diff --git a/backend/Property.py b/backend/Property.py
index 259ca724..370eca06 100644
--- a/backend/Property.py
+++ b/backend/Property.py
@@ -749,6 +749,7 @@ class Property(Definitions):
             "TOTAL_FLOOR_AREA": self.floor_area,
             **epc_raw_data,
             "BUILT_FORM": built_form,
+            "POSTCODE": self.data["postcode"],
         }
 
         return property_data
diff --git a/backend/app/plan/router.py b/backend/app/plan/router.py
index e531896e..4064452f 100644
--- a/backend/app/plan/router.py
+++ b/backend/app/plan/router.py
@@ -211,11 +211,18 @@ async def trigger_plan(body: PlanTriggerRequest):
         logger.info("Preparing data for scoring in sap change api")
         recommendations_scoring_data = pd.DataFrame(recommendations_scoring_data)
 
-        # Perform the same cleaning as in the model
+        # Perform the same cleaning as in the model - first clean number of room variables though
         recommendations_scoring_data = DataProcessor.apply_averages_cleaning(
             data_to_clean=recommendations_scoring_data,
             cleaning_data=cleaning_data,
-            cols_to_merge_on=COLUMNS_TO_MERGE_ON + ["LOCAL_AUTHORITY"]
+            cols_to_merge_on=['PROPERTY_TYPE', 'BUILT_FORM', 'CONSTRUCTION_AGE_BAND', 'LOCAL_AUTHORITY'],
+            colnames=["NUMBER_HABITABLE_ROOMS", "NUMBER_HEATED_ROOMS"],
+        )
+
+        recommendations_scoring_data = DataProcessor.apply_averages_cleaning(
+            data_to_clean=recommendations_scoring_data,
+            cleaning_data=cleaning_data,
+            cols_to_merge_on=COLUMNS_TO_MERGE_ON + ["LOCAL_AUTHORITY"],
         ).drop(columns=["LOCAL_AUTHORITY"])
 
         recommendations_scoring_data = DataProcessor.clean_missings_after_description_process(
@@ -303,7 +310,7 @@ async def trigger_plan(body: PlanTriggerRequest):
         # 3) the recommendations
 
         logger.info("Uploading recommendations to the database")
-        for i in tqdm(range(0, len(input_properties), BATCH_SIZE)):
+        for i in range(0, len(input_properties), BATCH_SIZE):
             try:
                 # Take a slice of the input_properties list to make a batch
                 batch_properties = input_properties[i:i + BATCH_SIZE]
diff --git a/etl/epc/DataProcessor.py b/etl/epc/DataProcessor.py
index 3ef485b8..0587fdbe 100644
--- a/etl/epc/DataProcessor.py
+++ b/etl/epc/DataProcessor.py
@@ -179,7 +179,6 @@ class DataProcessor:
         # We have some non-standard construction age bands which we'll clean for matching
         if not self.newdata:
             self.standardise_construction_age_band()
-
             self.clean_missing_rooms()
 
         self.recast_df_columns(
@@ -451,7 +450,7 @@ class DataProcessor:
         self.data["PHOTO_SUPPLY"] = self.data["PHOTO_SUPPLY"].fillna(0)
 
     @staticmethod
-    def apply_averages_cleaning(data_to_clean, cleaning_data, cols_to_merge_on):
+    def apply_averages_cleaning(data_to_clean, cleaning_data, cols_to_merge_on, colnames=None):
         """
         Clean the input DataFrame using averages from a cleaning DataFrame.
 
@@ -459,11 +458,16 @@ class DataProcessor:
         :param cleaning_data: DataFrame containing data for cleaning.
         :param cols_to_merge_on: Columns on which merging is based. We pass cols_to_merge_on to this function as this
                                  differs depending on where the function is being used.
+        :param colnames: If specified can be used to state exactly which columns to clean
         :return: Cleaned DataFrame.
         """
 
+        # The desired colnames to clean - which may not be present
+        if colnames is None:
+            colnames = ["TOTAL_FLOOR_AREA", "FLOOR_HEIGHT", "FIXED_LIGHTING_OUTLETS_COUNT"]
+
         cols_to_clean = [
-            c for c in ["TOTAL_FLOOR_AREA", "FLOOR_HEIGHT", "FIXED_LIGHTING_OUTLETS_COUNT"] if
+            c for c in colnames if
             c in data_to_clean.columns
         ]
 
@@ -492,6 +496,8 @@ class DataProcessor:
         for col in cols_to_clean:
             data_to_clean[col].fillna(data_to_clean[f"{col}_AVERAGE"], inplace=True)
             data_to_clean.drop(columns=[f"{col}_AVERAGE"], inplace=True)
+            # If we still have missings
+            data_to_clean[col].fillna(data_to_clean[col].mean(), inplace=True)
 
         return data_to_clean