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Accessing Real Time Residential Real Estate Data in 3 Steps via the Parcl Labs API 

Learn how to access real time real estate data in minutes by leveraging the Parcl Labs API.

The Parcl Labs Price Feed is a daily residential real estate price feed that represents real time price movements at any level of geography, to learn more about the Parcl Labs Price Feed, click here. You can access the price feed today in 3 simple steps:

  1. Register for the API
  2. Define the GraphQL query
  3. Call the API and visualize the results

Step 1: Register for the API

You can register for the API right now:

After you register you will receive an API key giving you access to the Parcl Labs API. The Parcl Labs API leverages GraphQL to give you access to real time real estate data, demographic data, geospatial data and much more. If you are not familiar with the Parcl Labs API or GraphQL, you can read our documentation to learn more.

You can retrieve API data with only a couple lines of GraphQL code. For demonstration purposes here we will show you a use case using Python, one of the most popular programming languages for data analysis, but the API can be used in combination with your favorite tech stack. Once you’ve received your key, you can store it as a variable in Python along with the API URL:

Step 2: Define the GraphQL query

Let’s use the API to see how prices are evolving in real time in the Phoenix Metropolitan Area to understand the price fluctuation over the course of 2022. First we name our query, in this case PHX_PLPF, and then we query the “Phoenix-Mesa-Chandler, AZ” MSA from the MSA table (see the Parcl Labs API docs for a comprehensive list of geographies and objects available in the API).

After defining the MSA, we choose the columns we want to output - MSA_Name, PARCL_ID (our unique identifier of different levels of geography), PARCL_PRICE_FEED and DATE. Before calling the price feed, we need to define the parameters of our query. We only want the price feed where the date is on or after 1/1/2022, ordered by the earliest date first. This is what the query would look like:

Step 3: Call the API and visualize the results 

Once you have plugged in your query, the URL and API key you can call the API with Python:

Finally we can easily flatten the JSON response into a dataframe and visualize it with your choice of visualization libraries, here we use Plotly:


# Flatten the JSON data and store in a dataframe
PHX_PPF_df = pd.json_normalize(data, 'parcl_price_feed', ['MSA_NAME'])
PHX_PPF_df['DATE'] = pd.to_datetime(PHX_PPF_df['DATE'])
 
#Plot the Phoenix Price Feed
PHX_PPF_fig = px.line(PHX_PPF_df, x='DATE', y='PARCL_PRICE_FEED', title='2022 Parcl Price Feed: Phoenix MSA', width=900, height=500,
               labels={
                    "PARCL_PRICE_FEED": "Parcl Price Feed ($)",
                    "DATE": "Date"
                },)
         
PHX_PPF_fig.update_traces(line_color='#4882db')
PHX_PPF_fig.show()

Based on our parameters, this query outputs the MSA name, Parcl ID, Date and Price at the MSA geography from 1/1/2022 to date.

Let’s take our output and visualize what these fluctuations look like at the MSA level, allowing us to see shifts across the Phoenix MSA for 2022, with all data coming from the GraphQL API.

Pilot API data is currently in beta and may not exactly match our published content.

As you can see the decline in prices in Phoenix is more pronounced than what lagged indices such as the Case Shiller would suggest, and has been continuing to decline since the Case Shiller’s most recent August numbers. The month over month decline has continued through to November according to the Parcl Labs Price Feed. 

Our proprietary Parcl Labs Price Feed (PLPF) can give you insights into the evolution of Phoenix, and many other geographies in real time, so you don’t have to make decisions with untimely data. It allows our users to track the evolution of real estate prices on a daily basis and can be pulled at multiple levels of geography (MSA, city, etc.) to understand micro and macro price shifts in a market. Additionally, since it updates daily, the Parcl Labs Price Feed empowers investors and homeowners to make decisions based on real time data.

You can register here for early access to our API and start getting insights into your markets well ahead of others.

Check out the full notebook to run this code yourself!

Disclaimer: The material contained on this website is provided for educational and informational purposes only, without any express or implied warranty of any kind. The information on this website does not constitute the provision of investment, tax, legal or other professional advice. No reliance may be placed for any purpose on the information and opinions contained herein or their accuracy or completeness, and nothing contained herein may be relied upon in making any investment decision.

Jason Lewris

Co-Founder

Jason leads the data team at Parcl Labs. Jason brings his experience from Microsoft and Deloitte where he worked on international data standardization and machine learning problems at scale.