diff --git a/backend/ml_models/AnnualBillSavings.py b/backend/ml_models/AnnualBillSavings.py
index 9be9d78a..99d67126 100644
--- a/backend/ml_models/AnnualBillSavings.py
+++ b/backend/ml_models/AnnualBillSavings.py
@@ -19,7 +19,8 @@ class AnnualBillSavings:
     PRICE_FACTOR = 0.09549999999999999
 
     # Daily standard charge, based on average across England, Scotland and Wales, and includes VAT
-    DAILY_STANDARD_CHARGE = 0.3143
+    DAILY_STANDARD_CHARGE_GAS = 0.3143
+    DAILY_STANDARD_CHARGE_ELECTRICITY = 0.601
 
     EPC_BANDS = ["G", "F", "E", "D", "C", "B", "A"]
 
@@ -45,11 +46,12 @@ class AnnualBillSavings:
     def calculate_annual_bill(cls, kwh):
         """
         This method will estimate the total annual bill for a property
+        It assumed gas & electricity are used
         :param kwh: The total kwh consumption
         :return: An estimate for annual bill
         """
 
-        return cls.PRICE_FACTOR * kwh + cls.DAILY_STANDARD_CHARGE * 365
+        return cls.PRICE_FACTOR * kwh + (cls.DAILY_STANDARD_CHARGE_GAS + cls.DAILY_STANDARD_CHARGE_ELECTRICITY * 365)
 
     @classmethod
     def adjust_energy_to_metered(cls, epc_energy_consumption, current_epc_rating):
diff --git a/etl/customers/gla_croydon_demo/slides.py b/etl/customers/gla_croydon_demo/slides.py
index cbd1f7e4..9f791bbd 100644
--- a/etl/customers/gla_croydon_demo/slides.py
+++ b/etl/customers/gla_croydon_demo/slides.py
@@ -112,6 +112,49 @@ def scenario_1():
         recommendations_summary["total_bill_savings"] / recommendations_summary["current_energy_bill"]
     )
 
+    ########################
+    # Overview
+    ########################
+    overview_totals = recommendations_summary.sum()
+    overview_means = recommendations_summary.mean()
+
+    ########################
+    # Measures
+    ########################
+    measures_count = recommendations_df.groupby("type")["id"].count().reset_index()
+    wall_insulation_measures = measures_count[
+        measures_count["type"].isin(["cavity_wall_insulation", "external_wall_insulation", "internal_wall_insulation"])
+    ]["id"].sum()
+    ventilation_measures = measures_count[
+        measures_count["type"].isin(["mechanical_ventilation"])
+    ]["id"].sum()
+    roof_insulation_measures = measures_count[
+        measures_count["type"].isin(["loft_insulation", "flat_roof_insulation"])
+    ]["id"].sum()
+    floor_insulation_measures = measures_count[
+        measures_count["type"].isin(["solid_floor_insulation", "suspended_floor_insulation"])
+    ]["id"].sum()
+    windows = measures_count[
+        measures_count["type"].isin(["windows_glazing"])
+    ]["id"].sum()
+    heating = measures_count[
+        measures_count["type"].isin(["heating"])
+    ]["id"].sum()
+    heating_controls = measures_count[
+        measures_count["type"].isin(["heating_control"])
+    ]["id"].sum()
+    solar = measures_count[
+        measures_count["type"].isin(["solar_pv"])
+    ]["id"].sum()
+    other = measures_count[
+        ~measures_count["type"].isin([
+            "cavity_wall_insulation", "external_wall_insulation", "internal_wall_insulation",
+            "loft_insulation", "flat_roof_insulation", "solid_floor_insulation",
+            "suspended_floor_insulation", "windows_glazing", "heating", "heating_control", "solar_pv",
+            "mechanical_ventilation"
+        ])
+    ]["id"].sum()
+
     # Summary information by each archetype
     ########################
     # Archetype 1
@@ -121,10 +164,54 @@ def scenario_1():
         recommendations_summary["uprn"].astype(str).isin(archetype_1["uprn"].values)
     ]
 
+    arch_1_property_details = property_details_df[
+        property_details_df["uprn"].astype(str).isin(archetype_1["uprn"].values)
+    ]
+    arch_1_property_details["co2_emissions"].sum() / property_details_df["co2_emissions"].sum()
+
     # Take the mean, median and maximum of each value
-    arch_1_recommendation_min = recommendations_arch_1_summary.min()
-    arch_1_recommendation_max = recommendations_arch_1_summary.max()
-    arch_1_recommendation_means = recommendations_arch_1_summary.mean()
+    cols_to_keep = ["total_cost", "total_carbon", "total_bill_savings", "total_sap_points", "adjusted_heat_demand",
+                    "energy_percent_change", "carbon_percent_change", "bills_percent_change"]
+    arch_1_recommendation_min = recommendations_arch_1_summary.min()[cols_to_keep]
+    arch_1_recommendation_max = recommendations_arch_1_summary.max()[cols_to_keep]
+    arch_1_recommendation_means = recommendations_arch_1_summary.mean()[cols_to_keep]
+    arch_1_totals = recommendations_arch_1_summary.sum()[cols_to_keep]
+
+    annual_total_co2 = recommendations_arch_1_summary["total_carbon"].sum()
+    annual_total_bills = recommendations_arch_1_summary["total_bill_savings"].sum()
+    annual_total_energy_savings = recommendations_arch_1_summary["adjusted_heat_demand"].sum()
+    archetype_measures = \
+        recommendations_df[recommendations_df["uprn"].astype(str).isin(archetype_1["uprn"].values)].groupby("type")[
+            "id"].count().reset_index()
+
+    cost_text = (f"{round(arch_1_recommendation_means['total_cost'], 2)}: "
+                 f"{arch_1_recommendation_min['total_cost']} - {arch_1_recommendation_max['total_cost']}")
+
+    sap_text = (f"{round(arch_1_recommendation_means['total_sap_points'], 2)}: "
+                f"{arch_1_recommendation_min['total_sap_points']} - {arch_1_recommendation_max['total_sap_points']}")
+
+    energy_text = (f"{round(arch_1_recommendation_means['adjusted_heat_demand'], 2)}: "
+                   f"{arch_1_recommendation_min['adjusted_heat_demand']} - "
+                   f"{arch_1_recommendation_max['adjusted_heat_demand']}")
+
+    energy_percent_text = (f"{round(arch_1_recommendation_means['energy_percent_change'], 2)}: "
+                           f"{arch_1_recommendation_min['energy_percent_change']} - "
+                           f"{arch_1_recommendation_max['energy_percent_change']}")
+
+    carbon_text = (f"{round(arch_1_recommendation_means['total_carbon'], 2)}: "
+                   f"{arch_1_recommendation_min['total_carbon']} - {arch_1_recommendation_max['total_carbon']}")
+
+    carbon_percent_text = (f"{round(arch_1_recommendation_means['carbon_percent_change'], 2)}: "
+                           f"{arch_1_recommendation_min['carbon_percent_change']} - "
+                           f"{arch_1_recommendation_max['carbon_percent_change']}")
+
+    bill_text = (f"{round(arch_1_recommendation_means['total_bill_savings'], 2)}: "
+                 f"{arch_1_recommendation_min['total_bill_savings']} - "
+                 f"{arch_1_recommendation_max['total_bill_savings']}")
+
+    bill_percent_text = (f"{round(arch_1_recommendation_means['bills_percent_change'], 2)}: "
+                         f"{arch_1_recommendation_min['bills_percent_change']} - "
+                         f"{arch_1_recommendation_max['bills_percent_change']}")
 
     ########################
     # Archetype 2
@@ -134,11 +221,53 @@ def scenario_1():
         recommendations_summary["uprn"].astype(str).isin(archetype_2["uprn"].values)
     ]
 
+    arch_2_property_details = property_details_df[
+        property_details_df["uprn"].astype(str).isin(archetype_2["uprn"].values)
+    ]
+    arch_2_property_details["co2_emissions"].sum() / property_details_df["co2_emissions"].sum()
+
     # Take the mean, median and maximum of each value
     arch_2_recommendation_min = recommendations_arch_2_summary.min()
     arch_2_recommendation_max = recommendations_arch_2_summary.max()
     arch_2_recommendation_means = recommendations_arch_2_summary.mean().round(2)
 
+    total_cost = recommendations_arch_2_summary["total_cost"].sum()
+    annual_total_co2 = recommendations_arch_2_summary["total_carbon"].sum()
+    annual_total_bills = recommendations_arch_2_summary["total_bill_savings"].sum()
+    annual_total_energy_savings = recommendations_arch_2_summary["adjusted_heat_demand"].sum()
+    archetype_measures = \
+        recommendations_df[recommendations_df["uprn"].astype(str).isin(archetype_2["uprn"].values)].groupby("type")[
+            "id"].count().reset_index()
+
+    cost_text = (f"{round(arch_2_recommendation_means['total_cost'], 2)}: "
+                 f"{arch_2_recommendation_min['total_cost']} - {arch_2_recommendation_max['total_cost']}")
+
+    sap_text = (f"{round(arch_2_recommendation_means['total_sap_points'], 2)}: "
+                f"{arch_2_recommendation_min['total_sap_points']} - {arch_2_recommendation_max['total_sap_points']}")
+
+    energy_text = (f"{round(arch_2_recommendation_means['adjusted_heat_demand'], 2)}: "
+                   f"{arch_2_recommendation_min['adjusted_heat_demand']} - "
+                   f"{arch_2_recommendation_max['adjusted_heat_demand']}")
+
+    energy_percent_text = (f"{round(arch_2_recommendation_means['energy_percent_change'], 2)}: "
+                           f"{arch_2_recommendation_min['energy_percent_change']} - "
+                           f"{arch_2_recommendation_max['energy_percent_change']}")
+
+    carbon_text = (f"{round(arch_2_recommendation_means['total_carbon'], 2)}: "
+                   f"{arch_2_recommendation_min['total_carbon']} - {arch_2_recommendation_max['total_carbon']}")
+
+    carbon_percent_text = (f"{round(arch_2_recommendation_means['carbon_percent_change'], 2)}: "
+                           f"{arch_2_recommendation_min['carbon_percent_change']} - "
+                           f"{arch_2_recommendation_max['carbon_percent_change']}")
+
+    bill_text = (f"{round(arch_2_recommendation_means['total_bill_savings'], 2)}: "
+                 f"{arch_2_recommendation_min['total_bill_savings']} - "
+                 f"{arch_2_recommendation_max['total_bill_savings']}")
+
+    bill_percent_text = (f"{round(arch_2_recommendation_means['bills_percent_change'], 2)}: "
+                         f"{arch_2_recommendation_min['bills_percent_change']} - "
+                         f"{arch_2_recommendation_max['bills_percent_change']}")
+
     ########################
     # Archetype 3
     ########################
@@ -147,11 +276,53 @@ def scenario_1():
         recommendations_summary["uprn"].astype(str).isin(archetype_3["uprn"].values)
     ]
 
+    arch_3_property_details = property_details_df[
+        property_details_df["uprn"].astype(str).isin(archetype_3["uprn"].values)
+    ]
+    arch_3_property_details["co2_emissions"].sum() / property_details_df["co2_emissions"].sum()
+
     # Take the mean, median and maximum of each value
     arch_3_recommendation_min = recommendations_arch_3_summary.min()
     arch_3_recommendation_max = recommendations_arch_3_summary.max()
     arch_3_recommendation_means = recommendations_arch_3_summary.mean()
 
+    total_cost = recommendations_arch_3_summary["total_cost"].sum()
+    annual_total_co2 = recommendations_arch_3_summary["total_carbon"].sum()
+    annual_total_bills = recommendations_arch_3_summary["total_bill_savings"].sum()
+    annual_total_energy_savings = recommendations_arch_3_summary["adjusted_heat_demand"].sum()
+    archetype_measures = \
+        recommendations_df[recommendations_df["uprn"].astype(str).isin(archetype_3["uprn"].values)].groupby("type")[
+            "id"].count().reset_index()
+
+    cost_text = (f"{round(arch_3_recommendation_means['total_cost'], 2)}: "
+                 f"{arch_3_recommendation_min['total_cost']} - {arch_3_recommendation_max['total_cost']}")
+
+    sap_text = (f"{round(arch_3_recommendation_means['total_sap_points'], 2)}: "
+                f"{arch_3_recommendation_min['total_sap_points']} - {arch_3_recommendation_max['total_sap_points']}")
+
+    energy_text = (f"{round(arch_3_recommendation_means['adjusted_heat_demand'], 2)}: "
+                   f"{arch_3_recommendation_min['adjusted_heat_demand']} - "
+                   f"{arch_3_recommendation_max['adjusted_heat_demand']}")
+
+    energy_percent_text = (f"{round(arch_3_recommendation_means['energy_percent_change'], 2)}: "
+                           f"{arch_3_recommendation_min['energy_percent_change']} - "
+                           f"{arch_3_recommendation_max['energy_percent_change']}")
+
+    carbon_text = (f"{round(arch_3_recommendation_means['total_carbon'], 2)}: "
+                   f"{arch_3_recommendation_min['total_carbon']} - {arch_3_recommendation_max['total_carbon']}")
+
+    carbon_percent_text = (f"{round(arch_3_recommendation_means['carbon_percent_change'], 2)}: "
+                           f"{arch_3_recommendation_min['carbon_percent_change']} - "
+                           f"{arch_3_recommendation_max['carbon_percent_change']}")
+
+    bill_text = (f"{round(arch_3_recommendation_means['total_bill_savings'], 2)}: "
+                 f"{arch_3_recommendation_min['total_bill_savings']} - "
+                 f"{arch_3_recommendation_max['total_bill_savings']}")
+
+    bill_percent_text = (f"{round(arch_3_recommendation_means['bills_percent_change'], 2)}: "
+                         f"{arch_3_recommendation_min['bills_percent_change']} - "
+                         f"{arch_3_recommendation_max['bills_percent_change']}")
+
     ########################
     # Archetype 4
     ########################
@@ -160,14 +331,52 @@ def scenario_1():
         recommendations_summary["uprn"].astype(str).isin(archetype_4["uprn"].values)
     ]
 
+    arch_4_property_details = property_details_df[
+        property_details_df["uprn"].astype(str).isin(archetype_4["uprn"].values)
+    ]
+    arch_4_property_details["co2_emissions"].sum() / property_details_df["co2_emissions"].sum()
+
     # Take the mean, median and maximum of each value
     arch_4_recommendation_min = recommendations_arch_4_summary.min()
     arch_4_recommendation_max = recommendations_arch_4_summary.max()
     arch_4_recommendation_means = recommendations_arch_4_summary.mean()
 
-    property_details_df[
-        property_details_df["uprn"].astype(str).isin(archetype_4["uprn"].values)
-    ]["total_floor_area"].mean()
+    total_cost = recommendations_arch_4_summary["total_cost"].sum()
+    annual_total_co2 = recommendations_arch_4_summary["total_carbon"].sum()
+    annual_total_bills = recommendations_arch_4_summary["total_bill_savings"].sum()
+    annual_total_energy_savings = recommendations_arch_4_summary["adjusted_heat_demand"].sum()
+    archetype_measures = \
+        recommendations_df[recommendations_df["uprn"].astype(str).isin(archetype_4["uprn"].values)].groupby("type")[
+            "id"].count().reset_index()
+
+    cost_text = (f"{round(arch_4_recommendation_means['total_cost'], 2)}: "
+                 f"{arch_4_recommendation_min['total_cost']} - {arch_4_recommendation_max['total_cost']}")
+
+    sap_text = (f"{round(arch_4_recommendation_means['total_sap_points'], 2)}: "
+                f"{arch_4_recommendation_min['total_sap_points']} - {arch_4_recommendation_max['total_sap_points']}")
+
+    energy_text = (f"{round(arch_4_recommendation_means['adjusted_heat_demand'], 2)}: "
+                   f"{arch_4_recommendation_min['adjusted_heat_demand']} - "
+                   f"{arch_4_recommendation_max['adjusted_heat_demand']}")
+
+    energy_percent_text = (f"{round(arch_4_recommendation_means['energy_percent_change'], 2)}: "
+                           f"{arch_4_recommendation_min['energy_percent_change']} - "
+                           f"{arch_4_recommendation_max['energy_percent_change']}")
+
+    carbon_text = (f"{round(arch_4_recommendation_means['total_carbon'], 2)}: "
+                   f"{arch_4_recommendation_min['total_carbon']} - {arch_4_recommendation_max['total_carbon']}")
+
+    carbon_percent_text = (f"{round(arch_4_recommendation_means['carbon_percent_change'], 2)}: "
+                           f"{arch_4_recommendation_min['carbon_percent_change']} - "
+                           f"{arch_4_recommendation_max['carbon_percent_change']}")
+
+    bill_text = (f"{round(arch_4_recommendation_means['total_bill_savings'], 2)}: "
+                 f"{arch_4_recommendation_min['total_bill_savings']} - "
+                 f"{arch_4_recommendation_max['total_bill_savings']}")
+
+    bill_percent_text = (f"{round(arch_4_recommendation_means['bills_percent_change'], 2)}: "
+                         f"{arch_4_recommendation_min['bills_percent_change']} - "
+                         f"{arch_4_recommendation_max['bills_percent_change']}")
 
     ########################
     # Overview
@@ -291,6 +500,38 @@ def scenario_2():
     # Measures
     ########################
     measures_count = recommendations_df.groupby("type")["id"].count().reset_index()
+    wall_insulation_measures = measures_count[
+        measures_count["type"].isin(["cavity_wall_insulation", "external_wall_insulation", "internal_wall_insulation"])
+    ]["id"].sum()
+    ventilation_measures = measures_count[
+        measures_count["type"].isin(["mechanical_ventilation"])
+    ]["id"].sum()
+    roof_insulation_measures = measures_count[
+        measures_count["type"].isin(["loft_insulation", "flat_roof_insulation"])
+    ]["id"].sum()
+    floor_insulation_measures = measures_count[
+        measures_count["type"].isin(["solid_floor_insulation", "suspended_floor_insulation"])
+    ]["id"].sum()
+    windows = measures_count[
+        measures_count["type"].isin(["windows_glazing"])
+    ]["id"].sum()
+    heating = measures_count[
+        measures_count["type"].isin(["heating"])
+    ]["id"].sum()
+    heating_controls = measures_count[
+        measures_count["type"].isin(["heating_control"])
+    ]["id"].sum()
+    solar = measures_count[
+        measures_count["type"].isin(["solar_pv"])
+    ]["id"].sum()
+    other = measures_count[
+        ~measures_count["type"].isin([
+            "cavity_wall_insulation", "external_wall_insulation", "internal_wall_insulation",
+            "loft_insulation", "flat_roof_insulation", "solid_floor_insulation",
+            "suspended_floor_insulation", "windows_glazing", "heating", "heating_control", "solar_pv",
+            "mechanical_ventilation"
+        ])
+    ]["id"].sum()
 
     z = recommendations_df[recommendations_df["uprn"].astype(str).isin(archetype_3_sample)]
 
@@ -305,11 +546,54 @@ def scenario_2():
         recommendations_summary["uprn"].astype(str).isin(archetype_1["uprn"].values)
     ]
 
+    arch_1_property_details = property_details_df[
+        property_details_df["uprn"].astype(str).isin(archetype_1["uprn"].values)
+    ]
+    arch_1_property_details["co2_emissions"].sum() / property_details_df["co2_emissions"].sum()
+
     # Take the mean, median and maximum of each value
     arch_1_recommendation_min = recommendations_arch_1_summary.min()
     arch_1_recommendation_max = recommendations_arch_1_summary.max()
     arch_1_recommendation_means = recommendations_arch_1_summary.mean()
 
+    arch_1_totals = recommendations_arch_1_summary.sum()
+
+    annual_total_co2 = recommendations_arch_1_summary["total_carbon"].sum()
+    annual_total_bills = recommendations_arch_1_summary["total_bill_savings"].sum()
+    annual_total_energy_savings = recommendations_arch_1_summary["adjusted_heat_demand"].sum()
+    archetype_measures = \
+        recommendations_df[recommendations_df["uprn"].astype(str).isin(archetype_1["uprn"].values)].groupby("type")[
+            "id"].count().reset_index()
+
+    cost_text = (f"{round(arch_1_recommendation_means['total_cost'], 2)}: "
+                 f"{arch_1_recommendation_min['total_cost']} - {arch_1_recommendation_max['total_cost']}")
+
+    sap_text = (f"{round(arch_1_recommendation_means['total_sap_points'], 2)}: "
+                f"{arch_1_recommendation_min['total_sap_points']} - {arch_1_recommendation_max['total_sap_points']}")
+
+    energy_text = (f"{round(arch_1_recommendation_means['adjusted_heat_demand'], 2)}: "
+                   f"{arch_1_recommendation_min['adjusted_heat_demand']} - "
+                   f"{arch_1_recommendation_max['adjusted_heat_demand']}")
+
+    energy_percent_text = (f"{round(arch_1_recommendation_means['energy_percent_change'], 2)}: "
+                           f"{arch_1_recommendation_min['energy_percent_change']} - "
+                           f"{arch_1_recommendation_max['energy_percent_change']}")
+
+    carbon_text = (f"{round(arch_1_recommendation_means['total_carbon'], 2)}: "
+                   f"{arch_1_recommendation_min['total_carbon']} - {arch_1_recommendation_max['total_carbon']}")
+
+    carbon_percent_text = (f"{round(arch_1_recommendation_means['carbon_percent_change'], 2)}: "
+                           f"{arch_1_recommendation_min['carbon_percent_change']} - "
+                           f"{arch_1_recommendation_max['carbon_percent_change']}")
+
+    bill_text = (f"{round(arch_1_recommendation_means['total_bill_savings'], 2)}: "
+                 f"{arch_1_recommendation_min['total_bill_savings']} - "
+                 f"{arch_1_recommendation_max['total_bill_savings']}")
+
+    bill_percent_text = (f"{round(arch_1_recommendation_means['bills_percent_change'], 2)}: "
+                         f"{arch_1_recommendation_min['bills_percent_change']} - "
+                         f"{arch_1_recommendation_max['bills_percent_change']}")
+
     ########################
     # Archetype 2
     ########################
@@ -318,11 +602,53 @@ def scenario_2():
         recommendations_summary["uprn"].astype(str).isin(archetype_2["uprn"].values)
     ]
 
+    arch_2_property_details = property_details_df[
+        property_details_df["uprn"].astype(str).isin(archetype_2["uprn"].values)
+    ]
+    arch_2_property_details["co2_emissions"].sum() / property_details_df["co2_emissions"].sum()
+
     # Take the mean, median and maximum of each value
     arch_2_recommendation_min = recommendations_arch_2_summary.min()
     arch_2_recommendation_max = recommendations_arch_2_summary.max()
     arch_2_recommendation_means = recommendations_arch_2_summary.mean().round(2)
 
+    total_cost = recommendations_arch_2_summary["total_cost"].sum()
+    annual_total_co2 = recommendations_arch_2_summary["total_carbon"].sum()
+    annual_total_bills = recommendations_arch_2_summary["total_bill_savings"].sum()
+    annual_total_energy_savings = recommendations_arch_2_summary["adjusted_heat_demand"].sum()
+    archetype_measures = \
+        recommendations_df[recommendations_df["uprn"].astype(str).isin(archetype_2["uprn"].values)].groupby("type")[
+            "id"].count().reset_index()
+
+    cost_text = (f"{round(arch_2_recommendation_means['total_cost'], 2)}: "
+                 f"{arch_2_recommendation_min['total_cost']} - {arch_2_recommendation_max['total_cost']}")
+
+    sap_text = (f"{round(arch_2_recommendation_means['total_sap_points'], 2)}: "
+                f"{arch_2_recommendation_min['total_sap_points']} - {arch_2_recommendation_max['total_sap_points']}")
+
+    energy_text = (f"{round(arch_2_recommendation_means['adjusted_heat_demand'], 2)}: "
+                   f"{arch_2_recommendation_min['adjusted_heat_demand']} - "
+                   f"{arch_2_recommendation_max['adjusted_heat_demand']}")
+
+    energy_percent_text = (f"{round(arch_2_recommendation_means['energy_percent_change'], 2)}: "
+                           f"{arch_2_recommendation_min['energy_percent_change']} - "
+                           f"{arch_2_recommendation_max['energy_percent_change']}")
+
+    carbon_text = (f"{round(arch_2_recommendation_means['total_carbon'], 2)}: "
+                   f"{arch_2_recommendation_min['total_carbon']} - {arch_2_recommendation_max['total_carbon']}")
+
+    carbon_percent_text = (f"{round(arch_2_recommendation_means['carbon_percent_change'], 2)}: "
+                           f"{arch_2_recommendation_min['carbon_percent_change']} - "
+                           f"{arch_2_recommendation_max['carbon_percent_change']}")
+
+    bill_text = (f"{round(arch_2_recommendation_means['total_bill_savings'], 2)}: "
+                 f"{arch_2_recommendation_min['total_bill_savings']} - "
+                 f"{arch_2_recommendation_max['total_bill_savings']}")
+
+    bill_percent_text = (f"{round(arch_2_recommendation_means['bills_percent_change'], 2)}: "
+                         f"{arch_2_recommendation_min['bills_percent_change']} - "
+                         f"{arch_2_recommendation_max['bills_percent_change']}")
+
     ########################
     # Archetype 3
     ########################
@@ -331,11 +657,53 @@ def scenario_2():
         recommendations_summary["uprn"].astype(str).isin(archetype_3["uprn"].values)
     ]
 
+    arch_3_property_details = property_details_df[
+        property_details_df["uprn"].astype(str).isin(archetype_3["uprn"].values)
+    ]
+    arch_3_property_details["co2_emissions"].sum() / property_details_df["co2_emissions"].sum()
+
     # Take the mean, median and maximum of each value
     arch_3_recommendation_min = recommendations_arch_3_summary.min()
     arch_3_recommendation_max = recommendations_arch_3_summary.max()
     arch_3_recommendation_means = recommendations_arch_3_summary.mean()
 
+    total_cost = recommendations_arch_3_summary["total_cost"].sum()
+    annual_total_co2 = recommendations_arch_3_summary["total_carbon"].sum()
+    annual_total_bills = recommendations_arch_3_summary["total_bill_savings"].sum()
+    annual_total_energy_savings = recommendations_arch_3_summary["adjusted_heat_demand"].sum()
+    archetype_measures = \
+        recommendations_df[recommendations_df["uprn"].astype(str).isin(archetype_3["uprn"].values)].groupby("type")[
+            "id"].count().reset_index()
+
+    cost_text = (f"{round(arch_3_recommendation_means['total_cost'], 2)}: "
+                 f"{arch_3_recommendation_min['total_cost']} - {arch_3_recommendation_max['total_cost']}")
+
+    sap_text = (f"{round(arch_3_recommendation_means['total_sap_points'], 2)}: "
+                f"{arch_3_recommendation_min['total_sap_points']} - {arch_3_recommendation_max['total_sap_points']}")
+
+    energy_text = (f"{round(arch_3_recommendation_means['adjusted_heat_demand'], 2)}: "
+                   f"{arch_3_recommendation_min['adjusted_heat_demand']} - "
+                   f"{arch_3_recommendation_max['adjusted_heat_demand']}")
+
+    energy_percent_text = (f"{round(arch_3_recommendation_means['energy_percent_change'], 2)}: "
+                           f"{arch_3_recommendation_min['energy_percent_change']} - "
+                           f"{arch_3_recommendation_max['energy_percent_change']}")
+
+    carbon_text = (f"{round(arch_3_recommendation_means['total_carbon'], 2)}: "
+                   f"{arch_3_recommendation_min['total_carbon']} - {arch_3_recommendation_max['total_carbon']}")
+
+    carbon_percent_text = (f"{round(arch_3_recommendation_means['carbon_percent_change'], 2)}: "
+                           f"{arch_3_recommendation_min['carbon_percent_change']} - "
+                           f"{arch_3_recommendation_max['carbon_percent_change']}")
+
+    bill_text = (f"{round(arch_3_recommendation_means['total_bill_savings'], 2)}: "
+                 f"{arch_3_recommendation_min['total_bill_savings']} - "
+                 f"{arch_3_recommendation_max['total_bill_savings']}")
+
+    bill_percent_text = (f"{round(arch_3_recommendation_means['bills_percent_change'], 2)}: "
+                         f"{arch_3_recommendation_min['bills_percent_change']} - "
+                         f"{arch_3_recommendation_max['bills_percent_change']}")
+
     ########################
     # Archetype 4
     ########################
@@ -344,11 +712,49 @@ def scenario_2():
         recommendations_summary["uprn"].astype(str).isin(archetype_4["uprn"].values)
     ]
 
+    arch_4_property_details = property_details_df[
+        property_details_df["uprn"].astype(str).isin(archetype_4["uprn"].values)
+    ]
+    arch_4_property_details["co2_emissions"].sum() / property_details_df["co2_emissions"].sum()
+
     # Take the mean, median and maximum of each value
     arch_4_recommendation_min = recommendations_arch_4_summary.min()
     arch_4_recommendation_max = recommendations_arch_4_summary.max()
     arch_4_recommendation_means = recommendations_arch_4_summary.mean()
 
-    property_details_df[
-        property_details_df["uprn"].astype(str).isin(archetype_4["uprn"].values)
-    ]["total_floor_area"].mean()
+    total_cost = recommendations_arch_4_summary["total_cost"].sum()
+    annual_total_co2 = recommendations_arch_4_summary["total_carbon"].sum()
+    annual_total_bills = recommendations_arch_4_summary["total_bill_savings"].sum()
+    annual_total_energy_savings = recommendations_arch_4_summary["adjusted_heat_demand"].sum()
+    archetype_measures = \
+        recommendations_df[recommendations_df["uprn"].astype(str).isin(archetype_4["uprn"].values)].groupby("type")[
+            "id"].count().reset_index()
+
+    cost_text = (f"{round(arch_4_recommendation_means['total_cost'], 2)}: "
+                 f"{arch_4_recommendation_min['total_cost']} - {arch_4_recommendation_max['total_cost']}")
+
+    sap_text = (f"{round(arch_4_recommendation_means['total_sap_points'], 2)}: "
+                f"{arch_4_recommendation_min['total_sap_points']} - {arch_4_recommendation_max['total_sap_points']}")
+
+    energy_text = (f"{round(arch_4_recommendation_means['adjusted_heat_demand'], 2)}: "
+                   f"{arch_4_recommendation_min['adjusted_heat_demand']} - "
+                   f"{arch_4_recommendation_max['adjusted_heat_demand']}")
+
+    energy_percent_text = (f"{round(arch_4_recommendation_means['energy_percent_change'], 2)}: "
+                           f"{arch_4_recommendation_min['energy_percent_change']} - "
+                           f"{arch_4_recommendation_max['energy_percent_change']}")
+
+    carbon_text = (f"{round(arch_4_recommendation_means['total_carbon'], 2)}: "
+                   f"{arch_4_recommendation_min['total_carbon']} - {arch_4_recommendation_max['total_carbon']}")
+
+    carbon_percent_text = (f"{round(arch_4_recommendation_means['carbon_percent_change'], 2)}: "
+                           f"{arch_4_recommendation_min['carbon_percent_change']} - "
+                           f"{arch_4_recommendation_max['carbon_percent_change']}")
+
+    bill_text = (f"{round(arch_4_recommendation_means['total_bill_savings'], 2)}: "
+                 f"{arch_4_recommendation_min['total_bill_savings']} - "
+                 f"{arch_4_recommendation_max['total_bill_savings']}")
+
+    bill_percent_text = (f"{round(arch_4_recommendation_means['bills_percent_change'], 2)}: "
+                         f"{arch_4_recommendation_min['bills_percent_change']} - "
+                         f"{arch_4_recommendation_max['bills_percent_change']}")
diff --git a/recommendations/HeatingRecommender.py b/recommendations/HeatingRecommender.py
index 9d2e99e3..2c075820 100644
--- a/recommendations/HeatingRecommender.py
+++ b/recommendations/HeatingRecommender.py
@@ -186,9 +186,18 @@ class HeatingRecommender:
         # This upgrade will only take the heating system to average energy efficiency
         heating_simulation_config["mainheat_energy_eff_ending"] = "Average"
 
+        # If the property is off-gas and has no heating system in place, the number of heated rooms will actually
+        # be 0, so we use the number of rooms as the figure
+        number_heated_rooms = (
+            self.property.data["number-heated-rooms"] if self.property.data["number-heated-rooms"] > 0
+            else (
+                self.property.number_of_rooms - 1 if self.property.number_of_rooms > 1 else
+                self.property.number_of_rooms
+            )
+        )
         # Upgrade to electric storage heaters
         costs = self.costs.high_heat_electric_storage_heaters(
-            number_heated_rooms=self.property.data["number-heated-rooms"]
+            number_heated_rooms=number_heated_rooms
         )
         description = "Install high heat retention electric storage heaters"
 
@@ -268,9 +277,9 @@ class HeatingRecommender:
             ] and self.property.data["mains-gas-flag"]
             is_combi = hotwater_from_mains or access_to_mains_no_system
             if is_combi:
-                description = "Upgrade to a low carbon combi boiler"
+                description = "Upgrade to a new combi boiler"
             else:
-                description = "Upgrade to a low carbon boiler"
+                description = "Upgrade to a new boiler"
 
             simulation_config = {"mainheat_energy_eff_ending": "Good"}
             if access_to_mains_no_system: