Home insurers are dropping customers based on aerial images
Home insurers in the US are increasingly using aerial and satellite imagery, paired with algorithms, to assess properties and non-renew policies—sometimes for issues like “old” roofs, overhanging trees, undeclared pools or trampolines, and even based on possibly inaccurate data. Commenters debate whether this is a rational evolution of risk-based pricing in an era of rising climate and repair costs, or a harmful shift toward opaque, one-sided surveillance that leaves homeowners with little recourse, especially in highly regulated or high-risk states like California and Florida. More broadly, the thread probes what happens to the social purpose of insurance when risk can be priced ever more precisely, and whether public policy should limit how far this data-driven underwriting can go.
Tech used for inspections & privacy concerns
- Imagery mostly from manned aircraft and high‑res aerial/satellite vendors, not consumer drones (yet).
- Several argue the aircraft type is irrelevant; the real issue is pervasive, cheap, large‑scale surveillance.
- Others say aerial views of roofs and yards are already public (e.g., Google Maps) and insurers have long done in‑person inspections, so this is just a cheaper method.
- Future daily high‑res satellite updates are flagged as potentially “Orwellian”.
- Some jurisdictions (e.g., Australia) already use aerial+AI to detect planning and tax violations, with lawyers noting there’s generally no legal privacy right in visible structures.
Insurance economics, risk pooling & “perfect pricing”
- Long debate on what insurance is for: pooling low‑probability, high‑cost risks vs. acting as a forced savings plan.
- With increasingly granular data (aerials, telematics, big data), insurers can better segment risk.
- One camp: more accurate pricing is fairer, reduces cross‑subsidies (e.g., between safe and risky roofs, trampoline vs no‑trampoline) and improves efficiency.
- Other camp: in the limit of near‑perfect prediction, pooling collapses; high‑risk people are either priced at near‑loss cost or dropped, turning insurers into “fortune tellers” rather than risk‑bearers. Socially, this leaves many unprotected.
Regulation, climate risk & market exits
- Commenters tie cancellations and pull‑outs from states like California and Florida to:
- Regulated rate caps and slow approvals for increases.
- Rising nat‑cat risk (wildfires, hurricanes) and higher rebuild costs.
- In Florida, extreme litigation and fraud (e.g., roofing scams).
- Disagreement over whether regulators mainly protect consumers or create shortages and instability by blocking actuarially justified rates.
Cancellations, fairness & recourse
- Multiple anecdotes of non‑renewals triggered by aerial findings: allegedly aged roofs, overhanging trees, junk piles, even when on‑the‑ground inspection or recent replacement contradicts the images.
- Complaints that customers often cannot see the photos or meaningfully appeal; some see this as “computer says no” behavior.
- Proposed safeguards: warning and cure periods, mandated offers at some price instead of outright denial, stronger complaint channels via regulators.
Liability risks: trampolines, pools, dogs, guests
- Many report insurers explicitly asking about trampolines, pools, playgrounds, dog breeds, wiring types, etc.
- Trampolines and pools are described as high sources of broken bones, paralysis, and guest lawsuits; insurers fear third‑party claims and subrogation from health insurers.
- Some countries report lower concern due to different tort and healthcare regimes, or separate liability policies.
Auto insurance & behavioral tracking
- Parallel trend: auto premiums rising despite clean records; offers to “earn” discounts with tracking devices or OEM telemetry.
- Some see this as coercive data collection, with suspicion that telemetry will mostly be used to justify higher baselines and penalties, not durable discounts.
Broader ethical and structural questions
- Arguments over whether basic insurance (home, health, auto) should be:
- For‑profit, competitive, and highly risk‑differentiated;
- Mutual/co‑op based; or
- Government‑run as part of social policy.
- Moral hazard discussed on both sides: insureds taking more risks vs. insurers maximizing profit by denying claims, cherry‑picking safe customers, and exploiting data asymmetry.
- Some see climate‑exposed exurbs as inherently “mistakes” that shouldn’t be subsidized; others stress freedom to choose where to live and worry about creating de facto uninsurable, unsellable communities.