We develop an AI system for real estate market analysis that automates data collection and processing from dozens of sources: CIAN, Avito, Rosreestr, DOM.RF, OpenStreetMap. The system gathers listings, transactions, macroeconomic indicators, and demographic data in minutes, cleans duplicates and errors, and delivers fresh market indicators with >95% deduplication accuracy and daily updates. Unlike manual collection, which takes up to 40 hours per district, AI processing completes in 5–10 minutes. With over 10 projects in AI/ML and 5 years in the market, we guarantee reliability and transparent methodology.
The system integrates with your CRM and BI tools via REST API and Webhook, and exports data in CSV or Parquet. We adapt the solution to your segment: residential, commercial, or suburban real estate. Contact us for a demo on your data — get a consultation on implementation.
Core Problems Solved by AI Analytics
Developers get objective data for location selection: pricing, competition, infrastructure. The system enables dynamic price adjustments during sales based on comparable properties and demand forecasts. Investors use scoring of an area's investment potential — yield, liquidity, risks. For real estate agencies, automatic market monitoring is available: trends, anomalies, competitive analysis, and listing competitiveness assessment.
How We Achieve >95% Deduplication Accuracy
One property can appear on multiple platforms with different prices or characteristics. We apply a fuzzy search algorithm based on TF-IDF and cosine similarity of descriptions, combined with geopositioning. After address normalization via Yandex Geocoder and DaData, the system standardizes apartment types and removes duplicates. Deduplication accuracy exceeds 95% — a guaranteed result confirmed on over 1 million processed listings.
Data Collection and Normalization
Parsing CIAN, Avito, Yandex Realty, Rosreestr API, and open data from DOM.RF is done with Scrapy, BeautifulSoup, and REST APIs. A Feature Store based on Feast (Redis) saves features for ML models. Geocoding links properties to quarters via PostGIS and OpenStreetMap, calculating distance to metro and POI.
Market Indicators We Calculate
Price metrics: median price per sqm by district (daily), Price to Rent ratio, YoY and MoM dynamics, premiums/discounts (first floor -8%, top floor -3%, near metro +12%). Market activity: Days on Market (DOM), List to Sale ratio, Absorption rate. In a typical seller's market (absorption rate >20%), purchase time is limited; in a buyer's market (<10%), the buyer has leverage. Comparison with risk-free OFZ yield shows investment attractiveness relative to alternatives.
Why Geoanalytics Is Critical for Real Estate
Location is the primary price factor. Heatmaps based on kriging (Gaussian Process) reveal a continuous price surface of the city down to the quarter level. Price correlation with distance to metro, parks, and schools provides objective data for site selection. More on Gaussian Process at Wikipedia. Example code for building a heatmap:
import geopandas as gpd import folium from sklearn.gaussian_process import GaussianProcessRegressor def create_price_heatmap(transactions_gdf, city_boundary): gpr = GaussianProcessRegressor() coords = np.column_stack([transactions_gdf.geometry.x, transactions_gdf.geometry.y]) gpr.fit(coords, transactions_gdf.price_per_sqm) grid = create_grid(city_boundary, resolution=100) predicted_prices = gpr.predict(grid) return create_folium_heatmap(grid, predicted_prices) This approach gives 3–5 times better accuracy compared to simple averaging by district.
Investment Scoring
A multi-factor model of the investment potential of a district or property:
def investment_score(location, property_type, holding_period=5): score_components = { 'price_momentum': price_growth_3y / benchmark_growth, 'rental_yield': annual_rent / purchase_price, 'infrastructure_development': count_approved_projects_500m, 'demographic_growth': population_change_3y, 'vacancy_rate': 1 - rental_occupancy_rate, 'price_to_income': median_price / median_household_income } weights = calibrate_weights_by_property_type(property_type) return weighted_score(score_components, weights) Component weights are adjusted per segment: commercial real estate, housing, land plots. The system processes up to 1 million listings per day — 10 times more than a manual team of 5 analysts.
Monitoring and Competitive Intelligence
For marketers and competitive intelligence: new construction permits (parsing Ministry of Construction data), construction stage from satellite imagery (Sentinel-2), sales dynamics of developers on CIAN, housing supply forecast for 24–48 months based on historical construction duration.
Efficiency Comparison: AI vs Manual Analysis
| Parameter | Manual Analysis | AI System |
|---|---|---|
| Data collection time per district | up to 40 hours | 5–10 minutes |
| Deduplication accuracy | ~70% | >95% |
| Update frequency | weekly | daily |
| Number of processed listings | 50,000 per month | 1 million per day |
The AI system analyzes listings 10 times faster than manual selection, and scoring accuracy is 25% higher.
What You Get
| Component | Description |
|---|---|
| Analytics Portal | Dashboards: Market Overview, Price Tracker, Investment Screener, Comparables |
| REST API | Real-time current indicators |
| Webhooks | Notifications of new transactions in a specified area |
| Bulk export | Export for custom analysis in CSV/Parquet |
Timeline and How to Start
A base system with CIAN parsing and core metrics — from 6 to 8 weeks. A full platform with geoanalytics, scoring, and monitoring — from 4 to 6 months. The exact scope is determined during a consultation. Get a consultation and demo of the system on your data — contact us to discuss your task and get a preliminary estimate.