diff --git a/etl/customers/aiha/xml_extraction.py b/etl/customers/aiha/xml_extraction.py
index 563ed7ca..29ac44c6 100644
--- a/etl/customers/aiha/xml_extraction.py
+++ b/etl/customers/aiha/xml_extraction.py
@@ -65,7 +65,7 @@ def main():
 
     # THis is the data we need for the AIHA project
     measures_data = extracted_surveys[
-        ["survey_key", "address", "postcode", "current-energy-efficiency", "current-energy-rating"]
+        ["survey_key", "address", "postcode", "current-energy-efficiency", "current-energy-rating", "number_of_floors"]
     ]
     measures_data = measures_data.sort_values("survey_key", ascending=True)
 
@@ -459,15 +459,20 @@ def main():
         },
     ]
 
-    descs = []
-    for r in recommended_measures:
-        for m in r["recommended_measures"]:
-            descs.append(m["description"])
-    descs = list(set(descs))
+    scaffolding_data = [
+        {
+            "number_of_floors": 2,
+            "price": 841,
+        },
+        {
+            "number_of_floors": 3,
+            "price": 1077,
+        }
+    ]
 
-    # TODO - need to add scaffolding
+    # TODO - Need an update cost for cylinder insulation
     pricing_data = [
-        {'item': '80mm cylinder insulation', 'unit_price': None, 'unit': 'unit'},
+        {'item': '80mm cylinder insulation', 'unit_price': 50, 'unit': 'unit'},
         {'item': '100mm internal wall insulation', 'unit_price': 244.8, 'unit': 'hlp_m2'},
         {'item': 'CWI to rdSAP default standard', 'unit_price': 14.21, 'unit': 'hlp_m2'},
         {'item': 'Window draught proofing improvements', 'unit_price': None, 'unit': 'unit'},
@@ -486,6 +491,31 @@ def main():
     ]
     pricing_data = pd.DataFrame(pricing_data)
 
+    for recommendation in recommended_measures:
+
+        property_data = measures_data[measures_data["survey_key"] == recommendation["survey_key"]].squeeze()
+
+        for measure in recommendation["recommended_measures"]:
+            measure_pricing = pricing_data[pricing_data["item"] == measure["description"]]
+            measure_unit = measure_pricing["unit"].values[0]
+            if measure_unit is None:
+                blah
+                continue
+
+            if measure_unit == "unit":
+                measure["Total Cost"] = float(measure_pricing["unit_price"].values[0])
+                continue
+
+            if measure_unit == "unit_needs_scaffolding":
+                # We need the number of floors
+                n_floors = property_data["number_of_floors"]
+                cost_of_scalfolding = [x for x in scaffolding_data if x["number_of_floors"] == n_floors][0]["price"]
+                measure["Total Cost"] = float(measure_pricing["unit_price"].values[0]) + cost_of_scalfolding
+
+            blah
+
+            measure["total"] = pricing_data[pricing_data["item"] == measure["measure"]]["unit_price"].values[0]
+
     # Step 1: Normalize the recommended_measures data into a DataFrame.
     normalized_measures = []