> For the complete documentation index, see [llms.txt](https://docs.layerlens.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.layerlens.ai/4.2-industry-use-cases/pattern-9.md).

# Pattern: automated property valuation

Real estate / proptech — automated valuation pattern. Per-segment accuracy, fair-housing parity, image-condition assessment.

A proptech company or lender uses AI to generate property valuations from comparable sales, property features, listing photos, and market data. Banking partners require fair-housing-compliant valuation accuracy; replicating historical appraisal bias is a non-starter.

This pattern shows how to evaluate AVM accuracy and fair-housing parity together.

## What's at stake

| Risk dimension                                                  | Magnitude                                          | Framework                              |
| --------------------------------------------------------------- | -------------------------------------------------- | -------------------------------------- |
| Fair-housing exposure on neighborhood-level valuation disparity | Per-violation civil penalties; consent-decree cost | Fair Housing Act / HUD enforcement     |
| Banking-partner rejection of an AVM with documented bias        | Loss of integration agreements, revenue impact     | OCC / FRB safety-and-soundness reviews |
| Bad-loan losses from inaccurate valuations                      | $50K–$500K per loan in the worst-tail outcome      | Industry mortgage-loss benchmarks      |
| Brookings-documented appraisal-undervaluation pattern           | $48K average undervaluation in Black neighborhoods | Brookings 2018 / HUD studies           |

## The evaluation pattern

A **fairness-aware evaluation** runs the AVM against a labeled dataset spanning diverse neighborhoods, price ranges, and property types.

1. **Numeric scorer (valuation accuracy)** — AVM estimate within ±5% of appraiser ground truth.
2. **Custom code grader (geographic disparity)** — per-Census-tract or per-MSA disparity in median accuracy. Disparity above the configured tolerance (commonly 1.25× ratio) flags as a regression.
3. **Demographic-correlation scorer** — accuracy disparity must not correlate with protected-class proxies (race demographics, income tier).
4. **Image-condition assessment scorer** — for AVMs that incorporate listing photos, condition-extraction accuracy from images is scored separately.
5. **Comparable-selection rationale judge** (GEPA-tuned against ≥50 appraiser-labeled examples — scored output) — the AI's choice of comparables is defensible against an appraiser's choice.

> Don't have labels yet? See [Bootstrap a judge before GEPA](https://github.com/LayerLens/gitbook-full/blob/main/08-evaluate/guides/bootstrap-judges.md) for the week-1 setup.

**Continuous trace evaluation:** sampled at 1% of production valuations, daily. Disparity trends visible to compliance and risk committees.

## Configuration in code

```python
# Python (SDK)
from layerlens import Stratix
client = Stratix()

geo_disparity = client.scorers.create_code(
 name="geographic-disparity",
 code="""
by_tract = group_by_census_tract(traces, scores)
ratio = max_median(by_tract) / min_median(by_tract)
result = {'passed': ratio <= 1.25, 'disparity_ratio': ratio}
""",
)

valuation_accuracy = client.scorers.create_code(
 name="valuation-accuracy",
 code="result = {'passed': abs(output['estimate'] - expected['appraiser_value']) / expected['appraiser_value'] <= 0.05}",
)

comparable_judge = client.judges.create(
 name="comparable-selection",
 evaluation_goal="Score 1-5: is the AI's choice of comparable sales defensible against an appraiser's selection?",
)

trace_eval = client.trace_evaluations.create(
 trace_set={"tags": {"feature": "avm"}, "sample_rate": 0.01},
 scorers=[geo_disparity.id, valuation_accuracy.id],
 judges=[comparable_judge.id],
 schedule="daily",
)
```

```typescript
// TypeScript (REST)
const r = await fetch("https://stratix.layerlens.ai/api/v1/trace-evaluations", {
 method: "POST",
 headers: {
 "X-API-Key": process.env.LAYERLENS_STRATIX_API_KEY!,
 "Content-Type": "application/json",
 },
 body: JSON.stringify({
 trace_set: { tags: { feature: "avm" }, sample_rate: 0.01 },
 scorers: [geoDisparityId, valuationAccuracyId],
 judges: [comparableJudgeId],
 schedule: "daily",
 }),
});
```

## What you get

* Fair-housing disparity becomes a measurement on the dashboard, not a discovery during a regulatory review.
* Banking-partner integration approvals come faster — the evaluation evidence packet shrinks the diligence timeline.
* Per-comp rationale auditability for borrower-facing valuation explanations.
* Pre- and post-deployment block on any model variant that worsens disparity.

## Stratix capabilities used

* [Custom code graders](https://github.com/LayerLens/gitbook-full/blob/main/08-evaluate/cookbook/custom-code-scorer.md) — geographic-disparity and demographic-correlation
* [Judges with GEPA optimization](/8.-evaluate-score-the-outputs/judges-1.md) — comparable-selection rationale
* [Compare models](/5.-select-pick-the-model/compare-models.md) — variant selection with fair-housing criteria
* [Trace evaluations](/8.-evaluate-score-the-outputs/trace-evaluations.md) — continuous sampled

## Replicate this

**Get started:** [Workflow: Govern](/9.-improve-tune-the-system/workflow.md) describes the cross-team gate this pattern sits under.

* [Industry → Real estate](/4.2-industry-use-cases/real-estate.md)
* [Use case: AI quality gates in CI/CD](/4.1-general-use-cases/ai-quality-gates-cicd.md)
* [Concept: Continuous evaluation](/7.-observe-see-whats-happening/continuous-evaluation.md)