Property appraisers and assessors: AI exposure and career outlook
Property appraisers and assessors (SOC 13-2020) sit at the 78th percentile for measured AI exposure among the 342 occupations tracked here, measured from a composite of Microsoft Research and Anthropic Economic Index telemetry. An estimated 50% of tasks are already automated and 70% are being reshaped rather than replaced — both modelled figures, not direct measurements. Of the Claude usage observed in this occupation, 49% follows an automation pattern and 51% an augmentation pattern — close enough to even that the grouping below should be read loosely, which places it in the Orchestrator opportunity group: AI reaches deep into this work and amplifies it. The leverage goes to whoever directs it, and that can be you. In Canada the role maps to NOC 12203 (Assessors, business valuators and appraisers), and ESDC's COPS 2024–2033 outlook for that unit group is balance.
Figures last updated 2026-06. Every number on this page is labelled measured or modelled; where a source has no coverage for this occupation, it says so rather than showing a zero.
Key facts
- SOC code
- 13-2020
- Canada NOC 2021
- 12203
- TEER level
- 2 — College / Apprenticeship 2+ yrs
- COPS outlook
- Balance
What the measurements actually say
Two of three independent studies measure AI exposure for this occupation directly; the other reports no coverage for it. The two percentages below them are ours, modelled from those measurements.
| Metric | Value | Provenance |
|---|---|---|
| AI applicability | 23% | Measured — Microsoft Research, from 200,000 Copilot conversations classified against O*NET work activities. |
| Observed AI usage | Not available | Not covered by Anthropic's occupation-level dataset. |
| Academic AI exposure | 71st percentile | Measured — Felten-Raj-Seamans AIOE index across 774 occupations; the index underlying Statistics Canada's Canadian estimates. Occupation-group average of 1 detailed roles. |
| Automation-pattern usage | 49% | Measured — the share of observed Claude usage in this occupation where the task is handed over rather than iterated on (Anthropic Economic Index). Occupation-group average of 2 detailed roles. |
| Augmentation-pattern usage | 51% | Measured — the complement of the row above; the two sum to 100% by construction. This is the pattern where the person stays in the loop. |
| Estimated task automation (modelled) | 50% | Modelled from this occupation's measured telemetry composite, mapped through anchors calibrated to Anthropic's published aggregate findings. Our estimate, not per-occupation telemetry. |
| Estimated task reshaping (modelled) | 70% | Share of tasks where AI acts as co-pilot rather than replacement. Modelled from the measured telemetry composite, calibrated to BCG's published aggregate reshaping rates. Our estimate. |
This skill domain faces significant AI substitution. The opportunity is in pivoting to AI orchestration and higher-order human judgment within the domain.
In Canada: NOC 12203
Property appraisers and assessors map to NOC 12203 — Assessors, business valuators and appraisers at TEER 2 (College / Apprenticeship 2+ yrs). ESDC's COPS 2024–2033 projection for this unit group is Balance.
Mapped via Statistics Canada's official SOC 2018 → NOC 2016 → NOC 2021 correspondence tables. TEER is the second digit of the official NOC code, so it cannot disagree with it.
What this job actually involves
These are the O*NET task statements for Property appraisers and assessors— the work itself, as reported by people doing the job — grouped by the work activity each task belongs to. Showing 12 of 42, core tasks first.
The exposure figures above are for the occupation as a whole. We do not have a per-task measurement: no published dataset tells us which of these specific tasks AI is doing. Read this as what the job is made of, not as a ranking of what is at risk.
4 of these 12statements do carry one measured signal: Anthropic publishes, per O*NET task, how much of the observed Claude usage on it looked like automation rather than iteration. Those are marked below. It still says how people use AI on that task, not how much of it AI can do — and the unmarked statements carry nothing rather than inheriting the occupation's figure.
Microsoft's applicability score was itself produced by classifying Copilot conversations against the O*NET work-activity catalogue — the same catalogue these groups come from. The published file gives one number per occupation, not one per activity, so we can tell you that 23% of this occupation's work activities are covered by observed AI usage, but not which ones.
SOC 13-2020 is a BLS occupation group. These statements are pooled across the 2 detailed occupations in it, so a given task may apply to only some of them.
Documenting/Recording Information · 4 tasks
Write descriptions of the property being appraised.
Core task · Importance 4.9/5 (O*NET) · 44% of observed AI use on this task is automation-pattern · reported by 1 of 2 occupations in this group
Document physical characteristics of property such as measurements, quality, and design.
Core task · Importance 4.8/5 (O*NET) · reported by 1 of 2 occupations in this group
Write and submit appraisal reports for property, such as jewelry, art, antiques, collectibles, and equipment.
Core task · Importance 4.7/5 (O*NET) · reported by 1 of 2 occupations in this group
Prepare written reports that estimate property values, outline methods by which the estimations were made, and meet appraisal standards.
Core task · Importance 4.7/5 (O*NET) · reported by 1 of 2 occupations in this group
Getting Information · 3 tasks
Locate and record data on sales of comparable property using specialized software, internet searches, or personal records.
Core task · Importance 4.7/5 (O*NET) · 73% of observed AI use on this task is automation-pattern · reported by 1 of 2 occupations in this group
Search public records for transactions such as sales, leases, and assessments.
Core task · Importance 4.7/5 (O*NET) · reported by 1 of 2 occupations in this group
Obtain county land values and sales information about nearby properties to aid in establishment of property values.
Core task · Importance 4.6/5 (O*NET) · reported by 1 of 2 occupations in this group
Judging the Qualities of Objects, Services, or People · 2 tasks
Calculate the value of property based on comparisons to recent sales, estimated cost to reproduce, and anticipated property income streams.
Core task · Importance 4.8/5 (O*NET) · 31% of observed AI use on this task is automation-pattern · reported by 1 of 2 occupations in this group
Compute final estimation of property values, taking into account such factors as depreciation, replacement costs, value comparisons of similar properties, and income potential.
Core task · Importance 4.7/5 (O*NET) · 59% of observed AI use on this task is automation-pattern · reported by 1 of 2 occupations in this group
Making Decisions and Solving Problems · 1 task
Determine the appropriate type of valuation to make, such as fair market, replacement, or liquidation, based on the needs of the property owner.
Core task · Importance 4.9/5 (O*NET) · reported by 1 of 2 occupations in this group
Thinking Creatively · 1 task
Photograph interiors and exteriors of properties to assist in estimating property value, substantiate findings, and complete appraisal reports.
Core task · Importance 4.7/5 (O*NET) · reported by 1 of 2 occupations in this group
Updating and Using Relevant Knowledge · 1 task
Maintain familiarity with aspects of local real estate markets.
Core task · Importance 4.6/5 (O*NET) · reported by 1 of 2 occupations in this group
Task statements and work activities from the O*NET Database by the U.S. Department of Labor, Employment and Training Administration (USDOL/ETA), used under CC BY 4.0. O*NET® is a trademark of USDOL/ETA. USDOL/ETA has not reviewed or approved this material.
That's the average. Is this you?
Everything above describes Property appraisers and assessors as a whole. Three questions, and nothing is stored or sent.
Where are you in your career?
What does your week mostly look like?
Is your employer deploying AI?
Where the leverage is
Transition to high-value advisory, complex case analysis, or AI system oversight. The pivot is to move from routine execution to AI orchestration and quality assurance.
Cognitive, information-based tasks are highly exposed. Entry-level displacement pressure is high, necessitating transition to advisory and governance roles.
Upskill in AI prompt engineering, model validation, and governance. Focus on transition playbooks to pivot toward high-value advisory services.
This guidance is written per exposure band — three texts across all 342 occupations, framed on the ILO's transformation-versus-displacement distinction. It is editorial guidance for the high risk band, not an occupation-specific research finding about property appraisers and assessors.
Sources for this occupation
Only the sources that hold data for property appraisers and assessors are listed. A study that does not cover this occupation is not cited here.
- Microsoft Research — Working with AI (Tomlinson et al., arXiv:2507.07935): Measured AI applicability, from 200,000 anonymised Copilot conversations classified against O*NET work activities.
- Felten, Raj & Seamans — AI Occupational Exposure index: Measured academic exposure percentile; the index underlying Statistics Canada's Canadian AI-exposure estimates (Mehdi & Morissette, 2024).
- O*NET Database 31.0 (USDOL/ETA), CC BY 4.0: The occupation's task statements and work activities, verbatim. Joined on SOC 2018 with no crosswalk — O*NET-SOC is built on SOC — and carrying no claim about which tasks AI touches.
- Anthropic Economic Index — collaboration split (release 2026-06-26, CC-BY 4.0): Measured share of observed Claude usage following an automation rather than an augmentation pattern. This is the second axis of the grouping above.
- Statistics Canada NOC 2021 concordance: Official SOC 2018 → NOC 2016 V1.3 → NOC 2021 V1.0 correspondence tables. TEER is the second digit of the resolved code, so we report it only where the concordance gave a single candidate.
- ESDC COPS 2024–2033: Projected labour-market assessment per NOC 2021 unit group, from the Canadian Occupational Projection System open dataset.
- BCG — AI Will Reshape More Jobs Than It Replaces (April 3, 2026): Published aggregate reshaping rates, used to calibrate our modelled percentages. The per-occupation figures here are our estimates, not BCG's data.
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