Added further placeholder valuation figures and added mvp valuation increase methodology

.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/db/models/materials.py

@@ -35,7 +35,7 @@ class MaterialType(enum.Enum):
     low_energy_lighting_installation = "low_energy_lighting_installation"
     flat_roof_preparation = "flat_roof_preparation"
     flat_roof_vapour_barrier = "flat_roof_vapour_barrier"
-    flat_roof_waterpoofing = "flat_roof_waterpoofing"
+    flat_roof_waterproofing = "flat_roof_waterproofing"
 
 
 class DepthUnit(enum.Enum):

backend/ml_models/Valuation.py

@@ -1,22 +1,117 @@
+import numpy as np
+
+
 class PropertyValuation:
     """
     This is a placeholder class for the property valuation model
     """
 
     UPRN_VALUE_LOOKUP = {
-        15038202: {"current_value": 202000, "increase_percentage": 0.05725},
-        37024763: {"current_value": 213000, "increase_percentage": 0.025},
+        15038202: 202000,
+        37024763: 213000,
+        100070478545: 212000,
+        100070297696: 235000,
+        100070476394: 222000,  # Based on Zoopla's estimation of next door, 20 Parkside
+        100071264896: 128000,
+        # Based on next door neighbour: https://themovemarket.com/tools/propertyprices/flat-2-queens-wood-house-219
+        # -brandwood-road-birmingham-b14-6pu
     }
 
+    # We base our valuation uplifts on a number of sources
+    # https://www.moneysupermarket.com/gas-and-electricity/value-of-efficiency/
+    MSM_MAPPING = [
+        {"start": "G", "end": "F", "increase_percentage": 0.06},
+        {"start": "F", "end": "E", "increase_percentage": 0.01},
+        {"start": "E", "end": "D", "increase_percentage": 0.01},
+        {"start": "D", "end": "C", "increase_percentage": 0.02},
+        {"start": "C", "end": "B", "increase_percentage": 0.04},
+        {"start": "B", "end": "A", "increase_percentage": 0.0},
+    ]
+
+    # https://www.lloydsbankinggroup.com/media/press-releases/2021/halifax/homebuyers-pay-a-green-premium-of-40000
+    # -for-the-most-energy-efficient-properties.html
+    LLOYDS_MAPPING = [
+        {"start": "G", "end": "F", "increase_percentage": 0.038},
+        {"start": "F", "end": "E", "increase_percentage": 0.029},
+        {"start": "E", "end": "D", "increase_percentage": 0.024},
+        {"start": "D", "end": "C", "increase_percentage": 0.02},
+        {"start": "C", "end": "B", "increase_percentage": 0.02},
+        {"start": "B", "end": "A", "increase_percentage": 0.018},
+    ]
+
+    KNIGHT_FRANK_MAPPING = [
+        {"start": "D", "end": "C", "increase_percentage": 0.03},
+        {"start": "D", "end": "B", "increase_percentage": 0.088},
+    ]
+
+    NATIONWIDE_MAPPING = [
+        {"start": "G", "end": "D", "increase_percentage": 0.035},
+        {"start": "F", "end": "D", "increase_percentage": 0.035},
+        {"start": "D", "end": "B", "increase_percentage": 0.017},
+        {"start": "D", "end": "A", "increase_percentage": 0.017},
+    ]
+
+    EPC_BANDS = ["G", "F", "E", "D", "C", "B", "A"]
+
+    @classmethod
+    def get_increase(cls, epc_band_range):
+
+        increases = []
+        for i in range(len(epc_band_range)):
+
+            if i == len(epc_band_range) - 1:
+                break
+
+            current = epc_band_range[i]
+            next = epc_band_range[i + 1]
+
+            msm_increase = [x for x in cls.MSM_MAPPING if x["start"] == current and x["end"] == next][0]
+            lloyds_increase = [x for x in cls.LLOYDS_MAPPING if x["start"] == current and x["end"] == next][0]
+
+            increases.append(
+                {
+                    "start": current,
+                    "end": next,
+                    "msm_increase": msm_increase["increase_percentage"],
+                    "lloyds_increase": lloyds_increase["increase_percentage"],
+                }
+            )
+
+        # We now aggregate the increases. The should be compound increases so we multiply them together
+        msm_increase = np.prod([1 + x["msm_increase"] for x in increases]) - 1
+        lloyds_increase = np.prod([1 + x["lloyds_increase"] for x in increases]) - 1
+
+        return msm_increase, lloyds_increase
+
     @classmethod
     def estimate(cls, property_instance, target_epc):
-        data = cls.UPRN_VALUE_LOOKUP.get(property_instance.uprn)
+        value = cls.UPRN_VALUE_LOOKUP.get(property_instance.uprn)
 
-        if not data:
+        if not value:
             raise ValueError("Have not implemented valuation for this property")
 
-        new_valuation = (1 + data["increase_percentage"]) * data["current_value"]
+        current_epc = property_instance.data["current-energy-rating"]
+        # We get the spectrum of ratings between the current and target EPC
+        epc_band_range = cls.EPC_BANDS[cls.EPC_BANDS.index(current_epc): cls.EPC_BANDS.index(target_epc) + 1]
 
-        increase = round(new_valuation - data["current_value"], 2)
+        msm_increase, lloyds_increase = cls.get_increase(epc_band_range)
 
-        return increase
+        # We now use the knight frank and nationwide 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]
+
+        kf_increase = kf_increase[0]["increase_percentage"] if kf_increase else None
+        nw_increase = nw_increase[0]["increase_percentage"] if nw_increase else None
+
+        all_increases = [x for x in [msm_increase, lloyds_increase, kf_increase, nw_increase] if x is not None]
+
+        max_increase = max(all_increases)
+        min_increase = min(all_increases)
+        avg_increase = np.mean(all_increases)
+
+        return {
+            "current_value": value,
+            "lower_bound_increased_value": value * (1 + min_increase),
+            "upper_bound_increased_value": value * (1 + max_increase),
+            "average_increased_value": value * (1 + avg_increase),
+        }