diff --git a/backend/app/plan/router.py b/backend/app/plan/router.py
index 48a78e15..c26a5217 100644
--- a/backend/app/plan/router.py
+++ b/backend/app/plan/router.py
@@ -133,8 +133,8 @@ def extract_portfolio_aggregation_data(
             [r["energy_cost_savings"] for r in default_recommendations]
         )
 
-        pre_retrofit_energy_consumption = p.current_adjusted_energy
-        post_retrofit_energy_consumption = p.current_adjusted_energy - sum(
+        pre_retrofit_energy_consumption = p.current_energy_consumption
+        post_retrofit_energy_consumption = p.current_energy_consumption - sum(
             [r["kwh_savings"] for r in default_recommendations]
         )
 
@@ -724,8 +724,9 @@ async def trigger_plan(body: PlanTriggerRequest):
         # We now insert kwh estimates and costs into the recommendations
         # TODO: We should join the methodology which maps the heating and hot water descriptions to the fuel types in
         #       Recommendations, but also the Property class
-        for property_id in tqdm(recommendations.keys()):
-            property_recommendations = recommendations[property_id]
+        logger.info("Calculating tenant savings - kwh and bills")
+        for property_id in tqdm([p.id for p in input_properties]):
+            property_recommendations = recommendations.get(property_id, [])
             property_instance = [p for p in input_properties if p.id == property_id][0]
 
             property_current_energy_bill = Recommendations.calculate_recommendation_tenant_savings(
diff --git a/recommendations/Recommendations.py b/recommendations/Recommendations.py
index af55031f..ef478426 100644
--- a/recommendations/Recommendations.py
+++ b/recommendations/Recommendations.py
@@ -16,6 +16,7 @@ from recommendations.HotwaterRecommendations import HotwaterRecommendations
 from recommendations.SecondaryHeating import SecondaryHeating
 from backend.ml_models.AnnualBillSavings import AnnualBillSavings
 from backend.apis.GoogleSolarApi import GoogleSolarApi
+import backend.app.assumptions as assumptions
 
 ASHP_COP = 3
 DESCRIPTIONS_TO_FUEL_TYPES = {
@@ -40,6 +41,8 @@ DESCRIPTIONS_TO_FUEL_TYPES = {
     "Electric instantaneous at point of use": {"fuel": "Electricity", "cop": 1},
     "Boiler and radiators, oil": {"fuel": "Oil", "cop": 0.9},
     "Electric storage heaters, Electric storage heaters": {"fuel": "Electricity", "cop": 1},
+    "Boiler and radiators, electric": {"fuel": "Electricity", "cop": 0.9},
+    "Gas boiler/circulator, no cylinder thermostat": {"fuel": "Natural Gas", "cop": 0.9},
 }
 STARTING_DUMMY_ID_VALUE = -9999
 
@@ -594,14 +597,15 @@ class Recommendations:
             {
                 "phase": r["phase"],
                 "recommendation_id": r["recommendation_id"],
-                "lighting_kwh_savings": r["kwh_savings"] * GoogleSolarApi.SOLAR_CONSUMPTION_PROPORTION,
+                "lighting_kwh_savings": r["kwh_savings"]
             } for recs in property_recommendations for r in recs if r["type"] == "low_energy_lighting"
         ], columns=["phase", "recommendation_id", "lighting_kwh_savings"])
+
         solar_recommendations = pd.DataFrame([
             {
                 "phase": r["phase"],
                 "recommendation_id": r["recommendation_id"],
-                "solar_kwh_savings": r["initial_ac_kwh_per_year"] * GoogleSolarApi.SOLAR_CONSUMPTION_PROPORTION,
+                "solar_kwh_savings": r["initial_ac_kwh_per_year"] * assumptions.SOLAR_CONSUMPTION_PROPORTION,
             } for recs in property_recommendations for r in recs if r["type"] == "solar_pv"
         ], columns=["phase", "recommendation_id", "solar_kwh_savings"])
 
@@ -673,17 +677,21 @@ class Recommendations:
             fuel_mapping, how="left", on="id"
         ).sort_values(["phase", "recommendation_id"], ascending=True).reset_index(drop=True)
 
-        kwh_impact_table["heating_fuel_type"] = np.where(
-            kwh_impact_table["id"] == STARTING_DUMMY_ID_VALUE,
-            property_instance.heating_energy_source,
-            kwh_impact_table["heating_fuel_type"]
-        )
+        if (pd.isnull(kwh_impact_table["heating_fuel_type"]).sum() or
+            pd.isnull(kwh_impact_table["hotwater_fuel_type"]).sum()):
+            raise Exception("Fuel type is missing")
 
-        kwh_impact_table["hotwater_fuel_type"] = np.where(
-            kwh_impact_table["id"] == STARTING_DUMMY_ID_VALUE,
-            property_instance.hot_water_energy_source,
-            kwh_impact_table["hotwater_fuel_type"]
-        )
+        # kwh_impact_table["heating_fuel_type"] = np.where(
+        #     kwh_impact_table["id"] == STARTING_DUMMY_ID_VALUE,
+        #     property_instance.heating_energy_source,
+        #     kwh_impact_table["heating_fuel_type"]
+        # )
+        #
+        # kwh_impact_table["hotwater_fuel_type"] = np.where(
+        #     kwh_impact_table["id"] == STARTING_DUMMY_ID_VALUE,
+        #     property_instance.hot_water_energy_source,
+        #     kwh_impact_table["hotwater_fuel_type"]
+        # )
 
         # We now calculate the fuel cost
         for k in ["heating", "hotwater"]: