I've made money in property. I've also lost it. After five investment purchases over ten years, the biggest lesson I can share is this: the suburbs where I got burned all had one thing in common — I bought on gut feel instead of data.
I'm not a property guru. I'm not selling a course. I'm an everyday investor who started with a two-bedroom unit in Western Sydney in 2016 and has since built a portfolio across three states. Some of those purchases were smart. Others cost me years of negative cash flow and sleepless nights.
This post is the honest version of my investing journey — the mistakes, the wins, and the framework I eventually adopted that made me stop guessing and start actually understanding what makes a suburb tick.
In this article
- My investing timeline at a glance
- Lesson 1: Price guides are theatre
- Lesson 2: "Expert picks" nearly broke me
- Lesson 3: Yield without growth is a trap
- Lesson 4: The suburb was fine — the timing was wrong
- Lesson 5: One metric is never enough
- The turning point: when I found the data
- Gut feel vs data: a real comparison
- The framework I use now
My Investing Timeline at a Glance
Before I get into the lessons, here's the quick overview of my five purchases so you can see the arc. Two wins, two painful lessons, and one that's still playing out.
Bought off a podcast recommendation. Decent price growth but rental yield was terrible. Held for 4 years, barely broke even after costs. Sold in 2020.
Chased a "hot tip" from a buyer's agent seminar. The town's major employer shut down 18 months later. Still holding it at a loss.
First purchase where I actually looked at the data — population growth, building approvals, infrastructure pipeline. Up 40% in value since purchase. Still holding.
Bought into a "growth corridor" at the peak. Oversupply from new estates has kept prices flat. Cash-flow negative for 2 years.
Most researched purchase I've ever made. Cross-referenced 8 different government data sources. Positive cash flow from month one, 12% growth in first year.
See the pattern? The more data I used, the better my outcomes got. That's not a coincidence.
Lesson 1: Price Guides Are Theatre
The mistake
On my first purchase, I trusted the agent's price guide of $420–$460K. I budgeted accordingly, got emotionally attached to the property during inspections, and ended up paying $502K at auction. I was $42K over my "absolute maximum" before I'd even started.
What I didn't know then: agents deliberately underquote to generate auction competition. The price guide has almost nothing to do with what the property will actually sell for.
The fix was embarrassingly simple. Every state in Australia publishes actual settled sale prices through the Valuer General. Not listing prices, not estimates — the real number that was recorded after settlement. If I'd checked what comparable units in that suburb actually sold for in the six months prior, I'd have known the real range was $480–$510K and budgeted accordingly.
What I do now: Before I even inspect a property, I check the Valuer General records for the suburb. I look at actual sale prices for comparable properties over the last 12 months. That gives me the real price range — not the agent's marketing fiction.
Lesson 2: "Expert Picks" Nearly Broke Me
The mistake
Property #2 was my most expensive lesson. I attended a "free" buyer's agent seminar where a charismatic speaker recommended regional towns with "incredible yields and untapped growth potential." I bought a house in one of those towns three months later.
What the seminar didn't mention: the town's economy was dependent on a single employer. When that employer downsized, the population dropped, vacancies spiked, and property values fell 15% in two years. The speaker, I later learned, had a commercial relationship with developers in the area.
Red flag I missed: I never checked the employment data. The ABS Census and Department of Employment's Small Area Labour Markets data would have shown me that the town had an unemployment rate nearly double the state average and dangerous employer concentration. That single data point would have killed the deal — and saved me $85K.
This was the purchase that fundamentally changed how I think about property. I realised that every "expert" has an angle. Every spruiker has a reason for recommending a specific area. The only thing that doesn't have an agenda is the raw government data.
Lesson 3: Yield Without Growth Is a Trap
The mistake
After getting burned on Property #2, I overcorrected. For Property #4, I focused almost entirely on the "growth corridor" narrative — new estates, population projections, future train lines. The yield was only 3.1%, but I figured the capital growth would make up for it.
Two years later: prices are flat because there's a massive oversupply of new builds in the area. The building approval data — which I didn't check — would have shown thousands of dwellings in the pipeline. Population growth couldn't keep up with supply.
The lesson here was nuanced. Growth corridors can be excellent investments, but only when population growth outpaces new supply. I was looking at population projections in isolation without checking how many new dwellings were being approved. The ABS Building Approvals data is published monthly and freely available — I just didn't know to look at it.
Lesson 4: The Suburb Was Fine — the Timing Was Wrong
The realisation
Looking back at Property #1 (the Western Sydney unit), the suburb itself wasn't a bad choice. It's actually performed well over the last decade. My problem was that I bought at a point in the cycle where prices had already run up, and the demographics were shifting from young professionals (who rent) to families (who buy houses, not units).
The Census data showed this shift clearly — the 25–34 age bracket was declining while the 35–49 bracket with children was growing. A unit was the wrong product type for where that suburb was heading.
This taught me that suburb selection and property type selection are two different decisions, and the demographic data tells you which product type is going to be in demand over the next 5–10 years. A suburb full of young families doesn't need more one-bedroom units. It needs three-bedroom houses with backyards.
Lesson 5: One Metric Is Never Enough
The pattern
Every single mistake I made came from focusing on one or two metrics in isolation. Great yield? Didn't check employment. Great growth? Didn't check supply. Great suburb? Wrong property type for the demographic trend.
Property #3 (Brisbane) and Property #5 (Mid-North Coast) both worked because I looked at the full picture: price trends, rental yields, population growth, building approvals, crime rates, employment, income growth, school catchments, and demographic shifts — all at the same time.
The Turning Point: When I Found the Data
After the Property #2 disaster, I spent an entire weekend trying to pull together the data I should have checked before buying. I'm talking about actual government sources — the ABS Census, Valuer General records, state crime stats, building approvals, employment reports.
It took me three days to compile a decent picture for a single suburb. Three days of navigating different government websites, downloading spreadsheets in incompatible formats, and trying to figure out which tables actually contained the numbers I needed.
That's when it hit me: the data that would have saved me $85K was freely available the entire time. It was just scattered across a dozen different government websites in formats that made it nearly impossible for a normal person to use.
For Property #3, I forced myself to do the work. I built my own spreadsheet tracking 8 different metrics across 15 suburbs in the Brisbane corridor. It took weeks, but the result spoke for itself: I picked a suburb that ticked nearly every box, and it's been my best-performing investment by far.
By Property #5, I'd refined my process even further. And that's also when I discovered tools that consolidate this data automatically, which saved me the weeks of manual spreadsheet work.
This is exactly the problem BuyersMate was built to solve. Every suburb report pulls together 23+ government data factors — from Valuer General sale prices to ABS demographics to state crime statistics — into a single, transparent assessment. It's the spreadsheet I used to build manually, except it covers every suburb in Australia and updates automatically. If I'd had access to something like this in 2018, Property #2 would never have happened.
Gut Feel vs Data: A Real Comparison
Here's a side-by-side comparison of how I made decisions before and after I started using data. The difference in outcomes isn't subtle.
| Decision | Gut Feel Approach | Data-Driven Approach |
|---|---|---|
| Suburb selection | Podcast tips, seminar picks, "hot lists" Risky | Multi-factor analysis across 10+ criteria Verified |
| Price assessment | Agent's price guide, Domain estimates | Valuer General actual settled sale prices |
| Yield calculation | Listing site rental estimates | State rental bond data + real sale prices |
| Growth potential | "It feels like it's up and coming" | Population growth + building approvals + infrastructure pipeline |
| Risk assessment | Drove around, looked nice enough | Crime trends, employment data, employer diversification |
| Demographic fit | Didn't think about it | Census age structure + household composition trends |
| My results | ~$125K in losses/dead money Ouch | ~$210K in equity growth + cash flow positive Winner |
The Framework I Use Now
I'm not going to pretend I've cracked the code. Property investing always carries risk. But after ten years and five properties, here's the checklist I now use before putting an offer on anything:
My pre-purchase checklist
Median price trend: Has the suburb shown consistent growth over 5+ years? I check Valuer General records, not listing estimates.
Rental yield: Is the gross yield above 4% for houses? I calculate from actual sale prices and rental bond data.
Population growth: Is the suburb growing faster than the city average? ABS Estimated Resident Population tells me this.
Supply pipeline: Are building approvals declining or stable? A flood of new supply kills growth.
Employment health: Is unemployment below the state average? Are there multiple major employers? Small Area Labour Markets data answers this.
Crime trajectory: Is crime trending down? A suburb with improving safety is often gentrifying.
Infrastructure: Is there funded (not just announced) infrastructure coming within 5km?
Demographics: Does the age structure and household composition align with the property type I'm buying?
Owner-occupier ratio: Is the suburb trending toward more owner-occupiers? That signals stability and gentrification.
School quality: Are there high-performing schools in the catchment? This drives long-term family demand.
Pulling all of this together for one suburb used to take me days. Now I run a report on BuyersMate and get the full picture in minutes. I still do my own drive-by inspection and due diligence, but the data gives me the confidence to know I'm looking in the right place before I ever get in the car.
A note on honesty: No tool, platform, or spreadsheet can guarantee a good investment. Property markets are affected by interest rates, government policy, global events, and countless other factors no model can predict. What data can do is dramatically reduce the risk of making a decision based on bad information or someone else's commercial agenda. That's the edge I wish I'd had from day one.
Don't Learn the Hard Way Like I Did
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