Commission Intelligence: What It Is and Why Recruiters Are Using It

Author: Kent Lardner Date: February 2026 Category: Commission Intelligence

Real estate recruitment in Australia has operated on the same information model for decades. A candidate tells you what they bill. You check their listing history on the portals. You call a few referees. You make a judgement.

The problem with this model is not that it's slow — although it is. The problem is that it relies almost entirely on self-reported data and subjective assessment. There is no independent, structured, comparable dataset that tells a recruiter what an agent is actually producing relative to every other agent in their market.

Commission intelligence changes that. It takes active listing data, applies a standardised commission estimation model, and produces a structured dataset where every agent, every suburb, and every region can be compared on the same terms. This article explains what it is, how it works, and why it has become a core part of how recruitment teams operate.

What commission intelligence is

Commission intelligence is the practice of estimating agent-level earning capacity from publicly observable listing data, then structuring those estimates into a dataset that can be queried, compared, and acted on.

The inputs are straightforward: active property listings across Australia, each with a street address, listing price (or imputed price where withheld), property attributes, and agent and agency attribution. The outputs are estimated commission values for every listing, aggregated to the agent level, the suburb level (SA2), the regional level (SA4), and the state and national level.

The result is a single dataset where a recruiter can answer questions that were previously unanswerable without extensive manual research: which agents are generating the most estimated commissions in a given area, how commission density varies between suburbs, how a specific market compares to adjacent regions, and where listing momentum is shifting week to week.

How commissions are estimated

The estimation model applies a tiered percentage structure to each listing's sale price, reflecting the way commission rates typically operate in Australian residential real estate — higher percentages at lower price points, declining as prices increase.

Where listing prices are not publicly available, the model imputes an estimated sale value using median prices by bedroom count within the relevant geography. This ensures every listing carries a commission estimate, not just those with visible pricing.

The commission estimate for each listing is then attributed to the listed agent and agency. When aggregated, this produces an estimated total commission figure for every agent in the dataset — a proxy for earning capacity that is consistent, comparable, and updated weekly.

It is important to be precise about what this is and what it is not. These are estimates based on a standardised model, not actual fee agreements. Individual agency commission structures vary. Vendor-paid advertising, conjunctional arrangements, and split commission deals are not captured. The model provides a consistent, comparable benchmark — not a precise accounting of actual income.

Its value lies in relative comparison, not absolute accuracy. When every agent in a market is measured using the same methodology, the rankings and concentration patterns that emerge are structurally reliable even if any individual estimate carries a margin of error.

What the dataset contains

Each record in the commission intelligence dataset represents a single active listing. For every listing, the dataset includes a verified street address, geocoded latitude and longitude coordinates, the assigned SA2 area (the Australian Bureau of Statistics geographic unit that represents a local community or market area), SA4 region, state, property attributes including bedroom count, the estimated or listed sale price, a tier-based commission percentage, the calculated commission estimate, and agent and agency attribution with listing date fields.

Because every listing is geocoded and mapped to its SA2, the dataset supports analysis at a geographic precision that postcodes and marketing labels cannot match. An SA2 typically represents a recognisable suburb cluster in metropolitan areas, making it the natural unit for local market analysis.

The dataset is delivered as a structured CSV file — formatted for direct interrogation in AI tools, spreadsheet software, or any analytical environment. No proprietary dashboards or software are required.

Why recruiters are adopting it

The adoption of commission intelligence by recruitment teams reflects three shifts in how real estate groups approach talent acquisition.

The resume problem. Agent resumes and interviews are unreliable indicators of actual production. Candidates overstate, selectively present, and frame their history in the most favourable terms — as anyone would. Commission intelligence provides an independent baseline. When a candidate claims to be a top-five agent in their market, the data either confirms or contradicts that claim before the first interview.

This is not about catching dishonesty. It is about having a shared factual foundation for conversations that are otherwise conducted in a fog of self-promotion and incomplete information. Recruiters who use commission data report that the quality of their initial conversations improves because both parties can engage with verifiable context rather than competing narratives.

The targeting problem. Traditional recruitment is reactive — groups advertise, wait for applications, or rely on networks and referrals. Commission intelligence enables proactive targeting. A recruitment director can identify the top 20 estimated producers in any SA4 region, filter by SA2 to find agents operating in specific suburb clusters, and approach them with knowledge of their market position.

This reverses the recruitment dynamic. Instead of evaluating candidates who have self-selected into the process, the recruiter is approaching agents who may not be actively looking but whose production makes them strategically valuable. The data makes the approach credible — it signals that the recruiter understands the agent's market and has done genuine analysis, not just a portal search.

The allocation problem. National groups with offices across multiple SA4 regions face a constant question: where to invest recruitment effort. Commission intelligence provides the structural data to answer it. Which regions have the largest commission pools? Where is agent density creating competitive pressure? Which markets are concentrated around a few dominant producers, and which are broadly distributed? Where is listing momentum increasing or declining?

These are strategic questions that affect headcount planning, office expansion decisions, and competitive positioning. Without structured data, they are answered by intuition and anecdote. With commission intelligence, they are answered by evidence.

How it is used in practice

The practical application varies by organisation, but three use cases dominate.

Agent benchmarking. The most common application is comparing agents within and across markets. A principal can see how their team's estimated production compares to competitors in the same SA2 areas. A recruitment director can rank every agent in a target SA4 by estimated commission output. A group can identify which of their offices are under-producing relative to the available commission pool in their region.

Market assessment. Before opening a new office or entering a new market, groups use commission intelligence to assess the opportunity. The total commission pool, the number of active agents, the commission per listing, and the concentration of top producers all inform whether a market justifies investment — and what level of team is required to compete.

Competitive monitoring. Weekly data refreshes make it possible to track changes over time. New agents appearing in a market, shifts in listing concentration between agencies, and changes in commission density by SA2 all provide early signals of competitive movement. Groups that monitor these signals can respond before the market impact becomes visible through conventional channels.

What it does not do

Commission intelligence is a structural tool, not a surveillance tool. It does not track individual agent movements between offices in real time. It does not capture actual commission splits or fee agreements. It does not replace the judgement that recruitment ultimately requires — cultural fit, management capability, long-term potential, and personal qualities are not measurable from listing data.

What it does is remove the information asymmetry that has historically defined real estate recruitment. The agents know what they produce. The portals know what is listed. Commission intelligence ensures that the recruiters and principals making hiring decisions have access to structured, comparable, independent data — rather than relying on the candidates themselves to provide it.

The Australian market context

Australia's real estate market is structured across 88 SA4 regions containing more than 2,300 SA2 areas. In the most recent quarter (November 2025 to January 2026), the national dataset tracked more than 112,000 active listings attributed to more than 33,000 agents, representing an estimated $2.07 billion in potential commissions.

The distribution of that $2.07 billion is highly uneven. The largest single SA4 commission pool is more than 70 times the size of the smallest. The highest commission per listing in Australia is more than double the lowest among major metro SA4s. The most dominant individual agent in their region captures more than 9% of the total pool; in the most distributed market, the top agent holds less than 1%.

These structural variations mean that recruitment strategy cannot be uniform. The data that makes those variations visible — consistently, independently, and at scale — is what commission intelligence provides.

Explore the national commission map at suburbtrends.com. For full agent rankings, SA2-level breakdowns, and weekly data access — Talk to Kent →