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 bb27029e..21af73ff 100644
--- a/etl/eligibility/ha_15_32/ha_analysis_batch_3.py
+++ b/etl/eligibility/ha_15_32/ha_analysis_batch_3.py
@@ -2728,15 +2728,22 @@ def forecast_remaining_sales(loader):
     # Produce the final output
     december_figures = loader.december_figures.copy()
     december_figures = december_figures.fillna(0)
+    # If we have negative remaining, it means that actually sold more gbis than they initially thought so we set
+    # remaining to 0
+    december_figures["ECO4 remaining"] = np.where(
+        december_figures["ECO4 remaining"] < 0, 0, december_figures["ECO4 remaining"]
+    )
+    december_figures["GBIS remaining"] = np.where(
+        december_figures["GBIS remaining"] < 0, 0, december_figures["GBIS remaining"]
+    )
+
     results = []
     for ha_name, input_data in loader.data.items():
-        # Original warmfront figures
+        # Original warmfront figures - ECO4
         original_warmfront_estimates = december_figures[december_figures["HA Name"] == ha_name]
 
         original_warmfront_eco4 = original_warmfront_estimates["ECO4"].values[0]
         original_warmfront_remaining_eco4 = original_warmfront_estimates["ECO4 remaining"].values[0]
-        original_warmfront_gbis = original_warmfront_estimates["GBIS"].values[0]
-        original_warmfront_remaining_gbis = original_warmfront_estimates["GBIS remaining"].values[0]
 
         original_warmfront_eco4_revenue = (
             original_warmfront_remaining_eco4 * eco4_rate +
@@ -2744,21 +2751,79 @@ def forecast_remaining_sales(loader):
         )
         original_warmfront_remaining_eco4_revenue = original_warmfront_remaining_eco4 * eco4_rate
 
+        # Original warmfront figures - GBIS
+
+        original_warmfront_gbis = original_warmfront_estimates["GBIS"].values[0]
+        original_warmfront_remaining_gbis = original_warmfront_estimates["GBIS remaining"].values[0]
+
         original_warmfront_gbis_revenue = (
             original_warmfront_remaining_gbis * gbis_rate +
             (original_warmfront_gbis - original_warmfront_remaining_gbis) * old_gbis_rate
         )
+        original_warmfront_remaining_gbis_revenue = original_warmfront_remaining_gbis * gbis_rate
+
+        # Asset list
+        asset_list = input_data["asset_list"].copy()
+        survey_list = input_data["survey_list"].copy()
+
+        asset_list_remaining = asset_list.merge(
+            survey_list[["asset_list_row_id", "installation_status"]],
+            how="left",
+            on="asset_list_row_id"
+        )
+        asset_list_remaining = asset_list_remaining[pd.isnull(asset_list_remaining["installation_status"])]
+
+        eligiblity_counts = pd.DataFrame(asset_list["ECO Eligibility"].value_counts()).reset_index()
+        eligiblity_counts_remaining = pd.DataFrame(asset_list_remaining["ECO Eligibility"].value_counts()).reset_index()
+
+        eco4_pre_ciga = eligiblity_counts[
+            eligiblity_counts["ECO Eligibility"].isin(
+                ["eco4", "eco4 (subject to ciga)", "eco4 - passed ciga", "failed ciga"]
+            )
+        ]["count"].sum()
+
+        eco4_pre_ciga_remaining = eligiblity_counts_remaining[
+            eligiblity_counts["ECO Eligibility"].isin(
+                ["eco4", "eco4 (subject to ciga)", "eco4 - passed ciga", "failed ciga"]
+            )
+        ]["count"].sum()
+
+        eco4_pre_ciga_revenue = eco4_pre_ciga * eco4_rate
+        eco4_pre_ciga_remaining_revenue = eco4_pre_ciga_remaining * eco4_rate
+
+        # We check if the property has done a CIGA check
+        has_ciga_check = not input_data["ciga_list"].empty
+
+        if has_ciga_check:
+            eco4_post_ciga = eligiblity_counts[
+                eligiblity_counts["ECO Eligibility"].isin(
+                    ["eco4", "eco4 (subject to ciga)", "eco4 - passed ciga", "failed ciga"]
+                )
+            ]["count"].sum()
 
         results.append(
             {
-                ("", "", "HA Name"): ha_name,
-                ("Original Warmfront estimate", "Total - #", "ECO4 - November"): original_warmfront_eco4,
-                ("", "Remaining - #", ""): original_warmfront_remaining_eco4,
-                ("", "Total - £", ""): original_warmfront_eco4_revenue,
-                ("", "Remaining - £", ""): original_warmfront_remaining_eco4_revenue,
+                ("", "", "", "HA Name"): ha_name,
+                # ECO4 - original warmfront figures
+                ("", "Original Warmfront estimate", "Total - #", "ECO4 - November"): original_warmfront_eco4,
+                ("ECO4", "", "Remaining - #", ""): original_warmfront_remaining_eco4,
+                ("ECO4", "", "Total - £", ""): original_warmfront_eco4_revenue,
+                ("ECO4", "", "Remaining - £", ""): original_warmfront_remaining_eco4_revenue,
+                # GBIS - original warmfront figures
+                ("", "Original Warmfront estimate", "Total - #", "GBIS - November"): original_warmfront_gbis,
+                ("GBIS", "", "Remaining - #", ""): original_warmfront_gbis,
+                ("GBIS", "", "Total - £", ""): original_warmfront_gbis_revenue,
+                ("GBIS", "", "Remaining - £", ""): original_warmfront_remaining_gbis_revenue,
+                # ECO4 - asset list
+                ("", "Warmfront post code list", "Total #", "ECO4 total (pre-ciga)"): eco4_pre_ciga,
+                ("ECO4", "", "Remaining - #", ""): eco4_pre_ciga_remaining,
+                ("ECO4", "", "Total - £", ""): eco4_pre_ciga_revenue,
+                ("ECO4", "", "Remaining - £", ""): eco4_pre_ciga_remaining_revenue,
             }
         )
 
+    results = pd.DataFrame(results)
+
 
 def app():
     """