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updating text for valuation improvement

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This commit is contained in:
Khalim Conn-Kowlessar 2024-04-16 11:18:36 +01:00
parent 83d472a710
commit 0f7e815379
1 changed files with 41 additions and 8 deletions
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49
backend/app/plan/router.py
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@ -59,7 +59,7 @@ def patch_epc(patch, epc_records):
def extract_portfolio_aggregation_data(
input_properties, total_valuation_increase, recommendations, new_epc_bands
input_properties, total_valuation_increase, recommendations, new_epc_bands, property_value_increase_ranges
):
# We aggregate a number of metrics for the portfolio:
# 1) A breakdown of the number of properties in each EPC band
@ -69,7 +69,7 @@ def extract_portfolio_aggregation_data(
# 3) Co2/unit
# a) before retrofit
# b) after retrofit
# 4) Energy bulls/unit
# 4) Energy bill/unit
# a) before retrofit
# b) after retrofit
# 5) Average valuation improvement/unit
@ -105,6 +105,8 @@ def extract_portfolio_aggregation_data(
# Get just the default recommendations
default_recommendations = [r for r in property_recommendations if r["default"]]
has_recommendations = len(default_recommendations) > 0
# We can now calculate multiple outputs based on default recommendations
carbon_savings = sum([r["co2_equivalent_savings"] for r in default_recommendations])
@ -125,6 +127,15 @@ def extract_portfolio_aggregation_data(
cost = sum([r["total"] for r in default_recommendations])
sap_point_improvement = sum([r["sap_points"] for r in default_recommendations])
lower_bound_valuation_uplift = (
property_value_increase_ranges[p.id]["lower_bound_increased_value"] -
property_value_increase_ranges[p.id]["current_value"]
)
upper_bound_valuation_uplift = (
property_value_increase_ranges[p.id]["upper_bound_increased_value"] -
property_value_increase_ranges[p.id]["current_value"]
)
agg_data.append({
"pre_retrofit_epc": p.data["current-energy-rating"],
"post_retrofit_epc": new_epc_bands[p.id],
@ -135,14 +146,22 @@ def extract_portfolio_aggregation_data(
"pre_retrofit_energy_consumption": pre_retrofit_energy_consumption,
"post_retrofit_energy_consumption": post_retrofit_energy_consumption,
"cost": cost,
"sap_point_improvement": sap_point_improvement
"sap_point_improvement": sap_point_improvement,
"lower_bound_valuation_uplift": lower_bound_valuation_uplift,
"upper_bound_valuation_uplift": upper_bound_valuation_uplift,
"has_recommendations": has_recommendations
})
agg_data = pd.DataFrame(agg_data)
n_units_to_retrofit = len(agg_data)
n_units_to_retrofit = agg_data["has_recommendations"].sum()
valuation_improvment_per_unit = total_valuation_increase / n_units_to_retrofit
valuation_improvement_lower_bound_per_unit = (
agg_data["lower_bound_valuation_uplift"].mean()
)
valuation_improvement_upper_bound_per_unit = (
agg_data["upper_bound_valuation_uplift"].mean()
)
total_carbon_saved = agg_data["pre_retrofit_co2"].sum() - agg_data["post_retrofit_co2"].sum()
total_sap_points = agg_data["sap_point_improvement"].sum()
@ -150,6 +169,17 @@ def extract_portfolio_aggregation_data(
def format_money(amount):
return f"£{amount:,.0f}"
valuation_improvment_per_unit = format_money(
total_valuation_increase / n_units) + (f" ({format_money(valuation_improvement_lower_bound_per_unit)} - "
f"{format_money(valuation_improvement_upper_bound_per_unit)})")
valuation_return_on_investment = (
str(round(total_valuation_increase / agg_data["cost"].sum(), 2)) +
f" ("
f"{agg_data['lower_bound_valuation_uplift'].sum() / agg_data['cost'].sum():,.2f} - "
f"{agg_data['upper_bound_valuation_uplift'].sum() / agg_data['cost'].sum():,.2f})"
)
aggregation_data = {
"epc_breakdown_pre_retrofit": json.dumps(
reformat_epc_data(agg_data["pre_retrofit_epc"].value_counts().to_dict())
@ -167,11 +197,11 @@ def extract_portfolio_aggregation_data(
round(agg_data["pre_retrofit_energy_consumption"].mean())) + "kWh",
"energy_consumption_per_unit_post_retrofit": str(
round(agg_data["post_retrofit_energy_consumption"].mean())) + "kWh",
"valuation_improvement_per_unit": format_money(valuation_improvment_per_unit),
"valuation_improvement_per_unit": valuation_improvment_per_unit,
"cost_per_unit": format_money(agg_data["cost"].mean()),
"cost_per_co2_saved": format_money(agg_data["cost"].sum() / total_carbon_saved),
"cost_per_sap_point": format_money(agg_data["cost"].sum() / total_sap_points),
"valuation_return_on_investment": str(round(total_valuation_increase / agg_data["cost"].sum(), 2))
"valuation_return_on_investment": valuation_return_on_investment,
# TODO: Could we add 10yr carbon credits value?
}
@ -446,6 +476,7 @@ async def trigger_plan(body: PlanTriggerRequest):
property_valuation_increases = []
session.commit()
new_epc_bands = {}
property_value_increase_ranges = {}
for i in range(0, len(input_properties), BATCH_SIZE):
try:
# Take a slice of the input_properties list to make a batch
@ -460,6 +491,7 @@ async def trigger_plan(body: PlanTriggerRequest):
new_epc_bands[p.id] = new_epc
valuations = PropertyValuation.estimate(property_instance=p, target_epc=new_epc)
property_value_increase_ranges[p.id] = valuations
# Your existing operations
property_details_epc = p.get_property_details_epc(
@ -527,7 +559,8 @@ async def trigger_plan(body: PlanTriggerRequest):
input_properties=input_properties,
total_valuation_increase=total_valuation_increase,
recommendations=recommendations,
new_epc_bands=new_epc_bands
new_epc_bands=new_epc_bands,
property_value_increase_ranges=property_value_increase_ranges
)
aggregate_portfolio_recommendations(