AI Real Estate Description Generator for Portals

A major developer with a catalog of 1,000+ properties spent 3 days writing listings for 5 portals. After deploying our AI generator, that time dropped to 2 hours. The problem is familiar to every realtor and developer: manual descriptions are templated, buyers skip them, and managers waste hours on edits. We developed an AI generator that creates unique descriptions in seconds based on characteristics, photos, and geolocation. Our expertise in NLP and Computer Vision allows us to adapt the solution for any portal — CIAN, Avito, Yandex.Realty, and international platforms. The GPT-4o model with Vision API analyzes up to 5 photos, determining renovation condition, finishing materials, and layout features. The result is a specific description with no clichés. We use prompt engineering with a system message that sets the company's tone and excludes empty adjectives. Request a demo version of the generator for your catalog.

How AI Generates a Description from Photos

Input includes structured data (type, area, floor, address, price, amenities) and up to 5 photos. The GPT-4o model combines textual and visual cues: from photos it identifies renovation quality, materials, layout; from data it takes square footage and location. The prompt is configured so the first paragraph contains key information (size, location, condition), and the description includes concrete facts rather than "beautiful apartment."

from openai import AsyncOpenAI
from dataclasses import dataclass

client = AsyncOpenAI()

@dataclass
class PropertyData:
    property_type: str      # apartment, house, commercial, land
    deal_type: str          # sale, rent
    rooms: int
    area: float             # m²
    floor: int
    total_floors: int
    address: str
    district: str
    metro_distance: int     # minutes walk
    price: float
    renovated: bool
    amenities: list[str]    # balcony, parking, elevator, storage...
    year_built: int = None
    ceiling_height: float = None

async def generate_property_listing(
    property_data: PropertyData,
    portal: str = "cian",
    tone: str = "professional"
) -> dict:
    PORTAL_CONFIGS = {
        "cian": {"max_title": 100, "max_desc": 4000},
        "avito": {"max_title": 80, "max_desc": 3000},
        "yandex_realty": {"max_title": 100, "max_desc": 4000},
    }
    config = PORTAL_CONFIGS.get(portal, PORTAL_CONFIGS["cian"])

    amenities_str = ", ".join(property_data.amenities)

    response = await client.chat.completions.create(
        model="gpt-4o",
        messages=[{
            "role": "system",
            "content": f"""You are a realtor writing sales listings for {portal}.
            Style: concrete, no fluff, numbers and facts.
            First paragraph — most important (size, location, condition).
            DO NOT write: "beautiful apartment", "gorgeous view", empty adjectives.
            Title: up to {config['max_title']} characters.
            Description: up to {config['max_desc']} characters.
            Return JSON: {{title, description, key_features (3-5 facts)}}"""
        }, {
            "role": "user",
            "content": f"""
            Type: {property_data.property_type}, {property_data.deal_type}
            Rooms: {property_data.rooms}, Area: {property_data.area} m²
            Floor: {property_data.floor}/{property_data.total_floors}
            Address: {property_data.address}, {property_data.district}
            Metro distance: {property_data.metro_distance} min walk
            Renovation: {'yes' if property_data.renovated else 'needed'}
            Amenities: {amenities_str}
            {'Year built: ' + str(property_data.year_built) if property_data.year_built else ''}
            """
        }],
        response_format={"type": "json_object"}
    )
    return json.loads(response.choices[0].message.content)

Photo Analysis via Vision API

async def describe_from_photos(photo_urls: list[str], property_type: str) -> str:
    """Analyze apartment photos, describe condition"""
    image_contents = [
        {"type": "image_url", "image_url": {"url": url}}
        for url in photo_urls[:5]
    ]

    response = await client.chat.completions.create(
        model="gpt-4o",
        messages=[{
            "role": "user",
            "content": [
                {"type": "text", "text": f"Describe the condition of the {property_type} from the photos. Indicate: renovation condition, finishing materials, layout features, visible advantages. 3-4 sentences, specific."}
            ] + image_contents
        }]
    )
    return response.choices[0].message.content

Why Automating Descriptions Is Profitable?

Manual writing of one listing takes 15–20 minutes — with a flow of 100 properties per month, that's 30+ hours of a copywriter. AI generation processes the same amount in 10 minutes without loss of quality. Moreover, budget savings on copywriting reach 90%. Comparison:

AI generation is 15 times faster than a human with comparable quality. We guarantee compliance with the company's tone of voice and eliminate spelling errors.

How Batch Catalog Processing Works?

For a large developer with 500+ listings, batch generation is necessary. We use asyncio.gather (see Python documentation) to process objects in parallel:

async def process_real_estate_catalog(
    properties: list[dict],
    portal: str = "cian"
) -> list[dict]:
    generator_tasks = [
        generate_property_listing(PropertyData(**p), portal=portal)
        for p in properties
    ]
    results = await asyncio.gather(*generator_tasks)
    return [{"property": p, "listing": r} for p, r in zip(properties, results)]

Processing a catalog of 500 objects takes 15–30 minutes. The cost per listing is negligible. Implementation time — from 2 weeks for the generation script and CRM integration. Specific cost is calculated individually based on catalog volume and model requirements.

Common Implementation Mistakes?

Ignoring portal limits — title gets truncated, description cut off. As specified in the CIAN API specification, the listing title must not exceed 100 characters. We strictly set character limits in the prompt for each portal. Lack of fallback on API failure — if GPT-4o is unavailable, the system switches to a backup LLaMA 3 model via custom inference. Generation without fact-checking — the model might "assume" floor or area. We add an instruction to the prompt to strictly rely on provided data.

Implementation Process

1. Analysis: audit current listings, gather requirements for format and style.
2. Design: develop prompt, configure Vision API, prepare batch generation scripts.
3. Implementation: write generator code in Python with async support, integrate with CRM via REST API.
4. Testing: A/B test on 50 listings, adjust prompt based on CTR metrics.
5. Deployment: deploy on your server or in the cloud, document, train the team.

Technical specifications:

What's Included

• Prompt engineering for your catalog and tone of voice.
• Generator code in Python (async, batch support).
• CRM integration (REST API, webhooks).
• Adaptation for 3 portals (CIAN, Avito, Yandex.Realty or any others) with Russian language support.
• Documentation and team training (2-hour webinar + recording).
• Support for 1 month after deployment.

Order a pilot project — get a ready script in 2 weeks. Contact us to evaluate your catalog: we'll prepare a demo generation on 50 properties for free. Our experience spans many years in AI development and numerous projects in the real estate sector. Get a consultation on automating your listings today.