Added rightmove property valuation increase estimates

.idea/Model.iml

@@ -7,7 +7,7 @@
       <sourceFolder url="file://$MODULE_DIR$/open_uprn" isTestSource="false" />
       <sourceFolder url="file://$MODULE_DIR$/recommendations" isTestSource="false" />
     </content>
-    <orderEntry type="jdk" jdkName="Python 3.10 (model_data)" jdkType="Python SDK" />
+    <orderEntry type="jdk" jdkName="Python 3.10 (backend)" jdkType="Python SDK" />
     <orderEntry type="sourceFolder" forTests="false" />
   </component>
   <component name="PyNamespacePackagesService">

.idea/misc.xml

@@ -3,7 +3,7 @@
   <component name="Black">
     <option name="sdkName" value="Python 3.10 (backend)" />
   </component>
-  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.10 (model_data)" project-jdk-type="Python SDK" />
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.10 (backend)" project-jdk-type="Python SDK" />
   <component name="PythonCompatibilityInspectionAdvertiser">
     <option name="version" value="3" />
   </component>

backend/app/plan/router.py

@@ -282,16 +282,16 @@ async def trigger_plan(body: PlanTriggerRequest):
             property_id, is_new = create_property(
                 session, body.portfolio_id, epc_searcher.address_clean, epc_searcher.postcode_clean, epc_searcher.uprn
             )
-            # if not is_new:
-            #     continue
-            #
-            # create_property_targets(
-            #     session,
-            #     property_id=property_id,
-            #     portfolio_id=body.portfolio_id,
-            #     epc_target=body.goal_value,
-            #     heat_demand_target=None
-            # )
+            if not is_new:
+                continue
+
+            create_property_targets(
+                session,
+                property_id=property_id,
+                portfolio_id=body.portfolio_id,
+                epc_target=body.goal_value,
+                heat_demand_target=None
+            )
 
             epc_records = {
                 'original_epc': epc_searcher.newest_epc.copy(),

backend/ml_models/Valuation.py

@@ -71,6 +71,14 @@ class PropertyValuation:
         90013980: 148_000,  # Based on Zoopla
         90087154: 184_000,  # Based on Zoopla
         90046817: 167_000,  # Based on Zoopla
+        # Goldman Sachs Pilot for inrto - search by going to https://www.zoopla.co.uk/property/uprn/{uprn}/
+        100070358888: 153_000,  # Based on Zoopla
+        10090436544: 282_000,  # Based on Zoopla
+        100070365751: 177_000,  # Based on Zoopla
+        10095952767: 168_000,  # Based on Zoopla
+        100070520130: 177_000,  # Based on Zoopla
+        100070333957: 185_000,  # Based on Zoopla
+        100070543258: 211_000,  # Based on Zoopla
     }
 
     # We base our valuation uplifts on a number of sources
@@ -108,6 +116,29 @@ class PropertyValuation:
         # {"start": "D", "end": "A", "increase_percentage": 0.017},
     ]
 
+    # Found here: https://www.rightmove.co.uk/news/articles/property-news/green-premium-epc-ratings/
+    # F -> C is + 15%
+    # E -> C is +7%
+    # D -> C is +3%
+    RIGHTMOVE_MAPPING = [
+        {"start": "G", "end": "C", "increase_percentage": 0.15},
+        {"start": "G", "end": "B", "increase_percentage": 0.15},
+        {"start": "G", "end": "A", "increase_percentage": 0.15},
+
+        {"start": "F", "end": "C", "increase_percentage": 0.15},
+        {"start": "F", "end": "B", "increase_percentage": 0.15},
+        {"start": "F", "end": "A", "increase_percentage": 0.15},
+
+        {"start": "E", "end": "C", "increase_percentage": 0.07},
+        {"start": "E", "end": "B", "increase_percentage": 0.07},
+        {"start": "E", "end": "A", "increase_percentage": 0.07},
+
+        {"start": "D", "end": "C", "increase_percentage": 0.03},
+        {"start": "D", "end": "B", "increase_percentage": 0.03},
+        {"start": "D", "end": "A", "increase_percentage": 0.03},
+
+    ]
+
     EPC_BANDS = ["G", "F", "E", "D", "C", "B", "A"]
 
     @classmethod
@@ -159,14 +190,18 @@ class PropertyValuation:
 
         msm_increase, lloyds_increase = cls.get_increase(epc_band_range)
 
-        # We now use the knight frank and nationwide data to get further valuation evidence, if we have it
+        # We now use the knight frank, nationwide and Rightmove data to get further valuation evidence, if we have it
         kf_increase = [x for x in cls.KNIGHT_FRANK_MAPPING if x["start"] == current_epc and x["end"] == target_epc]
         nw_increase = [x for x in cls.NATIONWIDE_MAPPING if x["start"] == current_epc and x["end"] == target_epc]
+        rm_increase = [x for x in cls.RIGHTMOVE_MAPPING if x["start"] == current_epc and x["end"] == target_epc]
 
         kf_increase = kf_increase[0]["increase_percentage"] if kf_increase else None
         nw_increase = nw_increase[0]["increase_percentage"] if nw_increase else None
+        rm_increase = rm_increase[0]["increase_percentage"] if rm_increase else None
 
-        all_increases = [x for x in [msm_increase, lloyds_increase, kf_increase, nw_increase] if x is not None]
+        all_increases = [
+            x for x in [msm_increase, lloyds_increase, kf_increase, nw_increase, rm_increase] if x is not None
+        ]
 
         max_increase = max(all_increases)
         min_increase = min(all_increases)

etl/customers/gla_croydon_demo/asset_list.py

@@ -34,10 +34,6 @@ def app():
         low_memory=False
     )
 
-    z = epc_data[epc_data["MAINHEAT_DESCRIPTION"] == "Boiler and radiators, mains gas"]
-    z["HOTWATER_DESCRIPTION"].value_counts()
-    z["MAIN_FUEL"].value_counts()
-
     # Filter on entries where we have a UPRN
     epc_data = epc_data[~pd.isnull(epc_data["UPRN"])]
 

etl/customers/goldman/asset_list.py

@@ -0,0 +1,63 @@
+import pandas as pd
+from utils.s3 import read_excel_from_s3
+from utils.s3 import save_csv_to_s3
+
+PORTFOLIO_ID = 75
+USER_ID = 8
+
+
+def app():
+    asset_list = [
+        {
+            "address": "19 Emily Gardens",
+            "postcode": "B16 0ED",
+        },
+        {
+            "address": "Flat 6 41 Bradford Street",
+            "postcode": "B5 6HX",
+        },
+        {
+            "address": "197 FIELD LANE",
+            "postcode": "B32 4HL",
+        },
+        {
+            "address": "FLAT 4 108 SUMMER ROAD",
+            "postcode": "B23 6DY",
+        },
+        {
+            "address": "1, St. Benedicts Road",
+            "postcode": "B10 9DP",
+        },
+        {
+            "address": "29 COOKSEY LANE",
+            "postcode": "B44 9QL",
+        },
+        {
+            "address": "40 TRITTIFORD ROAD",
+            "postcode": "B13 0HG",
+        }
+    ]
+
+    asset_list = pd.DataFrame(asset_list)
+
+    # Store the asset list in s3
+    filename = f"{USER_ID}/{PORTFOLIO_ID}/pilot.csv"
+    save_csv_to_s3(
+        dataframe=asset_list,
+        bucket_name="retrofit-plan-inputs-dev",
+        file_name=filename
+    )
+
+    # EPC C portoflio
+    body = {
+        "portfolio_id": str(PORTFOLIO_ID),
+        "housing_type": "Private",
+        "goal": "Increase EPC",
+        "goal_value": "B",
+        "trigger_file_path": filename,
+        "already_installed_file_path": "",
+        "patches_file_path": "",
+        "non_invasive_recommendations_file_path": "",
+        "budget": None,
+    }
+    print(body)

etl/customers/goldman/epc_f_g_properties.py

@@ -0,0 +1,25 @@
+import pandas as pd
+
+
+def app():
+    """
+    Pulling the list of EPC G & F properties in Birmingham for Goldman Sachs
+    """
+    epc_data = pd.read_csv(
+        "local_data/all-domestic-certificates/domestic-E08000025-Birmingham/certificates.csv",
+        low_memory=False
+    )
+
+    epc_data = epc_data[~pd.isnull(epc_data["UPRN"])]
+    epc_data["UPRN"] = epc_data["UPRN"].astype(int).astype(str)
+
+    # Get the newest EPC for each UPRN. We use LODGEMENT_DATE as a proxy for this
+    epc_data["LODGEMENT_DATETIME"] = pd.to_datetime(epc_data["LODGEMENT_DATETIME"], format='mixed')
+
+    epc_data = epc_data.sort_values("LODGEMENT_DATETIME", ascending=False).drop_duplicates("UPRN")
+
+    # Get G & F properties
+    epc_data = epc_data[epc_data["CURRENT_ENERGY_RATING"].isin(["G", "F"])]
+
+    # Save as an excel
+    epc_data.to_excel("Birmingham EPC F & G Properties.xlsx", index=False)

recommendations/HeatingRecommender.py

@@ -359,7 +359,6 @@ class HeatingRecommender:
                     **heating_simulation_config,
                     **hotwater_simulation_config,
                     **fuel_simulation_config,
-                    "hot_water_energy_eff_ending": "Good"
                 }
 
             boiler_costs = self.costs.boiler(