Input data and information sources
Sale prices, owners' information and objections to earlier valuations come from outsiders. Each value relates to a property and therefore its owners.
Automated property valuation (AVM)
Also known as: Automated valuation model (AVM)
A model estimates residential property market values from property characteristics and comparable sales.
Understand the use case
What defines this use case?
The model learns from actual sale prices and estimates what a property would fetch on the valuation date. Dutch municipalities use this to determine official annual property values (WOZ values); German property tax instead uses statutory valuation methods.
This page describes a typical system design. Actual data flows and permissions depend on your implementation.
- System design
- Analysis and processing
- Ability to act
- Action permissions depend on integration
- Fields of use
- municipal tax administration, property finance
- Also known as
- mass appraisal, WOZ valuation model
How it works, step by step
System design and data flows
Input data
The model receives location, area, year of construction and condition, alongside comparable sale prices.
Sale prices, owners' information and objections to earlier valuations come from outsiders. Using aerial images or photographs alongside property data introduces several input types.
Processing
The model uses comparable sales to estimate what the property would fetch on the valuation date.
The model combines available property, location and comparable-property data to estimate a property's value. The estimate relates to a specific valuation purpose and data snapshot. Particular property characteristics absent from the input data cannot be explicitly accounted for in this calculation.
Results
Each property receives an estimated value, sometimes with an uncertainty indication.
The result describes the defined model task. A match, category or forecast acquires its professional significance only in conjunction with the underlying data and intended use.
Use and impact
After review, the value enters the municipal notice or determines the lending value of loan collateral.
Integration determines whether the result is simply displayed, adopted after approval or written directly into a business application. These variants differ in their data flows and permissions.
Explore the system design: Select a component to see its task, incoming and outgoing data, and role in the use case. The guided walkthrough explains each phase. Then use Yes, Uncertain and No to represent your specific system variant.
8 review first10 clarify7 baseline threats
- Present
- Depends on your system
- Absent
- Review first
- To clarify
How to use the model
The diagram shows the typical architecture. Select a component to see what happens there and adapt its features to your system. Enable “Analysis guidance” to see which threats need reviewing for this architecture. The page's checklist continues to describe the typical case.
Changes from the typical case
None yet. Each change will appear here.
Where most threats arise
- 3 review first, 1 clarify
- 4 clarify
- 3 review first
- 2 clarify
In this system design: 8 to review first, 10 to clarify, 7 baseline threats.
Architecture as a list
External access: Depends on your system
To clarify
Input: Part of every AI system
Review first
To clarify
Baseline for every AI system
Third-party content: Depends on your system
AI model: Part of every AI system
Review first
- Model Drift & Degradation Review priority
Provider service: Depends on your system
To clarify
Document collection: Depends on your system
To clarify
Training and feedback: Present
Review first
Result: Part of every AI system
Baseline for every AI system
Business application or equipment: Depends on your system
To clarify
person: Part of every AI system
Review first
- Overreliance Review priority
Affected person: Present
Unapproved AI services: Depends on your system
To clarify
Supply chain and technical foundations: 5 baseline threats applying to every AI system
Baseline for every AI system
How it differs from similar use cases
Credit scoring and creditworthiness assessment
Assesses the borrowing person's ability to repay
Data and information
Processing and operating environment
Many municipalities and banks obtain valuation software from specialist providers.
Storage and further use
Some methods retrieve the most similar sold properties from a collection for each property. Municipalities and banks train models on sales data from their area or portfolio.
Results and data sharing
Depending on the municipality, the estimate enters the notice directly or only after a valuer's approval.
Using a provider does not automatically determine where processing takes place. Storage location, subcontractors, deletion periods and use for training must be checked for the specific deployment.
Assess risks and determine measures
From understanding the system to analysing risks
- Define the system boundary
Record data, participants, interfaces and permissions. Resolve uncertain characteristics in the model.
- Investigate threats
Relate review candidates to actual data flows: what could happen, and who would be affected?
- Assess risk
Assess impacts and likelihood in the deployment context. The number of review candidates is not a risk score.
- Provide evidence of measures
Select appropriate controls, assign responsibilities and define effectiveness checks. Document residual risks and approvals.
Key threats in the application context
- 3 particularly important
- 5 typical
- 10 depending on the system
- 7 additional threats for every AI system
A total of 25 threats from the threat catalogue may be relevant to this use case.
Also review: whether the system disadvantages groups
Check whether selection, ranking or assessment disadvantages people with protected characteristics, such as sex, age or origin.
Priority review points
The model learns from sales before the valuation date. Rapid price changes in particular locations make estimates inaccurate until new sales data is available.
Targeted Poisoning / Label Poisoning
Family sales or incorrectly recorded floor areas enter training data as comparables. The model then shifts values across entire streets or building types.
With thousands of properties, valuers often scrutinise only unusual estimates. An unremarkable but incorrect value enters the notice, leaving the owner to challenge it.
5 further review candidates in the typical design
Other relevant threats
Your own training or operational feedback can introduce manipulated data into training datasets.
Listed because of this characteristic
- Learns from your own data
Training data or an adopted base model can introduce hidden triggers for misbehaviour.
Listed because of this characteristic
- Learns from your own data
Targeted queries can recover some personal or protected training data from the model.
Listed because of these characteristics
- Processes personal data
- Processes confidential data
Sensitive Information Disclosure
Confidential content can appear in answers, supplied context or logs.
Listed because of these characteristics
- Processes personal data
- Processes confidential data
Exfiltration from ML Application
Protected data can leak through tools, network connections, storage or insiders.
Listed because of this characteristic
- Processes confidential data
Up to 10 further threats may apply, depending on your system design. Nine threats need to be reviewed for every AI system. Two of these are particularly important here and are therefore listed above.
System features and open questions
Each Yes adds further threats to your checklist, up to 10 in total. If you cannot yet answer a question, include its threats in your review.
- Added by this Yes
How your system is built
How you operate the system
How your system is built
Does the system search its own collection of documents or records for relevant content?
Some methods retrieve the most similar sold properties from a collection for each property.
If yes, add
Can the system change things itself, such as storing data, sending messages, initiating transactions or granting permissions?
Depending on the municipality, the estimate enters the notice directly or only after a valuer's approval.
If yes, add
Does the system process several input types together, such as text with images or speech?
Using aerial images or photographs alongside property data introduces several input types.
If yes, add
How you operate the system
These questions arise for almost every AI system.
Can people or programs query the model directly, for example through an interface?
If yes, add
Is the system accessible over a network or dependent on network services?
If yes, add
Do you pay per query or unit of computing time, so costs increase with use?
If yes, add
Does the system share servers or computing resources with other customers or applications?
If yes, add
Do staff also use AI services for this task that your organisation has not approved?
If yes, add
Controls and implementation
Measures for the priority review areas are expanded. Each linked measure explains how to implement it.
Review priorityModel Drift & Degradation3 measures
Review priorityTargeted Poisoning / Label Poisoning3 measures
Review priorityOverreliance3 measures
Training Data Poisoning2 measures
Backdoor ML Model2 measures
Privacy Attacks3 measures
Sensitive Information Disclosure3 measures
Exfiltration from ML Application3 measures
Controls for the remaining threats are available on their pages in the threat catalogue.
AI Act provisions
Applies to every AI system
Art. 4: AI literacy
Applies if staff or appointed representatives operate or use AI systems.
Applies since 2 February 2025.
Obligations and details for Art. 4: AI literacy
- Who bears the obligations
- Provider, Deployer, Product manufacturer, Provider of a general-purpose AI model
Detailed obligations
Generally does not apply
Annex III no. 5 point b: Creditworthiness
The system values property rather than a person's creditworthiness, even where that value serves as loan collateral.
Obligations and details for Annex III no. 5 point b: Creditworthiness
- The general condition is
- Applies if the system assesses people's creditworthiness or establishes their credit scores.
- Exception
- Systems intended to detect financial fraud.
- Who bears the obligations
- Provider, Deployer, Product manufacturer
Detailed obligations
Classify your system with the quick checker All obligations in the obligations compass
Cross-cutting threats
Review these threats for every AI system, whatever its purpose.
Atypical system features
View 16 further questions
If any of these features does apply to your system, add the listed threats.
Does the system generate its own text, such as answers, drafts or summaries?
The system produces an estimated value, rather than text.
If yes, add
Does the system generate or edit images, sounds, voices or videos?
If yes, add
Does the system create new content from open-ended requests that people read, see or hear directly? Translation, reading aloud and transcription do not count.
If yes, add
Can users give the system natural language tasks that it then performs?
If yes, add
Does the system formulate answers from retrieved documents?
If yes, add
Does the system use a knowledge graph storing concepts and their relationships?
If yes, add
Does the system remember content from earlier conversations or tasks and reuse it later?
If yes, add
Does the system call other programs, interfaces or search services itself, even if only to read?
If yes, add
Does the system determine its next steps itself based on earlier results?
If yes, add
Does the system delegate tasks to other AI agents or incorporate their results?
If yes, add
Is the system connected to tools or data sources through the Model Context Protocol (MCP)?
If yes, add
Do documents from several customers, departments or permission groups share one search store?
If yes, add
Can people outside your organisation use the system, including without login?
The municipality or bank uses the model; owners only see the result.
If yes, add
Does the system generate code, scripts or database queries for further use?
If yes, add
Does the system execute programs, scripts or commands itself?
If yes, add
Is the system intended to maximise a specified metric, such as revenue, utilisation or clicks?
If yes, add
Sources
- Register or case collection
Dutch Algorithm Register, WOZ-taxatiemodellen, Gemeente Ede
The entry describes a machine learning valuation model for WOZ values.
- Register or case collection
Dutch Algorithm Register, WOZ-taxatiemodellen, Gemeente Het Hogeland
The entry describes AI and machine learning valuation models in use since July 2025.
- Legislation, norm or standard
EBA, Guidelines on loan origination and monitoring (EBA/GL/2020/06), Section 7.4
The guidelines require criteria for advanced statistical models used to value, revalue and monitor collateral (paragraphs 236 et seq.).
Related use cases
Also in the field of Administration, justice and public safety
- Risk-based review selection for applications and benefitsAuthorities assess applications or payments for error or fraud risk and use this to select cases for closer review.
- Police data analysis platformsPolice analysis platforms combine data from different sources and display links between people, incidents and cases as graphs, tables or maps.
- AI assistance in high-volume court proceedingsIn high-volume proceedings, the system organises pleadings, extracts each case's features and prepares draft decisions.
- Language and dialect recognition in asylum proceduresFrom a short speech sample, the system estimates which language and dialect an asylum seeker is likely to speak.
- Automatic number plate recognition (ANPR)Cameras capture vehicle number plates, and the system automatically checks them against registration or police search data.
- Intelligent video analytics in public spacesThe system detects specific events or objects in camera images and alerts a control centre without identifying people.
Updated: 30 September 2026. Review checklist rules version 0.3.2, threat catalogue version 2026.10.24.2. How these pages are structured