Dutch Real Estate Vector Search API (Funda, VBO, Huispedia,..) avatar

Dutch Real Estate Vector Search API (Funda, VBO, Huispedia,..)

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Dutch Real Estate Vector Search API (Funda, VBO, Huispedia,..)

Dutch Real Estate Vector Search API (Funda, VBO, Huispedia,..)

Semantic vector search over Dutch property listings (Funda, VBO, Huispedia and Pararius). Drop-in Qdrant-compatible API: point qdrant-client at it, send a query vector, get scoped, ranked matches. Build AI property search, RAG, and recommendations. Get an API key at portal.househugger.nl.

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Pay per usage

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Ramin Ariana

Ramin Ariana

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Dutch Real Estate Vector Search API — Funda, Vastgoed Nederland & Huispedia listings, AI semantic search

Semantic vector search over Dutch property listings (Funda, Vastgoed Nederland, Huispedia, and more), served as a drop-in Qdrant-compatible API. Point the official qdrant-client at this endpoint, send a query vector, and get back the most similar woningen — ranked by meaning, not keywords. Built for developers shipping AI property search, RAG, and recommendation features over the Netherlands housing market.

Powered by Househugger. Same infrastructure that runs in production — this is a hosted, production endpoint you can call today.

What you get

  • Semantic / vector search over Dutch real estate listings — embeddings-based similarity, so "licht appartement met tuin bij het water" matches on intent, not exact words.
  • Qdrant-native — use the real qdrant-client (Python/JS/Rust) unchanged. No new SDK to learn. search, query, scroll, retrieve-by-id all supported.
  • Scoped & safe — every request is filtered to exactly the data slice your key is entitled to. You can narrow further; you can never see another tenant's data.
  • Fast, read-optimized — a thin proxy over a managed Qdrant Cloud collection.

Use cases

  • AI-powered property search & discovery for a portal or app
  • Retrieval-Augmented Generation (RAG) over housing data for an LLM assistant
  • "Similar homes" / recommendation engines
  • Lead enrichment and market analysis over Funda / Vastgoed Nederland listings

How to call it

This runs in Standby mode — a always-on HTTP server, not a batch job. Authenticate with your API key and use qdrant-client as usual:

from qdrant_client import QdrantClient
client = QdrantClient(
url="https://vectorapi.househugger.nl",
api_key="<your-househugger-key>", # sent as the api-key header
)
hits = client.query_points(
collection_name="vectors",
query=my_query_vector, # bring your own embedding
limit=10,
).points

You bring the query vector (embed with any model you like); the API returns scoped, ranked matches. Writes and admin calls are rejected — this endpoint is read-only by design.

Get access

You need a Househugger API key. It's the real gate — calls without a valid key return 401.

👉 Sign up & get your API key at portal.househugger.nl

Pick a plan, self-serve a key, and start querying in minutes.

Notes

  • Does: forward semantic reads (search / query / scroll / retrieve-by-id) with your entitlement enforced on every filter and prefetch.
  • Does not: embed text for you, ingest, upsert, or expose data outside your slice.

Keywords: Dutch real estate API, Funda data, Vastgoed Nederland, Huispedia, Netherlands property search, woning zoeken, vector database, Qdrant, semantic search, embeddings, RAG, AI real estate, huizen API.