Public data as the foundation for better rea...

The Netherlands has an unusually rich collection of public data on buildings, parcels, soil, water, energy, infrastructure and the living environment. Much of it is freely available through national facilities such as PDOK, the Kadaster, CBS, the Key Register of the Subsurface and the Climate Impact Atlas. Yet only a limited part of this data is used structurally in real estate decisions.

A developer looks at zoning and land price. An investor assesses rental income, maintenance and market value. An insurer focuses on claims history and rebuilding value. A municipality analyses housing production, climate adaptation and public space. All these parties in fact study the same area or building, but from different sources and systems.

That is where the opportunity lies. Not in yet another map viewer or another standalone dataset, but in bringing information together around one recognisable object: the building, the parcel or the property complex.

Public data is not ready-made real estate advice

Public data rarely delivers a definitive answer. A map showing that a property sits in an area with foundation risk does not prove that this specific foundation is poor. A model showing forty centimetres of water depth does not mean exactly forty centimetres will enter the building during the next downpour.

Public data mainly helps to assess large numbers of objects quickly, recognise striking risks or opportunities, and determine where further investigation is needed. It supports professional decision-making, but replaces no valuation, soil survey, building inspection or legal advice.

Start with the identity of a building

Almost every reliable analysis starts with the Key Register of Addresses and Buildings (BAG): identification numbers, construction year, use, floor area, status and geometry. Its real value is that it gives a building a shared identity. The same address may appear in four different spellings in an administration; for a system those are four different objects. Linking addresses to a BAG identification creates a stable foundation for enrichment and makes it possible to follow splits, mergers, renovations and changes of function over time.

What can you do with BAG data?

clean up a property portfolio;
identify duplicate or missing objects;
segment by construction year and function;
find properties with multiple residential units;
compare registered and internal floor areas;
recognise changes of use;
link buildings to parcels and neighbourhoods.

Construction year is a useful first indicator, but only an indication: a 1920 property may be fully renovated, while a younger building can suffer badly from poor maintenance.

Understanding the surroundings with the BGT

The Key Register of Large-scale Topography describes roads, pavements, water, greenery, terrain, railways, bridges, quay walls, trees and paving. Combined with building data it shows how much of a site is paved, where greenery is present and how close a building is to water or public infrastructure. That matters for flooding: a building surrounded by asphalt behaves very differently from one with gardens, ditches and green strips.

Practical uses include locating water storage, wadis, extra trees, green parking, open paving, shade and rainwater disconnection. Accessibility is relevant too: a building can sit outside a flood zone while its access road becomes impassable — often decisive for care properties, distribution centres and critical facilities.

Parcels, ownership and the cadastral map

The public cadastral map shows indicative parcel boundaries, parcel numbers, buildings and watercourses. Combined with the BAG it supports calculations such as built-up percentage, building-to-plot ratio, distance to boundaries, unbuilt space, room for expansion and multiple buildings on one parcel. Boundaries are not exact legal measurements, and ownership, mortgage rights and transaction data are not simply open data — official cadastral documents remain necessary for due diligence.

Elevation differences are increasingly relevant

The Actueel Hoogtebestand Nederland (AHN) contains detailed elevation data. Differences of a few decimetres already determine where water flows. AHN supports analysis of ground level, elevation differences around buildings, depressions where water collects, slopes and runoff routes, building heights, roof shapes and change between survey years. It also feeds first-pass solar potential calculations through roof pitch, orientation and shading.

What is under the building?

The Key Register of the Subsurface holds borehole, cone penetration test, groundwater monitoring and geological model data. A building on sand carries different risks than one on peat or soft clay. Subsurface data supports a first indication of foundation risk, geotechnical feasibility, ground energy analysis, groundwater nuisance, drought risk and the selection of sites for further soil investigation. Distance to the nearest measurement matters: a sounding a few streets away is context, not proof.

Soil contamination and historical use

Bodemloket and local sources reveal known soil investigations, remediations, former industrial activities and contaminated sites. Old industrial estates, petrol stations, printers, laundries, metal works and sites with underground storage tanks deserve extra attention. An empty map is not a certificate of clean soil — it may simply mean no research was ever done.

The Climate Impact Atlas as a first risk scan

The Klimaateffectatlas covers flooding, heat, drought, subsidence, groundwater and foundation risk.

Pluvial flooding

Key questions: how much water can gather around the building, is the entrance lower than the street, are there basements, where are technical installations, how much of the site is paved, and can water enter through doors, vents or car parks? Two adjacent properties can share the same map while only the one with a basement suffers major damage.

Flooding

Beyond the chance of inundation: does the location stay accessible, do power and communications fail, how fast does water arrive, how high can it rise, are there dry floors, can people leave in time, and how long before the area is usable again?

Heat

A public heat map says little about the indoor climate. Orientation, glazing, insulation, ventilation, sun shading, floor level, roof type, cooling and night ventilation determine which buildings actually overheat — especially care properties, schools, apartments and offices.

Drought and foundations

Combine construction year, soil type, groundwater development, subsidence, known pile rot or settlement risk and internal reports of cracks. The result is not a foundation survey; it is a way to decide which properties need attention first.

Energy performance at building level

EP-Online holds official energy labels and performance registrations, making it possible to see which buildings perform poorly, which labels expire soon and where decarbonisation matters most. Value grows when label data is combined with actual consumption, occupancy, maintenance plans and remaining exploitation period. A missing label does not automatically mean poor performance.

Spatial rules via the Digital System for the Environment Act

Environment documents, locations, rules and activities can be consulted to answer questions on permitted functions, maximum building height, extension, permit obligations, protected status, restrictions from water, noise, nature or heritage, and planned amendments. Interpretation remains specialist work: a geometric overlap with a regulated area does not automatically decide whether a development is allowed.

Include the socio-economic environment

CBS publishes district and neighbourhood data on population, households, age structure, income, housing stock, rent and ownership, business activity, employment, new build, demolition and car ownership. For care properties, growth in older residents matters; for housing development, household formation is often more relevant than population growth; for retail, income and density combine with accessibility and competition.

From separate sources to concrete applications

Acquisition and due diligence

identification of building and parcel;
construction year, function and floor area;
energy label and performance;
soil composition and known contamination;
pluvial flooding and river flooding;
heat, drought and subsidence;
noise and air quality;
environmental rules;
demographic and economic context.

Portfolio management

An integrated data model ranks the portfolio: buildings combining foundation and groundwater risk, locations with high water depth and vulnerable installations, care locations with severe heat load, poorly insulated buildings with high maintenance costs, and high-value objects with high risk.

Financing and mortgages

Lenders can map concentration in flood-prone areas, exposure to foundation problems, label distribution, estimated retrofit needs and regional value risks. Given uncertainty and regional bias, fully automated decisions at individual client level require restraint.

Insurance

Combining claims data with local rainfall, elevation, paved surface, nearby water, building type and basements shows which conditions actually cause damage, enabling targeted prevention.

Municipalities and public bodies

The same sources support prioritising vulnerable neighbourhoods, selecting housing sites, foundation programmes, greening, water storage, heat transition and supervision of spatial development. Combining physical and social data is essential.

Data management matters more than the map viewer

The hard work sits under the bonnet: which building exactly, from what date, measurement or model, which source version, what happens on a split, how are missing values handled, can a score be reconstructed and who owns quality?

Use stable identifiers

Use BAG building and residential unit identifications, cadastral parcel numbers, an internal asset ID and building-part or contract IDs, plus a link table recording relations. One complex may span several BAG buildings, dozens of units and multiple parcels while finance treats it as one asset.

Separate source data from calculations

A source layer stores original data unchanged with source holder, download moment, version, licence, format, coordinate system and technical checks. A harmonised layer standardises identifiers, dates, units and geometries. Only then does an application layer calculate indicators such as distance to water, paved percentage, maximum water depth, heat class, foundation priority, energy risk and adaptation need.

Time belongs in the data model

Store not only the value but the validity period, publication date, load date, scenario and calculation date. Without a time dimension, data from different years is presented as if it all describes today.

Not every spatial join means the same thing

Is a building in a risk zone when the full outline lies inside, one corner touches, the centroid falls inside, more than half overlaps, or the site is within ten metres? The right method depends on the application and must be recorded per indicator.

Make data quality visible

Assess completeness, currency, accuracy, spatial resolution, consistency, traceability and coverage. A simple grading: A registration or measurement at object level; B recent high-resolution measurement; C model value with usable resolution; D district average; E indirectly derived or incomplete.

Avoid false precision

A climate score of 7.8 looks precise but says little. "Increased priority due to low ground level, pluvial flood exposure, a basement and installations below street level" is explainable and points to the next step. Prefer risk classes, ranges, scenarios, source references, quality indications and clear decision rules.

Combination with your own data makes the difference

Public data mainly describes the exterior and surroundings. Property administration (market value, book value, rent, occupancy, insured value, exploitation period, maintenance budget, planned investment) translates physical risk into financial relevance. Inspections add foundation type, basements, threshold heights, cracks, damp, roof condition, installation locations and adaptation measures already taken. Smart meters and building management systems add electricity, gas, heat, water, indoor temperature, humidity, CO2 and faults. Claims history shows which risks actually materialised.

A practical object model

Identity — internal asset ID, BAG building, unit, parcel
Building — year, use, area, height, volume
Location — coordinates, neighbourhood, municipality, water authority
Energy — label, consumption, measures
Subsurface — soil type, groundwater, boreholes, soundings
Climate — pluvial flooding, flooding, heat, drought, subsidence
Living environment — noise, air quality, greenery, amenities
Legal — environmental rules, restrictions, permits
Financial — value, rent, maintenance, insured value
Own observations — inspections, faults, claims, sensors
Data quality — source, reference date, resolution, reliability

A realistic implementation

Step 1: put your own objects in order and link addresses to BAG buildings, units and parcels. Step 2: add core data (BAG, cadastral map, BGT, energy labels, AHN, CBS neighbourhood data, environmental rules). Step 3: add climate and subsurface. Step 4: connect internal systems for maintenance, inspections, energy, damage, rent and financial value. Step 5: start with explainable decision rules — high water depth plus a basement triggers an inspection; severe heat load in a care function triggers an adaptation plan; old construction year with soft soil and falling groundwater triggers a foundation survey; a poor label with long remaining exploitation triggers a retrofit study.

Limits of use

Not everything relevant is freely available: ownership, mortgage rights, detailed transactions, building surveys and part of the foundation information must be obtained separately. National coverage does not mean equal quality everywhere, and many maps are models rather than measurements. The rule is simple: the greater the consequences of a decision, the higher the demands on data and validation.

Conclusion

The Netherlands has nearly all the building blocks for a high-quality, data-driven approach to real estate and climate risk. The strength lies not in a single dataset but in bringing buildings, parcels, soil, water, energy, living environment and spatial rules together around one object. Technology is only part of the solution; good identifiers, current sources, historisation, transparent calculations and clear responsibilities matter just as much.

Liplyn Information Group publishes on how data, AI and digital information provision help organisations make complex information more accessible and usable.