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 e1d7db4d..53ce69e2 100644
--- a/etl/eligibility/ha_15_32/ha_analysis_batch_3.py
+++ b/etl/eligibility/ha_15_32/ha_analysis_batch_3.py
@@ -5200,6 +5200,22 @@ def fml_analysis(loader):
         "Cavity wall, as built, partial insulation (assumed)"
     ]
 
+    codes = [
+        "HA39", "HA14", "HA24", "HA15", "HA32", "HA28", "HA6", "HA1", "HA7",
+        "HA16", "HA107", "HA25", "HA50", "HA41", "HA48", "HA2", "HA63", "HA12",
+        "HA117", "HA13", "HA35", "HA34", "HA56", "HA19", "HA18", "HA9", "HA27",
+        "HA30", "HA31", "HA54", "HAXX", "HA49", "HAXXX"
+    ]
+
+    values = [
+        706, 2161, 1053, 793, 0, 656, 1200, 1647, 4248, 2703, 1087, 1876, 2135,
+        1078, 775, 538, 518, 401, 466, 2627, 98, 1050, 524, 191, 538, 384, 204,
+        281, 422, 74, 313, 71, 6
+    ]
+
+    # Create a dictionary mapping
+    remaining_eligible_mapping = dict(zip(codes, values))
+
     results = []
     for ha_name in has_bruh:
 
@@ -5207,6 +5223,7 @@ def fml_analysis(loader):
             loader.december_figures["HA Name"] == ha_name
             ].copy()
         original_remaining = original_figures["ECO4 remaining"].values[0]
+        postcode_list_remaining = remaining_eligible_mapping[ha_name]
 
         # Read in the epc data
         asset_list = loader.data[ha_name]["asset_list"].copy()
@@ -5271,7 +5288,7 @@ def fml_analysis(loader):
             raise Exception("SOMETHING WENT WRONG")
 
         if any(fuck_this["ECO Eligibility"].str.contains("subject to archetype")):
-            blah
+            raise Exception("DO THE DAMN ARCHETYPE CHECK BRO")
 
         # clean roof insulation
         fuck_this["roof_insulation_thickness"] = fuck_this["roof_insulation_thickness"].fillna("0")
@@ -5313,6 +5330,13 @@ def fml_analysis(loader):
             (no_ciga_check_needed["current-energy-efficiency"].astype(float) <= 80)
             ]
 
+        # For anything not needing a CIGA check, some of it will be GBIS
+        no_ciga_check_needed_eligible_gbis = no_ciga_check_needed[
+            (no_ciga_check_needed["walls-description"].isin(no_ciga_cavity_descriptions)) &
+            (no_ciga_check_needed["current-energy-efficiency"].astype(float) <= 80) &
+            (~no_ciga_check_needed["asset_list_row_id"].isin(no_ciga_check_needed_eligible["asset_list_row_id"].values))
+            ]
+
         # Characterise no CIGA check needed
 
         # TODO: WHAT ABOUT PASSED CIGA - don't need to apply the further deduction
@@ -5359,6 +5383,8 @@ def fml_analysis(loader):
         # Need to add on the non-ciga
         total_expectation = ciga_check_expectation + without_ciga_expectation + passed_ciga_expectation
 
+        total_gbis_expectation = no_ciga_check_needed_eligible_gbis.shape[0]
+
         if proportion_with_survey < 100:
             # We estimate the rest
             without_survey_needing_ciga = fuck_this[
@@ -5395,12 +5421,17 @@ def fml_analysis(loader):
 
             if without_survey_eco4.empty:
                 without_survey_eco4_expected = 0
+                without_survey_gbis_expected = 0
             else:
                 # We apply the same conversion rate as the properties with a survey
                 without_survey_eco4_expected = np.round(
                     without_survey_eco4.shape[0] * (without_ciga_expectation / no_ciga_check_needed.shape[0])
                 )
 
+                without_survey_gbis_expected = np.round(
+                    without_survey_eco4.shape[0] * (total_gbis_expectation / no_ciga_check_needed.shape[0])
+                )
+
             total_expectation = (
                 total_expectation +
                 without_survey_without_ciga_expected +
@@ -5408,6 +5439,8 @@ def fml_analysis(loader):
                 without_survey_eco4_expected
             )
 
+            total_gbis_expectation = total_gbis_expectation + without_survey_gbis_expected
+
         surveys = loader.data[ha_name]["survey_list"]
         sold_now = 0
         if not surveys.empty:
@@ -5421,20 +5454,22 @@ 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 ECO4 Eligible - Remaining": int(total_expectation),
+                "Of which GBIS Eligibile - Remaining": int(total_gbis_expectation),
                 "Proportion with a survey": proportion_with_survey,
             }
         )
 
     results_df = pd.DataFrame(results)
 
-    results_df["Delta vs November"] = 100 * (
-        results_df["Of which ECO4 Eligible - Remaining"] - results_df["Original ECO4 Estimate - Remaining"]
-    ) / results_df["Original ECO4 Estimate - Remaining"]
+    # results_df["Delta vs November"] = 100 * (
+    #     results_df["Of which ECO4 Eligible - Remaining"] - results_df["Original ECO4 Estimate - Remaining"]
+    # ) / results_df["Original ECO4 Estimate - Remaining"]
 
-    # TODO: Split into high and low confidence?
-    #
+    # TODO: Add in estimated GBIS (for eco jobs, of which look like gbis)
+    # TODO: Change the left hand side number for our post CIGA estimates
 
 
 def app():