We're on a mission to create the world's best API developer experience and community for housing data.
Our SDK is designed to supercharge your API experience and accelerate your time to insight. It enables you to efficiently pull the data you need, analyze it, and visualize your findings.
The Parcl Labs API provides instant insights into the U.S. housing market, delivering data on housing supply, sales, listings, rentals, investor activities, and market trends.
The most complete picture of US residential real estate
| Category | Coverage |
|---|---|
| Property Types | 🏘️ All Residential Assets: ✅ Single Family ✅ Townhouses ✅ Condos ✅ Other |
| Markets | 🇺🇸 Complete National Coverage, 70k+ Unique Markets at Any Level of Granularity: ✅ Regions ✅ States ✅ Metros ✅ Cities ✅ Counties ✅ Towns ✅ Zips ✅ Census Places |
| Housing Events | 🔄 The Full Property Lifecycle: ✅ Sales ✅ For Sale Listings ✅ Rentals |
We maintain a repository of examples that demonstrate how to use the Parcl Labs API for analysis. You can find the examples in the Parcl Labs Cookbook
Are you a premium user? See the premium features section for more information on how to access premium features including:
turbo_mode for faster data retrievalTo use the Parcl Labs API, you need an API key. To get an API key, sign up at ParclLabs. In the subsequent examples, the API key is stored in the PARCLLABS_API_KEY environment variable.
You can install the package via pip:
pip install -U parcllabs
The ParclLabsClient class is the entry point to the Parcl Labs API. You can use the client to access methods that allow you to retrieve and analyze data from the Parcl Labs API. You'll need to pass in your API key when you create an instance of the ParclLabsClient class.
import os
from parcllabs import ParclLabsClient
api_key = os.getenv('PARCL_LABS_API_KEY')
client = ParclLabsClient(api_key)
Search is your entry point into finding one or many of over 70,000 markets in the United States. You can search for markets by name, state, region, fips, or zip code. You can also search for markets by their unique parcl_id.
# get top 2 metros by population
markets = client.search.markets.retrieve(
location_type='CBSA',
sort_by='TOTAL_POPULATION',
sort_order='DESC',
limit=2
)
# top 2 metros based on population. We will use these markets to query other services in the remainder of this readme
top_market_parcl_ids = markets['parcl_id'].tolist()
# parcl_id country geoid state_fips_code name state_abbreviation region location_type total_population median_income parcl_exchange_market pricefeed_market case_shiller_10_market case_shiller_20_market
# 2900187 USA 35620 None New York-Newark-Jersey City, Ny-Nj-Pa None None CBSA 19908595 93610 0 1 1 1
# 2900078 USA 31080 None Los Angeles-Long Beach-Anaheim, Ca None None CBSA 13111917 89105 0 1 1 1
Services are the core of the Parcl Labs API. They provide access to a wide range of data and analytics on the housing market. The services are divided into the following categories: Price Feeds, Rental Market Metrics, For Sale Market Metrics, Market Metrics, Investor Metrics, Portfolio Metrics and Property.
Gets the percent gross yield for a specified parcl_id. At the market level, identified by parcl_id, gross yield is calculated by dividing the annual median rental income—derived from multiplying the monthly median new rental listing price by 12—by its median new listings for sale price.
Gets the number of rental units, total units, and percent rental unit concentration for a specified parcl_id.
Gets weekly updated rolling counts of newly listed for rent properties, segmented into 7, 30, 60, and 90 day periods ending on a specified date, based on a given parcl_id.
start_date = '2024-04-01'
end_date = '2024-04-01'
results_rental_units_concentration = client.rental_market_metrics.rental_units_concentration.retrieve(
parcl_ids=top_market_parcl_ids,
start_date=start_date,
end_date=end_date
)
results_gross_yield = client.rental_market_metrics.gross_yield.retrieve(
parcl_ids=top_market_parcl_ids,
start_date=start_date,
end_date=end_date
)
rentals_new_listings_rolling_counts = client.rental_market_metrics.new_listings_for_rent_rolling_counts.retrieve(
parcl_ids=top_market_parcl_ids
)
Gets weekly updated rolling counts of newly listed for sale properties, segmented into 7, 30, 60, and 90 day periods ending on a specified date, based on a given parcl_id.
Gets the weekly updated current count of total inventory listed on market for sale, based on a specified parcl_id . The data series for the for sale inventory begins on September 1, 2022 (2022-09-01).
Gets weekly updated metrics on the price behavior of current for sale inventory, based on a specified parcl_id. Available metrics include the count of price changes, count of price drops, median days between price changes, median price change, and the percentage of inventory with price changes. The data series for the for sale inventory metrics begins on September 1, 2022 (2022-09-01).
start_date = '2024-04-01'
end_date = '2024-04-01'
property_type = 'single_family'
results_for_sale_new_listings = client.for_sale_market_metrics.new_listings_rolling_counts.retrieve(
parcl_ids=top_market_parcl_ids,
start_date=start_date,
end_date=end_date,
property_type=property_type
)
for_sale_inventory = client.for_sale_market_metrics.for_sale_inventory.retrieve(
parcl_ids=top_market_parcl_ids,
start_date=start_date,
end_date=end_date
)
for_sale_inventory_price_changes = client.for_sale_market_metrics.for_sale_inventory_price_changes.retrieve(
parcl_ids=top_market_parcl_ids,
start_date=start_date,
end_date=end_date,
)
Gets monthly counts of housing events, including sales, new sale listings, and new rental listings, based on a specified parcl_id.
Gets housing stock for a specified parcl_id. Housing stock represents the total number of properties, broken out by single family homes, townhouses, and condos.
Gets monthly statistics on prices for housing events, including sales, new for-sale listings, and new rental listings, based on a specified parcl_id.
Gets monthly statistics on the physical attributes of properties involved in housing events, including sales, new for sale listings, and new rental listings, based on a specified parcl_id.
Gets monthly counts of all cash transactions and their percentage share of total sales, based on a specified parcl_id.
start_date = '2024-01-01'
end_date = '2024-04-01'
results_housing_event_prices = client.market_metrics.housing_event_prices.retrieve(
parcl_ids=top_market_parcl_ids,
start_date=start_date,
end_date=end_date
)
results_housing_stock = client.market_metrics.housing_stock.retrieve(
parcl_ids=top_market_parcl_ids,
start_date=start_date,
end_date=end_date
)
results_housing_event_counts = client.market_metrics.housing_event_counts.retrieve(
parcl_ids=top_market_parcl_ids,
start_date=start_date,
end_date=end_date
)
housing_event_property_attributes = client.market_metrics.housing_event_property_attributes.retrieve(
parcl_ids=top_market_parcl_ids,
start_date=start_date,
end_date=end_date
)
results_all_cash = client.market_metrics.all_cash.retrieve(
parcl_ids=top_market_parcl_ids,
start_date=start_date,
end_date=end_date
)
Gets monthly counts of new construction housing events, including sales, new for sale listings, and new rental listings, based on a specified parcl_id.
Gets monthly median prices for new construction housing events, including sales, new for sale listings, and new rental listings, based on a specified parcl_id.
start_date = '2024-01-01'
end_date = '2024-04-01'
results_new_construction_housing_event_prices = client.new_construction_metrics.housing_event_prices.retrieve(
parcl_ids=top_market_parcl_ids,
start_date=start_date,
end_date=end_date
)
results_new_construction_housing_event_counts = client.new_construction_metrics.housing_event_counts.retrieve(
parcl_ids=top_market_parcl_ids,
start_date=start_date,
end_date=end_date
)
Gets monthly counts of investor housing events, including acquisitions, dispositions, new sale listings, and new rental listings, based on a specified parcl_id.
Gets the monthly investor purchase to sale ratio for a specified parcl_id.
Gets weekly updated rolling counts of investor-owned properties newly listed for sale, and their corresponding percentage share of the total for-sale listings market. These metrics are segmented into 7, 30, 60, and 90-day periods ending on a specified date, based on a given parcl_id
Gets counts of investor-owned properties and their corresponding percentage ownership share of the total housing stock, for a specified parcl_id.
Gets monthly median prices for investor housing events, including acquisitions, dispositions, new sale listings, and new rental listings, based on a specified parcl_id.
start_date = '2024-01-01'
end_date = '2024-04-01'
results_housing_stock_ownership = client.investor_metrics.housing_stock_ownership.retrieve(
parcl_ids=top_market_parcl_ids,
start_date=start_date,
end_date=end_date
)
results_new_listings_for_sale_rolling_counts = client.investor_metrics.new_listings_for_sale_rolling_counts.retrieve(
parcl_ids=top_market_parcl_ids,
start_date=start_date,
end_date=end_date
)
results_purchase_to_sale_ratio = client.investor_metrics.purchase_to_sale_ratio.retrieve(
parcl_ids=top_market_parcl_ids,
start_date=start_date,
end_date=end_date
)
results_housing_event_counts = client.investor_metrics.housing_event_counts.retrieve(
parcl_ids=top_market_parcl_ids,
start_date=start_date,
end_date=end_date
)
results = client.investor_metrics.housing_event_prices.retrieve(
parcl_ids=top_market_parcl_ids,
start_date=start_date,
end_date=end_date,
)
Gets monthly counts of investor-owned single family property housing events, segmented by portfolio size, for a specified parcl_id. Housing events include acquisitions, dispositions, new for sale listings, and new rental listings.
Gets counts of investor-owned single family properties and their corresponding percentage of the total single family housing stock, segmented by portfolio size, for a specified parcl_id. The data series for portfolio metrics begins on March 1, 2024 (2024-03-01).
Gets counts of investor-owned single family properties and their corresponding percentage of the total single family housing stock, segmented by portfolio size, for a specified parcl_id. The data series for portfolio metrics begins on April 15, 2024 (2024-04-15).
Gets weekly updated rolling counts of investor-owned single family properties newly listed for rent, segmented by portfolio size, and their corresponding percentage share of the total single family for rent listings market. These metrics are divided into 7, 30, 60, and 90 day periods ending on a specified date, based on a given parcl_id. The data series for portfolio metrics begins on April 22, 2024 (2024-04-22).
results_housing_stock_ownership = client.portfolio_metrics.sf_housing_stock_ownership.retrieve(
parcl_ids=top_market_parcl_ids,
)
# get new listings for specific portfolio sizes
portfolio_metrics_new_listings = client.portfolio_metrics.sf_new_listings_for_sale_rolling_counts.retrieve(
parcl_ids=top_market_parcl_ids,
portfolio_size='PORTFOLIO_1000_PLUS',
)
results = client.portfolio_metrics.sf_housing_event_counts.retrieve(
parcl_ids=top_market_parcl_ids,
portfolio_size='PORTFOLIO_1000_PLUS'
)
results = client.portfolio_metrics.sf_new_listings_for_rent_rolling_counts.retrieve(
parcl_ids=top_market_parcl_ids,
portfolio_size='PORTFOLIO_1000_PLUS'
)
The Parcl Labs Price Feed (PLPF) is a daily-updated, real-time indicator of residential real estate prices, measured by price per square foot, across select US markets.
The Price Feeds category allows you to access our daily-updated PLPF and derivative metrics, such as volatility.
Gets the daily price feed for a specified parcl_id.
Gets the daily price feed volatility for a specified parcl_id.
Gets the daily updated Parcl Labs Rental Price Feed for a given parcl_id.
# get 2 price feeds trading on the Parcl Exchange
pricefeed_markets = client.search.markets.retrieve(
sort_by='PARCL_EXCHANGE_MARKET', # use PRICEFEED_MARKET for all price feed markets
sort_order='DESC',
limit=2
)
# top 2 metros based on population. We will use these markets to query other services in the remainder of this readme
pricefeed_ids = pricefeed_markets['parcl_id'].tolist()
start_date = '2024-06-01'
end_date = '2024-06-05'
price_feeds = client.price_feed.price_feed.retrieve(
parcl_ids=pricefeed_ids,
start_date=start_date,
end_date=end_date
)
rental_price_feeds = client.price_feed.rental_price_feed.retrieve(
parcl_ids=pricefeed_ids,
start_date=start_date,
end_date=end_date
)
price_feed_volatility = client.price_feed.volatility.retrieve(
parcl_ids=pricefeed_ids,
start_date=start_date,
end_date=end_date
)
# want to save to csv? Use .to_csv method as follow:
# price_feeds.to_csv('price_feeds.csv', index=False)
# rental_price_feeds.to_csv('rental_price_feeds.csv', index=False)
# price_feed_volatility.to_csv('price_feed_volatility.csv', index=False)
A premium Parcl Labs API key unlocks several critical features. This includes:
turbo_mode for faster data retrievalYou can register for a premium Parcl Labs API key through your account dashboard.
Gets a list of unique identifiers (parcl_property_id) for units that correspond to specific markets or parameters defined by the user. The parcl_property_id is key to navigating the Parcl Labs API, serving as the core mechanism for retrieving unit-level information.
# search by operators
invitation_homes_tampa_units = client.property.search.retrieve(
parcl_ids=[2900417],
property_type='single_family',
# square_footage_min=1000,
# quare_footage_max=2500,
# bedrooms_min=2,
# bedrooms_max=5,
# bathrooms_min=2,
# bathrooms_max=3,
# year_built_min=2010,
# year_built_max=2023,
current_entity_owner_name='invitation_homes',
# event_history_sale_flag=True,
# event_history_rental_flag=True,
# event_history_listing_flag=True,
# current_new_oncstruciton_flag=True,
# current_owner_occupied_flag=True,
# current_investor_owned_flag=True,
)
# search by buy box - only look at units that have rented
# and review rental rates
rental_buy_box = client.property.search.retrieve(
parcl_ids=[2900417],
property_type='single_family',
square_footage_min=1000,
square_footage_max=2500,
bedrooms_min=2,
bedrooms_max=5,
# bathrooms_min=2,
# bathrooms_max=3,
year_built_min=2010,
year_built_max=2023,
# current_entity_owner_name='invitation_homes',
# event_history_sale_flag=True,
event_history_rental_flag=True,
# event_history_listing_flag=True,
# current_new_oncstruciton_flag=True,
# current_owner_occupied_flag=True,
# current_investor_owned_flag=True,
)
# to extract parcl_property_id's to retrieve expanded history for
# any of these queries, use:
parcl_property_id_list = rental_buy_box['parcl_property_id'].tolist()
Gets unit-level properties and their housing event history, including sales, listings, and rentals. The response includes detailed property information and historical event data for each specified property.
sale_events = client.property.events.retrieve(
parcl_property_ids=parcl_property_id_list[0:10],
event_type='SALE',
start_date='2020-01-01',
end_date='2024-06-30'
)
rental_events = client.property.events.retrieve(
parcl_property_ids=parcl_property_id_list[0:10],
event_type='RENTAL',
start_date='2020-01-01',
end_date='2024-06-30'
)
Pass in a list of addresses -- address, unit, city, state_abbreviation, zip_code, source_id -- and receive the associated parcl_property_id, if there is a match. unit and source_id are optional fields.
addresses = client.property_address.search.retrieve(
addresses=[
{
"address": "123 Main St",
"city": "New York",
"state_abbreviation": "NY",
"zip_code": "10001",
"source_id": "123",
},
{
"address": "6251 coldwater canyon ave",
"unit": "unit 311",
"city": "north hollywood",
"state_abbreviation": "CA",
"zip_code": "91606",
"source_id": "456",
},
]
)
Turbo mode is a premium feature that allows you to retrieve data faster. To enable turbo mode, set the turbo_mode parameter to True when creating an instance of the ParclLabsClient class.
client = ParclLabsClient(api_key, turbo_mode=True)
This will enable turbo mode for all subsequent API calls which is a smart switch to route API calls through more efficient, premium endpoints designed for bulk data retrieval.
FAQs
Python SDK for ParclLabs API
We found that parcllabs demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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