Document

Methodology

How we compute property intelligence from open data.

1

Overview

HindaAI combines official registers, open datasets, and derived enrichment layers to compute property intelligence for residential and commercial addresses in Estonia. The platform transforms public source data into practical signals through transparent statistical scoring models.

2

Data Sources

Address Data System (ADS)

Maa-amet

Open Data

Building Registry and Energy Certificates

Ministry of Economic Affairs

Open Data

Market Statistics and Land Cadastre

Maa-amet

Open Data

Statistics Estonia and ECB Rental Index

Statistics Estonia / European Central Bank

CC BY 4.0

OpenStreetMap POIs

OSM Contributors

ODbL

HindaAI Solar Baseline

HindaAI

Internal computed model

Peatus.ee Transit Data

Estonian transit data

Open Data

EHIS Education and Healthcare Enrichment

Estonian public registers and enrichment layers

Open Data / Derived layer

Weather, Climate, and Air Quality

OpenWeather; Keskkonnaagentuur for climate baseline

Provider terms / CC BY 4.0 climate data

EELIS Protected Areas and Pollution Layers

Environmental Board / Environmental Agency

Open Data

Flood and Radon Risk Layers

Flood-risk sources / Geological Survey of Estonia (EGT)

Open Data

Traffic Volumes and Noise Context

Transpordiamet

Open Data

Tallinn and Tartu Open Data

Municipal open data portals

Open Data
3

Scoring Methodology

HindaAI Score

A 5-pillar composite score (0–100) covering neighborhood context, financial signals, building condition, livability, and sustainability. Method weights adjust when some source layers are unavailable.

Livability Score

A neighborhood quality assessment using proximity and access signals across education, transport, retail, health, parks, sport, and culture.

Building Risk Score

A structural condition assessment using building age, construction materials, heating, energy label, building status, floors, ventilation, secondary heating, and utility connections.

Environmental Risk

An evaluation of environmental factors including flood exposure, mapped radon class, pollution proximity, protected area status, traffic volumes, and noise context.

4

Valuation Engine

Our valuation engine uses a weighted multi-method statistical approach to produce property value estimates. The ensemble provides transparent methodology with confidence intervals.

Hedonic Pricing

statistical method that estimates value from area-level market signals, property attributes, location factors, and available context

Cost Approach

estimates replacement cost minus depreciation, using construction cost indices and building age

Tax Assessment

incorporates official tax assessment values as a baseline reference point

5

Open Data Attribution

HindaAI uses open data and provider datasets published under their respective licenses and terms. OpenStreetMap data © OpenStreetMap contributors (ODbL). Statistics Estonia data is used under CC BY 4.0. Solar assessment uses HindaAI's local Estonia baseline and EHR building facts; no request-time external solar API is used. Air quality is shown as derived HindaAI scoring with visible attribution: Weather data © OpenWeather; climate baseline data is attributed to Keskkonnaagentuur/Keskkonnaportaal where used. Transit, education, radon, traffic, and environmental layers are attributed to their respective source providers and may be redacted until source terms are confirmed.

6

Computed Scores

All scores, ratings, and assessments are computed products derived from raw open data using proprietary algorithms. They represent statistical estimates and should not be treated as certified appraisals.