Suggested Renovations API

1. Introduction

Unlock smarter renovation planning with our API. It automatically transforms EPC certificates and property data into actionable renovation roadmaps. By processing uploaded EPC documents, enriching gaps with trusted external data, and calculating optimal step-by-step renovation sequences, the API helps you:

Maximize energy efficiency gains
Optimize cost vs. benefit
Leverage available subsidies

Deliver clear, EPC-driven renovation plans that empower property owners, advisors, and financial institutions to make confident, sustainable decisions while improving their property’s energy performance rating.

2. Process Overview

The API follows these EPC-centric steps:

EPC Document Processing: Extracts comprehensive property information from uploaded EPC certificates, capturing:
- Current EPC rating
- Energy consumption breakdown (heating, cooling, hot water)
- Building characteristics (construction year, type, dimensions)
- Insulation levels (walls, roof, floor, windows)
- Heating system efficiency and fuel type
- Ventilation system
Data Enrichment: Enhances property data with:
- LiDAR Integration: Provides accurate property measurements using LiDAR datasets for building dimensions, roof surface, and facade areas
- VEKA Provider: Calculates energy efficiency indicators and validates renovation impact on EPC scores
- Missing Value Prediction: ML model fills gaps in property attributes when direct data is unavailable
EPC-Driven Renovation Selection: Determines optimal renovation components based on:
- Current EPC score and improvement potential
- EPC impact per euro invested (energy efficiency ROI)
- Regulatory compliance with local EPC standards
- Subsidy eligibility for EPC improvements
Supporting Works: Automatically includes essential costs:
- Scaffolding for facade renovations
- Architecture Costs for structural changes
Trajectory optimization: The API sequences renovations for maximum efficiency and compliance by prioritizing:
- EPC improvement per euro invested
- Logical execution order (e.g., insulation before heating upgrades)
- Regulatory compliance with local building codes
- Budget optimization including subsidies and incentives

Budget optimization including subsidies and incentives

3. Business Logic & Data Enrichment

3.1 EPC Document Processing

The API’s core functionality revolves around intelligent EPC (Energy Performance Certificate) document processing. When an EPC document is uploaded, the system performs comprehensive analysis to extract critical property information and identify renovation opportunities.

3.1.1 Document Analysis Pipeline

The EPC processing follows a multi-stage pipeline:

Document Validation & Parsing
- Validates EPC document format and completeness
- Extracts structured data using OCR and NLP techniques
- Identifies document type (residential, commercial, mixed-use)
- Verifies document authenticity and recency
Property Data Extraction
- Building Characteristics: Construction year, building type, total floor area
- Energy Performance: Current EPC score, energy consumption indicators
- Insulation Levels: Wall, roof, floor, and window insulation values
- Heating Systems: Type, efficiency rating, fuel source
- Ventilation: Current ventilation system type and efficiency
- Renewable Energy: Existing solar panels, heat pumps, etc.
Data Quality Assessment
- Flags missing or incomplete data points
- Identifies potential data inconsistencies
- Assigns confidence scores to extracted values
- Determines which external data sources are needed for enrichment

3.1.2 EPC Score Analysis

The system performs deep analysis of the EPC score to understand improvement potential:

Current EPC Classification: Maps to energy efficiency scale (A++ to G)
Energy Consumption Breakdown: Analyzes heating, cooling, hot water, lighting consumption
Improvement Potential: Calculates theoretical maximum EPC improvement
Priority Areas: Identifies which building components offer the highest ROI for EPC improvement

3.1.3 Property Component Mapping

EPC data is mapped to renovation components:

EPC Data Point	EPC Score Impact	Priority Level
Wall(s) U-value	High	🔥 Critical
Roof(s) U-value	High	🔥 Critical
Floor(s) U-value	Medium	⚡ Important
Window(s) U-value	Medium	⚡ Important
Heating efficiency	Very High	🚀 Highest
Ventilation type	Medium	⚡ Important

3.1.4 Missing Data Prediction

When EPC documents lack specific measurements, the system uses ML models to predict missing values:

Surface Areas: Predicts facade, roof, and floor areas based on building dimensions
U-values: Estimates insulation levels based on construction year and building type
System Efficiency: Predicts heating system efficiency based on age and type
Energy Consumption: Estimates consumption patterns based on building characteristics

3.2 LiDAR Data Enrichment

The API integrates with LiDAR data sources to provide accurate property measurements. If certain data points are missing (e.g., roof area, building width, depth), the API queries LiDAR datasets to retrieve them.

3.3 Machine Learning Predictions

A trained ML model refines missing values and predicts energy efficiency scores. The confidence_score in the response indicates how much of the final result is based on direct data vs. predictions.

3.3.1 Confidence Score & Pricing Accuracy

The confidence_score parameter helps users assess the accuracy of the returned results:

Confidence Score	Expected Price Error (avg)	Standard Deviation	Notes
`< 0.60`	>20%	>30%	High error margin, model relied on many assumptions
`0.60 - 0.70`	~15%	~13%	Moderate accuracy, reasonable estimates
`0.70 - 0.80`	~14%	~13%	Good accuracy, confidence in most cases
`> 0.80`	~5%	~8%	Very high accuracy, minimal deviations

If LiDAR data is available, the expected confidence score is ~78%.
Lower scores indicate the API made more assumptions due to missing data.

3.4 Renovation Step Selection Criteria

The suggested renovation trajectory follows a priority-based approach:

Maximizing EPC improvement
Choosing cost-effective solutions when impact is equal
Selecting actions based on subsidy availability

4. Implementation Considerations

4.1 Authentication

The API requires an OAuth2 Bearer Token.
Use the Create new Access Token endpoint to obtain an access token.

4.2 Polling Mechanism

Since calculations are asynchronous, clients should poll the status endpoint periodically until the job status changes to FINISHED.

5. Frequently Asked Questions (FAQs)

What if the EPC score seems incorrect?

Check the confidence_score. A low value indicates the model had to rely heavily on predictions instead of actual EPC data.
Verify that the uploaded EPC document is complete and recent.
Consider providing additional property measurements for more accurate EPC calculations.

How accurate are the EPC score improvements?

EPC score improvements are calculated using VEKA provider validation and LiDAR data for maximum accuracy.
The energy_efficiency_indication field shows the final EPC score after all renovations.
High confidence scores (>0.80) indicate very reliable EPC predictions.

Can I get EPC-specific subsidies?

Yes! The API automatically identifies EPC-related subsidies and incentives available for each renovation component.
Subsidy amounts are calculated based on current EPC score and proposed improvements.

Final Notes

This EPC-powered API is particularly useful for real estate developers and homeowners looking for automated, EPC-optimized renovation trajectories.
LiDAR data and ML predictions are integrated to deliver optimal EPC improvement recommendations, maximizing energy efficiency gains from all available data.
Users are encouraged to cross-check low-confidence EPC results with additional manual data input to improve accuracy and ensure regulatory compliance.

Authorizations

Authorization

string

header

required

Bearer authentication header of the form Bearer <token>, where <token> is your auth token.

Body

application/json

Body with required input data.

year_built

number

required

Required range: x >= 1

estate_format

enum<string>

required

Available options:

SEMI_OPEN,

OPEN,

CLOSED

habitable_space_m2

number

required

Required range: x >= 1

zip_code

string

required

country

enum<string>

required

Available options:

BE,

NL

roof_type

enum<string>

Available options:

INCLINED,

FLAT,

MIXED

inclined_roof_pane_surface

number

Required range: x >= 1

flat_roof_pane_surface

number

Required range: x >= 1

inclined_roof_insulation_type

enum<string>

Available options:

NONE,

LIGHT,

MODERATE,

STRONG

flat_roof_insulation_type

enum<string>

Available options:

NONE,

LIGHT,

MODERATE,

STRONG

attic_usage

enum<string>

Available options:

STORAGE,

LIVING_SPACE,

NOT_USED

heating_type

enum<string>

Available options:

ELECTRIC,

FUEL_OIL,

GAS,

HEAT_PUMP,

PELLET_FUEL,

WOOD,

NONE

heating_device

enum<string>

Available options:

DIRECT_ACCUMULATION,

CONDENSING_BOILER,

NON_CONDENSING_BOILER,

STOVE,

AIR_AIR_HEAT_PUMP,

AIR_WATER_HEAT_PUMP,

GEOTHERMAL_HEAT_PUMP

sanitary_heating_device

enum<string>

Available options:

GAS_BOILER,

FUEL_OIL_BOILER,

ELECTRIC_BOILER,

HEAT_PUMP_BOILER,

NOT_SEPARATED,

NONE

installation_age

enum<string>

Available options:

ZERO_TO_FIVE_YEARS_OLD,

FIVE_TO_TEN_YEARS_OLD,

OLDER_THAN_TEN_YEARS

has_solar_panels

boolean

has_solar_boiler

boolean

ground_floor_surface

number

Required range: x >= 1

basement_type

enum<string>

Available options:

NONE,

FULL,

PARTIAL,

UNSPECIFIED

basement_ceiling_surface

number

Required range: x >= 1

basement_ceiling_height

enum<string>

Available options:

HIGHER_THAN_120_CM,

LOWER_THAN_120_CM

solid_ground_floor_insulation_type

enum<string>

Available options:

NONE,

LIGHT,

MODERATE,

STRONG

floor_above_basement_insulation_type

enum<string>

Available options:

NONE,

MODERATE,

STRONG

outer_wall_insulation_type

enum<string>

Available options:

NONE,

LIGHT,

MODERATE,

STRONG

has_cavity_wall

boolean

cavity_wall_width

enum<string>

Available options:

EQUAL_OR_MORE_THAN_5_CM,

LESS_THAN_5_CM

has_cavity_wall_insulation

boolean

window_glazing_type

enum<string>

Available options:

SINGLE_GLAZING,

DOUBLE_GLAZING,

HR,

TRIPLE_GLAZING

window_glazing_surface

number

Required range: x >= 1

building_width

number

Required range: x >= 1

building_depth

number

Required range: x >= 1

cornice_height

number

Required range: x >= 1

energy_efficiency_score

number

street

string

house_number

string

house_number_extra

string

place

string

price

number

Required range: x >= 1

title

string

description

string

epc_url

string

external_id

string

webhook_url

string

Response

Successfully processed request.

string

required

created_at

string<date-time>

required

updated_at

string<date-time>

required

external_id

string

required

deactivated_at

string<date-time>

required

calculation_job_type

enum<string>

required

Available options:

EPC_DOCUMENT_PROCESSING,

ENERGY_EFFICIENCY_INDICATION,

SUGGESTED_RENOVATION_TRAJECTORY_CALCULATION

calculation_job_status

enum<string>

required

Available options:

NOT_STARTED,

RUNNING,

FINISHED,

FAILED,

STOPPED

metadata

object

required

estate_id

string

required

name

string

required

description

string

required

expires_at

string<date-time>

required

published_at

string<date-time>

required

API Documentation

Authentication

Estate

Renovation

Calculation

Suggested Renovations API

1. Introduction

2. Process Overview

3. Business Logic & Data Enrichment

3.1 EPC Document Processing

3.1.1 Document Analysis Pipeline

3.1.2 EPC Score Analysis

3.1.3 Property Component Mapping

3.1.4 Missing Data Prediction

3.2 LiDAR Data Enrichment

3.3 Machine Learning Predictions

3.3.1 Confidence Score & Pricing Accuracy

3.4 Renovation Step Selection Criteria

4. Implementation Considerations

4.1 Authentication

4.2 Polling Mechanism

5. Frequently Asked Questions (FAQs)

What if the EPC score seems incorrect?

How accurate are the EPC score improvements?

Can I get EPC-specific subsidies?

Final Notes

Authorizations

Body

Response

API Documentation

Authentication

Estate

Renovation

Calculation

​1. Introduction

​2. Process Overview

​3. Business Logic & Data Enrichment

​3.1 EPC Document Processing

​3.1.1 Document Analysis Pipeline

​3.1.2 EPC Score Analysis

​3.1.3 Property Component Mapping

​3.1.4 Missing Data Prediction

​3.2 LiDAR Data Enrichment

​3.3 Machine Learning Predictions

​3.3.1 Confidence Score & Pricing Accuracy

​3.4 Renovation Step Selection Criteria

​4. Implementation Considerations

​4.1 Authentication

​4.2 Polling Mechanism

​5. Frequently Asked Questions (FAQs)

​What if the EPC score seems incorrect?

​How accurate are the EPC score improvements?

​Can I get EPC-specific subsidies?

​Final Notes

Authorizations

Body

Response

1. Introduction

2. Process Overview

3. Business Logic & Data Enrichment

3.1 EPC Document Processing

3.1.1 Document Analysis Pipeline

3.1.2 EPC Score Analysis

3.1.3 Property Component Mapping

3.1.4 Missing Data Prediction

3.2 LiDAR Data Enrichment

3.3 Machine Learning Predictions

3.3.1 Confidence Score & Pricing Accuracy

3.4 Renovation Step Selection Criteria

4. Implementation Considerations

4.1 Authentication

4.2 Polling Mechanism

5. Frequently Asked Questions (FAQs)

What if the EPC score seems incorrect?

How accurate are the EPC score improvements?

Can I get EPC-specific subsidies?

Final Notes