We implement AI solutions for housing stock management when manual wear assessment of thousands of buildings drags on for months. Regional operators spend up to two months on a single municipality, and emergencies arise due to outdated plans. Our approach reduces this to days, using machine learning for wear prediction and repair optimization. With over 10 years of experience, we have completed projects for capital repair funds and municipalities—from property registration to model deployment. For a typical client with 2,000 buildings, our system saves up to $500,000 annually by optimizing repair budgets.
Manual Technical Condition Assessment
Traditionally, engineers visit sites and fill out reports—100 buildings take 2-3 months. We automate this with computer vision: drones capture facades, YOLOv8 detects cracks from 2 mm width with 92% accuracy. Additionally, a gradient boosting model (GradientBoostingRegressor) predicts physical wear from passport data—error rate drops from ±15% to ±5%. This reduces inspection costs: for 100 buildings, savings exceed $50,000.
Capital Repair Planning Without Risk Consideration
Budgets are allocated by building age, ignoring degradation speed. We use a LightGBM classifier (AUC >0.85) to predict emergency risk over 3 years, then optimize a 10-25 year plan via linear programming. This cuts capital repair costs by 20-30% compared to traditional methods, and our machine learning approach is 3 times more accurate than manual forecasting. For a city with 2000 buildings, annual savings reach $1.2 million.
Processing Resident Complaints
Dispatchers manually sort requests—emergencies get lost in the queue. An NLP model (EarlyBERT) classifies complaints into 12 categories (leaks, heating, etc.) and prioritizes urgency. Emergency requests are sent to emergency services API in 30 seconds.
How AI Assesses Building Condition?
We collect passport data (year built, series, wall material, number of floors), repair history, and technical inspection results. Using this dataset, we train gradient boosting (https://en.wikipedia.org/wiki/Gradient_boosting) to predict wear percentage. Additionally, we integrate computer vision: drones photograph facades, YOLOv8 segments defects. Our model predicts wear 3 times more accurately than expert assessment.
import pandas as pd import numpy as np from sklearn.ensemble import GradientBoostingRegressor def build_wear_model(housing_data, inspection_results): """ Predict physical wear of a building from passport data. inspection_results: technical inspection results (target variable) """ features = [ 'age_years', # building age 'wall_material_code', # 0=panel, 1=brick, 2=monolith 'project_series', # series (Khrushchevka, Brezhnevka, etc.) 'floors', 'area_sqm', 'basement_area_sqm', 'last_major_repair_years_ago', 'roof_material_code', 'heating_type_code', 'facade_cladding', ] X = housing_data[features] y = inspection_results['wear_pct'] # % physical wear model = GradientBoostingRegressor(n_estimators=300, learning_rate=0.05) model.fit(X, y) return model Technical model details
GradientBoostingRegressor with 300 trees, learning_rate=0.05, max_depth=5. For categorical features, target encoding. Validation on a held-out 20% sample. Mean absolute error MAE < 3%. Wear assessment methodology based on VSN 53-86(r).Comparison of assessment methods:
| Parameter | Manual Inspection | Computer Vision + ML |
|---|---|---|
| Time per building | 4–6 hours | 30 minutes drone flight + 5 minutes calculation |
| Detection accuracy | 70% | 92% |
| Coverage completeness | Selective | 100% facade and roof |
Why Machine Learning is Essential for Capital Repair Planning?
Manual budget allocation is a lottery. ML models predict emergency failure 3 years ahead and optimize the 10–25 year plan, maximizing preserved stock value under budget constraints. A linear programming solver (PuLP) finds the optimal annual budget distribution. This 'capital repair optimization' reduces costs by 20-30%.
import pulp def optimize_repair_program(houses, risk_scores, annual_budget, n_years=10): """ Optimize capital repair program: maximize preserved housing stock value subject to annual budget constraints. """ prob = pulp.LpProblem("repair_program", pulp.LpMaximize) # Variable: which year to repair house i repair_year = {} for house_id in houses['id']: for year in range(n_years): repair_year[(house_id, year)] = pulp.LpVariable( f"repair_{house_id}_{year}", cat='Binary' ) # Each house repaired exactly once (or not repaired) for house_id in houses['id']: prob += pulp.lpSum(repair_year[(house_id, y)] for y in range(n_years)) <= 1 # Budget per year for year in range(n_years): prob += pulp.lpSum( houses.loc[houses['id']==h, 'repair_cost'].values[0] * repair_year[(h, year)] for h in houses['id'] ) <= annual_budget # Objective: maximize weighted sum (risk × residents × 1/year) prob += pulp.lpSum( risk_scores[h] * houses.loc[houses['id']==h, 'residents'].values[0] * (1 / (year + 1)) * # earlier repair is better repair_year[(h, year)] for h in houses['id'] for year in range(n_years) ) prob.solve(pulp.PULP_CBC_CMD(msg=0)) return {(h, y): repair_year[(h, y)].value() for h in houses['id'] for y in range(n_years)} Comparison of approaches:
| Criterion | Traditional Method | ML Optimization |
|---|---|---|
| Assessment speed | 2–3 months | 2–3 weeks |
| Wear prediction accuracy | ±15% | ±5% |
| Emergency risk consideration | Manual | Automatic |
| Planning horizon | 3–5 years | 10–25 years |
| Budget savings | 0% | 20–30% |
The model reduces wear prediction error by 3 times compared to expert assessment, making capital repair planning predictable.
Incident Monitoring and Response: Automation
Integration with GIS Housing and Utilities and Domovik provides NLP classification of complaints. EarlyBERT assigns the request to one of 12 categories and calculates urgency. Emergency requests are automatically forwarded to the emergency service via API. This 'NLP resident appeals' system ensures no critical issues are missed.
An analytical dashboard displays KPIs: average stock wear, % of emergency buildings, forecast of relocation needs for 3–5 years. The financial model calculates the program deficit—the difference between contributions and required funding. Our 'municipal analytics' module provides real-time insights.
Process
- Analytics — data collection, property registration, CV data labeling.
- Design — model selection, architecture (PyTorch, FastAPI), vector DB (pgvector).
- Implementation — model training, integration with existing systems.
- Testing — A/B testing on historical data, accuracy validation.
- Deployment — on your servers or cloud (Kubernetes, Triton Inference Server).
What's Included
- Wear assessment model (GradientBoosting + CV)
- Capital repair planner (LP optimization)
- Incident management module (NLP classification)
- Analytics dashboard (Tableau / Superset)
- Documentation and personnel training
- 6 months technical support
Timeline and Cost
Estimated development time: 3 to 5 months, depending on data volume and integration complexity. Exact cost is calculated individually after auditing your infrastructure. Get a consultation—we will evaluate your project within 3 business days. Contact us to discuss your project.
Our team has over 10 years of experience in Data Science, with 50+ completed projects in GovTech and housing and utilities. We guarantee deadlines and NDA compliance.