diff --git a/backend/Property.py b/backend/Property.py
index 45c0b406..3dbcc2b8 100644
--- a/backend/Property.py
+++ b/backend/Property.py
@@ -8,6 +8,7 @@ import pandas as pd
 from etl.epc.DataProcessor import DataProcessor
 from etl.epc.settings import POTENTIAL_COLUMNS, EFFICIENCY_FEATURES, BUILT_FORM_REMAP
 from etl.epc_clean.epc_attributes.all_cleaners import all_cleaner_map
+from etl.solar.SolarPhotoSupply import SolarPhotoSupply
 from utils.logger import setup_logger
 from utils.s3 import read_dataframe_from_s3_parquet
 from BaseUtility import Definitions
@@ -849,37 +850,19 @@ class Property(Definitions):
                 "Need to set insulation floor area and pitched roof area before setting solar pv roof area"
             )
 
-        # TODO: Create a class for the solar etl process and make this one of the functions, which applies a different
-        #     method depending on the data type
-        def classify_floor_area(new_area, thresholds):
-            for i, threshold in enumerate(thresholds):
-                if new_area <= threshold:
-                    return i  # Returns the decile index (0 to 9)
-            return len(thresholds)
-
-        floor_area_decile = classify_floor_area(
-            self.floor_area, floor_area_decile_thresholds["floor_area_decile_thresholds"].values
+        photo_supply_matched = SolarPhotoSupply.filter_photo_supply_lookup(
+            photo_supply_lookup=photo_supply_lookup,
+            floor_area_decile_thresholds=floor_area_decile_thresholds,
+            tenure=self.data["tenure"],
+            built_form=self.data["built-form"],
+            property_type=self.data["property-type"],
+            construction_age_band=self.construction_age_band,
+            is_flat=self.roof["is_flat"],
+            is_pitched=self.roof["is_pitched"],
+            is_roof_room=self.roof["is_roof_room"],
+            floor_area=self.floor_area
         )
 
-        # Given the photo_supply_lookup, we esimate the percentage of the roof that is suitable for solar panels
-
-        # TODO: Move this to the ETL process, since we need to know that tenure should be lower
-        tenure = self.data["tenure"].lower()
-        photo_supply_matched = photo_supply_lookup[
-            (photo_supply_lookup["tenure"] == tenure) &
-            (photo_supply_lookup["built_form"] == self.data["built-form"]) &
-            (photo_supply_lookup["property_type"] == self.data["property-type"]) &
-            (photo_supply_lookup["construction_age_band"] == self.construction_age_band) &
-            (photo_supply_lookup["is_flat"] == self.roof["is_flat"]) &
-            (photo_supply_lookup["is_pitched"] == self.roof["is_pitched"]) &
-            (photo_supply_lookup["is_roof_room"] == self.roof["is_roof_room"])
-            ]
-
-        if floor_area_decile in photo_supply_matched["floor_area_decile"].values:
-            photo_supply_matched = photo_supply_matched[
-                photo_supply_matched["floor_area_decile"] == floor_area_decile
-                ]
-
         percentage_of_roof = photo_supply_matched["photo_supply_median"].mean()
         percentage_of_roof = percentage_of_roof / 100