diff --git a/etl/testing_data/birmingham_pilot.py b/etl/testing_data/birmingham_pilot.py
index ab39df7e..56f9cc37 100644
--- a/etl/testing_data/birmingham_pilot.py
+++ b/etl/testing_data/birmingham_pilot.py
@@ -22,49 +22,130 @@ def app():
 
     # Birmingham has a Local Authority Code of E08000025
 
+    # ~~~~~~~~~~~~~~~~~~~~
+    # First example
+    # ~~~~~~~~~~~~~~~~~~~~
     # Let's take an EPC D property
     example_1_reponse = epc_client.domestic.search(
         params={
             "local-authority": "E08000025",
             "property-type": "house",
-        }
+        },
+        size=1000
     )
+    example_1_reponse = example_1_reponse["rows"]
+    # Get a property with a cavity wall
+    example_1_reponse_filtered = [
+        x for x in example_1_reponse if
+        "cavity wall, as built, no insulation (assumed)" in x["walls-description"].lower()
+    ]
+    example_1_reponse_filtered = [
+        x for x in example_1_reponse_filtered if "pitched, no insulation (assumed)" in x["roof-description"].lower()
+    ]
+    print(example_1_reponse_filtered[0]["postcode"])
+    # 21 Penshaw Grove
+    print(example_1_reponse_filtered[0]["address1"])
+    # B13 9NL
+    print(example_1_reponse_filtered[0]["built-form"])
+    # Mid-Terrace
+    print(example_1_reponse_filtered[0]["current-energy-rating"])
+    # 'D'
 
-    g_data = epc_client.domestic.search(params={"energy-band": "g"}, size=n_g)
-    f_data = epc_client.domestic.search(params={"energy-band": "f"}, size=n_f)
-    e_data = epc_client.domestic.search(params={"energy-band": "e"}, size=n_e)
-    d_data = epc_client.domestic.search(params={"energy-band": "d"}, size=n_d)
-    c_data = epc_client.domestic.search(params={"energy-band": "c"}, size=n_c)
-    b_data = epc_client.domestic.search(params={"energy-band": "b"}, size=n_b)
-    a_data = epc_client.domestic.search(params={"energy-band": "a"}, size=n_a)
+    # ~~~~~~~~~~~~~~~~~~~~
+    # Second example
+    # ~~~~~~~~~~~~~~~~~~~~
 
-    # Combine the final data
-    final_data = (
-        g_data["rows"] + f_data["rows"] + e_data["rows"] + d_data["rows"] + c_data["rows"] + b_data["rows"]
-        + a_data["rows"]
+    # Let's take an EPC E property
+    example_2_reponse = epc_client.domestic.search(
+        params={
+            "local-authority": "E08000025",
+            "property-type": "house",
+            "energy-band": "e"
+        },
+        size=1000
     )
-
-    # TODO: We also take homes with just a specific type of wall
-
-    final_data = [
-        x for x in final_data if ("cavity wall" in x["walls-description"].lower()) or (
-            "solid brick" in x["walls-description"].lower()
-        ) or ("average thermal transmittance" in x["walls-description"].lower())
+    example_2_reponse = example_2_reponse["rows"]
+    # Get a solid wall example
+    example_2_reponse_filtered = [
+        x for x in example_2_reponse if
+        "solid brick, as built, no insulation (assumed)" in x["walls-description"].lower()
+    ]
+    # With some existing loft insulation
+    example_2_reponse_filtered = [
+        x for x in example_2_reponse_filtered if "pitched, 100 mm loft insulation" in x["roof-description"].lower()
     ]
 
-    # TODO: For the moment, don't use park homes
-    final_csv_data = pd.DataFrame(
-        [{"address": x["address"], "postcode": x["postcode"], "Notes": None} for x
-         in final_data if
-         x["property-type"] not in ["Park home"]]
-    )
+    print(example_2_reponse_filtered[0]["postcode"])
+    # B13 9BY
+    print(example_2_reponse_filtered[0]["address1"])
+    # 2 Bloomfield Road
+    print(example_2_reponse_filtered[0]["built-form"])
+    # Semi-Detached
+    print(example_2_reponse_filtered[0]["current-energy-rating"])
+    # E
 
-    final_csv_data = pd.concat([starting_csv, final_csv_data]).reset_index(drop=True)
+    # ~~~~~~~~~~~~~~~~~~~~
+    # Third example
+    # ~~~~~~~~~~~~~~~~~~~~
+    example_3_reponse = epc_client.domestic.search(
+        params={
+            "local-authority": "E08000025",
+            "property-type": "house",
+            "energy-band": "f"
+        },
+        size=1000
+    )
+    example_3_reponse = example_3_reponse["rows"]
+    print(example_3_reponse[2]["walls-description"])
+    print(example_3_reponse[2]["floor-description"])
+    print(example_3_reponse[3]["roof-description"])
+    print(example_3_reponse[3]["postcode"])
+    # B32 3DG
+    print(example_3_reponse[3]["address1"])
+    # 18 Parkside
+    print(example_3_reponse[3]["built-form"])
+    # End-Terrace
+
+    # ~~~~~~~~~~~~~~~~~~~~
+    # Final example
+    # ~~~~~~~~~~~~~~~~~~~~
+    # Let's take a flat that is a D
+    example_4_reponse = epc_client.domestic.search(
+        params={
+            "local-authority": "E08000025",
+            "property-type": "flat",
+            "energy-band": "d"
+        },
+        size=1000
+    )
+    example_4_reponse = example_4_reponse["rows"]
+
+    example_4_reponse_filtered = [
+        x for x in example_4_reponse if
+        "cavity wall, as built, no insulation (assumed)" in x["walls-description"].lower()
+    ]
+    print(example_4_reponse_filtered[0]["postcode"])
+    # B14 6PU
+    print(example_4_reponse_filtered[0]["address1"])
+    # Flat 3
+
+    print(example_4_reponse_filtered[0]["floor-description"])
+    print(example_4_reponse_filtered[0]["property-type"])
+    # Flat
+
+    test_file = pd.DataFrame(
+        [
+            {"address": "21 Penshaw Grove", "postcode": "B13 9NL", "Notes": None},
+            {"address": "2 Bloomfield Road", "postcode": "B13 9BY", "Notes": None},
+            {"address": "18 Parkside", "postcode": "B32 3DG", "Notes": None},
+            {"address": "Flat 3", "postcode": "B14 6PU", "Notes": None},
+        ]
+    )
 
     # Store the data in s3
     filename = f"{USER_ID}/{PORTFOLIO_ID}/test_inputs.csv"
     save_csv_to_s3(
-        dataframe=final_csv_data,
+        dataframe=test_file,
         bucket_name="retrofit-plan-inputs-dev",
         file_name=filename
     )
@@ -73,7 +154,7 @@ def app():
         "portfolio_id": str(PORTFOLIO_ID),
         "housing_type": "Social",
         "goal": "Increase EPC",
-        "goal_value": "B",
+        "goal_value": "C",
         "trigger_file_path": filename
     }
     print(body)