I Used AI to Merge 10 Government Databases Into One Property Risk Map. Here Is What It Found.

Mount Waverley. One suburb. The Victorian government recognises 54 separate planning overlays and risk layers that apply to properties within its boundaries 1.

Flood zones. Bushfire-prone areas. Heritage controls. High-voltage transmission line easements. Significant vegetation overlays. Environmental significance overlays. Development plan overlays. Design and development overlays.

All of this data is publicly available. Free. Updated weekly by the Victorian government through VicPlan and Vicmap. Every buyer in Australia has the legal right to access it before purchasing any property.

But here is the problem: the data is scattered across more than 20 separate government databases. VicPlan shows planning zones and overlays but requires you to search one address at a time. Vicmap provides spatial data but requires GIS software to interpret. ABS Census data sits in a completely separate portal. School zone boundaries are on the Department of Education's website. Flood data is split between Melbourne Water and council-specific flood studies.

No single government tool lets you see all of this information on one screen for one suburb, let alone compare across suburbs.

So we built one. Using AI, we merged seven primary data sources into a single interactive map that shows every risk overlay, every planning control, and key demographic indicators for any suburb in Melbourne. Today, I want to show you what it reveals about Mount Waverley — and why this kind of analysis matters before you spend $1 million on a property.

Category 1: Hazard overlays that destroy value

Start with the risks that directly affect property value and insurability.

High-voltage transmission lines. A 220 kV transmission line crosses through Mount Waverley. Peer-reviewed research from Queensland University of Technology found that properties within 50 metres of high-voltage lines experience a price discount of 15 to 20 per cent compared to equivalent properties further away 2. That is not sentiment. That is regression analysis on thousands of transactions.

A $1.2 million house within 50 metres of the transmission line is worth $960,000 to $1,020,000. The $180,000 to $240,000 discount is permanent and structural — it does not recover because the power line does not move.

Land Subject to Inundation Overlay (LSIO). Flood-prone properties in Mount Waverley face three financial penalties: higher insurance premiums (often 50-100 per cent above non-flood properties), bank lending restrictions (some lenders reduce maximum LVR or decline the application entirely), and council-imposed construction constraints that increase renovation costs for any work below the designated flood level 3.

Bushfire-prone areas. While Mount Waverley itself has limited bushfire exposure, nearby suburbs in the Yarra Ranges corridor face devastating insurance costs. Kalorama properties have reported annual insurance premiums of $4,738 to $6,527 — over $500 per month just for building and contents coverage 4. If you are looking at properties east of Mount Waverley toward the Dandenong Ranges, bushfire risk should be your first due diligence item, not an afterthought.

Any one of these hazards can eliminate your expected return on investment. Combined, they can render a property uninvestable. And none of them are visible from the street.

Category 2: Planning overlays that block your strategy

Heritage Overlay is the one that catches investors most often.

In 2019, Victoria introduced a planning reform that allows granny flat construction in General Residential Zone areas without a planning permit, provided certain conditions are met. This was a significant win for investors pursuing dual-income strategies — buy a house, add a granny flat, collect two rents 5.

But if the property falls within a Heritage Overlay, the permit exemption does not apply. You must submit a full planning application, which adds 3 to 6 months of processing time and requires heritage-sensitive design that can cost 30 to 50 per cent more than standard construction.

Do the maths. You planned to buy a $800,000 property, spend $110,000 on a granny flat, and add $350 per week in rental income within four months of settlement. But the Heritage Overlay means the permit alone takes six months, the heritage-compliant design adds $40,000 to the build cost, and the council may reject the application entirely. Your dual-income strategy is either delayed by a year or cancelled completely.

This is not hypothetical. I have watched buyers discover Heritage Overlays after settlement. The information was freely available in VicPlan before they signed the contract. They just did not check.

Our map flags Heritage Overlays as a distinct layer with colour-coded severity. When you are comparing three properties across two suburbs, you can see instantly which ones have heritage restrictions and which ones allow permit-free granny flat construction 6.

Category 3: Demand indicators that drive rental performance

Risk avoidance is one side of due diligence. Demand analysis is the other.

School zones are a primary driver of rental demand in Melbourne's eastern suburbs. But school zone boundaries are not aligned with suburb boundaries. The same street can be split between two different public secondary school catchments, with dramatically different NAPLAN rankings and demand profiles. A property on the "right" side of the street commands 10 to 15 per cent higher rent from families targeting the higher-ranked school 7.

Our map overlays school zone boundaries from the Department of Education directly onto the property map. You can see, at a glance, which school catchment each property falls within.

Population structure is the second demand indicator. The ABS Census divides Mount Waverley into 78 SA1 statistical areas — tiny geographic units containing approximately 200 to 400 people each. Within a single suburb, two adjacent SA1 areas can have completely different demographics: median household income, proportion of renters versus owners, age distribution, and family composition 8.

This granularity matters for investment. A property in an SA1 area dominated by young families with two incomes has a different rental demand profile than a property in an SA1 area of elderly owner-occupiers. The first will attract long-term family tenants willing to pay premium rent for proximity to schools and childcare. The second may have lower tenant demand but higher owner-occupier competition if you decide to sell.

Most buyers look at suburb-level medians and assume the entire suburb is homogeneous. It is not. One street can sit in a high-income SA1 while the next street sits in a moderate-income SA1. The rental demand, and therefore the investment case, is different.

How we built it (and what AI cannot do)

The tool draws on seven primary data sources: VicPlan zoning and overlay data, Vicmap spatial layers, ABS Census demographics, Melbourne Water flood mapping, Department of Education school zones, CFA bushfire mapping, and council-specific planning scheme amendments.

AI handled the heavy lifting: automated data ingestion from government APIs, spatial data cleaning and alignment (each data source uses slightly different coordinate systems), suburb-boundary clipping, and layer-by-layer rendering on an interactive map interface.

This was not a "built an app in 10 minutes" stunt. The data cleaning alone — reconciling geographic boundaries across seven sources that update on different schedules — took weeks of iterative work. AI accelerated that process by an order of magnitude, but a human still had to define what clean data looks like and validate the output against known ground truth 9.

But I want to be honest about something: the tool shows public data. Everyone has access to the same inputs. In a world where AI makes public data freely queryable, the competitive advantage in property investment does not come from public data. It comes from private information.

Private information means: walking the street and noticing the water staining on the boundary fence that indicates subfloor drainage issues. Having a 10-minute conversation with the selling agent and learning that the vendor has already bought their next home and needs to settle in 45 days. Knowing from repeated inspections that the council is quietly rezoning three blocks on the eastern side of a suburb. Understanding that a particular house has been on the market for 87 days because the vendor rejected two reasonable offers early on and is now negotiating from weakness.

AI cannot walk streets. It cannot read body language. It cannot build relationships with agents. It cannot inspect a subfloor.

Public data tools like this map eliminate obvious mistakes. They prevent you from buying in a flood zone you did not know existed. They prevent you from paying a premium for a heritage-controlled property when a non-heritage equivalent sits two streets away. They save you from hazards you would have discovered only after settlement.

But they do not replace the human work of due diligence. Clicking a mouse is not buying a property. It is the starting line 10.

The tool is currently in limited beta testing. We plan to open it publicly for free. If you want to be notified when it launches, follow our page.

I am Yan, an actuary and buyer's agent. My team of 40 in Melbourne is your property advisory unit. If you have questions about any of the data layers mentioned in this article, leave them in the comments.

The real competitive advantage in property is not public data

I want to close with a point that may seem counterintuitive given that I just spent an article promoting a data tool we built.

Public data tools — including ours — are necessary but not sufficient for making profitable property investment decisions. They prevent mistakes. They flag risks that would otherwise remain invisible until after settlement. They save you from the $200,000 error of buying in a flood zone or the $150,000 error of paying full price for a heritage-restricted property where your renovation strategy cannot be executed.

But they do not, by themselves, generate above-market returns.

Above-market returns come from private information that cannot be digitised. They come from the agent who calls you before a listing goes live because you have settled reliably on four prior transactions. They come from the building inspector who tells you the foundation crack is cosmetic, not structural — saving you from walking away from a $50,000 discount that other buyers will not claim. They come from knowing that the council planning officer for Precinct 6 interprets the ResCode setback provisions more conservatively than the officer for Precinct 3, and adjusting your building plans accordingly.

They come from walking a street at 7am on a weekday and noticing that the parking is full of tradesman vehicles, which means the area is gentrifying through renovation rather than through new construction — a signal that land values are about to accelerate because the resident population is investing in their properties rather than leaving.

None of this information appears in any database. AI cannot learn it from internet text. It exists only in the heads and notebooks of people who spend their days in the field.

Our tool handles the data layer. Our team handles the human layer. The combination of both is what produces results. Neither alone is enough.

If you are using our tool and finding it useful, I am glad. But do not mistake it for a substitute for ground-level research. The map shows you where to look. Walking the streets shows you what to buy.

What we are building next

The current tool shows planning overlays, hazard layers, and demographic indicators. The next version will add three additional features that our internal team has been testing.

First, historical price growth by SA1 area. Instead of looking at a single suburb median, you will see price growth at the micro-geographic level — block by block, street by street. Two streets in the same suburb can have growth differentials of 3 to 5 per cent per year, and those differentials compound into enormous value gaps over a 10-year hold period.

Second, rental yield heat maps. Overlaying actual rental income data onto the geographic map, colour-coded by gross yield. This lets you instantly identify pockets within a suburb where rental returns are above or below the suburb average — and investigate why.

Third, development potential scoring. A composite score based on zoning, lot size, overlay restrictions, access width, slope, and easement position. Properties with a high development score can support granny flat additions, dual-occupancy splits, or multi-unit development. Properties with a low score are limited to single-dwelling use regardless of the owner's ambitions.

All of this will be free when it launches. Our business model is not data subscriptions. It is buyer's advocacy and property management. The tool exists to help people make better initial decisions — and when they are ready to act, we are here to execute.