LO
Property manager — how we know
The page itself gives the judgements. This one gives what they rest on: which technologies bear on the work, how the estimate moved since language models reached the public, and the method behind both.
Which technologies matter here#
Four separate signals. They are deliberately not added together — a job exposed to two technologies is not twice as exposed.
How it got here#
The index is not a static number. This is where it would have sat at each capability checkpoint since ChatGPT — reconstructed, and labelled as such.
—— this stretch contains a verified event- - - no event in this stretch — reconstruction only0 = no task exposed, 100 = every task exposed
● 7 verified events for this occupation, plotted at the date it happened — the parts of the line near a marker are anchored to something checkable.
Starts at 20 because listing sites, accounting and payment software and online rent collection were standard before this chart begins, while enquiries, showings, screening calls and repair dispatch were handled by people. It climbs as large landlords add AI leasing assistants, self-guided tours and AI replies to enquiries, and move on-site staff into hubs. It stays below the middle because inspecting buildings and dealing with residents still need someone there, and courts and lawmakers are restricting screening scores and rent-setting software.
A flat line is not a forecast of safety. It says which tasks automation has reached so far — the occupations that moved least here are the ones where the constraint is physical or regulatory, and both of those can change.
Method and sources#
- Assessment date
- 2026-10-07
- Basis of the task judgements
- 5 evidence-backed · 1 platform inference · 0 not enough evidence
- Verified events
- 7