AI Residential Housing Stock Management System

AI System for Residential Housing Stock Management and Repair Planning

We develop AI systems for municipal and regional housing stock management: building condition assessment using computer vision and registry data, emergency risk prediction, and capital repair program optimization. Our solutions deliver data-driven planning tools for housing authorities, including dashboards for portfolio analysis and complaint routing. We deliver end-to-end solutions in 3–5 months, including integration with GIS HCS, 1C:HCS, and existing municipal information systems. Contact us to evaluate your housing stock project.

Management of residential housing stock at regional or municipal level requires balancing resources across thousands of properties with different technical conditions. AI transforms repair planning from intuitive to analytical — enabling objective prioritization of investment and earlier detection of emergency risk.

Assessment of Building Technical Condition

Housing stock registry plus ML: assessment inputs include construction year, project series, wall material, area, floor count, repair history, and current inspection data. ML regression predicts an aggregate wear index:

import pandas as pd
import numpy as np
from sklearn.ensemble import GradientBoostingRegressor

def build_wear_model(housing_data, inspection_results):
    """
    Building physical wear forecast from registry data.
    inspection_results: technical inspection results (target variable)
    """
    features = [
        'age_years',           # building age
        'wall_material_code',  # 0=panel, 1=brick, 2=monolithic
        'project_series',      # series (Khrushchyovka, Brezhniovka, 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

Computer vision for inspection: facade photographs feed a CV model for condition assessment. YOLOv8 Instance Segmentation detects cracks and measures their area and width. The model also identifies plaster peeling, balcony damage, and leaks. Condition classification outputs: satisfactory, unsatisfactory, or emergency. Drones with cameras enable roof inspection of areas that are inaccessible without scaffolding.

Major Repair Planning

Short-term emergency risk model: predicts failure probability over 3 years. Features include current wear index, degradation rate, and incident history. LightGBM binary classification outputs P(emergency_3y). Priority queue: high-risk buildings with large resident populations are scheduled first.

Regional capital repair program: optimization of the 10–25 year plan:

import pulp

def optimize_repair_program(houses, risk_scores, annual_budget, n_years=10):
    """
    Major repair program optimization: maximize preserved fund value
    under annual budget constraints.
    """
    prob = pulp.LpProblem("repair_program", pulp.LpMaximize)

    # Variable: in 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 at all)
    for house_id in houses['id']:
        prob += pulp.lpSum(repair_year[(house_id, y)] for y in range(n_years)) <= 1

    # Annual budget
    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 preferred
        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)}

Monitoring and Response

Incident management: integration with dispatch services (GIS HCS, Domovyk, 1C:HCS). NLP classifies resident complaints by type and routes them to the right service. Prioritization rules: a water leak on the 5th floor is urgent; a squeaky door is planned maintenance.

Analytics for the municipality: KPI dashboard showing average portfolio wear and percentage of emergency buildings. Resource forecasting for emergency housing relocations covers 3–5 years. Financial model compares contribution receipts against program funding requirements.

Method Comparison

ML reduces wear forecast error 3× compared to manual expert assessment. Capital repair scheduling becomes predictable and defensible.

Deliverables for Housing Stock Management

Our housing stock management AI system includes a building condition assessment module with CV inspection support, an emergency risk prediction model (LightGBM binary classification), a capital repair program optimizer (LP-based scheduling), a complaint classification and routing module using NLP, and a municipal analytics dashboard.

We deliver integration with existing housing information systems (GIS HCS, 1C:HCS, Domovyk) and 2 weeks of post-launch support. The system handles housing registries from several hundred to hundreds of thousands of buildings.

Our team has 10+ years in Data Science and 50+ completed projects in GovTech and municipal services. We guarantee deadline adherence and sign NDA.

Timeline and Cost

Development timeline: 3–5 months for the complete solution covering condition assessment, repair planner, and analytics dashboard. The average return on investment is 1.5–2 years. Annual savings for a city with 2,000 buildings can reach tens of millions in reduced emergency response costs and optimized repair budgets. We provide a free initial project evaluation within 3 business days of your request.