A client brief on Monday. A shortlist you can defend by lunch.
Your client will ask why this suburb and not the other forty. Increasingly they will check your answer themselves, against a subscription of their own. HtAG turns a client brief into a ranked shortlist, a scored list of live listings and a report with your logo on it, and leaves the reasoning written down behind every one of them.
| Market | Beds | Price | Score |
|---|---|---|---|
| Doreen VIC | 3 | $778,688 | 1822 |
| Medowie NSW | 3 | $847,998 | 1711 |
| Miners Rest VIC | All | $692,951 | 1701 |
| Norwood TAS | 3 | $656,955 | 1689 |
| Hillside VIC | 3 | $772,690 | 1659 |
Eleven stages, one screen
Area explains 85.7% of a property’s capital growth, measured across 1,157 Australian house markets, which is why six of these eleven stages happen before you open a single listing. Select any stage to see what happens and what your client ends up holding.
Two conversations decide everything downstream: what the client actually needs, and which prebuilt strategy matches it.
Take the client brief
Four answers set the hierarchy of every metric used from here on: budget band, how long they will hold, when they need to extract equity, and how much volatility they can tolerate. Growth or cashflow sits on top as the primary objective.
- The equity extraction timeframe is the question most agents never ask, and it changes which markets qualify
- Budget band changes which metrics matter. Below $550,000 socio economic scores barely move five year growth
- Where the client’s stated goal and their actual constraints disagree, that gap is the conversation that wins the mandate
A brief they sign off on, with the trade offs written in their own words rather than assumed.
You cannot recommend a market without a documented brief, and you cannot defend a recommendation without one either.
Match the brief to a strategy
The four answers map to one of 16 pre built strategies, each carrying only the filters that must be present for that kind of brief. You never build a filter set from scratch, and you never start from a blank screen.
- A typical preset carries 10 or 11 conditions, grouped into essentials, supply and market timing
- Need something extra, such as a yield floor inside a growth strategy? Add one filter and save it as your own
- Saved strategies repopulate in one click, and can be shared by link across your whole team
A named strategy attached to their file, so the next brief for the same client starts where the last one finished.
For an agency, it also means every agent is running the same method rather than their own.
Seven thousand suburbs down to a ranked shortlist, with a reason attached to every market that survives the cut.
Filter down to a working shortlist
Filters are only for conditions that genuinely must be there. Every extra hard threshold silently deletes good markets that miss it by a fraction, which is the most common and most expensive mistake in suburb research.
- Set a yield filter at 4.5% and you have quietly removed a market sitting at 4.4% that may have been the right answer
- Ten domains available: price, rent, returns, fundamentals, supply, demand, supporting, risk, composite scores and geography
- Stop filtering at 15 to 40 markets. Filtering says these are worth considering. Ranking says which one first
markets met the conditions on the real brief shown at the top of this page, drawn from a universe of 7,000+ suburbs.
Every filter traces back to a line in their brief, which is what makes the shortlist explainable.
Weight what matters, then rank
This is the part no other platform does. Every metric gets a direction, higher is better or lower is better, and a significance multiplier from 0 to 15. Both are pre populated from the brief, and every one of them can be overridden.
- On the real growth brief above, supply carried the heaviest group weighting, then demand, then fundamentals
- Short term supply and demand trends outrank current values, because direction beats snapshot
- Leave auto rank on and the list re-orders as you drag a weighting, which shows you instantly which metrics move the needle for this brief and which are noise
- Turn auto rank off when you want to walk a client through the raw shortlist before applying your view
This stage and the next are a loop, not a line. If something on the suburb page reads badly, you come back here, raise that metric’s weighting and re-rank. You repeat until the top of the list earns its position.
one year growth for Dex top decile picks against 9.1% for the market, in a 14 year point in time backtest across roughly 150,000 suburb observations.
A ranked shortlist with a reason attached to every weighting decision, produced by a ranking method with a published track record rather than a house view.
Verify how the market actually behaves
The ranking has already crunched the numbers. The suburb pages show you behaviour: whether supply is tightening or loosening, whether demand is building or fading, and how deep the worst historical downturn went.
- Composite RCS scores for overall quality, lower risk, capital growth and cashflow
- Ten year price, rent and yield history with a modelled forward range shown as a dashed extension
- The growth rate cycle, plus the worst twelve month period the market has ever recorded
- Supply and demand trends on both short and long horizons, so you can see the direction, not just today’s value
A branded suburb report carrying your logo, with the sections you choose and houses and units separated.
Each market also has a plain English written summary you can lift straight into your own advice.
Cut the list at the drop off
The most common question buyers agents ask is how many suburbs to show a client. The answer is a rule anyone can apply: look for the point where the scores fall away, and cut there.
- A fall of roughly 100 to 150 points marks the natural cut off
- On the real run above, the leader sat 111 points clear, then ranks two to seven clustered inside a 65 point band
- Everything above the cut off is worth sourcing in. Securing the right asset in the fourth ranked market beats missing out in the first
- If you cannot rank, you are carrying the cost of buying the market that returns 7% when the one beside it on your list returned 12%
point gap between first and second on the real run, with ranks two to seven inside a 65 point band.
A target list of three to seven markets and a stated reason for where the line was drawn.
From the right market to the right streets, then to the individual listings worth booking an inspection for.
Narrow to the right streets
A suburb is not one market. StreetLens maps the inside of a suburb at roughly block level, so you can show a client that the listing they found sits on the wrong side of it.
- 15 layers including socio economic index, public housing share, renter share, flood risk, bushfire risk and estimated price, rent and yield
- Live listings plotted on top, coloured by asking price
- Also carries days on market, days on rental market, hold period, lot size and estimated dwelling age at the same resolution
- Views are shareable, so the map itself becomes part of the advice
map layers at roughly block level, rather than one number for the whole suburb.
A shareable map showing exactly which pockets are in scope and which are not, and why.
Push to the portals and vet every listing
Select up to 100 suburbs from the ranked list and send them straight to the portal, with property type and budget already filled in. Nothing is retyped. The Chrome extension then turns the results page into a scored, sortable table.
- Every listing on the page scored and re-ordered best first in about ten seconds
- Five weighted criteria you set per client: socio economic index, public housing, renter to owner ratio, flood rating and bushfire rating
- Auto mode sweeps up to 20 pages of results without supervision
- Expand any listing for years to own, hold period, ownership, build date, land and floor area, and an under or over valued flag against HtAG’s own estimate
- Add off market properties manually so private stock is scored on the same basis
A client ready shortlist report with their name on it, showing must haves, deal breakers and your notes against each property.
Plus a CSV export of the processed data if you want it inside your own systems.
Turn the research into a mandate: answer the objections, model the whole plan, and run the same method across the team.
Answer the pushback before it lands
There is always one negative metric, because no perfect suburb exists. Your client will find it. The weighting already accounted for it, and HtAG AI Copilot turns that into a paragraph you can send.
- Every metric in an answer is clickable. Stack two, three or ten and ask how they relate
- Answers are pitched at the expertise level you set, and can be rewritten in first timer language for the client
- 11 tools across client and portfolio work, market research and property analysis
- Professional includes 150 credits a month. A full suburb analysis costs 10, a side by side comparison 12
A written explanation in their language, not a metric definition, and research they can read themselves if they want to go deeper.
This is usually the difference between a deal that stalls and one that proceeds.
Model the plan, not just the purchase
The brief already contains an equity extraction date, which means it already contains purchase number two. Modelling it turns a single transaction into a plan the client comes back to you to execute.
- Actual portfolio against unlimited scenarios, with growth assumptions you can change per property
- Equity release modelling, lump sum events, multiple loan splits and selective offset
- Individual name, SMSF and trust structures
- Agency mode adds bulk client upload, branded invitation links and onboarding progress per client
A branded plan with the next three purchases in it, and a login to a tracker carrying your name rather than ours.
Clients who can see the plan tend to come back to execute it.
Scale it past one agent
HtAG data can go directly into whichever AI assistant your agency already uses, so research happens where you are already working instead of in another browser tab you have to copy figures out of.
- 104+ API endpoints and 70+ MCP tools across 6 servers, covering all 537 local government areas
- Connection guides for Claude, Perplexity, Manus and ChatGPT Codex Desktop. Setup takes about five minutes
- Skills teach the assistant HtAG’s interpretation rules, so it reads a score the right way round rather than telling your client the opposite
- Free to join through the HtAG Developer Portal, billed per row of data with the first 25 rows per endpoint free. Professional subscribers receive 50% off, Investor 25%
API endpoints, plus 70+ MCP tools, available to plug into the tools your agency already runs.
The workflow stays yours. Your CRM, your email, your drive. The data comes to your stack rather than moving your business into someone else’s.
When your client turns up having already done the research
They arrive with ten suburbs filtered, a data subscription of their own, and an AI summary they generated the night before. That is not a threat to your fee. It is the moment your fee gets decided.
| What they ask | What they arrived with | What you put next to it |
|---|---|---|
| Why these ten suburbs? | A filter on yield and median price, run across whichever fields their subscription happens to expose. | A ranking where every metric carries a direction and a significance weighting taken from their own brief, applied to every market that qualified. |
| Why not the other forty? | Nothing. Exclusions rarely survive a spreadsheet. | A documented cut. Filtering says which markets are worth considering. Ranking says which one first, and why the rest are not. |
| What did you rule out, and why? | Silence, or a story told from memory. | The markets you walked away from, with the condition that removed each one still attached to it. |
| How do I know the ranking works? | Confidence, and last year’s testimonial. | A fourteen year backtest they can read themselves, including the years it won by least and the proportion of picks that lost money. |
| Could an AI have told me this? | It did, last night, assembled from published articles. | A signal rebuilt from the data available on each historical date, with no hindsight, judged against other suburbs in the same price band. |
Why these ten suburbs?
A filter on yield and median price, run across whichever fields their subscription happens to expose.
A ranking where every metric carries a direction and a significance weighting taken from their own brief, applied to every market that qualified.
Why not the other forty?
Nothing. Exclusions rarely survive a spreadsheet.
A documented cut. Filtering says which markets are worth considering. Ranking says which one first, and why the rest are not.
What did you rule out, and why?
Silence, or a story told from memory.
The markets you walked away from, with the condition that removed each one still attached to it.
How do I know the ranking works?
Confidence, and last year’s testimonial.
A fourteen year backtest they can read themselves, including the years it won by least and the proportion of picks that lost money.
Could an AI have told me this?
It did, last night, assembled from published articles.
A signal rebuilt from the data available on each historical date, with no hindsight, judged against other suburbs in the same price band.
The research is not the deliverable. The defensibility is. A client who can rank suburbs still cannot show a lender, a partner or an accountant why the eleventh one was cut, and that is the part they are paying you for.
Assertion versus evidence
Most buyers agents can find a decent suburb. Far fewer can show a client, in writing, why that suburb rather than the forty others that also looked decent. Here is the same five jobs, done by hand and done through the eleven stages above. For the data layers underneath the process, see the buyers agent data stack.
| Stage | What you can assert | What you can prove |
|---|---|---|
| Narrowing the country | A spreadsheet, a weekend, and a shortlist built from suburbs you already had in mind. | 7,000+ suburbs filtered to a working shortlist on one screen, using only the conditions the brief actually requires. |
| Ranking the shortlist | Every metric treated as equally important, which is mathematically the same as treating none of them as important. | Each metric carries a direction and a significance weighting taken from the brief, then the whole list is scored against it. |
| Checking listings | One browser tab at a time. A search returning 147 results does not get checked, it gets skimmed. | Every listing on the results page scored and re-ordered in about ten seconds, with auto mode sweeping up to 20 pages. |
| The client report | Rebuilt by hand in Word for every client, and out of date the week after you send it. | Generated with your logo, sections you choose, houses and units separated. |
| The client pushing back | You find the one negative metric they found, and improvise. | The weighting already accounted for it. Copilot writes the explanation in language you can send. |
Narrowing the country
A spreadsheet, a weekend, and a shortlist built from suburbs you already had in mind.
7,000+ suburbs filtered to a working shortlist on one screen, using only the conditions the brief actually requires.
Ranking the shortlist
Every metric treated as equally important, which is mathematically the same as treating none of them as important.
Each metric carries a direction and a significance weighting taken from the brief, then the whole list is scored against it.
Checking listings
One browser tab at a time. A search returning 147 results does not get checked, it gets skimmed.
Every listing on the results page scored and re-ordered in about ten seconds, with auto mode sweeping up to 20 pages.
The client report
Rebuilt by hand in Word for every client, and out of date the week after you send it.
Generated with your logo, sections you choose, houses and units separated.
The client pushing back
You find the one negative metric they found, and improvise.
The weighting already accounted for it. Copilot writes the explanation in language you can send.
Fourteen years on trial. Fourteen wins.
Dex top decile picks grew 17.6% over the following year against 9.1% for the market, and beat their price band in all 14 years from 2012 to 2025.
years beating the benchmark, with an annual edge of 4.2 to 16.4 percentage points and no losing year in the series.
- No hindsight. The signal was rebuilt from data available on each historical date.
- Judged against other suburbs in the same price band, not a national average.
- Horizon matched, so the long term strategy drove the five year test.
- Roughly 150,000 suburb observations across every house suburb in the country.
- The losing picks are published too. Over five years the worst 5% still returned +0.9% a year.
| Hold | Dex picks | Market | Edge | Beat rate | Lost money |
|---|---|---|---|---|---|
| 1 year | 17.6% | 9.1% | +8.9pp | 87% | 3.1% |
| 3 years | 9.4% | 7.0% | +2.5pp a year | 67% | 6.0% |
| 5 years | 8.1% | 6.8% | +1.2pp a year | 64% | 3.9% |
HtAG states the limits as well as the result: over a full ten year hold the edge converges with the market, so the reliable window is one to five years. Separately, the HtAG evidence portal documents an outcome for 135 of 135 past suburb recommendations, with the underlying data available for independent audit, including three buyers agencies whose results are documented in full and one engagement written up from first contact to contract.
Run it once on a brief you are already working
The fastest way to judge this is to watch it run on a live client brief rather than a demo one. Book a session and we will take one of yours end to end.
Filter 7,000+ suburbs and rank the survivors against the brief, on one screen.
Score a whole portal search in about ten seconds and export a client report.
Branded suburb reports, shortlist reports and a portfolio plan under your name.
104+ endpoints and 70+ MCP tools, connected in about five minutes.
What buyers agents ask before they start
Used by buyers agents, brokers and institutional clients across Australia