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Model/backend/ml_models/Valuation.py
Khalim Conn-Kowlessar a2c61395de new environment working
2024-10-21 18:34:18 +01:00

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Python
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import numpy as np
from scipy.constants import value
class PropertyValuation:
"""
This is a placeholder class for the property valuation model
"""
UPRN_VALUE_LOOKUP = {
15038202: 202000,
37024763: 213000,
100070478545: 212000,
100070297696: 662000, # Based on Zoopla's estimation of nearby house, 8 bloomfield road
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
100070533688: 218000, # Based on Zoopla's estimation of 95 Tenby Road, which is also mid terrace
100070505235: 344000, # Based on Zoopla's estimation of 131 School road, which is also semi-detached
100070513306: 182000, # Based on Zoopla's estimation of 61 Simmons Drive
100071306896: 77000, # Based on Flat 2 of 44 Wedgewood Road on Zoopla
100021192109: 650000, # Based on Zoopla
766249482: 358000, # Based on Zoopla estimate for 19 Spring Lane, 3 bedroom semi-detached
100120703802: 277000, # Based on Zoopla
10014469685: 286000, # Based on Zoopla
10001328782: 196000, # Based on Zoopla
# Urban Splash - valuations from The Move Market
10023345430: 74_000,
10023345435: 99_000,
10023345436: 62_000,
10023345441: 62_000,
10094183503: 2_988_000,
10094183499: 123_000,
10070056824: 70_000,
110070056242: 100_000,
10070056243: 130_000,
10070056817: 130_000,
10094183501: 185_000,
10070056250: 71_000,
10094183500: 185_000,
10070056843: 67_000,
10070056844: 67_000,
10070056241: 76_000,
10070056834: 63_000,
10023345439: 62_000,
10070056815: 101_000,
10070056816: 101_000,
10094183498: 101_000,
10070056840: 673_000,
10070056848: 76_000,
10070056849: 76_000,
10070056829: 76_000,
10070056920: 76_000,
10023345463: 76_000,
# IMMO Dudley Pilot - search by going to https://www.zoopla.co.uk/property/uprn/{uprn}/
90070461: 172_000, # Based on Zoopla
90022227: 181_000, # Based on Zoopla
90106884: 180_000, # Based on Zoopla
90051858: 201_000, # Based on Zoopla
90060989: 172_000, # Based on Zoopla
90048026: 196_000, # Based on Zoopla
90077535: 192_000, # Based on Zoopla
90093693: 279_000, # Based on Zoopla
90055152: 149_000, # Based on Zoopla
90028499: 238_000, # Based on Zoopla
# IMMO Dudley Pilot 2- search by going to https://www.zoopla.co.uk/property/uprn/{uprn}/
90039318: 177_000, # Based on Zoopla
90038384: 170_000, # Based on Zoopla
90105380: 185_000, # Based on Zoopla
90124001: 165_000, # Based on Zoopla
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
# Vander Elliot Pilot - search by going to https://www.zoopla.co.uk/property/uprn/{uprn}/
41018850: 104_000, # Based on Zoopla
38237316: 74_000, # Based on Zoopla
38237317: 74_000, # Based on Zoopla
41052320: 70_000, # Based on Zoopla
41052321: 70_000, # Based on Zoopla
41052322: 38_000, # Based on Zoopla
41222759: 38_000, # Based on Zoopla
41222760: 46_000, # Based on Zoopla
41222761: 270_000, # Based on Zoopla
41212534: 38_000, # Based on Zoopla
# Northern Group Pilot - search by going to https://www.zoopla.co.uk/property/uprn/{uprn}/
10070868263: 194_000, # Based on Zoopla
10070868244: 195_000, # Based on Zoopla
# Places For People Pilot
200140644: 385_000,
200140645: 481_000,
200140646: 372_000,
200140647: 481_000,
200140648: 373_000,
200140649: 373_000,
# Vander Elliot Intrusive surveys
12103116: 1_537_000,
12103117: 1_404_000,
# GLA Proposal
100020606627: 409_000
}
# 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},
{"start": "D", "end": "A", "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},
]
# 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},
]
# Additional sources:
# https://superhomes.org.uk/wp-content/uploads/2024/05/The-Impact-of-Retrofit-on-Residential-Property-Market
# -Values-7-rotated-1.pdf
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):
current_value = (
property_instance.valuation if property_instance.valuation else
cls.UPRN_VALUE_LOOKUP.get(property_instance.uprn)
)
if not current_value:
return {
"current_value": 0,
"lower_bound_increased_value": 0,
"upper_bound_increased_value": 0,
"average_increased_value": 0,
"average_increase": 0
}
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]
msm_increase, lloyds_increase = cls.get_increase(epc_band_range)
# 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, rm_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": current_value,
"lower_bound_increased_value": float(current_value * (1 + min_increase)),
"upper_bound_increased_value": float(current_value * (1 + max_increase)),
"average_increased_value": float(current_value * (1 + avg_increase)),
"average_increase": float(current_value * (1 + avg_increase) - current_value)
}
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