diff --git a/etl/customers/immo/pilot/asset_list.py b/etl/customers/immo/pilot/asset_list.py
index 269ffe00..7939a555 100644
--- a/etl/customers/immo/pilot/asset_list.py
+++ b/etl/customers/immo/pilot/asset_list.py
@@ -1,18 +1,7 @@
-import os
-
 import pandas as pd
-from tqdm import tqdm
-
-from dotenv import load_dotenv
 from utils.s3 import read_excel_from_s3
-from backend.SearchEpc import SearchEpc
-from epc_api.client import EpcClient
 from utils.s3 import save_csv_to_s3
 
-# Read in the .env file in backend
-load_dotenv(dotenv_path="backend/.env")
-EPC_AUTH_TOKEN = os.getenv("EPC_AUTH_TOKEN")
-
 USER_ID = 8
 PORTFOLIO_ID = 70
 
@@ -28,6 +17,7 @@ council_tax_bands = [
     {'address': '6 Beech Road', 'postcode': 'DY1 4BP', 'band': 'A'},
     {'address': '5 Oaklands', 'postcode': 'B62 0JA', 'band': 'A'},
 ]
+council_tax_bands = pd.DataFrame(council_tax_bands)
 
 
 def app():
@@ -41,7 +31,6 @@ def app():
     raw_asset_list["address"] = raw_asset_list["Full Address"].str.split(",").str[0]
     raw_asset_list["postcode"] = raw_asset_list["Full Address"].str.split(",").str[-1].str.strip()
 
-    council_tax_bands = pd.DataFrame(council_tax_bands)
     asset_list = raw_asset_list.merge(council_tax_bands, how="left", on=["address", "postcode"])
 
     # Store the data in s3
diff --git a/recommendations/optimiser/optimiser_functions.py b/recommendations/optimiser/optimiser_functions.py
index 27838d6e..9860c5ea 100644
--- a/recommendations/optimiser/optimiser_functions.py
+++ b/recommendations/optimiser/optimiser_functions.py
@@ -20,10 +20,6 @@ def prepare_input_measures(property_recommendations, goal, housing_type):
     if not goal_key:
         raise NotImplementedError("Not implemented this gain type - investigate me")
 
-    # We don't include suspended and solid floor insulation as possible measures in private housing, because
-    # of the need to decant the tenant
-    ignored_measures = ["suspended_floor_insulation", "solid_floor_insulation"] if housing_type == "Private" else []
-
     input_measures = []
     for recs in property_recommendations:
         input_measures.append(
@@ -34,7 +30,7 @@ def prepare_input_measures(property_recommendations, goal, housing_type):
                     "gain": rec[goal_key],
                     "type": rec["type"]
                 }
-                for rec in recs if rec["type"] not in ignored_measures
+                for rec in recs
             ]
         )