When Algorithms Can See Dangerous Buildings Coming But Inspections Still Miss Them

1. The problem

Structure fires kill tens of thousands of people and injure hundreds of thousands more worldwide every year, with damage running into tens of billions of dollars. In many cities, aging building stock, informal conversions, and climate‑driven heat waves and wildfires raise the odds that a small incident escalates into a major fire. Traditional fire prevention relies on slow inspection cycles, complaint‑driven visits, and simple checklists that struggle to keep pace with risk in dense urban areas.

Fire departments and building regulators are often badly outmatched. One recent statewide analysis in the U.S. examined over 48,000 structure fire incidents in Oregon from 2012 to 2023 and found that factors like victim age, response time, and presence of working detectors strongly shaped the risk of severe casualties. Yet most prevention programs still spread inspectors thinly across large inventories, visiting many low‑risk properties while high‑risk buildings wait years for a visit. The result is a quiet implementation gap: we know a subset of buildings accounts for a disproportionate share of serious fires, but inspections rarely focus there.

2. What research shows

Retrospective studies show that ML models can predict which buildings or incidents are likely to escalate much more accurately than traditional heuristics. A 2025 urban fire impact study built a model to forecast whether an incident would become a major fire, achieving 85.6% overall accuracy and an AUROC of 0.83 using building structure, use, and number of floors plus temporal and spatial features. The authors found that fires in brick and wood structures had an 85.45% likelihood of becoming major incidents, highlighting clear risk gradients that could guide resource allocation.

Building‑level risk models show even sharper concentration. A recent stacking ensemble model for building fire risk prediction integrated 16 different ML algorithms and 34 variables, from building attributes to land and demographic characteristics. When it divided properties into five risk bands, the highest‑risk category contained only 22% of buildings but accounted for 54% of actual fires, a more than twofold concentration of risk. In other words, if inspectors prioritized the top quintile, they would focus on just over one in five buildings but cover over half of observed fires.

Casualty‑focused models perform similarly well. The Oregon statewide analysis used random forests and a Bayesian regularized neural network ensemble to classify structural fire casualty severity. The full model achieved 92.5% accuracy at the incident level; when retrained using only spatially available data aggregated to the census block, accuracy remained a strong 87.6%. That suggests departments can generate usable risk maps even without detailed incident‑level data for every building.

Heuristic “high‑risk” rules (e.g., “old + tall building”) typically capture far less of the fire burden within the same number of properties, while published ML models reach AUROCs around 0.8–0.9 and concentrate over half of fires in roughly the top fifth of buildings.

3. What the real world shows

Real‑world deployments are starting to test whether these models actually change outcomes. In one urban case study, researchers simulated what would have happened if the 2025 escalation‑prediction model had guided dispatch decisions. Their retrospective simulation suggested that directing extra resources to incidents the model flagged as high risk could have reduced property damage by 25%, firefighter injuries by 21%, and average response times by 18% compared with the historical baseline. While simulated, these are large improvements relative to standard practice.

Other work moves from response to prevention. A 2025 ML‑based risk analysis and predictive modeling study projected a spatial classification of severe‑casualty risk at the census block level, using the Oregon data. The resulting risk maps were designed for “resource allocation, risk factor reduction, and safety education efforts,” giving agencies a concrete tool to target outreach campaigns, smoke‑alarm distribution, and inspection priorities. Though the paper focused on model performance, it emphasized that these maps had already begun informing where state fire marshals focused limited prevention resources.

Emerging operational pilots around inspections show similar promise. Technology vendors now offer AI‑supported fire permit review and inspection platforms that use ML to triage permit applications, cluster high‑hazard occupancies, and optimize inspector routes. Early adopter agencies report shorter permit turnaround times and more inspections of high‑hazard properties, although peer‑reviewed outcome analyses (e.g., documented reductions in violations or incidents) are still limited.

In simulated deployment, escalation‑prediction cut estimated property damage by 25%, injuries by 21%, and response times by 18% versus historical dispatch.
Spatial risk mapping in Oregon maintained 87.6% severity prediction accuracy using only census‑level data, enabling statewide prioritization of prevention activities.

4. The implementation gap

Despite these results, most fire departments and building agencies still schedule inspections by crude rules: fixed rotations, simple occupancy codes, or complaint queues. The share of agencies using ML‑driven risk scores as a core part of inspection or dispatch is small, and even where models exist, they often sit in pilot dashboards rather than feeding into daily decisions.

One barrier is data fragmentation and quality. Fire incident records, building permits, code violations, and demographic data usually live in separate systems, with inconsistent identifiers and missing fields. Cleaning and linking these sources well enough to train and maintain ML models requires sustained investment that many local departments, especially smaller ones, cannot spare. Without reliable data pipelines, even the best model quickly degrades.

A second barrier is regulation and governance. As New York’s experience with “smart buildings” shows, most cities do not yet treat AI systems as regulated life‑safety equipment. There is often no requirement to document, test, or periodically re‑inspect the algorithms that influence access control, smoke control, or inspection priorities. That makes leaders understandably cautious about leaning on ML scores for high‑stakes decisions like where to send inspectors or how many units to dispatch on first alarm.

There are also operational and cultural hurdles. Inspectors and fire officers have deep experience-based intuition and may distrust “black box” scores that seem to second‑guess their judgment. If a model flags a modern mid‑rise as high risk while a visibly dilapidated building scores low, staff can reasonably question whether the inputs or labels capture the real hazards. Without interpretable models that highlight key drivers—such as lack of detectors, combustible cladding, or prior violations—adoption stalls.

Finally, there is uneven evidence on downstream outcomes. While model performance metrics like accuracy and AUROC look strong, there are still few prospective, peer‑reviewed studies showing clear reductions in actual fire incidence, casualties, or insurance losses after sustained use of predictive targeting. Vendors claim improvements, but public agencies face scrutiny if they reorganize inspection schedules around algorithms without rock‑solid proof.

Published ML fire‑risk models show AUROCs around 0.8–0.9 and strong concentration of risk in the top 20–25% of buildings, but only a small slice of departments report using such scores in standard workflows.

5. Where it actually works

The clearest wins so far come where predictive models are embedded into existing processes rather than bolted on. In the Oregon risk‑mapping work, results were delivered as simple spatial layers integrated into familiar GIS tools, allowing prevention officers to overlay risk with schools, nursing homes, or prior violation clusters. That kept the model “behind the scenes” while giving staff a more informative map to guide outreach and smoke‑alarm campaigns.

Vendor systems for permit review and inspection prioritization have gained traction when they automate tedious triage steps instead of replacing human judgment. For example, systems that automatically flag high‑hazard occupancies for earlier inspection and optimize daily routes—while leaving final scheduling with supervisors—fit more easily into current practice. In these setups, inspectors still make the call, but they start from a smarter, risk‑ranked list.

6. The opportunity

Targeted use of predictive fire‑risk models could let departments focus scarce prevention and inspection capacity on the buildings and neighborhoods where catastrophic fires are most likely, cutting casualties and losses without dramatically increasing budgets.

Concrete steps that would boost adoption include:

Build linked data foundations that connect fire incidents, permits, inspections, and demographics, making it practical to train and update models.
Favor interpretable models and dashboards that show why a building scores high risk, so inspectors can validate or challenge the result.
Treat critical AI systems as regulated life‑safety equipment, with plan review, permitting, and periodic re‑inspection similar to fire alarms and sprinklers.
Start with pilot programs where risk scores augment—not replace—existing triage, while tracking clear outcome metrics like violations found per inspection or reduction in high‑severity fires.
Partner with researchers and insurers to run prospective evaluations that measure real reductions in fires, injuries, and losses when predictive targeting guides prevention.

References

“Machine learning-based forecasting of urban fire impact in city environments.” PMC, 2025.
“Machine Learning Based Risk Analysis and Predictive Modeling of Structural Fire Casualty Severity in Oregon.” ScienceDirect, 2025.
“A Machine Learning Framework for Fire Risk Prediction With Multi-source Data.” IEEE, 2025.
“Building fire risk prediction with stacking ensemble methods.” Fire, 2024.
“Fire Risk Predictive Models Overview.” Emergent Mind, 2025.
“AI system detects fires before alarms sound, NYU study shows.” International Fire & Safety Journal, 2025.
“AI for Fire Permit Review & Inspection.” Datagrid, 2025.
“It’s Time to Treat New York’s AI Building Systems the Same as Fire Systems.” Commercial Observer, 2025.