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 0c9f685f..4ae881d2 100644
--- a/etl/eligibility/ha_15_32/ha_analysis_batch_3.py
+++ b/etl/eligibility/ha_15_32/ha_analysis_batch_3.py
@@ -155,6 +155,10 @@ class DataLoader:
         "HA24": {
             "address": "Address",
             "postcode": "Postcode"
+        },
+        "HA25": {
+            "address": "T1_Address",
+            "postcode": "matching_postcode"
         }
     }
 
@@ -178,7 +182,7 @@ class DataLoader:
 
     def create_asset_list_matching_address(self, ha_name, asset_list):
 
-        if ha_name in ["HA1", "HA6", "HA16", "HA24"]:
+        if ha_name in ["HA1", "HA6", "HA16", "HA24", "HA25"]:
             asset_list["matching_address"] = asset_list[
                 self.COLUMN_CONFIG[ha_name]["address"]
             ].astype(str).str.lower().str.strip()
@@ -374,13 +378,23 @@ class DataLoader:
         asset_sheetname = self.get_asset_sheetname(workbook)
         asset_sheet = workbook[asset_sheetname]
         asset_sheet_colnames = [cell.value for cell in asset_sheet[1]]
+        if ha_name == "HA25":
+            asset_sheet_colnames[11] = "matching_postcode"
+
+        values_only = not ha_name != "HA25"
 
         rows_data = []
-        for row in asset_sheet.iter_rows(min_row=2, values_only=False):
-            row_data = [cell.value for cell in row]  # This will get you the cell values
-            rows_data.append(row_data)
+        if not values_only:
+            for row in asset_sheet.iter_rows(min_row=2, values_only=values_only):
+                row_data = [cell.value for cell in row]  # This will get you the cell values
+                rows_data.append(row_data)
+        else:
+            for row in asset_sheet.iter_rows(min_row=2, values_only=values_only):  # use values_only=True to get values
+                row_data = list(row)  # No need for comprehension, values_only=True returns a tuple of values
+                rows_data.append(row_data)
 
         asset_list = pd.DataFrame(rows_data, columns=asset_sheet_colnames)
+
         asset_list = asset_list.loc[:, asset_list.columns.notnull()]
 
         # Remove entirely empty rows - consider all rows apart from row_color
@@ -403,9 +417,10 @@ class DataLoader:
             asset_list_correction_function = getattr(self, f"correct_{ha_name.lower()}_asset_list")
             asset_list = asset_list_correction_function(asset_list)
 
-        # For HA1, there is an exception in the structure of the data. We don't have any survey or ciga lists, and so
+        # For HA1 and HA25, there is an exception in the structure of the data. We don't have any survey or ciga
+        # lists, and so
         # we can return the asset list now
-        if ha_name == "HA1":
+        if ha_name in ["HA1", "HA25"]:
             return asset_list, pd.DataFrame(), pd.DataFrame()
 
         # We check if there is a survey list
@@ -1149,7 +1164,8 @@ class DataLoader:
             "ECO4": "ECO4",
             "AFFORDABLE WARMTH": "ECO4",
             "ECO4 A/W": "ECO4",
-            "ECO4 GBIS (ECO+)": "GBIS"
+            "ECO4 GBIS (ECO+)": "GBIS",
+            "ECO4 GBIS (ECO+) JJC UNDER 73m²": "GBIS"
         }
 
         eco_eligibility_map = {
@@ -3305,6 +3321,8 @@ def app():
     december_figures_filepath = "local_data/ha_data/HA_December_figures.csv"
 
     priority_has = ["HA1", "HA6", "HA7", "HA14", "HA16", "HA24", "HA25", "HA39", "HA107"]
+    # Next HAs to do: 15, 32, 33,
+    # Then: 28, 41, 38, 10, 14, 20, 48
     # Filter down the directories to only the priority HAs
     directories = [d for d in directories if d.split("/")[2] in priority_has]