What Does Landlord Insurance Actually Cost? Predicting Premiums for Every Australian Address

Key Findings

Why This Matters for Investors

A typical value-tier premium is $595 per year, while premium tier averages $2,337. Insurance is 2-8% of gross rental income depending on location and product. For a portfolio with 10 properties on premium tier, that is more than $23,000 per year. Most investors only find the cost after they buy. Our research changes that. We can now estimate the premium for any address. This helps investors compare true net yields. For example, a house in Killarney Heights (Sydney), NSW costs $607. This assumes $670 weekly rent. The same rent in Upper Coomera (Gold Coast), QLD costs $1,144 with value tier or $3,384 with premium tier. The Gold Coast bill is nearly double. This is due to higher natural hazard risks. An investor comparing gross yields alone would miss this difference.

What We Studied

We analysed 8,806 landlord insurance quotes from two insurers in April 2026. These cover all Australian states and territories. The data spans 1,289 postcodes and 2,258 suburbs. This includes both city and regional properties. Each quote includes base premiums and taxes. These taxes include the Emergency Services Levy, GST, and stamp duty. We collected quotes at different rent levels. We added building costs and hazard risks to each property. This helped us build our predictive model. We tested it on properties the model had not seen before.

Results by State

The model is most accurate in the ACT. Premiums there are tightly clustered. It is weakest in Tasmania. Premiums there are low relative to property features. Our model tends to overestimate Tasmanian costs. This suggests that Tasmanian risk pricing follows a unique structure.

Price Sensitivity: How Rent Affects Your Premium

Weekly rent directly impacts premium cost. Insurers use rent to estimate liability risks. The magnitude of this effect is striking. For example, a house in Newman (Pilbara), WA costs $898. This is for $600 weekly rent. The same house at $900 rent costs $1,324. That is a 47% increase in premium. A property in Coomera (Gold Coast), QLD also jumps. It goes from $1,282 to $2,076 as rent rises. Investors should factor in these higher costs when raising rents.

The Geography of Insurance Cost

Location is the strongest predictor of cost. It outweighs building type and age. Coastal Queensland is the most expensive region. Properties in high-cyclone zones pay significantly more. Premiums also vary by microburb. A house in Lavington (Albury), NSW costs $379. A similar property in inner Sydney costs over $1,800. That large gap shows why state averages are misleading.

Building Characteristics Matter Less Than You Think

Building features matter less than location. Dwelling type and age have a smaller effect. Upper-floor units are the cheapest to insure. They cost 10% less than houses. This reflects lower flood and storm risks. But geographic location causes much larger price shifts.

How Well Does the Model Actually Generalise?

The headline accuracy is within $103 of an actual value-tier quote on a typical $595 policy (about 17% in dollar terms), and within $275 of a premium-tier quote on a typical $2,337 policy (about 12%). Both come from standard 5-fold cross-validation: holding out one fifth of properties at a time and asking the model to predict them.

The model now uses 8,806 quotes split per tier (4,662 value-tier quotes + 4,144 premium-tier quotes). LightGBM with log-target, AVM and relative-altitude features added in v11. Per-insurer 5-fold OOF results below.

The model is strongest at suburb-level interpolation and weakest at predicting entirely new geographic regions. Investors and analysts using this work should keep that in mind. A premium estimate for an inner-Sydney suburb backed by 50 nearby quotes is much more reliable than a premium estimate for a Northern Territory town the model has never seen.

The five biggest predictors of premium turn out to be (in order): the underwriter, the weekly rent, whether the building is elevated above ground level, the property’s longitude, and its latitude. Elevation above ground specifically — a building on stumps or piers rather than a slab — is the single most decisive structural feature, more than construction material or year built. Flood-prone properties on slabs pay much more than equivalent properties lifted clear of likely water depth.

One important caveat. The underwriter indicator carries a large share of the model’s lift on cross-state predictions. Because the dataset is still dominated by one landlord specialist (value tier, 89.6%), cross-insurer generalisation is not yet proven. Adding two or three more underwriters to the training data is the next step.

Defence Against Criticism

Single insurer limitation

All quotes come from one major landlord insurance specialist. The model predicts that insurer’s pricing. Different insurers use different risk models. However, this insurer is a dominant market specialist. Their pricing is a reasonable proxy for the whole market. Future work will include quotes from more underwriters.

Point-in-time data

We collected all quotes in April 2026. Premiums change with major events and repricing. The model captures a snapshot. But the core relationships are likely stable. Location and rent will always drive the final cost.

Standardised property profile

We kept some variables constant across quotes. This includes no claims history and self-management. Real premiums will vary based on individual factors. The model estimates the cost for a standard profile.

Limitations

• Tasmania remains poorly modelled. Its insurance pricing is unique. More data from that market is needed.
• The model does not account for claims history. Landlords with recent claims will pay more.
• About 8% of attempted addresses (387 of 5,000) were referred for manual underwriting (jeopardy declines), typically high-risk properties with known flood, subsidence, or compliance issues. A larger 22% (1,087 of 5,000) returned no quote because the insurer’s address autocomplete did not recognise the address — these are mostly remote or newly built dwellings, not insurer declines. The model trains on 8,806 successfully quoted properties across both insurers.
• The model is trained on contents-only landlord cover. Combined building and contents cover will differ.
• All quotes are new-business prices. New-business policies typically attract a first-year discount that rolls off at renewal. Real-world renewal premiums are commonly 10 to 30% higher than the new-business prices used here, even with no claims. Investors should treat the predicted figure as a year-one budget and plan for the typical renewal step-up.

Conclusion

Landlord insurance costs are predictable. Location, rent, and hazard risks explain most price shifts. Our model estimates premiums within $103 of a typical $595 value-tier quote and within $275 of a typical $2,337 premium-tier quote. This is useful for investment analysis. Investors should check insurance costs before they buy. A property with high gross yield may have high insurance costs. This is common in storm-prone or flood-prone areas. Microburbs will add these estimates to our reports soon. These will sit alongside data on rental yields and capital growth.