diff --git a/etl/customers/stonewater/Wave 3 Preparation.py b/etl/customers/stonewater/Wave 3 Preparation.py
index 24a8e9bb..e471211c 100644
--- a/etl/customers/stonewater/Wave 3 Preparation.py	
+++ b/etl/customers/stonewater/Wave 3 Preparation.py	
@@ -125,13 +125,13 @@ def extract_summary_report(pdf_path):
     - Address
     """
 
-    blah
     data = {
         "Address": None,
         "Postcode": None,
         "Current SAP Rating": None,
         "Current EPC Band": None,
         "Fuel Bill": None,
+        "Main Building Age Band": None,
         "Number of Storeys": None,
         "Window Age Description": None,
         "Window Age Description Proportion (%)": None,
@@ -181,6 +181,10 @@ def extract_summary_report(pdf_path):
         sap_match = re.search(r"Current SAP rating:\s*([A-Z] \d+)", text)
         data["Current SAP Rating"] = sap_match.group(1).split(" ")[1]
 
+        # Extract age
+        age_band_match = re.search(r"3\.0 Date Built:\s*Main Property\s*[A-Z]?\s*(\d{4}-\d{4})", text)
+        data["Main Building Age Band"] = age_band_match.group(1)
+
         # Number of storeys
         storeys_match = re.search(r"Number of Storeys:\s*(\d+)", text)
         data["Number of Storeys"] = int(storeys_match.group(1))
@@ -3027,6 +3031,7 @@ def revised_model():
 
     # We now do a large pull of all of the data
     extracted_data = []
+    mtp_extracted_data = []  # Additional data to extract from the medium term plans
     for survey_folder in tqdm(survey_folders):
         survey_folder_path = os.path.join(CUSTOMER_FOLDER_PATH, survey_folder)
 
@@ -3048,6 +3053,58 @@ def revised_model():
             None
         )
 
+        mtp_folder = next(
+            (name for name in survey_subfolders if "mid-term" in name.lower() or "mtp" in name.lower()),
+            None
+        )
+        if mtp_folder:
+            # We have a mid term plan:
+            mtp_folder_path = os.path.join(survey_folder_path, mtp_folder)
+            # Get the contents - files and not folder
+            mtp_contents = [
+                os.path.join(mtp_folder, file) for file in os.listdir(mtp_folder_path)
+                if ".DS_Store" not in file and not os.path.isdir(os.path.join(mtp_folder_path, mtp_folder, file))
+            ]
+            # We check the the IMA
+            for file_name in mtp_contents:
+                filepath = os.path.join(survey_folder_path, file_name)
+                # We expect a pdf so try and parse it
+                try:
+                    with open(filepath, "rb") as file:
+                        reader = PyPDF2.PdfReader(file)
+                        # Just the first page
+                        text = reader.pages[0].extract_text()
+
+                except Exception as e:
+                    continue
+
+                # We check if this is an IMA
+                ima_heading_search = re.search(
+                    r"Improvement measure\s+Capital Cost\s+Lifetime of\s*\n\s*measureFuel saving\s*Lifetime fuel", text
+                )
+
+                is_ima = bool(ima_heading_search)
+                if not is_ima:
+                    continue
+
+                # Otherwise, extract: RIR, PV
+                pv_search = re.search(r"PV \(\d+Kwp\)", text)
+                has_pv = bool(pv_search)
+                pv_system = pv_search.group(0) if has_pv else None
+
+                rir_search = re.search(r"RIR \(\d+(\.\d+)?\)", text)
+                has_rir = bool(rir_search)
+                rir_spec = rir_search.group(0) if has_rir else None
+
+                mtp_extracted_data.append({
+                    "survey_folder": survey_folder,
+                    "has_pv": has_pv,
+                    "PV System": pv_system,
+                    "RIR Specification": rir_spec,
+                    "has_rir": has_rir
+                })
+                continue
+
         # If retrofit assessment folder exists, check if it has content
         if retrofit_folder or ra_folder:
             if retrofit_folder:
@@ -3094,7 +3151,7 @@ def revised_model():
     retrofit_assessment_data = pd.DataFrame(extracted_data)
 
     # retrofit_assessment_data.to_csv(
-    #     os.path.join(CUSTOMER_FOLDER_PATH, "Jan 2025 Project/Retrofit Assessment Data Sheet 2.csv"), index=False
+    #     os.path.join(CUSTOMER_FOLDER_PATH, "Jan 2025 Project/Retrofit Assessment Data Sheet 3.csv"), index=False
     # )
     retrofit_assessment_data = pd.read_csv(
         os.path.join(CUSTOMER_FOLDER_PATH, "Jan 2025 Project/Retrofit Assessment Data Sheet 2.csv"),