Updated: February 2026
Want to estimate a home’s value and run comps without hiring a realtor?
In 2026, tools like ChatGPT,
Google Gemini,
Claude, and
Perplexity
can help you organize data, build a comp grid, and sanity-check your assumptions.
TL;DR
- AI doesn’t “know” your market unless you feed it real data.
- Your comp set is everything: neighborhood + similarity + recency.
- Use AI for structure: comp tables, adjustment notes, and a value range.
- Verify: compare against sold comps, actives, days-on-market, and price cuts.
What AI can (and can’t) do for pricing
AI is great at turning messy inputs into a clean workflow: summaries, tables, checklists, and draft narratives.
But it can still be wrong if your inputs are incomplete, outdated, or biased.
AI is useful for
- Creating a “property sheet” (features, upgrades, condition, constraints).
- Building a comp shortlist + comp grid.
- Generating adjustment logic and a pricing range (low/mid/high).
- Stress-testing with “what if” scenarios (rate changes, market shift, repairs needed).
AI is not a substitute for
- A licensed appraisal for lending or legal purposes.
- Local, on-the-ground micro-market nuance (street-by-street demand).
- Verifying your sources (AI can hallucinate or assume facts you didn’t provide).
Step 1: Gather data (your “pricing inputs”)
Start by collecting facts that a human appraiser would want. In Los Angeles, you can use public sources like the
LA County Assessor Property Search
(beds/baths, assessed history) and recorded documents through the
LA County Registrar-Recorder/County Clerk
(ownership and record requests).
Then cross-check consumer portals for listing history and market context (actives/pending patterns).
For broader macro signals, you can reference mortgage rate releases like
Freddie Mac PMMS
or housing market datasets published through
FRED.
Your minimum “subject property” checklist
- Address (keep private if sharing with AI tools), property type, year built
- Beds/baths, living area (SqFt), lot size
- Condition: renovated vs original, major systems age (roof/HVAC/plumbing)
- Parking, view, yard, pool, ADU potential (if applicable)
- Deal-breakers: busy street, odd layout, unpermitted work, rental restrictions
Step 2: Build a real comp set (the part that actually matters)
To price accurately, your comps must be truly comparable.
A simple rule: same neighborhood, similar size and layout, and recent.
If you can’t find enough comps, expand one variable at a time (slightly older sale dates or a slightly wider radius),
and document what changed.
Comp selection filters (use these first)
- Recency: ideally last 90–180 days (longer if your area has low turnover)
- Distance: start close (same neighborhood); expand only if needed
- Similarity: same property type, similar SqFt, similar bed/bath count
- Context: compare street influence, school pocket, view/no-view, parking
Step 3: Use AI to generate a comp grid + adjustment notes
Now that you have real inputs, AI becomes powerful. Ask it to produce a comp grid, identify differences,
and propose a pricing range. You can also run the same prompt in multiple AI tools and compare results
for consistency.
Copy/paste prompt templates
Prompt A — Build comps and a comp grid
You are my comp analyst. Subject property: - Location: [Neighborhood/ZIP], Los Angeles - Type: [SFR / Condo / Townhome] - Beds/Baths: [ ] - Living area: [ ] SqFt - Lot: [ ] (if relevant) - Condition: [updated/original/mixed] - Notes: [parking/view/pool/ADU/etc.] Comps (paste the addresses + key details + sold price + sold date). Task: 1) Select the best 5–8 comps. 2) Create a comp grid table. 3) List differences vs the subject and propose reasonable adjustment notes. 4) Output a low/mid/high value range with confidence notes. 5) List what information is missing and how to verify it.
Prompt B — Quality control and sanity-check
Here is my comp grid and my proposed value range: [paste]. Please do a strict QA review: - Identify weak comps and why they are weak - Flag any assumptions that need verification - Suggest 2 alternative comp sets (more conservative / more aggressive) - Give me a final recommended range with a short explanation
Step 4: Do the math (simple, transparent, defensible)
You don’t need fancy models to be practical—you need transparent logic.
A straightforward way to think about adjustments:
Adjusted Comp Price = Comp Sale Price + (Size Difference × local $/SqFt signal) + Feature adjustments (parking, view, pool, condition) + Location adjustments (busy street, premium pocket)
Important: $/SqFt is not universal. It changes by neighborhood, condition, and price bracket.
Use AI to help you document assumptions—but don’t let it invent numbers you didn’t validate.
Step 5: Reality-check (this is where DIY pricing fails most often)
- Compare to actives: if similar homes are sitting, your price may be high.
- Watch price cuts: reductions reveal where buyers say “no.”
- Track days-on-market: speed is a pricing signal.
- Check condition gaps: turnkey vs fixer spreads can widen fast in rate-sensitive markets.
Fair Housing + ethics note (do not skip)
When you use AI, be intentional: do not use or request pricing logic based on protected characteristics.
The Fair Housing Act prohibits housing discrimination based on race, color, national origin, religion, sex, familial status, and disability.
For reference, see the official HUD overview of protected classes.
In your prompts, focus on property facts and market data—not people.
Disclaimer: This article is educational and not financial, legal, or tax advice. For lending, disputes, or high-stakes decisions,
consult a licensed professional (e.g., appraiser).
Optional: Want a second set of eyes?
Even if you DIY your comps, it can help to get an experienced review—especially for unique homes, busy streets, view lots,
or properties with unpermitted work.
Contact: Tai Savet • Agents of LA •