Pricing | API Documentation

1 import requests 2 3 url = "https://api.hellodata.ai/property/pricing" 4 5 payload = { "subject": { 6 "id": "52907745-7672-470e-a803-a2f8feb52944", 7 "street_address": "111 West Wacker Drive", 8 "city": "Chicago", 9 "state": "IL", 10 "zip_code": "60601", 11 "lat": 41.88651, 12 "lon": -87.63141, 13 "created_on": "2021-01-01", 14 "building_availability": [ 15 { 16 "id": "52907745-7672-470e-a803-a2f8feb52944", 17 "is_floorplan": True, 18 "tags": ["ada_unit"], 19 "history": [ 20 { 21 "from_date": "2021-01-01", 22 "to_date": "2021-01-01", 23 "period_id": 0 24 } 25 ], 26 "availability_periods": [{ "period_id": 0 }], 27 "created_on": "2021-01-01", 28 "price_plans": [{ "duration_in_months": 12 }], 29 "amenities": ["amenities"] 30 } 31 ], 32 "street_address_alias": ["111 West Wacker", "111 West Wacker Drive"], 33 "building_name_alias": ["building_name_alias"], 34 "pricing_strategy": {}, 35 "building_quality": {} 36 } } 37 headers = { 38 "x-api-key": "<apiKey>", 39 "Content-Type": "application/json" 40 } 41 42 response = requests.post(url, json=payload, headers=headers) 43 44 print(response.json())

1 [ 2 { 3 "unit": { 4 "building_name": "OneEleven", 5 "unit_name": "203", 6 "floorplan_name": "2B2B", 7 "bed": 2, 8 "bath": 2, 9 "partial_bath": 1, 10 "sqft": 850, 11 "floor": 2, 12 "price": 1500, 13 "effective_price": 1350, 14 "enter_market": "2021-01-01", 15 "availability": "2021-01-01", 16 "exit_market": "2021-01-01", 17 "tags": [ 18 "ada_unit" 19 ] 20 }, 21 "supporting_units": [ 22 { 23 "unit": { 24 "building_name": "OneEleven", 25 "unit_name": "203", 26 "floorplan_name": "2B2B", 27 "bed": 2, 28 "bath": 2, 29 "partial_bath": 1, 30 "sqft": 850, 31 "price": 1500, 32 "effective_price": 1350, 33 "enter_market": "2021-01-01", 34 "exit_market": "2021-01-01" 35 }, 36 "similarity": 0.9 37 } 38 ], 39 "predicted_price": 2500, 40 "predicted_price_sqft": 2.08 41 } 42 ]

Get pricing recommendations for active units among a subject property given its details. We use all property and unit details (characteristics, amenities, location, etc.) to find similar units in the market that have been rented recently. We then use the rent prices of these units to estimate the unit pricing.

Request

This endpoint expects an object.

subjectobjectRequired

The content of the property for which to compute the recommended pricing.

settingsobjectOptional

The settings for computing the recommended pricing. For example to exclude the subject property from the comps analysis, to limit the search radius for comparable properties, or to filter the data to include only units occurring within a specific date range.

Response

The unit pricing.

unitobject

The PricingUnit object represents rudimentary information about a unit and its pricing. It is returned when querying a property's recommended pricing instead of the full unit details.

supporting_unitslist of objects

predicted_pricedouble or null

The recommended rent for the unit, based on an analysis of comparable units.

predicted_price_sqftdouble or null

The recommended rent per square foot of the unit, based on an analysis of comparable units.

Headers

Request

Response