parcllabs 0.1.18

Python SDK for ParclLabs API

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',
    )