> For the complete documentation index, see [llms.txt](https://docs.altum.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.altum.ai/english/sustainability/nta-8800-energylabel-v2-api/apartments.md).
# Apartments
The endpoint described in this section applies to **apartment units within multi-unit residential buildings**. These include:
* `Galerijflat`
* `Portiekflat`
* `Maisonnette`
* `Benedenwoning`
* `Bovenwoning`
* `Portiekwoning`
The model assumes a **dwelling within a multi-unit residential building**, where roofs, walls, and installations may be shared with other units. The **position of the apartment within the building and the number of floors** are important factors, allowing the API to estimate insulation levels, heating systems, and other measures based on building characteristics and machine learning predictions.
## Validation & Prediction Performance
Altum AI conducts quarterly performance testing of the NTA 8800 Energy Label API for Houses using a representative sample of 10,000 addresses with an NTA 8800 determination method. The performance is assessed using Mean Absolute Error (MAE), Median Absolute Error (MedAE), and the percentage of results falling within defined BENG2 and energy-label error ranges.
#### **BENG2 / Energy Label Information Available**
Uses a **statistical model** combined with the **definitive energy label or BENG2** information. The results therefore measure the consistency of the API with the available energy information.
{% tabs %}
{% tab title="Q3 2026" %}
**MAE:** 29.56
**MedAE:** 17.0
**BENG2**
```
15.95% predictions in 5 BENG2 units error range.
30.74% predictions in 10 BENG2 units error range.
54.89% predictions in 20 BENG2 units error range.
71.83% predictions in 30 BENG2 units error range.
81.53% predictions in 40 BENG2 units error range.
87.32% predictions in 50 BENG2 units error range.
89.95% predictions in 60 BENG2 units error range.
91.27% predictions in 70 BENG2 units error range.
92.54% predictions in 80 BENG2 units error range.
93.75% predictions in 90 BENG2 units error range.
94.74% predictions in 100 BENG2 units error range.
97.50% predictions in 150 BENG2 units error range.
98.68% predictions in 200 BENG2 units error range.
99.82% predictions in 400 BENG2 units error range.
99.90% predictions in 500 BENG2 units error range.
```
**Energy Label**
```
81.39% predictions in 0 energy label difference.
91.07% predictions in 1 energy label difference.
95.60% predictions in 2 energy label difference.
98.02% predictions in 3 energy label difference.
99.08% predictions in 4 energy label difference.
99.55% predictions in 5 energy label difference.
99.75% predictions in 6 energy label difference.
99.84% predictions in 7 energy label difference.
99.95% predictions in 8 energy label difference.
99.99% predictions in 9 energy label difference.
100.00% predictions in 10 energy label difference.
```
{% endtab %}
{% endtabs %}
#### **BENG2 / Energy Label Information Unavailable**
Uses **machine learning to predict the energy label or BENG2 value** when definitive information is unavailable. These results represent the API's actual predictive performance.
{% tabs %}
{% tab title="Q3 2026" %}
**MAE:** 47.64
**MedAE:** 33.0
**BENG2**
```
7.54% predictions in 5 BENG2 units error range.
15.66% predictions in 10 BENG2 units error range.
31.39% predictions in 20 BENG2 units error range.
45.71% predictions in 30 BENG2 units error range.
57.50% predictions in 40 BENG2 units error range.
66.88% predictions in 50 BENG2 units error range.
73.82% predictions in 60 BENG2 units error range.
79.23% predictions in 70 BENG2 units error range.
83.12% predictions in 80 BENG2 units error range.
86.38% predictions in 90 BENG2 units error range.
89.01% predictions in 100 BENG2 units error range.
95.89% predictions in 150 BENG2 units error range.
98.15% predictions in 200 BENG2 units error range.
99.87% predictions in 400 BENG2 units error range.
99.93% predictions in 500 BENG2 units error range.
```
**Energy Label**
```
40.19% predictions in 0 energy label difference.
76.58% predictions in 1 energy label difference.
90.36% predictions in 2 energy label difference.
96.42% predictions in 3 energy label difference.
99.33% predictions in 4 energy label difference.
99.75% predictions in 5 energy label difference.
99.91% predictions in 6 energy label difference.
99.97% predictions in 7 energy label difference.
99.99% predictions in 8 energy label difference.
100.00% predictions in 9 energy label difference.
100.00% predictions in 10 energy label difference.
```
{% endtab %}
{% endtabs %}
---
# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.
## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.
Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:
```
GET https://docs.altum.ai/english/sustainability/nta-8800-energylabel-v2-api/apartments.md?ask=&goal=
```
`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.
Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.
