avm

Description

Input features

Field name	Type	Example	Description	Importance
property_sub_type	string	'SingleFamilyResidence'	Building structure type of home. Accepted values are 'SingleFamilyResidence', 'Apartment', 'Townhouse', 'Duplex', 'Terraced', 'Cabin', 'Farm'	Necessary
occupant_type	string	'Owner'	Ownership type of home. Accepted values are 'Owner', 'TenantOwnership', 'Lease'	Necessary
living_area	int	75	Living area of home in square meters	Necessary
lot_size_area	int	1000	Lot area in square meters	Low
secondary_area	int	40	Secondary area of home in square meters	Medium
rooms_total	int	4	Number of rooms in home	Low
association_fee	int	3500	Monthly co-operation member fee	Medium
year_built	int	1950	Building construction year	High
latitude	decimal	59.405589	Latitude of current residence	Necessary
longitude	decimal	18.323536	Longitude of current residence	Necessary
purchase_contract_date	string	'2024-01-14'	Usually left empty. Specifies the date on which the valuation occurs. Note that the transactions that the valuations are based on are timestamped on the purchase contract date.	Optional

Note on Importance

A general measurement of the importance of the variable for the valuation model. Note that something with "low" importance can still be very important in some cases. Our recommendation is to include all information.

Note on Property_sub_type

We follow the RESO standard to the largest possible extent. However, the Swedish house types "kedjehus" ('chain house') and "radhus" ('row house') are not separated in the RESO standard. We use 'Terraced' for "kedjehus" and 'Townhouse' for "radhus". The difference is that for a "kedjehus" there is a building, usually a garage, between the houses that is connecting them.

Model Usage

SDK

import modelmarket as mm
import pandas as pd

mm_client = mm.Client()
# Login
mm_client.authenticate("USER","PASSWORD")

# Perform inference and get the predicted value
result = mm_client.models(input_features={
					  "property_sub_type": "SingleFamilyResidence",
					  "living_area": 150,
					  "lot_size_area": 32,
					  "secondary_area": None,
					  "rooms_total": 3,
					  "purchase_contract_date": "2022-04-01",
					  "latitude": 59.405589084302875,
					  "longitude": 18.32353631202982,
					  "occupant_type": "Owner",
					  "association_fee": None,
					  "year_built": 1992
				   }, 
				   provider="realai",
				   model_name="avm")


# Print the predicted probability of moving
print("Predicted Value of Home:", result['pred'])

Raw version

curl --location --request POST  'https://api.modelmarket.io/v1/models/normal/realai/avm' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer <ACCESS_TOKEN>' \
--data '{"property_sub_type":"SingleFamilyResidence",
      "living_area":150.0,
      "lot_size_area":32,
      "secondary_area":null,
      "rooms_total":3,
      "purchase_contract_date":"2022-04-01",
      "latitude":59.405589084302875,
      "longitude":18.32353631202982,
      "occupant_type":"Owner",
      "association_fee":null,
      "year_built":1992}'