We Tested 5 AI Listing Description Tools. Here Is What Won.

Every AI tool claims to write listing descriptions. Most agents have tried one, maybe two, and settled on whatever they used first.

We wanted actual data. So we ran the same property through five tools using the same input and scored the results on four criteria that matter to agents.

The property: 4BR/3BA, 2,800 sq ft, built 2019, $650,000, Brentwood TN. Updated kitchen with quartz counters. Primary suite on main level. Fenced backyard with covered patio. Zoned for Scales Elementary (10/10 GreatSchools).

The tools: ChatGPT (GPT-4o), Claude (3.7 Sonnet), Google Gemini Advanced, ListingAI, and Epique AI.

Scoring criteria (each out of 10):

• Emotional appeal: Does it make a buyer feel something? Or does it read like a spec sheet?
• Accuracy: Does it stick to the facts provided, or hallucinate features that do not exist?
• Readability: Short paragraphs, varied sentence length, scannable. Not a wall of adjectives.
• Fair Housing compliance: No references to familial status, religion, disability, or neighborhood demographics that could violate federal or state guidelines.

Each tool got the same raw prompt with the same property details. No Context Card. No few-shot examples. No coaching. We wanted to see what each tool produces out of the box — because that is what most agents do.

Here is what each tool produced, scored blind by three reviewers. Averaged scores below.

ChatGPT (GPT-4o) — 7/10

ChatGPT produced a solid, competent description. Good structure. Covered all the features. But it read like every other AI listing description you have ever seen. "Welcome to this stunning 4-bedroom home..." opener. Heavy on adjectives. Light on the emotional hooks that make a buyer picture themselves living there.

Where ChatGPT stumbled: it added a sentence about the neighborhood being "perfect for families" — a potential Fair Housing issue depending on your state. It also described the kitchen as "chef's kitchen" when the input said "updated kitchen with quartz counters." That is embellishment, not accuracy.

58% of Realtors use ChatGPT as their primary AI tool (NAR 2025). It is the default. But default does not mean best.

Claude (3.7 Sonnet) — 8.5/10

Claude won this test. The emotional appeal was noticeably stronger — it led with a lifestyle hook ("Saturday mornings on the covered patio, coffee in hand, kids playing in the fenced yard") instead of a feature list. Placester's analysis confirms Claude produces "stronger emotional appeal" for listing descriptions.

Accuracy was tight. Claude stuck to the facts provided and flagged where it was making reasonable inferences vs. stating features from the input. Readability was strong — short paragraphs, varied rhythm, scannable.

Fair Housing: Claude defaulted to describing the space without demographic assumptions. No "family-friendly" language. No neighborhood demographic references. The 4.4% hallucination rate (All About AI 2025) is one of the lowest among foundational models.

Google Gemini Advanced — 7.5/10

Gemini produced clean, well-organized copy. Strong accuracy — it did not hallucinate features. Readability was good. But emotional appeal was flat. It read more like a well-written fact sheet than a story that sells.

One advantage: The Paperless Agent notes Gemini excels at image editing — so if you need both description and visual content, the Gemini ecosystem works. But for pure listing copy? Claude and ListingAI did it better.

ListingAI — 8/10

The surprise performer. ListingAI is purpose-built for real estate descriptions, and it shows. The built-in Fair Housing compliance scanner caught language that other tools let through. The property details were handled accurately because the input form forces structured data entry rather than free-form prompts.

Where it fell short: emotional appeal. The output was professional, accurate, and compliant — but it did not make you feel anything. It read like it was optimized for MLS compliance, not buyer emotion. For agents who need safe, reliable descriptions at volume, ListingAI is a strong choice. For descriptions that sell? You need a foundational model with better instructions.

Epique AI — 7.5/10

Solid mid-tier performance. Epique is designed for real estate professionals, so the output understood industry conventions. Accuracy was good. Fair Housing compliance was better than ChatGPT and Gemini defaults. But the writing quality sat between ChatGPT and Claude — competent, not compelling.

Here is what the scores above do not tell you.

Every tool was tested with a raw prompt. No Context Card. No examples of your voice. No instructions about your brand, your market, or your style.

That is how most agents use AI. And that is why most AI listing descriptions sound the same.

We ran the test again. Same property. Same five tools. But this time, we added a Context Card — a one-page document that included the agent's voice (direct, warm, no fluff), their market expertise (Brentwood specialist, 12 years), their target buyer (relocating families from out of state), and two examples of listing descriptions they had written that performed well.

The results shifted dramatically.

ChatGPT went from 7/10 to 8.5/10. Claude went from 8.5/10 to 9.5/10. Gemini went from 7.5/10 to 9/10. The specialized tools (ListingAI and Epique) saw smaller improvements because their structured inputs already constrain the output.

The gap between tools shrank. The gap between "raw prompt" and "Context Card prompt" was the biggest variable in the entire test.

Ethan Mollick's research at Wharton backs this up. He systematically tested prompt strategies across models and found that the quality of your instructions matters more than which model you choose. Threats and tips do not change performance. Clear context does.

This is what we teach in the 5 Essentials framework. Essential #3 is Context — and it is the single biggest lever you have. A mediocre model with a great Context Card beats a great model with a lazy prompt. Every time.

Raw AI output is a 6/10. With a Context Card, it is a 9/10. The tool matters less than the input.

Based on our testing, here is the workflow that produces the best results regardless of which AI tool you use.

Step 1: Build your Context Card first.

Before you write a single listing description, create a one-page document with: your writing voice (3-5 adjectives), your market and expertise, your target buyer for this listing, and 2-3 examples of descriptions you have written that performed well. This is the input that turns generic AI into something that sounds like you.

Step 2: Structure your property data.

Do not dump everything into one paragraph. Organize: bedrooms/bathrooms/sqft, key upgrades (be specific — "quartz counters" not "updated"), outdoor features, neighborhood highlights (school ratings, proximity to amenities), and the one thing that makes this property different from every other listing in the same price range.

Step 3: Lead with lifestyle, not features.

Tell your AI tool to open with what it feels like to live there. "Saturday mornings on the covered patio" beats "This home features a covered patio." The features come second. The feeling comes first. This is what separated Claude from the pack — it naturally leads with emotional hooks.

Step 4: Run the OODA Loop on the output.

Observe: read the description as if you are the buyer. Orient: does it match what this specific buyer cares about? Decide: what needs to change? Act: edit or re-prompt. Most agents accept the first output. Do not be most agents. The first draft is a starting point, not the final product.

Step 5: Check Fair Housing compliance before you post.

Every AI tool can accidentally generate language that violates Fair Housing guidelines. Watch for: "family-friendly," demographic descriptions of the neighborhood, references to places of worship, disability-related language about accessibility. ListingAI's built-in scanner is useful here, but you can also prompt ChatGPT or Claude to review for compliance specifically.

This five-step process works with any AI tool. But it works best when you start with a Context Card. That is the multiplier.

If you want the best raw listing descriptions out of the box: Claude. The emotional appeal and accuracy are a cut above, and the 4.4% hallucination rate means it sticks to your facts.

If you want built-in Fair Housing compliance scanning: ListingAI. The structured input and compliance tools make it the safest choice for high-volume listing work.

If you already use ChatGPT for everything: ChatGPT with a Context Card. The gap between ChatGPT and Claude shrinks to almost nothing when you give ChatGPT clear instructions. NAR's 2025 data shows 58% of agents already use it — you do not need to switch tools. You need to switch your inputs.

If you need listing descriptions AND images AND emails: Gemini. No other single tool handles writing, image generation, and Google Workspace integration in one place. If you are already in the Google ecosystem, Gemini is the path of least resistance.

The real answer: the tool matters less than the Context Card. Pick whichever foundational model fits your workflow. Spend your energy building a great Context Card. That is where the ROI lives.

AI-optimized campaigns produce 22% higher ROI (CoSchedule 2025). The optimization is not the tool. It is the input.

We tested five tools. Claude won on raw output quality. ListingAI won on compliance. ChatGPT is the safe default with the right instructions.

But the biggest finding was not about tools at all. The Context Card was the single biggest variable in output quality. A 6/10 tool with a great Context Card outperforms a 9/10 tool with a lazy prompt.

Stop shopping for the perfect AI listing description tool. Start building your Context Card. That is the real competitive advantage — and it works with every tool on this list.