diff --git a/backend/Property.py b/backend/Property.py
index 4b5e1304..dc847750 100644
--- a/backend/Property.py
+++ b/backend/Property.py
@@ -58,7 +58,7 @@ class Property(Definitions):
         self.full_sap_epc = full_sap_epc
         self.property_dimensions = None
 
-        self.uprn = None if data is None else data["uprn"]
+        self.uprn = None if data is None else int(data["uprn"])
 
         self.in_conservation_area, self.is_listed, self.is_heritage = None, None, None
         self.restricted_measures = False
diff --git a/backend/SearchEpc.py b/backend/SearchEpc.py
index 9d3ecd42..4953b04c 100644
--- a/backend/SearchEpc.py
+++ b/backend/SearchEpc.py
@@ -584,6 +584,11 @@ class SearchEpc:
 
             estimated_epc[key] = estimated_value
 
+        # Insert an estimated lodgement datetime, with a weighted average
+        estimated_epc["lodgement-datetime"] = self.calculate_weighted_lodgement_datetime(epc_data=epc_data)
+        # Extract logement date
+        estimated_epc["lodgement-date"] = estimated_epc["lodgement-datetime"].strftime("%Y-%m-%d")
+
         estimated_epc["postcode"] = self.postcode
         estimated_epc["uprn"] = self.uprn
         # Indicate that this epc was estimated
@@ -591,6 +596,24 @@ class SearchEpc:
 
         return estimated_epc
 
+    @staticmethod
+    def calculate_weighted_lodgement_datetime(epc_data):
+        numeric_dates = pd.to_datetime(epc_data['lodgement-datetime']).view('int64')
+
+        # Calculate the weighted sum of dates
+        weighted_sum = (numeric_dates * epc_data['weight']).sum()
+
+        # Calculate the sum of weights
+        total_weights = epc_data['weight'].sum()
+
+        # Calculate the weighted mean in numeric format
+        weighted_mean_numeric = weighted_sum / total_weights
+
+        # Convert the numeric weighted mean back to datetime
+        weighted_mean_datetime = pd.to_datetime(weighted_mean_numeric)
+
+        return weighted_mean_datetime
+
     @staticmethod
     def _estimate_int(estimation_data, key):
         return round(np.average(a=estimation_data[key], weights=estimation_data["weight"]))