diff --git a/model_data/simulation_system/app.py b/model_data/simulation_system/app.py
index c11e7ae7..ab687d74 100644
--- a/model_data/simulation_system/app.py
+++ b/model_data/simulation_system/app.py
@@ -71,6 +71,7 @@ LATEST_FIELD = [
 MANDATORY_FIXED_FEATURES = [
     "PROPERTY_TYPE",
     "BUILT_FORM",
+    "CONSTITUENCY"
 ]
 
 # For particularly old EPC data, we have inconsistent records so we'll only include EPCS that were
@@ -139,20 +140,25 @@ def clean_multi_glaze_proportion(df):
     return df
 
 
+def ordinal(n):
+    if 10 <= n % 100 <= 20:
+        suffix = 'th'
+    else:
+        suffix = {1: 'st', 2: 'nd', 3: 'rd'}.get(n % 10, 'th')
+
+    return str(n) + suffix
+
+
 FLOOR_LEVEL_MAP = {
-    "00": 0,
-    "01": 1,
-    "02": 2,
-    "03": 3,
     "Basement": -1,
     "Ground": 0,
-    "1st": 1,
-    "2nd": 2,
-    "3rd": 3,
     "ground floor": 0,
-    # Put in estimates for these
-    "mid floor": 2,
-    "top floor": 4
+    "20+": 20,
+    "21st or above": 21,
+    **{str(i).zfill(2): i for i in range(0, 21)},
+    **{ordinal(i): i for i in range(-1, 21)},
+    **{str(i): i for i in range(-1, 21)},
+    **{i: i for i in range(-1, 21)},
 }
 
 BUILT_FORM_REMAP = {
@@ -170,8 +176,10 @@ def app():
     for directory in tqdm(directories):
         filepath = os.path.join(DATA_DIRECTORY, directory, "certificates.csv")
         df = pd.read_csv(filepath, low_memory=False)
+        # UPRN is a unique identifier for a property, so we remove any EPCs that don't have one
         df = df[~pd.isnull(df["UPRN"])]
-
+        # Lodgement date is the date the EPC was lodged, so we remove any EPCs that were lodged
+        # before the introduction of SAP09
         df = df[df["LODGEMENT_DATE"] >= EARLIEST_EPC_DATE]
 
         cleaning_averages, general_averages = make_cleaning_averages(df)
@@ -182,6 +190,9 @@ def app():
 
         df = clean_multi_glaze_proportion(df)
 
+        # We remove floor level in top floor or mid floor since this is ambiguous
+        df = df[~df["FLOOR_LEVEL"].isin(["top floor", "mid floor"])]
+
         df["UPRN"] = df["UPRN"].astype(int).astype(str)
         counts = df.groupby("UPRN").size().reset_index()
         counts.columns = ["UPRN", "count"]