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 0daf239b..b5c6835b 100644
--- a/etl/eligibility/ha_15_32/ha_analysis_batch_3.py
+++ b/etl/eligibility/ha_15_32/ha_analysis_batch_3.py
@@ -3059,20 +3059,18 @@ def forecast_remaining_sales(loader):
             totals_row[col] = results[col].sum()
 
     # For the delta columns, we calculate the delta on the totals
-    totals_row[("ECO4 post-ciga", "", "Delta vs original estimate - %", "")] = round(
-        100 * (
+    totals_row[("ECO4 post-ciga", "", "Delta vs original estimate - %", "")] = (
+        (
             totals_row[("ECO4 post-ciga", "", "Estimated total eligible - #", "ECO4 total (post-ciga)")] -
             totals_row[("", "Original Warmfront estimate", "Total - #", "ECO4 - November")]
-        ) / totals_row[("", "Original Warmfront estimate", "Total - #", "ECO4 - November")],
-        1
+        ) / totals_row[("", "Original Warmfront estimate", "Total - #", "ECO4 - November")]
     )
 
-    totals_row[("GBIS Postcode list", "", "Delta vs original estimate - %", "")] = round(
-        100 * (
+    totals_row[("GBIS Postcode list", "", "Delta vs original estimate - %", "")] = (
+        (
             totals_row[("GBIS Postcode list", "Warmfront post code list", "Total - #", "GBIS total")] -
             totals_row[("", "Original Warmfront estimate", "Total - #", "GBIS - November")]
-        ) / totals_row[("", "Original Warmfront estimate", "Total - #", "GBIS - November")],
-        1
+        ) / totals_row[("", "Original Warmfront estimate", "Total - #", "GBIS - November")]
     )
 
     blank_row = pd.DataFrame([{col: "" for col in results.columns}])