rebrowser/iaai-dataset

language:

• —en license: cc-by-nc-4.0 pretty_name: "IAAI Insurance Auto Auction Dataset" tags:
• —open-data
• —data-science
• —web-scraping
• —scraper
• —crawler
• —data-collection
• —iaai
• —salvage-auction
• —insurance-auto-auction
• —total-loss
• —salvage-vehicles
• —automotive-data
• —vehicle-auction
• —parquet size_categories:
• —1M<n<10M task_categories:
• —other configs:
• —configname: auction-listings datafiles:
• —path: "auction-listings/data/*.parquet" split: train ---

IAAI Insurance Auto Auction Dataset

Daily sample of IAAI insurance auto auction lots with damage assessments, title status, bidding data, and branch locations across North America.

This dataset is a preview sample of the IAAI dataset published by Rebrowser. If you're doing academic research, you may be eligible for free access to a much larger slice — see Free Datasets for Research.

This dataset contains 1 entity, each in its own folder: Auction Listings (auction-listings). See below for a full field breakdown, sample counts, and data distributions for each.

Found this useful? ❤️ Like this dataset on HuggingFace to help us keep publishing fresh data. Found an error? [Let us know](https://rebrowser.net/contact-us).

Auction Listings

IAAI insurance auto auction lots with vehicle specs, damage classifications, loss types, title status, mileage, drivetrain, branch locations, and auction scheduling.

1,027,826 total records from 2025-11-16 to 2026-08-09, up to 30,000 rows in this sample (2.9% of full dataset). Exported as one file per day, up to 1,000 rows each, last 30 days retained.

[image]

🔒 Premium fields are included in the data files but their values are replaced with [PREMIUM]. To access real values, use our website.

Field Distributions

<details> <summary><strong>Loss Type Distribution</strong> (<code>lossType</code>)</summary>

</details>

<details> <summary><strong>Primary Damage Location</strong> (<code>primaryDamage</code>)</summary>

</details>

<details> <summary><strong>Vehicle Class Distribution</strong> (<code>vehicleClass</code>)</summary>

</details>

<details> <summary><strong>Title Code Distribution</strong> (<code>titleCode</code>)</summary>

</details>

<details> <summary><strong>Vehicle Origin</strong> (<code>origin</code>)</summary>

</details>

<details> <summary><strong>Listings by Branch State</strong> (<code>branchState</code>)</summary>

</details>

Pre-built Views on Rebrowser

Rebrowser web viewer lets you filter, sort, and export any slice of this dataset interactively. These pre-built views are ready to open:

Auction Listings

Listings with Buy Now Price — 139,268 records

↳ [{"field":"buyNowIndicator","op":"isTrue","value":true},{"sort":"buyNowPrice ASC"}]

Run and Drive Vehicles — 810,458 records

↳ [{"field":"runAndDrive","op":"isTrue","value":true},{"sort":"_lastSeenAt DESC"}]

Collision Total Loss Vehicles — 786,344 records

↳ [{"field":"lossType","op":"is","value":"Collision"},{"sort":"_lastSeenAt DESC"}]

Theft Recovery Vehicles — 16,768 records

↳ [{"field":"lossType","op":"is","value":"Theft"},{"sort":"_lastSeenAt DESC"}]

Insurance Company Listings — 878,609 records

↳ [{"field":"origin","op":"is","value":"Insurance"},{"sort":"_lastSeenAt DESC"}]

[See all 40 views →](https://rebrowser.net/products/datasets/iaai/auction-listings)

Code Examples

import pandas as pd
from pathlib import Path

# ── Auction Listings ─────────────────────────────────────────────────────────
files = sorted(Path('rebrowser/iaai-dataset/auction-listings/data').glob('*.parquet'))[-7:]
df = pd.concat([pd.read_parquet(f) for f in files])

# Top 10 makes by listing volume
print(df['make'].value_counts().head(10).to_string())

# Average mileage by vehicle class for collision total losses
collision = df[df['lossType'] == 'Collision']
print(collision.groupby('vehicleClass')['mileage'].mean().sort_values().round(0).to_string())

# Loss type distribution across all listings
print(df['lossType'].value_counts().to_string())

# Run-and-drive listings by branch state (top 10 states)
drivable = df[df['runAndDrive'] == True]
print(drivable['branchState'].value_counts().head(10).to_string())

# Title code breakdown for theft recovery vehicles
theft = df[df['lossType'] == 'Theft']
print(theft['titleCode'].value_counts().to_string())

Use Cases

Salvage Valuation Modeling

Build pricing models using loss type, damage location, mileage, title status, and vehicle specs. Compare across branches and states to identify regional price variations.

Theft Recovery Analysis

Filter by loss type to study theft recovery patterns by make, model, and state. Identify which vehicles are most frequently recovered and their typical condition at auction.

Inventory Sourcing

Monitor auction listings by vehicle class, drivetrain, and branch location to find inventory matching rebuild or parts sourcing criteria. Track run-and-drive status and key availability.

Market Trend Research

Track shifts in loss type distribution, vehicle class mix, and geographic auction volume over time. Analyze how catastrophe events and seasonal patterns affect regional supply.

Full Dataset on Rebrowser

This is a 1,000-row preview sample. The full dataset is at rebrowser.net/products/datasets/iaai

Doing academic research? You may qualify for free access to a larger slice. See Free Datasets for Research.

On Rebrowser you can:

• —Filter before you buy — use the web UI to apply documented filters and sortable columns. Preview results before purchasing; paid exports freeze their exact selected identities before billing.
• —Export in your format — CSV, JSON, JSONL, or Parquet depending on your plan.
• —Access via API — integrate dataset queries into your pipelines and workflows.
• —Choose your freshness — plans range from a 14-day lag to real-time data with no delay.
• —Select only the fields you need — keep exports lean. Premium fields with richer data are available on higher plans.

Pricing starts at $2 per 1,000 rows with volume discounts.

License & Terms

Free for research and non-commercial use with attribution. See license terms and how to cite.

@misc{rebrowser_iaai,
  author       = {Rebrowser},
  title        = {IAAI Insurance Auto Auction Dataset},
  year         = {2026},
  howpublished = {\url{https://rebrowser.net/products/datasets/iaai}},
  note         = {Accessed: YYYY-MM-DD}
}

Commercial use requires a paid license — see pricing. Use of this data is governed by the Rebrowser Terms of Use, which may be updated at any time independently of this dataset.

Disclaimer

Rebrowser is an independent data provider and is not affiliated with, endorsed by, or sponsored by IAAI. Any trademarks are the property of their respective owners. This dataset is compiled from publicly available information; we do not request or collect IAAI user credentials. By using this dataset, you agree to comply with IAAI's Terms of Service and all applicable laws and regulations. Images, logos, descriptions, and other materials included in this dataset remain the intellectual property of their respective owners and are provided solely for informational purposes. Rebrowser makes no warranties regarding the accuracy, completeness, or legality of the data and assumes no liability for how the data is used. You are solely responsible for ensuring that your use of this dataset does not infringe on the rights of any third party.

You can also find this data on GitHub, Kaggle, Zenodo.