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 61a90d14..05c0299c 100644
--- a/etl/eligibility/ha_15_32/ha_analysis_batch_3.py
+++ b/etl/eligibility/ha_15_32/ha_analysis_batch_3.py
@@ -142,7 +142,6 @@ class DataLoader:
         # We now do the matching between the asset list and the survey list.
         # What we'll get from this is a lookup table from the asset list to the survey list
 
-        matched_lookup = pd.DataFrame()
         if ha_name == "ha_6":
             matched_lookup = self.merge_ha_6(asset_list, survey_list)
         else:
@@ -261,8 +260,9 @@ class DataLoader:
             "Brittania Avenue", "Brittain Avenue"
         )
 
-        # Moffat Way
-        # Moffatt Way
+        survey_list["Street / Block Name"] = survey_list["Street / Block Name"].str.replace(
+            "Hawthorn Road", "Hawthorne Road"
+        )
 
         matching_lookup = []
         for _, row in tqdm(survey_list.iterrows(), total=len(survey_list)):
@@ -274,6 +274,7 @@ class DataLoader:
             df = asset_list[
                 asset_list["matching_address"].str.contains(row["Street / Block Name"].lower().strip())
             ].copy()
+
             df = df[df["matching_address"].str.contains(str(house_number))]
             if df.shape[0] != 1:
                 df = df[df["HouseNo"] == str(house_number)]