diff --git a/backend/Property.py b/backend/Property.py
index 4d5a93a7..a1bfe265 100644
--- a/backend/Property.py
+++ b/backend/Property.py
@@ -76,12 +76,15 @@ class Property:
         already_installed=None,
         non_invasive_recommendations=None,
         measures=None,
+        energy_assessment=None,
+        is_new=True,
         **kwargs
     ):
 
         self.epc_record = epc_record
 
         self.id = id
+        self.is_new = is_new
 
         self.address = address
         self.postcode = postcode
@@ -158,13 +161,14 @@ class Property:
         self.floor_height = epc_record.prepared_epc.get("floor_height")
         self.insulation_wall_area = None
         self.floor_area = epc_record.prepared_epc.get("total_floor_area")
-        self.pitched_roof_area = None
+        self.roof_area = None
         self.insulation_floor_area = None
         self.number_lighting_outlets = epc_record.prepared_epc.get(
             "fixed_lighting_outlets_count"
         )
         self.floor_level = None
         self.number_of_windows = None
+        self.windows_area = None
         self.solar_pv_percentage = None
 
         self.current_adjusted_energy = None
@@ -178,6 +182,12 @@ class Property:
         self.recommendations_scoring_data = []
         self.simulation_epcs = {}
 
+        # This additional condition data should change how we pass kwargs to this. We should no longer need to pass
+        # kwargs to this class, but instead, we should pass the energy assessment condition data
+        self.energy_assessment_condition_data = energy_assessment["condition"]
+        self.energy_assessment_is_newer = energy_assessment["energy_assessment_is_newer"]
+
+        # TODO: We keep this but only temporarily until we add bathrooms, bedrooms, building id to the condition data
         self.parse_kwargs(kwargs)
 
     @classmethod
@@ -188,6 +198,10 @@ class Property:
         :param kwargs:
         :return:
         """
+
+        # Note - none of this data is contained in an energy asssessment, but we should consider how this is done
+        # as we collect more data from the energy assessment
+
         n_bathrooms = kwargs.get("n_bathrooms", None)
         if n_bathrooms not in [None, ""]:
             # We add on a small value to ensure that the number of bathrooms is rounded up, in case the value is 0.5
@@ -593,18 +607,12 @@ class Property:
     def get_components(
         self,
         cleaned,
-        photo_supply_lookup,
-        floor_area_decile_thresholds,
         energy_consumption_client
     ):
         """
         Given the cleaning that has been performed, we'll use this to identify the property
         components, from roof to walls to windows, heating and hot water
         :param cleaned: This is the dictionary of components found in cleaner.cleaned
-        :param photo_supply_lookup: This is the lookup table for the photo supply, used to estimate the percentage
-                                    of the roof that is suitable for solar panels
-        :param floor_area_decile_thresholds: This is the decile thresholds for the floor area, used in estimating the
-                                             solar pv roof area
         :param energy_consumption_client: Contains the heating and hot water kwh models - used to predict current
                                         energy annual consumption in kWh
         :return:
@@ -669,20 +677,21 @@ class Property:
         self.set_floor_type()
         self.set_floor_level()
         self.set_windows_count()
-        self.set_solar_panel_area(
-            photo_supply_lookup=photo_supply_lookup,
-            floor_area_decile_thresholds=floor_area_decile_thresholds,
-        )
         self.set_energy_source()
         self.find_energy_sources()
         self.set_current_energy_bill(energy_consumption_client)
 
-    def set_solar_panel_configuration(self, solar_panel_configuration):
+    def set_solar_panel_configuration(
+        self, solar_panel_configuration, roof_area
+    ):
         """
         This funtion inserts the solar panel configuration into the property object
         """
         self.solar_panel_configuration = solar_panel_configuration
 
+        # We also set the roof area
+        self.roof_area = roof_area
+
     def set_current_energy_bill(self, energy_consumption_client):
         """
         Given what we know about the property now, estimates the current energy consumption using the UCL paper
@@ -697,17 +706,20 @@ class Property:
         # Today's costs
         todays_heating_cost = energy_consumption_client.convert_cost_to_today(
             original_cost=float(self.data["heating-cost-current"]),
-            lodgement_date=pd.Timestamp(self.epc_record.prepared_epc["lodgement_date"])
+            lodgement_date=pd.Timestamp(self.epc_record.prepared_epc["lodgement_date"]).tz_localize(None)
         )
         todays_hot_water_cost = energy_consumption_client.convert_cost_to_today(
             original_cost=float(self.data["hot-water-cost-current"]),
-            lodgement_date=pd.Timestamp(self.epc_record.prepared_epc["lodgement_date"])
+            lodgement_date=pd.Timestamp(self.epc_record.prepared_epc["lodgement_date"]).tz_localize(None)
         )
         todays_lighting_cost = energy_consumption_client.convert_cost_to_today(
             original_cost=float(self.data["lighting-cost-current"]),
-            lodgement_date=pd.Timestamp(self.epc_record.prepared_epc["lodgement_date"])
+            lodgement_date=pd.Timestamp(self.epc_record.prepared_epc["lodgement_date"]).tz_localize(None)
         )
 
+        # If we have the kwh figures, we don't need to predict them
+        condition_data = self.energy_assessment_condition_data.copy()
+
         scoring_df = pd.DataFrame([self.epc_record.prepared_epc])
         # Change columns from underscores to hyphens
         scoring_df.columns = [
@@ -717,13 +729,20 @@ class Property:
             scoring_df[col] = None
 
         energy_consumption_client.data = None
-        heating_prediction = energy_consumption_client.score_new_data(
-            new_data=scoring_df, target="heating_kwh"
-        )[0]
 
-        hot_water_prediction = energy_consumption_client.score_new_data(
-            new_data=scoring_df, target="hot_water_kwh"
-        )[0]
+        heating_prediction = (
+            float(condition_data["space_heating_kwh"]) if condition_data.get("space_heating_kwh") is not None
+            else energy_consumption_client.score_new_data(
+                new_data=scoring_df, target="heating_kwh"
+            )[0]
+        )
+
+        hot_water_prediction = (
+            float(condition_data["water_heating_kwh"]) if condition_data.get("water_heating_kwh") is not None
+            else energy_consumption_client.score_new_data(
+                new_data=scoring_df, target="hot_water_kwh"
+            )[0]
+        )
 
         # We convert the lighting cost into kwh, just using the price cap
         lighting_kwh = float(self.data["lighting-cost-current"]) / AnnualBillSavings.ELECTRICITY_PRICE_CAP
@@ -861,7 +880,10 @@ class Property:
         property_data = {
             "creation_status": "READY",
             "uprn": int(self.data["uprn"]),
-            "building_reference_number": int(self.data["building-reference-number"]),
+            "building_reference_number": (
+                int(self.data["building-reference-number"]) if
+                self.data["building-reference-number"] is not None else None
+            ),
             "has_pre_condition_report": True,
             "has_recommendations": True,
             "property_type": self.data["property-type"],
@@ -1030,27 +1052,33 @@ class Property:
         medians across the EPC data
         :return:
         """
+        # Many of these pieces of information are now contained in the condition data
+        condition_data = self.energy_assessment_condition_data.copy()
 
-        # TODO: These functions should work on an EPCRecord object, so that the format is more standardised.
-        #       They could also be added as attributes to the EPC Record
+        # We can update the number of floors if we have this information in the condition data
+        self.number_of_floors = int(self.energy_assessment_condition_data["number_of_floors"]) \
+            if condition_data.get("number_of_floors") is not None \
+            else self.number_of_floors
 
-        self.perimeter = estimate_perimeter(
-            self.floor_area / self.number_of_floors,
-            self.number_of_rooms / self.number_of_floors,
+        self.perimeter = float(self.energy_assessment_condition_data["perimeter"]) \
+            if condition_data.get("perimeter") is not None \
+            else estimate_perimeter(
+            floor_area=self.floor_area / self.number_of_floors,
+            num_rooms=self.number_of_rooms / self.number_of_floors
         )
 
-        self.insulation_wall_area = estimate_external_wall_area(
+        self.insulation_wall_area = float(self.energy_assessment_condition_data["insulation_wall_area"]) \
+            if condition_data.get("insulation_wall_area") is not None \
+            else estimate_external_wall_area(
             num_floors=self.number_of_floors,
             floor_height=self.floor_height,
             perimeter=self.perimeter,
             built_form=self.data["built-form"],
         )
 
-        self.insulation_floor_area = self.floor_area / self.number_of_floors
-
-        self.pitched_roof_area = esimtate_pitched_roof_area(
-            floor_area=self.insulation_floor_area, floor_height=self.floor_height
-        )
+        self.insulation_floor_area = float(self.energy_assessment_condition_data["main_dwelling_ground_floor_area"]) \
+            if condition_data.get("main_dwelling_ground_floor_area") is not None \
+            else self.floor_area / self.number_of_floors
 
     def set_floor_level(self):
         self.floor_level = (
@@ -1148,7 +1176,11 @@ class Property:
         :return:
         """
 
-        self.number_of_windows = estimate_windows(
+        condition_data = self.energy_assessment_condition_data.copy()
+
+        self.number_of_windows = int(condition_data["number_of_windows"]) \
+            if condition_data.get("number_of_windows") is not None \
+            else estimate_windows(
             property_type=self.data["property-type"],
             built_form=self.data["built-form"],
             construction_age_band=self.construction_age_band,
@@ -1156,47 +1188,9 @@ class Property:
             number_habitable_rooms=self.number_of_rooms,
         )
 
-    def set_solar_panel_area(self, photo_supply_lookup, floor_area_decile_thresholds):
-        """
-        Sets the approximate area of the solar panels
-        :return:
-        """
-
-        if (self.insulation_floor_area is None) and (self.pitched_roof_area is None):
-            raise ValueError(
-                "Need to set insulation floor area and pitched roof area before setting solar pv roof area"
-            )
-
-        photo_supply_matched = SolarPhotoSupply.filter_photo_supply_lookup(
-            photo_supply_lookup=photo_supply_lookup,
-            floor_area_decile_thresholds=floor_area_decile_thresholds,
-            tenure=self.data["tenure"],
-            built_form=self.data["built-form"],
-            property_type=self.data["property-type"],
-            construction_age_band=self.construction_age_band,
-            is_flat=self.roof["is_flat"],
-            is_pitched=self.roof["is_pitched"],
-            is_roof_room=self.roof["is_roof_room"],
-            floor_area=self.floor_area,
-        )
-
-        percentage_of_roof = photo_supply_matched["photo_supply_median"].mean()
-        percentage_of_roof = percentage_of_roof / 100
-
-        self.solar_pv_percentage = percentage_of_roof
-
-    def get_solar_pv_roof_area(self, percentage_of_roof):
-        """
-        Given a percentage of the roof, this method will return the estimated area of the solar panels
-        :param percentage_of_roof:
-        :return:
-        """
-
-        return (
-            self.insulation_floor_area * percentage_of_roof
-            if self.roof["is_flat"]
-            else self.pitched_roof_area * percentage_of_roof
-        )
+        self.windows_area = float(condition_data["windows_area"]) \
+            if condition_data.get("windows_area") is not None \
+            else None
 
     def set_energy_source(self):
         """
@@ -1282,3 +1276,79 @@ class Property:
                 self.hot_water_energy_source = self.heating_energy_source
             else:
                 raise Exception("Investiage me")
+
+    def is_ashp_valid(self, exclusions):
+
+        if "air_source_heat_pump" in self.non_invasive_recommendations:
+            return True
+
+        if "air_source_heat_pump" in exclusions:
+            return False
+
+        suitable_property_type = self.data["property-type"] in ["House", "Bungalow"]
+        has_air_source_heat_pump = self.main_heating["has_air_source_heat_pump"]
+
+        return suitable_property_type and not has_air_source_heat_pump
+
+    def is_solar_pv_valid(self):
+
+        # If the property is a flat but we are looking at building solar potential, we can include this
+        if (self.building_id is not None) and (self.solar_panel_configuration is not None):
+            return True
+
+        is_valid_property_type = self.data["property-type"] in ["House", "Bungalow", "Maisonette"]
+        is_valid_roof_type = (
+            self.roof["is_flat"] or self.roof["is_pitched"] or self.roof["is_roof_room"]
+        )
+        # If there is no existing solar PV, the photo-supply field will be None or a missing value
+        has_no_existing_solar_pv = self.data["photo-supply"] in [
+            None, 0, self.DATA_ANOMALY_MATCHES
+        ]
+
+        return is_valid_property_type and is_valid_roof_type and has_no_existing_solar_pv
+
+    def estimate_electrical_consumption(self, assumed_ashp_efficiency, exclusions):
+        """
+        Given a property, this method estimates the electrical consumption of the property, based on the energy
+        consumption, the assumed efficiency of an ASHP and the exclusions.
+
+        What we're trying to do here is size up the future electricicty demand of the property, assuming that the
+        home is eligible for an ASHP. If the property is not eligible for an ASHP, we don't need to adjust the
+        consumption.
+
+        This figure is used to size up solar panels, so they can cover heat generation, even if the property
+        today doesn't generate its heat from electricity
+
+        :param assumed_ashp_efficiency:
+        :param exclusions:
+        :return:
+        """
+
+        exclusions = [] if exclusions is None else exclusions
+
+        if (self.main_fuel["fuel_type"] == "electricity") or (
+            self.main_fuel["fuel_type"] == "mains gas" and not self.is_ashp_valid(exclusions=exclusions)
+        ):
+            # if the primary fuel is already electricity, we don't need to adjust the consumpion
+            return self.current_adjusted_energy
+
+        if self.main_fuel["fuel_type"] == "mains gas" and self.is_ashp_valid(exclusions=exclusions):
+            # if the primary fuel is gas, we need to adjust the consumption to reflect the expected
+            # efficiency of an ASHP.
+            # We should adjust the energy consumption to reflect the 200-400% efficiency of an ASHP with
+            # electrified heating, so that the solar panel can cover heating generation.
+            heating_consumption = self.energy_consumption_estimates["adjusted"]["heating"]
+            hot_water_consumption = self.energy_consumption_estimates["adjusted"]["hot_water"]
+
+            systems_consumptions = heating_consumption + hot_water_consumption
+
+            adjusted_consumption = systems_consumptions / (assumed_ashp_efficiency / 100)
+            electric_consumption = (
+                adjusted_consumption +
+                self.energy_consumption_estimates["adjusted"]["lighting"] +
+                self.energy_consumption_estimates["adjusted"]["appliances"]
+            )
+
+            return electric_consumption
+
+        raise NotImplementedError("Have not implemented estimating electrical consumption for this fuel type")
diff --git a/backend/apis/GoogleSolarApi.py b/backend/apis/GoogleSolarApi.py
index 8d08b083..579e985d 100644
--- a/backend/apis/GoogleSolarApi.py
+++ b/backend/apis/GoogleSolarApi.py
@@ -8,6 +8,7 @@ import time
 from backend.app.db.functions.solar_functions import get_solar_data, store_batch_data
 from utils.logger import setup_logger
 from sklearn.preprocessing import MinMaxScaler
+from recommendations.Costs import Costs
 
 logger = setup_logger()
 
@@ -107,7 +108,14 @@ class GoogleSolarApi:
 
     @lru_cache(maxsize=128)
     def get(
-        self, longitude, latitude, energy_consumption, required_quality="MEDIUM", is_building=False, session=None,
+        self,
+        longitude,
+        latitude,
+        energy_consumption,
+        property_instance=None,
+        required_quality="MEDIUM",
+        is_building=False,
+        session=None,
         uprn=None
     ):
         """
@@ -115,7 +123,9 @@ class GoogleSolarApi:
 
         :param longitude: The longitude of the location.
         :param latitude: The latitude of the location.
-        :param energy_consumption: The energy consumption of the building/unit associated to the longitude and latitude.
+        :param energy_consumption: The energy consumption of the building/unit associated to the longitude and latitude,
+                                    that we wish to size the solar panels up against
+        :param property_instance: The property instance associated to the longitude and latitude.
         :param required_quality: The required quality of the data (default is "MEDIUM").
         :param is_building: Whether the energy consumption is for a building or a unit.
         :param session: The database session to use for the query (default is None).
@@ -158,7 +168,9 @@ class GoogleSolarApi:
         self.roof_segment_indexes = [segment['segmentIndex'] for segment in self.roof_segments]
 
         # We now start finding the solar panel configurations
-        self.optimise_solar_configuration(energy_consumption=energy_consumption, is_building=is_building)
+        self.optimise_solar_configuration(
+            energy_consumption=energy_consumption, is_building=is_building, property_instance=property_instance
+        )
 
     def save_to_db(self, session, uprns_to_location, scenario_type):
         if self.insights_data is None:
@@ -178,7 +190,7 @@ class GoogleSolarApi:
                 "yearly_dc_energy",
                 "total_cost",
                 "panneled_roof_area",
-                "array_warrage",
+                "array_wattage",
                 "initial_ac_kwh_per_year",
                 "lifetime_ac_kwh",
                 "roi",
@@ -191,7 +203,7 @@ class GoogleSolarApi:
                 "yearly_dc_energy": "yearly_dc_kwh",
                 "total_cost": "cost",
                 "panneled_roof_area": "panelled_roof_area",
-                "array_warrage": "array_kwhp",
+                "array_wattage": "array_kwhp",
                 "initial_ac_kwh_per_year": "yearly_ac_kwh",
             }
         )
@@ -226,12 +238,14 @@ class GoogleSolarApi:
                 installation_life_span)) /
             (1 - efficiency_depreciation_factor))
 
-    def optimise_solar_configuration(self, energy_consumption, is_building=False):
+    def optimise_solar_configuration(self, energy_consumption, is_building=False, property_instance=None):
         """
         Optimise the solar panel configuration for the building.
         :return:
         """
 
+        cost_instance = Costs(property_instance=property_instance) if property_instance is not None else None
+
         # Remove any north facing roof segments
         panel_performance = []
         for config in self.insights_data["solarPotential"]["solarPanelConfigs"]:
@@ -246,7 +260,14 @@ class GoogleSolarApi:
                 wattage = segment["panelsCount"] * self.insights_data["solarPotential"]["panelCapacityWatts"]
                 generated_dc_energy = segment["yearlyEnergyDcKwh"]
                 ratio = generated_dc_energy / wattage
-                cost = MCS_SOLAR_PV_COST_DATA["average_cost_per_kwh"] * (wattage / 1000)
+
+                if cost_instance is None:
+                    cost = MCS_SOLAR_PV_COST_DATA["average_cost_per_kwh"] * (wattage / 1000)
+                else:
+                    cost = cost_instance.solar_pv(
+                        wattage=wattage, has_battery=False
+                    )["total"]
+
                 roi_summary.append(
                     {
                         "segmentIndex": segment["segmentIndex"],
@@ -274,7 +295,7 @@ class GoogleSolarApi:
                     "total_cost": total_cost,
                     "weighted_ratio": weighted_ratio,
                     "panneled_roof_area": roi_summary["panneled_roof_area"].sum(),
-                    "array_warrage": roi_summary["n_panels"].sum() * self.panel_wattage
+                    "array_wattage": roi_summary["n_panels"].sum() * self.panel_wattage
                 }
             )
 
@@ -290,7 +311,7 @@ class GoogleSolarApi:
 
         # Remove anything where the total ac energy is less than half of the array wattage
         panel_performance = panel_performance[
-            (panel_performance["initial_ac_kwh_per_year"] / panel_performance["array_warrage"]) >= 0.5
+            (panel_performance["initial_ac_kwh_per_year"] / panel_performance["array_wattage"]) >= 0.5
             ]
 
         # 2) Calculate the liftime solar energy production
@@ -311,12 +332,19 @@ class GoogleSolarApi:
         )
 
         # Now that we know the lifetime cnsumption of ac kwh, we can estimate the roi
+        # Key things we estimate:
+        # - generation_value: this is the gbp value of the electricity generated
+        # - roi: the return on investment, calcualated as generation_value / total_cost
+        # - surplus: this is the amount of additional energy generated, and therefore how much will be exported
+        # - surplus_value: the value of the surplus energy - this feeds into generation_value, when relevant
+        # - expected_payback_years: the number of years it will take to pay back the initial investment
         lifetime_energy_consumption = energy_consumption * self.installation_life_span
         roi_results = []
         for _, panel_config in panel_performance.iterrows():
             lifetime_ac_kwh = panel_config["lifetime_ac_kwh"]
 
             surplus = 0
+            generation_deficit = 0
             if lifetime_ac_kwh < lifetime_energy_consumption:
                 # We estimate the amount of electricity generated, based on the price cap
                 generation_value = lifetime_ac_kwh * AnnualBillSavings.ELECTRICITY_PRICE_CAP
@@ -329,7 +357,6 @@ class GoogleSolarApi:
                 surplus_value = surplus * AnnualBillSavings.ELECTRICITY_EXPORT_PAYMENT
                 generation_value = lifetime_energy_consumption * AnnualBillSavings.ELECTRICITY_PRICE_CAP
                 roi = (generation_value + surplus_value) / panel_config["total_cost"]
-                generation_deficit = surplus_value
 
             # Calculate expected payback years
             if generation_value > 0:
diff --git a/backend/app/assumptions.py b/backend/app/assumptions.py
new file mode 100644
index 00000000..13bd913f
--- /dev/null
+++ b/backend/app/assumptions.py
@@ -0,0 +1,3 @@
+# Assumes that the average efficiency of an air source heat pump is 300%, taking the median of the 200-400% range,
+# which is often quoted as a sensible efficiency range for air source heat pumps.
+AVERAGE_ASHP_EFFICIENCY = 300
diff --git a/backend/app/db/functions/energy_assessment_functions.py b/backend/app/db/functions/energy_assessment_functions.py
new file mode 100644
index 00000000..b223d2f5
--- /dev/null
+++ b/backend/app/db/functions/energy_assessment_functions.py
@@ -0,0 +1,62 @@
+from backend.app.db.models.energy_assessments import EnergyAssessment
+from sqlalchemy.orm import Session
+from sqlalchemy.exc import IntegrityError
+from typing import Optional
+from sqlalchemy import desc
+
+
+def bulk_insert_energy_assessments(session: Session, data_list):
+    """
+    This function inserts or updates multiple energy assessment records into the database.
+
+    :param session: The SQLAlchemy session.
+    :param data_list: A list of dictionaries containing energy assessment data.
+    """
+    try:
+        for data in data_list:
+            uprn = data.get('uprn')
+            inspection_date = data.get('inspection_date')
+
+            # Check if a record with the same uprn and inspection_date exists
+            existing_record = session.query(EnergyAssessment).filter_by(
+                uprn=uprn,
+                inspection_date=inspection_date
+            ).first()
+
+            if existing_record:
+                # Update the existing record with new data
+                for key, value in data.items():
+                    setattr(existing_record, key, value)
+                session.add(existing_record)
+            else:
+                # Insert a new record
+                new_assessment = EnergyAssessment(**data)
+                session.add(new_assessment)
+
+        # Commit the transaction
+        session.commit()
+        print("All records inserted or updated successfully.")
+
+    except IntegrityError as e:
+        # Rollback the session in case of error
+        session.rollback()
+        print(f"Error occurred: {e}")
+
+
+def get_latest_assessment_by_uprn(session: Session, uprn: int) -> Optional[EnergyAssessment]:
+    """
+    Retrieve the latest energy assessment for a given UPRN based on the inspection date.
+
+    :param session: The database session
+    :param uprn: The unique property reference number
+    :return: The latest EnergyAssessment object or None if not found
+    """
+    try:
+        # Query the EnergyAssessment model, filter by uprn, order by inspection_date in descending order
+        latest_assessment = session.query(EnergyAssessment).filter_by(uprn=uprn).order_by(
+            desc(EnergyAssessment.inspection_date)).first()
+
+        return latest_assessment.to_dict() if latest_assessment else EnergyAssessment.empty_response()
+    except Exception as e:
+        print(f"An error occurred: {e}")
+        return None
diff --git a/backend/app/db/functions/portfolio_functions.py b/backend/app/db/functions/portfolio_functions.py
index 402675e8..ac340ab5 100644
--- a/backend/app/db/functions/portfolio_functions.py
+++ b/backend/app/db/functions/portfolio_functions.py
@@ -1,10 +1,14 @@
 from sqlalchemy import func
-from backend.app.db.models.recommendations import Plan, PlanRecommendations, Recommendation
-from backend.app.db.models.portfolio import Portfolio
+from backend.app.db.models.recommendations import Plan, PlanRecommendations, Recommendation, Scenario
 
 
 def aggregate_portfolio_recommendations(
-    session, portfolio_id: int, total_valuation_increase: float, labour_days: float, aggregated_data: dict
+    session,
+    portfolio_id: int,
+    scenario_id: int,
+    total_valuation_increase: float,
+    labour_days: float,
+    aggregated_data: dict
 ):
     # Aggregate multiple fields
     aggregates = (
@@ -17,7 +21,11 @@ def aggregate_portfolio_recommendations(
         )
         .join(PlanRecommendations, PlanRecommendations.recommendation_id == Recommendation.id)
         .join(Plan, Plan.id == PlanRecommendations.plan_id)
-        .filter(Plan.portfolio_id == portfolio_id, Plan.is_default == True, Recommendation.default == True)
+        .filter(
+            Plan.portfolio_id == portfolio_id,
+            Plan.scenario_id == scenario_id,
+            Recommendation.default == True
+        )
         .one()
     )
 
@@ -30,16 +38,17 @@ def aggregate_portfolio_recommendations(
         **aggregated_data
     }
 
-    # Get the portfolio and update the fields
-    portfolio = session.query(Portfolio).filter_by(id=portfolio_id).one()
+    # Get the scenario and update the fields. This data needs to be stored against the scenario, not the portfolio
+    portfolio_scenario = session.query(Scenario).filter_by(id=scenario_id).one()
+
     # Update the data
     for key, value in aggregates_dict.items():
-        setattr(portfolio, key, value)
+        setattr(portfolio_scenario, key, value)
 
     # Insert total valuation increase and labour days
-    portfolio.property_valuation_increase = total_valuation_increase
-    portfolio.labour_days = labour_days
+    portfolio_scenario.property_valuation_increase = total_valuation_increase
+    portfolio_scenario.labour_days = labour_days
 
-    # Merge the updated portfolio back into the session
-    session.merge(portfolio)
+    # Merge the updated portfolio plan back into the session
+    session.merge(portfolio_scenario)
     session.flush()
diff --git a/backend/app/db/functions/recommendations_functions.py b/backend/app/db/functions/recommendations_functions.py
index 365829e4..b03909ee 100644
--- a/backend/app/db/functions/recommendations_functions.py
+++ b/backend/app/db/functions/recommendations_functions.py
@@ -1,8 +1,12 @@
 from sqlalchemy import insert, delete
 from sqlalchemy.orm import Session
-from backend.app.db.models.recommendations import Plan, Recommendation, RecommendationMaterials, PlanRecommendations
-from backend.app.db.models.portfolio import PropertyModel, PropertyTargetsModel, PropertyDetailsMeter, \
-    PropertyDetailsEpcModel
+from sqlalchemy.exc import SQLAlchemyError
+from backend.app.db.models.recommendations import (
+    Plan, Recommendation, RecommendationMaterials, PlanRecommendations, Scenario
+)
+from backend.app.db.models.portfolio import (
+    PropertyModel, PropertyTargetsModel, PropertyDetailsMeter, PropertyDetailsEpcModel
+)
 
 
 def create_plan(session: Session, plan):
@@ -11,12 +15,38 @@ def create_plan(session: Session, plan):
     :param session: The database session
     :param plan: dictionary of data representing a plan to be created
     """
+    try:
+        new_plan = Plan(**plan)
+        session.add(new_plan)
+        session.flush()
+        session.commit()
+        return new_plan.id
+    except SQLAlchemyError as e:
+        session.rollback()
+        raise e
 
-    new_plan = Plan(**plan)
-    session.add(new_plan)
-    session.flush()
 
-    return new_plan.id
+def create_scenario(session: Session, scenario):
+    """
+    This function will create a record for the scenario in the database if it does not exist.
+    :param session: The database session
+    :param scenario: dictionary of data representing a scenario to be created
+    """
+    try:
+
+        # Before creating a new scenario, we check if there is a scenario for this portfolio id already
+        # If there is, it means that any new scnario created will NOT be the default scenario
+        existing_scenario = session.query(Scenario).filter_by(portfolio_id=scenario["portfolio_id"]).first()
+        scenario["is_default"] = True if not existing_scenario else False
+
+        new_scenario = Scenario(**scenario)
+        session.add(new_scenario)
+        session.flush()
+        session.commit()
+        return new_scenario
+    except SQLAlchemyError as e:
+        session.rollback()
+        raise e
 
 
 def create_recommendation(session: Session, recommendation):
@@ -25,12 +55,15 @@ def create_recommendation(session: Session, recommendation):
     :param session: The database session
     :param recommendation: dictionary of data representing a recommendation to be created
     """
-
-    new_recommendation = Recommendation(**recommendation)
-    session.add(new_recommendation)
-    session.flush()
-
-    return new_recommendation.id
+    try:
+        new_recommendation = Recommendation(**recommendation)
+        session.add(new_recommendation)
+        session.flush()
+        session.commit()
+        return new_recommendation.id
+    except SQLAlchemyError as e:
+        session.rollback()
+        raise e
 
 
 def create_recommendation_material(session: Session, recommendation_id, material_id, depth):
@@ -68,62 +101,68 @@ def create_plan_recommendations(session: Session, plan_id, recommendation_ids):
     session.execute(insert(PlanRecommendations).values(data))
 
 
-def upload_recommendations(session: Session, recommendations_to_upload, property_id):
-    # Prepare data for bulk insert for Recommendation
-    recommendations_data = [
-        {
-            "property_id": property_id,
-            "type": rec["type"],
-            "description": rec["description"],
-            "estimated_cost": rec["total"],
-            "default": rec["default"],
-            "starting_u_value": rec.get("starting_u_value"),
-            "new_u_value": rec.get("new_u_value"),
-            "sap_points": rec["sap_points"],
-            "energy_savings": rec["heat_demand"],
-            "kwh_savings": rec["kwh_savings"],
-            "co2_equivalent_savings": rec["co2_equivalent_savings"],
-            "total_work_hours": rec["labour_hours"],
-            "energy_cost_savings": rec["energy_cost_savings"],
-            "labour_days": rec["labour_days"],
-            "already_installed": rec["already_installed"],
-        }
-        for rec in recommendations_to_upload
-    ]
+def upload_recommendations(session: Session, recommendations_to_upload, property_id, new_plan_id):
+    try:
+        # Prepare data for bulk insert for Recommendation
+        recommendations_data = [
+            {
+                "property_id": property_id,
+                "type": rec["type"],
+                "description": rec["description"],
+                "estimated_cost": rec["total"],
+                "default": rec["default"],
+                "starting_u_value": rec.get("starting_u_value"),
+                "new_u_value": rec.get("new_u_value"),
+                "sap_points": rec["sap_points"],
+                "energy_savings": rec["heat_demand"],
+                "kwh_savings": rec["kwh_savings"],
+                "co2_equivalent_savings": rec["co2_equivalent_savings"],
+                "total_work_hours": rec["labour_hours"],
+                "energy_cost_savings": rec["energy_cost_savings"],
+                "labour_days": rec["labour_days"],
+                "already_installed": rec["already_installed"],
+            }
+            for rec in recommendations_to_upload
+        ]
 
-    session.bulk_insert_mappings(Recommendation, recommendations_data)
+        # Insert the recommendations, get back the IDs
+        stmt = insert(Recommendation).returning(Recommendation.id).values(recommendations_data)
+        result = session.execute(stmt)
+        uploaded_recommendation_ids = [row[0] for row in result]
 
-    # To get the IDs of the newly inserted recommendations, we need to flush the session
-    session.flush()
+        # Prepare data for bulk insert for RecommendationMaterials
+        recommendation_materials_data = [
+            {
+                "recommendation_id": recommendation_id,
+                "material_id": part["id"],
+                "depth": int(part["depth"]) if part["depth"] else None,
+                "quantity": part["quantity"],
+                "quantity_unit": part["quantity_unit"],
+                "estimated_cost": part["total"],
+            }
+            for rec, recommendation_id in zip(recommendations_to_upload, uploaded_recommendation_ids)
+            for part in rec["parts"]
+        ]
 
-    # Map the uploaded_recommendation_ids with the original data for reference
-    uploaded_recommendation_ids = [rec.id for rec in session.query(Recommendation).filter(
-        Recommendation.property_id == property_id,
-        Recommendation.description.in_([rec["description"] for rec in recommendations_to_upload])
-    )]
+        session.bulk_insert_mappings(RecommendationMaterials, recommendation_materials_data)
 
-    # Prepare data for bulk insert for RecommendationMaterials
-    # We can have multiple materials per recommendation. The aggregation of the materials will total the
-    # recommendation figures
-    recommendation_materials_data = [
-        {
-            "recommendation_id": recommendation_id,
-            "material_id": part["id"],
-            "depth": int(part["depth"]) if part["depth"] else None,
-            "quantity": part["quantity"],
-            "quantity_unit": part["quantity_unit"],
-            "estimated_cost": part["total"],
-        }
-        for rec, recommendation_id in zip(recommendations_to_upload, uploaded_recommendation_ids)
-        for part in rec["parts"]
-    ]
+        # flush the changes to get the newly created IDs
+        session.flush()
 
-    session.bulk_insert_mappings(RecommendationMaterials, recommendation_materials_data)
+        create_plan_recommendations(
+            session, plan_id=new_plan_id, recommendation_ids=uploaded_recommendation_ids
+        )
 
-    # flush the changes to get the newly created IDs
-    session.flush()
+        # Commit the transaction
+        session.commit()
 
-    return uploaded_recommendation_ids
+        return True
+
+    except SQLAlchemyError as e:
+        # Rollback the transaction in case of an error
+        session.rollback()
+        print(f"An error occurred: {e}")
+        return False
 
 
 def clear_portfolio(session: Session, portfolio_id: int):
@@ -148,6 +187,9 @@ def clear_portfolio(session: Session, portfolio_id: int):
     # Delete all Plans associated with the portfolio
     session.execute(delete(Plan).where(Plan.portfolio_id == portfolio_id))
 
+    # Delete all Scenarios associated with the portfolio
+    session.execute(delete(Scenario).where(Scenario.portfolio_id == portfolio_id))
+
     # Delete all Recommendations associated with the properties
     session.execute(delete(Recommendation).where(Recommendation.property_id.in_(property_ids)))
 
diff --git a/backend/app/db/models/energy_assessments.py b/backend/app/db/models/energy_assessments.py
new file mode 100644
index 00000000..3928f9fa
--- /dev/null
+++ b/backend/app/db/models/energy_assessments.py
@@ -0,0 +1,165 @@
+from sqlalchemy import Column, Integer, BigInteger, Text, Float, DateTime, Boolean, Date
+from sqlalchemy.ext.declarative import declarative_base
+
+Base = declarative_base()
+
+
+class EnergyAssessment(Base):
+    __tablename__ = 'energy_assessments'
+    id = Column(BigInteger, primary_key=True, autoincrement=True)
+    uprn = Column(BigInteger, nullable=False)
+    uprn_source = Column(Text, nullable=False)
+    property_type = Column(Text, nullable=False)
+    building_reference_number = Column(Text)
+    current_energy_efficiency = Column(Text, nullable=False)
+    current_energy_rating = Column(Text, nullable=False)
+    address1 = Column(Text, nullable=False)
+    address2 = Column(Text, nullable=False)
+    address3 = Column(Text)
+    posttown = Column(Text, nullable=False)
+    postcode = Column(Text, nullable=False)
+    address = Column(Text, nullable=False)
+    county = Column(Text)
+    constituency = Column(Text)
+    constituency_label = Column(Text)
+    low_energy_fixed_light_count = Column(Text, nullable=False)
+    construction_age_band = Column(Text, nullable=False)
+    mainheat_energy_eff = Column(Text, nullable=False)
+    windows_env_eff = Column(Text, nullable=False)
+    lighting_energy_eff = Column(Text, nullable=False)
+    environment_impact_potential = Column(Text, nullable=False)
+    mainheatcont_description = Column(Text, nullable=False)
+    sheating_energy_eff = Column(Text, nullable=False)
+    local_authority = Column(Text, nullable=False)
+    local_authority_label = Column(Text, nullable=False)
+    fixed_lighting_outlets_count = Column(Text, nullable=False)
+    energy_tariff = Column(Text, nullable=False)
+    mechanical_ventilation = Column(Text, nullable=False)
+    solar_water_heating_flag = Column(Text, nullable=False)
+    co2_emissions_potential = Column(Text, nullable=False)
+    number_heated_rooms = Column(Text, nullable=False)
+    floor_description = Column(Text, nullable=False)
+    energy_consumption_potential = Column(Text, nullable=False)
+    built_form = Column(Text, nullable=False)
+    number_open_fireplaces = Column(Text, nullable=False)
+    windows_description = Column(Text, nullable=False)
+    glazed_area = Column(Text, nullable=False)
+    inspection_date = Column(DateTime(timezone=True), nullable=False)
+    mains_gas_flag = Column(Text, nullable=False)
+    co2_emiss_curr_per_floor_area = Column(Text, nullable=False)
+    heat_loss_corridor = Column(Text, nullable=False)
+    unheated_corridor_length = Column(Text)
+    flat_storey_count = Column(Text)
+    roof_energy_eff = Column(Text, nullable=False)
+    total_floor_area = Column(Text, nullable=False)
+    environment_impact_current = Column(Text, nullable=False)
+    roof_description = Column(Text, nullable=False)
+    floor_energy_eff = Column(Text, nullable=False)
+    number_habitable_rooms = Column(Text, nullable=False)
+    hot_water_env_eff = Column(Text, nullable=False)
+    mainheatc_energy_eff = Column(Text, nullable=False)
+    main_fuel = Column(Text, nullable=False)
+    lighting_env_eff = Column(Text, nullable=False)
+    windows_energy_eff = Column(Text, nullable=False)
+    floor_env_eff = Column(Text, nullable=False)
+    sheating_env_eff = Column(Text, nullable=False)
+    lighting_description = Column(Text, nullable=False)
+    roof_env_eff = Column(Text, nullable=False)
+    walls_energy_eff = Column(Text, nullable=False)
+    photo_supply = Column(Text, nullable=False)
+    lighting_cost_potential = Column(Text, nullable=False)
+    mainheat_env_eff = Column(Text, nullable=False)
+    multi_glaze_proportion = Column(Text, nullable=False)
+    main_heating_controls = Column(Text, nullable=False)
+    flat_top_storey = Column(Text)
+    secondheat_description = Column(Text, nullable=False)
+    walls_env_eff = Column(Text, nullable=False)
+    transaction_type = Column(Text, nullable=False)
+    extension_count = Column(Text, nullable=False)
+    mainheatc_env_eff = Column(Text, nullable=False)
+    lmk_key = Column(Text)
+    wind_turbine_count = Column(Text, nullable=False)
+    tenure = Column(Text, nullable=False)
+    floor_level = Column(Text, nullable=False)
+    potential_energy_efficiency = Column(Text, nullable=False)
+    potential_energy_rating = Column(Text, nullable=False)
+    hot_water_energy_eff = Column(Text, nullable=False)
+    low_energy_lighting = Column(Text, nullable=False)
+    walls_description = Column(Text, nullable=False)
+    hotwater_description = Column(Text, nullable=False)
+    co2_emissions_current = Column(Text, nullable=False)
+    heating_cost_current = Column(Text, nullable=False)
+    heating_cost_potential = Column(Text, nullable=False)
+    hot_water_cost_current = Column(Text, nullable=False)
+    hot_water_cost_potential = Column(Text, nullable=False)
+    lighting_cost_current = Column(Text, nullable=False)
+    energy_consumption_current = Column(Text, nullable=False)
+    lodgement_date = Column(Date, nullable=False)
+    lodgement_datetime = Column(DateTime(timezone=False), nullable=False)
+    mainheat_description = Column(Text, nullable=False)
+    floor_height = Column(Float, nullable=False)
+    glazed_type = Column(Text, nullable=False)
+    file_location = Column(Text, nullable=False)
+    surveyor_name = Column(Text, nullable=False)
+    surveyor_company = Column(Text, nullable=False)
+    space_heating_kwh = Column(Text, nullable=False)
+    water_heating_kwh = Column(Text, nullable=False)
+    number_of_doors = Column(Integer, nullable=False)
+    number_of_insulated_doors = Column(Integer, nullable=False)
+    number_of_floors = Column(Integer, nullable=False)
+    insulation_wall_area = Column(Float, nullable=False)
+    heat_loss_perimeter = Column(Float, nullable=False)
+    party_wall_length = Column(Float, nullable=False)
+    perimeter = Column(Float, nullable=False)
+    rooms_with_bath_and_or_shower = Column(Integer)
+    rooms_with_mixer_shower_no_bath = Column(Integer)
+    room_with_bath_and_mixer_shower = Column(Integer)
+    percent_draftproofed = Column(Integer)
+    has_hot_water_cylinder = Column(Boolean)
+    cylinder_insulation_type = Column(Text)
+    cylinder_insulation_thickness = Column(Integer)
+    cylinder_thermostat = Column(Boolean)
+    main_dwelling_ground_floor_area = Column(Float)
+    number_of_windows = Column(Integer)
+    windows_area = Column(Float)
+
+    EPC_KEYS = [
+        'low_energy_fixed_light_count', 'address', 'uprn_source', 'floor_height', 'heating_cost_potential',
+        'unheated_corridor_length', 'hot_water_cost_potential', 'construction_age_band', 'potential_energy_rating',
+        'mainheat_energy_eff', 'windows_env_eff', 'lighting_energy_eff', 'environment_impact_potential', 'glazed_type',
+        'heating_cost_current', 'address3', 'mainheatcont_description', 'sheating_energy_eff', 'property_type',
+        'local_authority_label', 'fixed_lighting_outlets_count', 'energy_tariff', 'mechanical_ventilation',
+        'hot_water_cost_current', 'county', 'postcode', 'solar_water_heating_flag', 'constituency',
+        'co2_emissions_potential', 'number_heated_rooms', 'floor_description', 'energy_consumption_potential',
+        'local_authority', 'built_form', 'number_open_fireplaces', 'windows_description', 'glazed_area',
+        'inspection_date', 'mains_gas_flag', 'co2_emiss_curr_per_floor_area', 'address1', 'heat_loss_corridor',
+        'flat_storey_count', 'constituency_label', 'roof_energy_eff', 'total_floor_area', 'building_reference_number',
+        'environment_impact_current', 'co2_emissions_current', 'roof_description', 'floor_energy_eff',
+        'number_habitable_rooms', 'address2', 'hot_water_env_eff', 'posttown', 'mainheatc_energy_eff', 'main_fuel',
+        'lighting_env_eff', 'windows_energy_eff', 'floor_env_eff', 'sheating_env_eff', 'lighting_description',
+        'roof_env_eff', 'walls_energy_eff', 'photo_supply', 'lighting_cost_potential', 'mainheat_env_eff',
+        'multi_glaze_proportion', 'main_heating_controls', 'lodgement_datetime', 'flat_top_storey',
+        'current_energy_rating', 'secondheat_description', 'walls_env_eff', 'transaction_type', 'uprn',
+        'current_energy_efficiency', 'energy_consumption_current', 'mainheat_description', 'lighting_cost_current',
+        'lodgement_date', 'extension_count', 'mainheatc_env_eff', 'lmk_key', 'wind_turbine_count', 'tenure',
+        'floor_level', 'potential_energy_efficiency', 'hot_water_energy_eff', 'low_energy_lighting',
+        'walls_description', 'hotwater_description'
+    ]
+
+    def to_dict(self):
+        """
+        Convert the SQLAlchemy object to a dictionary.
+        """
+
+        epc = {key.replace("_", "-"): getattr(self, key) for key in self.EPC_KEYS}
+        # Get everything else
+        condition = {
+            column.name: getattr(self, column.name)
+            for column in self.__table__.columns if column.name not in self.EPC_KEYS
+        }
+
+        return {"epc": epc, "condition": condition}
+
+    @staticmethod
+    def empty_response():
+        return {"epc": {}, "condition": {}}
diff --git a/backend/app/db/models/recommendations.py b/backend/app/db/models/recommendations.py
index 8ab7908f..a1743436 100644
--- a/backend/app/db/models/recommendations.py
+++ b/backend/app/db/models/recommendations.py
@@ -50,8 +50,10 @@ class Plan(Base):
     __tablename__ = 'plan'
 
     id = Column(BigInteger, primary_key=True, autoincrement=True)
+    name = Column(String, nullable=True, default="")
     portfolio_id = Column(BigInteger, ForeignKey(Portfolio.id), nullable=False)
     property_id = Column(BigInteger, ForeignKey(PropertyModel.id), nullable=False)
+    scenario_id = Column(BigInteger, ForeignKey('scenario.id'))  # Doesn't have to be linked to a scenario
     created_at = Column(TIMESTAMP, nullable=False, server_default=func.now())
     is_default = Column(Boolean, nullable=False)
     valuation_increase_lower_bound = Column(Float)
@@ -65,3 +67,46 @@ class PlanRecommendations(Base):
     id = Column(BigInteger, primary_key=True, autoincrement=True)
     plan_id = Column(BigInteger, ForeignKey('plan.id'), nullable=False)
     recommendation_id = Column(BigInteger, ForeignKey('recommendation.id'), nullable=False)
+
+
+class Scenario(Base):
+    __tablename__ = 'scenario'
+
+    id = Column(BigInteger, primary_key=True, autoincrement=True)
+    name = Column(String, nullable=False)
+    created_at = Column(TIMESTAMP, nullable=False, server_default=func.now())
+    budget = Column(Float)
+    portfolio_id = Column(BigInteger, ForeignKey(Portfolio.id), nullable=False)
+    housing_type = Column(String, nullable=False)
+    goal = Column(String, nullable=False)
+    trigger_file_path = Column(String, nullable=False)
+    already_installed_file_path = Column(String)
+    patches_file_path = Column(String)
+    non_invasive_recommendations_file_path = Column(String)
+    exclusions = Column(String)
+    multi_plan = Column(Boolean, default=False)
+    is_default = Column(Boolean, default=False, nullable=False)
+
+    # Add in the fields we need, which were previously sitting at the portfolio level
+    cost = Column(Float)
+    total_work_hours = Column(Float)
+    energy_savings = Column(Float)
+    co2_equivalent_savings = Column(Float)
+    energy_cost_savings = Column(Float)
+    epc_breakdown_pre_retrofit = Column(String)
+    epc_breakdown_post_retrofit = Column(String)
+    number_of_properties = Column(BigInteger)
+    n_units_to_retrofit = Column(BigInteger)
+    co2_per_unit_pre_retrofit = Column(String)
+    co2_per_unit_post_retrofit = Column(String)
+    energy_bill_per_unit_pre_retrofit = Column(String)
+    energy_bill_per_unit_post_retrofit = Column(String)
+    energy_consumption_per_unit_pre_retrofit = Column(String)
+    energy_consumption_per_unit_post_retrofit = Column(String)
+    valuation_improvement_per_unit = Column(String)
+    cost_per_unit = Column(String)
+    cost_per_co2_saved = Column(String)
+    cost_per_sap_point = Column(String)
+    valuation_return_on_investment = Column(String)
+    property_valuation_increase = Column(Float)
+    labour_days = Column(Float)
diff --git a/backend/app/plan/router.py b/backend/app/plan/router.py
index 00e73b56..a0d4e585 100644
--- a/backend/app/plan/router.py
+++ b/backend/app/plan/router.py
@@ -10,6 +10,7 @@ from sqlalchemy.exc import IntegrityError, OperationalError
 from sqlalchemy.orm import sessionmaker
 from starlette.responses import Response
 
+import backend.app.assumptions as assumptions
 from backend.app.config import get_settings, get_prediction_buckets
 from backend.app.db.connection import db_engine
 from backend.app.db.functions.materials_functions import get_materials
@@ -19,8 +20,9 @@ from backend.app.db.functions.property_functions import (
     update_or_create_property_spatial_details
 )
 from backend.app.db.functions.recommendations_functions import (
-    create_plan, create_plan_recommendations, upload_recommendations
+    create_plan, create_plan_recommendations, upload_recommendations, create_scenario
 )
+from backend.app.db.functions.energy_assessment_functions import get_latest_assessment_by_uprn
 from backend.app.db.models.portfolio import rating_lookup
 from backend.app.dependencies import validate_token
 from backend.app.plan.schemas import PlanTriggerRequest, MdsRequest
@@ -219,6 +221,68 @@ def extract_portfolio_aggregation_data(
     return aggregation_data
 
 
+def create_epc_records(epc_searcher: SearchEpc, energy_assessment: dict):
+    """
+    This function will set up with epc_records dictionary with the newest EPC, the full SAP EPC and the older EPCs
+    and will factor in an energy assessment that we have performed for a client.
+    :param epc_searcher: An instance of the SearchEpc class
+    :param energy_assessment: The energy assessment we have performed. If we have not performed an energy assessment,
+                              this should be an empty response as defined by the models's
+                              EnergyAssessment.empty_response() method
+    """
+
+    if not energy_assessment["epc"]:
+        energy_assessment_is_newer = False
+        return {
+            'original_epc': epc_searcher.newest_epc.copy(),
+            'full_sap_epc': epc_searcher.full_sap_epc.copy(),
+            'old_data': epc_searcher.older_epcs.copy(),
+        }, energy_assessment_is_newer
+
+    epc = energy_assessment["epc"]
+    energy_assessment_date = epc["inspection-date"].strftime("%Y-%m-%d")
+
+    # We insert county into the epc, since right now this isn't something that we pull out from the energy
+    # assessment
+    epc["county"] = epc_searcher.newest_epc["county"]
+    epc["constituency"] = epc_searcher.newest_epc["constituency"]
+
+    # We check if the energy assessment is newer than the newest EPC
+    if pd.to_datetime(energy_assessment_date) > pd.to_datetime(epc_searcher.newest_epc["inspection-date"]):
+        # In this case, our energy assessment is newer than the EPCs available for this property
+        energy_assessment_is_newer = True
+        return {
+            "original_epc": epc,
+            "full_sap_epc": epc_searcher.full_sap_epc.copy(),
+            "old_data": epc_searcher.older_epcs.copy() + [epc_searcher.newest_epc.copy()]
+        }, energy_assessment_is_newer
+
+    # We check if the EPC we have produced is contained in the set of EPCs done for the property
+    # We do this based on inspection-date and SAP
+    epc_in_historicals = [
+        x for x in epc_searcher.older_epcs + [epc_searcher.newest_epc]
+        if x["inspection-date"] == energy_assessment_date and
+                   x["current-energy-efficiency"] == epc["current-energy-efficiency"]
+    ]
+    energy_assessment_is_newer = False
+
+    if epc_in_historicals:
+        # Then the EPC we have produced is already in the set of EPCs, and our EPC is older than the newest
+        return {
+            "original_epc": epc_searcher.newest_epc.copy(),
+            "full_sap_epc": epc_searcher.full_sap_epc.copy(),
+            "old_data": epc_searcher.older_epcs.copy()
+        }, energy_assessment_is_newer
+
+    # In this case, our EPC is older than the newest publically avaible one, but is not contained in
+    # the historicals, so it can't have been lodged, so we include it in the old data
+    return {
+        'original_epc': epc_searcher.newest_epc.copy(),
+        'full_sap_epc': epc_searcher.full_sap_epc.copy(),
+        'old_data': epc_searcher.older_epcs.copy() + [epc],
+    }, energy_assessment_is_newer
+
+
 router = APIRouter(
     prefix="/plan",
     tags=["plan"],
@@ -233,9 +297,6 @@ async def trigger_plan(body: PlanTriggerRequest):
     session = sessionmaker(bind=db_engine)()
     created_at = datetime.now().isoformat()
 
-    # TODO: We should store the trigger file path in the database with the plan so we can track the file that
-    #       triggered the plan
-
     # TODO: if the measure is already installed, it should actually be the very first phase
 
     try:
@@ -265,6 +326,7 @@ async def trigger_plan(body: PlanTriggerRequest):
 
         input_properties = []
         for config in tqdm(plan_input):
+
             # We validate each record in the file. If the record is NOT valid, we need to handle this accordingly
             uprn = config.get("uprn", None)
             if uprn:
@@ -281,27 +343,33 @@ async def trigger_plan(body: PlanTriggerRequest):
             epc_searcher.ordnance_survey_client.property_type = config.get("property_type", None)
             # For the moment, our OS API access is unavailable, so we skip and interpolate
             epc_searcher.find_property(skip_os=True)
+
+            # We check for an energy assessment we have performed on this property:
+            energy_assessment = get_latest_assessment_by_uprn(session, uprn if uprn is not None else epc_searcher.uprn)
+
             # Create a record in db
             property_id, is_new = create_property(
                 session, body.portfolio_id, epc_searcher.address_clean, epc_searcher.postcode_clean, epc_searcher.uprn
             )
-            if not is_new:
+            if not is_new and not body.multi_plan:
                 continue
 
-            create_property_targets(
-                session,
-                property_id=property_id,
-                portfolio_id=body.portfolio_id,
-                epc_target=body.goal_value,
-                heat_demand_target=None
+            if is_new:
+                create_property_targets(
+                    session,
+                    property_id=property_id,
+                    portfolio_id=body.portfolio_id,
+                    epc_target=body.goal_value,
+                    heat_demand_target=None
+                )
+
+            # If we have an energy assessment in place, that is newer than all of the previous EPCs, we use that.
+            # Otherwise, we use the newest EPC
+            # energy_assessment_is_newer will tell us if the energy assessment is newer than the newest EPC that
+            # has been publically lodged
+            epc_records, energy_assessment["energy_assessment_is_newer"] = create_epc_records(
+                epc_searcher, energy_assessment
             )
-
-            epc_records = {
-                'original_epc': epc_searcher.newest_epc.copy(),
-                'full_sap_epc': epc_searcher.full_sap_epc.copy(),
-                'old_data': epc_searcher.older_epcs.copy(),
-            }
-
             patch = next((
                 x for x in patches if (x["address"] == config["address"]) and (x["postcode"] == config["postcode"])
             ), {})
@@ -326,18 +394,39 @@ async def trigger_plan(body: PlanTriggerRequest):
             input_properties.append(
                 Property(
                     id=property_id,
+                    is_new=is_new,
                     address=epc_searcher.address_clean,
                     postcode=epc_searcher.postcode_clean,
                     epc_record=prepared_epc,
                     already_installed=property_already_installed,
                     non_invasive_recommendations=property_non_invasive_recommendations,
-                    **Property.extract_kwargs(config)
+                    energy_assessment=energy_assessment,
+                    **Property.extract_kwargs(config),  # TODO: Depraecate this
                 )
             )
 
         if not input_properties:
             return Response(status_code=204)
 
+        # If we have any work to do, we create a new scenario
+        engine_scenario = create_scenario(
+            session=session,
+            scenario={
+                "name": body.scenario_name,
+                "created_at": created_at,
+                "budget": body.budget,
+                "portfolio_id": body.portfolio_id,
+                "housing_type": body.housing_type,
+                "goal": body.goal,
+                "trigger_file_path": body.trigger_file_path,
+                "already_installed_file_path": body.already_installed_file_path,
+                "patches_file_path": body.patches_file_path,
+                "non_invasive_recommendations_file_path": body.non_invasive_recommendations_file_path,
+                "exclusions": body.exclusions,
+                "multi_plan": body.multi_plan
+            }
+        )
+
         # The materials data could be cached or local so we don't need to make
         # consistent requests to the backend for
         # the same data
@@ -348,7 +437,6 @@ async def trigger_plan(body: PlanTriggerRequest):
         uprn_filenames = read_dataframe_from_s3_parquet(
             bucket_name=get_settings().DATA_BUCKET, file_key="spatial/filename_meta.parquet"
         )
-        photo_supply_lookup, floor_area_decile_thresholds = SolarPhotoSupply.load(bucket=get_settings().DATA_BUCKET)
         solar_api_client = GoogleSolarApi(api_key=get_settings().GOOGLE_SOLAR_API_KEY)
 
         dataset_version = "2024-07-08"
@@ -365,25 +453,48 @@ async def trigger_plan(body: PlanTriggerRequest):
 
         logger.info("Getting spatial data")
         for p in input_properties:
-            p.get_components(cleaned, photo_supply_lookup, floor_area_decile_thresholds, energy_consumption_client)
+            p.get_components(cleaned=cleaned, energy_consumption_client=energy_consumption_client)
             p.get_spatial_data(uprn_filenames)
 
-        # TODO: Handle the case of modelling some units as buildings and some as properties individually
+        logger.info("Performing solar analysis")
+        # TODO: Tidy this up
         building_ids = [
             {
                 "building_id": p.building_id,
                 "longitude": p.spatial["longitude"],
                 "latitude": p.spatial["latitude"],
                 # Energy consumption is adjusted for the property's expected post retrofit state
+                # We set the target rating to EPC C, which is the typical EPC rating we would expect the
+                # property to achieve post retrofit of just the fabric
                 "energy_consumption": energy_consumption_client.estimate_new_consumption(
-                    current_rating=p.data["current-energy-rating"],
-                    target_rating=body.goal_value,
-                    current_consumption=p.current_adjusted_energy
+                    current_energy_efficiency=p.data["current-energy-efficiency"],
+                    target_efficiency="69",
+                    current_consumption=p.estimate_electrical_consumption(
+                        assumed_ashp_efficiency=assumptions.AVERAGE_ASHP_EFFICIENCY, exclusions=body.exclusions
+                    )
                 ),
                 "property_id": p.id,
                 "uprn": p.uprn
             } for p in input_properties if p.building_id is not None
         ]
+        individual_units = [
+            {
+                "longitude": p.spatial["longitude"],
+                "latitude": p.spatial["latitude"],
+                # Energy consumption is adjusted for the property's expected post retrofit state
+                # We set the target rating to EPC C, which is the typical EPC rating we would expect the
+                # property to achieve post retrofit of just the fabric
+                "energy_consumption": energy_consumption_client.estimate_new_consumption(
+                    current_energy_efficiency=p.data["current-energy-efficiency"],
+                    target_efficiency="69",
+                    current_consumption=p.estimate_electrical_consumption(
+                        assumed_ashp_efficiency=assumptions.AVERAGE_ASHP_EFFICIENCY, exclusions=body.exclusions
+                    ),
+                ),
+                "property_id": p.id,
+                "uprn": p.uprn
+            } for p in input_properties if p.building_id is None
+        ]
         if building_ids:
             # Find the unique longitude and latitude pairs for each building id
             unique_coordinates = {}
@@ -447,14 +558,46 @@ async def trigger_plan(body: PlanTriggerRequest):
                         )
                         p.set_solar_panel_configuration(unit_solar_panel_configuration)
 
-        else:
-            # # Model the solar potential at the property level
-            # for p in input_properties:
-            #     # TODO: Complete me! - we probably won't do this for individual flats
-            #     solar_performance = solar_api_client.get(
-            #         longitude=p.spatial["longitude"], latitude=p.spatial["latitude"]
-            #     )
-            print("Implement me")
+        if individual_units:
+            # Model the solar potential at the property level
+            for unit in individual_units:
+                property_instance = [p for p in input_properties if p.id == unit["property_id"]][0]
+                # At this level, we check if the property is suitable for solar and if now, skip
+                if not property_instance.is_solar_pv_valid():
+                    continue
+
+                solar_api_client.get(
+                    longitude=unit["longitude"],
+                    latitude=unit["latitude"],
+                    energy_consumption=unit["energy_consumption"],
+                    is_building=False,
+                    session=session,
+                    uprn=unit["uprn"],
+                    property_instance=property_instance
+                )
+
+                # Store the data in the database
+                # TODO: Rather than just doing a straight insert, we should overwrite what's already there if it exists
+                solar_api_client.save_to_db(
+                    session=session,
+                    uprns_to_location=[
+                        {
+                            "uprn": property_instance.uprn,
+                            "longitude": property_instance.spatial["longitude"],
+                            "latitude": property_instance.spatial["latitude"]
+                        }
+                    ],
+                    scenario_type="unit"
+                )
+
+                property_instance.set_solar_panel_configuration(
+                    solar_panel_configuration={
+                        "insights_data": solar_api_client.insights_data,
+                        "panel_performance": solar_api_client.panel_performance,
+                        "unit_share_of_energy": 1
+                    },
+                    roof_area=solar_api_client.roof_area
+                )
 
         logger.info("Getting components and epc recommendations")
         recommendations = {}
@@ -610,18 +753,18 @@ async def trigger_plan(body: PlanTriggerRequest):
                     valuations = PropertyValuation.estimate(property_instance=p, target_epc=new_epc)
                     property_value_increase_ranges[p.id] = valuations
 
-                    # Your existing operations
-                    property_details_epc = p.get_property_details_epc(
-                        portfolio_id=body.portfolio_id, rating_lookup=rating_lookup,
-                    )
-                    create_property_details_epc(session, property_details_epc)
+                    if p.is_new:
+                        property_details_epc = p.get_property_details_epc(
+                            portfolio_id=body.portfolio_id, rating_lookup=rating_lookup,
+                        )
+                        create_property_details_epc(session, property_details_epc)
 
-                    update_or_create_property_spatial_details(session, p.uprn, p.spatial)
+                        update_or_create_property_spatial_details(session, p.uprn, p.spatial)
 
-                    property_data = p.get_full_property_data(current_valuation=valuations["current_value"])
-                    update_property_data(
-                        session, property_id=p.id, portfolio_id=body.portfolio_id, property_data=property_data
-                    )
+                        property_data = p.get_full_property_data(current_valuation=valuations["current_value"])
+                        update_property_data(
+                            session, property_id=p.id, portfolio_id=body.portfolio_id, property_data=property_data
+                        )
 
                     if not recommendations_to_upload:
                         continue
@@ -629,7 +772,9 @@ async def trigger_plan(body: PlanTriggerRequest):
                     new_plan_id = create_plan(session, {
                         "portfolio_id": body.portfolio_id,
                         "property_id": p.id,
-                        "is_default": True,
+                        "scenario_id": engine_scenario.id,
+                        "is_default": True if p.is_new else False,
+                        "name": body.scenario_name,
                         "valuation_increase_lower_bound": (
                             valuations["lower_bound_increased_value"] - valuations["current_value"]
                         ),
@@ -641,10 +786,8 @@ async def trigger_plan(body: PlanTriggerRequest):
                         ),
                     })
 
-                    uploaded_recommendation_ids = upload_recommendations(session, recommendations_to_upload, p.id)
-
-                    create_plan_recommendations(
-                        session, plan_id=new_plan_id, recommendation_ids=uploaded_recommendation_ids
+                    upload_recommendations(
+                        session, recommendations_to_upload, p.id, new_plan_id
                     )
 
                     property_valuation_increases.append(
@@ -683,6 +826,7 @@ async def trigger_plan(body: PlanTriggerRequest):
         aggregate_portfolio_recommendations(
             session,
             portfolio_id=body.portfolio_id,
+            scenario_id=engine_scenario.id,
             total_valuation_increase=total_valuation_increase,
             labour_days=labour_days,
             aggregated_data=aggregated_data
@@ -817,6 +961,7 @@ async def build_mds(body: MdsRequest):
                     # already_installed=property_already_installed,
                     # non_invasive_recommendations=property_non_invasive_recommendations,
                     measures=measures,
+                    is_new=is_new,
                     **Property.extract_kwargs(config)
                 )
             )
diff --git a/backend/app/plan/schemas.py b/backend/app/plan/schemas.py
index 77ac4217..108eb1ae 100644
--- a/backend/app/plan/schemas.py
+++ b/backend/app/plan/schemas.py
@@ -13,6 +13,10 @@ class PlanTriggerRequest(BaseModel):
     patches_file_path: Optional[str] = None
     non_invasive_recommendations_file_path: Optional[str] = None
     exclusions: Optional[conlist(str, min_items=1)] = None
+    scenario_name: Optional[str] = ""
+    # If true, will allow us to create multiple plans for the same portfolio, whereas if this is false, if this property
+    # exists in the portfolio, it will be ignored
+    multi_plan: Optional[bool] = False
 
     # Pre-defined list of possibilities for exclusions
     _allowed_exclusions = {
@@ -31,7 +35,7 @@ class PlanTriggerRequest(BaseModel):
         "air_source_heat_pump",
     }
 
-    _allowed_goals = {"Increase EPC"}
+    _allowed_goals = {"Increasing EPC"}
 
     _allowed_housing_types = {"Social", "Private"}
 
diff --git a/backend/ml_models/Valuation.py b/backend/ml_models/Valuation.py
index b87f156b..cbcebb9f 100644
--- a/backend/ml_models/Valuation.py
+++ b/backend/ml_models/Valuation.py
@@ -100,6 +100,9 @@ class PropertyValuation:
         200140647: 481_000,
         200140648: 373_000,
         200140649: 373_000,
+        # Vander Elliot Intrusive surveys
+        12103116: 1_537_000,
+        12103117: 1_404_000,
     }
 
     # We base our valuation uplifts on a number of sources
diff --git a/etl/bill_savings/EnergyConsumptionModel.py b/etl/bill_savings/EnergyConsumptionModel.py
index 9a7d6523..ff225073 100644
--- a/etl/bill_savings/EnergyConsumptionModel.py
+++ b/etl/bill_savings/EnergyConsumptionModel.py
@@ -102,6 +102,7 @@ class EnergyConsumptionModel:
             # We also retrieve the newest retail price comparison data which comes from Ofgem:
             # https://www.ofgem.gov.uk/energy-data-and-research/data-portal/retail-market-indicators
             # We use the detail price comparison by company and tariff type data
+            print("Reading retail price comparison - make sure this is up-to-date")
             self.read_retail_price_comparison()
 
     def read_retail_price_comparison(self):
@@ -506,31 +507,36 @@ class EnergyConsumptionModel:
         return prediction
 
     @staticmethod
-    def calculate_percentage_decrease(start_rating, end_rating, consumption_averages):
+    def calculate_percentage_decrease(start_efficiency, end_efficiency, consumption_averages):
 
         start_consumption = consumption_averages.loc[
-            consumption_averages["current-energy-rating"] == start_rating, "total_consumption"
+            consumption_averages["current-energy-efficiency"].astype(str) == str(start_efficiency), "total_consumption"
         ].values[0]
+
         end_consumption = consumption_averages.loc[
-            consumption_averages["current-energy-rating"] == end_rating, "total_consumption"
+            consumption_averages["current-energy-efficiency"].astype(str) == str(end_efficiency), "total_consumption"
         ].values[0]
 
         percentage_decrease = ((start_consumption - end_consumption) / start_consumption) * 100
+        # percentage_decrease cannot be nehative
+        if percentage_decrease < 0:
+            percentage_decrease = 0
         return percentage_decrease
 
-    def estimate_new_consumption(self, current_rating, target_rating, current_consumption):
+    def estimate_new_consumption(self, current_energy_efficiency, target_efficiency, current_consumption):
         """
         Given then consumption_averages dataset, which is produced as a result of the data_combining.py script,
         for the energy kwh models, this function will estimate the new consumption based on the current consumption,
         based on the expected reduction in consumption from the current rating to the target rating.
-        :param current_rating:
-        :param target_rating:
+        :param current_energy_efficiency:
+        :param target_efficiency:
         :param current_consumption:
-        :param df:
         :return:
         """
         percentage_decrease = self.calculate_percentage_decrease(
-            current_rating, target_rating, self.consumption_averages
+            start_efficiency=current_energy_efficiency,
+            end_efficiency=target_efficiency,
+            consumption_averages=self.consumption_averages
         )
         new_consumption = current_consumption * (1 - percentage_decrease / 100)
         return new_consumption
diff --git a/etl/bill_savings/data_collection.py b/etl/bill_savings/data_collection.py
index d2283ac4..e6f6de6f 100644
--- a/etl/bill_savings/data_collection.py
+++ b/etl/bill_savings/data_collection.py
@@ -133,7 +133,7 @@ def app():
     energy_consumption_data = []
     for i, directory in tqdm(enumerate(epc_directories), total=len(epc_directories)):
         # Skip the first 50
-        if i < 250:
+        if i < 57:
             continue
 
         data = pd.read_csv(directory / "certificates.csv", low_memory=False)
@@ -146,12 +146,12 @@ def app():
         # Take just the newest EPC per uprn, based on lodgement-date
         data = data.sort_values("lodgement-date", ascending=False).drop_duplicates("uprn")
 
-        data = data.sample(sample_size)
+        data = data.sample(sample_size, replace=False)
         # We use the addreess data to find the related information
 
         collected_data = []
         for _, property_data in data.iterrows():
-            time.sleep(np.random.uniform(0.3, 2))
+            time.sleep(np.random.uniform(0.2, 1.5))
 
             uprn = int(property_data["uprn"])
             address = property_data["address1"]
diff --git a/etl/bill_savings/data_combining.py b/etl/bill_savings/data_combining.py
index 11366360..dece3834 100644
--- a/etl/bill_savings/data_combining.py
+++ b/etl/bill_savings/data_combining.py
@@ -94,7 +94,7 @@ def app():
 
     # We also estimate the energy consumption reduction from this data, by band
     df["total_consumption"] = df["heating_kwh"] + df["hot_water_kwh"]
-    consumption_averages = df.groupby("current-energy-rating")["total_consumption"].meam().reset_index()
+    consumption_averages = df.groupby("current-energy-efficiency")["total_consumption"].mean().reset_index()
 
     # Save the consumption averages back to s3
     save_dataframe_to_s3_parquet(
diff --git a/etl/customers/goldman/property_ownership.py b/etl/customers/goldman/property_ownership.py
index 500963a1..ebd72732 100644
--- a/etl/customers/goldman/property_ownership.py
+++ b/etl/customers/goldman/property_ownership.py
@@ -11,7 +11,10 @@ from utils.s3 import read_dataframe_from_s3_parquet
 # The mode EPC rating is D, so we associate the £238k valuation with an EPC D property
 # Therefore value_of_F * 1.15 = value_of_D * 1.03
 # Therefore value_of_F = value_of_D * 1.03/1.15 = 238k * (1.03/1.15) = 213165
-PROPERTY_VALUE_ESTIMATE = 213_165
+PROPERTY_VALUE_ESTIMATE = 200_000
+
+# UPRNs of properties we need
+MANUAL_EXCLUSIONS = []
 
 
 def aggregate_matches(matching_lookup, company_ownership, properties):
@@ -73,7 +76,7 @@ def find_f_g_properties(paths):
         epc_data["UPRN"] = epc_data["UPRN"].astype(int).astype(str)
 
         # Get the newest EPC for each UPRN. We use LODGEMENT_DATE as a proxy for this
-        epc_data["LODGEMENT_DATETIME"] = pd.to_datetime(epc_data["LODGEMENT_DATETIME"], format='mixed')
+        epc_data["LODGEMENT_DATETIME"] = pd.to_datetime(epc_data["LODGEMENT_DATETIME"], format='mixed', errors="coerce")
 
         epc_data = epc_data.sort_values("LODGEMENT_DATETIME", ascending=False).drop_duplicates("UPRN")
 
@@ -84,7 +87,7 @@ def find_f_g_properties(paths):
     data = pd.concat(data)
 
     # Save as an excel
-    data.to_excel("EPC F & G Properties.xlsx", index=False)
+    data.to_excel("EPC F & G Properties - V2.xlsx", index=False)
 
 
 def remove_text_in_brackets(address: str) -> str:
@@ -196,7 +199,7 @@ def remove_duplicate_matches(matching_lookup, properties, company_ownership):
             matches_to_drop[["UPRN", "Title Number"]].copy()
         )
 
-    to_drop = pd.concat(to_drop)
+    to_drop = pd.concat(to_drop) if to_drop else pd.DataFrame()
 
     if not to_drop.empty:
         merged = pd.merge(matching_lookup, to_drop, on=['UPRN', 'Title Number'], how='left', indicator=True)
@@ -245,6 +248,74 @@ def remove_duplicate_uprn_matches(matching_lookup, properties, company_ownership
     return matching_lookup
 
 
+def filter_land_registry(properties):
+    column_names = [
+        "transaction_id",
+        "price",
+        "date_of_transfer",
+        "postcode",
+        "property_type",
+        "old_new",
+        "duration",
+        "paon",
+        "saon",
+        "street",
+        "locality",
+        "town_city",
+        "district",
+        "county",
+        "ppd_category_type",
+        "record_status",
+    ]
+    land_registry = pd.read_csv("/Users/khalimconn-kowlessar/Downloads/pp-complete.csv", header=None)
+    land_registry.columns = column_names
+    land_registry = land_registry[
+        land_registry["postcode"].str.lower().isin(properties["POSTCODE"].str.lower().unique())
+    ]
+    land_registry["date_of_transfer"] = pd.to_datetime(
+        land_registry["date_of_transfer"], format="%Y-%m-%d", errors="coerce"
+    )
+    # Take data from the last 5 years
+    land_registry = land_registry[
+        (land_registry["date_of_transfer"] >= "2019-01-01")
+    ]
+
+    # Filter this
+    land_registry.to_csv(
+        "/Users/khalimconn-kowlessar/Downloads/land_registry_prices_paid_filtered.csv", index=False
+    )
+
+
+def is_substring(x, match_string):
+    if pd.isnull(x):
+        return False
+    return x in match_string.lower()
+
+
+def house_number_match(paon, house_number):
+    # Firstly try and convert to numberic
+    try:
+        paon_numeric = int(paon)
+        house_number_numeric = int(house_number)
+        return paon_numeric == house_number_numeric
+    except Exception as e:  # noqa
+        # If we can't convert both to numeric, we do an equality
+
+        return paon == house_number
+
+
+def check_equalities(lr_filtered):
+    all_paon_equal = all(lr_filtered["paon"] == lr_filtered["paon"].values[0])
+    if pd.isnull(lr_filtered["saon"].values[0]):
+        all_saon_equal = all(pd.isnull(lr_filtered["saon"]))
+    else:
+        all_saon_equal = all(lr_filtered["saon"] == lr_filtered["saon"].values[0])
+
+    all_street_equal = all(lr_filtered["street"] == lr_filtered["street"].values[0])
+
+    return all_paon_equal, all_saon_equal, all_street_equal
+
+
 def app():
     """
     This script is for scoping property ownership for EPC F & G rated properties in Birmingam, for Goldman Sachs
@@ -254,8 +325,8 @@ def app():
     #       https://epc.opendatacommunities.org/domestic/search?address=&postcode=&local-authority=&constituency
     #       =&uprn=100031179243&from-month=1&from-year=2008&to-month=12&to-year=2024
     #       is actually listed in two local authorities causing us to think it's an EPC F & G property, but it's
-    #       it's actually EPC E. Need to handle this, probably by reading in all of the EPC data, concatenating together
-    #       and performing a singular filter for most recent EPC by UPRN
+    #       it's actually EPC E. Need to handle this, probably by reading in all of the EPC data, concatenating
+    #       together and performing a singular filter for most recent EPC by UPRN
     # paths = [
     #     "local_data/all-domestic-certificates/domestic-E08000025-Birmingham/certificates.csv",
     #     "local_data/all-domestic-certificates/domestic-E08000031-Wolverhampton/certificates.csv",
@@ -293,17 +364,19 @@ def app():
     # paths = list(set(paths))
     # find_f_g_properties(paths)
 
-    properties = pd.read_excel("EPC F & G Properties.xlsx")
-    company_ownership = pd.read_csv("/Users/khalimconn-kowlessar/Downloads/CCOD_FULL_2024_04.csv")
+    properties = pd.read_excel("EPC F & G Properties - V2.xlsx")
+    # filter_land_registry(properties)
+    company_ownership = pd.read_csv("/Users/khalimconn-kowlessar/Downloads/CCOD_FULL_2024_07.csv")
     company_ownership["is_overseas"] = False
-    overseas_company_ownership = pd.read_csv("/Users/khalimconn-kowlessar/Downloads/OCOD_FULL_2024_04 2.csv")
+    overseas_company_ownership = pd.read_csv("/Users/khalimconn-kowlessar/Downloads/OCOD_FULL_2024_07.csv")
     overseas_company_ownership["is_overseas"] = True
 
     company_ownership = pd.concat([company_ownership, overseas_company_ownership])
 
     # FIlter on relevant postcodes
     company_ownership = company_ownership[
-        company_ownership["Postcode"].str.lower().isin(properties["POSTCODE"].str.lower().unique())]
+        company_ownership["Postcode"].str.lower().isin(properties["POSTCODE"].str.lower().unique())
+    ]
 
     # Now we filter properties the other way around
     properties = properties[properties["POSTCODE"].str.lower().isin(company_ownership["Postcode"].str.lower().unique())]
@@ -414,13 +487,11 @@ def app():
 
     freehold_matching_lookup = pd.DataFrame(freehold_matching_lookup)
     leasehold_matching_lookup = pd.DataFrame(leasehold_matching_lookup)
-    shared_leasehold_match = pd.concat(shared_leasehold_match)
-    shared_freehold_match = pd.concat(shared_freehold_match)
 
-    # freehold_matching_lookup.to_excel("freehold_matching_lookup_new.xlsx")
-    # leasehold_matching_lookup.to_excel("leasehold_matching_lookup_new.xlsx")
-    # shared_leasehold_match.to_excel("shared_leasehold_match_new.xlsx")
-    # shared_freehold_match.to_excel("shared_freehold_match_new.xlsx")
+    # freehold_matching_lookup.to_excel("freehold_matching_lookup V2.xlsx")
+    # leasehold_matching_lookup.to_excel("leasehold_matching_lookup V2.xlsx")
+    # freehold_matching_lookup = pd.read_excel("freehold_matching_lookup V2.xlsx")
+    # leasehold_matching_lookup = pd.read_excel("leasehold_matching_lookup V2.xlsx")
 
     # The approximate matches aren't very good
     freehold_matching_lookup = freehold_matching_lookup[freehold_matching_lookup["match_type"] == "exact"]
@@ -429,23 +500,309 @@ def app():
     # Combine
     combined_matching_lookup = pd.concat([freehold_matching_lookup, leasehold_matching_lookup])
     # Remove duplicates
-    combined_matching_lookup = remove_duplicate_matches(combined_matching_lookup, properties, company_ownership)
+    combined_matching_lookup = remove_duplicate_matches(
+        matching_lookup=combined_matching_lookup, properties=properties, company_ownership=company_ownership
+    )
     # We also have duplicates at a UPRN level
     combined_matching_lookup = remove_duplicate_uprn_matches(combined_matching_lookup, properties, company_ownership)
 
-    # There are some cases where we have duplicates
-    # freehold_matching_lookup = remove_duplicate_matches(freehold_matching_lookup, properties, company_ownership)
-    # leasehold_matching_lookup = remove_duplicate_matches(leasehold_matching_lookup, properties, company_ownership)
-
     matched_addresses = combined_matching_lookup.merge(
-        properties[["UPRN", "ADDRESS", "CURRENT_ENERGY_EFFICIENCY", "CURRENT_ENERGY_RATING"]].rename(
-            columns={"ADDRESS": "epc_address"}),
+        properties[
+            [
+                "UPRN",
+                "ADDRESS",
+                "ADDRESS1",
+                "CURRENT_ENERGY_EFFICIENCY",
+                "CURRENT_ENERGY_RATING",
+                "POSTCODE",
+                "LODGEMENT_DATE",
+                "TRANSACTION_TYPE"
+            ]
+        ].rename(
+            columns={
+                "ADDRESS": "epc_address",
+                "ADDRESS1": "epc_address1",
+                "POSTCODE": "epc_postcode"
+            }
+        ),
         how="left", on="UPRN"
     ).merge(
-        company_ownership[["Title Number", "Property Address", "Company Registration No. (1)", "Proprietor Name (1)"]],
+        company_ownership[
+            [
+                "Title Number",
+                "Property Address",
+                "Postcode",
+                "Company Registration No. (1)",
+                "Proprietor Name (1)",
+                "Date Proprietor Added",
+            ]
+        ],
         how="left", on="Title Number"
     )
 
+    # Let's try and get the house number
+    matched_addresses["house_number"] = (
+        matched_addresses["epc_address"]
+        .apply(remove_text_in_brackets)
+        .apply(SearchEpc.get_house_number)
+        .str.lower()
+        .str.replace(",", "")
+    )
+
+    # Read in land registry
+    land_registry = pd.read_csv(
+        "/Users/khalimconn-kowlessar/Downloads/land_registry_prices_paid_filtered.csv",
+    )
+
+    # We now perform a match between the land registry data and the matched address, in an attempt to find
+    # out when these properties last sold. The land registry data has been pre filtered on the postcodes in this
+    # data, and for sales within the last 5 years, to ensure the file isn't too large.
+
+    land_registry["postcode"] = land_registry["postcode"].str.lower().str.strip()
+    land_registry["street"] = land_registry["street"].str.lower().str.strip()
+    land_registry["paon"] = land_registry["paon"].str.lower().str.strip()
+    land_registry["saon"] = land_registry["saon"].str.lower().str.strip()
+    land_registry["date_of_transfer"] = pd.to_datetime(land_registry["date_of_transfer"])
+
+    land_registry_matches = []
+    for _, match in tqdm(matched_addresses.iterrows(), total=len(matched_addresses)):
+
+        # Filter land registry on the postcode
+        lr_filtered = land_registry[
+            (land_registry["postcode"] == match["epc_postcode"].lower().strip())
+        ]
+
+        # Filter further, when the street is in in the address
+        # street should be contained in epc_address
+        lr_filtered = lr_filtered[
+            lr_filtered["street"].apply(lambda x: is_substring(x, match["epc_address"].lower())) |
+            lr_filtered["street"].apply(lambda x: is_substring(x, match["Property Address"].lower()))
+            ]
+
+        if lr_filtered.empty:
+            continue
+
+        # We now check if paon is in address 1
+        lr_filtered["paon_match"] = lr_filtered["paon"].apply(lambda x: house_number_match(x, match["house_number"]))
+        # We also try the secondary match
+        lr_filtered["saon_match"] = (
+            lr_filtered["saon"].apply(
+                lambda x: False if pd.isnull(x) else is_substring(x, match["epc_address1"])
+            )
+        )
+        # We fileter where we have a primary or secondary match
+        lr_filtered = lr_filtered[
+            lr_filtered["paon_match"] | lr_filtered["saon_match"]
+            ]
+
+        if lr_filtered.empty:
+            continue
+        elif lr_filtered.shape[0] == 1:
+            land_registry_matches.append(
+                {
+                    "uprn": match["UPRN"],
+                    "transaction_id": lr_filtered['transaction_id'].values[0],
+                    "price": lr_filtered["price"].values[0],
+                    "date_of_transfer": lr_filtered["date_of_transfer"].values[0],
+                }
+            )
+            continue
+        elif lr_filtered.shape[0] > 1:
+            # We make sure all records are the same and take the newest
+            all_paon_equal, all_saon_equal, all_street_equal = check_equalities(lr_filtered)
+            has_paon_match = any(lr_filtered["paon_match"])
+
+            if all_paon_equal and all_street_equal and all_saon_equal:
+                # Take the newest record, append and continue
+                lr_filtered = lr_filtered.sort_values("date_of_transfer", ascending=False)
+                lr_filtered = lr_filtered.head(1)
+                land_registry_matches.append(
+                    {
+                        "uprn": match["UPRN"],
+                        "transaction_id": lr_filtered['transaction_id'].values[0],
+                        "price": lr_filtered["price"].values[0],
+                        "date_of_transfer": lr_filtered["date_of_transfer"].values[0],
+                    }
+                )
+                continue
+            elif has_paon_match and all_street_equal:
+                # Peform filter on paon
+                lr_filtered = lr_filtered[lr_filtered["paon_match"]]
+                # Do an addtiioanl equality check
+                all_paon_equal, all_saon_equal, all_street_equal = check_equalities(lr_filtered)
+                if all_paon_equal and all_street_equal and all_saon_equal:
+                    lr_filtered = lr_filtered.sort_values("date_of_transfer", ascending=False)
+                    lr_filtered = lr_filtered.head(1)
+                    land_registry_matches.append(
+                        {
+                            "uprn": match["UPRN"],
+                            "transaction_id": lr_filtered['transaction_id'].values[0],
+                            "price": lr_filtered["price"].values[0],
+                            "date_of_transfer": lr_filtered["date_of_transfer"].values[0],
+                        }
+                    )
+                else:
+                    # We do a match on saon
+                    lr_filtered["saon_match2"] = lr_filtered["saon"].apply(
+                        lambda x: False if pd.isnull(x) else is_substring(x, match["epc_address"])
+                    )
+
+                    lr_filtered = lr_filtered[lr_filtered["saon_match2"]]
+
+                    if lr_filtered.empty:
+                        continue
+                    elif lr_filtered.shape[0] == 1:
+                        land_registry_matches.append(
+                            {
+                                "uprn": match["UPRN"],
+                                "transaction_id": lr_filtered['transaction_id'].values[0],
+                                "price": lr_filtered["price"].values[0],
+                                "date_of_transfer": lr_filtered["date_of_transfer"].values[0],
+                            }
+                        )
+                        continue
+                    else:
+                        raise NotImplementedError("wtf")
+            else:
+                # We have a final check, based on an observed case
+                lr_address_1 = " ".join([x.lower().strip() for x in match["Property Address"].split(",")[0:2]])
+
+                lr_filtered["paon_match2"] = lr_filtered["paon"].apply(
+                    lambda x: False if pd.isnull(x) else is_substring(x, lr_address_1)
+                )
+
+                lr_filtered = lr_filtered[lr_filtered["paon_match2"]]
+
+                if lr_filtered.empty:
+                    continue
+                elif lr_filtered.shape[0] == 1:
+                    land_registry_matches.append(
+                        {
+                            "uprn": match["UPRN"],
+                            "transaction_id": lr_filtered['transaction_id'].values[0],
+                            "price": lr_filtered["price"].values[0],
+                            "date_of_transfer": lr_filtered["date_of_transfer"].values[0],
+                        }
+                    )
+                    continue
+                else:
+                    # Check all the same
+                    all_paon_equal, all_saon_equal, all_street_equal = check_equalities(lr_filtered)
+
+                    # Check saon is house number with exact match
+                    lr_filtered["saon_match2"] = lr_filtered["saon"].apply(
+                        lambda x: False if pd.isnull(x) else house_number_match(x, match["house_number"])
+                    )
+                    # We check if we have a flat
+                    match_flat_number = re.match("flat (\d+)", match["epc_address1"].lower())
+                    match_apartment_number = re.match("apartment (\d+)", match["epc_address1"].lower())
+                    lr_filtered["saon_match3"] = False
+                    if match_flat_number is not None:
+                        # Get out the match
+                        match_flat_number = "flat " + match_flat_number.group(1)
+                        lr_filtered["saon_match3"] = lr_filtered["saon"].apply(
+                            lambda x: False if pd.isnull(x) else x == match_flat_number
+                        )
+
+                    if match_apartment_number is not None:
+                        # Get out the match
+                        match_apartment_number = "apartment " + match_apartment_number.group(1)
+                        lr_filtered["saon_match3"] = lr_filtered["saon"].apply(
+                            lambda x: False if pd.isnull(x) else x == match_apartment_number
+                        )
+
+                    if all_paon_equal and all_saon_equal and all_street_equal:
+                        # Take the newest record
+                        lr_filtered = lr_filtered.sort_values("date_of_transfer", ascending=False)
+                        lr_filtered = lr_filtered.head(1)
+                        land_registry_matches.append(
+                            {
+                                "uprn": match["UPRN"],
+                                "transaction_id": lr_filtered['transaction_id'].values[0],
+                                "price": lr_filtered["price"].values[0],
+                                "date_of_transfer": lr_filtered["date_of_transfer"].values[0],
+                            }
+                        )
+                        continue
+                    elif any(lr_filtered["saon_match2"]):
+                        lr_filtered = lr_filtered[lr_filtered["saon_match2"]]
+                        all_saon_equal, all_paon_equal, all_street_equal = check_equalities(lr_filtered)
+                        if all_paon_equal and all_saon_equal and all_street_equal:
+                            # Filter on the newest record
+                            lr_filtered = lr_filtered.sort_values("date_of_transfer", ascending=False)
+                            lr_filtered = lr_filtered.head(1)
+                        if lr_filtered.shape[0] == 1:
+                            land_registry_matches.append(
+                                {
+                                    "uprn": match["UPRN"],
+                                    "transaction_id": lr_filtered['transaction_id'].values[0],
+                                    "price": lr_filtered["price"].values[0],
+                                    "date_of_transfer": lr_filtered["date_of_transfer"].values[0],
+                                }
+                            )
+                            continue
+                    elif any(lr_filtered["saon_match3"]):
+                        lr_filtered = lr_filtered[lr_filtered["saon_match3"]]
+                        if lr_filtered.shape[0] == 1:
+                            land_registry_matches.append(
+                                {
+                                    "uprn": match["UPRN"],
+                                    "transaction_id": lr_filtered['transaction_id'].values[0],
+                                    "price": lr_filtered["price"].values[0],
+                                    "date_of_transfer": lr_filtered["date_of_transfer"].values[0],
+                                }
+                            )
+                            continue
+
+                    raise NotImplementedError("wtf")
+        else:
+            raise NotImplementedError("What happened here?")
+
+    land_registry_matches = pd.DataFrame(land_registry_matches)
+    # land_registry_matches.to_excel("land_registry_matches.xlsx")
+
+    # Check the matches against the addresses
+    # lr_to_addresses = matched_addresses[
+    #     ["UPRN", "epc_address", "epc_postcode", "Property Address", "Postcode"]
+    # ].merge(
+    #     land_registry_matches,
+    #     how="inner",
+    #     left_on="UPRN",
+    #     right_on="uprn"
+    # ).drop(columns=["uprn"]).merge(
+    #     land_registry[["transaction_id", "paon", "saon", "street", "postcode"]],
+    #     how="left", on="transaction_id"
+    # )
+
+    # Merge onto matched addresses
+    matched_addresses = matched_addresses.merge(
+        land_registry_matches,
+        how="left",
+        left_on="UPRN",
+        right_on="uprn"
+    ).drop(columns=["uprn"])
+
+    # Flat anything that sold in the last year
+    matched_addresses["sold_recently"] = (
+        matched_addresses["date_of_transfer"] >= pd.Timestamp.now() - pd.DateOffset(years=1)
+    )
+
+    matched_addresses["sale_lodged_recently"] = (
+        (pd.to_datetime(matched_addresses["LODGEMENT_DATE"]) >= pd.Timestamp.now() - pd.DateOffset(months=12)) &
+        (matched_addresses["TRANSACTION_TYPE"].isin(["marketed sale", "non marketed sale"]))
+    )
+
+    # Drop rows on the booleans
+    matched_addresses = matched_addresses[
+        ~matched_addresses["sold_recently"] &
+        ~matched_addresses["sale_lodged_recently"]
+        ]
+
+    # Filter combined_matching_lookup accordingly
+    combined_matching_lookup = combined_matching_lookup[
+        combined_matching_lookup["UPRN"].isin(matched_addresses["UPRN"])
+    ]
+
     # shared_freehold_match = pd.DataFrame(shared_freehold_match)
     # Strore these files
     # freehold_matching_lookup.to_excel("freehold_matching_lookup.xlsx")
@@ -457,33 +814,28 @@ def app():
     # leasehold_matching_lookup = pd.read_excel("leasehold_matching_lookup.xlsx")
     # shared_leasehold_match = pd.read_excel("shared_leasehold_match.xlsx")
 
-    freehold_aggregate = aggregate_matches(freehold_matching_lookup, company_ownership, properties)
-    leasehold_aggregate = aggregate_matches(leasehold_matching_lookup, company_ownership, properties)
+    # freehold_aggregate = aggregate_matches(freehold_matching_lookup, company_ownership, properties)
+    # leasehold_aggregate = aggregate_matches(leasehold_matching_lookup, company_ownership, properties)
 
     combined_aggregate = aggregate_matches(
-        combined_matching_lookup, company_ownership, properties
+        matching_lookup=combined_matching_lookup,
+        company_ownership=company_ownership,
+        properties=properties
     )
 
-    investment_20m = combined_aggregate[combined_aggregate["cumulative_value"] <= 20_500_000]
     investment_50m = combined_aggregate[combined_aggregate["cumulative_value"] <= 51_000_000]
 
-    investment_20m_properties = matched_addresses[
-        matched_addresses["Company Registration No. (1)"].isin(investment_20m["Company Registration No. (1)"])
-    ]
-
     investment_50m_properties = matched_addresses[
         matched_addresses["Company Registration No. (1)"].isin(investment_50m["Company Registration No. (1)"])
     ]
 
     portfolio_epc_data_50m = properties[properties["UPRN"].isin(investment_50m_properties["UPRN"])]
-    portfolio_epc_data_20m = properties[properties["UPRN"].isin(investment_20m_properties["UPRN"])]
 
-    investment_20m_properties.to_excel("investment_20m_properties 28th May.xlsx", index=False)
-    investment_50m_properties.to_excel("investment_50m_properties 28th May.xlsx", index=False)
+    # Storing data
+    # investment_50m_properties.to_excel("investment_50m_properties 28th July.xlsx", index=False)
 
     # Store the EPC data
-    portfolio_epc_data_50m.to_excel("portfolio_epc_data_50m 28th May.xlsx", index=False)
-    portfolio_epc_data_20m.to_excel("portfolio_epc_data_20m 28th May.xlsx", index=False)
+    # portfolio_epc_data_50m.to_excel("portfolio_epc_data_50m 29th July.xlsx", index=False)
 
     # We check if any of these properties are in a conservation area
     valuations = pd.read_excel("property value.xlsx")
@@ -529,6 +881,48 @@ def company_aggregation():
     aggregation.to_excel("Company ownership aggregation.xlsx")
 
 
+def extract_price_info(text):
+    # Use regex to find the relevant price information
+    match = re.search(r'Estimated price\n\nLow£([\d,]+)k\n\n£([\d,]+)k\n\nHigh£([\d,]+)k', text)
+    if match:
+        low_price = int(match.group(1).replace(',', '')) * 1000
+        est_price = int(match.group(2).replace(',', '')) * 1000
+        high_price = int(match.group(3).replace(',', '')) * 1000
+
+        price_info = {
+            'Zoopla Valuation': est_price,
+            'Zoopla Lower Bound': low_price,
+            'Zoopla Upper Bound': high_price
+        }
+
+        return price_info
+
+    return None
+
+
+def get_valuations(portfolio_epc_data_50m):
+    # This gets blocked pretty quickly by Zoopla
+    import requests
+    import time
+    from tqdm import tqdm
+    valuation_data = []
+    for _, property_data in tqdm(portfolio_epc_data_50m.iterrows(), total=len(portfolio_epc_data_50m)):
+        uprn = property_data["UPRN"]
+        response = requests.get(
+            f"https://r.jina.ai/https://www.zoopla.co.uk/property/uprn/{uprn}/"
+        )
+
+        pricing = extract_price_info(response.text)
+        valuation_data.append(
+            {
+                "UPRN": uprn,
+                **pricing
+            }
+        )
+
+        time.sleep(2)
+
+
 def prepare_anonymised_data():
     investment_50m_properties = pd.read_excel("investment_50m_properties 28th May.xlsx", header=0)
     investment_epc_data = pd.read_excel("portfolio_epc_data_50m 28th May.xlsx", header=0)
diff --git a/etl/xml_survey_extraction/XmlParser.py b/etl/xml_survey_extraction/XmlParser.py
new file mode 100644
index 00000000..0bc3d56b
--- /dev/null
+++ b/etl/xml_survey_extraction/XmlParser.py
@@ -0,0 +1,721 @@
+import re
+import numpy as np
+import usaddress
+from datetime import datetime
+from xml.dom.minidom import parseString
+from backend.app.utils import sap_to_epc
+from etl.xml_survey_extraction.pcdb import heating_data
+
+PROPERTY_TYPE_LOOKUP = {
+    "0": "House",
+    "House": "House",
+}
+
+
+def get_house_number(address: str) -> str | None:
+    """
+    This method will use the usaddress library to parse an address and extract the house number
+    :return:
+    """
+
+    parsed = usaddress.parse(address)
+    parsed_house_number = [x for x in parsed if (x[1] == "AddressNumber")]
+    parsed_house_number = parsed_house_number[0][0] if parsed_house_number else None
+
+    if parsed_house_number is None:
+        # Because usaddress isn't optimal for parsing addresses with some prefixes such as 'Flat',
+        # we also add a custom approach
+
+        # Pattern to look for 'Flat' or 'Apartment' followed by a number, or just a number at the beginning
+        pattern = r'(?i)(?:flat|apartment)\s*(\d+)|^\s*(\d+)'
+
+        match = re.search(pattern, address)
+
+        if match:
+            # Return the first non-None group found
+            return next(g for g in match.groups() if g is not None)
+        else:
+            return None
+
+    # Remove training commas
+    parsed_house_number = parsed_house_number.replace(",", "")
+
+    return parsed_house_number
+
+
+class XmlParser:
+    epc = {}
+    additional_data = {}
+    uprn = None
+
+    # heating/emissions information
+    space_heating_kwh = None
+    water_heating_kwh = None
+    heating_system = None
+    heating_controls = None
+
+    # Assessor details
+    surveyor_name = None
+
+    number_of_doors = None
+    number_of_insulated_doors = None
+    windows = None
+
+    # Property dimensions
+    number_of_floors = None
+    perimeter = None
+    heat_loss_perimeter = None
+    party_wall_length = None
+    total_floor_area = None
+    floor_height = None
+    insulation_wall_area = None
+
+    floor_dimensions = None
+
+    # The age band lookup is based on the country code
+    AGE_BAND_LOOKUP = {
+        # England & Wales
+        "EAW": {
+            "A": "England and Wales: before 1900",
+            "B": "England and Wales: 1900-1929",
+            "C": "England and Wales: 1930-1949",
+            "D": "England and Wales: 1950-1966",
+            "E": "England and Wales: 1967-1975",
+            "F": "England and Wales: 1976-1982",
+            "G": "England and Wales: 1983-1990",
+            "H": "England and Wales: 1991-1995",
+            "I": "England and Wales: 1996-2002",
+            "J": "England and Wales: 2003-2006",
+            "K": "England and Wales: 2007-2011",
+            "L": "England and Wales: 2012 onwards",
+        }
+    }
+
+    RATINGS_MAP = {
+        "0": "N/A",
+        "1": "Very Poor",
+        "2": "Poor",
+        "3": "Average",
+        "4": "Good",
+        "5": "Very Good"
+    }
+
+    MECHANICAL_VENTILATION_MAP = {
+        "0": "natural"
+    }
+
+    BUILT_FORM_MAP = {
+        "1": "Detached",
+    }
+
+    GLAZED_AREA_MAP = {
+        "4": "Much More Than Typical"
+    }
+
+    FUEL_TYPE_MAP = {
+        "26": "mains gas (not community)"
+    }
+
+    TRANSACTION_TYPE_MAP = {
+        "13": "ECO assessment"
+    }
+
+    TENURE_MAP = {
+        '1': "Owner-occupied"
+    }
+
+    TARIFF_MAP = {
+        "1": "Dual",
+        "2": "Single"
+    }
+
+    def __init__(self, file, filekey, surveyor_company, uprn=None):
+        file.seek(0)  # Ensure the file pointer is at the beginning
+        xml_string = file.read().decode('utf-8')
+        self.xml = parseString(xml_string)
+        self.filekey = filekey
+        self.surveyor_company = surveyor_company
+
+        # We check if we have a lig xml or rdsap xml
+        # We look for the presence of the Schema-Version-Original tag
+        self.is_lig = len(self.xml.getElementsByTagName("Schema-Version-Original")) > 0
+
+        self.get_uprn(uprn)
+
+    @staticmethod
+    def get_node(node):
+        """
+        Utility function to get the node value from the xml, where data might be optional
+        :return:
+        """
+
+        node_first_child = node.firstChild
+        if node_first_child is None:
+            return None
+
+        return node_first_child.nodeValue
+
+    def run(self):
+
+        if not self.is_lig:
+            return
+
+        self.get_assessor_details()
+
+        self.get_heating_and_emissions_data()
+
+        # self.get_detailed_heating_specs()
+
+        # Building fabric
+        self.get_doors()
+
+        self.get_floor_dimensions()
+
+        self.get_windows()
+
+        # Get all of the EPC data
+        self.extract_epc()
+
+        # Put together all of the additional data we capture
+        self.extract_additional_data()
+
+    def extract_epc(self):
+
+        if self.floor_dimensions is None:
+            raise ValueError("Run get_floor_dimensions() first")
+
+        if self.windows is None:
+            raise ValueError("Run get_windows() first")
+
+        property_type = self.get_property_type()
+
+        if property_type == "Flat":
+            raise NotImplementedError(
+                "Need to handle: heat-loss-corridor, unheated-corridor-length, flat-storey-count, flat-top-storey, "
+                "floor-level"
+            )
+        heat_loss_corridor = "NO DATA!"
+        unheated_corridor_length = ""
+        flat_storey_count = ""
+        flat_top_storey = ""
+        floor_level = "NO DATA!"
+
+        floor_height = np.mean([
+            float(x['room_height']) for x in self.floor_dimensions if
+            x['building_part_identifier'] == 'Main Dwelling' and not x['room_roof']
+        ])
+
+        # Take the most prevelant glazing type
+        glazed_type = [w["glazing_type"] for w in self.windows if w['window_location'] == '0']
+        glazed_type = max(glazed_type, key=glazed_type.count)
+
+        energy_tariff = (
+            self.xml.getElementsByTagName("SAP-Energy-Source")[0]
+            .getElementsByTagName("Meter-Type")[0]
+            .firstChild.nodeValue
+        )
+        energy_tariff = self.TARIFF_MAP[energy_tariff]
+
+        self.epc = {
+            "uprn": self.uprn,
+            "uprn-source": "Address Matched",
+            "property-type": property_type,
+            "building-reference-number": "",
+            **self.get_sap(),
+            **self.get_property_address(),
+            "low-energy-fixed-light-count": self.get_node_value('Low-Energy-Fixed-Lighting-Outlets-Count'),
+            "construction-age-band": self.AGE_BAND_LOOKUP[
+                self.get_node_value('Country-Code')
+            ][self.get_node_value('Construction-Age-Band')],
+            "mainheat-energy-eff": self.RATINGS_MAP[
+                self.get_property_summary_value('Main-Heating', 'Energy-Efficiency-Rating')
+            ],
+            "windows-env-eff": self.RATINGS_MAP[
+                self.get_property_summary_value('Window', 'Environmental-Efficiency-Rating')
+            ],
+            "lighting-energy-eff": self.RATINGS_MAP[
+                self.get_property_summary_value('Lighting', 'Energy-Efficiency-Rating')
+            ],
+            "environment-impact-potential": self.get_energy_assessment_value('Environmental-Impact-Potential'),
+            "mainheatcont-description":
+                self.get_property_summary_value('Main-Heating-Controls', 'Description'),
+            "sheating-energy-eff": self.RATINGS_MAP[
+                self.get_property_summary_value('Secondary-Heating', 'Energy-Efficiency-Rating')
+            ],
+            "local-authority": "",  # Not included in the xml
+            "local-authority-label": "",
+            "fixed-lighting-outlets-count": self.get_node_value('Fixed-Lighting-Outlets-Count'),
+            "energy-tariff": energy_tariff,
+            "mechanical-ventilation": self.MECHANICAL_VENTILATION_MAP[self.get_node_value('Mechanical-Ventilation')],
+            "solar-water-heating-flag": self.get_node_value('Solar-Water-Heating'),
+            "co2-emissions-potential": self.get_energy_assessment_value('CO2-Emissions-Potential'),
+            "number-heated-rooms": self.get_node_value('Heated-Room-Count'),
+            "floor-description": self.get_property_summary_value('Floor', 'Description'),
+            "energy-consumption-potential": self.get_energy_assessment_value('Energy-Consumption-Potential'),
+            "built-form": self.BUILT_FORM_MAP[self.get_node_value('Built-Form')],
+            "number-open-fireplaces": self.get_node_value('Open-Fireplaces-Count'),
+            "windows-description": self.get_property_summary_value('Window', 'Description'),
+            "glazed-area": self.GLAZED_AREA_MAP[self.get_node_value('Glazed-Area')],
+            "inspection-date": self.get_node_value('Inspection-Date'),
+            "mains-gas-flag": self.get_node_value('Mains-Gas'),
+            "co2-emiss-curr-per-floor-area": self.get_energy_assessment_value('CO2-Emissions-Current-Per-Floor-Area'),
+            "heat-loss-corridor": heat_loss_corridor,
+            "unheated-corridor-length": unheated_corridor_length,
+            "flat-storey-count": flat_storey_count,
+            "roof-energy-eff": self.RATINGS_MAP[
+                self.get_property_summary_value('Roof', 'Energy-Efficiency-Rating')
+            ],
+            "total-floor-area": self.get_node_value('Total-Floor-Area'),
+            "environment-impact-current": self.get_energy_assessment_value('Environmental-Impact-Current'),
+            "roof-description": self.get_property_summary_value('Roof', 'Description'),
+            "floor-energy-eff": self.RATINGS_MAP[
+                self.get_property_summary_value('Floor', 'Energy-Efficiency-Rating')
+            ],
+            "number-habitable-rooms": self.get_node_value('Habitable-Room-Count'),
+            "hot-water-env-eff": self.RATINGS_MAP[
+                self.get_property_summary_value('Hot-Water', 'Environmental-Efficiency-Rating')
+            ],
+            "mainheatc-energy-eff": self.RATINGS_MAP[
+                self.get_property_summary_value('Main-Heating-Controls', 'Energy-Efficiency-Rating')
+            ],
+            "main-fuel": self.FUEL_TYPE_MAP[self.get_node_value('Main-Fuel-Type')],
+            "lighting-env-eff": self.RATINGS_MAP[
+                self.get_property_summary_value('Lighting', 'Environmental-Efficiency-Rating')
+            ],
+            "windows-energy-eff": self.RATINGS_MAP[
+                self.get_property_summary_value('Window', 'Energy-Efficiency-Rating')
+            ],
+            "floor-env-eff": self.RATINGS_MAP[
+                self.get_property_summary_value('Floor', 'Environmental-Efficiency-Rating')
+            ],
+            "sheating-env-eff": self.RATINGS_MAP[
+                self.get_property_summary_value('Secondary-Heating', 'Environmental-Efficiency-Rating')
+            ],
+            "lighting-description": self.get_property_summary_value('Lighting', 'Description'),
+            "roof-env-eff": self.RATINGS_MAP[
+                self.get_property_summary_value('Roof', 'Environmental-Efficiency-Rating')
+            ],
+            "walls-energy-eff": self.RATINGS_MAP[
+                self.get_property_summary_value('Wall', 'Energy-Efficiency-Rating')
+            ],
+            "photo-supply": self.get_photo_supply(),
+            "lighting-cost-potential": self.get_energy_assessment_value('Lighting-Cost-Potential'),
+            "mainheat-env-eff": self.RATINGS_MAP[
+                self.get_property_summary_value('Main-Heating', 'Environmental-Efficiency-Rating')
+            ],
+            "multi-glaze-proportion": self.get_node_value('Multiple-Glazed-Proportion'),
+            "main-heating-controls": self.get_property_summary_value('Main-Heating-Controls', 'Description'),
+            "flat-top-storey": flat_top_storey,
+            "secondheat-description": self.get_property_summary_value('Secondary-Heating', 'Description'),
+            "walls-env-eff": self.RATINGS_MAP[
+                self.get_property_summary_value('Wall', 'Environmental-Efficiency-Rating')
+            ],
+            "transaction-type": self.TRANSACTION_TYPE_MAP[self.get_node_value('Transaction-Type')],
+            "extension-count": self.get_node_value('Extensions-Count'),
+            "mainheatc-env-eff": self.RATINGS_MAP[
+                self.get_property_summary_value('Main-Heating-Controls', 'Environmental-Efficiency-Rating')
+            ],
+            "lmk-key": "",  # Doesn't exist for non-EPC xmls
+            "wind-turbine-count": self.get_node_value('Wind-Turbines-Count'),
+            "tenure": self.TENURE_MAP[self.get_node_value('Tenure')],
+            "floor-level": floor_level,
+            "potential-energy-efficiency": self.get_energy_assessment_value('Energy-Rating-Potential'),
+            "potential-energy-rating": sap_to_epc(float(self.get_energy_assessment_value('Energy-Rating-Potential'))),
+            "hot-water-energy-eff": self.RATINGS_MAP[
+                self.get_property_summary_value('Hot-Water', 'Energy-Efficiency-Rating')
+            ],
+            "low-energy-lighting": self.get_node_value('Low-Energy-Lighting'),
+            "walls-description": self.get_property_summary_value('Wall', 'Description'),
+            "hotwater-description": self.get_property_summary_value('Hot-Water', 'Description'),
+            "co2-emissions-current": self.get_node_value('CO2-Emissions-Current'),
+            "heating-cost-current": self.get_node_value('Heating-Cost-Current'),
+            "heating-cost-potential": self.get_energy_assessment_value('Heating-Cost-Potential'),
+            "hot-water-cost-current": self.get_node_value('Hot-Water-Cost-Current'),
+            "hot-water-cost-potential": self.get_energy_assessment_value('Hot-Water-Cost-Potential'),
+            "lighting-cost-current": self.get_node_value('Lighting-Cost-Current'),
+            "energy-consumption-current": self.get_node_value('Energy-Consumption-Current'),
+            "lodgement-date": self.get_node_value('Inspection-Date'),
+            "lodgement-datetime":
+                datetime.strptime(self.get_node_value('Inspection-Date'), "%Y-%m-%d").isoformat(),
+            "mainheat-description": self.get_property_summary_value('Main-Heating', 'Description'),
+            "floor-height": floor_height,
+            "glazed-type": glazed_type,
+        }
+
+    def get_insulation_wall_area(self):
+        """
+        Extracts the insulation wall area for the main dwelling
+
+        Note that this doesn't include any extensions. We don't have recommendations for extensions right now, so we
+        don't currently calculate the insulation wall area for them, since it's not used in the recommendations.
+
+        """
+
+        main_dwelling_floors = [
+            f for f in self.floor_dimensions if f["building_part_identifier"] == "Main Dwelling" and not f["room_roof"]
+        ]
+        main_dwelling_windows = [
+            w for w in self.windows if w["window_location"] == "0"
+        ]
+
+        wall_areas = sum([float(f["heat_loss_perimeter"]) * float(f["room_height"]) for f in main_dwelling_floors])
+        window_areas = sum([float(w["window_area"]) for w in main_dwelling_windows])
+        return wall_areas - window_areas
+
+    def extract_additional_data(self):
+
+        self.insulation_wall_area = self.get_insulation_wall_area()
+
+        # We pull this out which is used as the insulation floor area
+        main_dwelling_ground_floor_area = [
+            f for f in self.floor_dimensions if f["building_part_identifier"] == "Main Dwelling" and f["floor"] == "0"
+        ][0]["total_floor_area"]
+
+        main_dwelling_windows = [w for w in self.windows if w["window_location"] == "0"]
+
+        number_of_windows = len(main_dwelling_windows)
+        windows_area = sum([float(w["window_area"]) for w in main_dwelling_windows])
+
+        boolean_lookup = {
+            "true": True,
+            "false": False,
+            "Y": True,
+            "N": False
+        }
+
+        cylinder_insulation_type = {
+            "1": "Foam",
+        }
+
+        self.additional_data = {
+            "file_location": self.filekey,
+            "surveyor_name": self.surveyor_name,
+            "surveyor_company": self.surveyor_company,
+            "space_heating_kwh": self.space_heating_kwh,
+            "water_heating_kwh": self.water_heating_kwh,
+            # "heating_system": self.heating_system,
+            # "heating_controls": self.heating_controls,
+            "number_of_doors": self.number_of_doors,
+            "number_of_insulated_doors": self.number_of_insulated_doors,
+            "number_of_floors": self.number_of_floors,
+            "insulation_wall_area": self.insulation_wall_area,
+            "heat_loss_perimeter": self.heat_loss_perimeter,
+            "party_wall_length": self.party_wall_length,
+            "perimeter": self.perimeter,
+            "rooms_with_bath_and_or_shower": int(self.get_node_value('Rooms-With-Bath-And-Or-Shower')),
+            "rooms_with_mixer_shower_no_bath": int(self.get_node_value('Rooms-With-Mixer-Shower-No-Bath')),
+            "room_with_bath_and_mixer_shower": int(self.get_node_value('Rooms-With-Bath-And-Mixer-Shower')),
+            "percent_draftproofed": int(self.get_node_value('Percent-Draughtproofed')),
+            "has_hot_water_cylinder": boolean_lookup[self.get_node_value('Has-Hot-Water-Cylinder')],
+            "cylinder_insulation_type": cylinder_insulation_type[self.get_node_value('Cylinder-Insulation-Type')],
+            "cylinder_insulation_thickness": int(self.get_node_value('Cylinder-Insulation-Thickness')),
+            "cylinder_thermostat": boolean_lookup[self.get_node_value('Cylinder-Thermostat')],
+            "main_dwelling_ground_floor_area": float(main_dwelling_ground_floor_area),
+            "number_of_windows": int(number_of_windows),
+            "windows_area": float(windows_area),
+        }
+
+    def get_node_value(self, tag_name):
+        nodes = self.xml.getElementsByTagName(tag_name)
+        if nodes and nodes[0].firstChild:
+            return nodes[0].firstChild.nodeValue
+        return None
+
+    def get_node_value_from_floor_dimensions(self, tag_name):
+        nodes = self.xml.getElementsByTagName('SAP-Floor-Dimension')
+        if nodes:
+            tag = nodes[0].getElementsByTagName(tag_name)
+            if tag and tag[0].firstChild:
+                return tag[0].firstChild.nodeValue
+        return None
+
+    def get_property_summary_value(self, section, tag_name):
+        nodes = self.xml.getElementsByTagName('Property-Summary')[0].getElementsByTagName(section)
+        if nodes:
+            tag = nodes[0].getElementsByTagName(tag_name)
+            if tag and tag[0].firstChild:
+                return tag[0].firstChild.nodeValue
+        return None
+
+    def get_energy_assessment_value(self, tag_name):
+        nodes = self.xml.getElementsByTagName('Energy-Assessment')[0]
+        if nodes:
+            tag = nodes.getElementsByTagName(tag_name)
+            if tag and tag[0].firstChild:
+                return tag[0].firstChild.nodeValue
+        return None
+
+    def get_uprn(self, uprn):
+
+        if uprn is not None:
+            self.uprn = uprn
+            return
+
+        uprn_tag = self.xml.getElementsByTagName('UPRN')[0].firstChild
+        if uprn_tag is None:
+            self.uprn = -1
+            return
+
+        self.uprn = uprn_tag.nodeValue
+        # If all of the characters in the UPRN are 0, then there is not set UPRN
+        if self.uprn.count("0") == len(self.uprn):
+            self.uprn = 0
+        else:
+            self.uprn = self.uprn.lower().split("uprn-")[1]
+
+    def get_property_type(self):
+        if not self.xml:
+            raise ValueError("You need to read the file first")
+
+        property_type = self.xml.getElementsByTagName('Property-Type')
+        if not property_type:
+            property_type = self.xml.getElementsByTagName('PropertyType1')
+
+        return PROPERTY_TYPE_LOOKUP[property_type[0].firstChild.nodeValue]
+
+    def get_sap(self):
+        sap_score = self.xml.getElementsByTagName('Energy-Rating-Current')
+        sap_score = int(sap_score[0].firstChild.nodeValue)
+        epc_rating = sap_to_epc(sap_score)
+
+        return {
+            "current-energy-efficiency": str(sap_score),
+            "current-energy-rating": epc_rating
+        }
+
+    def get_heating_and_emissions_data(self):
+        """
+        This method will extract the following pieces of information:
+        1) Space heating requirement
+        2) Water heating requirement
+        3) CO2 emissions
+        4) Heat demand per square meter per year
+        5) Bills
+
+        :return:
+        """
+
+        self.space_heating_kwh = self.xml.getElementsByTagName(
+            'Space-Heating-Existing-Dwelling'
+        )[0].firstChild.nodeValue
+
+        self.water_heating_kwh = self.xml.getElementsByTagName('Water-Heating')[0].firstChild.nodeValue
+
+    def get_detailed_heating_specs(self):
+        """
+        Given the heating data that is found in the <SAP-Heating> tag, we extract the detailed about the heating
+        system
+        :return:
+        """
+        sap_main_heating_details = (
+            self.xml.getElementsByTagName('SAP-Heating')[0]
+            .getElementsByTagName("Main-Heating-Details")[0]
+            .getElementsByTagName("Main-Heating")[0]
+        )
+
+        heating_code = sap_main_heating_details.getElementsByTagName("Main-Heating-Number")[0].firstChild.nodeValue
+
+        # Get the heating system
+        heating_system = heating_data[heating_data["code"] == int(heating_code)]["description"]
+        heating_system = heating_system.values[0] if not heating_system.empty else f"Heating code: {heating_code}"
+
+        # Get the heating controls
+        heating_controls_code = (
+            sap_main_heating_details.getElementsByTagName("Main-Heating-Control")[0].firstChild.nodeValue
+        )
+
+        heating_controls = heating_data[heating_data["code"] == int(heating_controls_code)]["description"]
+        heating_controls = (
+            heating_controls.values[0] if not heating_controls.empty else f"Heating Controls code: {heating_code}"
+        )
+
+        self.heating_system = heating_system
+        self.heating_controls = heating_controls
+
+    def get_doors(self):
+
+        # Doors can be found in the SAP-Property-Details tag
+        self.number_of_doors = int(
+            self.xml.getElementsByTagName('SAP-Property-Details')[0]
+            .getElementsByTagName('Door-Count')[0]
+            .firstChild.nodeValue
+        )
+
+        self.number_of_insulated_doors = int(
+            self.xml.getElementsByTagName('SAP-Property-Details')[0]
+            .getElementsByTagName('Insulated-Door-Count')[0]
+            .firstChild.nodeValue
+        )
+
+    def get_photo_supply(self):
+        photo_supply_tag = self.xml.getElementsByTagName("Photovoltaic-Supply")[0]
+        # Check if the "None-Or-No-Details" tag is present
+        if photo_supply_tag.getElementsByTagName("None-Or-No-Details"):
+            return (
+                photo_supply_tag.
+                getElementsByTagName("None-Or-No-Details")[0].
+                getElementsByTagName("Percent-Roof-Area")[0].
+                firstChild.nodeValue
+            )
+        else:
+            raise NotImplementedError("Implement me")
+
+    def get_assessor_details(self):
+
+        energy_assessor_tag = self.xml.getElementsByTagName('Energy-Assessor')[0]
+
+        self.surveyor_name = (
+            energy_assessor_tag.getElementsByTagName("Name")[0].firstChild.nodeValue
+        )
+
+    def get_property_address(self):
+
+        property_tag = self.xml.getElementsByTagName("Property")[0]
+
+        address1 = self.get_node(property_tag.getElementsByTagName("Address-Line-1")[0])
+        address2 = self.get_node(property_tag.getElementsByTagName("Address-Line-2")[0])
+        address3 = self.get_node(property_tag.getElementsByTagName("Address-Line-3")[0])
+        posttown = self.get_node(property_tag.getElementsByTagName("Post-Town")[0])
+        postcode = self.get_node(property_tag.getElementsByTagName("Postcode")[0])
+        address = ", ".join(
+            [x for x in [address1, address2, address3] if x is not None]
+        )
+        county = property_tag.getElementsByTagName("County")
+        if county:
+            county = county[0].firstChild.nodeValue
+        else:
+            county = ""
+
+        # Seems to be unavailable in the xml
+        constituency = None
+        constituency_label = None
+
+        return {
+            "address1": address1,
+            "address2": address2,
+            "address3": address3,
+            "posttown": posttown,
+            "postcode": postcode,
+            "address": address,
+            "county": county,
+            "constituency": constituency,
+            "constituency-label": constituency_label
+        }
+
+    def get_floor_dimensions(self):
+
+        """
+        Extracts physical measurements of the property such as the floor area, room height, etc.
+        across the main dwelling and any extensions.
+        :return:
+        """
+
+        def get_part_value(node, tag_name):
+            element = node.getElementsByTagName(tag_name)
+            if element and element[0].firstChild:
+                return element[0].firstChild.nodeValue
+            return None
+
+        # Each part will correspond to the main
+        sap_building_parts = self.xml.getElementsByTagName("SAP-Building-Part")
+
+        floor_dimensions = []
+        for building_part in sap_building_parts:
+            building_part_identifier = building_part.getElementsByTagName("Identifier")[0].firstChild.nodeValue
+            sap_floor_dimensions = building_part.getElementsByTagName("SAP-Floor-Dimension")
+
+            data = [
+                {
+                    'building_part_identifier': building_part_identifier,
+                    'floor': get_part_value(floor_dimension, 'Floor'),
+                    'floor_construction': get_part_value(floor_dimension, 'Floor-Construction'),
+                    'floor_insulation': get_part_value(floor_dimension, 'Floor-Insulation'),
+                    'heat_loss_perimeter': get_part_value(floor_dimension, 'Heat-Loss-Perimeter'),
+                    'party_wall_length': get_part_value(floor_dimension, 'Party-Wall-Length'),
+                    'total_floor_area': get_part_value(floor_dimension, 'Total-Floor-Area'),
+                    'room_height': get_part_value(floor_dimension, 'Room-Height'),
+                    "room_roof": False
+                } for floor_dimension in sap_floor_dimensions
+            ]
+
+            room_roofs = building_part.getElementsByTagName("SAP-Room-In-Roof")
+            room_roof_data = [
+                {
+                    "building_part_identifier": building_part_identifier,
+                    "floor": str(max([int(d["floor"]) for d in data]) + 1),
+                    "floor_construction": "",
+                    "floor_insulation": rr.getElementsByTagName("Insulation")[0].firstChild.nodeValue,
+                    "heat_loss_perimeter": "",
+                    "party_wall_length": "",
+                    "total_floor_area": rr.getElementsByTagName("Floor-Area")[0].firstChild.nodeValue,
+                    "room_height": "",
+                    "room_roof": True
+                } for rr in room_roofs
+            ]
+
+            floor_dimensions.extend(data)
+            floor_dimensions.extend(room_roof_data)
+
+        self.floor_dimensions = floor_dimensions
+
+        self.number_of_floors = len(
+            [f for f in self.floor_dimensions if f["building_part_identifier"] == "Main Dwelling"]
+        )
+
+        # We extract the maximum heat loss perimeter, per building part
+        max_heat_loss_perimeters = {d['building_part_identifier']: max(
+            (float(x['heat_loss_perimeter']) for x in self.floor_dimensions if
+             x['building_part_identifier'] == d['building_part_identifier'] and x['heat_loss_perimeter']),
+            default=float('-inf')
+        ) for d in self.floor_dimensions}
+
+        self.heat_loss_perimeter = sum(max_heat_loss_perimeters.values())
+
+        max_party_walls = {
+            d['building_part_identifier']: max(
+                (float(x['party_wall_length']) for x in self.floor_dimensions if
+                 x['building_part_identifier'] == d['building_part_identifier'] and x['party_wall_length']),
+                default=float('-inf')
+            ) for d in self.floor_dimensions
+        }
+
+        self.party_wall_length = sum(max_party_walls.values())
+
+        self.perimeter = self.heat_loss_perimeter + self.party_wall_length
+
+    def get_windows(self):
+        """
+        Extracts data about the windows in the property, including the number of windows and the window type.
+        :return:
+        """
+
+        sap_windows = self.xml.getElementsByTagName("SAP-Windows")[0].getElementsByTagName("SAP-Window")
+
+        glazing_type_lookup = {
+            "3": "double glazing, unknown install date"
+        }
+
+        orientation_lookup = {
+            "1": "North",
+            "2": "North East",
+            "3": "East",
+            "4": "South East",
+            "5": "South",
+            "6": "South West",
+            "7": "West",
+            "8": "North West"
+        }
+
+        self.windows = [
+            {
+                "window_location": window.getElementsByTagName("Window-Location")[0].firstChild.nodeValue,
+                "window_area": window.getElementsByTagName("Window-Area")[0].firstChild.nodeValue,
+                "window_type": window.getElementsByTagName("Window-Type")[0].firstChild.nodeValue,
+                "glazing_type": glazing_type_lookup[
+                    window.getElementsByTagName("Glazing-Type")[0].firstChild.nodeValue
+                ],
+                "pvc_frame": window.getElementsByTagName("PVC-Frame")[0].firstChild.nodeValue,
+                "glazing_gap": window.getElementsByTagName("Glazing-Gap")[0].firstChild.nodeValue,
+                "orientation": orientation_lookup[window.getElementsByTagName("Orientation")[0].firstChild.nodeValue]
+            } for window in sap_windows
+        ]
diff --git a/etl/xml_survey_extraction/app.py b/etl/xml_survey_extraction/app.py
index 6f53e4e2..a8bffc73 100644
--- a/etl/xml_survey_extraction/app.py
+++ b/etl/xml_survey_extraction/app.py
@@ -1,3 +1,108 @@
+from backend.app.db.functions.energy_assessment_functions import bulk_insert_energy_assessments
+from sqlalchemy.orm import sessionmaker
+from backend.app.db.connection import db_engine
+from utils.s3 import read_from_s3, list_files_and_subfolders_in_s3_folder, list_xmls_in_s3_folder, save_csv_to_s3
+from utils.logger import setup_logger
+from etl.xml_survey_extraction.XmlParser import XmlParser
+import os
+import pandas as pd
+from io import BytesIO
+
+logger = setup_logger()
+
+BUCKET = "retrofit-energy-assessments-dev"
+USER_ID = 8
+SCENARIOS = {
+    86: {
+        "project_code": "VDE001",
+        "surveyor": "JAFFERSONS ENERGY CONSULTANTS",
+        "bodies": [
+            # Scenario A: Cavity wall insulation
+            {
+                "portfolio_id": str(86),
+                "housing_type": "Private",
+                "goal": "Increasing EPC",
+                "goal_value": "A",
+                "trigger_file_path": "",
+                "already_installed_file_path": "",
+                "patches_file_path": "",
+                "non_invasive_recommendations_file_path": "",
+                "exclusions": ["floor_insulation", "fireplace", "solar_pv", "heating"],
+                "budget": None,
+                "scenario_name": "Low Hanging Fruit",
+                "multi_plan": True,
+            },
+            # Scenario B: CWI, Solar PV, AHSP
+            {
+                "portfolio_id": str(86),
+                "housing_type": "Private",
+                "goal": "Increasing EPC",
+                "goal_value": "A",
+                "trigger_file_path": "",
+                "already_installed_file_path": "",
+                "patches_file_path": "",
+                "non_invasive_recommendations_file_path": "",
+                "exclusions": ["floor_insulation", "fireplace"],
+                "budget": None,
+                "scenario_name": "Deep Retrofit",
+                "multi_plan": True,
+            },
+            # Scenario C, CWI, floor insulation, PV, AHSP
+            {
+                "portfolio_id": str(86),
+                "housing_type": "Private",
+                "goal": "Increasing EPC",
+                "goal_value": "A",
+                "trigger_file_path": "",
+                "already_installed_file_path": "",
+                "patches_file_path": "",
+                "non_invasive_recommendations_file_path": "",
+                "exclusions": ["fireplace"],
+                "budget": None,
+                "scenario_name": "Whole House Retrofit",
+                "multi_plan": True,
+            }
+        ]
+    },
+    87: {
+        "project_code": "VDE002",
+        "surveyor": "JAFFERSONS ENERGY CONSULTANTS",
+        "bodies": [
+            # Scenario A: Solar PV, AHSP
+            {
+                "portfolio_id": str(87),
+                "housing_type": "Private",
+                "goal": "Increasing EPC",
+                "goal_value": "A",
+                "trigger_file_path": "",
+                "already_installed_file_path": "",
+                "patches_file_path": "",
+                "non_invasive_recommendations_file_path": "",
+                "exclusions": ["floor_insulation", "fireplace"],
+                "budget": None,
+                "scenario_name": "Deep Retrofit",
+                "multi_plan": True,
+            },
+            # Scenario B, floor insulation, PV, AHSP
+            {
+                "portfolio_id": str(87),
+                "housing_type": "Private",
+                "goal": "Increasing EPC",
+                "goal_value": "A",
+                "trigger_file_path": "",
+                "already_installed_file_path": "",
+                "patches_file_path": "",
+                "non_invasive_recommendations_file_path": "",
+                "exclusions": ["fireplace"],
+                "budget": None,
+                "scenario_name": "Whole House Retrofit",
+                "multi_plan": True,
+            }
+        ]
+    }
+}
+
+
 def main():
     """
     This function executes the main process, which will retrieve data from the specified locations, extract the data
@@ -6,4 +111,123 @@ def main():
     """
 
     # TODO: Build solution to get this data from Onedrive and store what we need in S3
-    #       In s3, we have a bucket called retrofit-energy-assessments-{stage} which
+    #       In s3, we have a bucket called retrofit-energy-assessments-{stage} which contains the data we need
+    #      The data is stored in a folder called {surveyors}/{project_code}/{uprn}
+    #       We'll need to get the uprn from the folder name, which we can do with EpcSearcher class
+
+    # TODO: Pull out county, as in create_epc_records in the router, we pull it from the latest EPC, but we should
+    #       be able to deduce it from just the address. Same for constituency and constituency_label
+
+    # TODO: Store the project code in the database
+    #
+
+    for scenario_config in SCENARIOS.values():
+        energy_assessments = list_files_and_subfolders_in_s3_folder(
+            bucket_name=BUCKET, folder_name=f"{scenario_config['surveyor']}/{scenario_config['project_code']}/"
+        )
+
+        logger.info(
+            f"Found {len(energy_assessments)} energy assessments for {scenario_config['surveyor']} and "
+            f"{scenario_config['project_code']}"
+        )
+        assessments_map = {}
+        for assessment in energy_assessments:
+            uploaded_xmls = list_xmls_in_s3_folder(
+                bucket_name=BUCKET, folder_name=os.path.join(assessment, "docs & plans")
+            )
+            uprn = int(assessment.rstrip("/").split("/")[-1])
+            assessments_map[uprn] = uploaded_xmls
+
+        logger.info(f"Exatracted XMLS for the energy assessments")
+
+        # TODO: IF we have many uploads, we can do them in a batch so we don't try and upload huge amounts of data to
+        #       the database at onece
+
+        # TODO: We now have detailed information about primary and secondary walls, so we should use this information
+        #       in our recommendations when we have it
+        #       For example, for 77 Peryn Road, W3 7LT, the energy assessment has a main dwelling and two extensions,
+        #       where
+        #       the physical dimensions and the fabric of each building is constructed in a way as if each building is
+        #       separate. We should use this information to make recommendations that are specific to each building
+        #       part, though the problem here is that while the fabric and dimensions are separate, the actual SAP,
+        #       CO2, etc
+        #       figures span across the entire property.
+        #       Idea: We can collect all of this information by building part and store it separately in the database
+        #             against the uprn. We can have key data for the EPC, but then also additional data for each
+        #             building
+        #             part. We can then use this data to make recommendations that are specific to each building part
+        #       We should probably re-think this data model, so we break up the data in a more considered fasion and
+        #       produce
+        #       the underlying EPC data as a summary of the building parts. Not only do we have data against the main
+        #       dwelling and extensions, but we also have multiple windows with individiaul pieces of information that
+        #       we can use to make recommendations. We should store this data in a way that we can easily access it and
+        #       use it to make recommendations (e.g. we should have a Windows table)
+
+        # For each property, we download the xmls and extract the data
+        database_data = []
+        for uprn, xmls in assessments_map.items():
+            extracted_data = {}
+            for xml in xmls:
+                xml_data = read_from_s3(bucket_name=BUCKET, s3_file_name=xml)
+                xml_data_io = BytesIO(xml_data)
+                xml_parser = XmlParser(
+                    file=xml_data_io,
+                    filekey=os.path.join(f"s3://{BUCKET}", xml),
+                    uprn=uprn,
+                    surveyor_company=scenario_config["surveyor"],
+                )
+                xml_parser.run()
+                if xml_parser.is_lig:
+                    logger.info(f"Extracted data from {xml}")
+                extracted_epc = xml_parser.epc
+                extracted_additional_data = xml_parser.additional_data
+
+                data_to_update = {
+                    **extracted_epc, **extracted_additional_data
+                }
+
+                # We need to update the keys to match the database schema - i.e. we should replace all hyphens with
+                # underscores
+                data_to_update = {k.replace("-", "_"): v for k, v in data_to_update.items()}
+
+                extracted_data.update(data_to_update)
+
+            database_data.append(extracted_data)
+
+        logger.info("Uploading data to the database")
+        session = sessionmaker(bind=db_engine)()
+        bulk_insert_energy_assessments(session, database_data)
+        session.close()
+
+        # Create the asset list
+        asset_list = [
+            {"uprn": x["uprn"], "address": x["address1"], "postcode": x["postcode"]} for x in database_data
+        ]
+        asset_list = pd.DataFrame(asset_list)
+
+        # Store the asset list in s3
+        filename = f"{USER_ID}/{scenario_config['bodies'][0]['portfolio_id']}/non_intrusives.csv"
+        save_csv_to_s3(
+            dataframe=asset_list,
+            bucket_name="retrofit-plan-inputs-dev",
+            file_name=filename
+        )
+
+        for body in scenario_config["bodies"]:
+            body["trigger_file_path"] = filename
+            print(body)
+
+    # TODO: In order to get the full data associated to the heating system, we need to download and parse the pcdb which
+    #       can be found here: https://www.ncm-pcdb.org.uk/pcdb/pcdb10.dat
+    #                          https://www.ncm-pcdb.org.uk/sap/download
+    #       However retrieving this data is not a priority, so we can leave this for now as parsing the database
+    #       is a non-trivial task
+
+    # TODO: The condition report contains additional data such as the number of bedrooms and the number of bathrooms
+    #       We can extract this data and store it in the database as well. We can then update our kwargs methodology
+    #       that is passed to the property class, where instead we store this additional data in our database (it could
+    #       be stored in the energy assessment table, or in a separate table) and then when we're passed additional data
+    #       we can query the database for this data and use it to update the property object, instead of storing it
+    #       in the asset list and pulling it out of the asset list
+    #       1) Bathrooms
+    #       2) Bedrooms
diff --git a/etl/xml_survey_extraction/pcdb.py b/etl/xml_survey_extraction/pcdb.py
new file mode 100644
index 00000000..64d65708
--- /dev/null
+++ b/etl/xml_survey_extraction/pcdb.py
@@ -0,0 +1,1129 @@
+"""
+This script contains the systems data, contained in the BRE product characteristics database (PCDB).
+
+For SAP 10.2, this can be found in the following document:
+https://files.bregroup.com/SAP/SAP%2010.2%20-%2017-12-2021.pdf
+
+From page 157 onwards
+"""
+import pandas as pd
+
+no_heating_system = [
+    {
+        "category": "No heating system present",
+        "description": "Electric heaters (assumed)",
+        "efficiency": 100,
+        "heating_type": 1,
+        "responsiveness": 1.0,
+        "code": 699
+    }
+]
+
+boiler_systems_with_radiators_or_underfloor_heating = [
+    # Solid fuel boilers
+    {
+        "category": "Boiler systems with radiators or underfloor heating",
+        "description": "Solid fuel boiler - Manual feed independent boiler",
+        "efficiency_A": 65,
+        "efficiency_B": 60,
+        "heating_type": 2,
+        "responsiveness": 0.75,
+        "code": 151
+    },
+    {
+        "category": "Boiler systems with radiators or underfloor heating",
+        "description": "Solid fuel boiler - Auto (gravity) feed independent boiler",
+        "efficiency_A": 70,
+        "efficiency_B": 65,
+        "heating_type": 2,
+        "responsiveness": 0.75,
+        "code": 153
+    },
+    {
+        "category": "Boiler systems with radiators or underfloor heating",
+        "description": "Solid fuel boiler - Wood chip/pellet independent boiler",
+        "efficiency_A": 75,
+        "efficiency_B": 70,
+        "heating_type": 2,
+        "responsiveness": 0.75,
+        "code": 155
+    },
+    {
+        "category": "Boiler systems with radiators or underfloor heating",
+        "description": "Solid fuel boiler - Open fire with back boiler to radiators",
+        "efficiency_A": 63,
+        "efficiency_B": 55,
+        "heating_type": 3,
+        "responsiveness": 0.50,
+        "code": 156
+    },
+    {
+        "category": "Boiler systems with radiators or underfloor heating",
+        "description": "Solid fuel boiler - Closed room heater with boiler to radiators",
+        "efficiency_A": 67,
+        "efficiency_B": 65,
+        "heating_type": 3,
+        "responsiveness": 0.50,
+        "code": 158
+    },
+    {
+        "category": "Boiler systems with radiators or underfloor heating",
+        "description": "Solid fuel boiler - Stove (pellet-fired) with boiler to radiators",
+        "efficiency_A": 75,
+        "efficiency_B": 70,
+        "heating_type": 2,
+        "responsiveness": 0.75,
+        "code": 159
+    },
+    {
+        "category": "Boiler systems with radiators or underfloor heating",
+        "description": "Solid fuel boiler - Range cooker boiler (integral oven and boiler)",
+        "efficiency_A": 50,
+        "efficiency_B": 45,
+        "heating_type": 3,
+        "responsiveness": 0.50,
+        "code": 160
+    },
+    {
+        "category": "Boiler systems with radiators or underfloor heating",
+        "description": "Solid fuel boiler - Range cooker boiler (independent oven and boiler)",
+        "efficiency_A": 60,
+        "efficiency_B": 55,
+        "heating_type": 3,
+        "responsiveness": 0.50,
+        "code": 161
+    },
+    # Electric boilers
+    {
+        "category": "Boiler systems with radiators or underfloor heating",
+        "description": "Electric boiler - Direct acting electric boiler",
+        "efficiency": 100,
+        "heating_type": "From Table 4d",
+        "responsiveness": None,
+        "code": 191
+    },
+    {
+        "category": "Boiler systems with radiators or underfloor heating",
+        "description": "Electric boiler - CPSU in heated space – radiators or underfloor",
+        "efficiency": 100,
+        "heating_type": 1,
+        "responsiveness": 1.0,
+        "code": 192
+    },
+    {
+        "category": "Boiler systems with radiators or underfloor heating",
+        "description": "Electric boiler - Dry core storage boiler in heated space",
+        "efficiency": 100,
+        "heating_type": 2,
+        "responsiveness": 0.75,
+        "code": 193
+    },
+    {
+        "category": "Boiler systems with radiators or underfloor heating",
+        "description": "Electric boiler - Dry core storage boiler in unheated space",
+        "efficiency": 85,
+        "heating_type": 2,
+        "responsiveness": 0.75,
+        "code": 194
+    },
+    {
+        "category": "Boiler systems with radiators or underfloor heating",
+        "description": "Electric boiler - Water storage boiler in heated space",
+        "efficiency": 100,
+        "heating_type": 2,
+        "responsiveness": 0.75,
+        "code": 195
+    },
+    {
+        "category": "Boiler systems with radiators or underfloor heating",
+        "description": "Electric boiler - Water storage boiler in unheated space",
+        "efficiency": 85,
+        "heating_type": 2,
+        "responsiveness": 0.75,
+        "code": 196
+    }
+]
+
+heat_pumps_with_radiators_or_underfloor_heating = [
+    # Electric heat pumps
+    {
+        "category": "Heat pumps with radiators or underfloor heating",
+        "description": "Electric heat pumps - Ground source heat pump with flow temperature <= 35°C",
+        "space": 230,
+        "water": 170,
+        "heating_type": "From Table 4d",  # Replace with specific value as needed
+        "responsiveness": None,  # Not provided, assuming 'None'
+        "code": 211
+    },
+    {
+        "category": "Heat pumps with radiators or underfloor heating",
+        "description": "Electric heat pumps - Water source heat pump with flow temperature <= 35°C",
+        "space": 230,
+        "water": 170,
+        "heating_type": "From Table 4d",  # Replace with specific value as needed
+        "responsiveness": None,  # Not provided, assuming 'None'
+        "code": 213
+    },
+    {
+        "category": "Heat pumps with radiators or underfloor heating",
+        "description": "Electric heat pumps - Air source heat pump with flow temperature <= 35°C",
+        "space": 170,
+        "water": 170,
+        "heating_type": "From Table 4d",  # Replace with specific value as needed
+        "responsiveness": None,  # Not provided, assuming 'None'
+        "code": 214
+    },
+    {
+        "category": "Heat pumps with radiators or underfloor heating",
+        "description": "Electric heat pumps - Ground source heat pump in other cases",
+        "space": 170,
+        "water": 170,
+        "heating_type": "From Table 4d",  # Replace with specific value as needed
+        "responsiveness": None,  # Not provided, assuming 'None'
+        "code": 221
+    },
+    {
+        "category": "Heat pumps with radiators or underfloor heating",
+        "description": "Electric heat pumps - Water source heat pump, in other cases",
+        "space": 170,
+        "water": 170,
+        "heating_type": "From Table 4d",  # Replace with specific value as needed
+        "responsiveness": None,  # Not provided, assuming 'None'
+        "code": 223
+    },
+    {
+        "category": "Heat pumps with radiators or underfloor heating",
+        "description": "Electric heat pumps - Air source heat pump in other cases",
+        "space": 170,
+        "water": 170,
+        "heating_type": "From Table 4d",  # Replace with specific value as needed
+        "responsiveness": None,  # Not provided, assuming 'None'
+        "code": 224
+    },
+    # Gast fired heat pumps
+    {
+        "category": "Heat pumps with radiators or underfloor heating",
+        "description": "Gas-fired heat pumps - Ground source heat pump with flow temperature <= 35°C",
+        "space": 120,
+        "water": 84,
+        "heating_type": "From Table 4d",  # Replace with specific value as needed
+        "responsiveness": None,  # Not provided, assuming 'None'
+        "code": 215
+    },
+    {
+        "category": "Heat pumps with radiators or underfloor heating",
+        "description": "Gas-fired heat pumps - Water source heat pump with flow temperature <= 35°C",
+        "space": 120,
+        "water": 84,
+        "heating_type": "From Table 4d",  # Replace with specific value as needed
+        "responsiveness": None,  # Not provided, assuming 'None'
+        "code": 216
+    },
+    {
+        "category": "Heat pumps with radiators or underfloor heating",
+        "description": "Gas-fired heat pumps - Air source heat pump with flow temperature <= 35°C",
+        "space": 110,
+        "water": 77,
+        "heating_type": "From Table 4d",  # Replace with specific value as needed
+        "responsiveness": None,  # Not provided, assuming 'None'
+        "code": 217
+    },
+    {
+        "category": "Heat pumps with radiators or underfloor heating",
+        "description": "Gas-fired heat pumps - Ground source heat pump in other cases",
+        "space": 84,
+        "water": 84,
+        "heating_type": "From Table 4d",  # Replace with specific value as needed
+        "responsiveness": None,  # Not provided, assuming 'None'
+        "code": 225
+    },
+    {
+        "category": "Heat pumps with radiators or underfloor heating",
+        "description": "Gas-fired heat pumps - Water source heat pump in other cases",
+        "space": 84,
+        "water": 84,
+        "heating_type": "From Table 4d",  # Replace with specific value as needed
+        "responsiveness": None,  # Not provided, assuming 'None'
+        "code": 226
+    },
+    {
+        "category": "Heat pumps with radiators or underfloor heating",
+        "description": "Gas-fired heat pumps - Air source heat pump in other cases",
+        "space": 77,
+        "water": 77,
+        "heating_type": "From Table 4d",  # Replace with specific value as needed
+        "responsiveness": None,  # Not provided, assuming 'None'
+        "code": 227
+    }
+]
+
+electric_heat_pumps_warm_air_distribution = [
+    {
+        "category": "Heat pumps with warm air distribution",
+        "description": "Electric heat pumps - Ground source heat pump",
+        "space": 230,
+        "water": 170,
+        "heating_type": 1,
+        "responsiveness": 1.0,
+        "code": 521
+    },
+    {
+        "category": "Heat pumps with warm air distribution",
+        "description": "Electric heat pumps - Water source heat pump",
+        "space": 230,
+        "water": 170,
+        "heating_type": 1,
+        "responsiveness": 1.0,
+        "code": 523
+    },
+    {
+        "category": "Heat pumps with warm air distribution",
+        "description": "Electric heat pumps - Air source heat pump",
+        "space": 170,
+        "water": 170,
+        "heating_type": 1,
+        "responsiveness": 1.0,
+        "code": 524
+    }
+]
+
+gas_fired_heat_pumps_warm_air_distribution = [
+    {
+        "category": "Heat pumps with warm air distribution",
+        "description": "Gas-fired heat pumps - Ground source heat pump",
+        "space": 120,
+        "water": 84,
+        "heating_type": 1,
+        "responsiveness": 1.0,
+        "code": 525
+    },
+    {
+        "category": "Heat pumps with warm air distribution",
+        "description": "Gas-fired heat pumps - Water source heat pump",
+        "space": 120,
+        "water": 84,
+        "heating_type": 1,
+        "responsiveness": 1.0,
+        "code": 526
+    },
+    {
+        "category": "Heat pumps with warm air distribution",
+        "description": "Gas-fired heat pumps - Air source heat pump",
+        "space": 110,
+        "water": 77,
+        "heating_type": 1,
+        "responsiveness": 1.0,
+        "code": 527
+    }
+]
+
+heat_networks = [
+    {
+        "category": "Heat networks",
+        "description": "Boilers (SAP)",
+        "efficiency": 80,
+        "heating_type": "From table 4d",  # Replace with specific value as needed
+        "code": 2
+    },
+    {
+        "category": "Heat networks",
+        "description": "CHP (SAP)",
+        "efficiency": 75,
+        "heating_type": "From table 4d",  # Replace with specific value as needed
+        "code": 1
+    },
+    {
+        "category": "Heat networks",
+        "description": "Waste heat from power station (SAP)",
+        "efficiency": 100,
+        "heating_type": "From table 4d",  # Replace with specific value as needed
+        "code": 4
+    },
+    {
+        "category": "Heat networks",
+        "description": "Heat pump (SAP)",
+        "efficiency": 300,
+        "heating_type": "From table 4d",  # Replace with specific value as needed
+        "code": 3
+    },
+    {
+        "category": "Heat networks",
+        "description": "Geothermal heat source (SAP)",
+        "efficiency": 100,
+        "heating_type": "From table 4d",  # Replace with specific value as needed
+        "code": 5
+    },
+    {
+        "category": "Heat networks",
+        "description": "Boilers only (RdSAP)",
+        "efficiency": 80,
+        "heating_type": "From table 4d",  # Replace with specific value as needed
+        "code": 301
+    },
+    {
+        "category": "Heat networks",
+        "description": "CHP and boilers (RdSAP)",
+        "efficiency": 75,
+        "heating_type": "From table 4d",  # Replace with specific value as needed
+        "code": 302
+    },
+    {
+        "category": "Heat networks",
+        "description": "Heat pump (RdSAP)",
+        "efficiency": 300,
+        "heating_type": "From table 4d",  # Replace with specific value as needed
+        "code": 304
+    }
+]
+
+electric_storage_systems = [
+    {
+        "category": "Electric Storage Systems",
+        "description": "Old (large volume) storage heaters",
+        "efficiency": 100,
+        "heating_type": 6,
+        "responsiveness": 0.0,
+        "code": 401
+    },
+    {
+        "category": "Electric Storage Systems",
+        "description": "Slimline storage heaters",
+        "code": 402,
+        "options": [
+            {"sub_description": "Off-peak tariffs", "efficiency": 100, "heating_type": 5, "responsiveness": 0.2},
+            {"sub_description": "24-hour heating tariff", "efficiency": 100, "heating_type": 4, "responsiveness": 0.4}
+        ]
+    },
+    {
+        "category": "Electric Storage Systems",
+        "description": "Convector storage heaters",
+        "code": 403,
+        "options": [
+            {"sub_description": "Off-peak tariffs", "efficiency": 100, "heating_type": 5, "responsiveness": 0.2},
+            {"sub_description": "24-hour heating tariff", "efficiency": 100, "heating_type": 4, "responsiveness": 0.4}
+        ]
+    },
+    {
+        "category": "Electric Storage Systems",
+        "description": "Fan storage heaters",
+        "code": 404,
+        "options": [
+            {"sub_description": "Off-peak tariffs", "efficiency": 100, "heating_type": 4, "responsiveness": 0.4},
+            {"sub_description": "24-hour heating tariff", "efficiency": 100, "heating_type": 4, "responsiveness": 0.4}
+        ]
+    },
+    {
+        "category": "Electric Storage Systems",
+        "description": "Slimline storage heaters with Celect-type control",
+        "code": 405,
+        "options": [
+            {"sub_description": "Off-peak tariffs", "efficiency": 100, "heating_type": 4, "responsiveness": 0.4},
+            {"sub_description": "24-hour heating tariff", "efficiency": 100, "heating_type": 3, "responsiveness": 0.6}
+        ]
+    },
+    {
+        "category": "Electric Storage Systems",
+        "description": "Convector storage heaters with Celect-type control",
+        "code": 406,
+        "options": [
+            {"sub_description": "Off-peak tariffs", "efficiency": 100, "heating_type": 4, "responsiveness": 0.4},
+            {"sub_description": "24-hour heating tariff", "efficiency": 100, "heating_type": 3, "responsiveness": 0.6}
+        ]
+    },
+    {
+        "category": "Electric Storage Systems",
+        "description": "Fan storage heaters with Celect-type control",
+        "code": 407,
+        "options": [
+            {"sub_description": "Off-peak tariffs", "efficiency": 100, "heating_type": 3, "responsiveness": 0.6},
+            {"sub_description": "24-hour heating tariff", "efficiency": 100, "heating_type": 3, "responsiveness": 0.6}
+        ]
+    },
+    {
+        "category": "Electric Storage Systems",
+        "description": "Integrated storage + direct-acting heater",
+        "efficiency": 100,
+        "heating_type": 3,
+        "responsiveness": 0.6,
+        "code": 408
+    },
+    {
+        "category": "Electric Storage Systems",
+        "description": "High heat retention storage heaters",
+        "code": 409,
+        "options": [
+            {"sub_description": "Off-peak tariffs", "efficiency": 100, "heating_type": 2, "responsiveness": 0.8},
+            {"sub_description": "24-hour heating tariff", "efficiency": 100, "heating_type": 2, "responsiveness": 0.8}
+        ]
+    }
+]
+
+off_peak_tariffs_electric_underfloor_heating = [
+    {
+        "category": "Electric Underfloor Heating",
+        "description": "Off-peak tariffs - In concrete slab (off-peak only)",
+        "efficiency": 100,
+        "heating_type": 5,
+        "responsiveness": 0.0,
+        "code": 421
+    },
+    {
+        "category": "Electric Underfloor Heating",
+        "description": "Off-peak tariffs - Integrated (storage+direct-acting)",
+        "efficiency": 100,
+        "heating_type": 4,
+        "responsiveness": 0.25,
+        "code": 422
+    },
+    {
+        "category": "Electric Underfloor Heating",
+        "description": "Off-peak tariffs - Integrated (storage+direct-acting) with low (off-peak) tariff control",
+        "efficiency": 100,
+        "heating_type": 3,
+        "responsiveness": 0.50,
+        "code": 423
+    }
+]
+
+standard_or_off_peak_tariff_electric_underfloor_heating = [
+    {
+        "category": "Electric Underfloor Heating",
+        "description": "Standard or off-peak tariff - In screed above insulation",
+        "efficiency": 100,
+        "heating_type": 2,
+        "responsiveness": 0.75,
+        "code": 424
+    },
+    {
+        "category": "Electric Underfloor Heating",
+        "description": "Standard or off-peak tariff - In timber floor, or immediately below floor covering",
+        "efficiency": 100,
+        "heating_type": 1,
+        "responsiveness": 1.0,
+        "code": 425
+    }
+]
+
+gas_fired_warm_air_fan_assisted = [
+    {
+        "category": "Warm Air Systems",
+        "description": "Gas-fired warm air with fan-assisted flue - Ducted, on-off control, pre 1998", "efficiency": 70,
+        "heating_type": 1,
+        "responsiveness": 1.0,
+        "code": 501
+    },
+    {
+        "category": "Warm Air Systems",
+        "description": "Gas-fired warm air with fan-assisted flue - Ducted, on-off control, 1998 or later",
+        "efficiency": 76,
+        "heating_type": 1,
+        "responsiveness": 1.0,
+        "code": 502
+    },
+    {
+        "category": "Warm Air Systems",
+        "description": "Gas-fired warm air with fan-assisted flue - Ducted, modulating control, pre 1998",
+        "efficiency": 72,
+        "heating_type": 1,
+        "responsiveness": 1.0,
+        "code": 503
+    },
+    {
+        "category": "Warm Air Systems",
+        "description": "Gas-fired warm air with fan-assisted flue - Ducted, modulating control, 1998 or later",
+        "efficiency": 78,
+        "heating_type": 1,
+        "responsiveness": 1.0,
+        "code": 504
+    },
+    {
+        "category": "Warm Air Systems",
+        "description": "Gas-fired warm air with fan-assisted flue - Room heater with in-floor ducts",
+        "efficiency": 69,
+        "heating_type": 1,
+        "responsiveness": 1.0,
+        "code": 505
+    },
+    {
+        "category": "Warm Air Systems",
+        "description": "Gas-fired warm air with fan-assisted flue - Condensing",
+        "efficiency": 81,
+        "heating_type": 1,
+        "responsiveness": 1.0,
+        "code": 520
+    }
+]
+
+gas_fired_warm_air_balanced_or_open_flue = [
+    {"category": "Warm Air Systems",
+     "description": "Gas-fired warm air with balanced or open flue - Ducted or stub-ducted, on-off control, pre 1998",
+     "efficiency": 70, "heating_type": 1, "responsiveness": 1.0, "code": 506},
+    {"category": "Warm Air Systems",
+     "description": "Gas-fired warm air with balanced or open flue - Ducted or stub-ducted, on-off control, "
+                    "1998 or later",
+     "efficiency": 76, "heating_type": 1, "responsiveness": 1.0, "code": 507},
+    {"category": "Warm Air Systems",
+     "description": "Gas-fired warm air with balanced or open flue - Ducted or stub-ducted, modulating control, "
+                    "pre 1998",
+     "efficiency": 72, "heating_type": 1, "responsiveness": 1.0, "code": 508},
+    {"category": "Warm Air Systems",
+     "description": "Gas-fired warm air with balanced or open flue - Ducted or stub-ducted, modulating control, "
+                    "1998 or later",
+     "efficiency": 78, "heating_type": 1, "responsiveness": 1.0, "code": 509},
+    {"category": "Warm Air Systems",
+     "description": "Gas-fired warm air with balanced or open flue - Ducted or stub-ducted with flue heat recovery",
+     "efficiency": 85, "heating_type": 1, "responsiveness": 1.0, "code": 510},
+    {"category": "Warm Air Systems", "description": "Gas-fired warm air with balanced or open flue - Condensing",
+     "efficiency": 81, "heating_type": 1, "responsiveness": 1.0, "code": 511}
+]
+
+liquid_fired_warm_air = [
+    {"category": "Warm Air Systems", "description": "Liquid-fired warm air - Ducted output (on/off control)",
+     "efficiency": 70, "heating_type": 1, "responsiveness": 1.0, "code": 512},
+    {"category": "Warm Air Systems", "description": "Liquid-fired warm air - Ducted output (modulating control)",
+     "efficiency": 72, "heating_type": 1, "responsiveness": 1.0, "code": 513},
+    {"category": "Warm Air Systems", "description": "Liquid-fired warm air - Stub duct system", "efficiency": 70,
+     "heating_type": 1, "responsiveness": 1.0, "code": 514}
+]
+
+electric_warm_air_systems = [
+    {
+        "category": "Warm Air Systems",
+        "description": "Electric warm air - Electricaire system",
+        "efficiency": 100,
+        "heating_type": 2,
+        "responsiveness": 0.75,
+        "code": 515
+    }
+]
+
+room_heaters = [
+    # Gas (including LPG and biogas) room heaters
+    {"category": "Room Heaters",
+     "description": "Gas (including LPG and biogas) room heaters - Gas fire, open flue, pre-1980 (open fronted)",
+     "flue": "OF", "efficiency_A": 50, "efficiency_B": 50, "heating_type": 1, "responsiveness": 1.0, "code": 601},
+    {"category": "Room Heaters",
+     "description": "Gas (including LPG and biogas) room heaters - Gas fire, open flue, pre-1980 (open fronted), "
+                    "with back boiler unit",
+     "flue": "OF*", "efficiency_A": 50, "efficiency_B": 50, "heating_type": 1, "responsiveness": 1.0, "code": 602},
+    {"category": "Room Heaters",
+     "description": "Gas (including LPG and biogas) room heaters - Gas fire, open flue, 1980 or later (open fronted), "
+                    "sitting proud of, and sealed to, fireplace opening",
+     "flue": "OF", "efficiency_A": 63, "efficiency_B": 64, "heating_type": 1, "responsiveness": 1.0, "code": 603},
+    {"category": "Room Heaters",
+     "description": "Gas (including LPG and biogas) room heaters - Gas fire, open flue, 1980 or later (open fronted), "
+                    "sitting proud of, and sealed to, fireplace opening, with back boiler unit",
+     "flue": "OF*", "efficiency_A": 63, "efficiency_B": 64, "heating_type": 1, "responsiveness": 1.0, "code": 604},
+    {"category": "Room Heaters",
+     "description": "Gas (including LPG and biogas) room heaters - Flush fitting Live Fuel Effect gas fire (open "
+                    "fronted), sealed to fireplace opening",
+     "flue": "OF", "efficiency_A": 40, "efficiency_B": 41, "heating_type": 1, "responsiveness": 1.0, "code": 605},
+    {"category": "Room Heaters",
+     "description": "Gas (including LPG and biogas) room heaters - Flush fitting Live Fuel Effect gas fire (open "
+                    "fronted), sealed to fireplace opening, with back boiler unit",
+     "flue": "OF*", "efficiency_A": 40, "efficiency_B": 41, "heating_type": 1, "responsiveness": 1.0, "code": 606},
+    {"category": "Room Heaters",
+     "description": "Gas (including LPG and biogas) room heaters - Flush fitting Live Fuel Effect gas fire (open "
+                    "fronted), fan assisted, sealed to fireplace opening",
+     "flue": "OF", "efficiency_A": 45, "efficiency_B": 46, "heating_type": 1, "responsiveness": 1.0, "code": 607},
+    {"category": "Room Heaters",
+     "description": "Gas (including LPG and biogas) room heaters - Gas fire or wall heater, balanced flue",
+     "flue": "RS", "efficiency_A": 58, "efficiency_B": 60, "heating_type": 1, "responsiveness": 1.0, "code": 609},
+    {"category": "Room Heaters",
+     "description": "Gas (including LPG and biogas) room heaters - Gas fire, closed fronted, fan assisted",
+     "flue": "RS", "efficiency_A": 72, "efficiency_B": 73, "heating_type": 1, "responsiveness": 1.0, "code": 610},
+    {"category": "Room Heaters", "description": "Gas (including LPG and biogas) room heaters - Condensing gas fire",
+     "flue": "RS", "efficiency_A": 85, "efficiency_B": 85, "heating_type": 1, "responsiveness": 1.0, "code": 611},
+    {"category": "Room Heaters",
+     "description": "Gas (including LPG and biogas) room heaters - Decorative Fuel Effect gas fire, open to chimney",
+     "flue": "C", "efficiency_A": 20, "efficiency_B": 20, "heating_type": 1, "responsiveness": 1.0, "code": 612},
+    {"category": "Room Heaters",
+     "description": "Gas (including LPG and biogas) room heaters - Flueless gas fire, secondary heating only",
+     "flue": "none", "efficiency_A": 90, "efficiency_B": 92, "heating_type": 1, "responsiveness": 1.0, "code": 613},
+
+    # Liquid fuel room heaters
+    {"category": "Room Heaters", "description": "Liquid fuel room heaters - Room heater, pre 2000", "efficiency": 55,
+     "heating_type": 1, "responsiveness": 1.0, "code": 621},
+    {"category": "Room Heaters",
+     "description": "Liquid fuel room heaters - Room heater, pre 2000, with boiler (no radiators)", "efficiency": 65,
+     "heating_type": 1, "responsiveness": 1.0, "code": 622},
+    {"category": "Room Heaters", "description": "Liquid fuel room heaters - Room heater, 2000 or later",
+     "efficiency": 60, "heating_type": 1, "responsiveness": 1.0, "code": 623},
+    {"category": "Room Heaters",
+     "description": "Liquid fuel room heaters - Room heater, 2000 or later with boiler (no radiators)",
+     "efficiency": 70, "heating_type": 1, "responsiveness": 1.0, "code": 624},
+    {"category": "Room Heaters", "description": "Liquid fuel room heaters - Bioethanol heater, secondary heating only",
+     "efficiency": 94, "heating_type": 1, "responsiveness": 1.0, "code": 625},
+
+    # Solid fuel room heaters
+    {"category": "Room Heaters", "description": "Solid fuel room heaters - Open fire in grate", "efficiency_A": 37,
+     "efficiency_B": 32, "heating_type": 3, "responsiveness": 0.5, "code": 631},
+    {"category": "Room Heaters", "description": "Solid fuel room heaters - Open fire with back boiler (no radiators)",
+     "efficiency_A": 50, "efficiency_B": 50, "heating_type": 3, "responsiveness": 0.5, "code": 632},
+    {"category": "Room Heaters", "description": "Solid fuel room heaters - Closed room heater", "efficiency_A": 65,
+     "efficiency_B": 60, "heating_type": 3, "responsiveness": 0.5, "code": 633},
+    {"category": "Room Heaters",
+     "description": "Solid fuel room heaters - Closed room heater with boiler (no radiators)", "efficiency_A": 67,
+     "efficiency_B": 65, "heating_type": 3, "responsiveness": 0.5, "code": 634},
+    {"category": "Room Heaters", "description": "Solid fuel room heaters - Stove (pellet fired)", "efficiency_A": 70,
+     "efficiency_B": 65, "heating_type": 2, "responsiveness": 0.75, "code": 635},
+    {"category": "Room Heaters",
+     "description": "Solid fuel room heaters - Stove (pellet fired) with boiler (no radiators)", "efficiency_A": 75,
+     "efficiency_B": 70, "heating_type": 2, "responsiveness": 0.75, "code": 636},
+
+    # Electric (direct acting) room heaters
+    {"category": "Room Heaters",
+     "description": "Electric (direct acting) room heaters - Panel, convector or radiant heaters", "efficiency": 100,
+     "heating_type": 1, "responsiveness": 1.0, "code": 691},
+    {"category": "Room Heaters",
+     "description": "Electric (direct acting) room heaters - Water- or oil-filled radiators", "efficiency": 100,
+     "heating_type": 1, "responsiveness": 1.0, "code": 694},
+    {"category": "Room Heaters", "description": "Electric (direct acting) room heaters - Fan heaters",
+     "efficiency": 100, "heating_type": 1, "responsiveness": 1.0, "code": 692},
+    {"category": "Room Heaters", "description": "Electric (direct acting) room heaters - Portable electric heaters",
+     "efficiency": 100, "heating_type": 1, "responsiveness": 1.0, "code": 693}
+]
+
+other_space_heating_systems = [
+    {
+        "category": "Other Space Heating Systems",
+        "description": "Electric ceiling heating",
+        "efficiency": 100,
+        "heating_type": 2,
+        "responsiveness": 0.75,
+        "code": 701
+    }
+]
+
+hot_water_systems = [
+    {"category": "Hot Water Systems", "description": "No hot water system present - electric immersion assumed",
+     "efficiency": 100, "code": 999},
+    {
+        "category": "Hot Water Systems",
+        "description": "HWP from the primary heating system",
+        "code": 901,
+        "options": [
+            {"sub_description": "Back boiler (hot water only), gas*", "efficiency": 65},
+            {"sub_description": "Circulator built into a gas warm air system, pre 1998", "efficiency": 65},
+            {"sub_description": "Circulator built into a gas warm air system, 1998 or later", "efficiency": 73},
+            {"sub_description": "Heat exchanger in a gas warm air system, condensing unit", "efficiency": 74},
+        ]
+    },
+    {"category": "Hot Water Systems",
+     "description": "From second main system", "efficiency": None,
+     "code": 914},
+    {"category": "Hot Water Systems", "description": "From secondary system",
+     "efficiency": None, "code": 902},
+    {"category": "Hot Water Systems", "description": "Electric immersion", "efficiency": 100, "code": 903},
+    {"category": "Hot Water Systems",
+     "description": "Single-point gas-fired water heater (instantaneous at point of use)", "efficiency": 70,
+     "code": 907},
+    {"category": "Hot Water Systems",
+     "description": "Multi-point gas-fired water heater (instantaneous serving several taps)", "efficiency": 65,
+     "code": 908},
+    {"category": "Hot Water Systems", "description": "Electric instantaneous at point of use", "efficiency": 100,
+     "code": 909},
+    {"category": "Hot Water Systems", "description": "Gas boiler/circulator for water heating only*", "efficiency": 65,
+     "code": 911},
+    {"category": "Hot Water Systems", "description": "Liquid fuel boiler/circulator for water heating only*",
+     "efficiency": 70, "code": 912},
+    {"category": "Hot Water Systems", "description": "Solid fuel boiler/circulator for water heating only",
+     "efficiency": 55, "code": 913},
+    # Range cookers with boiler for water heating only
+    {"category": "Hot Water Systems",
+     "description": "Range cooker with boiler for water heating only: Gas, single burner with permanent pilot",
+     "efficiency": 46, "code": 921},
+    {"category": "Hot Water Systems",
+     "description": "Range cooker with boiler for water heating only: Gas, single burner with automatic ignition",
+     "efficiency": 50,
+     "code": 922},
+    {"category": "Hot Water Systems",
+     "description": "Range cooker with boiler for water heating only: Gas, twin burner with permanent pilot pre 1998",
+     "efficiency": 60,
+     "code": 923},
+    {"category": "Hot Water Systems",
+     "description": "Range cooker with boiler for water heating only: Gas, twin burner with automatic ignition pre "
+                    "1998",
+     "efficiency": 65, "code": 924},
+    {"category": "Hot Water Systems",
+     "description": "Range cooker with boiler for water heating only: Gas, twin burner with permanent pilot 1998 or "
+                    "later",
+     "efficiency": 65, "code": 925},
+    {"category": "Hot Water Systems",
+     "description": "Range cooker with boiler for water heating only: Gas, twin burner with automatic ignition 1998 "
+                    "or later",
+     "efficiency": 70, "code": 926},
+    {"category": "Hot Water Systems",
+     "description": "Range cooker with boiler for water heating only: Liquid fuel, single burner", "efficiency": 60,
+     "code": 927},
+    {"category": "Hot Water Systems",
+     "description": "Range cooker with boiler for water heating only: Liquid fuel, twin burner pre 1998",
+     "efficiency": 70,
+     "code": 928},
+    {"category": "Hot Water Systems",
+     "description": "Range cooker with boiler for water heating only: Liquid fuel, twin burner 1998 or later",
+     "efficiency": 75,
+     "code": 929},
+    {"category": "Hot Water Systems",
+     "description": "Range cooker with boiler for water heating only: Solid fuel, integral oven and boiler",
+     "efficiency": 45,
+     "code": 930},
+    {"category": "Hot Water Systems",
+     "description": "Range cooker with boiler for water heating only: Solid fuel, independent oven and boiler",
+     "efficiency": 55,
+     "code": 931},
+    # Electric heat pump for water heating only
+    {"category": "Hot Water Systems", "description": "Electric heat pump for water heating only*", "efficiency": 170,
+     "code": 941},
+    # Hot-water only heat network
+    # Remove the SAP version
+    # {"category": "Hot Water Systems",
+    #  "description": "Hot-water only heat network (SAP)", "efficiency": None,
+    #  "code": 950},
+    {"category": "Hot Water Systems", "description": "Hot-water only heat network (RdSAP) - boilers", "efficiency": 80,
+     "code": 950},
+    {"category": "Hot Water Systems", "description": "Hot-water only heat network (RdSAP) - CHP", "efficiency": 75,
+     "code": 951},
+    {"category": "Hot Water Systems", "description": "Hot-water only heat network (RdSAP) - heat pump",
+     "efficiency": 300, "code": 952}
+]
+
+boilers_seasonal = [
+    {"category": "Boilers - seasonal",
+     "description": "Gas boilers (including mains gas, LPG and biogas) 1998 or later - Regular non-condensing with "
+                    "automatic ignition",
+     "efficiency_winter": 74, "efficiency_summer": 64, "code": 101},
+    {"category": "Boilers - seasonal",
+     "description": "Gas boilers (including mains gas, LPG and biogas) 1998 or later - Regular condensing with "
+                    "automatic ignition",
+     "efficiency_winter": 84, "efficiency_summer": 74, "code": 102},
+    {"category": "Boilers - seasonal",
+     "description": "Gas boilers (including mains gas, LPG and biogas) 1998 or later - Non-condensing combi with "
+                    "automatic ignition",
+     "efficiency_winter": 74, "efficiency_summer": 65, "code": 103},
+    {"category": "Boilers - seasonal",
+     "description": "Gas boilers (including mains gas, LPG and biogas) 1998 or later - Condensing combi with "
+                    "automatic ignition",
+     "efficiency_winter": 84, "efficiency_summer": 75, "code": 104},
+    {"category": "Boilers - seasonal",
+     "description": "Gas boilers (including mains gas, LPG and biogas) 1998 or later - Regular non-condensing with "
+                    "permanent pilot light",
+     "efficiency_winter": 70, "efficiency_summer": 60, "code": 105},
+    {"category": "Boilers - seasonal",
+     "description": "Gas boilers (including mains gas, LPG and biogas) 1998 or later - Regular condensing with "
+                    "permanent pilot light",
+     "efficiency_winter": 80, "efficiency_summer": 70, "code": 106},
+    {"category": "Boilers - seasonal",
+     "description": "Gas boilers (including mains gas, LPG and biogas) 1998 or later - Non-condensing combi with "
+                    "permanent pilot light",
+     "efficiency_winter": 70, "efficiency_summer": 61, "code": 107},
+    {"category": "Boilers - seasonal",
+     "description": "Gas boilers (including mains gas, LPG and biogas) 1998 or later - Condensing combi with "
+                    "permanent pilot light",
+     "efficiency_winter": 80, "efficiency_summer": 71, "code": 108},
+    {"category": "Boilers - seasonal",
+     "description": "Gas boilers (including mains gas, LPG and biogas) 1998 or later - Back boiler to radiators",
+     "efficiency_winter": 66, "efficiency_summer": 56, "code": 109},
+    {"category": "Boilers - seasonal",
+     "description": "Gas boilers (including mains gas, LPG and biogas) pre-1998, with fan-assisted flue - Regular, "
+                    "low thermal capacity",
+     "efficiency_winter": 73, "efficiency_summer": 63, "code": 110},
+    {"category": "Boilers - seasonal",
+     "description": "Gas boilers (including mains gas, LPG and biogas) pre-1998, with fan-assisted flue - Regular, "
+                    "high or unknown thermal capacity",
+     "efficiency_winter": 69, "efficiency_summer": 59, "code": 111},
+    {"category": "Boilers - seasonal",
+     "description": "Gas boilers (including mains gas, LPG and biogas) pre-1998, with fan-assisted flue - Combi",
+     "efficiency_winter": 71, "efficiency_summer": 62, "code": 112},
+    {"category": "Boilers - seasonal",
+     "description": "Gas boilers (including mains gas, LPG and biogas) pre-1998, with fan-assisted flue - Condensing "
+                    "combi",
+     "efficiency_winter": 84, "efficiency_summer": 75, "code": 113},
+    {"category": "Boilers - seasonal",
+     "description": "Gas boilers (including mains gas, LPG and biogas) pre-1998, with fan-assisted flue - Regular, "
+                    "condensing",
+     "efficiency_winter": 84, "efficiency_summer": 74, "code": 114},
+    {"category": "Boilers - seasonal",
+     "description": "Gas boilers (including mains gas, LPG and biogas) pre-1998, with balanced or open flue - "
+                    "Regular, wall mounted",
+     "efficiency_winter": 66, "efficiency_summer": 56, "code": 115},
+    {"category": "Boilers - seasonal",
+     "description": "Gas boilers (including mains gas, LPG and biogas) pre-1998, with balanced or open flue - "
+                    "Regular, floor mounted, pre 1979",
+     "efficiency_winter": 56, "efficiency_summer": 46, "code": 116},
+    {"category": "Boilers - seasonal",
+     "description": "Gas boilers (including mains gas, LPG and biogas) pre-1998, with balanced or open flue - "
+                    "Regular, floor mounted, 1979 to 1997",
+     "efficiency_winter": 66, "efficiency_summer": 56, "code": 117},
+    {"category": "Boilers - seasonal",
+     "description": "Gas boilers (including mains gas, LPG and biogas) pre-1998, with balanced or open flue - Combi",
+     "efficiency_winter": 66, "efficiency_summer": 57, "code": 118},
+    {"category": "Boilers - seasonal",
+     "description": "Gas boilers (including mains gas, LPG and biogas) pre-1998, with balanced or open flue - Back "
+                    "boiler to radiators",
+     "efficiency_winter": 66, "efficiency_summer": 56, "code": 119},
+    {"category": "Boilers - seasonal",
+     "description": "Combined Primary Storage Units (CPSU) (mains gas, LPG and biogas) - With automatic ignition ("
+                    "non-condensing)",
+     "efficiency_winter": 74, "efficiency_summer": 72, "code": 120},
+    {"category": "Boilers - seasonal",
+     "description": "Combined Primary Storage Units (CPSU) (mains gas, LPG and biogas) - With automatic ignition ("
+                    "condensing)",
+     "efficiency_winter": 83, "efficiency_summer": 81, "code": 121},
+    {"category": "Boilers - seasonal",
+     "description": "Combined Primary Storage Units (CPSU) (mains gas, LPG and biogas) - With permanent pilot ("
+                    "non-condensing)",
+     "efficiency_winter": 70, "efficiency_summer": 68, "code": 122},
+    {"category": "Boilers - seasonal",
+     "description": "Combined Primary Storage Units (CPSU) (mains gas, LPG and biogas) - With permanent pilot ("
+                    "condensing)",
+     "efficiency_winter": 79, "efficiency_summer": 77, "code": 123},
+    {"category": "Boilers - seasonal", "description": "Liquid fuel boilers - Standard oil boiler pre-1985",
+     "efficiency_winter": 66, "efficiency_summer": 54, "code": 124},
+    {"category": "Boilers - seasonal", "description": "Liquid fuel boilers - Standard oil boiler 1985 to 1997",
+     "efficiency_winter": 71, "efficiency_summer": 59, "code": 125},
+    {"category": "Boilers - seasonal", "description": "Liquid fuel boilers - Standard oil boiler, 1998 or later",
+     "efficiency_winter": 80, "efficiency_summer": 68, "code": 126},
+    {"category": "Boilers - seasonal", "description": "Liquid fuel boilers - Condensing oil boiler",
+     "efficiency_winter": 84, "efficiency_summer": 72, "code": 127},
+    {"category": "Boilers - seasonal", "description": "Liquid fuel boilers - Combi oil boiler, pre-1998",
+     "efficiency_winter": 71, "efficiency_summer": 62, "code": 128},
+    {"category": "Boilers - seasonal", "description": "Liquid fuel boilers - Combi oil boiler, 1998 or later",
+     "efficiency_winter": 77, "efficiency_summer": 68, "code": 129},
+    {"category": "Boilers - seasonal", "description": "Liquid fuel boilers - Condensing combi oil boiler",
+     "efficiency_winter": 82, "efficiency_summer": 73, "code": 130},
+    {"category": "Boilers - seasonal",
+     "description": "Liquid fuel boilers - Oil room heater with boiler to radiators, pre 2000", "efficiency_winter": 66,
+     "efficiency_summer": 54, "code": 131},
+    {"category": "Boilers - seasonal",
+     "description": "Liquid fuel boilers - Oil room heater with boiler to radiators, 2000 or later",
+     "efficiency_winter": 71, "efficiency_summer": 59, "code": 132},
+    {"category": "Boilers - seasonal",
+     "description": "Range cooker boilers (mains gas, LPG and biogas) - Single burner with permanent pilot",
+     "efficiency_winter": 47, "efficiency_summer": 37, "code": 133},
+    {"category": "Boilers - seasonal",
+     "description": "Range cooker boilers (mains gas, LPG and biogas) - Single burner with automatic ignition",
+     "efficiency_winter": 51, "efficiency_summer": 41, "code": 134},
+    {"category": "Boilers - seasonal",
+     "description": "Range cooker boilers (mains gas, LPG and biogas) - Twin burner with permanent pilot ("
+                    "non-condensing) pre 1998",
+     "efficiency_winter": 61, "efficiency_summer": 51, "code": 135},
+    {"category": "Boilers - seasonal",
+     "description": "Range cooker boilers (mains gas, LPG and biogas) - Twin burner with automatic ignition ("
+                    "non-condensing) pre 1998",
+     "efficiency_winter": 66, "efficiency_summer": 56, "code": 136},
+    {"category": "Boilers - seasonal",
+     "description": "Range cooker boilers (mains gas, LPG and biogas) - Twin burner with permanent pilot ("
+                    "non-condensing) 1998 or later",
+     "efficiency_winter": 66, "efficiency_summer": 56, "code": 137},
+    {"category": "Boilers - seasonal",
+     "description": "Range cooker boilers (mains gas, LPG and biogas) - Twin burner with automatic ignition ("
+                    "non-condensing) 1998 or later",
+     "efficiency_winter": 71, "efficiency_summer": 61, "code": 138},
+    {"category": "Boilers - seasonal", "description": "Range cooker boilers (liquid fuel) - Single burner",
+     "efficiency_winter": 61, "efficiency_summer": 49, "code": 139},
+    {"category": "Boilers - seasonal",
+     "description": "Range cooker boilers (liquid fuel) - Twin burner (non-condensing) pre 1998",
+     "efficiency_winter": 71, "efficiency_summer": 59, "code": 140},
+    {"category": "Boilers - seasonal",
+     "description": "Range cooker boilers (liquid fuel) - Twin burner (non-condensing) 1998 or later",
+     "efficiency_winter": 76, "efficiency_summer": 64, "code": 141},
+]
+
+# Heating controls
+no_heating_system_controls = [
+    {
+        "category": "No heating system present",
+        "description": "None",
+        "control": 2,
+        "temperature_adjustment_c": "+0.3",
+        "code": 2699
+    }
+]
+
+boiler_system_controls = [
+    {"category": "Boiler Systems with Radiators or Underfloor Heating",
+     "description": "Not applicable", "control": None, "temperature_adjustment_c": None,
+     "code": 2100},
+    {"category": "Boiler Systems with Radiators or Underfloor Heating",
+     "description": "No time or thermostatic control of room temperature", "control": 1,
+     "temperature_adjustment_c": "+0.6", "code": 2101},
+    {"category": "Boiler Systems with Radiators or Underfloor Heating", "description": "Programmer, no room thermostat",
+     "control": 1, "temperature_adjustment_c": "+0.6", "code": 2102},
+    {"category": "Boiler Systems with Radiators or Underfloor Heating", "description": "Room thermostat only",
+     "control": 1, "temperature_adjustment_c": "0", "code": 2103},
+    {"category": "Boiler Systems with Radiators or Underfloor Heating", "description": "Programmer and room thermostat",
+     "control": 1, "temperature_adjustment_c": "0", "code": 2104},
+    {"category": "Boiler Systems with Radiators or Underfloor Heating",
+     "description": "Programmer and at least two room thermostats", "control": 2, "temperature_adjustment_c": "0",
+     "code": 2105},
+    {"category": "Boiler Systems with Radiators or Underfloor Heating", "description": "Room thermostat and TRVs",
+     "control": 2, "temperature_adjustment_c": "0", "code": 2113},
+    {"category": "Boiler Systems with Radiators or Underfloor Heating",
+     "description": "Programmer, room thermostat and TRVs", "control": 2, "temperature_adjustment_c": "0",
+     "code": 2106},
+    {"category": "Boiler Systems with Radiators or Underfloor Heating", "description": "TRVs and bypass", "control": 2,
+     "temperature_adjustment_c": "0", "code": 2111},
+    {"category": "Boiler Systems with Radiators or Underfloor Heating", "description": "Programmer, TRVs and bypass",
+     "control": 2, "temperature_adjustment_c": "0", "code": 2107},
+    {"category": "Boiler Systems with Radiators or Underfloor Heating",
+     "description": "Programmer, TRVs and flow switch", "control": 2, "temperature_adjustment_c": "0", "code": 2108},
+    {"category": "Boiler Systems with Radiators or Underfloor Heating",
+     "description": "Programmer, TRVs and boiler energy manager", "control": 2, "temperature_adjustment_c": "0",
+     "code": 2109},
+    {"category": "Boiler Systems with Radiators or Underfloor Heating",
+     "description": "Time and temperature zone control by arrangement of plumbing and electrical services",
+     "control": 3, "temperature_adjustment_c": "0", "code": 2110},
+    {"category": "Boiler Systems with Radiators or Underfloor Heating",
+     "description": "Time and temperature zone control by device in PCDB", "control": 3,
+     "temperature_adjustment_c": "0", "code": 2112},
+]
+
+heat_pump_controls = [
+    # We have a previous 2100 code for not applicable
+    # {"category": "Heat Pumps with Radiators or Underfloor Heating",
+    #  "description": "Not applicable (heat pump provides DHW only)", "control": None, "temperature_adjustment_c": None,
+    #  "code": 2100},
+    {"category": "Heat Pumps with Radiators or Underfloor Heating",
+     "description": "No time or thermostatic control of room temperature", "control": 1,
+     "temperature_adjustment_c": "+0.3", "code": 2201},
+    {"category": "Heat Pumps with Radiators or Underfloor Heating", "description": "Programmer, no room thermostat",
+     "control": 1, "temperature_adjustment_c": "+0.3", "code": 2202},
+    {"category": "Heat Pumps with Radiators or Underfloor Heating", "description": "Room thermostat only", "control": 1,
+     "temperature_adjustment_c": "0", "code": 2203},
+    {"category": "Heat Pumps with Radiators or Underfloor Heating", "description": "Programmer and room thermostat",
+     "control": 1, "temperature_adjustment_c": "0", "code": 2204},
+    {"category": "Heat Pumps with Radiators or Underfloor Heating",
+     "description": "Programmer and at least two room thermostats", "control": 2, "temperature_adjustment_c": "0",
+     "code": 2205},
+    {"category": "Heat Pumps with Radiators or Underfloor Heating", "description": "Room thermostat and TRVs",
+     "control": 2, "temperature_adjustment_c": "0", "code": 2209},
+    {"category": "Heat Pumps with Radiators or Underfloor Heating",
+     "description": "Programmer, room thermostat and TRVs", "control": 2, "temperature_adjustment_c": "0",
+     "code": 2210},
+    {"category": "Heat Pumps with Radiators or Underfloor Heating", "description": "Programmer, TRVs and bypass",
+     "control": 2, "temperature_adjustment_c": "0", "code": 2206},
+    {"category": "Heat Pumps with Radiators or Underfloor Heating",
+     "description": "Time and temperature zone control by arrangement of plumbing and electrical services",
+     "control": 3, "temperature_adjustment_c": "0", "code": 2207},
+    {"category": "Heat Pumps with Radiators or Underfloor Heating",
+     "description": "Time and temperature zone control by device in PCDB", "control": 3,
+     "temperature_adjustment_c": "0", "code": 2208},
+]
+
+heat_network_controls = [
+    {"category": "Heat Networks", "description": "Flat rate charging*, no thermostatic control of room temperature",
+     "control": 1, "temperature_adjustment_c": "+0.3", "code": 2301},
+    {"category": "Heat Networks", "description": "Flat rate charging*, programmer, no room thermostat", "control": 1,
+     "temperature_adjustment_c": "+0.3", "code": 2302},
+    {"category": "Heat Networks", "description": "Flat rate charging*, room thermostat only", "control": 1,
+     "temperature_adjustment_c": "0", "code": 2303},
+    {"category": "Heat Networks", "description": "Flat rate charging*, programmer and room thermostat", "control": 1,
+     "temperature_adjustment_c": "0", "code": 2304},
+    {"category": "Heat Networks", "description": "Flat rate charging*, room thermostat and TRVs", "control": 2,
+     "temperature_adjustment_c": "0", "code": 2313},
+    {"category": "Heat Networks", "description": "Flat rate charging*, TRVs", "control": 2,
+     "temperature_adjustment_c": "0", "code": 2307},
+    {"category": "Heat Networks", "description": "Flat rate charging*, programmer and TRVs", "control": 2,
+     "temperature_adjustment_c": "0", "code": 2305},
+    {"category": "Heat Networks", "description": "Flat rate charging*, programmer and at least two room thermostats",
+     "control": 2, "temperature_adjustment_c": "0", "code": 2311},
+    {"category": "Heat Networks", "description": "Charging system linked to use of heating, room thermostat only",
+     "control": 2, "temperature_adjustment_c": "0", "code": 2308},
+    {"category": "Heat Networks",
+     "description": "Charging system linked to use of heating, programmer and room thermostat", "control": 2,
+     "temperature_adjustment_c": "0", "code": 2309},
+    {"category": "Heat Networks", "description": "Charging system linked to use of heating, room thermostat and TRVs",
+     "control": 3, "temperature_adjustment_c": "0", "code": 2314},
+    {"category": "Heat Networks", "description": "Charging system linked to use of heating, TRVs", "control": 3,
+     "temperature_adjustment_c": "0", "code": 2310},
+    {"category": "Heat Networks", "description": "Charging system linked to use of heating, programmer and TRVs",
+     "control": 3, "temperature_adjustment_c": "0", "code": 2306},
+    {"category": "Heat Networks",
+     "description": "Charging system linked to use of heating, programmer and at least two room thermostats",
+     "control": 3, "temperature_adjustment_c": "0", "code": 2312},
+]
+
+electric_storage_systems_controls = [
+    {"category": "Electric Storage Systems", "description": "Manual charge control", "control": 3,
+     "temperature_adjustment_c": "+0.7", "code": 2401},
+    {"category": "Electric Storage Systems", "description": "Automatic charge control", "control": 3,
+     "temperature_adjustment_c": "+0.4", "code": 2402},
+    {"category": "Electric Storage Systems", "description": "Celect-type controls", "control": 3,
+     "temperature_adjustment_c": "+0.4", "code": 2403},
+    {"category": "Electric Storage Systems", "description": "Controls for high heat retention storage heaters §",
+     "control": 3, "temperature_adjustment_c": "0", "code": 2404},
+]
+
+warm_air_systems_controls = [
+    {"category": "Warm Air Systems", "description": "No time or thermostatic control of room temperature", "control": 1,
+     "temperature_adjustment_c": "+0.3", "code": 2501},
+    {"category": "Warm Air Systems", "description": "Programmer, no room thermostat", "control": 1,
+     "temperature_adjustment_c": "+0.3", "code": 2502},
+    {"category": "Warm Air Systems", "description": "Room thermostat only", "control": 1,
+     "temperature_adjustment_c": "0", "code": 2503},
+    {"category": "Warm Air Systems", "description": "Programmer and room thermostat", "control": 1,
+     "temperature_adjustment_c": "0", "code": 2504},
+    {"category": "Warm Air Systems", "description": "Programmer and at least two room thermostats", "control": 2,
+     "temperature_adjustment_c": "0", "code": 2505},
+    {"category": "Warm Air Systems", "description": "Time and temperature zone control", "control": 3,
+     "temperature_adjustment_c": "0", "code": 2506},
+]
+
+room_heater_systems_controls = [
+    {"category": "Room Heater Systems", "description": "No thermostatic control of room temperature", "control": 2,
+     "temperature_adjustment_c": "+0.3", "code": 2601},
+    {"category": "Room Heater Systems", "description": "Appliance thermostats", "control": 3,
+     "temperature_adjustment_c": "0", "code": 2602},
+    {"category": "Room Heater Systems", "description": "Programmer and appliance thermostats", "control": 3,
+     "temperature_adjustment_c": "0", "code": 2603},
+    {"category": "Room Heater Systems", "description": "Room thermostats only", "control": 3,
+     "temperature_adjustment_c": "0", "code": 2604},
+    {"category": "Room Heater Systems", "description": "Programmer and room thermostats", "control": 3,
+     "temperature_adjustment_c": "0", "code": 2605},
+]
+
+other_systems_controls = [
+    {"category": "Other Systems", "description": "No time or thermostatic control of room temperature", "control": 1,
+     "temperature_adjustment_c": "+0.3", "code": 2701},
+    {"category": "Other Systems", "description": "Programmer, no room thermostat", "control": 1,
+     "temperature_adjustment_c": "+0.3", "code": 2702},
+    {"category": "Other Systems", "description": "Room thermostat only", "control": 1, "temperature_adjustment_c": "0",
+     "code": 2703},
+    {"category": "Other Systems", "description": "Programmer and room thermostat", "control": 1,
+     "temperature_adjustment_c": "0", "code": 2704},
+    {"category": "Other Systems", "description": "Temperature zone control", "control": 2,
+     "temperature_adjustment_c": "0", "code": 2705},
+    {"category": "Other Systems", "description": "Time and temperature zone control", "control": 3,
+     "temperature_adjustment_c": "0", "code": 2706},
+]
+
+heating_data = (
+    no_heating_system +
+    boiler_systems_with_radiators_or_underfloor_heating +
+    heat_pumps_with_radiators_or_underfloor_heating +
+    electric_heat_pumps_warm_air_distribution +
+    gas_fired_heat_pumps_warm_air_distribution +
+    heat_networks +
+    electric_storage_systems +
+    off_peak_tariffs_electric_underfloor_heating +
+    standard_or_off_peak_tariff_electric_underfloor_heating +
+    gas_fired_warm_air_fan_assisted +
+    gas_fired_warm_air_balanced_or_open_flue +
+    liquid_fired_warm_air +
+    electric_warm_air_systems +
+    room_heaters +
+    other_space_heating_systems +
+    hot_water_systems +
+    boilers_seasonal +
+    no_heating_system_controls +
+    boiler_system_controls +
+    heat_pump_controls +
+    heat_network_controls +
+    electric_storage_systems_controls +
+    warm_air_systems_controls +
+    room_heater_systems_controls +
+    other_systems_controls
+)
+
+heating_data = pd.DataFrame(heating_data)
diff --git a/recommendations/FireplaceRecommendations.py b/recommendations/FireplaceRecommendations.py
index 601a8eb0..9a9d7f76 100644
--- a/recommendations/FireplaceRecommendations.py
+++ b/recommendations/FireplaceRecommendations.py
@@ -50,5 +50,8 @@ class FireplaceRecommendations(Definitions):
                 # Take a very basic estimate of 6 hours, multipled by the number of open fireplaces to seal
                 "labour_hours": 6 * number_open_fireplaces,
                 "labour_days": 6 * number_open_fireplaces / 8,  # Assume 8 hour day
+                "description_simulation": {
+                    "number-open-fireplaces": 0
+                }
             }
         ]
diff --git a/recommendations/HeatingRecommender.py b/recommendations/HeatingRecommender.py
index 07bac2cd..1d409be6 100644
--- a/recommendations/HeatingRecommender.py
+++ b/recommendations/HeatingRecommender.py
@@ -116,7 +116,7 @@ class HeatingRecommender:
         # In the future, we'll allow overrides, so that non-intrusive surveys can contradict these conditions
         # and either allow or prevent the recommendation of an air source heat pump
 
-        if self.is_ashp_valid(exclusions=exclusions):
+        if self.property.is_ashp_valid(exclusions=exclusions):
             self.recommend_air_source_heat_pump(
                 phase=phase, has_cavity_or_loft_recommendations=has_cavity_or_loft_recommendations
             )
@@ -186,19 +186,6 @@ class HeatingRecommender:
             description = ("Replace the existing boiler and cylinder without a thermostat with a new electric combi "
                            "boiler")
 
-    def is_ashp_valid(self, exclusions):
-
-        if "air_source_heat_pump" in self.property.non_invasive_recommendations:
-            return True
-
-        if "air_source_heat_pump" in exclusions:
-            return False
-
-        suitable_property_type = self.property.data["property-type"] in ["House", "Bungalow"]
-        has_air_source_heat_pump = self.property.main_heating["has_air_source_heat_pump"]
-
-        return suitable_property_type and not has_air_source_heat_pump
-
     def recommend_air_source_heat_pump(self, phase, has_cavity_or_loft_recommendations, _return=False):
         """
         This method will implement the recommendation for an air source heat pump
diff --git a/recommendations/RoofRecommendations.py b/recommendations/RoofRecommendations.py
index a1f8c67c..56f3721a 100644
--- a/recommendations/RoofRecommendations.py
+++ b/recommendations/RoofRecommendations.py
@@ -87,6 +87,25 @@ class RoofRecommendations:
 
         return (self.insulation_thickness > self.MINIMUM_LOFT_ISULATION_MM) and self.property.roof["is_pitched"]
 
+    def is_room_roof_insulated(self):
+
+        """
+        Check if the room roof is already insulated
+        """
+
+        full_insulated_room_roof = (
+            self.property.roof["is_roof_room"] and
+            self.property.roof["insulation_thickness"] in ["average", "above_average"]
+        )
+
+        room_roof_insulated_at_rafters = (
+            self.property.roof["is_pitched"] and
+            self.property.roof["is_at_rafters"] and
+            self.property.roof["insulation_thickness"] in ["average", "above_average"]
+        )
+
+        return full_insulated_room_roof or room_roof_insulated_at_rafters
+
     def recommend(self, phase):
 
         if self.property.roof["has_dwelling_above"]:
@@ -105,8 +124,8 @@ class RoofRecommendations:
         if (self.insulation_thickness >= self.MINIMUM_FLAT_ROOF_ISULATION_MM) and self.property.roof["is_flat"]:
             return
 
-        if self.property.roof["is_roof_room"]:
-            raise ValueError("Update convert_thickness_to_numeric for room roof and implement")
+        if self.is_room_roof_insulated():
+            return
 
         # If we have a u-value already, need to implement this
         if u_value:
@@ -118,7 +137,17 @@ class RoofRecommendations:
                 return
             raise NotImplementedError("Implement me")
 
-        u_value = get_roof_u_value(**{**self.property.roof, "age_band": self.property.age_band})
+        u_value = get_roof_u_value(
+            insulation_thickness=self.property.roof["insulation_thickness"],
+            has_dwelling_above=self.property.roof["has_dwelling_above"],
+            is_loft=self.property.roof["is_loft"],
+            is_roof_room=self.property.roof["is_roof_room"],
+            is_thatched=self.property.roof["is_thatched"],
+            age_band=self.property.age_band,
+            is_flat=self.property.roof["is_flat"],
+            is_pitched=self.property.roof["is_pitched"],
+            is_at_rafters=self.property.roof["is_at_rafters"],
+        )
 
         self.estimated_u_value = u_value
         if (u_value <= self.BUILDING_REGULATIONS_PART_L_MAX_U_VALUE) and (
diff --git a/recommendations/SolarPvRecommendations.py b/recommendations/SolarPvRecommendations.py
index 276573ec..63519d02 100644
--- a/recommendations/SolarPvRecommendations.py
+++ b/recommendations/SolarPvRecommendations.py
@@ -78,23 +78,6 @@ class SolarPvRecommendations:
             }
         ]
 
-    def is_solar_pv_valid(self):
-
-        # If the property is a flat but we are looking at building solar potential, we can include this
-        if (self.property.building_id is not None) and (self.property.solar_panel_configuration is not None):
-            return True
-
-        is_valid_property_type = self.property.data["property-type"] in ["House", "Bungalow", "Maisonette"]
-        is_valid_roof_type = (
-            self.property.roof["is_flat"] or self.property.roof["is_pitched"] or self.property.roof["is_roof_room"]
-        )
-        # If there is no existing solar PV, the photo-supply field will be None or a missing value
-        has_no_existing_solar_pv = self.property.data["photo-supply"] in [
-            None, 0, self.property.DATA_ANOMALY_MATCHES
-        ]
-
-        return is_valid_property_type and is_valid_roof_type and has_no_existing_solar_pv
-
     def recommend_building_analysis(self, phase):
         """
         This recommendation approach handles the case of producing solar PV recommendations at the building level,
@@ -117,7 +100,7 @@ class SolarPvRecommendations:
             roof_coverage_percent = round(recommendation_config["panneled_roof_area"] / total_roof_area * 100)
             # Spread the cost to the individual units - adding a 20% contingency
             total_cost = recommendation_config["total_cost"] / n_units
-            kw = np.floor(recommendation_config["array_warrage"] / 100) / 10
+            kw = np.floor(recommendation_config["array_wattage"] / 100) / 10
             # Default to a weeks work for a team of 3 people doing 8 hour days
             labour_days = 5
             labour_hours = 3 * 8 * labour_days
@@ -159,7 +142,7 @@ class SolarPvRecommendations:
         :return:
         """
 
-        if not self.is_solar_pv_valid():
+        if not self.property.is_solar_pv_valid():
             return
 
         # If we have a buiilding level analysis, we implement separate logic
@@ -167,84 +150,47 @@ class SolarPvRecommendations:
             self.recommend_building_analysis(phase)
             return
 
-        solar_pv_percentage = self.property.solar_pv_percentage
-        # We round up to the neaest 10%
-        solar_pv_percentage = np.ceil(solar_pv_percentage * 10) / 10
+        panel_performance = self.property.solar_panel_configuration["panel_performance"]
+        roof_area = self.property.roof_area
 
-        # For the solar recommendations, we produce the following scenarios:
-        # 1) Solar panels only, we present a high, medium and low coverage
-        # 2) With and without battery
-        roof_coverage_scenarios = [
-            solar_pv_percentage - 0.1, solar_pv_percentage,
-        ]
-        if solar_pv_percentage <= 0.4:
-            roof_coverage_scenarios.append(solar_pv_percentage + 0.1)
-        # We make sure we haven't gone too low or high - we allow no more than 60% coverage
-        roof_coverage_scenarios = [v for v in roof_coverage_scenarios if 0 <= v <= 0.6]
-        # If we only have two scenarios, we add a coverage scenario 10% less than the smallest
-        if len(roof_coverage_scenarios) == 2:
-            roof_coverage_scenarios.insert(0, roof_coverage_scenarios[0] - 0.1)
-        battery_scenarios = [False, True]
+        solar_configurations = panel_performance.head(3).reset_index(drop=True)
 
-        scenarios_with_wattage = []
-        for roof_coverage in roof_coverage_scenarios:
-            # We now have a property which is potentially suitable for solar PV
-            solar_pv_roof_area = self.property.get_solar_pv_roof_area(roof_coverage)
+        # We combine each of these configurations with estimates with and without a battery
+        for rank, recommendation_config in solar_configurations.iterrows():
+            roof_coverage_percent = round(recommendation_config["panneled_roof_area"] / roof_area * 100)
+            for has_battery in [False, True]:
+                cost_result = self.costs.solar_pv(
+                    wattage=recommendation_config["array_wattage"], has_battery=has_battery
+                )
+                kw = np.floor(recommendation_config["array_wattage"] / 100) / 10
+                if has_battery:
+                    description = (f"Install a {kw} kilowatt-peak (kWp) solar photovoltaic (PV) panel system on "
+                                   f"{round(roof_coverage_percent)}% the roof, with a battery storage system.")
+                else:
+                    description = (f"Install a {kw} kilowatt-peak (kWp) solar photovoltaic (PV) p"
+                                   f"anel system on {round(roof_coverage_percent)}% the roof.")
 
-            number_solar_panels = np.floor(solar_pv_roof_area / self.SOLAR_PANEL_AREA)
-            solar_panel_wattage = number_solar_panels * self.SOLAR_PANEL_WATTAGE
+                already_installed = "solar_pv" in self.property.already_installed
+                if already_installed:
+                    cost_result = override_costs(cost_result)
 
-            if solar_panel_wattage < self.MIN_SYSTEM_WATTAGE:
-                continue
-
-            solar_panel_wattage = np.clip(
-                a=solar_panel_wattage, a_min=self.MIN_SYSTEM_WATTAGE, a_max=self.MAX_SYSTEM_WATTAGE
-            )
-            scenarios_with_wattage.append((roof_coverage, solar_panel_wattage))
-
-        # We trim the scenarios, so that we don't have duplicate wattages
-        scenarios_with_wattage = self.trim_solar_wattage_options(scenarios_with_wattage)
-
-        # Produce the cross product of the scenarios
-        scenarios = [
-            (roof, wattage, battery) for roof, wattage in scenarios_with_wattage for battery in battery_scenarios
-        ]
-        # We deduce the wattage of the solar panels based on the roof coverage
-
-        for roof_coverage, solar_panel_wattage, has_battery in scenarios:
-            # We now have a property which is potentially suitable for solar PV
-            roof_coverage_percent = round(roof_coverage * 100)
-            # Given the wattage, we estimate the cost of the solar PV system. This is based on the MCS database
-            # of solar PV installations
-            cost_result = self.costs.solar_pv(wattage=solar_panel_wattage, has_battery=has_battery)
-            kw = np.floor(solar_panel_wattage / 100) / 10
-
-            if has_battery:
-                description = (f"Install a {kw} kilowatt-peak (kWp) solar photovoltaic (PV) panel system on "
-                               f"{round(roof_coverage_percent)}% the roof, with a battery storage system.")
-            else:
-                description = (f"Install a {kw} kilowatt-peak (kWp) solar photovoltaic (PV) p"
-                               f"anel system on {round(roof_coverage_percent)}% the roof.")
-
-            already_installed = "solar_pv" in self.property.already_installed
-            if already_installed:
-                cost_result = override_costs(cost_result)
-
-            self.recommendation.append(
-                {
-                    "phase": phase,
-                    "parts": [],
-                    "type": "solar_pv",
-                    "description": description,
-                    "starting_u_value": None,
-                    "new_u_value": None,
-                    "sap_points": None,
-                    "already_installed": already_installed,
-                    **cost_result,
-                    # This is required for simulating the SAP impact. solar_pv_percentage is between 0 & 1 so we scale
-                    # back up here
-                    "photo_supply": 100 * roof_coverage,
-                    "has_battery": has_battery,
-                    "description_simulation": {"photo-supply": 100 * roof_coverage},
-                }
-            )
+                self.recommendation.append(
+                    {
+                        "phase": phase,
+                        "parts": [],
+                        "type": "solar_pv",
+                        "description": description,
+                        "starting_u_value": None,
+                        "new_u_value": None,
+                        "sap_points": None,
+                        "already_installed": already_installed,
+                        **cost_result,
+                        # This is required for simulating the SAP impact. solar_pv_percentage is between 0 & 1 so we
+                        # scale
+                        # back up here
+                        "photo_supply": roof_coverage_percent,
+                        "has_battery": has_battery,
+                        "initial_ac_kwh_per_year": recommendation_config["initial_ac_kwh_per_year"],
+                        "description_simulation": {"photo-supply": roof_coverage_percent},
+                    }
+                )
diff --git a/recommendations/WindowsRecommendations.py b/recommendations/WindowsRecommendations.py
index 29c75989..3826a470 100644
--- a/recommendations/WindowsRecommendations.py
+++ b/recommendations/WindowsRecommendations.py
@@ -48,6 +48,7 @@ class WindowsRecommendations:
         is_secondary_glazing = self.property.restricted_measures or (
             self.property.windows["glazing_type"] == "secondary"
         )
+        windows_area = self.property.windows_area
 
         if not number_of_windows:
             raise ValueError("Number of windows not specified")
@@ -57,6 +58,9 @@ class WindowsRecommendations:
         ):
             return
 
+        if windows_area is not None:
+            raise Exception("We have windows area, we should use this data for our recommendations!!!")
+
         # We scale the number of windows based on the proportion of existing glazing
         if self.property.data["multi-glaze-proportion"] != "":
             n_windows_scalar = 1 - (
diff --git a/recommendations/optimiser/optimiser_functions.py b/recommendations/optimiser/optimiser_functions.py
index 083a7c25..c1123e3d 100644
--- a/recommendations/optimiser/optimiser_functions.py
+++ b/recommendations/optimiser/optimiser_functions.py
@@ -9,7 +9,7 @@ def prepare_input_measures(property_recommendations, goal):
     """
 
     goal_map = {
-        "Increase EPC": "sap_points"
+        "Increasing EPC": "sap_points"
     }
 
     goal_key = goal_map[goal]
diff --git a/recommendations/recommendation_utils.py b/recommendations/recommendation_utils.py
index 9b5e22d1..ce32e061 100644
--- a/recommendations/recommendation_utils.py
+++ b/recommendations/recommendation_utils.py
@@ -205,10 +205,22 @@ def get_wall_u_value(
     return float(mapped_value)
 
 
-def get_u_value_from_s9(thickness, s9, is_loft, is_roof_room, is_thatched):
+def get_u_value_from_s9(thickness, s9, is_loft, is_roof_room, is_thatched, is_at_rafters):
     """Get the U-value from table S9 based on the insulation thickness."""
+
+    # If the roof as pitched & insulated at the rafters, it's a room roof
+    if is_roof_room or is_at_rafters:
+        # We re-map the thickness
+        thickness_map = {
+            "below average": "50",
+            "average": "100",
+            "above average": "270",
+            "none": "0",
+        }
+        thickness = thickness_map[thickness]
+
     if thickness in ["below average", "average", "above average", "none", None] or (
-        not is_loft and not is_roof_room
+        not is_loft and not is_roof_room and not is_at_rafters
     ):
         return None
     elif thickness.endswith("+"):
@@ -280,6 +292,7 @@ def get_roof_u_value(
         is_loft=is_loft,
         is_roof_room=is_roof_room,
         is_thatched=is_thatched,
+        is_at_rafters=is_at_rafters
     )
 
     if u_value is not None:
@@ -676,7 +689,7 @@ def estimate_windows(
     property_type, built_form, construction_age_band, floor_area, number_habitable_rooms
 ):
     # If there is an extension, that will boost the number of habitable rooms
-    
+
     # Base window count based on habitable rooms
     window_count = number_habitable_rooms
 
diff --git a/utils/s3.py b/utils/s3.py
index 1b14ca97..b3553824 100644
--- a/utils/s3.py
+++ b/utils/s3.py
@@ -276,3 +276,86 @@ def list_files_in_s3_folder(bucket_name, folder_name):
     except Exception as e:
         logger.error(f'Failed to list files in folder {folder_name} in bucket {bucket_name}: {str(e)}')
         return []
+
+
+def list_files_and_subfolders_in_s3_folder(bucket_name, folder_name):
+    """
+    List all files and immediate subfolders in a given folder in an S3 bucket.
+
+    E.g. if we have a folder structure in S3 like this:
+    - folder1/
+        - file1.csv
+        - file2.csv
+        - subfolder1/
+            - file3.csv
+
+    Then calling list_files_and_subfolders_in_s3_folder(bucket_name='my-bucket', folder_name='folder1/')
+    would return ['folder1/file1.csv', 'folder1/file2.csv', 'folder1/subfolder1/'].
+
+    Namely, the nested files are not included in the list, only the immediate files and subfolders.
+
+    :param bucket_name: The name of the S3 bucket.
+    :param folder_name: The folder name within the S3 bucket.
+    :return: A list of file keys and subfolder prefixes in the specified S3 folder.
+    """
+
+    # For this function, folder_name should end with a forward slash
+    if not folder_name.endswith('/'):
+        folder_name += '/'
+
+    try:
+        s3 = boto3.client('s3')
+        response = s3.list_objects_v2(Bucket=bucket_name, Prefix=folder_name, Delimiter='/')
+
+        items = []
+
+        # Add files to the list
+        if 'Contents' in response:
+            items.extend([content['Key'] for content in response['Contents'] if content['Key'] != folder_name])
+
+        # Add immediate subfolders to the list
+        if 'CommonPrefixes' in response:
+            items.extend([prefix['Prefix'] for prefix in response['CommonPrefixes']])
+
+        return items
+
+    except NoCredentialsError:
+        logger.error("Credentials not available.")
+        return []
+    except PartialCredentialsError:
+        logger.error("Incomplete credentials provided.")
+        return []
+    except Exception as e:
+        logger.error(f'Failed to list files and subfolders in folder {folder_name} in bucket {bucket_name}: {str(e)}')
+        return []
+
+
+def list_xmls_in_s3_folder(bucket_name, folder_name):
+    """
+    List all XML files in a given folder in an S3 bucket.
+
+    :param bucket_name: The name of the S3 bucket.
+    :param folder_name: The folder name within the S3 bucket.
+    :return: A list of XML file keys in the specified S3 folder.
+    """
+    try:
+        s3 = boto3.client('s3')
+        response = s3.list_objects_v2(Bucket=bucket_name, Prefix=folder_name)
+
+        if 'Contents' not in response:
+            logger.info(f"No files found in folder {folder_name} in bucket {bucket_name}.")
+            return []
+
+        # Filter XML files
+        xml_files = [content['Key'] for content in response['Contents'] if content['Key'].endswith('.xml')]
+        return xml_files
+
+    except NoCredentialsError:
+        logger.error("Credentials not available.")
+        return []
+    except PartialCredentialsError:
+        logger.error("Incomplete credentials provided.")
+        return []
+    except Exception as e:
+        logger.error(f'Failed to list XML files in folder {folder_name} in bucket {bucket_name}: {str(e)}')
+        return []