mirror of
https://github.com/Hestia-Homes/Model.git
synced 2026-06-08 11:17:27 +00:00
162 lines
6.7 KiB
Python
162 lines
6.7 KiB
Python
import numpy as np
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class PropertyValuation:
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"""
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This is a placeholder class for the property valuation model
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"""
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UPRN_VALUE_LOOKUP = {
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15038202: 202000,
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37024763: 213000,
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100070478545: 212000,
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100070297696: 662000, # Based on Zoopla's estimation of nearby house, 8 bloomfield road
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100070476394: 222000, # Based on Zoopla's estimation of next door, 20 Parkside
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100071264896: 128000,
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# Based on next door neighbour: https://themovemarket.com/tools/propertyprices/flat-2-queens-wood-house-219
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# -brandwood-road-birmingham-b14-6pu
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100070533688: 218000, # Based on Zoopla's estimation of 95 Tenby Road, which is also mid terrace
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100070505235: 344000, # Based on Zoopla's estimation of 131 School road, which is also semi-detached
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100070513306: 182000, # Based on Zoopla's estimation of 61 Simmons Drive
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100071306896: 77000, # Based on Flat 2 of 44 Wedgewood Road on Zoopla
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100021192109: 650000, # Based on Zoopla
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766249482: 358000, # Based on Zoopla estimate for 19 Spring Lane, 3 bedroom semi-detached
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100120703802: 277000, # Based on Zoopla
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10014469685: 286000, # Based on Zoopla
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10001328782: 196000, # Based on Zoopla
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# Urban Splash - valuations from The Move Market
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10023345430: 74_000,
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10023345435: 99_000,
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10023345436: 62_000,
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10023345441: 62_000,
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10094183503: 2_988_000,
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10094183499: 123_000,
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10070056824: 70_000,
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110070056242: 100_000,
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10070056243: 130_000,
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10070056817: 130_000,
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10094183501: 185_000,
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10070056250: 71_000,
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10094183500: 185_000,
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10070056843: 67_000,
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10070056844: 67_000,
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10070056241: 76_000,
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10070056834: 63_000,
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10023345439: 62_000,
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10070056815: 101_000,
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10070056816: 101_000,
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10094183498: 101_000,
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10070056840: 673_000,
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10070056848: 76_000,
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10070056849: 76_000,
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10070056829: 76_000,
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10070056920: 76_000,
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10023345463: 76_000,
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}
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# We base our valuation uplifts on a number of sources
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# https://www.moneysupermarket.com/gas-and-electricity/value-of-efficiency/
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MSM_MAPPING = [
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{"start": "G", "end": "F", "increase_percentage": 0.06},
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{"start": "F", "end": "E", "increase_percentage": 0.01},
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{"start": "E", "end": "D", "increase_percentage": 0.01},
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{"start": "D", "end": "C", "increase_percentage": 0.02},
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{"start": "C", "end": "B", "increase_percentage": 0.04},
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{"start": "B", "end": "A", "increase_percentage": 0.0},
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]
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# https://www.lloydsbankinggroup.com/media/press-releases/2021/halifax/homebuyers-pay-a-green-premium-of-40000
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# -for-the-most-energy-efficient-properties.html
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LLOYDS_MAPPING = [
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{"start": "G", "end": "F", "increase_percentage": 0.038},
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{"start": "F", "end": "E", "increase_percentage": 0.029},
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{"start": "E", "end": "D", "increase_percentage": 0.024},
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{"start": "D", "end": "C", "increase_percentage": 0.02},
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{"start": "C", "end": "B", "increase_percentage": 0.02},
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{"start": "B", "end": "A", "increase_percentage": 0.018},
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]
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KNIGHT_FRANK_MAPPING = [
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{"start": "D", "end": "C", "increase_percentage": 0.03},
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{"start": "D", "end": "B", "increase_percentage": 0.088},
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{"start": "D", "end": "A", "increase_percentage": 0.088},
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]
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NATIONWIDE_MAPPING = [
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# {"start": "G", "end": "D", "increase_percentage": 0.035},
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# {"start": "F", "end": "D", "increase_percentage": 0.035},
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# {"start": "D", "end": "B", "increase_percentage": 0.017},
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# {"start": "D", "end": "A", "increase_percentage": 0.017},
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]
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EPC_BANDS = ["G", "F", "E", "D", "C", "B", "A"]
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@classmethod
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def get_increase(cls, epc_band_range):
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increases = []
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for i in range(len(epc_band_range)):
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if i == len(epc_band_range) - 1:
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break
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current = epc_band_range[i]
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next = epc_band_range[i + 1]
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msm_increase = [x for x in cls.MSM_MAPPING if x["start"] == current and x["end"] == next][0]
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lloyds_increase = [x for x in cls.LLOYDS_MAPPING if x["start"] == current and x["end"] == next][0]
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increases.append(
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{
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"start": current,
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"end": next,
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"msm_increase": msm_increase["increase_percentage"],
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"lloyds_increase": lloyds_increase["increase_percentage"],
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}
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)
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# We now aggregate the increases. The should be compound increases so we multiply them together
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msm_increase = np.prod([1 + x["msm_increase"] for x in increases]) - 1
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lloyds_increase = np.prod([1 + x["lloyds_increase"] for x in increases]) - 1
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return msm_increase, lloyds_increase
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@classmethod
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def estimate(cls, property_instance, target_epc):
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value = cls.UPRN_VALUE_LOOKUP.get(property_instance.uprn)
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if not value:
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return {
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"current_value": 0,
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"lower_bound_increased_value": 0,
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"upper_bound_increased_value": 0,
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"average_increased_value": 0,
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"average_increase": 0
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}
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current_epc = property_instance.data["current-energy-rating"]
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# We get the spectrum of ratings between the current and target EPC
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epc_band_range = cls.EPC_BANDS[cls.EPC_BANDS.index(current_epc): cls.EPC_BANDS.index(target_epc) + 1]
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msm_increase, lloyds_increase = cls.get_increase(epc_band_range)
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# We now use the knight frank and nationwide data to get further valuation evidence, if we have it
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kf_increase = [x for x in cls.KNIGHT_FRANK_MAPPING if x["start"] == current_epc and x["end"] == target_epc]
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nw_increase = [x for x in cls.NATIONWIDE_MAPPING if x["start"] == current_epc and x["end"] == target_epc]
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kf_increase = kf_increase[0]["increase_percentage"] if kf_increase else None
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nw_increase = nw_increase[0]["increase_percentage"] if nw_increase else None
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all_increases = [x for x in [msm_increase, lloyds_increase, kf_increase, nw_increase] if x is not None]
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max_increase = max(all_increases)
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min_increase = min(all_increases)
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avg_increase = np.mean(all_increases)
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return {
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"current_value": value,
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"lower_bound_increased_value": value * (1 + min_increase),
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"upper_bound_increased_value": value * (1 + max_increase),
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"average_increased_value": value * (1 + avg_increase),
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"average_increase": value * (1 + avg_increase) - value
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}
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