Load HUD User data to DuckDB

HUD User provides a suite of public REST APIs for accessing datasets such as Fair Market Rents, Income Limits, USPS ZIP code crosswalks, and Comprehensive Housing Affordability Strategy (CHAS) data. Everything needed to build a working HUD User → DuckDB pipeline is on this page: the API's base URL, authentication, endpoints, pagination and incremental field — plus a prompt that hands the whole job to your coding agent.

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Build your HUD User to DuckDB pipeline

Paste this prompt into Claude, Codex, or Cursor. The agent does the rest.

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Run uvx dlthub-init@latest to build a pipeline from HUD User to DuckDB and run it on dltHub

That scaffolds a dltHub workspace and installs the dltHub AI harness — the project rules, the secrets-management skill, and the dlt MCP server your agent needs to work safely. From there it reads the HUD User API, proposes the endpoints to load, then writes, runs and validates the pipeline while you review rather than type. Credentials are inspected through MCP tools, so your agent never reads secrets.toml itself. How the LLM-native workflow works →

Prefer to write it yourself? Every fact the agent uses is below.

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HUD User API at a glance

These values come from the HUD User API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the HUD User API?

The API requires a bearer token obtained by registering on the HUD User portal. Pass the token in the HTTP Authorization header as 'Authorization: Bearer <your_token>'.

1. Get your credentials

To obtain API credentials for the HUD User REST API, follow these steps: 1. Navigate to the HUD User API registration page (https://www.huduser.gov/hudapi/public/register) or log in to your existing account at https://www.huduser.gov/hudapi/public/login. 2. If registering, complete the sign-up form and ensure you select the Datasets API you wish to access. 3. If you are a new user, check your email inbox for a confirmation message from HUD User to verify your account. 4. Once logged in, navigate to your account dashboard and click the 'Create New Token' button to generate your access token. This token will be used in your API requests.

2. Add them to .dlt/secrets.toml

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[sources.hud_user_source]
hud_user_api_key = "your_api_key_here"

dlt reads this file automatically at runtime. With the harness, the setup-secrets skill prompts you for the values and never handles the raw credential in chat. For production, see setting up credentials with dlt.

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What HUD User data can I load into DuckDB?

These are the HUD User endpoints dlt can load into DuckDB:

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How do I load only new HUD User records?

The HUD User API reference does not document a timestamp or sequence field for these endpoints, so there is nothing to advertise here as verified. Pick a field from the endpoints table above that increases with every write, then set it as the cursor_path.

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{"name": "fmr_data", "endpoint": {
    "path": "fmr/data/{entityid}",
    # Replace with a field that increases on every write.
    "incremental": {"cursor_path": "REPLACE_ME", "initial_value": "2024-01-01T00:00:00Z"},
}}

On the first run dlt loads everything from initial_value; on every run after that it requests only what changed and appends with write_disposition="merge" if you set a primary key. See incremental loading.

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What does the generated HUD User pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading fmr/data/{entityid} and chas (or generally usps) from the HUD User API into DuckDB:

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

@dlt.source
def hud_user_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://www.huduser.gov/hudapi/public",
            "auth": {"type": "bearer", "token": api_key},
        },
        "resources": [
            {"name": "fmr_data", "endpoint": {"path": "fmr/data/{entityid}", "data_selector": "basicdata"}},
            {"name": "fmr_state_data", "endpoint": {"path": "fmr/statedata/{statecode}", "data_selector": "metroareas"}}
        ],
    }
    yield from rest_api_resources(config)


def load_hud_user_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="hud_user_pipeline",
        destination="duckdb",
        dataset_name="hud_user_data",
    )
    load_info = pipeline.run(hud_user_source())
    print(load_info)


if __name__ == "__main__":
    load_hud_user_to_duckdb()

Run it with python hud_user_pipeline.py. The agent iterates on this until it loads cleanly — you review and approve, rather than write it from scratch.

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How do I query HUD User data in DuckDB?

dlt creates one table per resource. Query the loaded data with Python or SQL — or ask your agent to, through the MCP server's execute_sql_query tool.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("hud_user_pipeline").dataset()
df = data.fmr_data.df()
print(df.head())

SQL:

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SELECT * FROM hud_user_data.fmr_data LIMIT 10;

See querying your data with dataset and exploring it in marimo notebooks.

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How do I deploy the HUD User to DuckDB pipeline in production?

The pipeline runs locally, which is ideal for prototyping and one-off analysis. When you need it on a schedule, monitored on every load, and shared with your team, deploy the same dlt code on the dltHub platform — no infrastructure to maintain. The prompt above already ends with "run it on dltHub", so your agent can take it there directly.

• Deploy & schedule — run the pipeline as a managed job with automatic retries.
• Monitor — observable job queues, alerting, and load metrics for every run.
• Transform — promote raw HUD User loads into governed, documented models.
• Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.

Book a demo →

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What other destinations can I load HUD User data to?

dlt loads into any of these — only the destination argument changes:

Set dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. On the dltHub platform the same pipeline runs against a managed Iceberg lakehouse. See the full destinations list.

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Next steps

• Ready for production? Book a demo of the dltHub platform →
• dlt LLM-native workflow docs
• REST API source reference
• Join the dlt community on Slack