AI-Powered Housing Assistant
Automating Apartment Hunting in Berlin's Competitive Market
CLIENT:
Self-initiated project
YEAR:
2025
TECHNOLOGIES:
n8n, NocoDB, OpenAI API, Telegram Bot API, Apify
ROLE:
Full-stack developer, AI prompt engineer, workflow architect
challenge.
Berlin's housing market is one of the most competitive in Europe, with demand far outstripping supply. Finding an apartment involves:
Quickly filtering through options based on personal criteria
Writing personalized application letters for each potential apartment
Monitoring multiple websites daily for new listings
Responding rapidly before opportunities disappear
This process typically consumes 2-3 hours daily and involves significant repetitive work. The challenge was to create a system that could automate this entire workflow, from discovery to application.
Project Goals.
Automate data collection from multiple housing websites
Filter listings based on natural language user queries
Generate personalized application letters tailored to specific listings
Create a simple user interface accessible to non-technical users
Build a system that could operate autonomously with minimal supervision
The Solution
I designed a multi-agent AI system that handles the entire apartment hunting process through a conversational interface. The system consists of three main components:
1. Data Collection Layer
Daily automated scraping of Gewobag and eBay Kleinanzeigen housing platforms
Data normalization pipeline to standardize information from different sources
Deduplication system to prevent repeated listings
Structured database for efficient storage and retrieval
2. Intelligence Layer
Data Analyst Agent: Translates natural language queries into database filters
Writer Agent: Generates personalized application letters
Memory component: Retains user preferences and conversation history
3. User Interface Layer
Telegram bot providing a simple chat interface
Conversational UI requiring no technical knowledge to operate
Real-time notifications for new matches
Architecture Diagram
Data Collection Layer
Daily Scrapers (Gewobag & eBay)
Apify Web Scraping Platform
Automated Deduplication
Storage Layer
NocoDB Database
Normalized Schema
Intelligence Layer
Data Analyst Agent (powered by OpenAI)
Writer Agent (powered by OpenAI)
Memory Components
User Interface Layer
Telegram Bot
Conversational Interface
Real-time Notifications
Technical Challenges & Solutions
Challenge 1: Inconsistent Data Formats
Different housing platforms structure their listings in entirely different ways, making normalization difficult.
Solution: Created custom data mappers for each source that extracted and standardized critical fields (price, area, rooms, location) while preserving unique information.
Challenge 2: Natural Language Understanding
Users phrase their housing preferences in diverse and unpredictable ways.
Solution: Developed a specialized Data Analyst Agent with carefully crafted prompts that could translate human language into structured database queries, handling variations in how people express constraints.
Challenge 3: Application Personalization
Generic application letters are ineffective in competitive markets.
Solution: Implemented a two-phase approach where the system first asks users for personal details, then combines this information with listing-specific details to generate highly personalized application letters.
Results
The system successfully automated the entire apartment hunting process:
Time Savings: Reduced daily apartment search time from 2-3 hours to just 5-10 minutes
Coverage: Monitored multiple platforms simultaneously without additional effort
Speed: Generated personalized applications within seconds of listing discovery
Quality: Created application letters tailored to both listing details and personal profile
Key Metrics
Average of 15-20 new listings processed daily
95% accuracy in understanding natural language search queries
30+ personalized application letters generated
100% automation of the data collection process