diff --git a/etl/customers/goldman/property_ownership.py b/etl/customers/goldman/property_ownership.py
index 5d39f139..1c828566 100644
--- a/etl/customers/goldman/property_ownership.py
+++ b/etl/customers/goldman/property_ownership.py
@@ -754,6 +754,39 @@ def app():
         else:
             raise NotImplementedError("What happened here?")
 
+    land_registry_matches = pd.DataFrame(land_registry_matches)
+    # land_registry_matches.to_excel("land_registry_matches.xlsx")
+
+    # Check the matches against the addresses
+    # lr_to_addresses = matched_addresses[
+    #     ["UPRN", "epc_address", "epc_postcode", "Property Address", "Postcode"]
+    # ].merge(
+    #     land_registry_matches,
+    #     how="inner",
+    #     left_on="UPRN",
+    #     right_on="uprn"
+    # ).drop(columns=["uprn"]).merge(
+    #     land_registry[["transaction_id", "paon", "saon", "street", "postcode"]],
+    #     how="left", on="transaction_id"
+    # )
+
+    # Merge onto matched addresses
+    matched_addresses = matched_addresses.merge(
+        land_registry_matches,
+        how="left",
+        left_on="UPRN",
+        right_on="uprn"
+    ).drop(columns=["uprn"])
+
+    # Flat anything that sold in the last year
+    # TODO: Decide on what this logic should be!
+    matched_addresses["sold_recently"] = (
+        matched_addresses["date_of_transfer"] >= pd.Timestamp.now() - pd.DateOffset(years=1)
+    )
+
+    # Drop anything that sold recently
+    matched_addresses = matched_addresses[~matched_addresses["sold_recently"]]
+
     # shared_freehold_match = pd.DataFrame(shared_freehold_match)
     # Strore these files
     # freehold_matching_lookup.to_excel("freehold_matching_lookup.xlsx")
@@ -785,11 +818,30 @@ def app():
         matched_addresses["Company Registration No. (1)"].isin(investment_50m["Company Registration No. (1)"])
     ]
 
+    # Merge on the owner
+    al_rayan = investment_50m_properties[
+        investment_50m_properties["Proprietor Name (1)"].str.contains("AL RAYAN BANK PLC")]
+
     portfolio_epc_data_50m = properties[properties["UPRN"].isin(investment_50m_properties["UPRN"])]
     portfolio_epc_data_20m = properties[properties["UPRN"].isin(investment_20m_properties["UPRN"])]
 
-    investment_20m_properties.to_excel("investment_20m_properties 28th May.xlsx", index=False)
-    investment_50m_properties.to_excel("investment_50m_properties 28th May.xlsx", index=False)
+    # investment_20m_properties.to_excel("investment_20m_properties 28th July.xlsx", index=False)
+    # investment_50m_properties.to_excel("investment_50m_properties 28th July.xlsx", index=False)
+
+    z = pd.read_excel("investment_50m_properties 28th May.xlsx")
+    new = investment_50m_properties[~investment_50m_properties["UPRN"].isin(z["UPRN"])]
+    new_al_rayan = new[
+        new["Proprietor Name (1)"].str.contains("AL RAYAN BANK PLC")
+    ]
+    new_al_rayan = new_al_rayan.merge(
+        properties[["UPRN", "LODGEMENT_DATE"]],
+        how="left",
+        on="UPRN"
+    ).merge(
+        company_ownership[["Title Number", "Date Proprietor Added"]],
+        how="left",
+        on="Title Number",
+    )
 
     # Store the EPC data
     portfolio_epc_data_50m.to_excel("portfolio_epc_data_50m 28th May.xlsx", index=False)