avm

Description

Input features

| Field name | Type | Example | Description | Used for | Importance | | ------------- | ------------- | ------------- | ------------- |------------- | | property_sub_type | string | 'SingleFamilyResidence' | Building structure type of home. Accepted values are 'Apartment', 'SingleFamilyResidence', 'Townhouse', 'Duplex', 'Terraced', 'Cabin', 'Farm' | All | Necessary | occupant_type | string | 'Owner' | Ownership type of home. Accepted values are 'Owner', 'TenantOwnership', 'Lease' | ATT| Necessary | living_area | int | 75 | Living area of home in square meters | All |Necessary | lot_size_area | int | 1000 | Lot area in square meters | STDTCF |Low | secondary_area | int | 40 | Secondary area of home in square meters | STDTCF |Medium | rooms_total | int | 4 | Number of rooms in home | A |Low | association_fee | int | 3500 | Monthly co-operation member fee | occupant_type = TenantOwnership |Medium | year_built | int | 1950 | Building construction year | All |High | latitude | decimal | 59.405589 | Latitude of current residence | All |Necessary | longitude | decimal | 18.323536 | Longitude of current residence | All |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. | All |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}'