Platform · Funda

Funda Data Scraping

Q: What are k.k. and v.o.n.?

Two pricing bases. Kosten koper means the buyer pays transfer tax and associated costs on top of the asking price; vrij op naam means those costs are included in the seller's quoted price. The difference is several per cent of the purchase price, so two listings at the same headline number represent different total outlays.

Two Dutch asking prices are not comparable until you know who is paying the transfer costs. The listing says which, in two letters.

Funda data scraping collects Dutch property listings, asking prices and attributes. The convention that breaks a naive comparison: Dutch asking prices are quoted either kosten koper — buyer pays transfer costs — or vrij op naam, seller pays. Those are different bases, the gap is several per cent of the price, and a comparison that ignores it is systematically wrong in one direction.

The Dutch market marks this clearly on every listing in two letters, which makes it one of the easier conventions to handle correctly and one of the most commonly ignored.

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funda.jsonl LIVE FEED

{"listing_id":"fd-44120","municipality":"Example gemeente", "price":425000,"price_basis":"kosten_koper", "property_status":"existing", "vve_charge_amount":165,"vve_charge_period":"monthly", "living_area_sqm":78,"energy_label":"B"} {"listing_id":"fd-88120", "price":425000,"price_basis":"vrij_op_naam", "property_status":"new_build", "note":"same headline. different total outlay. and the basis tracks new-build share"} {"normalised_price":"not_computed", "coverage_note":"near-complete for agent-listed; excludes private sales", "caution":"adjustment depends on the buyer transfer-tax rate, which is not on the listing"}

3 of 244,110 listing rows · Netherlandsprice_basis MANDATORY · never normalised · schema v1.0

Independence and trademarks. Actowiz Solutions is not affiliated with, endorsed by or connected to Funda or its owners. Funda and related marks belong to their respective owners, used here only to name the publicly accessible source this service collects from.

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Funda at a glance

How we handle Funda specifically

Platform-specific handling, not a generic retail template pointed at a different domain.

Portal: Funda — Netherlands
The convention: k.k. (buyer pays costs) or v.o.n. (seller pays)
The gap: Several per cent of price, in one direction
So: price_basis is mandatory on every record
Typical pattern: Existing homes tend k.k.; new build tends v.o.n.
Energy label: Captured, with the Dutch scheme recorded
Apartments: VvE service charge sits outside the price
Refresh: Daily. Dutch listing turnover can be very fast in tight markets

Platform specifics

Two price bases, marked clearly and ignored constantly

These are the reasons a Funda dataset needs its own handling rather than a shared retail schema.

k.k. and v.o.n. are different numbers for the same house

A Dutch asking price carries one of two markers.

k.k. — kosten koper, the buyer pays transfer tax and associated costs on top of the asking price.
v.o.n. — vrij op naam, those costs are included in the price the seller quotes.

The difference is several per cent of the purchase price. So a v.o.n. listing and a k.k. listing at the same headline number represent different total outlays for the buyer.

price_basis is mandatory on every record. We do not normalise between them, because the adjustment depends on the transfer tax rate applicable to that buyer and property — which varies by buyer circumstance and is not on the listing.

It correlates with property type, which makes it worse

New-build is commonly quoted v.o.n. and existing stock commonly k.k. So a dataset that ignores the basis does not introduce random noise — it introduces a bias that tracks the new-build share, which is exactly the thing a market analysis is often trying to measure.

Coverage is unusually complete, and the gap is specific

Funda's position in the Dutch market gives it close to comprehensive coverage of agent-listed property, which is unusual among the portals we cover.

That has a practical consequence worth stating plainly: a Funda-based supply figure is much closer to the real agent-listed market than a comparable figure in Spain or Italy, where the same property appears several times under different agencies.

What it does not cover

Private sales that do not go through an agent. That share is small in the Netherlands but it is not zero, and a supply figure should say what it excludes rather than implying completeness.

We state coverage_note in the deliverable rather than letting near-complete be read as complete. The distinction matters more the closer coverage gets to total, because that is when people stop qualifying it.

Duplication

Much lower than in agent-mandate markets. We still cluster, because low is not zero, and cluster_confidence travels as it does everywhere.

VvE charges, energy labels and asking prices

VvE service charge

Dutch apartments sit in an owners' association (VvE) with a monthly service charge. As with the Swedish monthly fee and the UK service charge, it is outside the asking price and varies substantially.

Captured as its own field where stated. We do not compute a total cost figure, for the same reason as everywhere else: capitalising a monthly charge needs a horizon and a rate that belong with you.

Energy label

Dutch properties carry an energy label. We record it with the scheme, and unlike the French DPE it does not currently function as a letting gate — so it is an attribute here, not a gating field.

Asking, not transaction

price_type is a constant asking. Dutch transaction data exists through separate official sources with different coverage and lag.

What we never collect

Agent individual details, seller identity or any personal data. Agency name is a commercial entity.

Scope

What we collect on Funda, and what we do not

The right column matters more than the left. Anyone can list fields; the limits are what tell you whether the dataset will hold up.

✅ What we collect

price_basis mandatory — kosten_koper or vrij_op_naam — on every record
Never normalised between bases, since the adjustment is buyer-specific
coverage_note stating what the dataset excludes
Clustering still applied, since low duplication is not zero
VvE service charge as its own field where stated
Energy label with the scheme recorded
price_type constant asking
Agency name as a commercial entity
Property attributes, plot and living area as published

❌ What we do not, and why

A k.k. price compared against a v.o.n. price without the basis
A normalised price using an assumed transfer tax rate
Near-complete coverage presented as complete
A total cost figure capitalising the VvE charge
Agent, seller or any individual's details

Core Funda fields

The full dictionary is agreed during scoping. These are the fields specific to this platform.

Field	What it is on this platform
listing_id / agency_name / municipality / district	The listing, agency and geography
price / currency / price_basis	The price, and which basis. Mandatory
property_status	New build or existing, which correlates with the basis
vve_charge_amount / vve_charge_period	Where stated. Outside the price
energy_label / energy_label_scheme	Attribute here, not a gate
living_area_sqm / plot_area_sqm	Recorded separately
rooms / build_year	As published
property_cluster_id / cluster_confidence	Low duplication is not zero
coverage_note	What the dataset excludes, per batch
first_seen / last_seen	Lifecycle
observed_at	Timestamp

Use cases

What teams do with Funda data

Dutch price analysis on a consistent basis

price_basis on every record, so a market average is not biased by a new-build share that quietly carries a different pricing convention.

New-build versus existing stock comparison

Property status alongside the basis, which is the combination that makes the comparison valid rather than an artefact of who pays transfer costs.

Supply figures with stated exclusions

Near-complete agent coverage with a coverage note, since the closer coverage gets to total the more people stop qualifying it.

Apartment total-cost context

VvE charge as its own field alongside price, without a capitalised total that would rest on a horizon and rate we chose.

The 24-hour sample — run on your sources, not ours

Send us a Funda item or category list. We run real collection against it and return the output within 24 hours, with the platform-specific fields populated so you can check them yourself rather than take our word for it.

Real extraction from your actual sources
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Coverage and QA note included
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How we engage

Three ways to engage us

Same collection pipeline and QA underneath. The difference is who holds the schedule and how the data reaches you.

Managed service (most common)

We own the collection, the QA and the delivery. You receive clean data on a schedule and never touch a scraper.

Dedicated engineer assigned to your account
Site changes fixed by us, not reported to you
Scheduled delivery to your warehouse or S3
Named contact on Slack or email

Best fit: Teams who need the data, not the infrastructure.

API access

The same collection pipeline exposed as an authenticated REST endpoint your systems query directly.

On-demand and scheduled endpoints
Rate limits agreed to your load profile
Sandbox keys for integration testing
Versioned schema with deprecation notice

Best fit: Product and engineering teams building on live data.

One-time or project extraction

A defined pull for a specific question — market sizing, diligence, a pitch, a one-off audit.

Fixed scope agreed in writing upfront
Single delivery with full QA report
Methodology documented for your records
Converts to managed if you want continuity

Best fit: Research, strategy and diligence work with a deadline.

Pricing

Every engagement is quoted individually, because the honest answer depends on your scope: how many sources, how many records, how often, and how the data reaches you. We scope it with you, run a free pilot on your own sources, and then quote a fixed monthly figure — no per-request metering and no overage billing when volumes move. Request a quote and you will have a number after one call.

Funda is usually collected alongside its competitors

Almost nobody buys a single platform in isolation. Funda data becomes useful when it sits next to the competitor set on one schema, refreshed on one schedule, so a price index or availability comparison is genuinely like-for-like.

That is what real estate data covers, and a Funda-only engagement can be expanded into it without rebuilding. If you already know you need several platforms, start there instead — it is the same pipeline and usually the better scoping conversation.

FAQ

Funda data scraping: frequently asked questions

Platform-specific questions, including what cannot be collected here.

Two pricing bases. Kosten koper means the buyer pays transfer tax and associated costs on top of the asking price; vrij op naam means those costs are included in the seller's quoted price.

The difference is several per cent of the purchase price, so two listings at the same headline number represent different total outlays.

No. The adjustment depends on the transfer tax rate applicable to that buyer and property, which varies by buyer circumstance and is not on the listing.

price_basis is mandatory instead, so any normalisation you do rests on an assumption you can state.

Because it correlates with property type. New build is commonly quoted v.o.n. and existing stock commonly k.k., so ignoring the basis does not add random noise — it adds a bias that tracks the new-build share, which is frequently the thing being measured.

Close to comprehensive for agent-listed property, which is unusual among the portals we cover. It does not include private sales that bypass an agent — a small share in the Netherlands, but not zero.

We state a coverage note rather than letting near-complete be read as complete. That distinction matters more the closer coverage gets to total, because that is when people stop qualifying it.

Yes. Duplication is much lower than in agent-mandate markets like Spain or Italy, but low is not zero, so clustering runs with confidence recorded as it does everywhere.

We quote individually on listing volume, municipalities and refresh. Tight Dutch markets can move fast enough to warrant sub-daily runs in specific cities.

One scoping call, a free pilot within 24 hours, then a fixed monthly quote. Request a quote.

See real Funda data before you commit to anything

Send us an item or category list. We return the output within 24 hours with the platform-specific fields populated.

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