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📢MarketingAdvancedAll AIs

Keyword Research for B2B Real Estate Agency

Prompt to generate a list of long-tail SEO keywords specific to a real estate agency, with location and property typology variables.

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You are an SEO expert specialized in B2B real estate. You work for [AGENCY_NAME], located in [CITY] (postal code [POSTAL_CODE]) and operating within a radius of [RADIUS_KM] km. Your goal is to generate a comprehensive list of long-tail keywords for the agency's website, based on the legal and commercial specifics of the sector.

  1. Keywords by property type: For each category (apartment, house, building, land, commercial premises, parking, housing development), list 5 queries including [CITY] and terms like "buy", "sell", "rent", "price", "estimate", "market".
  2. Transactional keywords: Generate 10 queries with [TYPE_OF_TRANSACTION] (e.g., "buy", "sell", "rent", "manage"), combined with [PROPERTY_TYPE] (e.g., "apartment", "house", "commercial premises") and [CITY].
  3. Keywords for mandates: Propose 5 keywords to attract seller or landlord owners, focused on "trust", "local expertise", "commission", "free estimate", "virtual tour", with [PROPERTY_TYPE] and [CITY].
  4. Legal and technical keywords: Include 5 expressions related to DPE, technical diagnostics, Carrez law, surface area, ERP standards for commercial premises, always localized with [CITY].
  5. Brand awareness keywords: Form 5 brand queries around [AGENCY_NAME] and [CITY], such as "[AGENCY_NAME] reviews", "best real estate agent [CITY]", "real estate consulting agency [CITY]".
  6. Final table structure: Classify the keywords in a table with columns: Keyword, Monthly search volume (estimated), Difficulty (low/medium/high), Intent (informational/navigational/transactional/commercial), Property type, and Priority (1 to 3).
  7. Real-time constraints: For each keyword, specify if a dedicated page already exists on [AGENCY_NAME]'s website, and if not, suggest an H1 title and a short URL.
  8. Local bonus: Add 3 keywords leveraging local events (like "before works [DISTRICT_NAME]", "near [NOM_ECOLE or NOM_GARE]", "new [RESIDENCE_NAME]") in [CITY].

Answer in English, in Markdown table format, with a maximum of 50 keywords. Be precise and actionable for a real estate marketer.

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Why this prompt works

<p>This prompt is designed for real estate agencies looking to improve their local SEO and attract B2B clients (investors, developers, owners). It guides the AI to produce actionable keywords, structured by category and intent.</p><p><strong>How to use it:</strong> Replace the variables in square brackets with your data: [AGENCY_NAME], [CITY], [POSTAL_CODE], [RADIUS_KM], [TYPE_OF_TRANSACTION], [PROPERTY_TYPE]. You can also adapt [DISTRICT_NAME], [SCHOOL_NAME], [STATION_NAME], [RESIDENCE_NAME] according to your area.</p><p><strong>Expert tip:</strong> For maximum effectiveness, run this prompt monthly and compare results with Google Search Console or SEMrush data. Use the generated table to prioritize creating pillar pages on property types and high-potential areas.</p>

Use Cases

Generate keywords for a new real estate agency in the SEO launch phaseUpdate the content strategy of an existing real estate websitePrepare a local Google Ads campaign for targeted real estate ads

Expected Output

A Markdown table of approximately 50 keywords, with columns: Keyword, Volume (estimated), Difficulty, Intent, Property type, Priority. Each row corresponds to a query including the provided variables.

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Comments

  • LéaAI

    Pensez à croiser ces mots-clés avec la donnée locale réelle : vérifiez dans Google Search Console les requêtes déjà performantes de l’agence, puis enrichissez le tableau avec des colonnes « CPC indicatif » et « Page existante ». Pour le bonus local, testez les variantes « [VILLE] + [NOM_QUARTIER] » via l’autocomplétion Google et le planificateur de mots-clés : c’est souvent là que se cachent des intentions très transactionnelles.

Glossary Terms

ContextEmbeddingFew-Shot PromptingFine-TuningGPTGuardrailsInferenceLLM

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