diff --git a/.idea/Model.iml b/.idea/Model.iml
index c75af922..4413bb06 100644
--- a/.idea/Model.iml
+++ b/.idea/Model.iml
@@ -7,7 +7,7 @@
       <sourceFolder url="file://$MODULE_DIR$/open_uprn" isTestSource="false" />
       <sourceFolder url="file://$MODULE_DIR$/recommendations" isTestSource="false" />
     </content>
-    <orderEntry type="jdk" jdkName="non_invasive_surveys-photos" jdkType="Python SDK" />
+    <orderEntry type="jdk" jdkName="Python 3.10 (backend)" jdkType="Python SDK" />
     <orderEntry type="sourceFolder" forTests="false" />
   </component>
   <component name="PyNamespacePackagesService">
diff --git a/.idea/misc.xml b/.idea/misc.xml
index 1f2c584d..6f308057 100644
--- a/.idea/misc.xml
+++ b/.idea/misc.xml
@@ -3,7 +3,7 @@
   <component name="Black">
     <option name="sdkName" value="Python 3.10 (backend)" />
   </component>
-  <component name="ProjectRootManager" version="2" project-jdk-name="non_invasive_surveys-photos" project-jdk-type="Python SDK" />
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.10 (backend)" project-jdk-type="Python SDK" />
   <component name="PythonCompatibilityInspectionAdvertiser">
     <option name="version" value="3" />
   </component>
diff --git a/backend/Property.py b/backend/Property.py
index a8ed9129..7b5a6bc3 100644
--- a/backend/Property.py
+++ b/backend/Property.py
@@ -142,6 +142,8 @@ class Property:
 
         self.current_adjusted_energy = None
         self.expected_adjusted_energy = None
+        self.current_energy_bill = None
+        self.expected_energy_bill = None
 
         self.recommendations_scoring_data = []
 
@@ -892,12 +894,16 @@ class Property:
 
         return component_data
 
-    def set_adjusted_energy(self, current_adjusted_energy, expected_adjusted_energy):
+    def set_adjusted_energy(
+        self, current_adjusted_energy, expected_adjusted_energy, current_energy_bill, expected_energy_bill
+    ):
         """
         Stores these values for usage later
         """
         self.current_adjusted_energy = current_adjusted_energy
         self.expected_adjusted_energy = expected_adjusted_energy
+        self.current_energy_bill = current_energy_bill
+        self.expected_energy_bill = expected_energy_bill
 
     def set_windows_count(self):
         """
diff --git a/backend/app/db/functions/portfolio_functions.py b/backend/app/db/functions/portfolio_functions.py
index ead8280f..69203368 100644
--- a/backend/app/db/functions/portfolio_functions.py
+++ b/backend/app/db/functions/portfolio_functions.py
@@ -4,7 +4,7 @@ from backend.app.db.models.portfolio import Portfolio
 
 
 def aggregate_portfolio_recommendations(
-    session, portfolio_id: int, total_valuation_increase: float, labour_days: float
+    session, portfolio_id: int, total_valuation_increase: float, labour_days: float, aggregated_data: dict
 ):
     # Aggregate multiple fields
     aggregates = (
@@ -27,6 +27,7 @@ def aggregate_portfolio_recommendations(
         "energy_savings": aggregates.energy_savings or 0,
         "co2_equivalent_savings": aggregates.co2_equivalent_savings or 0,
         "energy_cost_savings": aggregates.energy_cost_savings or 0,
+        **aggregated_data
     }
 
     # Get the portfolio and update the fields
diff --git a/backend/app/plan/router.py b/backend/app/plan/router.py
index 49e14872..b8b2d5c8 100644
--- a/backend/app/plan/router.py
+++ b/backend/app/plan/router.py
@@ -1,3 +1,4 @@
+import json
 from datetime import datetime
 
 from tqdm import tqdm
@@ -57,6 +58,109 @@ def patch_epc(patch, epc_records):
     return epc_records
 
 
+def extract_portfolio_aggregation_data(
+    input_properties, total_valuation_increase, recommendations, new_epc_bands
+):
+    # We aggregate a number of metrics for the portfolio:
+    # 1) A breakdown of the number of properties in each EPC band
+    #    a) before retrofit
+    #    b) after retrofit
+    # 2) Number of units
+    # 3) Co2/unit
+    #    a) before retrofit
+    #    b) after retrofit
+    # 4) Energy bulls/unit
+    #    a) before retrofit
+    #    b) after retrofit
+    # 5) Average valuation improvement/unit
+    # 6) Total cost
+    # 7) Cost per unit
+    # 8) £ per CO2 saved
+    # 9) £ per SAP point
+
+    # We need to construct the underlyind data for this
+
+    # Helper function to reformat the EPC data
+    def reformat_epc_data(epc_counts):
+        # Define all possible EPC bands in the required order
+        epc_bands = ["G", "F", "E", "D", "C", "B", "A"]
+
+        # Create the formatted data list by checking each band in the order
+        formatted_data = []
+        for band in epc_bands:
+            # Get the count from the dictionary, defaulting to 0 if not present
+            count = epc_counts.get(band, 0)
+            # Append the formatted dictionary to the list
+            formatted_data.append({"name": band, band: count})
+
+        return formatted_data
+
+    n_units = len(input_properties)
+
+    agg_data = []
+    for p in input_properties:
+        # Get the recommendations for the property
+        property_recommendations = recommendations.get(p.id, [])
+        if not property_recommendations:
+            continue
+        # Get just the default recommendations
+        default_recommendations = [r for r in property_recommendations if r["default"]]
+
+        # We can now calculate multiple outputs based on default recommendations
+        carbon_savings = sum([r["co2_equivalent_savings"] for r in default_recommendations])
+
+        pre_retrofit_co2 = p.data["co2-emissions-current"]
+        post_retrofit_co2 = pre_retrofit_co2 - carbon_savings
+
+        pre_retrofit_energy_bill = p.current_energy_bill
+        post_retrofit_energy_bill = p.expected_energy_bill
+
+        cost = sum([r["total"] for r in default_recommendations])
+        sap_point_improvement = sum([r["sap_points"] for r in default_recommendations])
+
+        agg_data.append({
+            "pre_retrofit_epc": p.data["current-energy-rating"],
+            "post_retrofit_epc": new_epc_bands[p.id],
+            "pre_retrofit_co2": pre_retrofit_co2,
+            "post_retrofit_co2": post_retrofit_co2,
+            "pre_retrofit_energy_bill": pre_retrofit_energy_bill,
+            "post_retrofit_energy_bill": post_retrofit_energy_bill,
+            "cost": cost,
+            "sap_point_improvement": sap_point_improvement
+        })
+
+    agg_data = pd.DataFrame(agg_data)
+
+    n_units_to_retrofit = len(agg_data)
+
+    valuation_improvment_per_unit = total_valuation_increase / n_units_to_retrofit
+
+    total_carbon_saved = agg_data["pre_retrofit_co2"].sum() - agg_data["post_retrofit_co2"].sum()
+    total_sap_points = agg_data["sap_point_improvement"].sum()
+
+    aggregation_data = {
+        "epc_breakdown_pre_retrofit": json.dumps(
+            reformat_epc_data(agg_data["pre_retrofit_epc"].value_counts().to_dict())
+        ),
+        "epc_breakdown_post_retrofit": json.dumps(
+            reformat_epc_data(agg_data["post_retrofit_epc"].value_counts().to_dict())
+        ),
+        "number_of_properties": n_units,
+        "n_units_to_retrofit": n_units_to_retrofit,
+        "co2_per_unit_pre_retrofit": agg_data["pre_retrofit_co2"].mean(),
+        "co2_per_unit_post_retrofit": agg_data["post_retrofit_co2"].mean(),
+        "energy_bill_per_unit_pre_retrofit": agg_data["pre_retrofit_energy_bill"].mean(),
+        "energy_bill_per_unit_post_retrofit": agg_data["post_retrofit_energy_bill"].mean(),
+        "valuation_improvement_per_unit": valuation_improvment_per_unit,
+        "total_cost": agg_data["cost"].sum(),
+        "cost_per_unit": agg_data["cost"].mean(),
+        "cost_per_co2_saved": agg_data["cost"].sum() / total_carbon_saved,
+        "cost_per_sap_point": agg_data["cost"].sum() / total_sap_points
+    }
+
+    return aggregation_data
+
+
 router = APIRouter(
     prefix="/plan",
     tags=["plan"],
@@ -243,7 +347,13 @@ async def trigger_plan(body: PlanTriggerRequest):
 
             property_instance = [p for p in input_properties if p.id == property_id][0]
 
-            recommendations_with_impact, current_adjusted_energy, expected_adjusted_energy = (
+            (
+                recommendations_with_impact,
+                current_adjusted_energy,
+                expected_adjusted_energy,
+                current_energy_bill,
+                expected_energy_bill
+            ) = (
                 Recommendations.calculate_recommendation_impact(
                     property_instance=property_instance,
                     all_predictions=all_predictions,
@@ -254,7 +364,9 @@ async def trigger_plan(body: PlanTriggerRequest):
             # Store the resulting adjusted energy in the property instance
             property_instance.set_adjusted_energy(
                 current_adjusted_energy=current_adjusted_energy,
-                expected_adjusted_energy=expected_adjusted_energy
+                expected_adjusted_energy=expected_adjusted_energy,
+                current_energy_bill=current_energy_bill,
+                expected_energy_bill=expected_energy_bill
             )
 
             input_measures = prepare_input_measures(recommendations_with_impact, body.goal)
@@ -316,6 +428,7 @@ async def trigger_plan(body: PlanTriggerRequest):
         logger.info("Uploading recommendations to the database")
         property_valuation_increases = []
         session.commit()
+        new_epc_bands = {}
         for i in range(0, len(input_properties), BATCH_SIZE):
             try:
                 # Take a slice of the input_properties list to make a batch
@@ -327,6 +440,7 @@ async def trigger_plan(body: PlanTriggerRequest):
                     total_sap_points = sum([r["sap_points"] for r in default_recommendations])
                     new_sap_points = float(p.data["current-energy-efficiency"]) + total_sap_points
                     new_epc = sap_to_epc(new_sap_points)
+                    new_epc_bands[p.id] = new_epc
 
                     valuations = PropertyValuation.estimate(property_instance=p, target_epc=new_epc)
 
@@ -392,11 +506,19 @@ async def trigger_plan(body: PlanTriggerRequest):
             [sum(r["labour_days"] for r in rec_group if r["default"]) for p_id, rec_group in recommendations.items()]
         ))
 
+        aggregated_data = extract_portfolio_aggregation_data(
+            input_properties=input_properties,
+            total_valuation_increase=total_valuation_increase,
+            recommendations=recommendations,
+            new_epc_bands=new_epc_bands
+        )
+
         aggregate_portfolio_recommendations(
             session,
             portfolio_id=body.portfolio_id,
             total_valuation_increase=total_valuation_increase,
-            labour_days=labour_days
+            labour_days=labour_days,
+            aggregated_data=aggregated_data
         )
 
         # Commit final changes
diff --git a/recommendations/Recommendations.py b/recommendations/Recommendations.py
index 68fead16..659b41a8 100644
--- a/recommendations/Recommendations.py
+++ b/recommendations/Recommendations.py
@@ -281,6 +281,9 @@ class Recommendations:
             current_adjusted_energy - expected_adjusted_energy
         )
 
+        current_energy_bill = AnnualBillSavings.calculate_annual_bill(current_adjusted_energy)
+        expected_energy_bill = AnnualBillSavings.calculate_annual_bill(expected_adjusted_energy)
+
         for recommendations_by_type in property_recommendations:
             for rec in recommendations_by_type:
 
@@ -355,4 +358,10 @@ class Recommendations:
                     rec["heat_demand"] is None) or (rec["energy_cost_savings"] is None):
                     raise ValueError("sap points, co2 or heat demand is missing")
 
-        return property_recommendations, current_adjusted_energy, expected_adjusted_energy
+        return (
+            property_recommendations,
+            current_adjusted_energy,
+            expected_adjusted_energy,
+            current_energy_bill,
+            expected_energy_bill
+        )