diff --git a/backend/SearchEpc.py b/backend/SearchEpc.py
index 6fbdb3ea..1d5c2296 100644
--- a/backend/SearchEpc.py
+++ b/backend/SearchEpc.py
@@ -440,6 +440,8 @@ class SearchEpc:
             params = {"postcode": postcode}
             if property_type:
                 params["property-type"] = property_type_api_map[property_type]
+
+            # We take the 20 nearest homes of the relevant type, so not to pull in too many irrelevant homes
             epc_response = self.get_epc(params=params, size=100)
 
             if epc_response["status"] == 200:
diff --git a/etl/testing_data/estimate_epc.py b/etl/testing_data/estimate_epc.py
index 7a03edc3..9e460678 100644
--- a/etl/testing_data/estimate_epc.py
+++ b/etl/testing_data/estimate_epc.py
@@ -142,12 +142,44 @@ def app():
     avg_numeric_succes = results_df["numeric_success"].median()
     avg_categorical_sucess = results_df["categorical_success"].median()
 
-    # Before changing the search methodology: 0.7963985988175015, 0.5348837209302325
+    # With 20 nearest homes
+    # 0.7718100840549558
+    # 0.5116279069767442
+    # 100 nearest homes
+    # 0.7859617377809409
+    # 0.5348837209302325
 
     # Group by tenure
     by_tenure = results_df.groupby("tenure").agg(
         {"numeric_success": "median", "categorical_success": "median", "uprn": "count"}
     )
+    pd.set_option('display.max_rows', 500)
+    pd.set_option('display.max_columns', 500)
+    pd.set_option('display.width', 1000)
+
+    # With 20 nearest homes
+    #                                                     numeric_success  categorical_success   uprn
+    # tenure
+    # NO DATA!                                                   0.847840             0.581395    278
+    # Not defined - use in the case of a new dwelling...         0.930282             0.651163    617
+    # Owner-occupied                                             0.770330             0.511628   2588
+    # Rented (private)                                           0.791885             0.558140   1232
+    # owner-occupied                                             0.741088             0.488372  10912
+    # rental (private)                                           0.749064             0.488372   3252
+    # rental (social)                                            0.822109             0.581395   3878
+    # unknown                                                    0.895840             0.627907   1820
+
+    # 100 nearest homes
+    # tenure
+    # NO DATA!                                                   0.899566             0.604651    233
+    # Not defined - use in the case of a new dwelling...         0.927518             0.674419    608
+    # Owner-occupied                                             0.777026             0.511628   3167
+    # Rented (private)                                           0.805646             0.534884   1316
+    # owner-occupied                                             0.762180             0.488372  10835
+    # rental (private)                                           0.760503             0.511628   3181
+    # rental (social)                                            0.830057             0.604651   3705
+    # unknown                                                    0.899948             0.627907   1571
+
     # By property type - we also want to see how many properties we have for each property type
     by_property_type = results_df.groupby("property_type").agg(
         {"numeric_success": "median", "categorical_success": "median", "uprn": "count"}