diff --git a/backend/engine/engine.py b/backend/engine/engine.py
index 2dd08a1c..f71fbfaa 100644
--- a/backend/engine/engine.py
+++ b/backend/engine/engine.py
@@ -1330,9 +1330,7 @@ async def model_engine(body: PlanTriggerRequest):
                 )
             scenario_id = engine_scenario.id
 
-        # property_valuation_increases = []
         new_epc_bands = {}
-        # property_value_increase_ranges = {}
         for i in range(0, len(input_properties), BATCH_SIZE):
             try:
                 # Take a slice of the input_properties list to make a batch
@@ -1351,7 +1349,6 @@ async def model_engine(body: PlanTriggerRequest):
                         valuations = PropertyValuation.estimate(
                             property_instance=p, target_epc=new_epc, total_cost=total_cost
                         )
-                        # property_value_increase_ranges[p.id] = valuations
 
                         property_plan_data = db_funcs.recommendations_functions.prepare_plan_data(
                             p, body, scenario_id, eco_packages, valuations, new_sap_points, new_epc,
@@ -1386,47 +1383,13 @@ async def model_engine(body: PlanTriggerRequest):
                             session, p, new_plan_id, recommendations_to_upload
                         )
 
-                        # if valuations["current_value"] > 0:
-                        #     property_valuation_increases.append(
-                        #         valuations["average_increased_value"] - valuations["current_value"]
-                        #     )
-
             except Exception as e:
                 # Rollback the session if an error occurs
                 logger.warning("Failed i = %s" % str(i))
                 logger.error(f"An error occurred during batch starting at index {i}: {e}")
                 logger.error(f"property is uprn {p.uprn} id {p.id} address {p.address}")
 
-        logger.info("Creating portfolio aggregations")
-        # We implement this in the simplest way possible which will be just to query the database for all
-        # recommendations associated to the portfolio and then aggregate them. This is not the most efficient
-        # way to do this, but it's the simplest and will be a process that we can re-use since when we change a
-        # recommendation from being default to not default, we'll need to re-run this process to re-calculate the
-        # the portfolion level impact
-
-        # total_valuation_increase = sum(property_valuation_increases)
-        # labour_days = round(max(
-        #     [sum(r["labour_days"] for r in rec_group if r["default"]) for p_id, rec_group in recommendations.items()]
-        # ))
-        #
-        # # TODO - This code only pulls in the properties that have been updated in this run, but we need to
-        # #        aggregate all properties in the portfolio. We likely need to trigger a re-aggregation
-        # aggregated_data = extract_portfolio_aggregation_data(
-        #     input_properties=input_properties,
-        #     total_valuation_increase=total_valuation_increase,
-        #     recommendations=recommendations,
-        #     new_epc_bands=new_epc_bands,
-        #     property_value_increase_ranges=property_value_increase_ranges
-        # )
-        # with db_session() as session:
-        #     db_funcs.portfolio_functions.aggregate_portfolio_recommendations(
-        #         session,
-        #         portfolio_id=body.portfolio_id,
-        #         scenario_id=scenario_id,
-        #         total_valuation_increase=total_valuation_increase,
-        #         labour_days=labour_days,
-        #         aggregated_data=aggregated_data
-        #     )
+        logger.info("Work completed, updating log status")
 
     except IntegrityError as e:
         return handle_error("Database integrity error.", e, body.subtask_id, 500, start_ms)