Rentcast Python API Docs | dltHub

RentCast is a property and rental data API providing access to 140+ million US property records, owners, valuations (AVMs), listings, comps and market statistics. The REST API base URL is https://api.rentcast.io and all requests require an X-Api-Key header containing your API key.

dlt is an open-source Python library that handles authentication, pagination, and schema evolution automatically. dlthub provides AI context files that enable code assistants to generate production-ready pipelines. Install with uv pip install "dlt[workspace]" and start loading Rentcast data in under 10 minutes.

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What data can I load from Rentcast?

Here are some of the endpoints you can load from Rentcast:

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How do I authenticate with the Rentcast API?

Authentication is by API key. Include your RentCast API key in the X-Api-Key HTTP request header for all requests.

1. Get your credentials

1. Create a RentCast account and activate an API subscription at https://app.rentcast.io/app/api or via the developer portal.
2. Open the API dashboard and generate a new API key.
3. Copy the key and supply it in the X-Api-Key header for requests.

2. Add them to .dlt/secrets.toml

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[sources.rentcast_source]
api_key = "your_rentcast_api_key_here"

dlt reads this automatically at runtime — never hardcode tokens in your pipeline script. For production environments, see setting up credentials with dlt for environment variable and vault-based options.

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How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

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uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI Workbench:

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dlt ai init --agent <your-agent> # <agent>: claude | cursor | codex

This installs project rules, a secrets management skill, appropriate ignore files, and configures the dlt MCP server for your agent. Learn more →

2. Install the rest-api-pipeline toolkit:

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dlt ai toolkit rest-api-pipeline install

This loads the skills and context about dlt the agent uses to build the pipeline iteratively, efficiently, and safely. The agent uses MCP tools to inspect credentials — it never needs to read your secrets.toml directly. Learn more →

3. Start LLM-assisted coding:

Use /find-source to load data from the Rentcast API into DuckDB.

The rest-api-pipeline toolkit takes over from here — it reads relevant API documentation, presents you with options for which endpoints to load, and follows a structured workflow to scaffold, debug, and validate the pipeline step by step.

4. Run the pipeline:

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python rentcast_pipeline.py

If everything is configured correctly, you'll see output like this:

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Pipeline rentcast_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset rentcast_data
The duckdb destination used duckdb:/rentcast.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

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dlt pipeline rentcast_pipeline show

This opens the Pipeline Dashboard where you can verify pipeline state, load metrics, schema (tables, columns, types), and query the loaded data directly.

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Python pipeline example

This example loads property_data and property_listings from the Rentcast API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

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import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def rentcast_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.rentcast.io",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "property_data", "endpoint": {"path": "reference/property-data", "data_selector": "results"}},
            {"name": "property_listings", "endpoint": {"path": "reference/property-listings", "data_selector": "results"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="rentcast_pipeline",
        destination="duckdb",
        dataset_name="rentcast_data",
    )
    load_info = pipeline.run(rentcast_source())
    print(load_info)

To add more endpoints, append entries from the resource table to the "resources" list using the same name, path, and data_selector pattern.

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How do I query the loaded data?

Once the pipeline runs, dlt creates one table per resource. You can query with Python or SQL.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("rentcast_pipeline").dataset()
sessions_df = data.property_data.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM rentcast_data.property_data LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("rentcast_pipeline").dataset()
data.property_data.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

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What destinations can I load Rentcast data to?

dlt supports loading into any of these destinations — only the destination parameter changes:

Change the destination in dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. See the full destinations list.

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Troubleshooting

Authentication failures

If you receive 401 or 403 responses, verify your X-Api-Key header contains the correct API key and that your subscription is active. Regenerate the key in the RentCast dashboard if needed.

Rate limits and throttling

RentCast enforces plan‑based rate limits. If you receive 429 responses, back off and retry using exponential backoff. Check your plan limits in the developer portal and consider batching or pagination to reduce requests.

Pagination quirks

Most list endpoints return paginated results (page, per_page or cursor). Use the provided pagination fields in the response (page/cursor and total_pages or next cursor) to iterate through results. Respect rate limits when iterating.

Common response codes

400 — bad request (invalid parameters), 401/403 — auth errors, 404 — resource not found, 429 — rate limit exceeded, 500 — server error. For persistent 500s contact support@rentcast.io.

Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.

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Next steps

Continue your data engineering journey with the other toolkits of the dltHub AI Workbench:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-runtime — Deploy, schedule, and monitor your pipeline in production.

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dlt ai toolkit data-exploration install
dlt ai toolkit dlthub-runtime install

• How to deploy a pipeline
• How to explore your data in marimo Notebooks
• How to query your data in Python with dataset
• Join the dlt community on Slack