AI System for Real Estate Market Analysis

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

The AI system analyzes listings 10 times faster than manual selection, and scoring accuracy is 25% higher.

What You Get

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.

When does a time series forecasting model fail in production?

The CFO requests a quarterly sales forecast. An analyst builds SARIMA on three years of data, achieves MAPE 8.3% on the test set, and deploys. Two months later, the metric in production jumps to 23%. The root cause: the model was trained on pre‑COVID data, tested on a stable period, but production hit a promotion and supply chain disruption. Data leakage plus distribution shift—perfect notebook numbers, a broken forecast in reality. We have seen this pattern dozens of times across retail, fintech, and IoT. Our team has delivered more than 50 forecasting projects over 5+ years.

Incorrect cross-validation. Standard train_test_split for time series creates data leakage: the model sees future values during training. The correct approach is TimeSeriesSplit or walk‑forward validation with an expanding window.

Multiple seasonality. Hourly electricity consumption has three seasonalities: daily (24h), weekly (168h), yearly (8760h). SARIMA handles only one. Prophet can handle multiple but scales poorly to thousands of series.

Missing values and anomalies. A missing sensor reading is information (the sensor turned off), not NaN. Linear interpolation destroys this signal. Proper handling depends on the missingness mechanism.

Cold start. A new SKU in a 50,000‑item assortment has no history, yet a forecast is needed. Standard approaches fail; cross‑learning or feature‑based methods are required.

Why is model selection critical for your data?

Prophet (Meta) – a solid start for business data with clear seasonality and holidays. Fast setup, interpretable, built‑in outlier detection. Fails on irregular patterns and does not scale beyond ~10k series without parallelization.

Gradient boosting on features (LightGBM, XGBoost) – often underestimated. Engineer lags (t‑1, t‑7, t‑28), rolling means, day‑of‑week, holidays. The model trains on all series simultaneously, solving cold start via transfer learning. MAPE in retail often beats neural nets with proper feature engineering.

TFT (Temporal Fusion Transformer) – a transformer designed for interpretable forecasting with covariates. Built‑in variable selection, temporal attention, quantile outputs. Available in pytorch‑forecasting. Requires ~10,000+ records per series for stable training.

PatchTST – splits the series into patches (like ViT for images), capturing local patterns better than classic transformers. Excellent for long‑horizon forecasting (96–720 steps ahead).

N‑HiTS, N‑BEATS – attention‑free neural architectures, faster than TFT, competitive accuracy. N‑BEATS won the M4/M5 benchmarks for tasks without covariates.

How do we deploy TFT in production?

A typical pipeline via pytorch‑forecasting:

training = TimeSeriesDataSet(
    data,
    time_idx="time_idx",
    target="sales",
    group_ids=["store", "sku"],
    min_encoder_length=max_encoder_length // 2,
    max_encoder_length=max_encoder_length,  # 120 days
    min_prediction_length=1,
    max_prediction_length=max_prediction_length,  # 28 days
    static_categoricals=["store_type", "category"],
    time_varying_known_reals=["price", "promo_flag"],
    time_varying_unknown_reals=["sales"],
    target_normalizer=GroupNormalizer(groups=["store", "sku"], transformation="softplus"),
)

A common mistake: the default target_normalizer (StandardScaler) breaks predictions for series with zero values (no sales on weekends). GroupNormalizer with transformation="softplus" is the correct choice for count data.

Case study: retail demand forecasting

A chain of 120 stores, 8,000 SKUs, 28‑day forecast horizon. The original system: SARIMA per series, MAPE 18.4%, retraining cycle – 6 hours. We replaced it with TFT on PyTorch + pytorch‑forecasting: a single model for all series, MAPE 11.2%, retraining – 40 minutes on an A10G. Feature importance via variable selection revealed that day_before_holiday influences more than the holiday date itself. Annual savings on inference alone exceeded $50,000.

Step‑by‑step configuration

1. Data collection and preparation. Handle missing values (mark NaN, interpolate only for technical failures), aggregate to required frequency, engineer covariates (holidays, promotions, prices).
2. Create TimeSeriesDataSet. Set group_ids (store + SKU), time index, forecast horizon. Choose target_normalizer based on target distribution.
3. Train a baseline. Prophet or LightGBM first – to understand complexity.
4. Train TFT. Use TemporalFusionTransformer with loss=QuantileLoss(), tune learning rate and hidden layer sizes.
5. Validate and interpret. Walk‑forward test, analyze variable selection, build attention heatmaps.

How to properly evaluate forecast quality?

RMSE alone is misleading – it over‑penalizes large values. Our standard set:

• MAPE – interpretable, unstable near zero.
• sMAPE – symmetric, avoids division by small numbers.
• MASE (Mean Absolute Scaled Error) – normalized relative to a naive seasonal forecast, ideal for comparing series of different scales.
• Pinball loss – for probabilistic forecasting, inventory management.

We guarantee a model card with these metrics on the validation set and walk‑forward results on at least 6 months of history.

What deliverables do you receive?

• Documentation of chosen architecture and hyperparameter rationale.
• Reproducible training and inference pipeline (Docker + CI/CD + Airflow/Prefect).
• Committed code with unit tests for key components.
• Team training: retraining, output interpretation, deployment of new versions.
• 3 months of post‑delivery support (consultations, bug fixes, fine‑tuning).

The model is deployed via FastAPI or Triton Inference Server. Retraining is scheduled (e.g., weekly) via Airflow with drift validation and automatic rollback if metrics deteriorate.

Process and timeline

We start with EDA: visualization, ADF test, STL decomposition, analysis of missing values and outliers. This takes 2–3 days but often reveals systemic data issues that block forecasting. Then we build a baseline (naive seasonal, Prophet), engineer features for LightGBM, and select a neural architecture if needed. Walk‑forward validation with a realistic horizon. Deployment via API with automatic retraining scheduled via Airflow or Prefect.

Timeline: MVP forecast on one data type – 3–6 weeks. Hierarchical forecasting system with automation – 2–5 months. Cost is calculated individually based on data volume, number of series, and required accuracy.

Our team consists of certified ML engineers (AWS ML Specialty, GCP Professional ML Engineer) with 5+ years on the market and over 50 completed forecasting projects. Contact us for a free analysis of your data – we will assess the task and provide initial recommendations within 1–2 days. Request a consultation to ensure your forecasts work in production, not just in a notebook.