diff --git a/.idea/Model.iml b/.idea/Model.iml
index 09f2e496..c6561970 100644
--- a/.idea/Model.iml
+++ b/.idea/Model.iml
@@ -7,7 +7,7 @@
       <sourceFolder url="file://$MODULE_DIR$/open_uprn" isTestSource="false" />
       <sourceFolder url="file://$MODULE_DIR$/recommendations" isTestSource="false" />
     </content>
-    <orderEntry type="jdk" jdkName="AssetList" jdkType="Python SDK" />
+    <orderEntry type="jdk" jdkName="Fastapi-backend" jdkType="Python SDK" />
     <orderEntry type="sourceFolder" forTests="false" />
   </component>
 </module>
\ No newline at end of file
diff --git a/.idea/misc.xml b/.idea/misc.xml
index fb10c6b0..50cad4ca 100644
--- a/.idea/misc.xml
+++ b/.idea/misc.xml
@@ -3,7 +3,7 @@
   <component name="Black">
     <option name="sdkName" value="Python 3.10 (backend)" />
   </component>
-  <component name="ProjectRootManager" version="2" project-jdk-name="AssetList" project-jdk-type="Python SDK" />
+  <component name="ProjectRootManager" version="2" project-jdk-name="Fastapi-backend" project-jdk-type="Python SDK" />
   <component name="PyCharmProfessionalAdvertiser">
     <option name="shown" value="true" />
   </component>
diff --git a/asset_list/AssetList.py b/asset_list/AssetList.py
index ad3087c3..21376708 100644
--- a/asset_list/AssetList.py
+++ b/asset_list/AssetList.py
@@ -301,6 +301,14 @@ class AssetList:
         "Potential unsafe environment", "Date of Inspection", "Borescoped?"
     ]
 
+    # Another version of non-intrusives:
+    NON_INTRUSIVES_NEW_FORMAT_COLNAMES_V2 = [
+        'Archetype', 'Archetype 2', 'Construction', 'Insulated', 'Material', 'Boroscoped?',
+        'CIGA Check Required', 'ROOF ORIENTATION', 'TILE HUNG', 'RENDERED',
+        'CLADDING', 'ACCESS ISSUES', 'FURTHER SURVEYOR NOTES', 'DATE',
+        'NAME OF SURVEYOR'
+    ]
+
     NON_INTRUSIVES_ELIGIBILITY_COLUMN = "Eligibility (Red/Yellow/Green)"
 
     OLD_FORMAT_NON_INTRUSIVE_COLNAMES = ['WFT Findings', 'ECO Eligibility']
@@ -442,6 +450,8 @@ class AssetList:
         self.non_intrusives_present = "CIGA Check Required" in self.raw_asset_list.columns
         # We detect if we have the old format of non-intruvies
         self.old_format_non_intrusives_present = "WFT Findings" in self.raw_asset_list.columns
+        if self.old_format_non_intrusives_present:
+            self.non_intrusives_present = False
 
         self.non_intrusives_eligibility = "Eligibility (Red/Yellow/Green)" in self.raw_asset_list.columns
 
@@ -449,6 +459,8 @@ class AssetList:
             "Has the property been re-walled?" in self.raw_asset_list.columns
         )
 
+        self.new_format_non_insturives_present_v2 = 'TILE HUNG' in self.raw_asset_list.columns
+
         # Names of columns
         self.landlord_property_id = landlord_property_id
         self.address1_colname = address1_colname
@@ -750,7 +762,7 @@ class AssetList:
         self.rename_map = {k: v for k, v in self.rename_map.items() if k is not None}
 
         non_intrusive_columns = []
-        if self.non_intrusives_present:
+        if self.non_intrusives_present and not self.new_format_non_insturives_present_v2:
             non_intrusive_columns = self.NON_INTRUSIVES_COLNAMES
 
         if self.non_intrusives_eligibility:
@@ -759,6 +771,9 @@ class AssetList:
         if self.new_format_non_insturives_present:
             non_intrusive_columns += self.NON_INTRUSIVES_NEW_FORMAT_COLNAMES
 
+        if self.new_format_non_insturives_present_v2:
+            non_intrusive_columns += self.NON_INTRUSIVES_NEW_FORMAT_COLNAMES_V2
+
         if self.old_format_non_intrusives_present:
             # We check if we have the ECO Eligibility column, which we might not have
             non_intrusive_columns = [
@@ -827,52 +842,44 @@ class AssetList:
                 # We attempt to convert the year built to a datetime, by detecting the format and converting
 
                 def extract_year(date_str):
-                    """
-                    Extracts the year from a date string in the format '01-Jul-YYYY'.
-                    Returns the extracted year as an integer or None if the format is incorrect.
-                    """
-                    known_errors = [
+                    known_errors = {
                         "#MULTIVALUE",
+                        "ND",
+                        "PIMSS EMPTY",
+                        "UNKNOWN",
                         "This cell has an external reference that can't be shown or edited. Editing this cell will "
                         "remove the external reference.",
-                        "ND",
-                        'PIMSS EMPTY',
-                        "UNKNOWN"
-                    ]
+                        0
+                    }
 
-                    if pd.isnull(date_str) or date_str in known_errors or (date_str == 0):
+                    if pd.isnull(date_str) or date_str in known_errors:
                         return None
 
-                    if isinstance(date_str, str):
-                        match = re.match(r"\d{1,2}-[A-Za-z]{3}-(\d{4})", date_str)
-                        if match:
-                            return int(match.group(1))  # Extract the year and convert to integer
-                        if "-" in date_str:
-
-                            # Count the number of times we have "-", as we've seen double ranges
-                            # (when we have extensions) so the format is like this:
-                            # 'G: 1983-1990, H: 1991-1995'
-                            if date_str.count("-") == 2:
-                                # We have a range
-                                return int(date_str.split("-")[1].split(",")[0])
-                            # We probably have a range
-                            return int(date_str.split("-")[1].strip())
-
+                    # Handle datetime
                     if isinstance(date_str, datetime):
                         return date_str.year
 
-                    if isinstance(date_str, float):
-                        if str(int(date_str)).isdigit() & (len(str(int(date_str))) == 4):
+                    # Handle numeric year (float or int)
+                    if isinstance(date_str, (int, float)):
+                        if 1000 <= int(date_str) <= 2100:
                             return int(date_str)
 
-                    # Check if date_str is a year itself
-                    if str(date_str).isdigit() & (len(str(date_str)) == 4):
-                        return int(date_str)
+                    # Now handle string-based logic
+                    if isinstance(date_str, str):
+                        # Direct date match e.g. 01-Jul-2021
+                        match = re.match(r"\d{1,2}-[A-Za-z]{3}-(\d{4})", date_str)
+                        if match:
+                            return int(match.group(1))
 
-                    # Remove any non-numeric characters
-                    date_str = re.sub(r"\D", "", str(date_str))
-                    if str(date_str).isdigit() & (len(str(date_str)) == 4):
-                        return int(date_str)
+                        # Find all 4-digit years in string
+                        years = [int(y) for y in re.findall(r"\b(?:19|20)\d{2}\b", date_str)]
+                        if years:
+                            return max(years)  # Return most recent year
+
+                        # If only numbers are present without format
+                        numeric_str = re.sub(r"\D", "", date_str)
+                        if len(numeric_str) == 4 and numeric_str.isdigit():
+                            return int(numeric_str)
 
                     raise NotImplementedError(f"Unhandled format for year built, value is {date_str} - implement me")
 
@@ -1104,7 +1111,7 @@ class AssetList:
                 num_floors=x[self.ATTRIBUTE_NUMBER_OF_FLOORS],
                 floor_height=(
                     float(x[self.EPC_API_DATA_NAMES["floor-height"]]) if
-                    x[self.EPC_API_DATA_NAMES["floor-height"]] else 2.5
+                    not pd.isnull(x[self.EPC_API_DATA_NAMES["floor-height"]]) else 2.5
                 ),
                 perimeter=x[self.ATTRIBUTE_ESTIMATED_PERIMETER],
                 built_form=x[self.EPC_API_DATA_NAMES["built-form"]]
@@ -1315,10 +1322,16 @@ class AssetList:
 
         # Before we being, we identify if a property has solar already as we use this
         # for identifying cavity jobs
-        if self.non_intrusives_present:
-            existing_solar_non_intrusives_check = (
-                self.standardised_asset_list["non-intrusives: PV, ACCESS ISSUE, SEE NOTES"] == "SOLAR PV ON ROOF"
-            )
+        if self.non_intrusives_present and not self.old_format_non_intrusives_present:
+
+            if self.new_format_non_insturives_present_v2:
+                existing_solar_non_intrusives_check = (
+                    self.standardised_asset_list["non-intrusives: ROOF ORIENTATION"] == "ALREADY HAS SOLAR PV"
+                )
+            else:
+                existing_solar_non_intrusives_check = (
+                    self.standardised_asset_list["non-intrusives: PV, ACCESS ISSUE, SEE NOTES"] == "SOLAR PV ON ROOF"
+                )
         elif self.old_format_non_intrusives_present:
             existing_solar_non_intrusives_check = (
                 self.standardised_asset_list["non-intrusives: WFT Findings"].str.lower().str.strip().isin(
@@ -1557,7 +1570,7 @@ class AssetList:
             ) & (
                 ~self.standardised_asset_list[self.STANDARD_HEATING_SYSTEM].isin(
                     ["district heating", "communal heating", "communal gas boiler"]
-                ) & ~self.standardised_asset_list[self.STANDARD_HEATING_SYSTEM].str.contains("gas ")
+                ) & ~self.standardised_asset_list[self.STANDARD_HEATING_SYSTEM].astype(str).str.contains("gas ")
             )
         )
 
@@ -1596,12 +1609,17 @@ class AssetList:
         # With this in mind, we look for 2 clases
         # 1) The property is fully insulated apart from the loft (<200mm insulation)
         # 2) THe property is fully insulated
-
-        print("Should we include cavity properties where they might be uninsulated?")
         self.standardised_asset_list["solar_landlord_walls_insulated"] = (
             self.standardised_asset_list[self.STANDARD_WALL_CONSTRUCTION].isin(
                 [
-                    "filled cavity", "insulated solid brick", "insulated timber frame",
+                    "filled cavity",
+                    "insulated solid brick",
+                    "insulated timber frame",
+                    "uninsulated cavity",
+                    "insulated system built",
+                    "insulated granite or whinstone",
+                    "insulated sandstone or limestone",
+                    "new build - average thermal transmittance"
                 ]
             )
         )
@@ -1999,24 +2017,6 @@ class AssetList:
                     self.standardised_asset_list[col]
                 )
 
-        blocks_of_flats = self.standardised_asset_list[
-            self.standardised_asset_list[self.STANDARD_PROPERTY_TYPE] == "block of flats"
-            ]
-
-        non_blocks_of_flats = self.standardised_asset_list[
-            self.standardised_asset_list[self.STANDARD_PROPERTY_TYPE] != "block of flats"
-            ]
-
-        # Produce some aggregate figures
-        self.work_type_figures = {
-            **non_blocks_of_flats["cavity_reason"].value_counts().to_dict(),
-            **{
-                k + " (Block of flats)": v for k, v in
-                blocks_of_flats["solar_reason"].value_counts().to_dict().items()
-            },
-            **self.standardised_asset_list["solar_reason"].value_counts().to_dict()
-        }
-
         # We prepare outcomes for output
         if self.outcomes is not None:
             logger.info("Preparing outcomes for output")
@@ -2047,6 +2047,26 @@ class AssetList:
             )
         )
 
+    def get_work_figures(self):
+        blocks_of_flats = self.standardised_asset_list[
+            self.standardised_asset_list[self.STANDARD_PROPERTY_TYPE] == "block of flats"
+            ]
+
+        non_blocks_of_flats = self.standardised_asset_list[
+            self.standardised_asset_list[self.STANDARD_PROPERTY_TYPE] != "block of flats"
+            ]
+
+        # Produce some aggregate figures
+        self.work_type_figures = {
+            **non_blocks_of_flats["cavity_reason"].value_counts().to_dict(),
+            **{
+                k + " (Block of flats)": v for k, v in
+                blocks_of_flats["solar_reason"].value_counts().to_dict().items()
+            },
+            **self.standardised_asset_list["solar_reason"].value_counts().to_dict()
+        }
+        pprint(self.work_type_figures)
+
     def fill_landlord_block_reference(self, has_blocks_of_flats):
         if not has_blocks_of_flats:
             return
@@ -2082,15 +2102,40 @@ class AssetList:
 
         for _, row in blocks.iterrows():
             addr = str(row[self.STANDARD_ADDRESS_1])
+            full_addr = row[self.STANDARD_FULL_ADDRESS]
+
+            # We also look for terms like "Odd", "even", "all" in the address to indicate if it should be just
+            # the odds, evens or all of the numbers
+            has_odd = (
+                "(odd)" in addr.lower() or
+                "(odd)" in full_addr.lower() or
+                "(odds)" in addr.lower() or
+                "(odds)" in full_addr.lower()
+            )
+            has_even = (
+                "(even)" in addr.lower() or
+                "(even)" in full_addr.lower() or
+                "(evens)" in addr.lower() or
+                "(evens)" in full_addr.lower()
+            )
 
             # 1 ─ Range  (e.g. 1-7)
             m_range = RANGE_RE.search(addr)
             if m_range:
+
                 start, end = m_range.groups()
                 start, end = int(re.match(r'\d+', start)[0]), int(re.match(r'\d+', end)[0])
                 if start > end or (end - start) > 100:
                     raise ValueError(f"Suspicious range '{addr}'")
-                for n in range(start, end + 1):
+
+                # We define the looping range on whether we have odd, even or all numbers
+                house_number_range = range(start, end + 1)
+                if has_odd:
+                    house_number_range = [x for x in house_number_range if x % 2 != 0]
+                if has_even:
+                    house_number_range = [x for x in house_number_range if x % 2 == 0]
+
+                for n in house_number_range:
                     new = row.copy()
                     new_addr = RANGE_RE.sub(str(n), addr, count=1)
                     original_full_address = new[self.STANDARD_FULL_ADDRESS]
@@ -2108,9 +2153,9 @@ class AssetList:
                     expanded_rows.append(new)
                 continue
 
-            # 2 ─ Explicit list  (e.g. 1, 2, 5 Block)
+            # 2 ─ Explicit list  (e.g. 1, 2, 5 Block) or split by an ampersand (e.g. 1 & 2 Block)
             nums = NUM_RE.findall(addr)
-            if len(nums) > 1 and ',' in addr:
+            if len(nums) > 1 and (',' in addr or '&' in addr):
                 for n in nums:
                     new = row.copy()
                     new_addr = re.sub(NUM_RE, n, addr, count=1)  # replace the first number only
@@ -2320,7 +2365,7 @@ class AssetList:
         self.standardised_asset_list["cavity_reason"] = np.where(
             self.standardised_asset_list[self.STANDARD_BLOCK_REFERENCE].isin(eligible_blocks),
             self.standardised_asset_list["cavity_reason"]
-            + " " + "(Flat in block with more than 50% eligible, but not eligible itself)",
+            + " " + "(Flat in block with more than 50% eligible)",
             self.standardised_asset_list["cavity_reason"]
         )
 
@@ -2375,6 +2420,11 @@ class AssetList:
         none_details = [x for x in details_colnames if x is None]
         details_colnames = [x for x in details_colnames if x is not None]
 
+        if local_filepath is None:
+            # Create an empty DataFrame based on the fields in self.contact_detail_fields
+            self.contact_details = pd.DataFrame(columns=list(self.contact_detail_fields.keys()))
+            return
+
         contact_details = pd.read_excel(
             local_filepath, sheet_name=sheet_name
         )[[self.contact_detail_fields["landlord_property_id"]] + details_colnames]
@@ -2486,10 +2536,14 @@ class AssetList:
         if reconcile_programme:
             programme_data = programme_data[~pd.isnull(programme_data["project_code"])]
         else:
+
+            if programme_data["hubspot_status"].nunique() > 1:
+                logger.info("Multiple hubspot_status found - are you sure you don't want to reconcile the programme?")
+
             ready_to_be_scheduled = (
                 (
                     programme_data["hubspot_status"] == hubspot_config.HubspotProcessStatus.READY_TO_BE_SCHEDULED.label
-                ) & (~pd.isnull(programme_data["survey_date"]))
+                )
             )
             # completed_works = (
             #     (programme_data["hubspot_status"] !=
@@ -2540,13 +2594,13 @@ class AssetList:
             )
         else:
             # We shouldn't have any missing products
-            programme_data = programme_data[
-                ~pd.isnull(programme_data["survey_date"])
-            ]
+            # programme_data = programme_data[
+            #     ~pd.isnull(programme_data["survey_date"])
+            # ]
 
             if pd.isnull(programme_data["domna_product"]).sum():
                 raise ValueError("Missing products")
-            programme_data = programme_data.drop(columns=["solar_product", "cavity_product"])
+        programme_data = programme_data.drop(columns=["solar_product", "cavity_product"])
 
         product_df = (
             pd.DataFrame(self.CRM_PRODUCTS).T[["name", "id", "unit_price"]]
@@ -2587,6 +2641,13 @@ class AssetList:
                 programme_data[self.EPC_API_DATA_NAMES["uprn"]]
             )
 
+        # Remove any negative URPSN which are not valid
+        programme_data[uprn_column] = np.where(
+            programme_data["estimated"].isin([1, True]),
+            None,
+            programme_data[uprn_column]
+        )
+
         # Add in some columns if we have them
         date_of_inspections = (
             "Non-Intrusives: Date of Inspection" if
@@ -2753,6 +2814,7 @@ class AssetList:
             columns={v: k for k, v in schema_mappings.items() if v is not None}
         )
 
+        programme_data['Postcode <DEAL postcode>'] = programme_data['Postcode <LISTING hs_zip>'].copy()
         programme_data['Installer <DEAL installer>'] = installer_name
         programme_data['Name <LISTING hs_name>'] = (
             programme_data['Full Address <LISTING full_address>'] + " ," + programme_data['Postcode <LISTING hs_zip>']
@@ -2951,7 +3013,7 @@ class AssetList:
             outcomes["row_id"] = outcomes.index
 
             if outcomes_houseno[idx] is None:
-                outcomes_houseno = "houseno"
+                outcomes_houseno[idx] = "houseno"
                 outcomes["houseno"] = outcomes[outcomes_address[idx]].apply(
                     lambda x: SearchEpc.get_house_number(x, outcomes[outcomes_postcode])
                 )
@@ -3219,12 +3281,21 @@ class AssetList:
                 install_col = "INSTALL / CANCELLATION DATE"
             elif 'INSTALL/ CANCELLATION DATE' in master_data.columns:
                 install_col = 'INSTALL/ CANCELLATION DATE'
+            elif "INSTALL/CANCELLATION DATE" in master_data.columns:
+                install_col = "INSTALL/CANCELLATION DATE"
+            elif 'Measure 1 Install Date' in master_data.columns:
+                install_col = 'Measure 1 Install Date'
             else:
                 raise ValueError("No install or cancellation date")
 
-            submission_col = (
-                "SUBMISSION DATE" if "SUBMISSION DATE" in master_data.columns else "SUBMISSION DATE TO INSTALLERS"
-            )
+            if "SUBMISSION DATE" in master_data.columns:
+                submission_col = "SUBMISSION DATE"
+            elif "SUBMISSION DATE TO INSTALLERS" in master_data.columns:
+                submission_col = "SUBMISSION DATE TO INSTALLERS"
+            elif "Submission Date" in master_data.columns:
+                submission_col = "Submission Date"
+            else:
+                raise ValueError("No submission date column found in master data")
 
             master_data["row_id"] = master_data.index
 
@@ -3239,6 +3310,10 @@ class AssetList:
                 scheme_col = "AFFORDABLE WARMTH OR EPC FOR HOUSING ASSOCIATION"
             elif "AFFORDABLE WARMTH" in master_data.columns:
                 scheme_col = "AFFORDABLE WARMTH"
+            elif "Scheme" in master_data.columns:
+                scheme_col = "Scheme"
+            elif "Affordable Warmth" in master_data.columns:
+                scheme_col = "Affordable Warmth"
             else:
                 scheme_col = "OFFICE USE ONLY"
 
@@ -3254,12 +3329,30 @@ class AssetList:
                 property_type_col = "PROPERTY TYPE      As per table emailed"
             elif "PROPERTY TYPE As per table emailed" in master_data.columns:
                 property_type_col = "PROPERTY TYPE As per table emailed"
+            elif "PROPERTY TYPE" in master_data.columns:
+                property_type_col = "PROPERTY TYPE"
             else:
                 property_type_col = "PROPERTY TYPE (SEE DEEMED SCORES SHEET) Eg. 3W_Flat_1 (As per Matrix)"
 
+            if "INSTALLERS NOTES ; REASONS FOR CANCELLATIONS" in master_data.columns:
+                installer_notes_col = "INSTALLERS NOTES ; REASONS FOR CANCELLATIONS"
+            elif "INSTALLERS NOTES" in master_data.columns:
+                installer_notes_col = "INSTALLERS NOTES"
+            elif 'Installers Notes' in master_data.columns:
+                installer_notes_col = 'Installers Notes'
+            elif 'NOTES ; REASONS FOR CANCELLATIONS OR WHERE INSTALL DATE WAS OBTAINED FROM' in master_data.columns:
+                installer_notes_col = 'NOTES ; REASONS FOR CANCELLATIONS OR WHERE INSTALL DATE WAS OBTAINED FROM'
+            else:
+                raise ValueError("No installer notes column found in master data")
+
+            if "INSTALLER" in master_data.columns:
+                installer_col = "INSTALLER"
+            elif "Installer" in master_data.columns:
+                installer_col = "Installer"
+            else:
+                raise ValueError("No installer column found in master data")
+
             measure_mix_col = "MEASURE COMBO"
-            installer_notes_col = "INSTALLERS NOTES ; REASONS FOR CANCELLATIONS"
-            installer_col = "INSTALLER"
             town_colname = "TOWN" if "TOWN" in master_data.columns else 'Town/Area'
 
             logger.info("Matching master data to asset list")
@@ -3301,6 +3394,10 @@ class AssetList:
                 ]
 
                 house_no = row[house_no_col]
+
+                if pd.isnull(house_no):
+                    house_no = None
+
                 if isinstance(house_no, (float, int)):
                     house_no = str(int(house_no))
 
@@ -3401,6 +3498,9 @@ class AssetList:
                 master_data[measure_mix_col] = "Measure mix not recorded"
 
             matched = pd.DataFrame(matched)
+            if matched.empty:
+                continue
+
             master_to_append = master_data[
                 [scheme_col, "row_id", install_col, submission_col, measure_mix_col, installer_notes_col, installer_col]
             ].merge(
diff --git a/asset_list/abs_estimates.py b/asset_list/abs_estimates.py
new file mode 100644
index 00000000..58adcca6
--- /dev/null
+++ b/asset_list/abs_estimates.py
@@ -0,0 +1,229 @@
+"""
+Simple script to take a standardised asset list and calculate the abs. We'll use this code to estimate
+the ABS for properties, going forward
+"""
+import os
+import pandas as pd
+import numpy as np
+from dotenv import load_dotenv
+from etl.find_my_epc.AssetListEpcData import AssetListEpcData
+from backend.Funding import Funding
+from backend.app.utils import sap_to_epc
+
+load_dotenv(dotenv_path="backend/.env")
+EPC_AUTH_TOKEN = os.getenv("EPC_AUTH_TOKEN")
+
+asset_list = pd.read_excel(
+    "/Users/khalimconn-kowlessar/Documents/hestia/Instagroup Review/Livewest South-West - Standardised V2.xlsx",
+    sheet_name="Cavity Route (Insta Review)"
+)
+
+abs_matrix = pd.read_csv(
+    "/Users/khalimconn-kowlessar/Downloads/ECO4 Full Project Scores Matrix.csv"
+)
+pps_matrix = pd.read_excel(
+    "/Users/khalimconn-kowlessar/Downloads/ECO4 Partial Project Scores Matrix v5.xlsx",
+    header=1
+)
+pps_matrix.columns = [c.strip() for c in pps_matrix.columns]
+
+# We need to estimate the number of points the work will produce and the finishing band. For this, we assume 7 for
+# cavity and 15 for solar. We'll be more specific in the future, but for now, this is a good enough estimate.
+route = asset_list[["domna_address_1", "domna_postcode", "epc_os_uprn"]].rename(
+    columns={"domna_address_1": "address", "domna_postcode": "postcode", "epc_os_uprn": "upr"}
+)
+route["address"] = route["address"].astype(str)
+
+asset_list_epc_client = AssetListEpcData(
+    asset_list=route,
+    epc_auth_token=EPC_AUTH_TOKEN
+)
+
+asset_list_epc_client.get_data()
+asset_list_epc_client.get_non_invasive_recommendations()
+
+solar_sap_points = []
+for r in asset_list_epc_client.non_invasive_recommendations:
+    if not r.get("recommendations"):
+        continue
+    solar_recommendations = [
+        x for x in r["recommendations"] if "solar_pv" in x["type"]
+    ]
+    if solar_recommendations:
+        solar_recommendations = solar_recommendations[0]
+    else:
+        continue
+
+    address = r["address"]
+    postcode = r["postcode"]
+
+    solar_sap_points.append(
+        {
+            "address": address,
+            "postcode": postcode,
+            "sap_points": solar_recommendations["sap_points"]
+        }
+    )
+
+solar_sap_points = pd.DataFrame(solar_sap_points)
+solar_sap_points.drop_duplicates(subset=["address", "postcode"], inplace=True)
+# Store the sap points in the cavity route to csv
+# cwi_sap_points.to_csv(
+#     "/Users/khalimconn-kowlessar/Documents/hestia/Instagroup Review/cwi_sap_points_livewest_sw.csv",
+#     index=False
+# )
+
+avg_solar_points_by_postcode = solar_sap_points.groupby(["postcode"]).agg({"sap_points": "mean"}).reset_index()
+avg_solar_points = solar_sap_points["sap_points"].median()
+asset_list["domna_address_1"] = asset_list["domna_address_1"].astype(str)
+asset_list = asset_list.merge(
+    solar_sap_points, how="left", left_on=["domna_address_1", "domna_postcode"], right_on=["address", "postcode"]
+).drop(
+    columns=["address", "postcode"]
+)
+
+# Fill the sap points with the average cwi points
+asset_list = asset_list.merge(
+    avg_solar_points_by_postcode.rename(columns={"postcode": "domna_postcode"}),
+    how="left", on=["domna_postcode"], suffixes=("", "_avg")
+)
+asset_list["sap_points"] = asset_list["sap_points"].fillna(asset_list["sap_points_avg"])
+asset_list.drop(columns=["sap_points_avg"], inplace=True)
+
+asset_list["sap_points"] = asset_list["sap_points"].fillna(avg_solar_points)
+asset_list["post_works_sap"] = asset_list["epc_sap_score_on_register"] + asset_list["sap_points"]
+asset_list["post_works_epc"] = asset_list["post_works_sap"].apply(lambda x: sap_to_epc(x))
+asset_list["starting_half_band"] = asset_list["epc_sap_score_on_register"].apply(lambda x: Funding.get_sap_band(x))
+asset_list["ending_half_band"] = asset_list["post_works_sap"].apply(lambda x: Funding.get_sap_band(x))
+asset_list["floor_area_band"] = asset_list["epc_total_floor_area"].apply(lambda x: Funding.get_floor_area_band(x))
+
+asset_list["ending_half_band"] = np.where(
+    (asset_list["post_works_epc"] == asset_list["epc_rating_on_register"]),
+    "Low_C",
+    asset_list["ending_half_band"]
+)
+# Realistically, we'll take the properties to a low C at worst
+asset_list["ending_half_band"] = np.where(
+    (asset_list["post_works_sap"] < 69),
+    "Low_C",
+    asset_list["ending_half_band"]
+)
+
+asset_list = asset_list.merge(
+    abs_matrix, how="left", left_on=["starting_half_band", "ending_half_band", "floor_area_band"],
+    right_on=['Starting Band', 'Finishing Band', 'Floor Area Segment', ]
+)
+asset_list = asset_list.drop(columns=['Starting Band', 'Finishing Band', 'Floor Area Segment'])
+
+asset_list = asset_list.rename(
+    columns={"Cost Savings": "funding_abs"}
+)
+
+print(asset_list["domna_property_id"].duplicated().sum())
+
+# Store this data
+asset_list.to_csv(
+    "/Users/khalimconn-kowlessar/Documents/hestia/Instagroup Review/livewest_sw_solar_abs_estimates-solar.csv",
+    index=False
+)
+
+# Cavity process!
+# cwi_sap_points = []
+# for r in asset_list_epc_client.non_invasive_recommendations:
+#     if not r.get("recommendations"):
+#         continue
+#     cwi_recommendations = [
+#         x for x in r["recommendations"] if "cavity_wall_insulation" in x["type"]
+#     ]
+#     if cwi_recommendations:
+#         cwi_recommendations = cwi_recommendations[0]
+#     else:
+#         continue
+#
+#     address = r["address"]
+#     postcode = r["postcode"]
+#
+#     cwi_sap_points.append(
+#         {
+#             "address": address,
+#             "postcode": postcode,
+#             "sap_points": cwi_recommendations["sap_points"]
+#         }
+#     )
+#
+# cwi_sap_points = pd.DataFrame(cwi_sap_points)
+# cwi_sap_points = pd.read_csv(
+#     "/Users/khalimconn-kowlessar/Documents/hestia/Instagroup Review/cwi_sap_points_livewest_sw.csv"
+# )
+# cwi_sap_points.drop_duplicates(subset=["address", "postcode"], inplace=True)
+avg_cwi_points_by_postcode = cwi_sap_points.groupby(["postcode"]).agg({"sap_points": "mean"}).reset_index()
+avg_cwi_points = cwi_sap_points["sap_points"].median()
+asset_list = asset_list.merge(
+    cwi_sap_points, how="left", left_on=["domna_address_1", "domna_postcode"], right_on=["address", "postcode"]
+).drop(
+    columns=["address", "postcode"]
+)
+
+# Fill the sap points with the average cwi points
+asset_list = asset_list.merge(
+    avg_cwi_points_by_postcode.rename(columns={"postcode": "domna_postcode"}),
+    how="left", on=["domna_postcode"], suffixes=("", "_avg")
+)
+asset_list["sap_points"] = asset_list["sap_points"].fillna(asset_list["sap_points_avg"])
+asset_list.drop(columns=["sap_points_avg"], inplace=True)
+
+asset_list["sap_points"] = asset_list["sap_points"].fillna(avg_cwi_points)
+asset_list["post_works_sap"] = asset_list["epc_sap_score_on_register"] + asset_list["sap_points"]
+asset_list["post_works_epc"] = asset_list["post_works_sap"].apply(lambda x: sap_to_epc(x))
+asset_list["starting_half_band"] = asset_list["epc_sap_score_on_register"].apply(lambda x: Funding.get_sap_band(x))
+asset_list["ending_half_band"] = asset_list["post_works_sap"].apply(lambda x: Funding.get_sap_band(x))
+asset_list["floor_area_band"] = asset_list["epc_total_floor_area"].apply(lambda x: Funding.get_floor_area_band(x))
+
+asset_list["funding_scheme"] = np.where(
+    (
+        (asset_list["post_works_epc"] == asset_list["epc_rating_on_register"])
+    ),
+    "GBIS",
+    "ECO4"
+)
+asset_list = asset_list.merge(
+    abs_matrix, how="left", left_on=["starting_half_band", "ending_half_band", "floor_area_band"],
+    right_on=['Starting Band', 'Finishing Band', 'Floor Area Segment', ]
+)
+asset_list = asset_list.drop(columns=['Starting Band', 'Finishing Band', 'Floor Area Segment'])
+
+# Using CWI solid 1.7 -> 0.3 rates
+cwi_pps_matrix = pps_matrix[
+    pps_matrix["Measure_Type"].isin(["CWI_0.033"])
+]
+# Merge on
+asset_list = asset_list.merge(
+    cwi_pps_matrix[['Starting Band', 'Total Floor Area Band', 'Cost Savings']].rename(
+        columns={
+            "Cost Savings": "partial_project_score",
+            "Starting Band": "starting_half_band",
+            "Total Floor Area Band": "floor_area_band"
+        }
+    ),
+    how="left",
+    on=["starting_half_band", "floor_area_band"],
+)
+asset_list["partial_project_score"] = np.where(
+    (asset_list["epc_sap_score_on_register"] > 69),
+    None,
+    asset_list["partial_project_score"]
+)
+
+asset_list["funding_abs"] = np.where(
+    asset_list["funding_scheme"] == "GBIS",
+    asset_list["partial_project_score"],
+    asset_list["Cost Savings"]
+)
+
+asset_list["domna_property_id"].duplicated().sum()
+
+# Store this data
+asset_list.to_csv(
+    "/Users/khalimconn-kowlessar/Documents/hestia/Instagroup Review/livewest_sw_abs_estimates.csv",
+    index=False
+)
diff --git a/asset_list/app.py b/asset_list/app.py
index 7c0023ce..e431f723 100644
--- a/asset_list/app.py
+++ b/asset_list/app.py
@@ -1,7 +1,6 @@
 import os
 import json
 import pandas as pd
-from pprint import pprint
 from asset_list.AssetList import AssetList
 from asset_list.mappings.property_type import PROPERTY_MAPPING
 from asset_list.mappings.built_form import BUILT_FORM_MAPPINGS
@@ -60,39 +59,370 @@ def app():
     Property UPRN
     """
 
-    # NCHA
-    data_folder = "/Users/khalimconn-kowlessar/Documents/hestia/Customers/NCHA"
-    data_filename = "Energy Information MASTER June 2025.xlsx"
-    sheet_name = "Data"
+    # CDS
+    data_folder = "/Users/khalimconn-kowlessar/Documents/hestia/Customers/CDS"
+    data_filename = "Founder Estates - Asset List.xlsx"
+    sheet_name = "Combined"
     postcode_column = 'Postcode'
     fulladdress_column = "Address"
     address1_column = None
     address1_method = "house_number_extraction"
     address_cols_to_concat = []
     missing_postcodes_method = None
-    landlord_year_built = "Build Date (HAR10)"
+    landlord_year_built = None
     landlord_os_uprn = None
-    landlord_property_type = "Property Type (HAR10)"
-    landlord_built_form = "Build Form (EPC)"
-    landlord_wall_construction = "Wall Description"
-    landlord_roof_construction = None
-    landlord_heating_system = "HEAT Code"
+    landlord_property_type = None
+    landlord_built_form = None
+    landlord_wall_construction = None
+    landlord_heating_system = "Heating Type"
     landlord_existing_pv = None
-    landlord_property_id = "Place ref"
-    landlord_sap = "EPC SAP"
-    outcomes_filename = None
-    outcomes_sheetname = None
-    outcomes_postcode = None
-    outcomes_houseno = None
-    outcomes_id = None
-    outcomes_address = None
+    landlord_property_id = "Row ID"
+    outcomes_filename = []
+    outcomes_sheetname = []
+    outcomes_postcode = []
+    outcomes_houseno = []
+    outcomes_address = []
+    outcomes_id = []
     master_filepaths = []
     master_to_asset_list_filepath = None
-    phase = False
-    ecosurv_landlords = None
     asset_list_header = 0
     landlord_block_reference = None
     master_id_colnames = []
+    landlord_roof_construction = None
+    phase = False
+    landlord_sap = None
+    ecosurv_landlords = None
+
+    # Plus Dane
+    data_folder = "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Plus Dane/New Programme July 2025/"
+    data_filename = "20250711 Plus Dane Asset List.xlsx"
+    sheet_name = "Sheet1"
+    postcode_column = 'Postcode'
+    fulladdress_column = "Address"
+    address1_column = None
+    address1_method = "house_number_extraction"
+    address_cols_to_concat = []
+    missing_postcodes_method = None
+    landlord_year_built = "Property Age"
+    landlord_os_uprn = None
+    landlord_property_type = "Property Type"
+    landlord_built_form = "Built Form"
+    landlord_wall_construction = "Wall Construction"
+    landlord_heating_system = "Full Heating System"
+    landlord_existing_pv = None
+    landlord_property_id = "UPRN"
+    outcomes_filename = [
+        os.path.join(data_folder, "Outcomes - Plus Dane_CWI_2024.xlsx"),
+        os.path.join(data_folder, "Outcomes - Plus Dane_CWI_2025.xlsx"),
+        os.path.join(data_folder, "Outcomes - Plus Dane_PV_2025.xlsx"),
+    ]
+    outcomes_sheetname = [
+        "CWI & LI - 2024", "2025 - CWI", "PV - 2025",
+    ]
+    outcomes_postcode = ["Postcode", "Postcode", "Postcode"]
+    outcomes_houseno = ["No.", "No", "No"]
+    outcomes_address = ["Address", "Address", "Address"]
+    outcomes_id = ["Asset Reference", "LL UPRN", "LL UPRN"]
+    master_filepaths = [
+        os.path.join(data_folder, "submissions/JJC-Table 1.csv"),
+        os.path.join(data_folder, "submissions/SCIS-Table 1.csv")
+    ]
+    master_to_asset_list_filepath = None
+    asset_list_header = 1
+    landlord_block_reference = None
+    master_id_colnames = [None, None]
+    landlord_roof_construction = None
+    phase = False
+    landlord_sap = "SAP Rating"
+    ecosurv_landlords = "plus dane"
+
+    # data_folder = "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Brentwood/July 2025 New Programme"
+    # data_filename = "20250710 Asset List Brentwood.xlsx"
+    # sheet_name = "Sheet1"
+    # postcode_column = 'Postcode'
+    # fulladdress_column = None
+    # address1_column = "House Number"
+    # address1_method = None
+    # address_cols_to_concat = ["House Number", "Address Line 1", "Address Line 2", "Address Line 3"]
+    # missing_postcodes_method = None
+    # landlord_year_built = "Year Built"
+    # landlord_os_uprn = None
+    # landlord_property_type = "Dwelling"
+    # landlord_built_form = None
+    # landlord_wall_construction = None
+    # landlord_heating_system = "Heating"
+    # landlord_existing_pv = None
+    # landlord_property_id = "UPRN"
+    # outcomes_filename = [os.path.join(data_folder, "Brentwood - outcomes for analysis.xlsx")]
+    # outcomes_sheetname = ["OUTCOMES"]
+    # outcomes_postcode = ["POSTCODE"]
+    # outcomes_houseno = [None]
+    # outcomes_address = ["ADDRESS"]
+    # outcomes_id = [None]
+    # master_filepaths = [os.path.join(data_folder, "Submissions.csv")]
+    # master_to_asset_list_filepath = None
+    # asset_list_header = 1
+    # landlord_block_reference = None
+    # master_id_colnames = [None]
+    # landlord_roof_construction = None
+    # phase = False
+    # landlord_sap = None
+    # ecosurv_landlords = "brentwood"
+
+    # Brentwood
+    # data_folder = "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Brentwood/July 2025 New Programme"
+    # data_filename = "20250710 Asset List Brentwood.xlsx"
+    # sheet_name = "Sheet1"
+    # postcode_column = 'Postcode'
+    # fulladdress_column = None
+    # address1_column = "House Number"
+    # address1_method = None
+    # address_cols_to_concat = ["House Number", "Address Line 1", "Address Line 2", "Address Line 3"]
+    # missing_postcodes_method = None
+    # landlord_year_built = "Year Built"
+    # landlord_os_uprn = None
+    # landlord_property_type = "Dwelling"
+    # landlord_built_form = None
+    # landlord_wall_construction = None
+    # landlord_heating_system = "Heating"
+    # landlord_existing_pv = None
+    # landlord_property_id = "UPRN"
+    # outcomes_filename = [os.path.join(data_folder, "Brentwood - outcomes for analysis.xlsx")]
+    # outcomes_sheetname = ["OUTCOMES"]
+    # outcomes_postcode = ["POSTCODE"]
+    # outcomes_houseno = [None]
+    # outcomes_address = ["ADDRESS"]
+    # outcomes_id = [None]
+    # master_filepaths = [os.path.join(data_folder, "Submissions.csv")]
+    # master_to_asset_list_filepath = None
+    # asset_list_header = 1
+    # landlord_block_reference = None
+    # master_id_colnames = [None]
+    # landlord_roof_construction = None
+    # phase = False
+    # landlord_sap = None
+    # ecosurv_landlords = "brentwood"
+    #
+    # # Eastlight
+    # data_folder = "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Eastlight/New Programme"
+    # data_filename = "INSPECTIONS MASTER Non Tech.xlsx"
+    # sheet_name = "EASTLIGHT CW"
+    # postcode_column = 'Postcode'
+    # fulladdress_column = None
+    # address1_column = "HouseName"
+    # address1_method = None
+    # address_cols_to_concat = ["HouseName", "Block", "Address1", "Address2", "Address3"]
+    # missing_postcodes_method = None
+    # landlord_year_built = "Built In Year"
+    # landlord_os_uprn = None
+    # landlord_property_type = "AssetType"
+    # landlord_built_form = "Archetype"  # Using inspections archetype
+    # landlord_wall_construction = None
+    # landlord_roof_construction = None
+    # landlord_heating_system = "Main Heating Source"
+    # landlord_existing_pv = None
+    # landlord_property_id = "UPRN"
+    # landlord_sap = "SAP Score"
+    # outcomes_filename = [
+    #     os.path.join(data_folder, "Eastlight_CWI_JJC_2025.xlsx"),
+    #     os.path.join(data_folder, "Eastlight_CWI_SCIS_2025.xlsx"),
+    # ]
+    # outcomes_sheetname = ["Outcomes", "Feedback"]
+    # outcomes_postcode = ["Postcode", "Postcode"]
+    # outcomes_houseno = ["No", "No."]
+    # outcomes_id = [None, None]
+    # outcomes_address = ["Address", "Address"]
+    # master_filepaths = [
+    #     os.path.join(data_folder, "ECO 3-Table 1.csv"),
+    #     os.path.join(data_folder, "ECO 4-Table 1.csv"),
+    # ]
+    # master_to_asset_list_filepath = None
+    # phase = False
+    # ecosurv_landlords = "eastlight"
+    # asset_list_header = 0
+    # landlord_block_reference = None
+    # master_id_colnames = [None, None]
+    # landlord_sap = None
+
+    # Pickering and Ferens
+    # data_folder = "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Pickering & Ferens"
+    # data_filename = "SAP 9 vs SAP 10 Sava Intelligent Energy - Property List (190625).xlsx"
+    # sheet_name = "Sava Intelligent Energy - Prope"
+    # postcode_column = 'Postcode'
+    # fulladdress_column = 'Address'
+    # address1_column = None
+    # address1_method = "house_number_extraction"
+    # address_cols_to_concat = []
+    # missing_postcodes_method = None
+    # landlord_year_built = None
+    # landlord_os_uprn = None
+    # landlord_property_type = "Property Type"  # Using the inspections property type
+    # landlord_built_form = "Archetype 2"
+    # landlord_wall_construction = None
+    # landlord_roof_construction = None
+    # landlord_heating_system = None
+    # landlord_existing_pv = None
+    # landlord_property_id = "UPRN"
+    # landlord_sap = "SAP Rating (RdSAP 10)"
+    # outcomes_filename = []
+    # outcomes_sheetname = []
+    # outcomes_postcode = []
+    # outcomes_houseno = []
+    # outcomes_id = []
+    # outcomes_address = []
+    # master_filepaths = [
+    #     os.path.join(data_folder, "PICKERING & FERENS ROLLING MASTER SHEET HEDGEFUND - 26.7.24 - K.csv"),
+    #     os.path.join(data_folder, "PICKERING & FERENS NEW MASTER GBIS UPDATED 21.8.24 - M - For Analysis.csv"),
+    # ]
+    # master_to_asset_list_filepath = None
+    # phase = False
+    # ecosurv_landlords = "pickering"
+    # asset_list_header = 0
+    # landlord_block_reference = None
+    # master_id_colnames = [None, None]
+
+    # Colchester
+    # data_folder = "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Colchester"
+    # data_filename = "Warmfront data- Colchester Borough Homes (Complete).xlsx"
+    # sheet_name = "Sheet1"
+    # postcode_column = 'Full Address.1'
+    # fulladdress_column = "Full Address"
+    # address1_column = None
+    # address1_method = "first_word"
+    # address_cols_to_concat = []
+    # missing_postcodes_method = None
+    # landlord_year_built = "Build Date"
+    # landlord_os_uprn = None
+    # landlord_property_type = "Property Type"
+    # landlord_wall_construction = "Wallinsul"
+    # landlord_heating_system = "HeatSorc"
+    # landlord_existing_pv = None
+    # landlord_property_id = "Property Reference"
+    # outcomes_filename = []
+    # outcomes_sheetname = []
+    # outcomes_postcode = []
+    # outcomes_houseno = []
+    # outcomes_id = []
+    # outcomes_address = []
+    # master_filepaths = []
+    # master_to_asset_list_filepath = None
+    # asset_list_header = 0
+    # landlord_built_form = None
+    # landlord_roof_construction = None
+    # landlord_sap = None
+    # landlord_block_reference = None
+    # phase = False
+    # ecosurv_landlords = None
+    # master_id_colnames = []
+
+    # data_folder = "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Ealing/Hubspot"
+    # data_filename = "EalingFlats.xlsx"
+    # sheet_name = "Sheet1"
+    # postcode_column = 'Postcode'
+    # fulladdress_column = "Address"
+    # address1_column = None
+    # address1_method = "house_number_extraction"
+    # address_cols_to_concat = []
+    # missing_postcodes_method = None
+    # landlord_year_built = None
+    # landlord_os_uprn = None
+    # landlord_property_type = None  # Using the inspections property type
+    # landlord_built_form = None
+    # landlord_wall_construction = None
+    # landlord_roof_construction = None
+    # landlord_heating_system = None
+    # landlord_existing_pv = None
+    # landlord_property_id = "Property ref"
+    # landlord_sap = None
+    # outcomes_filename = []
+    # outcomes_sheetname = []
+    # outcomes_postcode = []
+    # outcomes_houseno = []
+    # outcomes_id = []
+    # outcomes_address = []
+    # master_filepaths = []
+    # master_to_asset_list_filepath = None
+    # phase = False
+    # ecosurv_landlords = None
+    # asset_list_header = 0
+    # landlord_block_reference = "Block Ref"
+    # master_id_colnames = []
+
+    # Southern - Jan list
+    # data_folder = "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Southern/New Programme/Jan 2025 List"
+    # data_filename = "SOUTHERN ASSETS January 2025 Additions Query 21.03.2025.xlsx"
+    # sheet_name = "Jan 2025 additions"
+    # postcode_column = 'Post Code'
+    # fulladdress_column = None
+    # address1_column = "NO."
+    # address1_method = None
+    # address_cols_to_concat = ["NO.", "Street / Block Name", "Town/Area"]
+    # missing_postcodes_method = None
+    # landlord_year_built = None
+    # landlord_os_uprn = None
+    # landlord_property_type = None  # Using the inspections property type
+    # landlord_built_form = None
+    # landlord_wall_construction = None
+    # landlord_roof_construction = None
+    # landlord_heating_system = None
+    # landlord_existing_pv = None
+    # landlord_property_id = "SH Property Reference"
+    # landlord_sap = None
+    # outcomes_filename = [
+    #     os.path.join(data_folder, "RT - Southern Housing Group - JJC.xlsx"),
+    #     os.path.join(data_folder, "RT - SOUTHERN OUTCOMES - SCIS Merged.xlsx"),
+    # ]
+    # outcomes_sheetname = ["Feedback", "Collated"]
+    # outcomes_postcode = ["Poscode", "Postcode"]
+    # outcomes_houseno = ["No.", "No"]
+    # outcomes_id = ["UPRNs", None]
+    # outcomes_address = ["Address", "Address"]
+    # master_filepaths = [
+    #     os.path.join(data_folder, "southern_submissions/CAVITY'S - DECEMBER 2018-Table 1.csv"),
+    #     os.path.join(data_folder, "southern_submissions/CAVITY'S 2019-Table 1.csv"),
+    #     os.path.join(data_folder, "southern_submissions/CAVITY'S ECO4-Table 1.csv"),
+    #     os.path.join(data_folder, "southern_submissions/LOFT'S-Table 1.csv"),
+    # ]
+    # master_to_asset_list_filepath = None
+    # phase = False
+    # ecosurv_landlords = "southern"
+    # asset_list_header = 0
+    # landlord_block_reference = None
+    # master_id_colnames = [None, None, None, None]
+
+    # NCHA
+    # data_folder = "/Users/khalimconn-kowlessar/Documents/hestia/Customers/NCHA"
+    # data_filename = "Energy Information MASTER June 2025.xlsx"
+    # sheet_name = "Data"
+    # postcode_column = 'Postcode'
+    # fulladdress_column = "Address"
+    # address1_column = None
+    # address1_method = "house_number_extraction"
+    # address_cols_to_concat = []
+    # missing_postcodes_method = None
+    # landlord_year_built = "Build Date (HAR10)"
+    # landlord_os_uprn = None
+    # landlord_property_type = "Property Type (HAR10)"
+    # landlord_built_form = "Build Form (EPC)"
+    # landlord_wall_construction = "Wall Description"
+    # landlord_roof_construction = None
+    # landlord_heating_system = "HEAT Code"
+    # landlord_existing_pv = None
+    # landlord_property_id = "Place ref"
+    # landlord_sap = "EPC SAP"
+    # outcomes_filename = None
+    # outcomes_sheetname = None
+    # outcomes_postcode = None
+    # outcomes_houseno = None
+    # outcomes_id = None
+    # outcomes_address = None
+    # master_filepaths = []
+    # master_to_asset_list_filepath = None
+    # phase = False
+    # ecosurv_landlords = None
+    # asset_list_header = 0
+    # landlord_block_reference = None
+    # master_id_colnames = []
 
     # data_folder = "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Calico"
     # data_filename = "07.04 CALICO - Final List.xlsx"
@@ -593,6 +923,7 @@ def app():
     # We now flag the status of the property
     asset_list.label_property_status()
     asset_list.analyse_geographies()
+    asset_list.get_work_figures()
 
     # Store as an excel
     filename = os.path.join(data_folder, ".".join(data_filename.split(".")[:-1])) + " - Standardised.xlsx"
diff --git a/asset_list/hubspot/config.py b/asset_list/hubspot/config.py
index 5110fb5f..23ff900a 100644
--- a/asset_list/hubspot/config.py
+++ b/asset_list/hubspot/config.py
@@ -17,7 +17,7 @@ class HubspotProcessStatus(IntEnum):
     # The property didn't get access and needs sign off
     SURVEYED_NO_ACCESS_NEEDS_SIGN_OFF = 2, "SURVEYED - NO ACCESS - NEED SIGN OFF"
     # The survey has been completed. We don't have any update as to whether the property has been installed
-    SURVEYED_COMPLETED_SIGNED_OFF = 3, "SURVEYED - COMPLETED - SIGNED OFF"
+    SURVEYED_COMPLETED_SIGNED_OFF = 3, "SURVEYED - AUTOMATED SIGNED OFF"
     # The property turned out to be ineligibile
     NOT_VIABLE = 4, "NOT VIABLE"
     # The property is with the installer. This will likely be the default for historic programmes
@@ -79,7 +79,7 @@ CRM_UPLOAD_COLUMNS = [
     'Last EPC: Room Height <LISTING last_epc__room_height>',
     'Last EPC: Age Band <LISTING last_epc__age_band>', 'Deal Stage <DEAL dealstage>',
     'Pipeline <DEAL pipeline>', 'Expected Commencement Date <DEAL expected_commencement_date>',
-    'Deal Name <DEAL dealname>', 'Project Code <DEAL project_code>',
+    'Deal Name <DEAL dealname>', 'Project Code <DEAL project_code>', 'Postcode <DEAL postcode>',
     'Product ID <LINE_ITEM hs_product_id>', 'Name <LINE_ITEM name>', 'Unit price <LINE_ITEM price>',
     'Quantity <LINE_ITEM quantity>', 'Deal Owner', 'Amount <DEAL amount>', 'Installer <DEAL installer>'
 ]
diff --git a/asset_list/hubspot/prepare_for_hubspot.py b/asset_list/hubspot/prepare_for_hubspot.py
index eed6d7e7..b12f4c04 100644
--- a/asset_list/hubspot/prepare_for_hubspot.py
+++ b/asset_list/hubspot/prepare_for_hubspot.py
@@ -2,6 +2,32 @@ import os
 import pandas as pd
 from asset_list.AssetList import AssetList
 
+import re
+
+
+def normalize_uk_phone(number: str | float | int) -> str | None:
+    if pd.isna(number):
+        return None
+
+    number = str(number)
+    number = re.sub(r"[^\d+]", "", number)
+
+    # Handle common short inputs: add '0' if likely missing
+    if re.match(r"^7\d{8,9}$", number) or re.match(r"^1\d{8,9}$", number):
+        number = "0" + number
+
+    # Convert to international format
+    if number.startswith("0"):
+        number = "+44" + number[1:]
+    elif number.startswith("0044"):
+        number = "+" + number[2:]
+
+    # Must be +44 followed by 10 digits (some area codes may vary)
+    if re.match(r"^\+44\d{9,10}$", number):
+        return number
+
+    return None
+
 
 def app():
     """
@@ -18,32 +44,28 @@ def app():
     """
 
     # inputs:
-    reconcile_programme = False  # If True, the hubspot upload will include all properties with a project code
-    customer_domain = "https://sandwell.gov.uk"
-    installer_name = "J & J CRUMP"
+    reconcile_programme = True  # If True, the hubspot upload will include all properties with a project code
+    customer_domain = "https://ealing.gov.uk"
+    installer_name = "SCIS"
     asset_list_filepath = (
-        "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Sandwell/Hubspot/Sandwell BC - Full Asset List MAIN - "
-        "Standardised.xlsx"
+        "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Ealing/Hubspot/20250707 Ealing Flats - Prepared "
+        "programme.xlsx"
     )
-    asset_list_sheet_name = "Proposed Program"
-    asset_list_header = 1
+    asset_list_sheet_name = "Standardised Asset List"
+    asset_list_header = 0
 
-    contact_details_filepath = (
-        "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Sandwell/Hubspot/Sandwell Contact Details.xlsx"
-    )
-    contacts_sheet_name = "Sheet1"
-    contacts_landlord_property_id = "landlord_property_id"
+    contact_details_filepath = None
+    contacts_sheet_name = "Sheet 1"
+    contacts_landlord_property_id = "UPRN"
     contacts_phone_number_column = "phone_number"
     contacts_secondary_phone_number_column = "secondary_phone_number"
     contacts_secondary_contact_full_name = "secondary_contact_full_name"
     contacts_email_column = "email"
     contacts_fullname_column = "fullname"
-    contacts_firstname_column = "firstname"
-    contacts_lastname_column = "lastname"
+    contacts_firstname_column = "First Name"
+    contacts_lastname_column = "Last Name"
 
-    existing_programme_filepath = (
-        "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Sandwell/Hubspot/property-status.csv"
-    )
+    existing_programme_filepath = None
 
     asset_list = AssetList.load_standardised_asset_list(
         asset_list_filepath, asset_list_sheet_name, asset_list_header
@@ -68,12 +90,12 @@ def app():
     )
 
     # Remove the existing programme
-    existing_programme = pd.read_csv(existing_programme_filepath, encoding="utf-8-sig")
-    asset_list.hubspot_data = asset_list.hubspot_data[
-        ~asset_list.hubspot_data["Domna Property ID <LISTING domna_property_id>"].isin(
-            existing_programme['Domna Property ID'].values
-        )
-    ]
+    # existing_programme = pd.read_csv(existing_programme_filepath, encoding="utf-8-sig")
+    # asset_list.hubspot_data = asset_list.hubspot_data[
+    #     ~asset_list.hubspot_data["Domna Property ID <LISTING domna_property_id>"].isin(
+    #         existing_programme['Domna Property ID'].values
+    #     )
+    # ]
 
     # Get the filepath and the filename. Append hubspot upload to the filename. We also change the file type to csv
     directory, filename = os.path.split(asset_list_filepath)
@@ -89,3 +111,66 @@ def app():
 
     # Just store locally
     asset_list.hubspot_data.to_csv(output_filepath, index=False, encoding="utf-8-sig")
+
+    # # TODO: Set this up separately, but we associate multiple contacts to the same deal
+    # contact_details = pd.read_csv(
+    #     "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Bromford/Solar Programme Hubspot "
+    #     "Upload/Hubspot/contact "
+    #     "details.csv"
+    # )
+    #
+    # # contacts_phone_number_column = "phone_number"
+    # #     contacts_secondary_phone_number_column = "secondary_phone_number"
+    # #     contacts_secondary_contact_full_name = "secondary_contact_full_name"
+    # #     contacts_email_column = "email"
+    # #     contacts_fullname_column = "fullname"
+    # #     contacts_firstname_column = "First Name"
+    # #     contacts_lastname_column = "Last Name"
+    # contact_details["phone_number"] = contact_details["Mobile Phone"].copy()
+    # # If phone number is NaN, we will use the landline number
+    # contact_details["phone_number"] = contact_details["phone_number"].fillna(contact_details["Landline"])
+    # contact_details["secondary_phone_number"] = contact_details["Landline"].copy()
+    # # If secondary phone number is the same as primary, we remove it
+    # import numpy as np
+    # contact_details["secondary_phone_number"] = np.where(
+    #     contact_details["secondary_phone_number"] == contact_details["phone_number"],
+    #     np.nan,
+    #     contact_details["secondary_phone_number"]
+    # )
+    # contact_details = contact_details[
+    #     ['Property Reference Number (Main Address) (Property)', "Email Address", "phone_number",
+    #      "secondary_phone_number", "First Name", "Last Name"]].copy().rename(
+    #     columns={"Property Reference Number (Main Address) (Property)": "landlord_proprty_id"}
+    # )
+    # contact_details["fullname"] = contact_details["First Name"] + " " + contact_details["Last Name"]
+    # # Format the phone numbers
+    #
+    # contact_details["phone_number"] = contact_details["phone_number"].astype(int).astype(str).apply(
+    # normalize_uk_phone)
+    # contact_details["secondary_phone_number"] = contact_details["secondary_phone_number"].astype("Int64").astype(
+    #     str).apply(
+    #     normalize_uk_phone)
+    #
+    # # Add in the Hubspot deal data
+    # hubspot_data = pd.read_csv(
+    #     "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Bromford/Solar Programme Hubspot Upload/Hubspot/"
+    #     "property-status.csv",
+    #     encoding="utf-8-sig"
+    # )
+    # # Merge on contact details
+    # contact_details = hubspot_data[["Landlord Property ID", "Deal ID"]].merge(
+    #     contact_details,
+    #     how="left",
+    #     right_on="landlord_proprty_id",
+    #     left_on="Landlord Property ID"
+    # )
+    #
+    # contact_details = contact_details.drop(columns=["landlord_proprty_id"])
+    #
+    # # Store as csv
+    # contact_details.to_csv(
+    #     "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Bromford/Solar "
+    #     "Programme Hubspot Upload/Hubspot/"
+    #     "contact_details.csv",
+    #     index=False, encoding="utf-8-sig"
+    # )
diff --git a/asset_list/mappings/built_form.py b/asset_list/mappings/built_form.py
index 45e45c54..c17e0ed4 100644
--- a/asset_list/mappings/built_form.py
+++ b/asset_list/mappings/built_form.py
@@ -3,7 +3,7 @@ import numpy as np
 STANDARD_BUILT_FORMS = {
     "unknown",
     # Houses
-    "end-terrace", "semi-detached", "detached", "mid-terrace",
+    "end-terrace", "semi-detached", "detached", "mid-terrace", "enclosed mid-terrace", "enclosed end-terrace",
     # Flats
     "ground floor", "mid-floor", "top-floor", "basement", "low rise", "high rise",
 }
@@ -358,6 +358,19 @@ BUILT_FORM_MAPPINGS = {
     '1983- 90 SEMI DET': 'semi-detached',
     '1983-90 MID TERR': 'mid-terrace',
     '1976-82 SEMI DET': 'semi-detached',
-    'PRE 1900 MID TERR': 'mid-terrace'
+    'PRE 1900 MID TERR': 'mid-terrace',
+    None: 'unknown',
 
+    'SEMI-DETACHED': 'semi-detached',
+    'DETACHED': 'detached',
+    'MID TERRACE': 'mid-terrace',
+    'END TERRACE': 'end-terrace',
+    'ENCLOSED MID': 'enclosed mid-terrace',
+
+    'BUILDING': 'unknown',
+    'FLAT COMMUNAL FACILITIES': 'unknown',
+    'MAISONETTE': 'unknown',
+    'HOUSE': 'unknown',
+    'FLAT': 'unknown',
+    'BLOCK': 'unknown'
 }
diff --git a/asset_list/mappings/heating_systems.py b/asset_list/mappings/heating_systems.py
index 1a46c429..010d49a5 100644
--- a/asset_list/mappings/heating_systems.py
+++ b/asset_list/mappings/heating_systems.py
@@ -28,6 +28,7 @@ STANDARD_HEATING_SYSTEMS = {
     "electric underfloor",
     "no heating",
     "non-electric underfloor",
+    "warm air heating",
 }
 
 HEATING_MAPPINGS = {
@@ -326,5 +327,42 @@ HEATING_MAPPINGS = {
     'ECO TEC PRO 24 (OLD TYPE)': 'gas combi boiler',
     'GREENSTAR 30SI COMPACT': 'gas combi boiler',
     'BAXI DUO TEC 28 COMBI ErP': 'gas combi boiler',
-    'Not applicable for this asset type': 'unknown'
+    'Not applicable for this asset type': 'unknown',
+    'Boiler: F rated Regular Boiler': 'gas condensing boiler',
+    'Warm Air Systems: Electric warm air: Electricaire system': 'warm air heating',
+    'Boiler: B rated Combi': 'gas condensing combi',
+    'Boiler: G rated Regular Boiler': 'gas condensing boiler',
+    'Electric Storage Systems: Modern (slimline) storage heaters': 'electric storage heaters',
+    'Boiler: C rated CPSU': 'gas condensing combi',
+    'Boiler: D rated Regular Boiler': 'gas condensing boiler',
+    'Warm Air Systems: Gas fired warm air with balanced or open flue: Ducted or stub-ducted, on-off control, '
+    'pre 1998': 'warm air heating',
+    'Electric Storage Systems: Integrated storage+direct-acting heater': 'electric storage heaters',
+    'Boiler: D rated Combi': 'gas condensing combi',
+    'Heat Pump: (from database)': 'air source heat pump',
+    'Community Heating Systems: Community CHP and boilers (RdSAP)': 'communal heating',
+    '': 'unknown',
+
+    'Solid Fuel Boiler': 'solid fuel',
+    'Heating (Other)': 'other',
+    'Solid Fuel Fire Only': 'solid fuel',
+    'No Main Heat Source': 'no heating',
+    'Electric Programmable': 'electric storage heaters',
+    'Linked to Communal Boiler': 'communal heating',
+    'Bio Mass Boiler': 'solid fuel',
+    'Electric Non Programmable': 'electric storage heaters',
+
+    'Room heaters, Mains gas': 'room heaters',
+    'Boiler, Solid fuel': 'solid fuel',
+    'Room heaters, Electricity': 'room heaters',
+    'Room heaters, Solid fuel': 'room heaters',
+    'Boiler, Oil': 'oil boiler',
+    'Boiler, Biomass': 'boiler - other fuel',
+    'Community heating, Community (non-gas)': 'communal heating',
+    'Heat pump (wet), Electricity': 'air source heat pump',
+    'Community heating, Community (mains gas)': 'communal gas boiler',
+    'Boiler, Electricity': 'electric boiler',
+    'Boiler, LPG': 'gas boiler, radiators',
+    'Boiler, Mains gas': 'gas boiler, radiators',
+    'Storage heating, Electricity': 'electric storage heaters'
 }
diff --git a/asset_list/mappings/property_type.py b/asset_list/mappings/property_type.py
index bdb6580e..caca0cf0 100644
--- a/asset_list/mappings/property_type.py
+++ b/asset_list/mappings/property_type.py
@@ -256,7 +256,6 @@ PROPERTY_MAPPING = {
     'HOUSE (3 STOREY)': 'house',
     'FLAT GROUND FLOOR': 'flat',
     'FLAT TOP FLOOR': 'flat',
-
     'SHARED HOUSE': 'house',
     'MAISONETTE': 'maisonette',
     'DIRECT ACCESS HOSTEL': 'other',
@@ -266,5 +265,11 @@ PROPERTY_MAPPING = {
     'SHOP': 'other',
     'Office Block': 'other',
     'BLOCK (Non-Communal)': 'block of flats',
-    'Refuge': 'other'
+    'Refuge': 'other',
+    None: 'unknown',
+    'HFOP FLAT': 'flat',
+    'HFOP BEDSIT': 'bedsit',
+    'LINKED FLAT': 'flat',
+    'LINKED BUNGALOW': 'bungalow'
+
 }
diff --git a/asset_list/mappings/roof.py b/asset_list/mappings/roof.py
index 13359ded..66860bec 100644
--- a/asset_list/mappings/roof.py
+++ b/asset_list/mappings/roof.py
@@ -9,6 +9,7 @@ STANDARD_ROOF_CONSTRUCTIONS = {
     "pitched less than 100mm insulation",
     "another dwelling above",
     "flat unknown insulation",
+    "flat insulated",
     "unknown insulated",
     "unknown",
 }
@@ -51,5 +52,127 @@ ROOF_CONSTRUCTION_MAPPINGS = {
     '100MM': 'pitched less than 100mm insulation',
     'U/K': 'unknown',
     'U/K - 250MM RIR FLAT CEILING': 'flat unknown insulation',
-    'U/K - 200MM RIR FLAT CEILING': 'flat unknown insulation'
+    'U/K - 200MM RIR FLAT CEILING': 'flat unknown insulation',
+
+    'AnotherDwellingAbove: Unknown, PitchedNormalLoftAccess: 100mm': 'another dwelling above',
+    'PitchedNormalNoLoftAccess: 150mm': 'pitched insulated',
+    'PitchedNormalLoftAccess: As Built, PitchedNormalNoLoftAccess: None': 'pitched insulated',
+    'PitchedNormalLoftAccess: 150mm, PitchedNormalNoLoftAccess: Unknown': 'pitched insulated',
+    'Flat: As Built, PitchedNormalLoftAccess: 200mm': 'flat unknown insulation',
+    'PitchedNormalLoftAccess: 200mm': 'pitched insulated',
+    'PitchedNormalNoLoftAccess: 50mm': 'unknown',
+    'PitchedNormalNoLoftAccess: No Insulation': 'pitched less than 100mm insulation',
+    'PitchedNormalLoftAccess: 50mm, PitchedNormalNoLoftAccess: None': 'pitched less than 100mm insulation',
+    'PitchedNormalLoftAccess: 50mm, PitchedNormalLoftAccess: No Insulation': 'pitched less than 100mm insulation',
+    'PitchedNormalLoftAccess: 150mm': 'unknown', 'Flat: None': 'pitched insulated',
+    'Flat: As Built, PitchedNormalLoftAccess: 150mm, PitchedNormalNoLoftAccess: None': 'flat unknown insulation',
+    'PitchedNormalNoLoftAccess: 250mm, PitchedNormalNoLoftAccess: Unknown': 'pitched insulated',
+    'PitchedNormalLoftAccess: 75mm': 'pitched less than 100mm insulation',
+    'Flat: Unknown, PitchedNormalLoftAccess: 200mm, SameDwellingAbove: Unknown': 'flat unknown insulation',
+    'Flat: As Built, PitchedNormalLoftAccess: 100mm, PitchedNormalNoLoftAccess: None': 'flat unknown insulation',
+    'PitchedNormalNoLoftAccess: 250mm': 'pitched insulated',
+    'PitchedNormalLoftAccess: 150mm, PitchedNormalNoLoftAccess: 100mm': 'pitched insulated',
+    'AnotherDwellingAbove: Unknown, PitchedNormalLoftAccess: 300mm': 'another dwelling above',
+    'PitchedNormalLoftAccess: 200mm, PitchedNormalNoLoftAccess: 50mm': 'pitched insulated',
+    'Flat: As Built, PitchedNormalNoLoftAccess: 100mm': 'flat unknown insulation',
+    'PitchedNormalLoftAccess: 250mm, PitchedNormalNoLoftAccess: Unknown': 'pitched insulated',
+    'PitchedNormalLoftAccess: 100mm, PitchedNormalLoftAccess: 150mm': 'pitched less than 100mm insulation',
+    'PitchedNormalLoftAccess: 100mm, PitchedNormalLoftAccess: 200mm': 'pitched less than 100mm insulation',
+    'PitchedNormalNoLoftAccess: 75mm': 'pitched less than 100mm insulation',
+    'Flat: As Built, PitchedNormalLoftAccess: 25mm': 'flat unknown insulation',
+    'PitchedNormalLoftAccess: 150mm, SameDwellingAbove': 'pitched insulated',
+    'PitchedNormalLoftAccess: 150mm, PitchedNormalLoftAccess: 50mm': 'pitched insulated',
+    'Flat: As Built, PitchedNormalLoftAccess: 100mm': 'flat unknown insulation',
+    'Flat: As Built, PitchedNormalNoLoftAccess: None': 'flat unknown insulation',
+    'PitchedNormalLoftAccess: 150mm, PitchedNormalLoftAccess: 200mm': 'pitched insulated',
+    'PitchedNormalNoLoftAccess: 300mm': 'pitched insulated',
+    'Flat: As Built, PitchedNormalNoLoftAccess: 150mm': 'flat unknown insulation',
+    'PitchedNormalLoftAccess: 150mm, PitchedNormalNoLoftAccess: None': 'pitched insulated',
+    'PitchedNormalNoLoftAccess: 200mm': 'pitched insulated',
+    'PitchedNormalLoftAccess: 300mm, PitchedNormalNoLoftAccess: Unknown': 'pitched insulated',
+    'PitchedNormalLoftAccess: None': 'pitched less than 100mm insulation',
+    'Flat: As Built': 'flat unknown insulation',
+    'PitchedNormalLoftAccess: 100mm, PitchedNormalLoftAccess: 250mm': 'pitched less than 100mm insulation',
+    'AnotherDwellingAbove: Unknown, PitchedNormalLoftAccess: 50mm': 'another dwelling above',
+    'PitchedNormalLoftAccess: 200mm, PitchedNormalLoftAccess: 250mm, PitchedNormalNoLoftAccess: None': 'pitched '
+                                                                                                       'insulated',
+    'PitchedNormalLoftAccess: 200mm, PitchedNormalLoftAccess: 250mm': 'pitched insulated',
+    'Flat: 50mm': 'flat unknown insulation',
+    'AnotherDwellingAbove: Unknown, PitchedNormalNoLoftAccess: None': 'another dwelling above',
+    'PitchedNormalNoLoftAccess: None': 'pitched uninsulated',
+    'AnotherDwellingAbove: Unknown, PitchedNormalLoftAccess: 25mm': 'another dwelling above',
+    'AnotherDwellingAbove: Unknown, Flat: As Built, PitchedNormalNoLoftAccess: Unknown': 'another dwelling above',
+    'Flat: As Built, PitchedNormalLoftAccess: 200mm, PitchedNormalNoLoftAccess: Unknown': 'flat unknown insulation',
+    'Flat: Unknown, PitchedNormalLoftAccess: 75mm, PitchedNormalLoftAccess: Unknown': 'flat unknown insulation',
+    'PitchedNormalLoftAccess: 100mm, PitchedNormalLoftAccess: Unknown': 'pitched less than 100mm insulation',
+    'PitchedNormalLoftAccess: 200mm, PitchedNormalLoftAccess: 300mm': 'pitched insulated',
+    'PitchedNormalLoftAccess: 100mm, PitchedNormalNoLoftAccess: 100mm': 'pitched less than 100mm insulation',
+    'PitchedNormalLoftAccess: 100mm, PitchedNormalLoftAccess: No Insulation': 'pitched less than 100mm insulation',
+    'AnotherDwellingAbove: Unknown, Flat: As Built, PitchedNormalLoftAccess: 150mm': 'another dwelling above',
+    'PitchedNormalLoftAccess: 75mm, PitchedNormalNoLoftAccess: Unknown': 'pitched less than 100mm insulation',
+    'Flat: As Built, PitchedNormalLoftAccess: 300mm': 'unknown', 'Flat: 100mm': 'flat unknown insulation',
+    'PitchedNormalNoLoftAccess: 150mm, PitchedNormalNoLoftAccess: Unknown': 'pitched insulated',
+    'PitchedNormalNoLoftAccess: 100mm': 'pitched less than 100mm insulation',
+    'PitchedNormalLoftAccess: 12mm': 'pitched less than 100mm insulation',
+    'AnotherDwellingAbove: Unknown, PitchedNormalNoLoftAccess: 150mm': 'another dwelling above',
+    'PitchedNormalLoftAccess: No Insulation': 'pitched less than 100mm insulation',
+
+    'PitchedNormalLoftAccess: 25mm, PitchedNormalNoLoftAccess: Unknown': 'pitched less than 100mm insulation',
+
+    'PitchedNormalLoftAccess: 250mm, PitchedNormalNoLoftAccess: None, PitchedNormalNoLoftAccess: Unknown': 'pitched '
+                                                                                                           'insulated',
+    'PitchedNormalNoLoftAccess: 100mm, PitchedNormalNoLoftAccess: Unknown': 'pitched less than 100mm insulation',
+    'Flat: As Built, PitchedNormalNoLoftAccess: 100mm, PitchedNormalNoLoftAccess: Unknown': 'flat unknown insulation',
+
+    'PitchedNormalNoLoftAccess: Unknown, SameDwellingAbove: Unknown': 'pitched no access to loft',
+    'PitchedNormalLoftAccess: 200mm, PitchedNormalNoLoftAccess: None': 'pitched insulated',
+    'PitchedNormalLoftAccess: 100mm, PitchedNormalNoLoftAccess: As Built': 'pitched less than 100mm insulation',
+    'PitchedNormalNoLoftAccess: 50mm, PitchedNormalNoLoftAccess: Unknown': 'pitched less than 100mm insulation',
+    'Flat: As Built, Flat: Unknown, PitchedNormalLoftAccess: 250mm': 'flat unknown insulation',
+    'PitchedNormalLoftAccess: 50mm': 'pitched less than 100mm insulation',
+    'PitchedNormalLoftAccess: 250mm, PitchedNormalNoLoftAccess: None': 'pitched insulated',
+    'Flat: 100mm, Flat: As Built': 'flat unknown insulation',
+    'PitchedNormalLoftAccess: 100mm, PitchedNormalNoLoftAccess: Unknown': 'pitched less than 100mm insulation',
+    'AnotherDwellingAbove: Unknown, PitchedNormalLoftAccess: No Insulation': 'another dwelling above',
+    'PitchedNormalLoftAccess: 100mm, PitchedNormalNoLoftAccess: None': 'pitched less than 100mm insulation',
+    'PitchedNormalLoftAccess: 300mm': 'pitched insulated',
+    'PitchedNormalLoftAccess: 100mm': 'pitched less than 100mm insulation',
+    'PitchedNormalLoftAccess: 270mm': 'pitched insulated',
+    'PitchedNormalNoLoftAccess: 200mm, PitchedNormalNoLoftAccess: Unknown': 'pitched insulated',
+    'PitchedNormalLoftAccess: 200mm, PitchedNormalNoLoftAccess: Unknown': 'pitched insulated',
+    'Flat: As Built, PitchedNormalLoftAccess: 250mm': 'flat unknown insulation',
+    'PitchedNormalLoftAccess: 50mm, PitchedNormalLoftAccess: 75mm': 'pitched less than 100mm insulation',
+    'PitchedNormalLoftAccess: 300mm, PitchedNormalLoftAccess: Unknown': 'pitched insulated',
+    'Flat: As Built, PitchedNormalNoLoftAccess: 250mm': 'flat unknown insulation',
+    'Flat: As Built, PitchedNormalNoLoftAccess: 50mm': 'flat unknown insulation',
+    'Flat: As Built, PitchedNormalLoftAccess: 75mm': 'flat unknown insulation',
+    'PitchedNormalLoftAccess: 250mm, PitchedNormalNoLoftAccess: No Insulation': 'pitched insulated',
+    'AnotherDwellingAbove: Unknown, PitchedNormalLoftAccess: 150mm': 'another dwelling above',
+    'PitchedNormalLoftAccess: 75mm, PitchedNormalNoLoftAccess: No Insulation': 'pitched less than 100mm insulation',
+    'AnotherDwellingAbove: Unknown, PitchedNormalNoLoftAccess: Unknown': 'another dwelling above',
+    'Flat: As Built, PitchedNormalNoLoftAccess: 200mm': 'flat unknown insulation',
+    'Flat: As Built, Flat: Unknown, PitchedNormalLoftAccess: 150mm': 'flat unknown insulation',
+    'Flat: As Built, PitchedNormalLoftAccess: 150mm': 'flat unknown insulation',
+    'PitchedNormalLoftAccess: 300mm, PitchedNormalNoLoftAccess: 100mm': 'pitched insulated',
+    'PitchedNormalLoftAccess: 100mm, PitchedNormalLoftAccess: 75mm': 'pitched less than 100mm insulation',
+    'AnotherDwellingAbove: Unknown, PitchedNormalNoLoftAccess: 50mm, PitchedNormalNoLoftAccess: No Insulation':
+        'another dwelling above',
+    'Flat: As Built, PitchedNormalLoftAccess: 50mm': 'flat unknown insulation',
+    'PitchedNormalLoftAccess: 25mm': 'pitched less than 100mm insulation',
+    'PitchedNormalLoftAccess: 50mm, PitchedNormalNoLoftAccess: Unknown': 'pitched less than 100mm insulation',
+    'PitchedNormalNoLoftAccess: 150mm, PitchedNormalNoLoftAccess: Unknown, PitchedThatched: 25mm': 'pitched insulated',
+    'Flat: 150mm+': 'flat insulated',
+    'Flat: Unknown, PitchedNormalLoftAccess: 100mm, PitchedNormalNoLoftAccess: Unknown': 'flat unknown insulation',
+    'PitchedNormalLoftAccess: 150mm, PitchedNormalLoftAccess: Unknown': 'pitched insulated',
+    'PitchedNormalLoftAccess: 150mm, PitchedNormalLoftAccess: 250mm': 'pitched insulated',
+    'Flat: As Built, PitchedNormalLoftAccess: 100mm, PitchedNormalNoLoftAccess: Unknown': 'flat unknown insulation',
+    'PitchedNormalLoftAccess: 250mm': 'pitched insulated',
+    'PitchedNormalLoftAccess: 250mm, PitchedNormalLoftAccess: 75mm': 'pitched insulated',
+    'PitchedNormalLoftAccess: 250mm, PitchedNormalLoftAccess: 50mm': 'pitched insulated',
+    'AnotherDwellingAbove: Unknown, PitchedNormalLoftAccess: 200mm': 'another dwelling above',
+
+    'PitchedNormalNoLoftAccess: Unknown': 'pitched no access to loft',
+    'PitchedNormalLoftAccess: Unknown': 'pitched unknown insulation',
+    'AnotherDwellingAbove: Unknown': 'another dwelling above'
+
 }
diff --git a/asset_list/mappings/walls.py b/asset_list/mappings/walls.py
index 2e0a332f..245b7f88 100644
--- a/asset_list/mappings/walls.py
+++ b/asset_list/mappings/walls.py
@@ -7,122 +7,163 @@ STANDARD_WALL_CONSTRUCTIONS = {
     "uninsulated solid brick", "insulated solid brick", "solid brick unknown insulation",
     # Timber Frame
     "timber frame unknown insulation", "insulated timber frame", "uninsulated timber frame",
-    "system built", "granite or whinstone", "other",
-    "unknown", "sandstone or limestone",
+    # System
+    "system built unknown insulation", "insulated system built", "uninsulated system built",
+    # Granite or Whinstone
+    "granite or whinstone unknown insulation", "insulated granite or whinstone", "uninsulated granite or whinstone",
+    # Sandstone or Limestone
+    "sandstone or limestone unknown insulation", "insulated sandstone or limestone",
+    "uninsulated sandstone or limestone",
+    # Other
+    "other",
     "cob",
     "new build - average thermal transmittance",
 }
 
 WALL_CONSTRUCTION_MAPPINGS = {
     "New Build - Average Thermal Transmittance": "new build - average thermal transmittance",
-    'Average thermal transmittance 0.25 W/m?K': 'unknown',
+    'Average thermal transmittance 0.25 W/m?K': 'new build - average thermal transmittance',
     'Cavity wall, as built, insulated (assumed)': 'filled cavity',
     'Average thermal transmittance 0.31 W/m?K': 'unknown',
     'Cavity wall, as built, no insulation (assumed)': 'uninsulated cavity',
-    'Average thermal transmittance 0.30 W/m?K': 'unknown', 'Average thermal transmittance 0.28 W/m-¦K': 'unknown',
-    'Average thermal transmittance 0.25 W/m-¦K': 'unknown', 'Average thermal transmittance 0.21 W/m-¦K': 'unknown',
-    'Average thermal transmittance 0.20 W/m-¦K': 'unknown', 'Average thermal transmittance 0.29 W/m?K': 'unknown',
-    'Average thermal transmittance 0.16 W/m?K': 'unknown',
-    'Average thermal transmittance 0.27 W/m&#0178;K': 'unknown',
-    'Average thermal transmittance 0.15 W/m-¦K': 'unknown', 'Average thermal transmittance 0.23 W/m-¦K': 'unknown',
-    'Average thermal transmittance 0.18 W/m?K': 'unknown',
-    'Granite or whin, with internal insulation': 'granite or whinstone',
-    "Granite or whinstone, as built, insulated (assumed)": "granite or whinstone",
-    'Average thermal transmittance 0.22 W/m-¦K': 'unknown', 'Average thermal transmittance 0.24 W/m?K': 'unknown',
-    'Average thermal transmittance 0.16 W/m-¦K': 'unknown', 'Average thermal transmittance 0.35 W/m?K': 'unknown',
-    'Average thermal transmittance 0.26 W/m-¦K': 'unknown', 'Average thermal transmittance 0.62 W/m?K': 'unknown',
-    'Average thermal transmittance 0.64 W/m?K': 'unknown', 'Average thermal transmittance 0.61 W/m?K': 'unknown',
-    'Sandstone or limestone, as built, no insulation (assumed)': 'sandstone or limestone',
-    'Average thermal transmittance 0.33 W/m?K': 'unknown',
+    'Average thermal transmittance 0.30 W/m?K': 'new build - average thermal transmittance',
+    'Average thermal transmittance 0.28 W/m-¦K': 'new build - average thermal transmittance',
+    'Average thermal transmittance 0.25 W/m-¦K': 'new build - average thermal transmittance',
+    'Average thermal transmittance 0.21 W/m-¦K': 'new build - average thermal transmittance',
+    'Average thermal transmittance 0.20 W/m-¦K': 'new build - average thermal transmittance',
+    'Average thermal transmittance 0.29 W/m?K': 'new build - average thermal transmittance',
+    'Average thermal transmittance 0.16 W/m?K': 'new build - average thermal transmittance',
+    'Average thermal transmittance 0.27 W/m&#0178;K': 'new build - average thermal transmittance',
+    'Average thermal transmittance 0.15 W/m-¦K': 'new build - average thermal transmittance',
+    'Average thermal transmittance 0.23 W/m-¦K': 'new build - average thermal transmittance',
+    'Average thermal transmittance 0.18 W/m?K': 'new build - average thermal transmittance',
+    'Granite or whin, with internal insulation': 'insulated granite or whinstone',
+    "Granite or whinstone, as built, insulated (assumed)": "uninsulated granite or whinstone",
+    'Average thermal transmittance 0.22 W/m-¦K': 'new build - average thermal transmittance',
+    'Average thermal transmittance 0.24 W/m?K': 'new build - average thermal transmittance',
+    'Average thermal transmittance 0.16 W/m-¦K': 'new build - average thermal transmittance',
+    'Average thermal transmittance 0.35 W/m?K': 'new build - average thermal transmittance',
+    'Average thermal transmittance 0.26 W/m-¦K': 'new build - average thermal transmittance',
+    'Average thermal transmittance 0.62 W/m?K': 'new build - average thermal transmittance',
+    'Average thermal transmittance 0.64 W/m?K': 'new build - average thermal transmittance',
+    'Average thermal transmittance 0.61 W/m?K': 'new build - average thermal transmittance',
+    'Sandstone or limestone, as built, no insulation (assumed)': 'uninsulated sandstone or limestone',
+    'Average thermal transmittance 0.33 W/m?K': 'new build - average thermal transmittance',
     'Cavity wall,': "cavity unknown insulation",
     'Cavity wall, as built, partial insulation (assumed)': 'partial insulated cavity',
-    'Average thermal transmittance 0.29 W/m-¦K': 'unknown', 'Average thermal transmittance 0.32 W/m-¦K': 'unknown',
-    'Average thermal transmittance 0.19 W/m-¦K': 'unknown', 'Average thermal transmittance 0.27 W/m?K': 'unknown',
-    'Average thermal transmittance 0.22 W/m?K': 'unknown', 'Average thermal transmittance 0.38 W/m?K': 'unknown',
-    'Average thermal transmittance 0.26 W/m?K': 'unknown', 'Average thermal transmittance 0.27 W/m-¦K': 'unknown',
-    'Average thermal transmittance 0.18 W/m-¦K': 'unknown', 'Average thermal transmittance = 0.27 W/m?K': 'unknown',
-    'Cavity wall, with external insulation': 'filled cavity', 'Average thermal transmittance 0.21 W/m?K': 'unknown',
-    'Average thermal transmittance 0.23 W/m?K': 'unknown', 'Average thermal transmittance 0.20 W/m?K': 'unknown',
-    'Average thermal transmittance 0.32 W/m?K': 'unknown', 'Average thermal transmittance 0.24 W/m-¦K': 'unknown',
+    'Average thermal transmittance 0.29 W/m-¦K': 'new build - average thermal transmittance',
+    'Average thermal transmittance 0.32 W/m-¦K': 'new build - average thermal transmittance',
+    'Average thermal transmittance 0.19 W/m-¦K': 'new build - average thermal transmittance',
+    'Average thermal transmittance 0.27 W/m?K': 'new build - average thermal transmittance',
+    'Average thermal transmittance 0.22 W/m?K': 'new build - average thermal transmittance',
+    'Average thermal transmittance 0.38 W/m?K': 'new build - average thermal transmittance',
+    'Average thermal transmittance 0.26 W/m?K': 'new build - average thermal transmittance',
+    'Average thermal transmittance 0.27 W/m-¦K': 'new build - average thermal transmittance',
+    'Average thermal transmittance 0.18 W/m-¦K': 'new build - average thermal transmittance',
+    'Average thermal transmittance = 0.27 W/m?K': 'new build - average thermal transmittance',
+    'Cavity wall, with external insulation': 'filled cavity',
+    'Average thermal transmittance 0.21 W/m?K': 'new build - average thermal transmittance',
+    'Average thermal transmittance 0.23 W/m?K': 'new build - average thermal transmittance',
+    'Average thermal transmittance 0.20 W/m?K': 'new build - average thermal transmittance',
+    'Average thermal transmittance 0.32 W/m?K': 'new build - average thermal transmittance',
+    'Average thermal transmittance 0.24 W/m-¦K': 'new build - average thermal transmittance',
     'Cavity wall, with internal insulation': 'filled cavity',
-    'Average thermal transmittance 0.17 W/m-¦K': 'unknown', 'Average thermal transmittance 0.28 W/m?K': 'unknown',
+    'Average thermal transmittance 0.17 W/m-¦K': 'new build - average thermal transmittance',
+    'Average thermal transmittance 0.28 W/m?K': 'new build - average thermal transmittance',
     'new build - average thermal transmittance': 'new build - average thermal transmittance',
-    'average thermal transmittance 0.25 w/m?k': 'unknown',
+    'average thermal transmittance 0.25 w/m?k': 'new build - average thermal transmittance',
     'cavity wall, as built, insulated (assumed)': 'filled cavity',
-    'average thermal transmittance 0.31 w/m?k': 'unknown',
+    'average thermal transmittance 0.31 w/m?k': 'new build - average thermal transmittance',
     'cavity wall, as built, no insulation (assumed)': 'uninsulated cavity',
-    'average thermal transmittance 0.30 w/m?k': 'unknown', 'average thermal transmittance 0.28 w/m-¦k': 'unknown',
-    'average thermal transmittance 0.25 w/m-¦k': 'unknown', 'average thermal transmittance 0.21 w/m-¦k': 'unknown',
-    'average thermal transmittance 0.20 w/m-¦k': 'unknown', 'average thermal transmittance 0.29 w/m?k': 'unknown',
-    'average thermal transmittance 0.16 w/m?k': 'unknown', 'average thermal transmittance 0.27 w/m&#0178;k': 'unknown',
-    'average thermal transmittance 0.15 w/m-¦k': 'unknown', 'average thermal transmittance 0.23 w/m-¦k': 'unknown',
-    'average thermal transmittance 0.18 w/m?k': 'unknown',
-    'granite or whin, with internal insulation': 'granite or whinstone',
-    'average thermal transmittance 0.22 w/m-¦k': 'unknown', 'average thermal transmittance 0.24 w/m?k': 'unknown',
-    'average thermal transmittance 0.16 w/m-¦k': 'unknown', 'average thermal transmittance 0.35 w/m?k': 'unknown',
-    'average thermal transmittance 0.26 w/m-¦k': 'unknown', 'average thermal transmittance 0.62 w/m?k': 'unknown',
-    'average thermal transmittance 0.64 w/m?k': 'unknown', 'average thermal transmittance 0.61 w/m?k': 'unknown',
-    'sandstone or limestone, as built, no insulation (assumed)': 'sandstone or limestone',
-    'average thermal transmittance 0.33 w/m?k': 'unknown', 'cavity wall,': "cavity unknown insulation",
+    'average thermal transmittance 0.30 w/m?k': 'new build - average thermal transmittance',
+    'average thermal transmittance 0.28 w/m-¦k': 'new build - average thermal transmittance',
+    'average thermal transmittance 0.25 w/m-¦k': 'new build - average thermal transmittance',
+    'average thermal transmittance 0.21 w/m-¦k': 'new build - average thermal transmittance',
+    'average thermal transmittance 0.20 w/m-¦k': 'new build - average thermal transmittance',
+    'average thermal transmittance 0.29 w/m?k': 'new build - average thermal transmittance',
+    'average thermal transmittance 0.16 w/m?k': 'new build - average thermal transmittance',
+    'average thermal transmittance 0.27 w/m&#0178;k': 'new build - average thermal transmittance',
+    'average thermal transmittance 0.15 w/m-¦k': 'new build - average thermal transmittance',
+    'average thermal transmittance 0.23 w/m-¦k': 'new build - average thermal transmittance',
+    'average thermal transmittance 0.18 w/m?k': 'new build - average thermal transmittance',
+    'granite or whin, with internal insulation': 'insulated granite or whinstone',
+    'average thermal transmittance 0.22 w/m-¦k': 'new build - average thermal transmittance',
+    'average thermal transmittance 0.24 w/m?k': 'new build - average thermal transmittance',
+    'average thermal transmittance 0.16 w/m-¦k': 'new build - average thermal transmittance',
+    'average thermal transmittance 0.35 w/m?k': 'new build - average thermal transmittance',
+    'average thermal transmittance 0.26 w/m-¦k': 'new build - average thermal transmittance',
+    'average thermal transmittance 0.62 w/m?k': 'new build - average thermal transmittance',
+    'average thermal transmittance 0.64 w/m?k': 'new build - average thermal transmittance',
+    'average thermal transmittance 0.61 w/m?k': 'new build - average thermal transmittance',
+    'sandstone or limestone, as built, no insulation (assumed)': 'uninsulated sandstone or limestone',
+    'average thermal transmittance 0.33 w/m?k': 'new build - average thermal transmittance',
+    'cavity wall,': "cavity unknown insulation",
     'cavity wall, as built, partial insulation (assumed)': 'partial insulated cavity',
-    'average thermal transmittance 0.29 w/m-¦k': 'unknown', 'average thermal transmittance 0.32 w/m-¦k': 'unknown',
-    'average thermal transmittance 0.19 w/m-¦k': 'unknown', 'average thermal transmittance 0.27 w/m?k': 'unknown',
-    'average thermal transmittance 0.22 w/m?k': 'unknown', 'average thermal transmittance 0.38 w/m?k': 'unknown',
-    'average thermal transmittance 0.26 w/m?k': 'unknown', 'average thermal transmittance 0.27 w/m-¦k': 'unknown',
-    'average thermal transmittance 0.18 w/m-¦k': 'unknown', 'average thermal transmittance = 0.27 w/m?k': 'unknown',
-    'cavity wall, with external insulation': 'filled cavity', 'average thermal transmittance 0.21 w/m?k': 'unknown',
-    'average thermal transmittance 0.23 w/m?k': 'unknown', 'average thermal transmittance 0.20 w/m?k': 'unknown',
-    'average thermal transmittance 0.32 w/m?k': 'unknown', 'average thermal transmittance 0.24 w/m-¦k': 'unknown',
-    'cavity wall, with internal insulation': 'filled cavity', 'average thermal transmittance 0.17 w/m-¦k': 'unknown',
-    'average thermal transmittance 0.28 w/m?k': 'unknown',
+    'average thermal transmittance 0.29 w/m-¦k': 'new build - average thermal transmittance',
+    'average thermal transmittance 0.32 w/m-¦k': 'new build - average thermal transmittance',
+    'average thermal transmittance 0.19 w/m-¦k': 'new build - average thermal transmittance',
+    'average thermal transmittance 0.27 w/m?k': 'new build - average thermal transmittance',
+    'average thermal transmittance 0.22 w/m?k': 'new build - average thermal transmittance',
+    'average thermal transmittance 0.38 w/m?k': 'new build - average thermal transmittance',
+    'average thermal transmittance 0.26 w/m?k': 'new build - average thermal transmittance',
+    'average thermal transmittance 0.27 w/m-¦k': 'new build - average thermal transmittance',
+    'average thermal transmittance 0.18 w/m-¦k': 'new build - average thermal transmittance',
+    'average thermal transmittance = 0.27 w/m?k': 'new build - average thermal transmittance',
+    'cavity wall, with external insulation': 'filled cavity',
+    'average thermal transmittance 0.21 w/m?k': 'new build - average thermal transmittance',
+    'average thermal transmittance 0.23 w/m?k': 'new build - average thermal transmittance',
+    'average thermal transmittance 0.20 w/m?k': 'new build - average thermal transmittance',
+    'average thermal transmittance 0.32 w/m?k': 'new build - average thermal transmittance',
+    'average thermal transmittance 0.24 w/m-¦k': 'new build - average thermal transmittance',
+    'cavity wall, with internal insulation': 'filled cavity',
+    'average thermal transmittance 0.17 w/m-¦k': 'new build - average thermal transmittance',
+    'average thermal transmittance 0.28 w/m?k': 'new build - average thermal transmittance',
     'Cavity wall, filled cavity': 'filled cavity',
     'Cavity wall, filled cavity and external insulation': 'filled cavity',
-    'Granite or whinstone, as built, no insulation (assumed)': 'granite or whinstone',
+    'Granite or whinstone, as built, no insulation (assumed)': 'uninsulated granite or whinstone',
     'Solid brick, as built, insulated (assumed)': 'insulated solid brick',
     'Solid brick, as built, no insulation (assumed)': 'uninsulated solid brick',
     'Solid brick, with external insulation': 'insulated solid brick',
     'Solid brick, with internal insulation': 'insulated solid brick',
-    'System built, as built, insulated (assumed)': 'system built',
-    'System built, as built, no insulation (assumed)': 'system built',
-    'System built, with external insulation': 'system built',
-    'System built, with internal insulation': 'system built',
-    'Timber frame, as built, insulated (assumed)': 'timber frame',
-    'Timber frame, as built, no insulation (assumed)': 'timber frame',
-    'Timber frame, as built, partial insulation (assumed)': 'timber frame',
-    'Timber frame, with additional insulation': 'timber frame',
+    'System built, as built, insulated (assumed)': 'insulated system built',
+    'System built, as built, no insulation (assumed)': 'uninsulated system built',
+    'System built, with external insulation': 'insulated system built',
+    'System built, with internal insulation': 'insulated system built',
+    'Timber frame, as built, insulated (assumed)': 'insulated timber frame',
+    'Timber frame, as built, no insulation (assumed)': 'uninsulated timber frame',
+    'Timber frame, as built, partial insulation (assumed)': 'insulated timber frame',
+    'Timber frame, with additional insulation': 'insulated timber frame',
     'CAVITY': 'cavity unknown insulation',
     'COMB': 'unknown',
     'NONE': 'unknown',
     'NOTKNOWN': 'unknown',
     'SOLID': 'solid brick unknown insulation',
     np.nan: 'unknown',
-    'RENDER/TIMBER FRAME': 'timber frame',
-    'SYSTEM BUILT': 'system built',
+    'RENDER/TIMBER FRAME': 'timber frame unknown insulation',
+    'SYSTEM BUILT': 'system built unknown insulation',
     'PCC PANELS': 'other',
     'NOT APPLICABLE - FLAT': 'unknown',
-    'BRICK/TIMBER FRAME': 'timber frame',
+    'BRICK/TIMBER FRAME': 'timber frame unknown insulation',
     'BRICK/BLOCK CAVITY': 'cavity unknown insulation',
-    'STONE SOLID': 'sandstone or limestone',
-    'EXT CLADDING SYSTEM': 'system built',
+    'STONE SOLID': 'sandstone or limestone unknown insulation',
+    'EXT CLADDING SYSTEM': 'system built unknown insulation',
     'BRICK/BLOCK SOLID': 'solid brick unknown insulation',
-
     'Cavity Filled cavity (with internal/external)': 'filled cavity',
     'ND (inferred) Filled cavity': 'filled cavity',
     'Cavity Filled cavity': 'filled cavity',
     'Cavity Unknown insulation': 'cavity unknown insulation',
-    'Timber frame As-built': 'timber frame',
-    'System build Unknown insulation': 'system built',
+    'Timber frame As-built': 'uninsulated timber frame',
+    'System build Unknown insulation': 'system built unknown insulation',
     'Cavity As-built': 'uninsulated cavity',
-    'System build External': 'system built',
+    'System build External': 'insulated system built',
     'ND (inferred) ND (inferred)': 'unknown',
     'Solid brick External': 'insulated solid brick',
     'Cavity External': 'filled cavity',
-    'System build As-built': 'system built',
+    'System build As-built': 'uninsulated system built',
     'Solid brick Internal': 'insulated solid brick',
     'Cavity Internal': 'filled cavity',
-    'System build Internal': 'system built',
-    'Solid brick As-built': 'solid brick unknown insulation',
-
+    'System build Internal': 'insulated system built',
+    'Solid brick As-built': 'uninsulated solid brick',
     'Cavity ': 'cavity unknown insulation',
     'Solid brick ': 'solid brick unknown insulation',
     'Timber frame Timber frame (good insulation)': 'insulated timber frame',
@@ -141,91 +182,90 @@ WALL_CONSTRUCTION_MAPPINGS = {
     'Cavity: Unknown': 'cavity unknown insulation',
     'Cavity: AsBuilt (Post 1995)': 'filled cavity',
     'Cavity: AsBuilt (1976-1982)': 'cavity unknown insulation',
-    'SystemBuilt: AsBuilt': 'system built',
-    'TimberFrame: AsBuilt': "timber frame unknown insulation",
-    'Cavity: AsBuilt (1983-1995)': 'cavity unknown insulation',
+    'SystemBuilt: AsBuilt': 'uninsulated system built',
+    'TimberFrame: AsBuilt': "uninsulated timber frame",
+    'Cavity: AsBuilt (1983-1995)': 'filled cavity',
     'Cavity: AsBuilt (1983-1995), Cavity: FilledCavity': 'filled cavity',
-    'SolidBrick: AsBuilt': 'solid brick unknown insulation',
+    'SolidBrick: AsBuilt': 'uninsulated solid brick',
     'Cavity: FilledCavity': 'filled cavity',
     'SolidBrick: Internal': 'insulated solid brick',
     'Cavity: External': 'filled cavity',
-    'Sandstone: Internal': 'sandstone or limestone',
-    'Cavity: AsBuilt (Pre 1976)': 'cavity unknown insulation',
-    'System build': 'system built',
+    'Sandstone: Internal': 'insulated sandstone or limestone',
+    'Cavity: AsBuilt (Pre 1976)': 'uninsulated cavity',
+    'System build': 'system built unknown insulation',
     'Solid brick': 'solid brick unknown insulation',
-    'Stone': 'sandstone or limestone',
+    'Stone': 'sandstone or limestone unknown insulation',
     'Timber frame': 'timber frame unknown insulation',
     '2017 onwards': 'new build - average thermal transmittance',
     'ND (inferred)': 'unknown',
-    'Flat / maisonette': 'other',
-    'Other': 'other',
+    'Flat / maisonette': 'unknown',
+    'Other': 'unknown',
     'Timber Frame': 'timber frame unknown insulation',
     'Cavity Wall': 'cavity unknown insulation',
-    'Non-Traditional': 'system built',
-    'PRC': 'system built',
-    'Cross Wall': 'system built',
+    'Non-Traditional': 'system built unknown insulation',
+    'PRC': 'system built unknown insulation',
+    'Cross Wall': 'system built unknown insulation',
     'Solid Wall': 'solid brick unknown insulation',
     'Traditional': 'unknown',
 
     'Solid': 'solid brick unknown insulation',
-    'Wates no fines': 'system built',
-    'Concrete Frame': 'system built',
-    'PRCWATES': 'system built',
-    'Refurbished Cornish': 'system built',
+    'Wates no fines': 'system built unknown insulation',
+    'Concrete Frame': 'system built unknown insulation',
+    'PRCWATES': 'system built unknown insulation',
+    'Refurbished Cornish': 'system built unknown insulation',
     'Bailey Stratton': 'other',
-    'Refurbished Reema': 'system built',
-    'PRCREEMA': 'system built',
-    'Trustsell Type': 'system built',
+    'Refurbished Reema': 'system built unknown insulation',
+    'PRCREEMA': 'system built unknown insulation',
+    'Trustsell Type': 'system built unknown insulation',
     'Petra Nissan': 'unknown',
-    'Reinstated Airey': 'system built',
-    'Refurbished Airey': 'system built',
+    'Reinstated Airey': 'system built unknown insulation',
+    'Refurbished Airey': 'system built unknown insulation',
     # From Abri- slightly unclear on types but not a large portion of the data
-    'No Fines Type': 'system built',
-    'Refurbished Unity': 'system built',
+    'No Fines Type': 'system built unknown insulation',
+    'Refurbished Unity': 'system built unknown insulation',
     'Timber Framed': 'timber frame unknown insulation',
-    'Refurbished Woolaway': 'system built',
+    'Refurbished Woolaway': 'system built unknown insulation',
     'Modern Methods of Construction': 'other',
-    'BISF - Brit Iron & Steel Federation': 'system built',
-    'Steel Framed': 'system built',
+    'BISF - Brit Iron & Steel Federation': 'system built unknown insulation',
+    'Steel Framed': 'system built unknown insulation',
     'Timber Framed with confirmed Fire Stopping': 'timber frame unknown insulation',
-    'Sipporex': 'system built',
+    'Sipporex': 'system built unknown insulation',
 
-    'Wates': 'system built',
-    'Bryants': 'system built',
-    'Gregory (Crosswall)': 'system built',
-    'Rsmit': 'system built',
-    'Dorman Long': 'system built',
-    'Tarmac': 'system built',
-    'RBIS': 'system built',
-    'Five Oaks': 'system built',
+    'Wates': 'system built unknown insulation',
+    'Bryants': 'system built unknown insulation',
+    'Gregory (Crosswall)': 'system built unknown insulation',
+    'Rsmit': 'system built unknown insulation',
+    'Dorman Long': 'system built unknown insulation',
+    'Tarmac': 'system built unknown insulation',
+    'RBIS': 'system built unknown insulation',
+    'Five Oaks': 'system built unknown insulation',
     'Not known': 'unknown',
-    'Smiths': 'system built',
-    'Kendrick': 'system built',
-    'IDC': 'system built',
-    'Wimpey (Part Brick)': 'system built',
-    'Whitehall': 'system built',
-    'Wimpey': 'system built',
-    'Bison': 'system built',
-    'Zinns': 'system built',
-    'Bisf': 'system built',
-    'Integer': 'system built',
-    'Cornish': 'system built',
-    'Rwate': 'system built',
-    'Hill Presweld Steel': 'system built',
+    'Smiths': 'system built unknown insulation',
+    'Kendrick': 'system built unknown insulation',
+    'IDC': 'system built unknown insulation',
+    'Wimpey (Part Brick)': 'system built unknown insulation',
+    'Whitehall': 'system built unknown insulation',
+    'Wimpey': 'system built unknown insulation',
+    'Bison': 'system built unknown insulation',
+    'Zinns': 'system built unknown insulation',
+    'Bisf': 'system built unknown insulation',
+    'Integer': 'system built unknown insulation',
+    'Cornish': 'system built unknown insulation',
+    'Rwate': 'system built unknown insulation',
+    'Hill Presweld Steel': 'system built unknown insulation',
     'Cavity Filled Cavity': 'filled cavity',
     'Cavity Unknown': 'cavity unknown insulation',
     'Cavity Filled Cavity (internal)': 'filled cavity',
     '': 'unknown',
     'Cavity Internal Insulation': 'filled cavity',
     'Cavity As Built': "uninsulated cavity",
-    'Non Trad Large Panel System': 'system built',
-    'Non Trad Cornish': 'system built',
-    'Non Trad Reema': 'system built',
+    'Non Trad Large Panel System': 'system built unknown insulation',
+    'Non Trad Cornish': 'system built unknown insulation',
+    'Non Trad Reema': 'system built unknown insulation',
     'Traditional Cavity Brickwork': 'cavity unknown insulation',
-    'System build (undefined)': 'system built',
-    'Non Trad Wimpey': 'system built',
-    'Non Trad Wates': 'system built',
-
+    'System build (undefined)': 'system built unknown insulation',
+    'Non Trad Wimpey': 'system built unknown insulation',
+    'Non Trad Wates': 'system built unknown insulation',
     'CAVITY FILLED 270MM': 'filled cavity',
     'CAVITY FILLED  270MM': 'filled cavity',
     'CAVITY FILLED  250MM': 'filled cavity',
@@ -238,17 +278,60 @@ WALL_CONSTRUCTION_MAPPINGS = {
     'CAVITY A/B  270MM': "uninsulated cavity",
     'SOLID BRICK/CAVITY EXT': 'solid brick unknown insulation',
     'CAVITY EWI': 'filled cavity',
-    'SANDSTONE/CAVITY EXT': 'sandstone or limestone',
-    'SYSTEM BUILD 100MM EWI': 'system built',
+    'SANDSTONE/CAVITY EXT': 'sandstone or limestone unknown insulation',
+    'SYSTEM BUILD 100MM EWI': 'insulated system built',
     'CAVITY A/B 260MM': "uninsulated cavity",
     'CAVITY A/B 270MM': "uninsulated cavity",
     'CAVITY A/B  250MM': "uninsulated cavity",
-    'System': 'system built',
-    'Sandstone/Limestone': 'sandstone or limestone',
-    'No Fines': 'system built',
-    'Granite/Whinstone': 'granite or whinstone',
+    'System': 'system built unknown insulation',
+    'Sandstone/Limestone': 'sandstone or limestone unknown insulation',
+    'No Fines': 'system built unknown insulation',
+    'Granite/Whinstone': 'granite or whinstone unknown insulation',
     'Not applicable to this asset type': 'unknown',
-    'Steel Frame': 'system built',
+    'Steel Frame': 'system built unknown insulation',
     'Solid Wall As Built': 'uninsulated solid brick',
-    'Solid As Built': 'uninsulated solid brick'
+    'Solid As Built': 'uninsulated solid brick',
+    'Cavity: FilledCavity, Cavity: Unknown': 'filled cavity',
+    'Cavity: AsBuilt (Pre 1976), TimberFrame: Unknown': 'uninsulated cavity',
+    'SolidBrick: AsBuilt, SolidBrick: Unknown': 'uninsulated solid brick',
+    'Cavity: FilledCavity, SolidBrick: Unknown': 'filled cavity',
+    'Cavity: AsBuilt (Pre 1976), SolidBrick: Unknown': 'uninsulated cavity',
+    'Cavity: FilledCavity, TimberFrame: Unknown': 'filled cavity',
+    'Cavity: AsBuilt (1976-1982), Cavity: Unknown': 'uninsulated cavity',
+    'Cavity: Unknown, SolidBrick: AsBuilt': 'cavity unknown insulation',
+    'Cavity: AsBuilt (1976-1982), Cavity: FilledCavity': 'filled cavity',
+    'Cavity: External, Cavity: FilledCavity': 'filled cavity',
+    'Cavity: AsBuilt (Post 1995), TimberFrame: AsBuilt': 'filled cavity',
+    'TimberFrame: AsBuilt, TimberFrame: Internal': 'timber frame unknown insulation',
+    'GraniteOrWhinstone: AsBuilt': 'uninsulated granite or whinstone',
+    'Cavity: AsBuilt (Post 1995), Cavity: FilledCavity, SolidBrick: Internal': 'filled cavity',
+    'Cavity: AsBuilt (Pre 1976), Cavity: FilledCavity': 'filled cavity',
+    'SolidBrick: AsBuilt, SolidBrick: External': 'insulated solid brick',
+    'Cavity: AsBuilt (Post 1995), Cavity: FilledCavity': 'filled cavity',
+    'Cavity: FilledCavity, SolidBrick: Internal': 'filled cavity',
+    'Cavity: AsBuilt (Post 1995), Cavity: FilledCavity, SolidBrick: Unknown': 'filled cavity',
+    'Cavity: AsBuilt (Pre 1976), SolidBrick: AsBuilt': 'uninsulated cavity',
+    'Cavity: AsBuilt (1976-1982), SolidBrick: AsBuilt': 'filled cavity',
+
+    'Cavity: FilledCavity, SolidBrick: AsBuilt': 'filled cavity',
+    'SolidBrick: External': 'insulated solid brick',
+    'Cavity: FilledCavity, Cavity: Internal': 'filled cavity',
+    'Cavity: External, SolidBrick: AsBuilt': 'filled cavity',
+    'SolidBrick: AsBuilt, TimberFrame: AsBuilt': 'uninsulated solid brick',
+    'Cavity: FilledCavity, SystemBuilt: AsBuilt': 'filled cavity',
+    'Cavity: AsBuilt (1976-1982), SystemBuilt: AsBuilt': 'system built',
+    'Cavity: AsBuilt (Post 1995), SolidBrick: AsBuilt': 'filled cavity',
+    'Cavity: AsBuilt (1983-1995), TimberFrame: AsBuilt': 'filled cavity',
+    'SystemBuilt: AsBuilt, TimberFrame: AsBuilt': 'uninsulated system built',
+    'TimberFrame: Internal': 'insulated timber frame',
+    'Cavity: Internal': 'filled cavity',
+    'SystemBuilt: External': 'filled cavity',
+    'Cavity: AsBuilt (Pre 1976), SystemBuilt: AsBuilt': 'uninsulated cavity',
+    'SystemBuilt: Internal': 'insulated system built',
+    'Cavity: AsBuilt (1983-1995), SolidBrick: AsBuilt': 'solid brick unknown insulation',
+    'Cavity: AsBuilt (Pre 1976), TimberFrame: AsBuilt': 'timber frame unknown insulation',
+    'SolidBrick: AsBuilt, SolidBrick: Internal': 'uninsulated solid brick',
+    'Cavity: FilledCavity, TimberFrame: AsBuilt': 'filled cavity',
+    'Cavity: FilledCavity, SolidBrick: AsBuilt, SolidBrick: Internal': 'filled cavity',
+    'Cavity: Internal, SolidBrick: AsBuilt': 'filled cavity',
 }
diff --git a/asset_list/requirements.txt b/asset_list/requirements.txt
index 99943397..b68706be 100644
--- a/asset_list/requirements.txt
+++ b/asset_list/requirements.txt
@@ -6,7 +6,10 @@ epc-api-python==1.0.2
 thefuzz
 boto3
 openpyxl
-openai
+openai>=1.3.5
 tiktoken
 msgpack
-beautifulsoup4
\ No newline at end of file
+beautifulsoup4
+pydantic>=1.10.7
+typing-extensions>=4.5.0
+requests>=2.28.2
diff --git a/asset_list/utils.py b/asset_list/utils.py
index 1678b8e9..fe2b7d14 100644
--- a/asset_list/utils.py
+++ b/asset_list/utils.py
@@ -1,5 +1,5 @@
 import time
-import numpy as np
+import random
 import pandas as pd
 from backend.SearchEpc import SearchEpc
 from etl.find_my_epc.RetrieveFindMyEpc import RetrieveFindMyEpc
@@ -37,7 +37,9 @@ def get_data(
         "mid-terrace": "Mid-Terrace",
         "end-terrace": "End-Terrace",
         "semi-detached": "Semi-Detached",
-        "detached": "Detached"
+        "detached": "Detached",
+        "enclosed end-terrace": "End-Terrace",
+        "enclosed mid-terrace": "Mid-Terrace",
     }
 
     epc_data = []
@@ -101,7 +103,6 @@ def get_data(
                     else:
                         # Try splitting on space
                         add1 = full_address.split(" ")[0].strip()
-
                 else:
                     add1 = str(house_number)
                 searcher = SearchEpc(
@@ -172,7 +173,7 @@ def get_data(
                     find_epc_data = {}
             except Exception as e:
                 raise Exception(f"Error retrieving FindMyEPC data: {e}")
-            time.sleep(np.random.uniform(0.1, 1))
+            time.sleep(random.sample(range(50, 100), 1)[0] / 100)
 
             epc = {
                 row_id_name: home[row_id_name],
@@ -182,6 +183,11 @@ def get_data(
             }
 
             epc_data.append(epc)
+
+            if len(epc_data) % 50 == 0 and len(epc_data) > 0:
+                logger.info("Sleeping for 10 seconds to avoid hitting API rate limit")
+                time.sleep(10)
+
         except Exception as e:
             errors.append(home[row_id_name])
             time.sleep(5)
diff --git a/backend/Property.py b/backend/Property.py
index 91c1265a..22eb2fc3 100644
--- a/backend/Property.py
+++ b/backend/Property.py
@@ -217,6 +217,9 @@ class Property:
         self.eco4_eligibility = None
         self.whlg_eligibility = None
 
+        # Ventilation
+        self.has_ventilation = self.identify_ventilation()
+
     @classmethod
     def extract_kwargs(cls, kwargs):
         """
@@ -1197,7 +1200,7 @@ class Property:
         self.heating_energy_source = self.heating_energy_source[0]
 
         if self.heating_energy_source == "Varied (Community Scheme)":
-            if self.main_fuel["fuel_type"] == "mains gas":
+            if self.main_fuel["fuel_type"] in ["mains gas", None]:  # We assume when None as it's unknown
                 self.heating_energy_source = "Natural Gas (Community Scheme)"
             else:
                 raise Exception("Implement me")
@@ -1233,6 +1236,13 @@ class Property:
         if "air_source_heat_pump" not in measures:
             return False
 
+        # If we have a house over a floor area threshold, we recommend an ASHP
+        if (
+            self.data["property-type"] in ["House", "Bungalow"] and
+            self.floor_area > assumptions.ASHP_FLOOR_AREA_THRESHOLD
+        ):
+            return True
+
         suitable_house = self.data["property-type"] == "House" and self.data["built-form"] in [
             "Detached", "Semi-Detached", "End-Terrace",
         ]
@@ -1342,3 +1352,12 @@ class Property:
         self.gbis_eligibiltiy = funding_calulator.gbis_eligibiltiy
         self.eco4_eligibility = funding_calulator.eco4_eligibility
         self.whlg_eligibility = funding_calulator.whlg_eligibility
+
+    def identify_ventilation(self):
+
+        ventilation_descriptions = [
+            'mechanical, extract only',
+            'mechanical, supply and extract'
+        ]
+
+        return self.data["mechanical-ventilation"] in ventilation_descriptions
diff --git a/backend/app/assumptions.py b/backend/app/assumptions.py
index f1090ef3..d36266d3 100644
--- a/backend/app/assumptions.py
+++ b/backend/app/assumptions.py
@@ -58,6 +58,19 @@ DESCRIPTIONS_TO_FUEL_TYPES = {
     "Room heaters, wood logs": {"fuel": "Wood Logs", "cop": 1},
     "Boiler and radiators, coal": {"fuel": "Coal", "cop": 0.85},
     "From main system, no cylinderstat": {"fuel": "Natural Gas", "cop": 0.85},
+    "Room heaters, coal": {"fuel": "Coal", "cop": 0.85},
+    "Electric underfloor heating, Electric storage heaters": {"fuel": "Electricity", "cop": 1},
+    'Room heaters, electric, Boiler and radiators, mains gas': {"fuel": "Natural Gas", "cop": 0.85},
+    'Boiler and radiators, mains gas, Boiler and radiators, mains gas': {"fuel": "Natural Gas", "cop": 0.85},
+    'Room heaters, electric, Electric storage heaters': {"fuel": "Electricity", "cop": 1},
+    "Boiler and radiators, mains gas, Electric storage heaters": {"fuel": "Natural Gas", "cop": 0.85},
+    "Boiler and radiators, anthracite": {"fuel": "Anthracite", "cop": 0.85},
+    'Electric immersion, off-peak, plus solar': {"fuel": "Electricity + Solar Thermal", "cop": 1},
+    'Ground source heat pump, radiators, electric': {
+        "fuel": "Electricity", "cop": AVERAGE_ASHP_EFFICIENCY / 100
+    },
+    'Electric instantaneous at point of use, plus solar': {"fuel": "Electricity + Solar Thermal", "cop": 1},
+    "Electric storage heaters, Room heaters, electric": {"fuel": "Electricity", "cop": 1},
 }
 
 # These are the measure types where if there is a ventilation recommendation, we force the inclusion of it
@@ -65,3 +78,6 @@ DESCRIPTIONS_TO_FUEL_TYPES = {
 measures_needing_ventilation = [
     "internal_wall_insulation", "external_wall_insulation", "cavity_wall_insulation"
 ]
+
+# If we have a property beyond this size, we assume it's likely large enough to have an ASHP
+ASHP_FLOOR_AREA_THRESHOLD = 120  # m2
diff --git a/backend/app/plan/schemas.py b/backend/app/plan/schemas.py
index 48300f2a..6b8b192d 100644
--- a/backend/app/plan/schemas.py
+++ b/backend/app/plan/schemas.py
@@ -96,3 +96,7 @@ class PlanTriggerRequest(BaseModel):
     # When performing a remote assessment, if this has been set, it will allow the engine to
     # pull data from the find my epc website, to utilise as part of a remote assessment
     event_type: Optional[Literal["remote_assessment"]] = None
+
+    # If true, before optimising the engine will select a slightly larger package, to account for the SAP 10 causing
+    # scores to drop by a few points
+    simulate_sap_10: Optional[bool] = False
diff --git a/backend/engine/engine.py b/backend/engine/engine.py
index 5316fd03..d631e349 100644
--- a/backend/engine/engine.py
+++ b/backend/engine/engine.py
@@ -30,7 +30,6 @@ import backend.app.assumptions as assumptions
 
 from backend.ml_models.api import ModelApi
 from backend.Property import Property
-from backend.Funding import Funding
 from backend.apis.GoogleSolarApi import GoogleSolarApi
 
 from recommendations.optimiser.CostOptimiser import CostOptimiser
@@ -507,7 +506,7 @@ async def model_engine(body: PlanTriggerRequest):
             )
 
             # if we have a remote assment data type, we pull the additional data and include it
-            if (body.event_type == "remote_assessment") and not (epc_searcher.newest_epc["estimated"]):
+            if (body.event_type == "remote_assessment") and not (epc_searcher.newest_epc.get("estimated")):
                 logger.info("Retrieving find my epc data")
                 try:
                     property_non_invasive_recommendations, patch = RetrieveFindMyEpc.get_from_epc(
@@ -728,7 +727,8 @@ async def model_engine(body: PlanTriggerRequest):
             # Additionally, if we have required measures, they should also be included. Therefore
             # we can discount the number of points required to get to the target SAP band (or increase)
             # in the case of ventilation
-            needs_ventilation = any(x in property_measure_types for x in assumptions.measures_needing_ventilation)
+            needs_ventilation = any(
+                x in property_measure_types for x in assumptions.measures_needing_ventilation) and not p.has_ventilation
 
             input_measures = prepare_input_measures(measures_to_optimise, body.goal, needs_ventilation)
 
@@ -772,6 +772,10 @@ async def model_engine(body: PlanTriggerRequest):
                     epc_to_sap_lower_bound(body.goal_value) - current_sap_points
                 ) - fixed_gain
 
+                if body.simulate_sap_10:
+                    # We add 3 additional SAP points to the required gain to account for SAP 10
+                    sap_gain += 3
+
                 if not body.optimise:
                     if body.goal != "Increasing EPC":
                         raise NotImplementedError("Only EPC optimisation is currently supported")
@@ -826,7 +830,11 @@ async def model_engine(body: PlanTriggerRequest):
                                 )
 
             # If wall insulation is selected, we also include mechanical ventilation as a best practice measure
-            if any(x in [r["type"] for r in solution] for x in assumptions.measures_needing_ventilation):
+            ventilation_selected = [
+                r for r in solution if "+mechanical_ventilation" in r["type"]
+            ]
+            if (any(x in [r["type"] for r in solution] for x in assumptions.measures_needing_ventilation) or
+                len(ventilation_selected)):
                 ventilation_rec = next(
                     (r[0] for r in recommendations[p.id] if r[0]["type"] == "mechanical_ventilation"),
                     None
diff --git a/backend/ml_models/AnnualBillSavings.py b/backend/ml_models/AnnualBillSavings.py
index b22837d8..4291b1d1 100644
--- a/backend/ml_models/AnnualBillSavings.py
+++ b/backend/ml_models/AnnualBillSavings.py
@@ -28,8 +28,8 @@ class AnnualBillSavings:
 
     # Latest price cap figures from Ofgem are for April 2024
     # https://www.ofgem.gov.uk/energy-price-cap
-    ELECTRICITY_PRICE_CAP = 0.2486
-    GAS_PRICE_CAP = 0.0634
+    ELECTRICITY_PRICE_CAP = 0.2573
+    GAS_PRICE_CAP = 0.0633
     # This is the most recent export payment figure, at 9.28p/kWh
     # Smart export guarantee rates can be found here:
     # https://www.sunsave.energy/solar-panels-advice/exporting-to-the-grid/best-seg-rates
@@ -39,8 +39,8 @@ class AnnualBillSavings:
     PRICE_FACTOR = 0.09549999999999999
 
     # Daily standard charge, based on average across England, Scotland and Wales, and includes VAT
-    DAILY_STANDARD_CHARGE_GAS = 0.3165
-    DAILY_STANDARD_CHARGE_ELECTRICITY = 0.6097
+    DAILY_STANDARD_CHARGE_GAS = 0.2982
+    DAILY_STANDARD_CHARGE_ELECTRICITY = 0.5137
 
     # Based on https://www.nottenergy.com/advice-and-tools/project-energy-cost-comparison
     # For July 2024. These quotes are based on the east midlands region, so we
diff --git a/etl/customers/Brentwood/compile_new_asset_list.py b/etl/customers/Brentwood/compile_new_asset_list.py
new file mode 100644
index 00000000..e3ced5ab
--- /dev/null
+++ b/etl/customers/Brentwood/compile_new_asset_list.py
@@ -0,0 +1,38 @@
+"""
+Brentwood sent us a new asset list in July 2025. This script will combine the data in the new asset list with the
+old, so we have a single picture
+"""
+
+import pandas as pd
+
+new_asset_list = pd.read_excel(
+    "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Brentwood/July 2025 New Programme/All Assets "
+    "29.05.2025.xlsx",
+    sheet_name="Sheet1",
+    header=1
+)
+
+old_asset_list = pd.read_excel(
+    "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Brentwood/July 2025 New Programme/BRENTWOOD Asset "
+    "list.xlsx",
+    sheet_name="Asset List"
+)
+
+# We combine based on the data we want
+compiled = new_asset_list.merge(
+    old_asset_list[["UPRN", "Asset Type", "Year Built", "Dwelling", "Bedrooms", "Ownership", 'Asbestos Full Survey',
+                    'Stock Condition Survey', 'Cat', 'Heating',
+                    'WFT Findings', 'ECO Eligibility', 'CIGA Requested', 'CIGA Guarantee',
+                    'ECO Survey completed']],
+    how="left",
+    on="UPRN"
+)
+
+compiled["WFT Findings"] = compiled["WFT Findings"].fillna("Not Inspected")
+
+# Store this data
+compiled.to_excel(
+    "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Brentwood/July 2025 New Programme/20250710 Asset List "
+    "Brentwood.xlsx",
+    index=False
+)
diff --git a/etl/customers/Colchester/July 2025 Finalised Route.py b/etl/customers/Colchester/July 2025 Finalised Route.py
new file mode 100644
index 00000000..f3ecf2d9
--- /dev/null
+++ b/etl/customers/Colchester/July 2025 Finalised Route.py	
@@ -0,0 +1,54 @@
+import pandas as pd
+
+comments_df = pd.read_excel(
+    "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Colchester/July Finalised "
+    "Project/CBH_RetroTeamList_amended_25-06-05.xlsx",
+)
+
+cavity_route = pd.read_excel(
+    "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Colchester/July Finalised "
+    "Project/20250708 Colchester Borough Homes- Standardised.xlsx",
+    sheet_name="July 2025 Route - Cavity"
+)
+
+solar_route = pd.read_excel(
+    "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Colchester/July Finalised "
+    "Project/20250708 Colchester Borough Homes- Standardised.xlsx",
+    sheet_name="July 2025 Route - Solar"
+)
+
+# Merge on the comments
+comments = comments_df[
+    ["URPN", 'Unnamed: 6', 'SHDF Live', 'SHDF Removed', 'SHDF Reserve', '25-26 List (138 to EPC)']
+].copy()
+
+cavity_route = cavity_route.merge(
+    comments, left_on="landlord_property_id", right_on="URPN", how="left"
+)
+solar_route = solar_route.merge(
+    comments, left_on="landlord_property_id", right_on="URPN", how="left"
+)
+
+# Get properties that are not on either route
+not_on_routes = comments_df[
+    ~comments_df["URPN"].isin(cavity_route["landlord_property_id"]) &
+    ~comments_df["URPN"].isin(solar_route["landlord_property_id"])
+    ]
+
+# Store
+not_on_routes.to_excel(
+    "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Colchester/July Finalised "
+    "Project/Properties not on routes.xlsx",
+    index=False
+)
+# Save the routes
+cavity_route.to_excel(
+    "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Colchester/July Finalised "
+    "Project/Cavity Route.xlsx",
+    index=False
+)
+solar_route.to_excel(
+    "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Colchester/July Finalised "
+    "Project/Solar Route.xlsx",
+    index=False
+)
diff --git a/etl/customers/acis/solid_wall_funding.py b/etl/customers/acis/solid_wall_funding.py
new file mode 100644
index 00000000..5515b29c
--- /dev/null
+++ b/etl/customers/acis/solid_wall_funding.py
@@ -0,0 +1,144 @@
+import os
+import pandas as pd
+import numpy as np
+from dotenv import load_dotenv
+from etl.find_my_epc.AssetListEpcData import AssetListEpcData
+from backend.Funding import Funding
+from backend.app.utils import sap_to_epc
+from recommendations.recommendation_utils import estimate_external_wall_area
+
+load_dotenv(dotenv_path="backend/.env")
+EPC_AUTH_TOKEN = os.getenv("EPC_AUTH_TOKEN")
+
+abs_matrix = pd.read_csv(
+    "/Users/khalimconn-kowlessar/Downloads/ECO4 Full Project Scores Matrix.csv"
+)
+pps_matrix = pd.read_excel(
+    "/Users/khalimconn-kowlessar/Downloads/ECO4 Partial Project Scores Matrix v5.xlsx",
+    header=1
+)
+pps_matrix.columns = [c.strip() for c in pps_matrix.columns]
+
+asset_list = pd.read_excel(
+    "/Users/khalimconn-kowlessar/Documents/hestia/Customers/ACIS/Solid Wall Properties - Standardised_2.xlsx",
+    sheet_name="Standardised Asset List"
+)
+
+asset_list = asset_list.rename(
+    columns={"domna_address_1": "address", "domna_postcode": "postcode"}
+)
+asset_list["address"] = asset_list["address"].astype(str)
+
+# Pull the find my EPC data and get the SAP points for solid wall
+asset_list_epc_client = AssetListEpcData(
+    asset_list=asset_list,
+    epc_auth_token=EPC_AUTH_TOKEN
+)
+asset_list_epc_client.get_data()
+asset_list_epc_client.get_non_invasive_recommendations()
+# We pull out solid wall insulation
+solid_wall_sap_points = []
+for r in asset_list_epc_client.non_invasive_recommendations:
+    solid_recommendations = [
+        x for x in r["recommendations"] if ("internal_wall_insulation" in x["type"]) or (
+            "external_wall_insulation" in x["type"]
+        )
+    ]
+    if solid_recommendations:
+        solid_recommendations = solid_recommendations[0]
+    else:
+        continue
+
+    address = r["address"]
+    postcode = r["postcode"]
+
+    solid_wall_sap_points.append(
+        {
+            "address": address,
+            "postcode": postcode,
+            "sap_points": solid_recommendations["sap_points"]
+        }
+    )
+
+solid_wall_sap_points = pd.DataFrame(solid_wall_sap_points)
+avg_points = solid_wall_sap_points["sap_points"].median()
+
+asset_list = asset_list.merge(solid_wall_sap_points, how="left", on=["address", "postcode"])
+asset_list["sap_points"] = asset_list["sap_points"].fillna(avg_points)
+asset_list["post_works_sap"] = asset_list["epc_sap_score_on_register"] + asset_list["sap_points"]
+asset_list["post_works_epc"] = asset_list["post_works_sap"].apply(lambda x: sap_to_epc(x))
+asset_list["starting_half_band"] = asset_list["epc_sap_score_on_register"].apply(lambda x: Funding.get_sap_band(x))
+asset_list["ending_half_band"] = asset_list["post_works_sap"].apply(lambda x: Funding.get_sap_band(x))
+asset_list["floor_area_band"] = asset_list["epc_total_floor_area"].apply(lambda x: Funding.get_floor_area_band(x))
+
+asset_list["funding_scheme"] = np.where(
+    (
+        (asset_list["post_works_epc"] == asset_list["epc_rating_on_register"])
+    ),
+    "GBIS",
+    "ECO4"
+)
+
+# Merge on the ABS matrix
+asset_list = asset_list.merge(
+    abs_matrix, how="left", left_on=["starting_half_band", "ending_half_band", "floor_area_band"],
+    right_on=['Starting Band', 'Finishing Band', 'Floor Area Segment', ]
+)
+asset_list = asset_list.drop(columns=['Starting Band', 'Finishing Band', 'Floor Area Segment'])
+
+# store for backup
+# asset_list.to_csv(
+#     "/Users/khalimconn-kowlessar/Documents/hestia/Customers/ACIS/Solid Wall Properties -
+#     Standardised_2_with_funding.csv",
+#     index=False
+# )
+
+# For GBIS, we use the PPS
+# Almost all properties are gas
+
+# Using IWI solid 1.7 -> 0.3 rates
+pps_matrix = pps_matrix[
+    pps_matrix["Measure_Type"].isin(["IWI_solid_1.7_0.3"])
+]
+
+# Merge on
+asset_list = asset_list.merge(
+    pps_matrix[['Starting Band', 'Total Floor Area Band', 'Cost Savings']].rename(
+        columns={
+            "Cost Savings": "partial_project_score",
+            "Starting Band": "starting_half_band",
+            "Total Floor Area Band": "floor_area_band"
+        }
+    ),
+    how="left",
+    on=["starting_half_band", "floor_area_band"],
+)
+asset_list["partial_project_score"] = np.where(
+    asset_list["starting_half_band"].isin(["Low_C", "High_C"]),
+    None,
+    asset_list["partial_project_score"]
+)
+
+asset_list["funding_abs"] = np.where(
+    asset_list["funding_scheme"] == "GBIS",
+    asset_list["partial_project_score"],
+    asset_list["Cost Savings"]
+)
+
+asset_list["heat_loss_area"] = asset_list.apply(
+    lambda x: estimate_external_wall_area(
+        num_floors=x["attribute_est_number_floors"],
+        floor_height=(
+            float(x["epc_floor_height"]) if
+            not pd.isnull(x["epc_floor_height"]) else 2.5
+        ),
+        perimeter=x["attribute_est_perimter"],
+        built_form=x["epc_archetype"]
+    ),
+    axis=1
+)
+
+filename = "/Users/khalimconn-kowlessar/Documents/hestia/Customers/ACIS/20250624 ACIS solid wall - standardised.xlsx"
+
+with pd.ExcelWriter(filename) as writer:
+    asset_list.to_excel(writer, sheet_name="Standardised Asset List", index=False)
diff --git a/etl/customers/blakeridge_mill/data.py b/etl/customers/blakeridge_mill/data.py
new file mode 100644
index 00000000..c9d7f9e6
--- /dev/null
+++ b/etl/customers/blakeridge_mill/data.py
@@ -0,0 +1,49 @@
+# Get units for postcodes WF17 8RA, WF17 8RB
+import os
+
+import pandas as pd
+from epc_api.client import EpcClient
+from dotenv import load_dotenv
+
+load_dotenv(dotenv_path="backend/.env")
+EPC_AUTH_TOKEN = os.getenv("EPC_AUTH_TOKEN")
+
+postcodes = [
+    "WF17 8RA",
+    "WF17 8RB",
+]
+
+client = EpcClient(auth_token=EPC_AUTH_TOKEN)
+
+data = []
+for postcode in postcodes:
+    resp = client.domestic.search(
+        params={"postcode": postcode, "address": None, "local-authority": None, "property-type": None,
+                "floor-area": None,
+                "energy-band": None, "from-month": None, "from-year": None, "to-month": None, "to-year": None,
+                'constituency': None},
+        size=1000
+    )
+    data.extend(resp["rows"])
+
+df = pd.DataFrame(data)
+# Get newest field by UPRN, inspection-date
+df["inspection-date"] = pd.to_datetime(df["inspection-date"])
+df = df.sort_values(by=["uprn", "inspection-date"], ascending=[True, False])
+df = df.drop_duplicates(subset=["uprn"], keep="first")
+
+df.to_excel(
+    "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Blakeridge Mill/blakeridge_mill_epc_data.xlsx", index=False
+)
+
+df = df[df["address"] != "The Tower Blakeridge Mill, Upper Blakeridge Lane"]
+df["walls-description"].value_counts()
+df["roof-description"].value_counts()
+
+df["total-floor-area"].astype(float).mean()
+df["current-energy-efficiency"] = pd.to_numeric(df["current-energy-efficiency"], errors='coerce')
+
+df.groupby("transaction-type")["current-energy-efficiency"].mean()
+df["transaction-type"].value_counts()
+
+df[df["transaction-type"] == "rental"]["built-form"].value_counts()
diff --git a/etl/customers/bromford/solar_pv_cleanup.py b/etl/customers/bromford/solar_pv_cleanup.py
new file mode 100644
index 00000000..c2c541da
--- /dev/null
+++ b/etl/customers/bromford/solar_pv_cleanup.py
@@ -0,0 +1,289 @@
+import pandas as pd
+from tqdm import tqdm
+from backend.SearchEpc import SearchEpc
+import numpy as np
+
+contact_list = pd.read_csv(
+    "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Bromford/Solar Programme Hubspot Upload/Bromford - Solar "
+    "PV address list - second wave KLD - PP.csv"
+)
+contact_list["house_no"] = contact_list.apply(lambda x: SearchEpc.get_house_number(
+    address=str(x["Address 1: Street 1"]).strip(),
+    postcode=str(x["Postal Code"]).strip(),
+), axis=1)
+
+asset_list = pd.read_excel(
+    "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Bromford/Solar Programme Hubspot Upload/asset_list - "
+    "Standardised (1).xlsx",
+    sheet_name="Standardised Asset List"
+)
+
+lookup = []
+missed = []
+for _, x in tqdm(contact_list.iterrows(), total=len(contact_list)):
+
+    if x["Address 1: Street 1"] == '1 The Beck':
+        lookup.append(
+            {
+                "UPRN": x["UPRN"],
+                "landlord_property_id": 40692,
+            }
+        )
+        continue
+
+    if x["Address 1: Street 1"] == '3 The Beck ':
+        lookup.append(
+            {
+                "UPRN": x["UPRN"],
+                "landlord_property_id": 40693,
+            }
+        )
+        continue
+
+    if x["Address 1: Street 1"] == '2 Orchard Close ':
+        lookup.append(
+            {
+                "UPRN": x["UPRN"],
+                "landlord_property_id": 7924,
+            }
+        )
+        continue
+
+    if x["Address 1: Street 1"] == '2 Orchard Close ':
+        lookup.append(
+            {
+                "UPRN": x["UPRN"],
+                "landlord_property_id": 7924,
+            }
+        )
+        continue
+
+    if x["Address 1: Street 1"] == '3 Croxall Road':
+        lookup.append(
+            {
+                "UPRN": x["UPRN"],
+                "landlord_property_id": 40650,
+            }
+        )
+        continue
+
+    if x["Address 1: Street 1"] == '4 Ward Road ':
+        lookup.append(
+            {
+                "UPRN": x["UPRN"],
+                "landlord_property_id": 33175,
+            }
+        )
+        continue
+
+    df = asset_list[
+        asset_list["domna_full_address"].str.replace(",", "").str.contains(x["Address 1: Street 1"].strip()) &
+        asset_list["domna_postcode"].str.contains(x["Postal Code"].strip())
+        ]
+
+    if df.shape[0] != 1:
+        df = asset_list[
+            asset_list["domna_full_address"].str.replace(",", "") == x["Address 1: Street 1"].strip() &
+            asset_list["domna_postcode"].str.contains(x["Postal Code"].strip())
+            ]
+
+    if df.shape[0] != 1:
+        df = asset_list[
+            (asset_list["domna_address_1"].astype(str) == str(x["house_no"])) &
+            (asset_list["domna_postcode"].str.contains(x["Postal Code"].strip()) == True)
+            ]
+
+    if df.shape[0] != 1:
+        missed.append(x["UPRN"])
+        continue
+
+    lookup.append(
+        {
+            "UPRN": x["UPRN"],
+            "landlord_property_id": df["landlord_property_id"].values[0],
+        }
+    )
+
+lookup = pd.DataFrame(lookup)
+
+contact_list = contact_list.merge(lookup, how="left", on="UPRN")
+# Store
+contact_list.to_csv(
+    "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Bromford/Solar Programme Hubspot Upload/Bromford - Solar "
+    "PV address list - second wave KLD - PP with landlord_property_id.csv",
+    index=False
+)
+
+# I manually completed the lookup for the missed ones. We now read it back in and pull in the properties for the
+# stndardised asset list
+contacts_complete = pd.read_csv(
+    "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Bromford/Solar Programme Hubspot Upload/Bromford - Solar "
+    "PV address list - second wave KLD - PP with landlord_property_id.csv"
+)
+
+new_data = pd.read_excel(
+    "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Bromford/Solar Programme Hubspot Upload/Master Sheet "
+    "Solar PV installs.xlsx",
+    sheet_name="Sheet1"
+)
+
+contact_list = contact_list.merge(
+    new_data,
+    how="left",
+    left_on="UPRN",
+    right_on="CE UPRN"
+)
+route = asset_list[
+    asset_list["landlord_property_id"].isin(contact_list["Legacy UPRN"].astype("Int64").astype(str))
+].copy()
+
+# Add the new heating data
+contact_list["Legacy UPRN"] = contact_list["Legacy UPRN"].astype("Int64").astype(str)
+route2 = contact_list.merge(
+    route,
+    how="left",
+    right_on="landlord_property_id",
+    left_on="Legacy UPRN"
+)
+
+# Because I did a data pull, we can fill the other bits of information
+missed = contact_list[~contact_list["Legacy UPRN"].isin(route["landlord_property_id"].astype(int))]
+
+# Store both the route and missed
+route2.to_csv(
+    "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Bromford/Solar Programme Hubspot Upload/route.csv",
+    index=False
+)
+
+# Add on phone number
+contact_details_filepath = ("/Users/khalimconn-kowlessar/Documents/hestia/Customers/Bromford/Solar Programme "
+                            "Hubspot Upload/Hubspot/Bromford - Solar PV address list - second wave KLD - PP with "
+                            "landlord_property_id.xlsx")
+
+contacts_filenames = [
+    "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Bromford/Solar Programme Hubspot Upload/contact "
+    "details/FAO Paul Contact Details-Table 1.csv",
+    "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Bromford/Solar Programme Hubspot Upload/contact "
+    "details/Green Contact Details-Table 1.csv",
+    "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Bromford/Solar Programme Hubspot Upload/contact "
+    "details/Main Contact Details-Table 1.csv",
+]
+
+merge_to = pd.read_excel(contact_details_filepath)
+
+lookup = []
+for fn in contacts_filenames:
+    df = pd.read_csv(fn, encoding="utf-8-sig")
+    # Merge on phone
+    details = df[
+        df["Property Reference Number (Main Address) (Property)"].isin(merge_to["UPRN"].astype(str))
+    ][[
+        "Property Reference Number (Main Address) (Property)", "Landline", "Mobile Phone", "Email Address",
+        "First Name", "Last Name"
+    ]]
+
+    lookup.append(details)
+
+lookup = pd.concat(lookup)
+
+# Drop entries where landline, mobile and email are all NaN
+lookup = lookup.dropna(subset=["Landline", "Mobile Phone", "Email Address"], how="all")
+lookup = lookup.drop_duplicates(["Landline", "Mobile Phone", "Email Address"])
+# Sort so email is first, then landline, then mobile
+lookup = lookup.sort_values(
+    ["Property Reference Number (Main Address) (Property)", "Email Address", "Landline", "Mobile Phone"],
+    ascending=[True, True, True, True]
+)
+
+# Store
+lookup.to_csv(
+    "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Bromford/Solar Programme Hubspot Upload/Hubspot/contact "
+    "details.csv",
+    index=False
+)
+
+lookup2 = []
+for _, x in lookup.groupby("Property Reference Number (Main Address) (Property)"):
+
+    # We any entries have an email, we take that
+    if x["Email Address"].notna().any():
+        x = x[x["Email Address"].notna()]
+    # We then take the entry with a phone number
+    if x["Landline"].notna().any() or x["Mobile Phone"].notna().any():
+        x = x[x["Landline"].notna() | x["Mobile Phone"].notna()]
+
+    # Take the first entry
+    x = x.iloc[0]
+    lookup2.append(x)
+
+lookup2 = pd.DataFrame(lookup2)
+
+import pandas as pd
+
+# Sample structure based on your columns
+columns = ['Property Reference Number (Main Address) (Property)', 'Landline', 'Mobile Phone', 'Email Address']
+
+# Simulating example input DataFrame
+# In practice, you would use: lookup = pd.read_csv(...) or similar
+lookup = pd.DataFrame(columns=columns)
+
+# Grouping and transforming
+results = []
+
+for prop_id, group in lookup.groupby("Property Reference Number (Main Address) (Property)"):
+    # Filter rows with any contact information
+    filtered = group[
+        group["Email Address"].notna() &
+        (group["Landline"].notna() | group["Mobile Phone"].notna())
+        ]
+
+    if filtered.empty:
+        continue
+
+    # Sort by presence of phone numbers (prioritize those with both)
+    filtered["contact_score"] = (
+        filtered["Landline"].notna().astype(int) +
+        filtered["Mobile Phone"].notna().astype(int)
+    )
+    filtered = filtered.sort_values("contact_score", ascending=False)
+
+    primary = filtered.iloc[0]
+    # Make sure secondary is not the same as primary
+    if not pd.isnull(primary["Mobile Phone"]):
+        secondary = filtered[
+            (filtered["Mobile Phone"] != primary["Mobile Phone"])
+        ]
+    elif not pd.isnull(primary["Landline"]):
+        secondary = filtered[
+            (filtered["Landline"] != primary["Landline"])
+        ]
+    else:
+        raise Exception("Look at me")
+
+    secondary = filtered.iloc[1] if len(filtered) > 1 else None
+
+    results.append({
+        "Property ID": prop_id,
+        "Primary Email": primary["Email Address"],
+        "Primary Phone": primary["Mobile Phone"] or primary["Landline"],
+        "Secondary Email": secondary["Email Address"] if secondary is not None else None,
+        "Secondary Phone": secondary["Mobile Phone"] or secondary["Landline"] if secondary is not None else None,
+    })
+
+final_df = pd.DataFrame(results)
+
+import ace_tools as tools;
+
+tools.display_dataframe_to_user(name="Cleaned Contact Lookup", dataframe=final_df)
+
+# We set up primary and secondary phone numbers. We use mobile as the primary
+
+
+# We have duplicates, we prioritise entries, by ID, that have a email
+lookup2 = lookup.sort_values("Property Reference Number (Main Address) (Property)").drop_duplicates(
+    "Property Reference Number (Main Address) (Property)", keep="last"
+)
+
+# TODO: Get into the standardised asset list format
+# TODO: Add the deal postcode to Hubspot
+# TODO: Upload the deal postcode
diff --git a/etl/customers/ealing/fixing houses asset list.py b/etl/customers/ealing/fixing houses asset list.py
new file mode 100644
index 00000000..4a39428a
--- /dev/null
+++ b/etl/customers/ealing/fixing houses asset list.py	
@@ -0,0 +1,45 @@
+import pandas as pd
+
+houses_list = pd.read_csv(
+    "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Ealing/Ealing BC - HOUSES(UNCHECKED).csv"
+)
+
+features = pd.read_csv(
+    "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Ealing/Ealing BC - HOUSES(IGNORE - FULL MAIN).csv"
+)
+features = features.drop(
+    columns=[
+        'Archetype', 'Construction', 'Insulated', 'Material',
+        'CIGA Check Required', 'PV, ACCESS ISSUE, SEE NOTES',
+        'OFF GAS - ROOF ORIENTATION', 'Any further surveyor notes', 'Surveyors Name',
+        'Unnamed: 30', 'Unnamed: 31'
+    ]
+)
+
+demolitions = pd.read_excel(
+    "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Ealing/Ealing - Demolished or due to be.xlsx",
+    sheet_name="Demolished or due to be"
+)
+
+inspections_data = houses_list[
+    [
+        "Property ref", "Postcode", 'Archetype', 'Construction', 'Insulated', 'Material',
+        'CIGA Check Required', 'PV, ACCESS ISSUE, SEE NOTES',
+        'OFF GAS - ROOF ORIENTATION', 'Any further surveyor notes', 'YET TO BE SURVEYED'
+    ]
+].rename(columns={"YET TO BE SURVEYED": "Surveyors Name"})
+
+asset_list = features.drop(
+    columns=[
+        'Archetype', 'Construction', 'Insulated', 'Material', 'CIGA Check Required',
+        'PV, ACCESS ISSUE, SEE NOTES', 'OFF GAS - ROOF ORIENTATION',
+        'Any further surveyor notes', 'Surveyors Name', "Postcode"
+    ]
+).merge(
+    inspections_data,
+    how="inner",
+    on="Property ref",
+)
+
+asset_list.to_csv("/Users/khalimconn-kowlessar/Documents/hestia/Customers/Ealing/Ealing_rechecked_cleaned_05042025.csv",
+                  index=False)
diff --git a/etl/customers/ealing/prepare_for_hubspot.py b/etl/customers/ealing/prepare_for_hubspot.py
new file mode 100644
index 00000000..8cffda57
--- /dev/null
+++ b/etl/customers/ealing/prepare_for_hubspot.py
@@ -0,0 +1,75 @@
+import numpy as np
+import pandas as pd
+from asset_list.hubspot.config import HubspotProcessStatus
+
+project_data = pd.read_excel(
+    "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Ealing/Hubspot/Ealing Flats Completion Tracker JW "
+    "170625.xlsx",
+    sheet_name="All_Flats"
+)
+
+project_data["hubspot_status"] = None
+project_data["hubspot_status"] = np.where(
+    (project_data["Status"] == "Submitted") & (project_data["PAS"] == "PAS2023"),
+    HubspotProcessStatus.SURVEYED_COMPLETED_SIGNED_OFF.label,
+    project_data["hubspot_status"]
+)
+project_data["hubspot_status"] = np.where(
+    (project_data["Status"] == "Submitted") & (project_data["PAS"] == "PAS2019"),
+    "SURVEYED UNDER 2019 - NEEDS RE-SURVEY",
+    project_data["hubspot_status"]
+)
+project_data["project_code"] = "EALING-FLATS-" + project_data["Block Ref"].astype(str)
+
+asset_list = pd.read_excel(
+    "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Ealing/Hubspot/20250707 Ealing Flats - Standardised.xlsx",
+    sheet_name="Standardised Asset List"
+)
+asset_list["landlord_property_id"] = asset_list["landlord_property_id"].astype(str)
+asset_list["incorrect_landlord_property_id"] = asset_list["incorrect_landlord_property_id"].astype(str)
+project_data["Property ref"] = project_data["Property ref"].astype(str)
+
+# We need to update the status of properties that already been surveyed
+asset_list2 = asset_list.merge(
+    project_data[["Property ref", "hubspot_status", "project_code"]],
+    how="left",
+    right_on="Property ref",
+    left_on="incorrect_landlord_property_id",
+    suffixes=("", "_project")
+)
+asset_list2["hubspot_status"] = np.where(
+    ~pd.isna(asset_list2["hubspot_status_project"]),
+    asset_list2["hubspot_status_project"],
+    asset_list2["hubspot_status"]
+)
+asset_list2["project_code"] = np.where(
+    ~pd.isna(asset_list2["project_code"]),
+    asset_list2["project_code"],
+    asset_list2["landlord_property_id"]
+)
+
+asset_list2 = asset_list2.drop(columns=["hubspot_status_project", "project_code_project"])
+asset_list2["cavity_reason"] = np.where(
+    pd.isnull(asset_list2["cavity_reason"]),
+    "Non-Intrusive Data Shows Empty Cavity: SAP Rating 55-68",
+    asset_list2["cavity_reason"]
+)
+asset_list2["solar_reason"] = None
+
+# Read in block analysis and geographical areas from standardised asset list
+block_analysis_df = pd.read_excel(
+    "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Ealing/Hubspot/20250707 Ealing Flats - Standardised.xlsx",
+    sheet_name="Block Analysis"
+)
+geographical_areas = pd.read_excel(
+    "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Ealing/Hubspot/20250707 Ealing Flats - Standardised.xlsx",
+    sheet_name="Geographical Areas"
+)
+
+# Update the new standardised asset list
+filename = ("/Users/khalimconn-kowlessar/Documents/hestia/Customers/Ealing/Hubspot/20250707 Ealing Flats - Prepared "
+            "programme.xlsx")
+with pd.ExcelWriter(filename) as writer:
+    asset_list2.to_excel(writer, sheet_name="Standardised Asset List", index=False)
+    block_analysis_df.to_excel(writer, sheet_name="Block Analysis", index=False)
+    geographical_areas.to_excel(writer, sheet_name="Geographical Areas", index=False)
diff --git a/etl/customers/mhs/new_programme.py b/etl/customers/mhs/new_programme.py
new file mode 100644
index 00000000..6f1caafe
--- /dev/null
+++ b/etl/customers/mhs/new_programme.py
@@ -0,0 +1,116 @@
+#
+import pandas as pd
+
+asset_list = pd.read_excel(
+    "/Users/khalimconn-kowlessar/Documents/hestia/Customers/MHS/New programme/12052025 MHS Standardised Asset List - "
+    "programme.xlsx",
+    sheet_name="Standardised Asset List"
+)
+
+new_cavity_programme = pd.read_excel(
+    "/Users/khalimconn-kowlessar/Documents/hestia/Customers/MHS/New programme/12052025 MHS Standardised Asset List - "
+    "programme.xlsx",
+    sheet_name="New Cavity Programme"
+)
+
+new_cavity_pilot = pd.read_excel(
+    "/Users/khalimconn-kowlessar/Documents/hestia/Customers/MHS/New programme/12052025 MHS Standardised Asset List - "
+    "programme.xlsx",
+    sheet_name="Empty Cavity Pilot"
+)
+
+new_solar_programme = pd.read_excel(
+    "/Users/khalimconn-kowlessar/Documents/hestia/Customers/MHS/New programme/12052025 MHS Standardised Asset List - "
+    "programme.xlsx",
+    sheet_name="New Solar Programme"
+)
+
+in_fill_properties_houses = pd.read_excel(
+    "/Users/khalimconn-kowlessar/Documents/hestia/Customers/MHS/New programme/Domna updated programme list May 2025 ("
+    "1).xlsx",
+    sheet_name="Houses and Bungalows"
+)
+in_fill_properties_flats = pd.read_excel(
+    "/Users/khalimconn-kowlessar/Documents/hestia/Customers/MHS/New programme/Domna updated programme list May 2025 ("
+    "1).xlsx",
+    sheet_name="Flats and Maistonettes"
+)
+# Q1) What are these properties? Do we have them on our list already?
+# All of the houses are already in the asset list
+in_fill_properties_houses["is_in_asset_list"] = in_fill_properties_houses["UPRN"].isin(
+    asset_list["landlord_property_id"].values
+)
+# All of the flats are already in the asset list
+in_fill_properties_flats["is_in_asset_list"] = in_fill_properties_flats["UPRN"].isin(
+    asset_list["landlord_property_id"].values
+)
+
+# Q2) Which properties are excluded from the new programme?
+in_fill_properties = pd.concat(
+    [in_fill_properties_houses, in_fill_properties_flats], ignore_index=True, sort=False
+)
+
+# Merge on the data
+in_fill_properties = in_fill_properties.merge(
+    asset_list,
+    left_on="UPRN",
+    right_on="landlord_property_id",
+    how="left"
+)
+# How many properties are in the new programme?
+
+in_fill_properties["in_new_cavity_programme"] = in_fill_properties["UPRN"].isin(
+    new_cavity_programme["landlord_property_id"].values
+)
+in_fill_properties["in_new_solar_programme"] = in_fill_properties["UPRN"].isin(
+    new_solar_programme["landlord_property_id"].values
+)
+in_fill_properties["in_new_cavity_pilot"] = in_fill_properties["UPRN"].isin(
+    new_cavity_pilot["landlord_property_id"].values
+)
+not_in_new_programme = in_fill_properties[
+    (~in_fill_properties["in_new_cavity_programme"] & ~in_fill_properties["in_new_solar_programme"] & ~
+    in_fill_properties["in_new_cavity_pilot"])
+].copy()
+
+# Why?
+not_in_new_programme["cavity_reason"].value_counts()
+not_in_new_programme["solar_reason"].value_counts()
+
+not_identified_for_anything = not_in_new_programme[
+    pd.isnull(not_in_new_programme["cavity_reason"]) &
+    pd.isnull(not_in_new_programme["solar_reason"])
+    ]
+
+# Flag the potential re-inspections which is 994 properties though any extractions we need to consider the HA funding
+# the extraction
+not_in_new_programme["funded_extractions"] = not_in_new_programme["cavity_reason"].isin(
+    [
+        "Non-Intrusive Data Shows Cavity Extraction: SAP Rating 69-75",
+        "Non-Intrusive Data Shows Cavity Extraction: SAP Rating 55-68",
+        "Non-Intrusive Data Shows Cavity Extraction: SAP Rating 76 or more",
+        "Non-Intrusive Data Shows Cavity Extraction: SAP Rating 54 or less",
+        "EPC Shows Empty Cavity, inspections show non-cavity build: SAP Rating 76 or more",
+        "EPC Shows Empty Cavity, inspections show non-cavity build: SAP Rating 54 or less",
+        "EPC Shows Empty Cavity, inspections show retro drilled: SAP Rating 54 or less",
+        "EPC Shows Empty Cavity, inspections show retro drilled: SAP Rating 76 or more",
+    ]
+)
+
+not_in_new_programme["excluded"] = not_identified_for_anything["landlord_property_id"].isin(
+    not_identified_for_anything["landlord_property_id"].values
+)
+
+not_in_new_programme[
+    not_in_new_programme["funded_extractions"]
+].to_csv(
+    "/Users/khalimconn-kowlessar/Documents/hestia/Customers/MHS/New programme/funded_extractions.csv",
+    index=False
+)
+
+not_in_new_programme[
+    not_in_new_programme["excluded"] == True
+    ].to_csv(
+    "/Users/khalimconn-kowlessar/Documents/hestia/Customers/MHS/New programme/excluded_properties.csv",
+    index=False
+)
diff --git a/etl/customers/ncha/portfolio.py b/etl/customers/ncha/portfolio.py
new file mode 100644
index 00000000..f47c87c8
--- /dev/null
+++ b/etl/customers/ncha/portfolio.py
@@ -0,0 +1,14 @@
+import pandas as pd
+
+cavity = pd.read_excel(
+    "/Users/khalimconn-kowlessar/Downloads/Energy Information MASTER June 2025 - Standardised.xlsx",
+    sheet_name="Cavity Properties (for review)",
+)
+solar = pd.read_excel(
+    "/Users/khalimconn-kowlessar/Downloads/Energy Information MASTER June 2025 - Standardised.xlsx",
+    sheet_name="Solar Properties",
+)
+
+cavity_al = cavity[["domna_address_1", "domna_postcode", "epc_os_uprn"]].rename(
+    columns={"domna_address_1": "address", "domna_postcode": "postcode", "epc_os_uprn": "uprn"}
+)
diff --git a/etl/customers/plus dane/prepare_asset_list.py b/etl/customers/plus dane/prepare_asset_list.py
new file mode 100644
index 00000000..430c7b5a
--- /dev/null
+++ b/etl/customers/plus dane/prepare_asset_list.py	
@@ -0,0 +1,48 @@
+"""
+July 2025, this script prepares the asset list for Plus Dane
+"""
+import pandas as pd
+
+oldest_asset_list = pd.read_excel(
+    "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Plus Dane/New Programme July 2025/PLUS DANE Asset List.xlsx"
+)
+solar_asset_list = pd.read_excel(
+    "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Plus Dane/New Programme July 2025/Plus Dane - potential "
+    "PV List 04.03.2025.xlsx"
+)
+newest_asset_list = pd.read_excel(
+    "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Plus Dane/New Programme July 2025/Sava Intelligent Energy "
+    "- Property List - March 2025.xlsx"
+)
+
+old_missed = oldest_asset_list[~oldest_asset_list["UPRN"].isin(newest_asset_list["UPRN"])]
+solar_missed = solar_asset_list[~solar_asset_list["UPRN"].isin(newest_asset_list["UPRN"])]  # Empty
+
+# Build new asset list
+# NEWEST
+# 'UPRN', 'Address', 'Postcode', 'Town', 'EPC SAP Band', 'SAP Rating',
+#        'CO₂ Emissions', 'EPC EI Band', 'Data Quality Indicator',
+#        'Results Calculated', 'Property Age', 'Property Type', 'Built Form',
+#        'Wall Construction', 'Wall Insulation', 'Roof Construction',
+#        'Joist Insulation', 'Space Heating System', 'Space Heating Fuel'
+#
+# SOlAR
+
+df = newest_asset_list.merge(
+    solar_asset_list, how="left", on="UPRN", suffixes=("", "_solar"),
+).merge(
+    oldest_asset_list, how="left", on="UPRN", suffixes=("", "_old")
+)
+df["asset_list_versiion"] = "July 2025"
+old_missed["asset_list_versiion"] = "Historic"
+
+# Append on the old missed?
+df = pd.concat(
+    [df, old_missed], ignore_index=True, sort=False
+)
+# Store excel
+df.to_excel(
+    "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Plus Dane/New Programme July 2025/Plus Dane Asset List "
+    "July 2025.xlsx",
+    index=False,
+)
diff --git a/etl/customers/remote_assessments/app.py b/etl/customers/remote_assessments/app.py
index a8805a71..df4a16fe 100644
--- a/etl/customers/remote_assessments/app.py
+++ b/etl/customers/remote_assessments/app.py
@@ -4,7 +4,7 @@ from dotenv import load_dotenv
 from utils.s3 import save_csv_to_s3
 from etl.find_my_epc.AssetListEpcData import AssetListEpcData
 
-PORTFOLIO_ID = 141
+PORTFOLIO_ID = 212
 USER_ID = 8
 
 load_dotenv(dotenv_path="backend/.env")
@@ -17,25 +17,15 @@ def app():
     :return:
     """
 
-    asset_list = [
-        {
-            "address": "196 Merrow Street",
-            "postcode": "SE17 2NP",
-            "uprn": 200003423454,
-            "patch": True
-        },
-        {
-            "address": "65 Liverpool Grove",
-            "postcode": "SE17 2HP",
-            "uprn": 200003423194
-        },
-        {
-            "address": "2 Brettell Street",
-            "postcode": "SE17 2NZ",
-            "uprn": 200003423607
-        },
-    ]
-    asset_list = pd.DataFrame(asset_list)
+    asset_list = pd.read_excel(
+        "/Users/khalimconn-kowlessar/Downloads/Energy Information MASTER June 2025 - Standardised.xlsx",
+        sheet_name="Solar Properties",
+    )
+    asset_list = asset_list[~asset_list["estimated"]]
+    asset_list["domna_address_1"] = asset_list["domna_address_1"].astype(str)
+    asset_list = asset_list[["domna_address_1", "domna_postcode", "epc_os_uprn"]].rename(
+        columns={"domna_address_1": "address", "domna_postcode": "postcode", "epc_os_uprn": "uprn"}
+    )
 
     # Store the asset list in s3
     filename = f"{USER_ID}/{PORTFOLIO_ID}/asset_list.csv"
@@ -98,14 +88,15 @@ def app():
         "portfolio_id": str(PORTFOLIO_ID),
         "housing_type": "Private",
         "goal": "Increasing EPC",
-        "goal_value": "C",
+        "goal_value": "A",
         "trigger_file_path": filename,
         "already_installed_file_path": "",
         "patches_file_path": patches_filename,
         "non_invasive_recommendations_file_path": non_invasive_recommendations_filename,
-        "valuation_file_path": valuation_filename,
+        "valuation_file_path": "",
         "scenario_name": "Full package remote assessment",
         "multi_plan": True,
         "budget": None,
+        "inclusions": ["cavity_wall_insulation", "ventilation"]
     }
     print(body)
diff --git a/etl/customers/thrive/Make Insepctions route.py b/etl/customers/thrive/Make Insepctions route.py
new file mode 100644
index 00000000..ec4f620b
--- /dev/null
+++ b/etl/customers/thrive/Make Insepctions route.py	
@@ -0,0 +1,40 @@
+"""
+This script will pull in properties, in neighbouring areas, that have been flagged for CWI
+"""
+import pandas as pd
+
+asset_list = pd.read_excel(
+    "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Thrive/July 2025 Inspections/Thrive Programme - "
+    "reconciled.xlsx",
+    sheet_name="Standardised Asset List"
+)
+
+cavity_areas = pd.read_excel(
+    "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Thrive/July 2025 Inspections/Thrive Programme - "
+    "reconciled.xlsx",
+    sheet_name="Cavity Areas"
+)
+
+existing_inspections_sheet = pd.read_excel(
+    "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Thrive/July 2025 Inspections/Thrive Programme - "
+    "reconciled.xlsx",
+    sheet_name="July 2025 Inspections"
+)
+
+empties = pd.read_excel(
+    "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Thrive/July 2025 Inspections/Thrive Programme - "
+    "reconciled.xlsx",
+    sheet_name="Cavity properties - for review"
+)
+
+cavity_inspections = asset_list[
+    asset_list["domna_postcode"].isin(cavity_areas["domna_postcode"].values)
+]
+cavity_inspections = cavity_inspections[
+    ~cavity_inspections["landlord_property_id"].isin(empties["landlord_property_id"].values)
+]
+
+cavity_inspections.to_csv(
+    "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Thrive/July 2025 Inspections/cavity_inspections.csv",
+    index=False
+)
diff --git a/etl/find_my_epc/AssetListEpcData.py b/etl/find_my_epc/AssetListEpcData.py
index f085c8fb..2ff9a3e0 100644
--- a/etl/find_my_epc/AssetListEpcData.py
+++ b/etl/find_my_epc/AssetListEpcData.py
@@ -1,3 +1,4 @@
+import random
 import time
 import pandas as pd
 from tqdm import tqdm
@@ -27,6 +28,7 @@ class AssetListEpcData:
         self.extracted_data = None
         self.non_invasive_recommendations = None
         self.patches = None
+        self.epc_data = None
 
     @staticmethod
     def check_asset_list(asset_list):
@@ -49,7 +51,7 @@ class AssetListEpcData:
                 "uprn": r.get("uprn"),
                 "address": r["address"],
                 "postcode": r["postcode"],
-                "recommendations": r["recommendations"]
+                "recommendations": r.get("recommendations")
             } for r in self.extracted_data
         ]
 
@@ -74,7 +76,9 @@ class AssetListEpcData:
 
         # Pull the additional data
         extracted_data = []
+        epc_data = []
         for _, home in tqdm(self.asset_list.iterrows(), total=len(self.asset_list)):
+
             add1 = home["address"]
             pc = home["postcode"]
             # Retrieve the EPC data
@@ -92,9 +96,6 @@ class AssetListEpcData:
             if epc_searcher.newest_epc is None:
                 continue
 
-            if not pd.isnull(home.get("patch")):
-                epc_searcher.newest_epc["address1"] = add1
-
             # Attempt both methods:
             try:
                 find_epc_searcher = RetrieveFindMyEpc(
@@ -104,16 +105,37 @@ class AssetListEpcData:
                 find_epc_data = find_epc_searcher.retrieve_newest_find_my_epc_data()
             except Exception as e:
                 logger.error(f"Error retrieving find my epc data: {e}")
-                find_epc_searcher = RetrieveFindMyEpc(
-                    address=epc_searcher.newest_epc["address1"],
-                    postcode=epc_searcher.newest_epc["postcode"]
-                )
-                find_epc_data = find_epc_searcher.retrieve_newest_find_my_epc_data()
-            time.sleep(0.5)
+                if not pd.isnull(home.get("patch")):
+                    epc_searcher.newest_epc["address1"] = add1
+
+                try:
+                    find_epc_searcher = RetrieveFindMyEpc(
+                        address=epc_searcher.newest_epc["address1"],
+                        postcode=epc_searcher.newest_epc["postcode"]
+                    )
+                    find_epc_data = find_epc_searcher.retrieve_newest_find_my_epc_data()
+                except Exception as e:
+                    logger.error("Error retrieving find my epc data with alternative address format: {e}")
+                    find_epc_data = {
+                        "current_epc_rating": epc_searcher.newest_epc["current-energy-rating"],
+                        "current_epc_efficiency": epc_searcher.newest_epc["current-energy-efficiency"],
+                        "potential_epc_rating": None,
+                        "potential_epc_efficiency": None,
+                        "epc_data": {}
+                    }
+
+            # Sleep for a random amount of time between 0.5 and 1 seconds to avoid hitting the API rate limit
+            time.sleep(random.sample(range(50, 100), 1)[0] / 100)
+
+            # Every 50 requests, we sleep for 10 seconds to avoid hitting the API rate limit
+            if len(extracted_data) % 50 == 0 and len(extracted_data) > 0:
+                logger.info("Sleeping for 10 seconds to avoid hitting API rate limit")
+                time.sleep(10)
+
             # We need uprn
 
             to_append = {
-                "uprn": home.get("uprn"),
+                "uprn": home.get("uprn", epc_searcher.newest_epc["uprn"]),
                 "address": home["address"],
                 "postcode": home["postcode"],
                 **find_epc_data,
@@ -128,6 +150,8 @@ class AssetListEpcData:
                 }
 
             extracted_data.append(to_append)
+            epc_data.append(epc_searcher.newest_epc)
 
         self.extracted_data = extracted_data
+        self.epc_data = epc_data
         logger.info("Data Extrction complete")
diff --git a/etl/find_my_epc/RetrieveFindMyEpc.py b/etl/find_my_epc/RetrieveFindMyEpc.py
index fad0c78e..50955377 100644
--- a/etl/find_my_epc/RetrieveFindMyEpc.py
+++ b/etl/find_my_epc/RetrieveFindMyEpc.py
@@ -1,3 +1,4 @@
+import time
 import re
 import pandas as pd
 import requests
@@ -55,9 +56,11 @@ class RetrieveFindMyEpc:
         results = {}
 
         # 1. Total floor area
-        results['total-floor-area'] = int(self.get_text(
+        # We have some isntances of very old EPCs where the total floor area is not available
+        tfa = self.get_text(
             soup.find("dt", string="Total floor area").find_next_sibling("dd")
-        ).split(" ")[0])
+        ).split(" ")[0]
+        results['total-floor-area'] = int(tfa) if tfa != "Not" else None
 
         # Table with features
         rows = soup.select("table.govuk-table tbody tr")
@@ -125,9 +128,156 @@ class RetrieveFindMyEpc:
 
         return results
 
-    def retrieve_newest_find_my_epc_data(self, sap_2012_date=None):
+    def _extract_epc_from_soup(self, soup, epc_certificate, sap_2012_date=None):
+
+        ratings = soup.find('desc', {'id': 'svg-desc'}).text
+        current_rating = ratings.split(".")[0]
+        potential_rating = ratings.split(".")[1]
+        current_sap = int(current_rating.split(' ')[-1])
+
+        # Retrieve the energy consumption
+        bills = soup.find('div', {'id': 'bills-affected'})
+        bills_list = bills.find_all('li')
+        if not bills_list:
+            # If this is the case, it's usually becaue the EPC was very old. Early EPCs did not have this information
+            heating_text = None
+            hot_water_text = None
+        else:
+            heating_text = bills_list[0].text
+            hot_water_text = bills_list[1].text
+
+        # Retrieve the recommendations and SAP points
+        recommendations = []
+        recommendations_div = soup.find('div', class_='epb-recommended-improvements')
+        if recommendations_div:
+            # Find all h3 headers for each step and extract their related information
+            step_headers = recommendations_div.find_all('h3', class_='govuk-heading-m')
+            previous_sap_score = current_sap
+            previous_epc = current_rating.split(' ')[-6]
+            for step_num, step_header in enumerate(step_headers, start=1):
+                # Extract the step title (the measure)
+                measure_title = step_header.text.strip().replace(f"Step {step_num}: ", "")
+
+                # Find the div containing the potential rating within the same section
+                potential_rating_div = step_header.find_next(
+                    'div', class_='epb-recommended-improvements__potential-rating'
+                )
+
+                # Check if the potential rating div is found
+                if potential_rating_div:
+                    # Extract the rating text within the SVG text element
+                    extracted_rating_text = potential_rating_div.find('text', class_='govuk-!-font-weight-bold')
+                    if extracted_rating_text is not None:
+                        rating_text = extracted_rating_text.text.strip()
+                    else:
+                        rating_text = " ".join([str(previous_sap_score), previous_epc])
+                    # Parse the rating text to separate the numeric rating and EPC letter
+                    new_rating = int(rating_text.split()[0])
+                    new_epc = rating_text.split()[1]
+
+                    # Append the information as a dictionary to the recommendations list
+                    recommendations.append({
+                        "step": step_num,
+                        "measure": measure_title,
+                        "new_rating": new_rating,
+                        "new_epc": new_epc,
+                        "sap_points": new_rating - previous_sap_score
+                    })
+                    previous_sap_score = new_rating
+                    previous_epc = new_epc
+
+        # Search for the assessment informaton
+        assessment_information = soup.find('div', {'id': 'information'})
+        # Parse this information
+        rows = assessment_information.find_all('div', class_='govuk-summary-list__row')
+        # Create a dictionary to hold the parsed information
+        assessment_data = {}
+        for row in rows:
+            key = row.find('dt').text.strip()
+            if key == "Type of assessment":
+                # We dont reliably extract this
+                continue
+            value_tag = row.find('dd')
+
+            # Check if value contains a link (email)
+            if value_tag.find('a'):
+                value = value_tag.find('a').text.strip()
+            elif value_tag.find('summary'):
+                value = value_tag.find('span').text.strip()
+            else:
+                value = value_tag.text.strip()
+
+            # These are keys that we have for both the surveyor and the acreditation scheme. Firstly, we'll
+            # get the surveyor's name and email so we make that information clear
+            if key in ["Telephone", "Email"]:
+                if "Assessor's " + key not in assessment_data:
+                    assessment_data["Assessor's " + key] = value
+                else:
+                    assessment_data["Accreditation Scheme's " + key] = value
+                continue
+
+            assessment_data[key] = value
+
+        expected_keys = [
+            'Assessor’s name',
+            "Assessor's Telephone",
+            "Assessor's Email",
+            'Assessor’s ID',
+            'Accreditation scheme',
+            'Assessor’s declaration',
+            "Accreditation Scheme's Telephone",
+            "Accreditation Scheme's Email",
+            'Date of assessment',
+            'Date of certificate'
+        ]
+        # Check we have all the expected keys
+        for key in expected_keys:
+            if key not in assessment_data:
+                raise ValueError(f"Missing key: {key}")
+
+        # The wall types of the property
+        property_features_table = soup.find("tbody", class_="govuk-table__body")
+        property_features_table = property_features_table.find_all("tr")
+
+        # Extract wall types
+        self.walls = []
+        for row in property_features_table:
+            cells = row.find_all("td")
+            if row.find("th").text.strip() == "Wall":
+                self.walls.append(cells[0].text.strip())
+
+        # Finally, we format the recommendations
+        recommendations = self.format_recommendations(recommendations, assessment_data, sap_2012_date)
+
+        # 4) Low and zero carbon energy sources
+        low_carbon_energy_sources = self.extract_low_carbon_sources(soup)
+
+        # 5) Pull out the EPC data
+        epc_data = self.extract_epc_data(soup)
+
+        resulting_data = {
+            'epc_certificate': epc_certificate,
+            'current_epc_rating': current_rating.split(' ')[-6],
+            'current_epc_efficiency': current_sap,
+            'potential_epc_rating': potential_rating.split(' ')[-6],
+            "potential_epc_efficiency": int(potential_rating.split(' ')[-1]),
+            "heating_text": heating_text,
+            "hot_water_text": hot_water_text,
+            "recommendations": recommendations,
+            "epc_data": epc_data,
+            **assessment_data,
+            **low_carbon_energy_sources,
+        }
+
+        return resulting_data
+
+    def retrieve_all_find_my_epc_data(self, sap_2012_date=None):
+
         """
-        For a post code and address, we pull out all the required data from the find my epc website
+        This is a quick function to retrieve all the data from the find my epc website for a given postcode and address.
+        Using this to fulfill a short term need to retrieve all history for a property
+        :param sap_2012_date:
+        :return:
         """
 
         postcode_input = self.postcode.replace(" ", "+")
@@ -182,6 +332,98 @@ class RetrieveFindMyEpc:
         address_response = requests.get(chosen_epc, headers=self.HEADERS)
         address_res = BeautifulSoup(address_response.text, features="html.parser")
 
+        # We check the section on "Other cerificates for this property and get the url"
+        # Find the section for other certificates
+        other_cert_section = address_res.find('div', id='other_certificates_and_reports')
+
+        # Extract all certificate number rows (anchor tags within a govuk-summary-list)
+        other_cert_links = other_cert_section.select('dd.govuk-summary-list__value a')
+
+        other_certificates = []
+        for link in other_cert_links:
+            cert_number = link.text.strip()
+            cert_url = link['href'].strip()
+            other_certificates.append({
+                "certificate_number": cert_number,
+                "certificate_url": f"https://find-energy-certificate.service.gov.uk{cert_url}"
+            })
+
+        # Always include the currently selected EPC first
+        soup_list = [address_res]
+
+        # Add additional historic certificates
+        for link in other_cert_links:
+            cert_url = f"https://find-energy-certificate.service.gov.uk{link['href'].strip()}"
+            response = requests.get(cert_url, headers=self.HEADERS)
+            time.sleep(0.3)
+            soup_list.append(BeautifulSoup(response.text, features="html.parser"))
+
+        all_find_my_epc_data = []
+        for soup in soup_list:
+            # Start with the primary one
+            all_find_my_epc_data.append(self._extract_epc_from_soup(soup, epc_certificate, sap_2012_date))
+
+        return all_find_my_epc_data
+
+    def retrieve_newest_find_my_epc_data(self, sap_2012_date=None):
+        """
+        For a post code and address, we pull out all the required data from the find my epc website
+        """
+
+        postcode_input = self.postcode.replace(" ", "+")
+        postcode_search = self.SEARCH_POSTCODE_URL.format(postcode_input=postcode_input)
+        postcode_response = requests.get(postcode_search, headers=self.HEADERS)
+
+        postcode_res = BeautifulSoup(postcode_response.text, features="html.parser")
+        rows = postcode_res.find_all('tr', class_='govuk-table__row')
+
+        extracted_table = []
+        for row in rows:
+            # Extract the address and URL
+            address_tag = row.find('a', class_='govuk-link')
+            if address_tag is None:
+                continue
+            extracted_address = None
+            extracted_address_url = None
+            if address_tag:
+                extracted_address = address_tag.text.strip()
+                extracted_address_url = address_tag['href']
+
+                extracted_address_cleaned = (
+                    extracted_address.replace(",", "").replace(" ", "").lower()
+                )
+                if not extracted_address_cleaned.startswith(self.address_cleaned):
+                    continue
+
+                # If the address is a match, we can extract the data
+
+            # Extract the expiry date
+            expiry_date_tag = row.find('td', class_='govuk-table__cell date')
+            expiry_date = None
+            if expiry_date_tag is not None:
+                expiry_date = expiry_date_tag.parent.find('span').text.strip()
+
+            extracted_table.append(
+                {
+                    "extracted_address": extracted_address,
+                    "extracted_address_url": extracted_address_url,
+                    "expiry_date": datetime.strptime(expiry_date, '%d %B %Y'),
+                }
+            )
+
+        if not extracted_table:
+            raise ValueError("No EPC found")
+
+        if len(extracted_table) > 1:
+            # We take the one with the most recent expiry date
+            extracted_table = sorted(extracted_table, key=lambda x: x['expiry_date'], reverse=True)
+
+        chosen_epc = self.BASE_ENERGY_URL + extracted_table[0]['extracted_address_url']
+        epc_certificate = chosen_epc.split('/')[-1]
+
+        address_response = requests.get(chosen_epc, headers=self.HEADERS)
+        address_res = BeautifulSoup(address_response.text, features="html.parser")
+
         # Key data we want to retrieve:
         # 1) Rating
         # 2) Bills estimates
@@ -195,9 +437,6 @@ class RetrieveFindMyEpc:
         potential_rating = ratings.split(".")[1]
         current_sap = int(current_rating.split(' ')[-1])
 
-        # Floor area
-        address_res.find()
-
         # Retrieve the energy consumption
         bills = address_res.find('div', {'id': 'bills-affected'})
         bills_list = bills.find_all('li')
@@ -432,6 +671,13 @@ class RetrieveFindMyEpc:
             "Condensing boiler (separate from the range cooker)": ["boiler_upgrade"],
             "Heating controls (programmer and thermostatic radiator valves)": [
                 "roomstat_programmer_trvs", "time_temperature_zone_control"
+            ],
+            'Heating controls (programmer room thermostat and thermostatic radiator valves)': [
+                "roomstat_programmer_trvs", "time_temperature_zone_control"
+            ],
+            "Internal wall insulation": ["internal_wall_insulation"],
+            "High heat retention storage heaters and dual immersion cylinder and dual rate meter": [
+                "high_heat_retention_storage_heater"
             ]
         }
 
@@ -466,8 +712,13 @@ class RetrieveFindMyEpc:
             find_epc_data = searcher.retrieve_newest_find_my_epc_data()
         except Exception as e:
             logger.error(f"Error retrieving find my epc data: {e}")
+            if epc["address1"] == epc["address"]:
+                # There's no benefit of using the same address, so we split on comma
+                address1 = epc["address"].split(",")[0]
+            else:
+                address1 = epc["address1"]
             # We attempt with the backup add
-            searcher = cls(address=epc["address1"], postcode=epc["postcode"])
+            searcher = cls(address=address1, postcode=epc["postcode"])
             find_epc_data = searcher.retrieve_newest_find_my_epc_data()
 
         non_invasive_recommendations = {
diff --git a/recommendations/Costs.py b/recommendations/Costs.py
index 96eb5d0e..0ef37add 100644
--- a/recommendations/Costs.py
+++ b/recommendations/Costs.py
@@ -194,7 +194,7 @@ class Costs:
     IWI_CONTINGENCY = 0.2
 
     # For air source heat pumps, we inflate the assume cost by quite a bit to account for design and installation
-    ASHP_CONTINGENCY = 0.35
+    ASHP_CONTINGENCY = 0.25
     # Where there is more uncertainty, a higher contingency rate is used
     HIGH_RISK_CONTINGENCY = 0.2
     # When there is less uncertainty, a lower contingency rate is used
@@ -871,10 +871,10 @@ class Costs:
 
         if needs_cylinder:
             # 1000 is the cost of a new hot water cylinder
-            total_cost = 1200 * number_heated_rooms + 1000
+            total_cost = 1300 * number_heated_rooms + 1000
         else:
             # 500 is the cost of a dual immersion heater - a rough estimate
-            total_cost = 1200 * number_heated_rooms + 500
+            total_cost = 1300 * number_heated_rooms + 500
         subtotal_before_vat = total_cost / (1 + self.VAT_RATE)
         vat = total_cost - subtotal_before_vat
 
diff --git a/recommendations/HeatingRecommender.py b/recommendations/HeatingRecommender.py
index 18e1110b..9d1a094e 100644
--- a/recommendations/HeatingRecommender.py
+++ b/recommendations/HeatingRecommender.py
@@ -57,6 +57,31 @@ class HeatingRecommender:
             },
             # These are the heating types we need to produce a dual heating recommendation
             "dual": None
+        },
+        'Electric underfloor heating, electric storage heaters': {
+            # For this, we would recommend a heat pump
+            "dual": None
+        },
+        "Room heaters, electric, boiler and radiators, mains gas": {
+            "hhr": {
+                "mainheating_description": "Electric storage heaters, radiators",
+                "recommendation_description": "Install high heat retention electric storage heaters.",
+                "controls_prefix": ""
+            },
+            "boiler": {
+                "mainheating_description": "Boiler and radiators, mains gas",
+                "recommendation_description": "Upgrade to a new condensing boiler.",
+                "controls_suffix": ""
+            },
+            "dual": None
+        },
+        "Room heaters, electric, electric storage heaters": {
+            "hhr": {
+                "mainheating_description": "Electric storage heaters, radiators",
+                "recommendation_description": "Install high heat retention electric storage heaters.",
+                "controls_prefix": ""
+            },
+            "dual": None
         }
     }
 
@@ -109,6 +134,10 @@ class HeatingRecommender:
 
         hhr_suitable = no_mains or self.has_electric_heating_description or self.has_room_heaters
 
+        hhr_suitable = hhr_suitable and (
+            "underfloor heating" not in self.property.main_heating["clean_description"]
+        )
+
         return (
             hhr_suitable and (not ashp_only_heating_recommendation) and not self.has_ashp and
             ("high_heat_retention_storage_heater" in measures)
@@ -165,7 +194,8 @@ class HeatingRecommender:
             ) and
             (not ashp_only_heating_recommendation) and
             ("boiler_upgrade" in measures) and
-            (not self.has_ashp)
+            (not self.has_ashp) and
+            (not self.property.main_heating["has_warm_air"])
         )
 
         return is_valid, has_gas_boiler
@@ -487,17 +517,30 @@ class HeatingRecommender:
             ]
 
         # This is a map from the heating controls description to the description of the air source heat pump set up
-        ashp_descriptions = {
-            "Time and temperature zone control": (
-                f"Install a {ashp_size}KW air source heat pump, and upgrade heating controls to Smart Thermostats, "
-                "room sensors and smart radiator valves (time & temperature zone control). Ensure you have an 18 or "
-                "24 hour tariff"
-            ),
-            "Programmer, TRVs and bypass": (
-                f"Install a {ashp_size}KW air source heat pump, with programmer, TRVs and a Bypass valve. Ensure you "
-                "have an 18 or 24 hour tariff"
-            ),
-        }
+        if ashp_size is None:
+            ashp_descriptions = {
+                "Time and temperature zone control": (
+                    f"Install two cascaded air source heat pumps, and upgrade heating controls to Smart Thermostats, "
+                    "room sensors and smart radiator valves (time & temperature zone control). Ensure you have an 18 "
+                    "or "
+                    "24 hour tariff"
+                )
+            }
+        else:
+
+            ashp_descriptions = {
+                "Time and temperature zone control": (
+                    f"Install a {ashp_size}KW air source heat pump, and upgrade heating controls to Smart Thermostats, "
+                    "room sensors and smart radiator valves (time & temperature zone control). Ensure you have an 18 "
+                    "or "
+                    "24 hour tariff"
+                ),
+                "Programmer, TRVs and bypass": (
+                    f"Install a {ashp_size}KW air source heat pump, with programmer, TRVs and a Bypass valve. Ensure "
+                    f"you "
+                    "have an 18 or 24 hour tariff"
+                ),
+            }
 
         new_heating_description = "Air source heat pump, radiators, electric"
         new_hot_water_description = "From main system"
@@ -924,6 +967,7 @@ class HeatingRecommender:
             return recommendations
 
         self.heating_recommendations.extend(recommendations)
+        return None
 
     @staticmethod
     def estimate_boiler_size(property_type, built_form, floor_area, floor_height, num_heated_rooms):
diff --git a/recommendations/Recommendations.py b/recommendations/Recommendations.py
index 0e73cffe..462d43aa 100644
--- a/recommendations/Recommendations.py
+++ b/recommendations/Recommendations.py
@@ -679,7 +679,7 @@ class Recommendations:
 
         # Handle the case of community schemes
         if (heating_description == "Community scheme") or (hotwater_description == "Community scheme"):
-            if main_fuel_description == "mains gas (community)":
+            if main_fuel_description in ["mains gas (community)", "UNKNOWN"]:
                 return {
                     "heating_fuel_type": "Natural Gas (Community Scheme)",
                     "hotwater_fuel_type": "Natural Gas (Community Scheme)",
diff --git a/recommendations/RoofRecommendations.py b/recommendations/RoofRecommendations.py
index fa8b831c..31ac2433 100644
--- a/recommendations/RoofRecommendations.py
+++ b/recommendations/RoofRecommendations.py
@@ -191,11 +191,22 @@ class RoofRecommendations:
 
         non_invasive_recommendations = self.property.non_invasive_recommendations
 
+        # We check a specific condition - which will imply loft insulation isn't appropriate but room in roof
+        # insulation is
+        # 1) We have an uninsulated loft (assumed)
+        # 2) We have a non-intrusive recommendation for room in roof insulation
+
+        rir_over_loft = (
+            self.property.roof["is_pitched"] and
+            self.property.roof["insulation_thickness"] == "none" and
+            "room_in_roof_insulation" in [x["type"] for x in non_invasive_recommendations]
+        )
+
         # We firstly handle non-intrusive recommendations, which may override the normal roof insulation recommendations
         if ("loft_insulation" in [x["type"] for x in non_invasive_recommendations]) or (
             self.property.roof["is_pitched"] and "loft_insulation" in measures and
             not self.property.roof["is_at_rafters"]
-        ):
+        ) and not rir_over_loft:
             self.recommend_roof_insulation(
                 u_value=u_value,
                 insulation_thickness=self.insulation_thickness,
@@ -223,7 +234,8 @@ class RoofRecommendations:
         # There are cases where the property might have a room roof as the second roof, but we have a recommendation for
         # it, so we allow this override
         if self.property.roof["is_roof_room"] and ("room_roof_insulation" in measures) or (
-            "room_roof_insulation" in [x["type"] for x in non_invasive_recommendations]
+            "room_roof_insulation" in [x["type"] for x in non_invasive_recommendations] or
+            rir_over_loft
         ):
             self.recommend_room_roof_insulation(u_value, phase, default_u_values)
             return
@@ -502,7 +514,7 @@ class RoofRecommendations:
         # and the cost of the materials
 
         rir_non_invasive_recommendation = next(
-            (x for x in self.property.non_invasive_recommendations if x["type"] == "room_roof_insulation"), {}
+            (x for x in self.property.non_invasive_recommendations if x["type"] == "room_in_roof_insulation"), {}
         )
 
         insulation_materials = pd.DataFrame(self.room_roof_insulation_materials)
diff --git a/recommendations/VentilationRecommendations.py b/recommendations/VentilationRecommendations.py
index a82e4df5..05113acf 100644
--- a/recommendations/VentilationRecommendations.py
+++ b/recommendations/VentilationRecommendations.py
@@ -10,11 +10,6 @@ class VentilationRecommendations(Definitions):
     crucial for prevent overheating risks in warmer months
     """
 
-    VENTILATION_DESCRIPTIONS = [
-        'mechanical, extract only',
-        'mechanical, supply and extract'
-    ]
-
     def __init__(
         self,
         property_instance: Property,
@@ -26,9 +21,6 @@ class VentilationRecommendations(Definitions):
         self.recommendation = None
         self.materials = [part for part in materials if part["type"] == "mechanical_ventilation"]
 
-    def identify_ventilation(self):
-        self.has_ventilaion = self.property.data["mechanical-ventilation"] in self.VENTILATION_DESCRIPTIONS
-
     def recommend(self, phase):
         """
         If there is no ventilation, we recommend installing ventilation
@@ -38,8 +30,8 @@ class VentilationRecommendations(Definitions):
         :return:
         """
 
-        self.identify_ventilation()
-        if self.has_ventilaion:
+        self.property.identify_ventilation()
+        if self.property.has_ventilation:
             return
 
         if len(self.materials) != 1:
diff --git a/recommendations/optimiser/optimiser_functions.py b/recommendations/optimiser/optimiser_functions.py
index 05b9ec42..6909a3f0 100644
--- a/recommendations/optimiser/optimiser_functions.py
+++ b/recommendations/optimiser/optimiser_functions.py
@@ -47,19 +47,19 @@ def prepare_input_measures(property_recommendations, goal, needs_ventilation):
             # We bundle the impact of ventilation with the measure
             total = (
                 rec["total"] + ventilation_recommendation["total"]
-                if rec["type"] in assumptions.measures_needing_ventilation
+                if rec["type"] in assumptions.measures_needing_ventilation and needs_ventilation
                 else rec["total"]
             )
             gain = (
                 rec[goal_key] + ventilation_recommendation[goal_key]
-                if rec["type"] in assumptions.measures_needing_ventilation
+                if rec["type"] in assumptions.measures_needing_ventilation and needs_ventilation
                 else rec[goal_key]
             )
 
             rec_type = (
                 "+".join(
                     [rec["type"], ventilation_recommendation["type"]]
-                ) if rec["type"] in assumptions.measures_needing_ventilation
+                ) if rec["type"] in assumptions.measures_needing_ventilation and needs_ventilation
                 else rec["type"]
             )
 
diff --git a/sfr/principal_pitch/0_prepare_sample.py b/sfr/principal_pitch/0_prepare_sample.py
new file mode 100644
index 00000000..8150d519
--- /dev/null
+++ b/sfr/principal_pitch/0_prepare_sample.py
@@ -0,0 +1,71 @@
+"""
+This is a script for preparing a sample for testing the end to end process, so that when Spring send us
+data, we know it will work.
+"""
+
+import pandas as pd
+from utils.s3 import read_csv_from_s3
+
+birmingham_epcs = pd.read_csv(
+    "/Users/khalimconn-kowlessar/Documents/hestia/sfr/Spring JV/domestic-E08000025-Birmingham/certificates.csv"
+)
+
+# We get the newest EPC, by UPRN and LODGEMENT_DATE
+birmingham_epcs['LODGEMENT_DATE'] = pd.to_datetime(birmingham_epcs['LODGEMENT_DATE'])
+
+birmingham_epcs = birmingham_epcs.sort_values(
+    by=['UPRN', 'LODGEMENT_DATE'],
+    ascending=[True, False]
+).drop_duplicates(subset='UPRN')
+
+birmingham_epcs["postal_region"] = birmingham_epcs["POSTCODE"].str.split(" ").str[0]
+
+addressable_market = birmingham_epcs[
+    (birmingham_epcs['CURRENT_ENERGY_RATING'].isin(['F', 'G', 'E', 'D'])) &
+    (birmingham_epcs['LODGEMENT_DATE'] >= '2020-01-01') &
+    (birmingham_epcs['PROPERTY_TYPE'].isin(['House', 'Bungalow'])) &
+    (birmingham_epcs['TENURE'].isin(
+        ['rental (private)', 'Rented (private)']
+    ))
+    ]
+
+# We take the Spring portfolio and remove the properties in their sample
+asset_list = read_csv_from_s3(bucket_name="retrofit-plan-inputs-dev", filepath='8/206/asset_list.csv')
+asset_list = pd.DataFrame(asset_list)
+asset_list["postal_region"] = asset_list["postcode"].str.split(" ").str[0]
+
+addressable_market = addressable_market[
+    ~addressable_market["UPRN"].astype(int).astype(str).isin(asset_list["uprn"].values)
+]
+addressable_market = addressable_market[
+    addressable_market["postal_region"].isin(asset_list["postal_region"].unique())
+]
+
+# Take a sample of properties, EPC F or G, EPC lodged in 2025. We focus on houses/bingalows
+sample = birmingham_epcs[
+    (birmingham_epcs['CURRENT_ENERGY_RATING'].isin(['F', 'G'])) &
+    (birmingham_epcs['LODGEMENT_DATE'] >= '2025-01-01') &
+    (birmingham_epcs['PROPERTY_TYPE'].isin(['House', 'Bungalow']))
+    ]
+
+# Prepare the sample, with just the columns we would expect to receive from Spring
+# 1) UPRN
+# 2) Address
+# 3) Postcode
+# 4) Property type
+# 5) Built form
+# 6) Number of bedrooms (we'll simulate this)
+# 7) Number of bathrooms (we'll simulate this)
+# 8) Valuation (We'll simulate this, around 200,000)
+
+sample = sample[['UPRN', 'ADDRESS', 'POSTCODE', 'PROPERTY_TYPE', 'BUILT_FORM']].copy()
+sample['BEDROOMS'] = 3  # Simulating number of bedrooms
+sample['BATHROOMS'] = 1  # Simulating number of bathrooms
+sample['VALUATION'] = 200000  # Simulating valuation
+sample.columns = [x.lower() for x in sample.columns]
+
+# Store this as a excel
+sample.to_excel(
+    "/Users/khalimconn-kowlessar/Documents/hestia/sfr/Spring JV/birmingham_sample.xlsx",
+    index=False
+)
diff --git a/sfr/principal_pitch/1_prepare_data.py b/sfr/principal_pitch/1_prepare_data.py
new file mode 100644
index 00000000..53969ec9
--- /dev/null
+++ b/sfr/principal_pitch/1_prepare_data.py
@@ -0,0 +1,124 @@
+"""
+This script prepares the data for the principal pitch modelling
+"""
+import os
+import pandas as pd
+from dotenv import load_dotenv
+from utils.s3 import save_csv_to_s3
+from etl.find_my_epc.AssetListEpcData import AssetListEpcData
+
+load_dotenv(dotenv_path="backend/.env")
+EPC_AUTH_TOKEN = os.getenv("EPC_AUTH_TOKEN")
+PORTFOLIO_ID = 206
+USER_ID = 8
+EPC_TARGET = "C"
+
+# Read the input file
+
+properties = pd.read_excel(
+    "/Users/khalimconn-kowlessar/Documents/hestia/sfr/Spring JV/Birmingham_price_top300.xlsx"
+)
+# Keep just the D's and below
+properties = properties[properties["current_energy_rating"].isin(["D", "E", "F", "G"])].copy()
+# Focus on houses
+properties = properties[properties["property_type_std"] != "Flat"]
+properties = properties[properties["property_type"] != "flat"]
+
+# Rename the key columns
+properties = properties.rename(
+    columns={
+        "address1": "address",
+        "number_of_bathrooms": "n_bathrooms",
+        "num_beds": "n_bedrooms"
+    }
+)
+properties["patch"] = True
+
+# Pull the non-invasive recommendations
+asset_list_epc_client = AssetListEpcData(
+    asset_list=properties,
+    epc_auth_token=EPC_AUTH_TOKEN
+)
+asset_list_epc_client.get_data()
+asset_list_epc_client.get_non_invasive_recommendations()
+asset_list_epc_client.get_patch()
+
+extracted_df = pd.DataFrame(asset_list_epc_client.extracted_data)
+epc_df = pd.DataFrame(asset_list_epc_client.epc_data)
+
+# Find examples where patches are different to the api
+compare_epc = []
+for patch in asset_list_epc_client.patches:
+    extracted = extracted_df[extracted_df["uprn"] == patch["uprn"]].squeeze()
+    epc = epc_df[epc_df["uprn"] == patch["uprn"]].squeeze()
+    compare_epc.append(
+        {
+            "uprn": extracted["uprn"],
+            "address": extracted["address"],
+            "postcode": extracted["postcode"],
+            "api_epc": int(extracted["current_epc_efficiency"]),
+            "fme_epc": int(epc["current-energy-efficiency"]),
+        }
+    )
+compare_epc = pd.DataFrame(compare_epc)
+diff = compare_epc[compare_epc["api_epc"] != compare_epc["fme_epc"]]
+# Compare matched addresses to make sure they are the same
+compare_addresses = extracted_df[["address", "postcode", "uprn"]].merge(
+    epc_df[["uprn", "address1", "postcode"]].rename(columns={"address1": "epc_address1", "postcode": "epc_postcode"}),
+    how="left",
+    on=["uprn"]
+)
+
+# Add on uprn
+properties = properties.merge(
+    extracted_df[["address", "postcode", "uprn"]],
+    how="left",
+    on=["address", "postcode"]
+)
+
+# Store the asset list in s3
+filename = f"{USER_ID}/{PORTFOLIO_ID}/asset_list.csv"
+save_csv_to_s3(
+    dataframe=properties,
+    bucket_name="retrofit-plan-inputs-dev",
+    file_name=filename
+)
+
+# Store non-invasive recommendations in S3
+non_invasive_recommendations_filename = f"{USER_ID}/{PORTFOLIO_ID}/non_invasive_recommendations.csv"
+save_csv_to_s3(
+    dataframe=pd.DataFrame(asset_list_epc_client.non_invasive_recommendations),
+    bucket_name="retrofit-plan-inputs-dev",
+    file_name=non_invasive_recommendations_filename
+)
+
+# Store patches in S3
+patches_filename = ""
+if asset_list_epc_client.patches:
+    patches_filename = f"{USER_ID}/{PORTFOLIO_ID}/patches.csv"
+    save_csv_to_s3(
+        dataframe=pd.DataFrame(asset_list_epc_client.patches),
+        bucket_name="retrofit-plan-inputs-dev",
+        file_name=patches_filename
+    )
+
+body = {
+    "portfolio_id": str(PORTFOLIO_ID),
+    "housing_type": "Private",
+    "goal": "Increasing EPC",
+    "goal_value": "C",
+    "trigger_file_path": filename,
+    "already_installed_file_path": "",
+    "patches_file_path": patches_filename,
+    "non_invasive_recommendations_file_path": non_invasive_recommendations_filename,
+    "valuation_file_path": "",
+    "scenario_name": "EPC C",
+    "multi_plan": True,
+    "budget": None,
+    "ashp_cop": 3.5,
+    # This is new - when optimising, we drop scores by a few points to account for SAP 10
+    "simulate_sap_10": True,
+    "exclusions": ["external_wall_insulation"],
+    "required_measures": ["cavity_wall_insulation", "loft_insulation"]
+}
+print(body)
diff --git a/sfr/principal_pitch/2_export_data.py b/sfr/principal_pitch/2_export_data.py
new file mode 100644
index 00000000..5660b78d
--- /dev/null
+++ b/sfr/principal_pitch/2_export_data.py
@@ -0,0 +1,224 @@
+"""
+This script prepares the data for the financial model
+"""
+
+import pandas as pd
+from backend.app.utils import sap_to_epc
+from sqlalchemy.orm import sessionmaker
+from backend.app.db.connection import db_engine
+from backend.app.db.models.recommendations import Recommendation, Plan, PlanRecommendations
+from backend.app.db.models.portfolio import PropertyModel, PropertyDetailsEpcModel
+
+PORTFOLIO_ID = 206
+SCENARIOS = [389]
+
+
+def get_data(portfolio_id, scenario_ids):
+    session = sessionmaker(bind=db_engine)()
+    session.begin()
+
+    # Get properties and their details for a specific portfolio
+    properties_query = session.query(
+        PropertyModel,
+        PropertyDetailsEpcModel
+    ).join(
+        PropertyDetailsEpcModel, PropertyModel.id == PropertyDetailsEpcModel.property_id
+    ).filter(
+        PropertyModel.portfolio_id == portfolio_id  # Filter by portfolio ID
+    ).all()
+
+    # Transform properties data to include all fields dynamically
+    properties_data = [
+        {**{col.name: getattr(prop.PropertyModel, col.name) for col in PropertyModel.__table__.columns},
+         **{col.name: getattr(prop.PropertyDetailsEpcModel, col.name) for col in
+            PropertyDetailsEpcModel.__table__.columns}}
+        for prop in properties_query
+    ]
+
+    # Get property IDs from fetched properties
+
+    # Get plans linked to the fetched properties
+    plans_query = session.query(Plan).filter(Plan.scenario_id.in_(scenario_ids)).all()
+
+    # Transform plans data to include all fields dynamically
+    plans_data = [
+        {col.name: getattr(plan, col.name) for col in Plan.__table__.columns}
+        for plan in plans_query
+    ]
+
+    # Extract plan IDs for filtering recommendations through PlanRecommendations
+    plan_ids = [plan['id'] for plan in plans_data]
+
+    # Get recommendations through PlanRecommendations for those plans and that are default
+    recommendations_query = session.query(
+        Recommendation,
+        Plan.scenario_id
+    ).join(
+        PlanRecommendations, Recommendation.id == PlanRecommendations.recommendation_id
+    ).join(
+        Plan, Plan.id == PlanRecommendations.plan_id  # Join with Plan to access scenario_id
+    ).filter(
+        PlanRecommendations.plan_id.in_(plan_ids),
+        Recommendation.default == True  # Filtering for default recommendations
+    ).all()
+
+    # Transform recommendations data to include all fields dynamically and include scenario_id
+    recommendations_data = [
+        {**{col.name: getattr(rec.Recommendation, col.name) if hasattr(rec, 'Recommendation') else getattr(rec,
+                                                                                                           col.name) for
+            col in Recommendation.__table__.columns},
+         "Scenario ID": rec.scenario_id}
+        for rec in recommendations_query
+    ]
+
+    session.close()
+
+    return properties_data, plans_data, recommendations_data
+
+
+properties_data, plans_data, recommendations_data = get_data(portfolio_id=PORTFOLIO_ID, scenario_ids=SCENARIOS)
+
+properties_df = pd.DataFrame(properties_data)
+plans_df = pd.DataFrame(plans_data)
+recommendations_df = pd.DataFrame(recommendations_data)
+
+recommended_measures_df = recommendations_df[
+    ["property_id", "measure_type", "estimated_cost", "default"]
+]
+recommended_measures_df = recommended_measures_df[recommended_measures_df["default"]]
+recommended_measures_df = recommended_measures_df.drop(columns=["default"])
+
+post_install_sap = recommendations_df[["property_id", "default", "sap_points"]]
+post_install_sap = post_install_sap[post_install_sap["default"]]
+# Sum up the sap points by property id
+post_install_sap = post_install_sap.groupby("property_id")[["sap_points"]].sum().reset_index()
+
+recommendations_measures_pivot = recommended_measures_df.pivot(
+    index='property_id',
+    columns='measure_type',
+    values='estimated_cost'
+)
+recommendations_measures_pivot = recommendations_measures_pivot.reset_index()
+
+# Total cost is the row sum, excluding the property_id column
+recommendations_measures_pivot["total_retrofit_cost"] = recommendations_measures_pivot.drop(
+    columns=["property_id"]
+).sum(axis=1)
+
+df = properties_df[
+    [
+        "property_id", "uprn", "address", "postcode", "property_type", "walls", "roof", "heating", "windows",
+        "current_epc_rating",
+        "current_sap_points", "total_floor_area", "number_of_rooms",
+    ]
+].merge(
+    recommendations_measures_pivot, how="left", on="property_id"
+).merge(
+    post_install_sap, how="left", on="property_id"
+)
+
+df = df.drop(columns=["property_id"])
+df["sap_points"] = df["sap_points"].fillna(0)
+
+df["predicted_post_works_sap"] = df["current_sap_points"] + df["sap_points"]
+df["predicted_post_works_sap"] = df["predicted_post_works_sap"].round()
+df["predicted_post_works_epc"] = df["predicted_post_works_sap"].apply(lambda x: sap_to_epc(x))
+
+# We merge this back to the main dataframe, which will contain the bathrooms
+from utils.s3 import read_csv_from_s3
+
+asset_list = read_csv_from_s3(bucket_name="retrofit-plan-inputs-dev", filepath='8/206/asset_list.csv')
+asset_list = pd.DataFrame(asset_list)
+df["uprn"] = df["uprn"].astype(str)
+asset_list = asset_list.merge(
+    df.drop(columns=["address", "postcode", "property_type", "total_floor_area"]),
+    how="left",
+    on="uprn"
+)
+
+condition_costs = pd.read_excel(
+    "/Users/khalimconn-kowlessar/Documents/hestia/sfr/Spring JV/Condition costs.xlsx",
+    sheet_name="Prices - Khalim",
+    header=35
+)
+# Remove unnamed columns and reset index
+condition_costs = condition_costs.loc[:, ~condition_costs.columns.str.contains('^Unnamed')]
+condition_costs = condition_costs.reset_index(drop=True)
+
+
+# We now estimate condition cost
+def simulate_condition(asset_list, condition_costs):
+    """
+    This function is for testing, and will simulate condition cost from 1-10 for each property to see what the
+    costing array looks like.
+    :param df:
+    :return:
+    """
+
+    condition_df = []
+    for _, row in asset_list.iterrows():
+
+        n_bathrooms = row["bathrooms"]
+
+        conditions = {}
+        for condition in reversed(range(1, 11)):
+            condition_cost = condition_costs[
+                condition_costs["Condition"] == condition
+                ].drop(columns=["Condition"]).iloc[0]
+
+            # Each cost is scaled by floor area
+            condition_cost = condition_cost * row["total_floor_area"]
+            condition_cost["Bathroom"] = condition_cost["Bathroom"] * n_bathrooms
+
+            total_condition_cost = condition_cost.sum()
+            conditions["Condition " + str(condition)] = (total_condition_cost)
+
+        condition_df.append(
+            {
+                "uprn": row["uprn"],
+                **conditions
+            }
+        )
+
+    condition_df = pd.DataFrame(condition_df)
+
+    asset_list = asset_list.merge(
+        condition_df,
+        how="left",
+        on="uprn"
+    )
+
+    return asset_list
+
+
+# asset_list = simulate_condition(asset_list, condition_costs)
+
+# We calculate the condition cost based on the condition
+for _, row in asset_list.iterrows():
+
+    condition = row["condition_score"]
+    if condition in [None, ""]:
+        continue
+    condition = int(float(condition))
+
+    condition_cost = condition_costs[
+        condition_costs["Condition"] == condition
+        ].drop(columns=["Condition"]).iloc[0]
+
+    # Each cost is scaled by floor area
+    condition_cost = condition_cost * float(row["total_floor_area"])
+    n_bathrooms = row["n_bathrooms"]
+    condition_cost["Bathroom"] = condition_cost["Bathroom"] * float(n_bathrooms)
+
+    total_condition_cost = condition_cost.sum()
+    asset_list.loc[asset_list["uprn"] == row["uprn"], "domna_condition_cost"] = total_condition_cost
+
+# Store output
+asset_list.to_excel(
+    "/Users/khalimconn-kowlessar/Documents/hestia/sfr/Spring JV/20250624_portfolio_retrofit_packages.xlsx",
+    index=False
+)
+
+condition_cost_comparison = asset_list[
+    ["condition_score", "decoration_sum_min ", "decoration_sum_max", "domna_condition_cost"]
+]