US dataswitch to UK
Property Appraisers and Assessors
writing descriptions of the property, obtaining county land values and sales information about nearby properties to aid in establishment of property values and inspecting personal or business property. If that's your week, this page is about your job.
The honest answer
Most tasks in this job are the kind AI has learned to do: writing descriptions of the property. The tasks, though, are not you.
It would be a lie to soften that; inspecting personal or business property is what this work rebuilds around. The plan below starts there.
Your week, as this page understands it
BLS publishes one employment and wage estimate for this SOC 2018 broad group rather than for the detailed occupations inside it, so it is reported here as a single occupation covering two detailed occupations: Appraisers of Personal and Business Property and Appraisers and Assessors of Real Estate. The task statements recorded against this occupation are the union of those detailed occupations' official O*NET tasks, kept grouped by detailed occupation so the weighting stays honest. The job title says “property appraisers” or “assessors”: officially one job, two names. The real job is the part underneath: inspecting personal or business property. 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 property appraisers and assessors is not one task. It is 7 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is inspecting personal or business property, and the ledger below shows exactly why.
Where the work sits, by task weight
- shifting to AI
- 67%
- changing shape
- 0%
- staying human
- 33%
These bars are tasks changing hands, not people being counted out. The ledger below shows which.
Whole-job exposure score 61 out of 100 (57–67 allowing for uncertainty): high exposure, across 7 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 property appraisers and assessors 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 2026-08-04. 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.
- 35 of this occupation's 42 published task statements are not yet scored and are omitted from this release. Every exposure figure on this page is computed from the 7 that are, so treat it as a partial reading of the job rather than a complete one.
- 7 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
5 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.
Writing descriptions of the property
This is reading one thing and writing another: descriptions of the property in, a record out. That is the shape today's tools are built for.
importance 5 · CoreSource: “Write descriptions of the property being appraised.” (O*NET task statement)
How this row was scored
Exposure score: 93 out of 100 (89–97 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: Writing property descriptions from recorded details is exactly the sort of drafting AI does at high quality.
The five ratings: output a model can produce 4/4 · 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.
Searching public records
This is reading one thing and writing another: public records in, a record out. That is the shape today's tools are built for.
importance 5 · CoreSource: “Search public records for transactions such as sales, leases, and assessments.” (O*NET task statement)
How this row was scored
Exposure score: 88 out of 100 (84–92 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: Public registers are searchable databases, so looking up sales, leases and assessments is quick, reliable computer work.
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 4/4.
Obtaining county land values and sales information about nearby properties to aid in establishment of property values
This is reading one thing and writing another: county land values in, a record out. That is the shape today's tools are built for.
importance 5 · CoreSource: “Obtain county land values and sales information about nearby properties to aid in establishment of property values.” (O*NET task statement)
How this row was scored
Exposure score: 93 out of 100 (86–100 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: Pulling sold prices and land values for nearby properties is a database lookup software completes in seconds.
The five ratings: output a model can produce 4/4 · 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.
Identifying the ownership of each piece of taxable property
This is reading one thing and writing another: the ownership of each piece of taxable property in, a record out. That is the shape today's tools are built for.
importance 4 · SupplementalSource: “Identify the ownership of each piece of taxable property.” (O*NET task statement)
How this row was scored
Exposure score: 93 out of 100 (86–100 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: Establishing who owns a property is a search of ownership records software runs directly.
The five ratings: output a model can produce 4/4 · 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.
Explaining real and personal property taxes to property owners
This is reading one thing and writing another: real in, a record out. That is the shape today's tools are built for.
importance 2 · SupplementalSource: “Explain real and personal property taxes to property owners.” (O*NET task statement)
How this row was scored
Exposure score: 64 out of 100 (57–71 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: Explaining how property taxes work is well-documented information software presents clearly.
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 1/4 · how much data exists 3/4.
Changing shape
0 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.
Nothing in this job’s scored task list landed in this group. That is the measurement, not an editorial choice, and it is worth knowing either way.
Staying human
2 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.
Inspecting personal or business property
This work happens in the physical world: personal or business property, in a real place. Software cannot follow it there.
importance 5 · CoreSource: “Inspect personal or business property.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Inspecting property means being there and looking at it.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Inspecting new construction and major improvements to existing structures to determine values
This work happens in the physical world: new construction, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Inspect new construction and major improvements to existing structures to determine values.” (O*NET task statement)
How this row was scored
Exposure score: 9 out of 100 (2–16 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: work that happens in the physical world; someone qualified has to answer for it.
The rating behind it: Valuing new building work means inspecting what has actually been built.
The five ratings: output a model can produce 2/4 · needs a body in a room 3/4 · needs an accountable person 2/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
What this job pays, and how many people do it
- Median pay
- $67,960a 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
- 57,070in 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: descriptions of the property in, a record out. The rows above are exactly that shape: writing descriptions of the property and searching public records. What it cannot do is be there in the room, and that is still where personal or business property gets done. 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
The exposed part of your job is the biggest part, and I am not going to dress that up: writing descriptions of the property is work today's tools do quickly and cheaply, and that is not coming back.
So, given all that: 67% of this job's task weight sits in rows the software is already learning, 0% in rows that change shape rather than disappear, and 33% in rows it is nowhere near. That is the position, measured across 7 scored tasks. It is not a forecast about you.
What you have that the software does not is inspecting personal or business property, plus the years of knowing when something looks wrong before you can say why. That is the raw material for everything below.
This week: one thing
Sit on the machine's side of the desk. Pick one real piece of descriptions of the property you would normally do yourself, let whatever software you already have take the first pass at it, and then go through what it produced and write down every single thing it got wrong. One evening this week. Do not fix anything yet. Just catch it.
- What you end up holding
- a written list of the machine’s mistakes, in your handwriting
- How long it takes
- an evening, or an hour if you pick one job rather than one client
If you can’t run software on your employer’s or your clients’ data: Do the same hunt on paper. Take one printed piece of descriptions of the property, work through it the way you always do, and mark every point where you made a call rather than followed a rule. Same evening, same list, nothing to log into and nobody to ask permission from. That list is the same artifact: it is the judgment written down.
Over the next 90 days
Change one sentence about what you do. Not on a CV. Out loud, to whoever pays you. From “my job is writing descriptions of the property” to “I check what the software does and tell you what it means.” Your error list from this week is what makes that sentence true instead of a claim, so use it: show it once, to one person, inside the next ninety days. Same skills, priced as judgment rather than as typing.
Over the next 12 months
Walk toward the end of this job that answers for things, and get it recognised. Pick the one part of inspecting personal or business property you are already best at, and spend the year making it formal: a qualification, a named responsibility, a specialism people ask for by name. Price it honestly: that is evenings, it is months rather than weeks, and the seats are competitive because everyone in your position is looking at the same door. 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 property appraisers and assessors (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was real estate brokers: only about 7% of its durable work is work you already do. I am not going to pretend that is comfortable news: 67% of your own task list is already in the top exposure band. But the answer on this evidence is not a sideways jump into a job with the same problem. It is to walk toward the end of this one that answers for things. “inspect personal or business property” is the part that stays with a person, and spending a year getting formally recognised for it beats a standing start somewhere else.
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 Brokers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already explain real and personal property taxes to property owners, and their equivalent is to obtain agreements from property owners to place properties for sale with real estate…. Across both published task lists that is about 7% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 7% of the durable side of that job. That is a different job, not a next step.
Real Estate Sales Agents
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already explain real and personal property taxes to property owners, and their equivalent is to contact property owners and advertise services to solicit property sales listings. Across both published task lists that is about 2% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 2% 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 $67,960, 22.3% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice.
Surveyors
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already write descriptions of the property being appraised, and their equivalent is to write descriptions of property boundary surveys for use in deeds, leases, or other…. Across both published task lists that is about 1% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 1% of the durable side of that job. That is a different job, not a next step.
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: 67% of its task weight, across 7 scored tasks. Every row behind it is printed above with the source. Check ours; ignore theirs.
“It’s too late for me to become something else”
You are not starting from zero, and the page shows why: inspecting personal or business property is work the software cannot do and you already do it. The move above is a repricing of what you know, not a new career. Nobody who has just left college has that.
“I should learn to code”
Almost certainly not. The value in your job is moving toward checking, deciding and answering for the output, not toward writing the software. A weekend of Python will not change your position; the error list from this week will.
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 Estimators, valuers and assessors 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 Estimators, valuers and assessors and Estate agents and auctioneers. 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 property appraisers / assessors, 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 67% 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 property appraisers / assessors. 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 property appraisers / assessors launches. Nothing else.
That did not look like an email address, so nothing was saved. Have another go below.
We could not save that. The fault is ours, not yours, and your address was not stored. Please try again later.
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 Property Appraisers and Assessors?
- Not as a job, but it is already doing parts of the work. Across the 7 official task statements scored for Property Appraisers and Assessors (United States, SOC 13-2020), 67% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 61 out of 100 (range 57–67, band: high). 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 “Property Appraisers and Assessors” can AI already do?
- The highest-scoring tasks in release 2026-q4.1 are: “Write descriptions of the property being appraised” (93/100, very high); “Obtain county land values and sales information about nearby properties to aid in establishment of property values” (93/100, very high); “Identify the ownership of each piece of taxable property” (93/100, 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 “Property Appraisers and Assessors” stay human?
- About 33% 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: “Inspect personal or business property” (0/100, minimal); “Inspect new construction and major improvements to existing structures to determine values” (9/100, minimal); “Explain real and personal property taxes to property owners” (64/100, high). 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 “Property Appraisers and Assessors” do about AI?
- Start from the ledger rather than the headline: 67% of this job's weighted core work is exposed, and roughly 33% 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 Property Appraisers and Assessors 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 7 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
About the data on this page
- The match between this job and its counterpart in the other country is partial, so the two pages count slightly different groups of people.
- 35 of this occupation's 42 published task statements are not yet scored and are omitted from this release. Every exposure figure on this page is computed from the 7 that are, so treat it as a partial reading of the job rather than a complete one.
- 7 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 2026-08-04.
- 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.