diff --git a/recommendations/SolarPvRecommendations.py b/recommendations/SolarPvRecommendations.py
index 3387fc7a..2de8f873 100644
--- a/recommendations/SolarPvRecommendations.py
+++ b/recommendations/SolarPvRecommendations.py
@@ -95,6 +95,50 @@ class SolarPvRecommendations:
 
         return is_valid_property_type and is_valid_roof_type and has_no_existing_solar_pv
 
+    def recommend_building_analysis(self, phase):
+        """
+        This recommendation approach handles the case of producing solar PV recommendations at the building level,
+        across multiple flats. For these recommendations, we don't include the battery option since it's impractical
+        from a space perspective.
+        :return:
+        """
+
+        panel_performance = self.property.solar_panel_configuration["panel_performance"]
+        total_roof_area = (
+            self.property.solar_panel_configuration["insights_data"]["solarPotential"]["wholeRoofStats"]["areaMeters2"]
+        )
+        n_units = self.property.solar_panel_configuration["n_units"]
+
+        best_configurations = panel_performance.head(3)
+
+        for _, recommendation_config in best_configurations.iterrows():
+            roof_coverage_percent = round(recommendation_config["panneled_roof_area"] / total_roof_area * 100)
+            # Spread the cost to the individual units
+            total_cost = recommendation_config["total_cost"] / n_units
+            kw = np.floor(recommendation_config["array_warrage"] / 100) / 10
+
+            description = (f"Install a {kw} kilowatt-peak (kWp) solar photovoltaic (PV) panel system on the roof "
+                           "of the building")
+
+            self.recommendation.append(
+                {
+                    "phase": phase,
+                    "parts": [],
+                    "type": "solar_pv",
+                    "description": description,
+                    "starting_u_value": None,
+                    "new_u_value": None,
+                    "sap_points": None,
+                    "already_installed": False,
+                    "total_cost": total_cost,
+                    # This is required for simulating the SAP impact. solar_pv_percentage is between 0 & 1 so we scale
+                    # back up here
+                    "photo_supply": roof_coverage_percent,
+                    "has_battery": False,
+                    "description_simulation": {"photo-supply": roof_coverage_percent}
+                }
+            )
+
     def recommend(self, phase):
         """
         We check if a property is potentially suitable for solar PV based on the following criteria:
@@ -107,6 +151,11 @@ class SolarPvRecommendations:
         if not self.is_solar_pv_valid():
             return
 
+        # If we have a buiilding level analysis, we implement separate logic
+        if self.property.building_id is not None:
+            self.recommend_building_analysis(phase)
+            return
+
         solar_pv_percentage = self.property.solar_pv_percentage
         # We round up to the neaest 10%
         solar_pv_percentage = np.ceil(solar_pv_percentage * 10) / 10