diff --git a/backend/Property.py b/backend/Property.py
index 3dbcc2b8..5713c179 100644
--- a/backend/Property.py
+++ b/backend/Property.py
@@ -150,7 +150,7 @@ class Property(Definitions):
         """
 
         solar_pv = self.data["photo-supply"]
-        if solar_pv == "":
+        if solar_pv in ["", None]:
             solar_pv = None
         else:
             solar_pv = float(solar_pv)
@@ -170,6 +170,7 @@ class Property(Definitions):
             "Y": True,
             "N": False,
             "": None,
+            None: None,
         }
 
         self.solar_hot_water = {
@@ -245,8 +246,8 @@ class Property(Definitions):
         # it
         self.data["built-form"] = BUILT_FORM_REMAP.get(self.data["built-form"], self.data["built-form"])
         if self.data["built-form"] in self.DATA_ANOMALY_MATCHES:
-            if self.data["property-type"] == "Flat":
-                self.data["built-form"] = "Semi-Detached"
+            if self.data["property-type"] in ["Flat", "Maisonette"]:
+                self.data["built-form"] = "End-Terrace"
 
         self.set_year_built()
         self.set_energy()
@@ -394,7 +395,8 @@ class Property(Definitions):
         map = {
             "no corridor": False,
             "unheated corridor": True,
-            "heated corridor": False
+            "heated corridor": False,
+            None: False
         }
 
         if self.data["heat-loss-corridor"] in self.DATA_ANOMALY_MATCHES:
@@ -403,7 +405,7 @@ class Property(Definitions):
             has_heat_loss_corridor = map[self.data["heat-loss-corridor"]]
 
         length = self.data["unheated-corridor-length"]
-        if length == "":
+        if length in ["", None]:
             length = None
         else:
             length = float(length)
@@ -579,7 +581,7 @@ class Property(Definitions):
         self.floor_area = float(self.data["total-floor-area"])
 
         if not self.data["number-habitable-rooms"] or (
-            self.data["floor-height"] == "" or self.data["floor-height"] in self.DATA_ANOMALY_MATCHES
+            self.data["floor-height"] in ["", None] or self.data["floor-height"] in self.DATA_ANOMALY_MATCHES
         ):
             if self.property_dimensions is None:
                 property_dimensions = read_dataframe_from_s3_parquet(
@@ -601,7 +603,7 @@ class Property(Definitions):
         else:
             raise NotImplementedError("Implement me")
 
-        if self.data["floor-height"] == "" or self.data["floor-height"] in self.DATA_ANOMALY_MATCHES:
+        if self.data["floor-height"] in [None, ""] or self.data["floor-height"] in self.DATA_ANOMALY_MATCHES:
             self.floor_height = float(self.property_dimensions["FLOOR_HEIGHT"].round(2))
         else:
             self.floor_height = float(self.data["floor-height"])
@@ -626,7 +628,7 @@ class Property(Definitions):
     def set_floor_level(self):
         self.floor_level = (
             FLOOR_LEVEL_MAP[self.data["floor-level"]] if
-            self.data["floor-level"] not in self.DATA_ANOMALY_MATCHES else None
+            self.data["floor-level"] not in list(self.DATA_ANOMALY_MATCHES) + [None] else None
         )
 
         if self.floor_level is None:
@@ -794,7 +796,7 @@ class Property(Definitions):
         :return:
         """
 
-        if self.data["fixed-lighting-outlets-count"] == "":
+        if self.data["fixed-lighting-outlets-count"] in [None, ""]:
 
             # We check old EPCs and the full SAP EPC
 
diff --git a/backend/SearchEpc.py b/backend/SearchEpc.py
index 2a2cdfba..d69d8d86 100644
--- a/backend/SearchEpc.py
+++ b/backend/SearchEpc.py
@@ -146,6 +146,7 @@ class SearchEpc:
         max_retries: int = None,
         uprn: [int, None] = None,
         size=None,
+        property_type=None,
     ):
         """
         Address lines 1 and postcode are mandatory fields. The other address lines are optional
@@ -157,6 +158,7 @@ class SearchEpc:
         :param uprn: int, optional, the uprn of the property
         :param size: int, optional, the number of results to return. If not provided, defaults to 25 which is the api's
                         default
+        :param property_type: str, optional, the property type of the property, if known before hand
         """
 
         self.address1 = address1
@@ -184,6 +186,8 @@ class SearchEpc:
 
         self.size = size if size is not None else 25
 
+        self.property_type = property_type
+
     @classmethod
     def get_house_number(cls, address: str) -> str | None:
         """
@@ -335,7 +339,7 @@ class SearchEpc:
 
         return address, postcode
 
-    def extract_epc_data(self, property_type=None, address=None):
+    def extract_epc_data(self, address=None):
 
         """
         Given a successful search, this method will format the data and return it
@@ -351,7 +355,7 @@ class SearchEpc:
         # Firstly, we should only have 1 urpn so if we have multiple, we'll need to filter down the
         # property further
 
-        rows = self.filter_rows(rows, property_type=property_type, address=None)
+        rows = self.filter_rows(rows, property_type=self.property_type, address=None)
         rows = self.filter_rows(rows, property_type=None, address=address)
 
         # We now check for a full sap epc:
@@ -366,9 +370,19 @@ class SearchEpc:
 
         # Ge the uprn from the newest record for this home
         uprns = {r["uprn"] for r in rows if r["uprn"]}
-        if len(uprns) != 1:
-            raise ValueError("Multiple UPRNs found - investigate me")
-        uprn = uprns.pop()
+        # We can sometimes have no uprn for a property
+        if (len(uprns) == 0) and len(rows) > 0:
+            logger.warning("Found data but missing uprn")
+        elif len(uprns) != 1:
+            # There is a possibility that we have multiple UPRNs for a single property, which is an error
+            addresses = {r["address"] for r in rows}
+            if len(addresses) == 1:
+                # Take the uprn from the most recent
+                uprns = {newest_epc["uprn"]}
+            else:
+                raise ValueError("Multiple UPRNs found - investigate me")
+
+        uprn = uprns.pop() if uprns else None
 
         return newest_epc, older_epcs, full_sap_epc, address_epc, postcode_epc, uprn
 
@@ -670,6 +684,19 @@ class SearchEpc:
 
         # Step 2: If we don't have an EPC, we use the ordnance survey api to find the uprn
         if skip_os:
+            if self.ordnance_survey_client.property_type is not None:
+                # We can try and estimate
+                estimated_epc = self.estimate_epc(
+                    property_type=self.ordnance_survey_client.property_type,
+                    built_form=self.ordnance_survey_client.built_form
+                )
+                self.newest_epc = estimated_epc
+                self.older_epcs = []
+                self.full_sap_epc = {}
+
+                # Finally, set a standardised address 1 and postcode
+                self.address_clean = self.ordnance_survey_client.address_os
+                self.postcode_clean = self.ordnance_survey_client.postcode_os
             return
 
         os_response = self.ordnance_survey_client.get_places_api()
diff --git a/etl/eligibility/Eligibility.py b/etl/eligibility/Eligibility.py
index 364be3cc..c9d75606 100644
--- a/etl/eligibility/Eligibility.py
+++ b/etl/eligibility/Eligibility.py
@@ -333,7 +333,8 @@ class Eligibility:
         """
 
         current_sap = int(self.epc["current-energy-efficiency"])
-        if current_sap > 54:
+
+        if current_sap >= 69:
             self.eco4_warmfront = {
                 "eligible": False,
                 "message": "sap too high"
@@ -347,7 +348,12 @@ class Eligibility:
         is_eligible = self.cavity["suitability"] & self.loft["suitability"]
 
         if post_retrofit_sap is None:
-            message = "subject to post retrofit sap" if is_eligible else "not eligible"
+
+            if current_sap >= 55:
+                message = "Possibly eligible but property currently EPC D"
+            else:
+                message = "subject to post retrofit sap" if is_eligible else "not eligible"
+
             self.eco4_warmfront = {
                 "eligible": is_eligible,
                 "message": message
diff --git a/etl/eligibility/ha_15_32/app.py b/etl/eligibility/ha_15_32/app.py
index 9a563770..3a0caec6 100644
--- a/etl/eligibility/ha_15_32/app.py
+++ b/etl/eligibility/ha_15_32/app.py
@@ -246,6 +246,8 @@ def merge_ha_15(asset_list, identified_addresses):
 
         identified_addresses = identified_addresses.drop_duplicates("merge_key")
 
+    # We pull out raw counts for the survey lists
+
     # Check asset list for dupes
     asset_list_dupes = asset_list["merge_key"].duplicated()
     if asset_list_dupes.sum():
@@ -336,7 +338,8 @@ def merge_ha_15(asset_list, identified_addresses):
 
 
 def prepare_model_data_row(
-    property_id, modelling_epc, cleaned, cleaning_data, created_at, old_data=None, full_sap_epc=None
+    property_id, modelling_epc, cleaned, cleaning_data, created_at,
+    photo_supply_lookup, floor_area_decile_thresholds, old_data=None, full_sap_epc=None,
 ):
     """
     This function prepares the data for modelling, in the same fashion as the recommendation engine
@@ -353,7 +356,8 @@ def prepare_model_data_row(
         full_sap_epc=full_sap_epc
     )
 
-    p.get_components(cleaned, None, None)
+    p.get_components(cleaned, photo_supply_lookup=photo_supply_lookup,
+                     floor_area_decile_thresholds=floor_area_decile_thresholds)
     # This is temp - this should happen after scoring
     cleaned_property_data = DataProcessor.apply_averages_cleaning(
         data_to_clean=pd.DataFrame([dict(**p.get_model_data(), LOCAL_AUTHORITY=p.data["local-authority"])]),
diff --git a/etl/eligibility/ha_15_32/ha16_app.py b/etl/eligibility/ha_15_32/ha16_app.py
index 7c1db158..bd19fe97 100644
--- a/etl/eligibility/ha_15_32/ha16_app.py
+++ b/etl/eligibility/ha_15_32/ha16_app.py
@@ -1,6 +1,6 @@
+import os
 import msgpack
 import openpyxl
-from openpyxl.styles.colors import COLOR_INDEX
 from pathlib import Path
 from datetime import datetime
 import pandas as pd
@@ -8,7 +8,7 @@ import numpy as np
 from utils.s3 import read_from_s3
 from utils.logger import setup_logger
 from dotenv import load_dotenv
-from backend.app.utils import read_parquet_from_s3
+from utils.s3 import read_dataframe_from_s3_parquet
 from tqdm import tqdm
 from backend.SearchEpc import SearchEpc
 from etl.eligibility.Eligibility import Eligibility
@@ -16,10 +16,12 @@ from etl.eligibility.ha_15_32.app import prepare_model_data_row
 from etl.epc.DataProcessor import DataProcessor
 from etl.epc.settings import COLUMNS_TO_MERGE_ON
 from backend.ml_models.api import ModelApi
+from etl.solar.SolarPhotoSupply import SolarPhotoSupply
 
 import re
 
 ENV_FILE = Path(__file__).parent / "etl" / "eligibility" / "ha_15_32" / ".env"
+EPC_AUTH_TOKEN = os.getenv("EPC_AUTH_TOKEN")
 
 logger = setup_logger()
 load_dotenv(ENV_FILE)
@@ -250,24 +252,55 @@ def load_data():
     return data, survey_list
 
 
-def get_epc_data(data, cleaned, cleaning_data, created_at):
+def get_epc_data(data, cleaned, cleaning_data, created_at, photo_supply_lookup, floor_area_decile_thresholds):
     scoring_data = []
     results = []
     nodata = []
 
-    for _, property_meta in tqdm(data.iterrows(), total=len(data)):
+    property_type_lookup = {
+        'Semi Detached Bungalow': {"property-type": "Bungalow", "built-form": "Semi-Detached"},
+        'Mid Terraced House': {"property-type": "House", "built-form": "Mid-Terrace"},
+        'End Terraced House': {"property-type": "House", "built-form": "End-Terrace"},
+        'Low Rise Flat': {"property-type": "Flat", "built-form": "Mid-Terrace"},
+        'Semi-Detached House': {"property-type": "House", "built-form": "Semi-Detached"},
+        'Detached Bungalow': {"property-type": "Bungalow", "built-form": "Detached"},
+        'End Terraced Bungalow': {"property-type": "Bungalow", "built-form": "End-Terrace"},
+        'Mid Terraced Bungalow': {"property-type": "Bungalow", "built-form": "Mid-Terrace"},
+        'Medium Rise Flat': {"property-type": "Flat", "built-form": "Mid-Terrace"},
+        'Detached House': {"property-type": "House", "built-form": "Detached"},
+        'Cottage Flat': {"property-type": "Flat", "built-form": "Semi-Detached"},
+        'Maisonette Medium Rise': {"property-type": "Flat", "built-form": "Mid-Terrace"},
+        'Maisonette Over Shop': {"property-type": "Flat", "built-form": "Mid-Terrace"},
+        'End Terraced Town House': {"property-type": "House", "built-form": "End-Terrace"},
+        'Flat Over Shop': {"property-type": "Flat", "built-form": "Mid-Terrace"},
+        'Mid Terraced Town House': {"property-type": "House", "built-form": "Mid-Terrace"},
+    }
+
+    for index, property_meta in tqdm(data.iterrows(), total=len(data)):
+
         searcher = SearchEpc(
             address1=property_meta["HouseNo"],
             postcode=property_meta["Postcode"],
-            size=1000
+            auth_token=EPC_AUTH_TOKEN,
+            os_api_key=None,
+            full_address=property_meta["Address"]
         )
-        searcher.search()
+        searcher.ordnance_survey_client.property_type = property_type_lookup[property_meta["Type"]]["property-type"]
+        searcher.ordnance_survey_client.built_form = property_type_lookup[property_meta["Type"]]["built-form"]
+        searcher.find_property(skip_os=True)
 
-        if searcher.data is None:
+        if searcher.newest_epc is None:
             nodata.append(property_meta)
             continue
 
-        newest_epc, older_epcs, full_sap_epc = searcher.retrieve(address=property_meta["Address"])
+        if searcher.newest_epc.get("estimated"):
+            # We insert the row ID as our proxy for UPRN
+            proxy_uprn = int(property_meta["row_id"].split("_")[1])
+            searcher.newest_epc["uprn"] = proxy_uprn
+
+        newest_epc = searcher.newest_epc
+        older_epcs = searcher.older_epcs
+        full_sap_epc = searcher.full_sap_epc
         # We also want to get the penultimate epc
         penultimate_epc, _ = searcher.filter_newest_epc(older_epcs)
         if not penultimate_epc:
@@ -277,16 +310,14 @@ def get_epc_data(data, cleaned, cleaning_data, created_at):
         eligibility.check_gbis_warmfront()
         eligibility.check_eco4_warmfront()
 
-        if (not eligibility.eco4_warmfront["eligible"]) and (not eligibility.gbis_warmfront) and (
-            property_meta["warmfront_identified"]
-        ):
+        if (not eligibility.eco4_warmfront["eligible"]) and (not eligibility.gbis_warmfront):
             eligibility = Eligibility(epc=penultimate_epc, cleaned=cleaned)
             eligibility.check_gbis_warmfront()
             eligibility.check_eco4_warmfront()
             # If this is the case, we need to update the older epcs
-            older_epcs = [
-                x for x in older_epcs if x["lmk-key"] not in [newest_epc["lmk-key"], penultimate_epc["lmk-key"]]
-            ]
+            # We don't update just to make data cleaning easier
+            if penultimate_epc.get("estimated") is None:
+                older_epcs = [x for x in searcher.data["rows"] if x["lmk-key"] != penultimate_epc["lmk-key"]]
 
         # Full checks
         eligibility.check_gbis()
@@ -303,7 +334,9 @@ def get_epc_data(data, cleaned, cleaning_data, created_at):
                 cleaning_data=cleaning_data,
                 created_at=created_at,
                 old_data=older_epcs,
-                full_sap_epc=full_sap_epc
+                full_sap_epc=full_sap_epc,
+                photo_supply_lookup=photo_supply_lookup,
+                floor_area_decile_thresholds=floor_area_decile_thresholds
             )
             scoring_data.extend(scoring_dictionary)
 
@@ -433,6 +466,18 @@ def analyse_results(results_df, data, survey_list):
         how="left", on="survey_key"
     )
 
+    all_identified_eco = analysis_data[
+        (analysis_data["warmfront_identified"] & analysis_data["funding_scheme"].isin(
+            ["ECO4 A/W", "AFFORDABLE WARMTH"])) |
+        (analysis_data["eco4_eligible"])
+        ]
+
+    all_identified_gbis = analysis_data[
+        (analysis_data["warmfront_identified"] & analysis_data["funding_scheme"].isin(
+            ["ECO4 GBIS (ECO+)"])) |
+        (analysis_data["gbis_eligible"] & analysis_data["eco4_eligible"].isin([False, None]))
+        ]
+
     warmfront_identified = analysis_data[analysis_data["warmfront_identified"]]
 
     # Of the ECO jobs, what proportion to we get right
@@ -482,17 +527,22 @@ def app():
     )
     cleaned = msgpack.unpackb(cleaned, raw=False)
 
-    cleaning_data = read_parquet_from_s3(
+    cleaning_data = read_dataframe_from_s3_parquet(
         bucket_name="retrofit-data-dev", file_key="sap_change_model/cleaning_dataset.parquet",
     )
 
     created_at = datetime.now().isoformat()
 
-    results_df, scoring_data, nodata = get_epc_data(data, cleaned, cleaning_data, created_at)
+    photo_supply_lookup, floor_area_decile_thresholds = SolarPhotoSupply.load(bucket="retrofit-data-dev")
+
+    results_df, scoring_data, nodata = get_epc_data(
+        data, cleaned, cleaning_data, created_at, photo_supply_lookup, floor_area_decile_thresholds
+    )
 
     # Store
+    # Old file was ha16.pickle
     # import pickle
-    # with open("ha16.pickle", "wb") as f:
+    # with open("ha16_8_jan_2.pickle", "wb") as f:
     #     pickle.dump(
     #         {
     #             "scoring_data": scoring_data,
@@ -500,3 +550,11 @@ def app():
     #             "nodata": nodata
     #         }, f
     #     )
+
+    # Read pickle
+    # import pickle
+    # with open("ha16.pickle", "rb") as f:
+    #     saved = pickle.load(f)
+    # scoring_data = saved["scoring_data"]
+    # results_df = saved["results"]
+    # nodata = saved["nodata"]
diff --git a/etl/eligibility/ha_15_32/ha24_app.py b/etl/eligibility/ha_15_32/ha24_app.py
index 3edf8735..0f82f30a 100644
--- a/etl/eligibility/ha_15_32/ha24_app.py
+++ b/etl/eligibility/ha_15_32/ha24_app.py
@@ -1,14 +1,13 @@
+import os
 import msgpack
 import openpyxl
-from openpyxl.styles.colors import COLOR_INDEX
 from pathlib import Path
 from datetime import datetime
 import pandas as pd
 import numpy as np
-from utils.s3 import read_from_s3
+from utils.s3 import read_from_s3, read_dataframe_from_s3_parquet
 from utils.logger import setup_logger
 from dotenv import load_dotenv
-from backend.app.utils import read_parquet_from_s3
 from tqdm import tqdm
 from backend.SearchEpc import SearchEpc
 from etl.eligibility.Eligibility import Eligibility
@@ -16,9 +15,9 @@ from etl.eligibility.ha_15_32.app import prepare_model_data_row
 from etl.epc.DataProcessor import DataProcessor
 from etl.epc.settings import COLUMNS_TO_MERGE_ON
 from backend.ml_models.api import ModelApi
+from etl.solar.SolarPhotoSupply import SolarPhotoSupply
 
-import re
-
+EPC_AUTH_TOKEN = os.getenv("EPC_AUTH_TOKEN")
 ENV_FILE = Path(__file__).parent / "etl" / "eligibility" / "ha_15_32" / ".env"
 
 logger = setup_logger()
@@ -170,24 +169,46 @@ def load_data():
     return data, survey_list
 
 
-def get_epc_data(data, cleaned, cleaning_data, created_at):
+def get_epc_data(data, cleaned, cleaning_data, created_at, photo_supply_lookup, floor_area_decile_thresholds):
     scoring_data = []
     results = []
     nodata = []
 
+    property_type_lookup = {
+        "01 HOUSE": "House",
+        "02 FLAT": "Flat",
+        "03 BUNGALOW": "Bungalow",
+        "05 BEDSIT": "Flat",
+        "04 MAISONETTE": "Maisonette",
+        "01 HOUSE MID": "House",
+        "10 PBUNGALOW": "Bungalow",
+        "14 SFLAT": "Flat",
+        "12 SBEDSIT": "Flat",
+        "11 PFLAT": "Flat",
+        "13 SBUNGALOW": "Bungalow",
+        " 01 HOUSE MID": "House",
+        "09 PBEDSIT": "Flat"
+    }
+
     for _, property_meta in tqdm(data.iterrows(), total=len(data)):
+
         searcher = SearchEpc(
             address1=property_meta["HouseNo"],
             postcode=property_meta["Postcode"],
-            size=1000
+            auth_token=EPC_AUTH_TOKEN,
+            os_api_key=None,
+            full_address=property_meta["Address"]
         )
-        searcher.search()
+        searcher.ordnance_survey_client.property_type = property_type_lookup[property_meta["Property Type"]]
+        searcher.find_property(skip_os=True)
 
-        if searcher.data is None:
+        if searcher.newest_epc is None:
             nodata.append(property_meta)
             continue
 
-        newest_epc, older_epcs, full_sap_epc = searcher.retrieve(address=property_meta["Address"])
+        newest_epc = searcher.newest_epc
+        older_epcs = searcher.older_epcs
+        full_sap_epc = searcher.full_sap_epc
         # We also want to get the penultimate epc
         penultimate_epc, _ = searcher.filter_newest_epc(older_epcs)
         if not penultimate_epc:
@@ -197,23 +218,25 @@ def get_epc_data(data, cleaned, cleaning_data, created_at):
         eligibility.check_gbis_warmfront()
         eligibility.check_eco4_warmfront()
 
-        if (not eligibility.eco4_warmfront["eligible"]) and (not eligibility.gbis_warmfront) and (
-            property_meta["warmfront_identified"]
-        ):
+        if (not eligibility.eco4_warmfront["eligible"]) and (not eligibility.gbis_warmfront):
             eligibility = Eligibility(epc=penultimate_epc, cleaned=cleaned)
             eligibility.check_gbis_warmfront()
             eligibility.check_eco4_warmfront()
             # If this is the case, we need to update the older epcs
-            older_epcs = [
-                x for x in older_epcs if x["lmk-key"] not in [newest_epc["lmk-key"], penultimate_epc["lmk-key"]]
-            ]
+            # older_epcs = [
+            #     x for x in older_epcs if x["lmk-key"] not in [newest_epc["lmk-key"], penultimate_epc["lmk-key"]]
+            # ]
+            # If this is the case, we need to update the older epcs
+            # We don't update just to make data cleaning easier
+            if penultimate_epc.get("estimated") is None:
+                older_epcs = [x for x in searcher.data["rows"] if x["lmk-key"] != penultimate_epc["lmk-key"]]
 
         # Full checks
         eligibility.check_gbis()
         eligibility.check_eco4()
 
         if eligibility.eco4_warmfront["eligible"]:
-            if eligibility.epc["uprn"] == "":
+            if eligibility.epc["uprn"] in ["", None]:
                 eligibility.epc["uprn"] = int(property_meta["row_id"].split("_")[1])
 
             scoring_dictionary = prepare_model_data_row(
@@ -223,7 +246,9 @@ def get_epc_data(data, cleaned, cleaning_data, created_at):
                 cleaning_data=cleaning_data,
                 created_at=created_at,
                 old_data=older_epcs,
-                full_sap_epc=full_sap_epc
+                full_sap_epc=full_sap_epc,
+                photo_supply_lookup=photo_supply_lookup,
+                floor_area_decile_thresholds=floor_area_decile_thresholds
             )
             scoring_data.extend(scoring_dictionary)
 
@@ -277,7 +302,7 @@ def get_epc_data(data, cleaned, cleaning_data, created_at):
     scoring_df = DataProcessor.clean_efficiency_variables(scoring_df)
     scoring_df["UPRN"] = scoring_df["UPRN"].astype(int)
 
-    model_api = ModelApi(portfolio_id="ha33-eligibility", timestamp=created_at)
+    model_api = ModelApi(portfolio_id="ha24-eligibility", timestamp=created_at)
     all_predictions = model_api.predict_all(
         df=scoring_df,
         bucket="retrofit-data-dev",
@@ -353,6 +378,18 @@ def analyse_results(results_df, data, survey_list):
         how="left", on="survey_key"
     )
 
+    all_identified_eco = analysis_data[
+        (analysis_data["warmfront_identified"] & analysis_data["funding_scheme"].isin(
+            ["ECO4 A/W"])) |
+        (analysis_data["eco4_eligible"])
+        ]
+
+    all_identified_gbis = analysis_data[
+        (analysis_data["warmfront_identified"] & analysis_data["funding_scheme"].isin(
+            ["ECO4 GBIS (ECO+)"])) |
+        (analysis_data["gbis_eligible"] & analysis_data["eco4_eligible"].isin([False, None]))
+        ]
+
     warmfront_identified = analysis_data[analysis_data["warmfront_identified"]]
 
     # Of the ECO jobs, what proportion to we get right
@@ -403,17 +440,21 @@ def app():
     )
     cleaned = msgpack.unpackb(cleaned, raw=False)
 
-    cleaning_data = read_parquet_from_s3(
+    cleaning_data = read_dataframe_from_s3_parquet(
         bucket_name="retrofit-data-dev", file_key="sap_change_model/cleaning_dataset.parquet",
     )
 
     created_at = datetime.now().isoformat()
 
-    results_df, scoring_data, nodata = get_epc_data(data, cleaned, cleaning_data, created_at)
+    photo_supply_lookup, floor_area_decile_thresholds = SolarPhotoSupply.load(bucket="retrofit-data-dev")
+
+    results_df, scoring_data, nodata = get_epc_data(
+        data, cleaned, cleaning_data, created_at, photo_supply_lookup, floor_area_decile_thresholds
+    )
 
     # Pickle results just in case
     # import pickle
-    # with open("ha24.pickle", "wb") as f:
+    # with open("ha24_8_jan.pickle", "wb") as f:
     #     pickle.dump(
     #         {
     #             "scoring_data": scoring_data,
@@ -421,3 +462,11 @@ def app():
     #             "nodata": nodata
     #         }, f
     #     )
+
+    # Read in pickle
+    # import pickle
+    # with open("ha24_8_jan.pickle", "rb") as f:
+    #     saved = pickle.load(f)
+    # scoring_data = saved["scoring_data"]
+    # results_df = saved["results"]
+    # nodata = saved["nodata"]
diff --git a/etl/eligibility/ha_15_32/ha7_app.py b/etl/eligibility/ha_15_32/ha7_app.py
index 0152ab91..62da5a52 100644
--- a/etl/eligibility/ha_15_32/ha7_app.py
+++ b/etl/eligibility/ha_15_32/ha7_app.py
@@ -16,6 +16,7 @@ from etl.eligibility.ha_15_32.app import prepare_model_data_row
 from etl.epc.DataProcessor import DataProcessor
 from etl.epc.settings import COLUMNS_TO_MERGE_ON
 from backend.ml_models.api import ModelApi
+from etl.solar.SolarPhotoSupply import SolarPhotoSupply
 
 ENV_FILE = Path(__file__).parent / "etl" / "eligibility" / "ha_15_32" / ".env"
 
@@ -67,12 +68,16 @@ def load_data():
     return df
 
 
-def get_ha7_data(data, cleaned, cleaning_data, created_at):
+def get_ha7_data(data, cleaned, cleaning_data, created_at, photo_supply_lookup, floor_area_decile_thresholds):
     property_type_lookup = {
-        "Mid Terrace": "Mid-Terrace",
-        "End Terrace": "End-Terrace",
-        "Semi Detached": "Semi-Detached",
-        "Detached": "Detached",
+        # "Mid Terrace": "Mid-Terrace",
+        # "End Terrace": "End-Terrace",
+        # "Semi Detached": "Semi-Detached",
+        # "Detached": "Detached",
+        "House": "House",
+        "Flat": "Flat",
+        "Bungalow": "Bungalow",
+        "Maisonette": "Maisonette",
     }
 
     scoring_data = []
@@ -80,7 +85,7 @@ def get_ha7_data(data, cleaned, cleaning_data, created_at):
     nodata = []
     for _, house in tqdm(data.iterrows(), total=len(data)):
 
-        if house["Address"] is not None:
+        if house["Address"]:
             address = house["Address"]
         else:
             address = house["Address2"]
@@ -89,7 +94,8 @@ def get_ha7_data(data, cleaned, cleaning_data, created_at):
             address1=address,
             postcode=house["Postcode"],
             auth_token=EPC_AUTH_TOKEN,
-            os_api_key=None
+            os_api_key=None,
+            property_type=property_type_lookup.get(house["Archetype"]),
         )
 
         searcher.find_property(skip_os=True)
@@ -118,7 +124,9 @@ def get_ha7_data(data, cleaned, cleaning_data, created_at):
                 cleaning_data=cleaning_data,
                 created_at=created_at,
                 old_data=older_epcs,
-                full_sap_epc=full_sap_epc
+                full_sap_epc=full_sap_epc,
+                photo_supply_lookup=photo_supply_lookup,
+                floor_area_decile_thresholds=floor_area_decile_thresholds
             )
             scoring_data.extend(scoring_dictionary)
 
@@ -285,9 +293,13 @@ def app():
         bucket_name="retrofit-data-dev", file_key="sap_change_model/cleaning_dataset.parquet",
     )
 
+    photo_supply_lookup, floor_area_decile_thresholds = SolarPhotoSupply.load(bucket="retrofit-data-dev")
+
     created_at = datetime.now().isoformat()
 
-    results_df, scoring_data, nodata = get_ha7_data(data, cleaned, cleaning_data, created_at)
+    results_df, scoring_data, nodata = get_ha7_data(
+        data, cleaned, cleaning_data, created_at, photo_supply_lookup, floor_area_decile_thresholds
+    )
 
     # Pickle results
     # import pickle
diff --git a/etl/solar/SolarPhotoSupply.py b/etl/solar/SolarPhotoSupply.py
index 6a225b5a..1a80c37a 100644
--- a/etl/solar/SolarPhotoSupply.py
+++ b/etl/solar/SolarPhotoSupply.py
@@ -210,7 +210,20 @@ class SolarPhotoSupply:
             ]
 
         if photo_supply_matched.empty:
-            raise ValueError("No photo supply matched")
+            # There are a small number of cases where we don't get a full match so try again with a more aggregated
+            # average
+            photo_supply_matched = photo_supply_lookup[
+                (photo_supply_lookup["tenure"] == tenure) &
+                (photo_supply_lookup["built_form"] == built_form) &
+                (photo_supply_lookup["property_type"] == property_type)
+                ]
+            if construction_age_band in photo_supply_matched["construction_age_band"].values:
+                photo_supply_matched = photo_supply_matched[
+                    photo_supply_matched["construction_age_band"] == construction_age_band
+                    ]
+
+            if photo_supply_matched.empty:
+                raise ValueError("No photo supply matches")
 
         floor_area_decile = cls.classify_floor_area(
             floor_area, floor_area_decile_thresholds["floor_area_decile_thresholds"].values
diff --git a/recommendations/tests/test_ventilation_recommendations.py b/recommendations/tests/test_ventilation_recommendations.py
index 893bb01a..3242b1d1 100644
--- a/recommendations/tests/test_ventilation_recommendations.py
+++ b/recommendations/tests/test_ventilation_recommendations.py
@@ -1,5 +1,4 @@
 from backend.Property import Property
-from unittest.mock import Mock
 from recommendations.VentilationRecommendations import VentilationRecommendations
 from recommendations.tests.test_data.materials import materials
 
@@ -7,7 +6,7 @@ from recommendations.tests.test_data.materials import materials
 class TestVentilationRecommendations:
 
     def test_natural_ventilation(self):
-        input_property1 = Property(id=1, postcode="F4k3 6", address1="623 fake street", epc_client=Mock())
+        input_property1 = Property(id=1, postcode="F4k3 6", address="623 fake street")
         input_property1.data = {"mechanical-ventilation": "natural"}
 
         recommender = VentilationRecommendations(
@@ -28,7 +27,7 @@ class TestVentilationRecommendations:
         assert recommender.recommendation[0]["parts"][0]["quantity"] == 2
 
     def test_missing_ventilation(self):
-        input_property2 = Property(id=1, postcode="F4k3 6", address1="623 fake street", epc_client=Mock())
+        input_property2 = Property(id=1, postcode="F4k3 6", address="623 fake street")
         input_property2.data = {"mechanical-ventilation": None}
 
         recommender2 = VentilationRecommendations(
@@ -49,7 +48,7 @@ class TestVentilationRecommendations:
         assert recommender2.recommendation[0]["parts"][0]["quantity"] == 2
 
     def test_nodata_ventilation(self):
-        input_property3 = Property(id=1, postcode="F4k3 6", address1="623 fake street", epc_client=Mock())
+        input_property3 = Property(id=1, postcode="F4k3 6", address="623 fake street")
         input_property3.data = {"mechanical-ventilation": "NO DATA!!"}
 
         recommender3 = VentilationRecommendations(
@@ -70,7 +69,7 @@ class TestVentilationRecommendations:
         assert recommender3.recommendation[0]["parts"][0]["quantity"] == 2
 
     def test_existing_ventilation_1(self):
-        input_property4 = Property(id=1, postcode="F4k3 6", address1="623 fake street", epc_client=Mock())
+        input_property4 = Property(id=1, postcode="F4k3 6", address="623 fake street")
         input_property4.data = {"mechanical-ventilation": 'mechanical, extract only'}
 
         recommender4 = VentilationRecommendations(
@@ -86,7 +85,7 @@ class TestVentilationRecommendations:
         assert recommender4.has_ventilaion
 
     def test_existing_ventilation_2(self):
-        input_property5 = Property(id=1, postcode="F4k3 6", address1="623 fake street", epc_client=Mock())
+        input_property5 = Property(id=1, postcode="F4k3 6", address="623 fake street")
         input_property5.data = {"mechanical-ventilation": 'mechanical, supply and extract'}
 
         recommender5 = VentilationRecommendations(
diff --git a/recommendations/tests/test_wall_recommendations.py b/recommendations/tests/test_wall_recommendations.py
index 0258e592..2fbf3239 100644
--- a/recommendations/tests/test_wall_recommendations.py
+++ b/recommendations/tests/test_wall_recommendations.py
@@ -231,7 +231,7 @@ class TestWallRecommendationsBase:
 class TestCavityWallRecommensations:
 
     def test_fill_empty_cavity(self):
-        input_property = Property(id=1, postcode="F4k3", address1="123 fake street", epc_client=Mock())
+        input_property = Property(id=1, postcode="F4k3", address="123 fake street")
         input_property.walls = {
             'original_description': 'Cavity wall, as built, no insulation (assumed)',
             'clean_description': 'Cavity wall, as built, no insulation',
@@ -265,7 +265,7 @@ class TestCavityWallRecommensations:
         assert np.isclose(recommender.recommendations[1]["total"], 2004.6600000000003)
 
     def test_fill_partial_filled_cavity(self):
-        input_property = Property(id=1, postcode="F4k3", address1="123 fake street", epc_client=Mock())
+        input_property = Property(id=1, postcode="F4k3", address="123 fake street")
         input_property.walls = {
             'original_description': 'Cavity wall, as built, partial insulation (assumed)',
             'clean_description': 'Cavity wall, as built, partial insulation',
@@ -299,7 +299,7 @@ class TestCavityWallRecommensations:
         assert np.isclose(recommender.recommendations[1]["total"], 1999.9350000000002)
 
     def test_system_built_wall(self):
-        input_property2 = Property(id=1, postcode="F4k3 2", address1="223 fake street", epc_client=Mock())
+        input_property2 = Property(id=1, postcode="F4k3 2", address="223 fake street")
         input_property2.walls = {
             'original_description': 'System built, as built, no insulation (assumed)',
             'clean_description': 'System built, as built, no insulation',
@@ -346,7 +346,7 @@ class TestCavityWallRecommensations:
         assert recommender2.recommendations[6]["parts"][0]["depth"] == 52.5
 
     def test_timber_frame_wall(self):
-        input_property3 = Property(id=1, postcode="F4k3 2", address1="223 fake street", epc_client=Mock())
+        input_property3 = Property(id=1, postcode="F4k3 2", address="223 fake street")
         input_property3.walls = {
             'original_description': 'Timber frame, as built, no insulation (assumed)',
             'clean_description': 'Timber frame, as built, no insulation',
@@ -388,7 +388,7 @@ class TestCavityWallRecommensations:
         assert recommender3.recommendations[1]["parts"][0]["depth"] == 150.0
 
     def test_granite_or_whinstone_wall(self):
-        input_property4 = Property(id=1, postcode="F4k3 2", address1="223 fake street", epc_client=Mock())
+        input_property4 = Property(id=1, postcode="F4k3 2", address="223 fake street")
         input_property4.walls = {
             'original_description': 'Granite or whinstone, as built, no insulation (assumed)',
             'clean_description': 'Granite or whinstone, as built, no insulation',
@@ -430,7 +430,7 @@ class TestCavityWallRecommensations:
         assert recommender4.recommendations[1]["parts"][0]["depth"] == 150
 
     def test_cob_wall(self):
-        input_property5 = Property(id=1, postcode="F4k3 2", address1="223 fake street", epc_client=Mock())
+        input_property5 = Property(id=1, postcode="F4k3 2", address="223 fake street")
         input_property5.walls = {
             'original_description': 'Cob, as built',
             'clean_description': 'Cob, as built',
@@ -472,7 +472,7 @@ class TestCavityWallRecommensations:
         assert recommender5.recommendations[3]["parts"][0]["depth"] == 100
 
     def test_sandstone_or_limestone_wall(self):
-        input_property6 = Property(id=1, postcode="F4k3 6", address1="623 fake street", epc_client=Mock())
+        input_property6 = Property(id=1, postcode="F4k3 6", address="623 fake street")
         input_property6.walls = {
             'original_description': 'Sandstone or limestone, as built, no insulation (assumed)',
             'clean_description': 'Sandstone or limestone, as built, no insulation',
diff --git a/recommendations/tests/test_window_recommendations.py b/recommendations/tests/test_window_recommendations.py
index ac461594..f103299d 100644
--- a/recommendations/tests/test_window_recommendations.py
+++ b/recommendations/tests/test_window_recommendations.py
@@ -1,6 +1,5 @@
 from recommendations.WindowsRecommendations import WindowsRecommendations
 from backend.Property import Property
-from unittest.mock import Mock
 from recommendations.tests.test_data.materials import materials
 
 
@@ -15,8 +14,7 @@ class TestWindowRecommendations:
         property_1 = Property(
             id=1,
             postcode='1',
-            address1='1',
-            epc_client=Mock(),
+            address='1',
             data={
                 "county": "Wychavon",
                 "multi-glaze-proportion": 0
@@ -52,8 +50,7 @@ class TestWindowRecommendations:
         property_2 = Property(
             id=1,
             postcode='1',
-            address1='1',
-            epc_client=Mock(),
+            address='1',
             data={
                 "county": "Wychavon",
                 "multi-glaze-proportion": 33
@@ -86,8 +83,7 @@ class TestWindowRecommendations:
         property_3 = Property(
             id=1,
             postcode='1',
-            address1='1',
-            epc_client=Mock(),
+            address='1',
             data={
                 "county": "Wychavon",
                 "multi-glaze-proportion": 80
@@ -110,8 +106,7 @@ class TestWindowRecommendations:
         property_4 = Property(
             id=1,
             postcode='1',
-            address1='1',
-            epc_client=Mock(),
+            address='1',
             data={
                 "county": "Wychavon",
                 "multi-glaze-proportion": 100
@@ -134,8 +129,7 @@ class TestWindowRecommendations:
         property_5 = Property(
             id=1,
             postcode='1',
-            address1='1',
-            epc_client=Mock(),
+            address='1',
             data={
                 "county": "Wychavon",
                 "multi-glaze-proportion": 50
@@ -164,8 +158,7 @@ class TestWindowRecommendations:
         property_6 = Property(
             id=1,
             postcode='1',
-            address1='1',
-            epc_client=Mock(),
+            address='1',
             data={
                 "county": "Wychavon",
                 "multi-glaze-proportion": 0
@@ -199,8 +192,7 @@ class TestWindowRecommendations:
         property_7 = Property(
             id=1,
             postcode='1',
-            address1='1',
-            epc_client=Mock(),
+            address='1',
             data={
                 "county": "Wychavon",
                 "multi-glaze-proportion": 100
@@ -227,11 +219,11 @@ class TestWindowRecommendations:
         property_8 = Property(
             id=1,
             postcode='1',
-            address1='1',
-            epc_client=Mock(),
+            address='1',
             data={
                 "county": "Wychavon",
-                "multi-glaze-proportion": 80
+                "multi-glaze-proportion": 80,
+                "uprn": 1
             }
         )
         property_8.windows = {'original_description': 'Mostly triple glazing', 'has_glazing': True,