Redfin · Real Estate

Redfin Estimate

Redfin's automated valuation model leveraging MLS data and machine learning to provide property value estimates with industry-leading accuracy for on-market homes.

Overview

The Redfin Estimate is an automated property valuation model that leverages Redfin's direct access to MLS data as a licensed brokerage combined with machine learning algorithms. This brokerage advantage gives Redfin access to more timely and detailed listing data than competitors who rely on third-party data feeds. The Redfin Estimate claims the lowest error rate among major online home valuation tools for on-market properties, making it a preferred reference for buyers and sellers seeking accurate pricing context.

Coverage

Major U.S. metropolitan areas

Accuracy

~1.5-2% median error (on-market homes)

Data Advantage

Direct MLS access as licensed brokerage

Update Frequency

Daily for active listings

Model Type

Machine learning AVM

Capabilities

Automated property valuation with MLS-direct data

On-market and off-market home value estimation

Comparable sales analysis and selection

Neighborhood and market trend analysis

Use Cases

Providing accurate home value estimates for listed properties

Supporting pricing decisions for home sellers and their agents

Informing buyer offers with data-driven property valuations

Tracking property value trends over time for homeowners

Pros

  • +Claims lowest error rate among major online AVM tools
  • +Direct MLS access provides more current and detailed data
  • +Transparent about accuracy metrics and methodology
  • +Integration with Redfin's brokerage provides full-service context

Cons

  • -Coverage limited to areas where Redfin operates as a brokerage
  • -Off-market estimates are significantly less accurate
  • -No standalone API or data product for developers
  • -Accuracy advantage diminishes for unique or rural properties

Pricing

Free for consumers on Redfin.com and Redfin app. No standalone API product. Integrated into Redfin's brokerage and lending services.

Related Models