Python SDK for ParclLabs API
Project description
parcllabs-python
Sign Up for an API Key
To 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.
Examples
We maintain a repository of examples that demonstrate how to use the Parcl Labs API for analysis. You can find the examples in the ParclLabs Examples
Installation
You can install the package via pip:
pip install parcllabs
Getting Started
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('PARCLLABS_API_KEY')
client = ParclLabsClient(api_key)
Search
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
.
Search Markets
import os
from parcllabs import ParclLabsClient
api_key = os.getenv('PARCLLABS_API_KEY')
client = ParclLabsClient(api_key)
# all cities in EAST_NORTH_CENTRAL census region
results = client.search_markets.retrieve(
location_type='CITY',
region='EAST_NORTH_CENTRAL',
as_dataframe=True
)
print(results.head())
# 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
# 0 5387853 USA 1714000 17 Chicago City IL EAST_NORTH_CENTRAL CITY 2721914 71673.0 1 1 0 0
# 1 5332060 USA 3918000 39 Columbus City OH EAST_NORTH_CENTRAL CITY 902449 62994.0 0 1 0 0
# 2 5288667 USA 1836003 18 Indianapolis City (Balance) IN EAST_NORTH_CENTRAL CITY 882006 59110.0 0 0 0 0
# 3 5278514 USA 2622000 26 Detroit City MI EAST_NORTH_CENTRAL CITY 636787 37761.0 0 1 0 0
# 4 5333209 USA 5553000 55 Milwaukee City WI EAST_NORTH_CENTRAL CITY 573299 49733.0 0 1 0 0
Services
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: Rental Market Metrics
, For Sale Market Metrics
, Market Metrics
, Investor Metrics
, and Portfolio Metrics
.
Rental Market Metrics
Gross Yield
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.
Rental Units Concentration
Gets the number of rental units, total units, and percent rental unit concentration for a specified parcl_id
.
Get all rental market metrics
import os
from parcllabs import ParclLabsClient
api_key = os.getenv('PARCLLABS_API_KEY')
client = ParclLabsClient(api_key)
# get all metros and sort by total population
markets = client.search_markets.retrieve(
location_type='CBSA',
sort_by='TOTAL_POPULATION',
sort_order='DESC',
as_dataframe=True
)
# top 10 metros based on population
top_market_parcl_ids = markets['parcl_id'].tolist()[0:10]
start_date = '2020-01-01'
end_date = '2024-04-01'
results_rental_units_concentration = client.rental_market_metrics_rental_units_concentration.retrieve_many(
parcl_ids=top_market_parcl_ids,
start_date=start_date,
end_date=end_date,
as_dataframe=True
)
results_gross_yield = client.rental_market_metrics_gross_yield.retrieve_many(
parcl_ids=top_market_parcl_ids,
start_date=start_date,
end_date=end_date,
as_dataframe=True
)
rentals_new_listings_rolling_counts = client.rental_market_metrics_new_listings_for_rent_rolling_counts.retrieve_many(
parcl_ids=[2900187, 5374167],
as_dataframe=True
)
For Sale Market Metrics
New Listings Rolling Counts
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
.
Get all for sale market metrics
import os
from parcllabs import ParclLabsClient
api_key = os.getenv('PARCLLABS_API_KEY')
client = ParclLabsClient(api_key)
# get all metros and sort by total population
markets = client.search_markets.retrieve(
location_type='CBSA',
sort_by='TOTAL_POPULATION',
sort_order='DESC',
as_dataframe=True
)
# top 10 metros based on population
top_market_parcl_ids = markets['parcl_id'].tolist()[0:10]
start_date = '2020-01-01'
end_date = '2024-04-01'
property_type = 'single_family'
results_for_sale_new_listings = client.for_sale_market_metrics_new_listings_rolling_counts.retrieve_many(
parcl_ids=top_market_parcl_ids,
start_date=start_date,
end_date=end_date,
property_type=property_type,
as_dataframe=True
)
Market Metrics
Housing Event Counts
Gets monthly counts of housing events, including sales, new sale listings, and new rental listings, based on a specified parcl_id
.
Housing Stock
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.
Housing Event Prices
Gets monthly statistics on prices for housing events, including sales, new for-sale listings, and new rental listings, based on a specified parcl_id
.
Get all market metrics
import os
from parcllabs import ParclLabsClient
api_key = os.getenv('PARCLLABS_API_KEY')
client = ParclLabsClient(api_key)
# get all metros and sort by total population
markets = client.search_markets.retrieve(
location_type='CBSA',
sort_by='TOTAL_POPULATION',
sort_order='DESC',
as_dataframe=True
)
# top 10 metros based on population
top_market_parcl_ids = markets['parcl_id'].tolist()[0:10]
start_date = '2020-01-01'
end_date = '2024-04-01'
results_housing_event_prices = client.market_metrics_housing_event_prices.retrieve_many(
parcl_ids=top_market_parcl_ids,
start_date=start_date,
end_date=end_date,
as_dataframe=True
)
results_housing_stock = client.market_metrics_housing_stock.retrieve_many(
parcl_ids=top_market_parcl_ids,
start_date=start_date,
end_date=end_date,
as_dataframe=True
)
results_housing_event_counts = client.market_metrics_housing_event_counts.retrieve_many(
parcl_ids=top_market_parcl_ids,
start_date=start_date,
end_date=end_date,
as_dataframe=True
)
Investor Metrics
Housing Event Counts
Gets monthly counts of investor housing events, including acquisitions, dispositions, new sale listings, and new rental listings, based on a specified parcl_id
.
Purchase to Sale Ratio
Gets the monthly investor purchase to sale ratio for a specified parcl_id
.
New Listings for Sale Rolling Counts
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
Housing Stock Ownership
Gets counts of investor-owned properties and their corresponding percentage ownership share of the total housing stock, for a specified parcl_id
.
Get all investor metrics
import os
from parcllabs import ParclLabsClient
api_key = os.getenv('PARCLLABS_API_KEY')
client = ParclLabsClient(api_key)
# get all metros and sort by total population
markets = client.search_markets.retrieve(
location_type='CBSA',
sort_by='TOTAL_POPULATION',
sort_order='DESC',
as_dataframe=True
)
# top 10 metros based on population
top_market_parcl_ids = markets['parcl_id'].tolist()[0:10]
start_date = '2020-01-01'
end_date = '2024-04-01'
results_housing_stock_ownership = client.investor_metrics_housing_stock_ownership.retrieve_many(
parcl_ids=top_market_parcl_ids,
start_date=start_date,
end_date=end_date,
as_dataframe=True
)
results_new_listings_for_sale_rolling_counts = client.investor_metrics_new_listings_for_sale_rolling_counts.retrieve_many(
parcl_ids=top_market_parcl_ids,
start_date=start_date,
end_date=end_date,
as_dataframe=True
)
results_purchase_to_sale_ratio = client.investor_metrics_purchase_to_sale_ratio.retrieve_many(
parcl_ids=top_market_parcl_ids,
start_date=start_date,
end_date=end_date,
as_dataframe=True
)
results_housing_event_counts = client.investor_metrics_housing_event_counts.retrieve_many(
parcl_ids=top_market_parcl_ids,
start_date=start_date,
end_date=end_date,
as_dataframe=True
)
Portfolio Metrics
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).
Single Family Home Portfolio Metrics
import os
from parcllabs import ParclLabsClient
api_key = os.getenv('PARCLLABS_API_KEY')
client = ParclLabsClient(api_key)
# get all metros and sort by total population
markets = client.search_markets.retrieve(
location_type='CBSA',
sort_by='TOTAL_POPULATION',
sort_order='DESC',
as_dataframe=True
)
# top 10 metros based on population
top_market_parcl_ids = markets['parcl_id'].tolist()[0:10]
results_housing_stock_ownership = client.portfolio_metrics_sf_housing_stock_ownership.retrieve_many(
parcl_ids=top_market_parcl_ids,
as_dataframe=True
)
# get new listings for specific portfolio sizes
portfolio_metrics_new_listings = client.portfolio_metrics_new_listings_for_sale_rolling_counts.retrieve_many(
parcl_ids=top_market_parcl_ids,
as_dataframe=True,
portfolio_size='PORTFOLIO_1000_PLUS',
)
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