Data Explorer — Mietspiegel Digitization

📦 Datasets

All data is processed, validated, and served as static JSON. All rent values are Nettokaltmiete (net cold rent) in €/m²/month.

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🔬 Methodology

1. Extraction: Official Mietspiegel PDFs downloaded from municipal websites. Tables extracted with pdfplumber (Python).

2. Normalization: Each city's table structure mapped to a standard schema (Lage × Baujahr × Size). Wohnlage categories harmonized across cities.

3. Validation: 14 automated checks: monotonicity, range, cross-source reconciliation. CI gate blocks deployment on failure.

4. Reconciliation: Immoscout24 (market) and Zensus 2022 (census) data matched to Mietspiegel grids using spatial joins.

5. Deployment: Validated JSON files deployed to GitHub Pages. No server, no database, no API keys.

📐 Schema

// Berlin Mietspiegel (berlin.json) { "city": "Berlin", "year": 2024, "lage_categories": ["einfach", "mittel", "gut"], "tables": [ { "lage": "einfach", "rows": [ { "baujahr": "bis 1918", "bis_40": 8.03, "40_60": 7.68, "60_90": 7.35, "ueber_90": 7.02 }, ... ] }, ... ] } // Immoscout Grid (berlin_immoscout.json) { "mean": 12.07, // €/m² net cold "std": 3.27, "clean": 361, "grids": [{ "lat": 52.5, "lng": 13.4, "rent": 12.5, "year": 2024, "plz": "10115" }, ...] } // Zensus Grid (berlin_zensus.json) { "mean": 7.97, // €/m² net cold "total_cells": 1155, "cells_slim": [[lat, lng, rent], ...] }

📊 CSV Exports

Pre-computed CSV exports for common analyses:

📥 Districts 📥 Historical Trend 📥 Mietspiegel 2024 📥 Rent Cells 📥 Mietpreisbremse

💾 Data Explorer

📦 Datasets

🔬 Methodology

📐 Schema

📊 CSV Exports