By Clear CapitalLearning Objectives
- Understand what a lending-grade automated valuation model (AVM) is and how it differs from a consumer home estimate
- See how computer-vision condition data can sharpen a statistical valuation
- Recognize why an AVM supports, but does not replace, a licensed appraiser in many lending decisions
What Is ClearAVM?
ClearAVM is a machine-learning automated valuation model (AVM) that estimates the market value of residential properties across the vast majority of US homes. Unlike a free consumer "home estimate," ClearAVM is built and tested to a lending-grade standard, meaning it is designed for use in real mortgage and lending workflows — origination, portfolio monitoring, and appraisal review — where accuracy and defensibility matter. The concrete problem it solves is speed and scale: lenders and investors need a reliable value on a property in seconds rather than waiting days for a full appraisal, and they need those values to be consistent across a large book of loans.
ClearAVM is a product of Clear Capital, a privately held real-estate valuation and analytics company that has operated in the mortgage and appraisal space for two decades. In May 2026 Clear Capital acquired Restb.ai, a long-established leader in AI-powered computer vision for real estate that processes billions of property photos and extracts condition and feature data. Combined with CubiCasa, the floor-plan company Clear Capital acquired in 2021, ClearAVM can now weave visual condition signals into its valuations rather than relying on public records and comparable sales alone.
💡Key Concept
Lending-grade AVM: An automated valuation model is a statistical algorithm that predicts a property's value from data such as comparable sales, property characteristics, and local market trends. "Lending-grade" means the model is accurate and well-tested enough that a lender can rely on it inside a regulated lending decision — a higher bar than the ballpark estimates on consumer real-estate websites.
✅Tip
Visit ClearAVM: clearcapital.com — for mortgage lenders, servicers, and institutional investors; enterprise licensing rather than consumer pricing.
Core Capabilities
Machine-learning valuation at scale
ClearAVM produces a value estimate and a confidence score for a property in seconds, drawing on comparable sales, property attributes, and market trend data. Because it runs on a model rather than a manual process, a lender can value an entire portfolio quickly and re-value it as markets move.
Computer-vision condition enrichment
Through Restb.ai, ClearAVM can incorporate condition and feature signals extracted from listing and inspection photos — for example, whether a home shows updated finishes or visible disrepair. This helps address a classic AVM blind spot: two homes with identical records can differ sharply in actual condition and therefore value.
Appraisal review and portfolio monitoring
Beyond a single value, ClearAVM supports appraisal review workflows that flag valuations worth a closer look, and portfolio monitoring that tracks how values shift across many properties over time. This lets institutions manage risk continuously rather than only at loan origination.
Strengths
- Built for lending, not just curiosity: ClearAVM is tested to a lending-grade standard, so its outputs are meant to withstand the scrutiny of a regulated mortgage process.
- Condition-aware valuations: The Restb.ai computer-vision layer adds visual condition data that records-only AVMs miss, reducing a common source of error.
- Speed and scale: Values return in seconds and can be applied across large portfolios, something manual appraisal cannot match.
- Backed by a broad valuation stack: ClearAVM sits alongside Clear Capital's floor-plan (CubiCasa) and inspection tools, so a customer can pull related property intelligence from one provider.
Limitations & Considerations
- An AVM is an estimate, not an appraisal. ClearAVM produces a statistical prediction; many lending decisions still legally require a licensed appraiser to inspect the property and stand behind the value. The AVM informs the decision — it does not sign off on it.
- Accuracy varies by market and data density. AVMs perform best where recent comparable sales are plentiful and property records are clean; in rural areas, unique homes, or thin markets, confidence drops and human judgment matters more.
- Condition data depends on available imagery. The computer-vision enrichment only helps where recent, representative photos exist; a property with stale or missing images falls back to records-based estimation.
- Model outputs must be used within fair-lending rules. Automated valuations feed decisions governed by fair-lending law, so lenders remain responsible for monitoring for unintended bias and disparate impact.
Best Use Cases
| Task | Why ClearAVM |
|---|---|
| Fast value on a single property | Machine-learning estimate returns in seconds with a confidence score |
| Valuing or monitoring a loan portfolio | Runs at scale and re-values as markets shift |
| Flagging questionable appraisals | Appraisal-review workflow surfaces values worth a second look |
| Reducing condition blind spots | Restb.ai computer vision adds visual condition signals |
Getting Started
- Engage Clear Capital about enterprise access and integration with your loan-origination or servicing systems.
- Define where an AVM value is appropriate in your workflow versus where a full appraisal is required.
- Use the confidence score to route low-confidence properties to human appraisers rather than accepting every automated value.
- Monitor AVM performance against actual appraisals and sales over time, and review outcomes for fair-lending compliance.
Key Takeaways
- ClearAVM is a lending-grade machine-learning AVM covering the vast majority of US homes.
- Clear Capital's 2026 acquisition of Restb.ai lets ClearAVM add computer-vision condition data to its valuations.
- It returns fast, scalable value estimates for origination, appraisal review, and portfolio monitoring.
- An AVM is a statistical estimate — a licensed appraiser is still required for many lending decisions.
