diff --git a/asset_list/AssetList.py b/asset_list/AssetList.py
index dc22a8a2..fc021034 100644
--- a/asset_list/AssetList.py
+++ b/asset_list/AssetList.py
@@ -354,6 +354,7 @@ class AssetList:
         self.contact_detail_fields = None
         self.outcomes = None
         self.outcomes_no_match = None
+        self.outcomes_for_output = None
         self.master_surveyed = None
 
         # We detect the presence of the non-intrusive columns
@@ -1414,8 +1415,6 @@ class AssetList:
             # Floor type check
             self.standardised_asset_list["solar_epc_floor_is_solid_no_recommendation"] &
             # SAP Below threshold
-            self.standardised_asset_list[self.ATTRIBUTE_SAP_THRESHOLD_AND_BELOW] &
-            # SAP above threshold
             self.standardised_asset_list[self.ATTRIBUTE_SAP_THRESHOLD_AND_BELOW]
         )
         # With heating upgrade, above threshold
@@ -1431,8 +1430,6 @@ class AssetList:
             self.standardised_asset_list["solar_epc_roof_insulated"] &
             # Floor type check
             self.standardised_asset_list["solar_epc_floor_is_solid_no_recommendation"] &
-            # SAP Below threshold
-            self.standardised_asset_list[self.ATTRIBUTE_SAP_THRESHOLD_AND_BELOW] &
             # SAP above threshold
             ~self.standardised_asset_list[self.ATTRIBUTE_SAP_THRESHOLD_AND_BELOW]
         )
@@ -1677,14 +1674,6 @@ class AssetList:
             # ~self.standardised_asset_list["solar_eligible_other_floor_needs_loft"]
         )
 
-        blocks_of_flats = self.standardised_asset_list[
-            self.standardised_asset_list[self.STANDARD_PROPERTY_TYPE] == "block of flats"
-            ]
-
-        non_blocks_of_flats = self.standardised_asset_list[
-            self.standardised_asset_list[self.STANDARD_PROPERTY_TYPE] != "block of flats"
-            ]
-
         # Finally, we note why each property has been flagged
         self.standardised_asset_list["cavity_reason"] = None
         self.standardised_asset_list["cavity_reason"] = np.where(
@@ -1794,12 +1783,35 @@ class AssetList:
                 self.standardised_asset_list["cavity_reason"]
             )
 
+        blocks_of_flats = self.standardised_asset_list[
+            self.standardised_asset_list[self.STANDARD_PROPERTY_TYPE] == "block of flats"
+            ]
+
+        non_blocks_of_flats = self.standardised_asset_list[
+            self.standardised_asset_list[self.STANDARD_PROPERTY_TYPE] != "block of flats"
+            ]
+
         # Produce some aggregate figures
         self.work_type_figures = {
-            **self.standardised_asset_list["cavity_reason"].value_counts().to_dict(),
+            **non_blocks_of_flats["cavity_reason"].value_counts().to_dict(),
+            **{
+                k + " (Block of flats)": v for k, v in
+                blocks_of_flats["solar_reason"].value_counts().to_dict().items()
+            },
             **self.standardised_asset_list["solar_reason"].value_counts().to_dict()
         }
 
+        # We prepare outcomes for output
+        if self.outcomes:
+            logger.info("Preparing outcomes for output")
+            identified_work = self.standardised_asset_list[
+                ~pd.isnull(self.standardised_asset_list["cavity_reason"]) |
+                ~pd.isnull(self.standardised_asset_list["solar_reason"])
+                ][self.DOMNA_PROPERTY_ID].values
+            self.outcomes_for_output = self.outcomes[
+                self.outcomes[self.DOMNA_PROPERTY_ID].isin(identified_work)
+            ]
+
     def flat_analysis(self):
 
         # We need to deduce the building name - we strip out the house number
@@ -2225,8 +2237,6 @@ class AssetList:
             self.outcomes[["row_id", "Outcome", "Notes", date_col]], how="left", on="row_id"
         )
 
-        df = lookup[lookup["domna_property_id"] == "44beckettavenuegainsboroughdn211en-1d4811cbb046"]
-
         visit_counts = (
             lookup.groupby(self.DOMNA_PROPERTY_ID)["row_id"]
             .count()
@@ -2245,15 +2255,15 @@ class AssetList:
 
         # We merge this data onto outcomes
         self.outcomes["matched_to_asset_list"] = self.outcomes["row_id"].isin(lookup["row_id"].values)
-        self.outcomes = self.outcomes.merge(
-            lookup, how="left", on="row_id"
-        )
+        self.outcomes = self.outcomes.merge(lookup[["row_id", "domna_property_id"]], how="left", on="row_id")
 
         # We merge out pivoted outcomes onto the asset list
         self.standardised_asset_list = self.standardised_asset_list.merge(
             pivot_df, how="left", left_on=self.DOMNA_PROPERTY_ID, right_on="domna_property_id"
         )
 
+        self.outcomes = self.outcomes.sort_values("domna_property_id", ascending=False)
+
     def flag_survey_master(
         self,
         master_filepaths,
diff --git a/asset_list/app.py b/asset_list/app.py
index 1a6dbc6b..0fe09767 100644
--- a/asset_list/app.py
+++ b/asset_list/app.py
@@ -684,6 +684,8 @@ def app():
         asset_list.standardised_asset_list.to_excel(writer, sheet_name="Standardised Asset List", index=False)
         asset_list.flat_data.to_excel(writer, sheet_name="Flat Data", index=False)
         # If we have outcomes, we add a tab with the outcomes
+        if asset_list.outcomes_for_output is not None:
+            asset_list.outcomes_for_output.to_excel(writer, sheet_name="Outcomes", index=False)
 
     # Store the Hubspot export as a csv
     hubspot_data.to_csv(os.path.join(data_folder, "Hubspot Export.csv"), index=False)