ned a CIGA check! pass
-    #  them!
+    #  them! Non-invasices will have checked the wall though
 
     codes = [
         "HA39", "HA14", "HA24", "HA15", "HA32", "HA28", "HA6", "HA1", "HA7",
@@ -5352,16 +5352,11 @@ def fml_analysis(loader):
             ]
 
         # Characterise no CIGA check needed
-
-        # TODO: WHAT ABOUT PASSED CIGA - don't need to apply the further deduction
-
         ciga_check_needed = had_survey[
             had_survey["ECO Eligibility"].str.contains("subject to ciga")
         ].copy()
 
-        ciga_check_passed = had_survey[
-            had_survey["ECO Eligibility"] == "eco4 - passed ciga"
-            ]
+        ciga_check_passed = had_survey[had_survey["ECO Eligibility"] == "eco4 - passed ciga"]
         # These should be treated the same as one that have passed their ciga checks, from a detection perspective
         ciga_check_passed_eligible = ciga_check_passed[
             (ciga_check_passed["walls-description"].str.lower().str.contains("cavity") == True) &
@@ -5469,18 +5464,15 @@ def fml_analysis(loader):
                 "HA Name": ha_name,
                 "Original ECO4 Estimate - Remaining": original_remaining,
                 "Postcode List - Remaining": postcode_list_remaining,
-                "Of which sold": sales_since_nov,
+                # "Of which sold": sales_since_nov,
                 "Of which ECO4 Eligible - Remaining": int(total_expectation),
                 "Of which GBIS Eligibile - Remaining": int(total_gbis_expectation),
-                "Proportion with a survey": proportion_with_survey,
+                # "Proportion with a survey": proportion_with_survey,
             }
         )
 
     results_df = pd.DataFrame(results)
-
-    wall_descriptions = list(set(wall_descriptions))
-    from pprint import pprint
-    pprint(wall_descriptions)
+    results_df.to_csv("analysis - revised.csv")
 
     # results_df["Delta vs November"] = 100 * (
     #     results_df["Of which ECO4 Eligible - Remaining"] - results_df["Original ECO4 Estimate - Remaining"]