US dataswitch to UK
Real Estate Brokers
selling for a fee, real estate, managing or operating real estate offices and comparing a property with similar properties that have recently sold to determine its competitive market price. If that's your week, this page is about your job.
The honest answer
This job is splitting in two: maintaining knowledge of real estate law is work AI now does quickly and cheaply, and selling for a fee, real estate is work it can't touch.
Which half fills your week decides your exposure. Moving toward the second half is a real, doable plan.
Your week, as this page understands it
Operate real estate office, or work for commercial real estate firm, overseeing real estate transactions. Other duties usually include selling real estate or renting properties and arranging loans. The job title says “real estate brokers”. The real job is the part underneath: selling for a fee, real estate. That is the thing someone has to be right about.
The exposed part of this job is specific, and we won’t pretend it is coming back. But real estate brokers is not one task. It is 17 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is selling for a fee, real estate, and the ledger below shows exactly why.
Where the work sits, by task weight
- shifting to AI
- 44%
- changing shape
- 15%
- staying human
- 40%
These bars are tasks changing hands, not people being counted out. The ledger below shows which.
Whole-job exposure score 47 out of 100 (41–52 allowing for uncertainty): partial exposure, across 17 scored tasks. The number is the support for the sentence above it, not a headline about anyone’s future.
How we know this
What is measured: Every published task statement for real estate brokers is rated on five dimensions: can a model produce the output, does the work need a body in a room, does it need a legally accountable person, does it depend on a person being trusted in the moment, and how much data exists. A published formula turns those five ratings into the score; the model never writes the number.
How the bar is built: Each task’s share of the bar is its published importance weight, so a task you do all day counts for more than one you do twice a year.
Release: 2026-q4.1, scores computed 2026-08-05. Read the full method.
Your job, task by task
These are the official task statements for this occupation, in plain English, sorted by what the evidence says is happening to each one. The official wording sits under every line so you can check the rewrite against it.
- 3 of this occupation's scored task statements carry a score that was measured under a different occupation's context, because the statement is shared between jobs and has only been scored once so far. Each one names the occupation it was measured under in the free routes below; none is presented as a measurement for this job.
- Task clusters are not derived in this release, so the task-cluster field is empty and no Collab365 Space routing is attached to this occupation yet.
Shifting to AI
7 tasksTasks today’s tools can already do most of. This is the part we will not soften: where these rows are the bulk of your week, the week changes.
Generating lists of properties
This is reading one thing and writing another: lists of properties in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Generate lists of properties for sale, their locations, descriptions, and available financing options, using computers.” (O*NET task statement)
How this row was scored
Exposure score: 81 out of 100 (77–85 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Generating property listings with locations, descriptions and financing options is database work software already does.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Monitoring fulfillment of purchase contract terms to ensure that they are handled in a timely manner
This is reading one thing and writing another: fulfillment of purchase contract terms in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Monitor fulfillment of purchase contract terms to ensure that they are handled in a timely manner.” (O*NET task statement)
How this row was scored
Exposure score: 69 out of 100 (65–73 allowing for uncertainty): high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Tracking contract deadlines and chasing outstanding items is scheduling work that software handles reliably.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Comparing a property with similar properties that have recently sold to determine its competitive market price
This is reading one thing and writing another: a property in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Compare a property with similar properties that have recently sold to determine its competitive market price.” (O*NET task statement)
How this row was scored
Exposure score: 62 out of 100 (55–69 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Comparing recent sales to price a home is data analysis software does well, though the property condition still needs seeing.
The five ratings: output a model can produce 3/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 4/4.
Maintaining knowledge of real estate law
This is reading one thing and writing another: knowledge of real estate law in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Maintain knowledge of real estate law, local economies, fair housing laws, types of available mortgages, financing options, and government programs.” (O*NET task statement)
How this row was scored
Exposure score: 83 out of 100 (76–90 allowing for uncertainty): very high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Keeping up with property law, mortgages and local markets is reading and summarising published material.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 4/4.
Changing shape
3 tasksTasks where the machine takes the producing and a person keeps the checking, the deciding, or the answering-for-it. For most jobs this is the biggest group, and it is where "transformation, not termination" is literally visible.
Checking work completed by loan officers
The software now makes the first pass at work, but someone has to be answerable for the result, and it cannot be the software. So the job becomes checking and deciding rather than producing.
importance 4 · CoreSource: “Check work completed by loan officers, attorneys, or other professionals to ensure that it is performed properly.” (O*NET task statement)
How this row was scored
Exposure score: 56 out of 100 (49–63 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; someone qualified has to answer for it.
The rating behind it: Checking others' paperwork against requirements is document comparison, but the broker carries responsibility for what is missed.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 2/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Appraising property values, assessing income potential
The software now makes the first pass at property values, assessing income potential, but someone has to be answerable for the result, and it cannot be the software. So the job becomes checking and deciding rather than producing.
importance 3 · CoreSource: “Appraise property values, assessing income potential when relevant.” (O*NET task statement)
How this row was scored
Exposure score: 56 out of 100 (49–63 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; someone qualified has to answer for it.
The rating behind it: Valuation models work well on market data, but a formal appraisal has to come from a licensed appraiser.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 2/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Giving buyers virtual tours of properties in which they
The software now makes the first pass at buyers virtual tours of properties, but the part that matters is a person saying it and standing behind it. So the job becomes checking and deciding rather than producing.
importance 3 · SupplementalSource: “Give buyers virtual tours of properties in which they are interested, using computers.” (O*NET task statement)
How this row was scored
Exposure score: 46 out of 100 (39–53 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; the value is that a specific person does it.
The rating behind it: A virtual tour can be assembled automatically, but the buyer's questions during it are what move the sale.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.
Staying human
7 tasksTasks that stay with a person, because they happen in the physical world, because the rules need someone accountable, or because the value is that a specific person does them.
Selling for a fee, real estate
This work happens in the physical world: a fee, real estate, in a real place. Software cannot follow it there.
importance 5 · CoreSource: “Sell, for a fee, real estate owned by others.” (O*NET task statement)
How this row was scored
Exposure score: 6 out of 100 (2–10 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world; someone qualified has to answer for it; the value is that a specific person does it.
The rating behind it: Selling someone's property means showings, negotiation and a licensed broker carrying legal responsibility for the deal.
The five ratings: output a model can produce 1/4 · needs a body in a room 2/4 · needs an accountable person 3/4 · needs to be trusted in the moment 3/4 · how much data exists 2/4.
Acting as an intermediary in negotiations between buyers and sellers over property prices and settlement details and during the closing of sales
The rules require a named, qualified person to answer for an intermediary, and that person cannot be a piece of software.
importance 4 · CoreSource: “Act as an intermediary in negotiations between buyers and sellers over property prices and settlement details and during the closing of sales.” (O*NET task statement)
How this row was scored
Exposure score: 8 out of 100 (4–12 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: someone qualified has to answer for it; the value is that a specific person does it.
The rating behind it: Standing between buyer and seller through a negotiation depends on both sides trusting the person in the middle.
The five ratings: output a model can produce 1/4 · needs a body in a room 1/4 · needs an accountable person 3/4 · needs to be trusted in the moment 3/4 · how much data exists 2/4.
Obtaining agreements from property owners to place properties for sale with real estate firms
The rules require a named, qualified person to answer for agreements, and that person cannot be a piece of software.
importance 4 · CoreSource: “Obtain agreements from property owners to place properties for sale with real estate firms.” (O*NET task statement)
How this row was scored
Exposure score: 10 out of 100 (6–14 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: someone qualified has to answer for it; the value is that a specific person does it.
The rating behind it: Winning a listing is about the owner trusting you enough to hand over their biggest asset.
The five ratings: output a model can produce 1/4 · needs a body in a room 1/4 · needs an accountable person 2/4 · needs to be trusted in the moment 3/4 · how much data exists 2/4.
Show the other 7 tasks
Arranging for title searches of properties being sold
shifting to AIThis is reading one thing and writing another: title searches of properties being sold in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Arrange for title searches of properties being sold.” (O*NET task statement)
How this row was scored
Exposure score: 81 out of 100 (77–85 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Ordering the right searches for a property is a rules-based step triggered from details already on file.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Maintaining awareness of current income tax regulations
shifting to AIThis is reading one thing and writing another: awareness of current income tax regulations in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Maintain awareness of current income tax regulations, local zoning, building and tax laws, and growth possibilities of a property's area.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (71–79 allowing for uncertainty): high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Tax rules and zoning are published, though a specific area's growth prospects need local knowledge.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Reviewing property details to ensure that environmental regulations are met
shifting to AIThis is reading one thing and writing another: property details in, a record out. That is the shape today's tools are built for.
importance 3 · SupplementalSource: “Review property details to ensure that environmental regulations are met.” (O*NET task statement)
How this row was scored
Exposure score: 66 out of 100 (62–70 allowing for uncertainty): high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Checking property details against environmental rules is comparison against published requirements.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Managing or operating real estate offices
staying humanThe ratings behind this row put real estate offices well outside what today's tools can do on their own.
importance 4 · CoreSource: “Manage or operate real estate offices, handling associated business details.” (O*NET task statement)
How this row was scored
Exposure score: 32 out of 100 (25–39 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Running an office is a mix of admin that automates well and judgement calls about people and premises.
The five ratings: output a model can produce 2/4 · needs a body in a room 1/4 · needs an accountable person 1/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Arranging for financing of property purchases
staying humanThe rules require a named, qualified person to answer for financing of property purchases, and that person cannot be a piece of software.
importance 4 · SupplementalSource: “Arrange for financing of property purchases.” (O*NET task statement)
How this row was scored
Exposure score: 30 out of 100 (23–37 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; someone qualified has to answer for it; the value is that a specific person does it.
The rating behind it: Lender options are documented, but arranging finance runs through licensed people and a client who has to be reassured.
The five ratings: output a model can produce 2/4 · needs a body in a room 0/4 · needs an accountable person 2/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.
Supervising agents who handle real estate transactions
staying humanThe rules require a named, qualified person to answer for agents who handle real estate transactions, and that person cannot be a piece of software.
importance 4 · SupplementalSource: “Supervise agents who handle real estate transactions.” (O*NET task statement)
How this row was scored
Exposure score: 11 out of 100 (7–15 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: someone qualified has to answer for it; the value is that a specific person does it.
The rating behind it: Supervising agents is a legal responsibility carried by a named person who oversees their conduct.
The five ratings: output a model can produce 1/4 · needs a body in a room 1/4 · needs an accountable person 3/4 · needs to be trusted in the moment 2/4 · how much data exists 2/4.
Renting properties or managing rental properties
staying humanThis work happens in the physical world: properties, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Rent properties or manage rental properties.” (O*NET task statement)
How this row was scored
Exposure score: 11 out of 100 (7–15 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world; someone qualified has to answer for it; the value is that a specific person does it.
The rating behind it: Managing rentals means viewings, repairs and dealing with tenants on the ground.
The five ratings: output a model can produce 1/4 · needs a body in a room 2/4 · needs an accountable person 2/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.
What this job pays, and how many people do it
- Median pay
- $73,220a year, the middle of the range, so half earn more and half earn less.bls-oews, 2025 · May 2025 estimates (national_M2025_dl.xlsx)
How we know this
Source: bls-oews
Reference period: May 2025 estimates (national_M2025_dl.xlsx)
Rounding: Shown as published.
- People doing this job
- 46,100in the US, 2025.bls-oews · May 2025 estimates (national_M2025_dl.xlsx)
What is deliberately not here: a forecast of how many of these jobs exist in ten years. Where an official projection exists for a market we publish it with its vintage; where it does not, we leave the space empty rather than borrow the other country’s number.
Why this is shifting
The reason is boringly specific. Most of what is shifting here is reading one thing and writing another: knowledge of real estate law in, a record out. The rows above are exactly that shape: maintaining knowledge of real estate law and generating lists of properties. What it cannot do is be answerable: a fee, real estate need a named person the rules will accept, and software cannot be that person. Which is why this page talks about your tasks changing, not your job ending.
Your move
Over a pint: what I’d tell you if you were my friend
Your week is splitting in two, and which half fills it is the whole question. Maintaining knowledge of real estate law is going; selling for a fee, real estate is not.
So, given all that: 44% of this job's task weight sits in rows the software is already learning, 15% in rows that change shape rather than disappear, and 40% in rows it is nowhere near. That is the position, measured across 17 scored tasks. It is not a forecast about you.
The occupation is an average and you are not, so the first job below is finding out where your own week actually sits.
This week: one thing
Print the task ledger above and put your own hours next to each row. Roughly, in pen, in one sitting. Then look at which group your hours actually pile up in. Twenty minutes, this week.
- What you end up holding
- your own week, on one page, sorted into what is shifting and what is not
- How long it takes
- about twenty minutes
If printing it isn’t practical: Read the rows off this page and write the same list on the back of an envelope. Same twenty minutes, same page, and it works just as well said out loud to someone who knows the job. The point is your hours next to the rows, not the paper it is on.
Over the next 90 days
Volunteer toward the durable end, visibly. Over the next ninety days put your hand up for the work in the bottom group (selling for a fee, real estate) and let people see you doing it. Not a new project: the same job, with your mix deliberately tilted. The point is that when the rota or the reorganisation comes, the version of you people picture is the one doing the part that stays.
Over the next 12 months
Claim a specialism at the durable end and let the other end go. Over a year, deliberately become the person who handles selling for a fee, real estate, and deliberately stop being the first choice for the rows in the top group. That trade costs something, because the exposed work is often the comfortable work. Decide it on purpose rather than by drift. Before you pay for anything, use CareerOneStop - Find local training. It is free, it is the Labor Department's own service, and it is listed below with the rest of the free routes.
The roads out of here, and why I am not sending you down them
I looked at the obvious moves out of this job, and here is what I found.
I checked the 12 nearest US occupations to real estate brokers (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was real estate sales agents: only about 8% of its durable work is work you already do and it pays 27.8% less. Your own job splits about 44/56: that share of the list sits in the top exposure band and the rest does not. On this evidence the honest move is inside the job rather than out of it. Become the person who owns “sell, for a fee, real estate owned by others”, and let the exposed end go.
How that was checked: this job was compared against all 830 US occupations in this release on their official task statements, and the 12 nearest were examined one by one. A move that turns on an industry, an employer or a qualification rather than on the work itself will not show up in a check like that. And this release carries no licence register, so anything you are weighing needs that looked up separately.
3 moves I checked and rejected
These are the obvious-looking jumps. They are here with their reasons rather than quietly dropped, because the ones that fail are worth knowing about. It is one less thing to turn over at night.
Real Estate Sales Agents
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already act as an intermediary in negotiations between buyers and sellers over property prices…, and their equivalent is to act as an intermediary in negotiations between buyers and sellers, generally representing one…. Across both published task lists that is about 8% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 8% of the durable side of that job. That is a different job, not a next step. It is a pay cut, in those words: $52,830 against your $73,220, 27.8% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice.
Title Examiners, Abstractors, and Searchers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already arrange for title searches of properties being sold, and their equivalent is to prepare and issue title commitments and title insurance policies, based on information compiled…. Across both published task lists that is about 6% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 6% of the durable side of that job. That is a different job, not a next step. I will not move you off one melting floe onto another: 67% of its own task list already scores in the top exposure band (65/100 in this release), so the same software is eating it. It is a pay cut, in those words: $58,650 against your $73,220, 19.9% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice.
Sales Managers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already sell, for a fee, real estate owned by others, and their equivalent is to direct and coordinate activities involving sales of manufactured products, services, commodities, real estate…. Across both published task lists that is about 4% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 4% of the durable side of that job. That is a different job, not a next step. The pay gap is the market pricing a barrier: $148,270 against your $73,220 is 2.02× (OEWS May 2025 (both)), and you would be crossing it holding about 4% of their durable work. A gap that size with an overlap that small is a wish, not a route.
What I’d stop worrying about
A friend tells you what not to spend fear on. This is that list.
The headline number you read somewhere
The big “X% of jobs” figures are about the whole economy, not about you. The number that describes your job is on this page: 44% of its task weight, across 17 scored tasks. Every row behind it is printed above with the source. Check ours; ignore theirs.
The whole-job doom story
Nothing on this page says this job ends. It says the mix inside it moves. Half the rows above are unchanged or changing shape, and the plan is about which half your week sits in. That is a very different problem, and a solvable one.
Panic-buying a course
Do the twenty-minute sorting exercise first. Most people who buy a course before they have done it buy the wrong one, and the free services listed below will tell you the same thing without charging for it.
The “obvious” next job everyone suggests
I checked the obvious moves and most of them did not survive. The reasons are printed with the routes above, including the pay and the gate. A move that fails on the numbers is worth knowing about so you can stop turning it over at night.
You are reading the United States figures
The United Kingdom splits this work across more than one official group, of which Estate agents and auctioneers is the closest. The pay and employment figures are not directly comparable, and we do not average them together.
Switch to the United Kingdom page →partial match
The other groups this work is counted across:
In UK official statistics this job is counted as Estate agents and auctioneers and Property, housing and estate managers. Pay is shown separately for each of those groups (medians cannot be averaged together), while the task list and the scores on this page are for this group only.
Your route through this
Where to go next, and what it costs
Free, and complete
The moves above cost nothing. These are the real services that go with them: public, government-funded, and free at the point of use. Nothing on this page is behind an email address or a payment.
Anywhere in the US:
CareerOneStop - Find local training
Search what's running near you, from the Labor Department's own database, before anyone sells you a course.
Free to search; individual programs vary, and some are funded
Anywhere in the US:
An American Job Center will sit down with you for free. Find yours by ZIP code.
Free
Anywhere in the US:
CareerOneStop - Licensed occupations finder
Check what your state actually requires before you pay for anything.
Free
Anywhere in the US:
Free
No Space for this job, but one for what is happening to it
Nothing Collab365 runs is built for real estate brokers, and we are not going to point you at the nearest one and call it a fit.
There is one that is not about a job title at all. The AI Authority is about being the person who directs these tools at work rather than the person they get compared to. That is worth saying here, because 44% of the work on this page is already inside what they can do.
7 days free, no card needed. Explore up to 2 Spaces before you choose a plan: you pick a plan later, not now.
The AI Authority is a general community about working with AI, not a course for real estate brokers. You do not need it to act on anything here: the moves above cost nothing and stand on their own. The data on this page is the same either way.
Noted, and thank you. We’ll email you if a Space for real estate brokers launches. Nothing else.
That did not look like an email address, so nothing was saved. Have another go below.
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No deadline on any of this. The page will still be here, and the data is refreshed on a published schedule rather than when someone wants a headline.
Questions people ask about this job
- Will AI replace Real Estate Brokers?
- Not as a job, but it is already doing parts of the work. Across the 17 official task statements scored for Real Estate Brokers (United States, SOC 41-9021), 44% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 47 out of 100 (range 41–52, band: partial). That is a statement about tasks, not about headcount: this measures what AI could do, not whether any employer adopts it, whether the law allows it, or whether doing the routine parts faster creates more demand for the human parts. Figures are from release 2026-q4.1.
- Which tasks in “Real Estate Brokers” can AI already do?
- The highest-scoring tasks in release 2026-q4.1 are: “Maintain knowledge of real estate law, local economies, fair housing laws, types of available mortgages, financing options, and government programs” (83/100, very high); “Generate lists of properties for sale, their locations, descriptions, and available financing options, using computers” (81/100, very high); “Arrange for title searches of properties being sold” (81/100, very high). Each score comes from five published 0–4 ratings turned into a number by a published formula, and each carries the model's one-sentence reason on the page.
- Which tasks in “Real Estate Brokers” stay human?
- About 40% of this job's task weight sits in work that scores low for AI exposure. The lowest-scoring tasks in release 2026-q4.1 are: “Sell, for a fee, real estate owned by others” (6/100, minimal); “Act as an intermediary in negotiations between buyers and sellers over property prices and settlement details and during the closing of sales” (8/100, minimal); “Obtain agreements from property owners to place properties for sale with real estate firms” (10/100, minimal). Low scores usually mean the task needs a body in a room, a legally accountable human, or trust built in real time. Those are the three things the scoring rubric treats as gates rather than obstacles.
- What should someone working in “Real Estate Brokers” do about AI?
- Start from the ledger rather than the headline: 44% of this job's weighted core work is exposed, and roughly 40% is not. The practical move is to spend more of your week on the tasks that score low, the ones above, and to get fluent at directing AI through the tasks that score high, because those are the parts that change whether or not you are ready for them. This page does not predict your job, and nothing here is career advice tailored to you: the score describes the occupation, not the person.
- How is the AI exposure score for Real Estate Brokers calculated?
- Each official task statement for the occupation is rated on five published 0–4 dimensions (output replicability, physical embodiment, licensed accountability, real-time human trust, and data availability) by claude-opus-5 using scoring prompt task_scoring_v1.0. The model never writes the score; a published formula turns the five ratings into a 0–100 number, so every score can be recomputed by hand. The occupation figure is the importance-weighted mean across 17 scored tasks. The prompt, the rubric, the formula and the full dataset are published at https://futureproof.collab365.com/method and https://futureproof.collab365.com/data/2026-q4.1 under CC BY 4.0.
Where these numbers come from
Worth knowing about these figures
- The match between this job and its counterpart in the other country is partial, so the two pages count slightly different groups of people.
- 3 of this occupation's scored task statements carry a score that was measured under a different occupation's context, because the statement is shared between jobs and has only been scored once so far. Each one names the occupation it was measured under in the free routes below; none is presented as a measurement for this job.
- Task clusters are not derived in this release, so the task-cluster field is empty and no Collab365 Space routing is attached to this occupation yet.
- Task statements
- onet-dbProcessing: catalogue-bridge → onet-im-rt-weighting → task-scoring → score-aggregation
- Task weights
- onet-db (im-rt)
- Scores
- Rubric task_scoring_v1.0, model claude-opus-5, computed 2026-08-05.
- Pay and employment
- bls-oews (May 2025 estimates (national_M2025_dl.xlsx))bls-oews (May 2025 estimates (national_M2025_dl.xlsx))
Figures on this page come from release 2026-q4.1, published 2026-08-05. Every release keeps its own permanent address, so a figure you cite in March is still there, unchanged, in November.
The plain-English wording on this page is assembled directly from the task statements and the published ratings, not written by hand for this occupation. That is why it is specific, and it is also why we say so.
The routes and free resources further up are today’s, not the release’s (last reviewed 2026-08-05). A route is an offer, not a historical fact, so it moves on its own clock.
Using these figures?
Cite this
Everything on this site is published under CC BY 4.0. Quote it, chart it, sell something built on it. Just say where it came from, and cite the dated release rather than the site, so the figure you quote stays checkable.
Plain text
Collab365 (2026). Collab365 Futureproof: task-level AI exposure for US and UK occupations, release 2026-q4.1 (methodVersion 2.0.0, promptVersion task_scoring_v1.0). https://futureproof.collab365.com/data/2026-q4.1. Licensed CC BY 4.0. Built with O*NET data (USDOL/ETA, CC BY 4.0); ONS data (Open Government Licence v3.0); GAISI task framework (arXiv:2507.22748, MIT); BLS data (public domain).
BibTeX
@misc{collab365futureproof2026q41,
title = {Collab365 Futureproof: task-level AI exposure for US and UK occupations, release 2026-q4.1},
author = {{Collab365}},
year = {2026},
url = {https://futureproof.collab365.com/data/2026-q4.1},
note = {Release 2026-q4.1, methodVersion 2.0.0, promptVersion task_scoring_v1.0, CC BY 4.0}
}Data as of release 2026-q4.1, published . Releases never change after publication; when the figures move, a new dated release is published beside this one and this one stays exactly where it is.