> ## Documentation Index
> Fetch the complete documentation index at: https://docs.shovels.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Understanding Shovels Resident Data

> Learn how Shovels assembles and validates resident demographic data, including data sources, available features, and how to access it via API.

Shovels provides resident data features including demographic information compiled from multiple sources.

## Available Data Features

### Demographics

* Gender
* Age range
* Income range

### Additional Consumer Attributes

* Number of kids
* Grandparent in household
* Net worth
* And more

## Data Sources

Shovels' resident data comes from multiple non-LinkedIn sources. We assemble data about individuals via common linkages between:

* Person's name
* Workplace
* Personal address
* Business or personal contact information

## Data Compilation Process

Our process uses **consensus and observation date methodology** to select the most likely and current data points for each individual.

### Consensus Validation

We require 3+ different data signals to confirm data linkages, with reliability ensured through exhaustive validation methods and confirmation from actual usage.

### Conformation

This process ensures data within the "Consensus" realm not only represents frequent occurrences but also adheres to proper formatting and data standards.

<Info>
  The data includes both directly observed information and modeled/probabilistic data.
</Info>

## Accessing Resident Data

### Via API

To access resident contact information, pass the `address_id` from a permit object into the [residents endpoint](/api-reference/addresses/get-residents):

```bash theme={null}
GET /v2/addresses/{address_id}/residents
```

This returns contact info like name, phone, and email for occupants associated with the address.

### Via EDL

EDL customers have access to:

* `PERSONAL_EMAILS` - Personal email addresses
* `BUSINESS_EMAIL` - Business email addresses
* `HOMEOWNER` field - Filter for `HOMEOWNER = Y`

## Using Demographics for Homeowner Analysis

You can use available features like:

* Income range
* Children present
* Net worth

To identify trends that could help determine homeownership likelihood at certain demographic levels.

## Related Articles

* [Homeowner field explained](/docs/knowledge-base/data/residents/homeowner-field)
* [Data Dictionary](https://www.shovels.ai/data-dictionary)