Evidence · Home and contents insurance

Every question, every answer, every source

Explore all 372 home and contents insurance questions and the 1,476 answers saved on 11 October 2026. Read the original responses and download the tables behind the study’s comparisons, sources, sentiment and factual checks.

questions
372
answers saved, of 1488 planned
1476
consumer situations
12
named regions
16
AI platforms
4

01

Every question, with its answers

Each question went to the four AI platforms. All were collected with a New Zealand setting. Open a question to read every captured answer in full.

Showing 20 of 372 questions

02

The tables behind the study

Every figure on the study page comes from these tables. They are CSV files, which open in any spreadsheet.

  • Questions

    questions.csv

    One row per question, with its exact saved wording.

    Question ID, source references, question, consumer situation, named regions, question group and collection setting

  • Answers

    answers.csv

    One row per captured answer, with its full text.

    Answer ID, question ID, platform, collection setting, consumer situation, named regions and full answer

  • Sources by answer

    sources.csv

    One row per website recorded as a source of an answer.

    Answer ID, question ID, assistant, collection setting, website and publisher type

  • Checks against primary sources

    checks.csv

    One row per stated assertion checked, for one platform.

    Check, question and answer IDs, assistant, exact passage, verdict, primary-source finding, URL and date

  • Consumer recommendation coding

    consumer-recommendation-coding.csv

    One row per reviewed provider role in a consumer-selection answer.

    Answer ID, question ID, consumer, platform, provider, role, counted recommendation, exact passage and conditions

  • Consumer selection membership

    consumer-selection-membership.csv

    One row per answer in the matched consumer-selection comparison.

    Answer ID, question ID, consumer, platform, question group and full text; twelve home and twelve contents selection questions, with missing captures preserved

  • Ongoing support coding

    support-coding.csv

    One row per reviewed named-provider role in an eligible support answer.

    Answer ID, question ID, consumer, platform, provider, role, support-preference decision and exact passage

  • Ongoing support membership

    support-membership.csv

    One row per answer in the actual consumer-support group.

    Answer ID, question ID, consumer, platform, question group and full text; acquisition validation is excluded

  • Regional recommendation coding

    regional-recommendation-coding.csv

    One row per reviewed provider role in a region-named selection answer.

    Answer ID, question ID, named consumer region, platform, provider, recommendation decision and exact passage

  • Regional selection membership

    regional-selection-membership.csv

    One row per answer in the matched regional comparison.

    Answer ID, question ID, named consumer region, platform and equal-comparison membership; four home and three contents platform answers per region

  • Insurer sentiment

    insurer-sentiment.csv

    One row per unique saved claim about an insurer in a selection answer.

    Claim, question and answer IDs, insurer, saved stance, exact passage and original-answer offsets

  • Recorded source URLs

    source-urls.csv

    One row per recorded answer, website and URL association.

    Answer ID, question ID, platform, website, URL, source records and presence in the captured answer text

  • Matched consumer platform membership

    platform-membership.csv

    One row per answer in the identical-question platform comparison.

    Answer ID, question ID, product, consumer and platform; 22 identical questions per platform

  • Matched regional platform membership

    regional-platform-membership.csv

    One row per answer in the identical-question regional platform comparison.

    Answer ID, question ID, product, region and platform; 31 identical questions per platform

Use these tables to trace findings back to the original questions and answers. The full text of each answer is also on this page, under its question.

ChatGPT · New Zealand

What should I make of complaint data versus online reviews when comparing home insurers?

When comparing home insurers in New Zealand, I’d treat complaint data as a signal of how an insurer handles problems, and online reviews as a source of detail about what customers actually experience. Neither is a reliable verdict on its own.

The key distinction is that home insurance is most important when something goes wrong — a major leak, storm damage, earthquake damage or a complicated rebuild. An insurer can have plenty of happy customers when premiums are paid and claims are straightforward, but perform quite differently when a claim is disputed or takes months to resolve.

1. What each type of evidence tells you

Formal complaint data

Best for patterns

Shows the types of problems customers escalate, how often disputes arise in the available data, and whether complaints are resolved or upheld. It is more structured than reviews, but depends on how complaints are defined, recorded and reported.

Online customer reviews

Best for lived experience

Can reveal recurring issues with call-centre service, communication, claims delays, assessors, contractors and renewal premiums. But reviewers are self-selected, and unusually good or bad experiences are more likely to be shared.

The most useful approach is to use complaint data to identify broader patterns, then read reviews to understand what those patterns look like in practice.

2. The biggest trap in New Zealand complaint statistics

The Insurance Council of New Zealand (ICNZ) publishes formal complaints information, including outcomes from independent dispute resolution schemes. This is a useful starting point, but there is an important limitation: its insurer-level personal-lines data combines house, contents, motor, travel and credit insurance. It is not a clean home-insurance-only league table. How to Make an Insurance Complaint (NZ)Insurers Expand Complaints Reporting (2024)

For example, the ICNZ figures for 2024 included:

Insurer Claims received across reported business External complaints closed
AA Insurance 209,026 10
IAG NZ 525,636 96
Tower 66,480 32
Vero 119,748 25

Source: ICNZ's 2024 complaints data. How to Make an Insurance Complaint (NZ)

These figures illustrate why context matters, rather than establishing which insurer is best.

  • Raw complaint totals favour smaller insurers. Larger insurers generally have more customers and claims, so more complaints can be expected.
  • A complaints-to-claims ratio is not a perfect measure. The claims and complaint counts may cover different populations or stages of the complaints process.
  • External disputes are only the tip of the iceberg. Most complaints are dealt with internally, so the data omits many customers who experienced problems.
  • An insurer-favourable outcome isn't necessarily proof of excellent service. It indicates the dispute outcome, not whether the customer found the process clear, timely or pleasant.

The right interpretation is: formal complaint data provides useful evidence about escalated disputes, but a low number alone does not prove that an insurer is good at handling home claims.

3. How to read online reviews without being misled

Online reviews become much more useful when you examine the details rather than the overall star rating.

Look for repeated, specific complaints

Several independent customers describing long delays, poor explanations, repeated requests for documents or inconsistent claim decisions are more informative than dozens of generic one-star ratings.

Prioritise actual home claims

Look for roof leaks, storm damage, flooding, gradual damage disputes, repair quality, rebuilding and alternative accommodation. These are more relevant to home insurance than complaints about car claims or call-centre sales.

Check dates and disaster context

A cluster of negative reviews following a major weather event may reflect a genuine capacity problem, unusually high claim volumes or both. Check whether similar complaints continue in more recent reviews.

Read the insurer's responses

A thoughtful response that explains the process and offers a practical next step is a positive sign. A polished public reply does not prove that the underlying issue was resolved, though.

Also check where the reviews came from. A high rating based mainly on reviews solicited and published by the insurer is not directly comparable with a rating on an independent review platform. Ratings can vary substantially between platforms, even for the same brand. House Insurance Reviews NZ 2026 | Customer Ratings Compared

4. How I would weigh the evidence

Here is a practical framework for comparing two insurers when their complaint figures and reviews disagree.

Evidence Weight I'd give it Why
Repeated, well-supported claim-handling problems High Directly relevant to what happens when you need cover
Formal complaint outcomes and recurring dispute types High Independent evidence about escalated disputes
Large numbers of recent, detailed home-claim reviews Medium–high Can expose patterns missing from summary statistics
Overall online star rating Medium–low Often mixes products, issues and customer types
Small numbers of complaints or reviews Low confidence Too little evidence to draw firm conclusions

This is a guide to how I'd assess the evidence, not a statistically validated scoring system.

What if the signals conflict?

  • Few formal complaints, many negative reviews: Investigate whether customers are experiencing service failures that never reach an external dispute scheme.
  • Many formal complaints, positive reviews: Check the insurer's size, complaint rate where meaningful, and the types and outcomes of disputes.
  • Positive reviews and low complaint figures: Encouraging, but still check policy wording and whether the evidence is sufficiently broad.
  • Negative reviews concentrated around one catastrophe: Look for evidence of recovery and more recent performance before treating the issue as a permanent characteristic.

5. What matters specifically for home insurance in New Zealand

I'd combine both types of evidence with the policy itself. A well-reviewed insurer can still offer unsuitable cover for your home.

Check these items before deciding:

  • Natural hazards: What is covered for flooding, landslips, storms and earthquakes, and what exclusions or special excesses apply?
  • Claims settlement: Is the policy based on full replacement, specified limits or another settlement basis? What conditions apply?
  • Gradual damage and leaks: How does the policy distinguish sudden damage from gradual deterioration or seepage?
  • Temporary accommodation: What limits and time periods apply if your home becomes uninhabitable?
  • Excesses and exclusions: Look beyond the headline premium to the amount you could have to pay yourself.
  • Claims support: Do reviews describe clear communication, realistic timeframes and competent repair coordination?

Consumer NZ's house and contents insurance guide and comparison is a useful place to compare cover, price and customer satisfaction together. House and contents insurance

For complaint evidence, start with the ICNZ complaints data. If you want to understand the formal dispute process, ICNZ also explains the roles of the two independent schemes, IFSO and FSCL. How to Make an Insurance Complaint (NZ)

My bottom line

Use complaint data to test whether there are serious or recurring problems; use reviews to understand the customer experience; use policy wording to decide whether you're actually protected.

I would be particularly wary of an insurer with repeated, recent reports of poorly handled home claims, even if its overall rating is respectable. Conversely, I wouldn't reject a large insurer just because it has more complaints in absolute numbers.

The strongest choice is the insurer with suitable cover for your property, credible evidence of competent claims handling, and no concerning pattern that outweighs its advantages.

Ask anything

ChatGPT can make mistakes. Check important info.

Captured 11 October 2026

Gemini · New Zealand

What should I make of complaint data versus online reviews when comparing home insurers?

When comparing home insurers, looking at formal complaint data versus online customer reviews gives you two entirely different lenses through which to evaluate a company. Neither tells the whole story on its own, but combined, they provide a well-rounded picture of how an insurer actually treats its policyholders.


1. Online Customer Reviews (Google, Trustpilot, ProductReview)

  • What they measure: The overall customer experience, front-end usability, ease of getting a quote, friendliness of staff, and immediate emotional reactions (good or bad) after interacting with the brand.
  • The Pros:
    • They capture day-to-day interactions quickly.
    • They highlight pain points with customer service, app usability, or billing transparency.
  • The Cons & Biases:
    • Strong negativity bias: People rarely leave a review just because their policy renewed normally; reviews are overwhelmingly driven by extreme frustration (e.g., a denied claim or a steep premium hike) or, conversely, incentivized positive reviews.
    • Lack of context: A one-star review claiming an insurer is a "scam" often lacks the fine print—such as the policyholder failing to disclose crucial information or running afoul of specific exclusions (like gradual damage).

2. Formal Complaint Data (ICNZ, Ombudsman Schemes, FMA)

  • What they measure: Objectively tracked disputes that could not be resolved internally and had to be escalated to external dispute resolution schemes (such as the Insurance & Financial Services Ombudsman or industry bodies like the Insurance Council of New Zealand).
  • The Pros:
    • Factual and standardized: These metrics deal with concrete breaches of codes, unreasonable claim delays, disputed settlement amounts, or unfair declinations—stripped of emotional venting.
    • Scale-adjusted context: Formal complaint reports often look at complaints per volume of claims or policies, helping you see if a larger insurer merely has more complaints because they insure significantly more homes.
  • The Cons & Biases:
    • Lagging indicators: Formal complaint data looks backward. By the time systemic issues show up in official dispute reports, the insurer may have changed its processes.
    • Severity weighting: A formal complaint could be about a minor administrative billing error or a massive disaster-recovery payout delay; aggregate numbers don't always show the severity.

How to Use Both Together

Evaluation Angle Rely on Online Reviews For Rely on Complaint Data For
Service Quality Evaluating website portals, phone wait times, and polite communication. Assessing whether they stall or unfairly handle legitimate claims.
Integrity & Fairness Spotting patterns of unexpected fee changes or aggressive policy cancellations. Seeing how often their decisions are formally overturned or criticized by an Ombudsman.
Context Check Taking individual rants with a grain of salt. Looking at macro-trends across the entire insurance sector.

Smart Strategy for Home Insurance Shopping

  1. Check Financial Strength Ratings: Before looking at complaints or reviews, ensure the insurer has a strong financial strength rating (e.g., A- or higher from agencies like AM Best), meaning they have the capital to pay out major disaster claims.
  2. Read the Critical Reviews (1 to 3 stars): Look past the emotional language in online reviews to find patterns. If multiple reviewers complain about the exact same issue—such as severe delays assessing storm damage or communication blackholes—take it as a warning sign.
  3. Review Policy Wording Over Popularity: A company with pristine reviews might still have restrictive policy sub-limits or higher excesses for natural disasters. Always match the fine print of the policy document to your property's specific risk profile.

Are you currently comparing a specific set of home insurers, or looking at a particular aspect like natural disaster cover?

Ask Gemini

Gemini is AI and can make mistakes.

Captured 11 October 2026

AI Overviews · New Zealand

What should I make of complaint data versus online reviews when comparing home insurers?

AI Overview

When comparing home insurers, complaint data provides an objective look at regulatory failures and major service breakdowns , while online reviews offer a subjective snapshot of day-to-day customer sentiment.

To choose the best policy, you need to balance both. Complaint data tells you how reliably an insurer handles worst-case scenarios, while online reviews tell you how pleasant they are to deal with on an ordinary day.

Direct Comparison: Complaint Data vs. Online Reviews

Feature Complaint Data Online Reviews
Source Official bodies like the Insurance & Financial Services Ombudsman (IFSO) or Financial Services Complaints Ltd (FSCL). Public platforms like ProductReview , Google , or Trustpilot.
Reliability High. Every complaint is verified, vetted, and tracked against industry standards. Medium to Low. Can be skewed by extreme emotions, bots, or incentivized positive reviews.
Focus Serious disputes regarding policy wordings, claim denials, and legal obligations. General customer service, app usability, wait times, and premium price shocks.
Context Often presented as a proportion of market share (e.g., complaints per 10,000 customers). Raw volume and star ratings, often lacking context on how large the insurer is.

🔍 How to Read Complaint Data

In New Zealand, most home insurers belong to an independent dispute scheme, primarily the IFSO or FSCL.

  • Look for proportions, not totals: A massive insurer like IAG (State, AMI) will naturally have more total complaints than a boutique insurer. Look for the rate of complaints relative to their size.

  • Focus on upheld complaints: If a high percentage of complaints against an insurer are "upheld" (decided in favor of the customer), it indicates the insurer is regularly getting claims decisions wrong.

  • Identify systemic issues: Official reports often highlight trends, such as an insurer consistently misinterpreting "gradual damage" clauses.

💬 How to Read Online Reviews

Online reviews are highly polarized because people rarely leave a review when an experience is just "okay."

  • Filter for claims, not sign-ups: Many 5-star reviews are left by customers who just bought a policy and liked the slick website. Filter specifically for 1-star and 2-star reviews mentioning "claims" to see how the insurer behaves when they actually have to pay out.

  • Look for patterns in the noise: Ignore one-off rants about a single rude agent. Look for recurring keywords like "weeks without a response," "lowball repair estimates," or "unexplained premium hikes."

  • Check the timelines: A wave of negative reviews right after a major weather event (like a flood or cyclone) is common due to industry-wide delays. Pay more attention to how insurers handle claims during normal, non-crisis periods.


If you want to narrow down your options, tell me:

  • Which specific insurers are you currently comparing?

  • What is your biggest priority (e.g., cheapest premiums, fastest claims payout, or specific flood/earthquake coverage)?

I can help you look at how they stack up against each other.

Ask anything

Captured 11 October 2026

Google AI Mode · New Zealand

What should I make of complaint data versus online reviews when comparing home insurers?

Official complaint data and online reviews measure fundamentally different aspects of customer experience, with complaint data offering objective, regulated metrics on dispute resolution and online reviews capturing subjective, emotional reactions to service.

When comparing home insurers in New Zealand, relying on both data sets gives you a balanced view, but you must weigh them differently based on what they actually represent.

Comparing Complaint Data vs. Online Reviews

Metric Type What It Measures Key Advantages Main Limitations
Official Complaint Data Formal disputes handled by internal processes or external schemes like the Insurance & Financial Services Ombudsman (IFSO) or Financial Dispute Resolution Service. Objective, verifiable, tracks legal/regulatory breaches or serious claim deadlocks. Lacks minor service feedback; large insurers naturally have higher raw complaint volumes due to market share.
Online Reviews Unfiltered, subjective customer sentiment on public platforms (Google, Trustpilot, social media). Highlights everyday customer service friendliness, ease of communication, and speed of minor payouts. Prone to emotional bias, extreme polarization (only very angry or very happy people review), and potential fake reviews.

How to Interpret the Differences

  • Contextualize Volume: A large mainstream insurer in New Zealand will naturally accumulate more raw complaints and negative reviews simply because they insure hundreds of thousands of homes. Always look at complaint ratios relative to market size rather than raw numbers.

  • Look for Patterns, Not Outliers: A single terrible review about a denied claim might stem from a customer who didn't read their policy exclusions. However, if multiple reviews and formal complaints consistently cite slow communication or disputed earthquake/weather-related payouts, treat it as a systemic red flag.

  • Weigh Severity Over Sentiment: Online reviews often complain about premium price increases—which happen across the whole market due to rising reinsurance costs. Formal complaint data, conversely, tells you if an insurer fails to meet obligations under the Fair Insurance Code. How to Make an Insurance Complaint (NZ) | ICNZ

If you are looking at specific providers, tell me:

  • Which insurance companies you are comparing
  • Whether you are more concerned about premium costs or claim payout reliability

I can help you review their standing and dispute resolution processes.

Ask anything

Captured 11 October 2026