Real Estate Agents

Sales & Marketing

AI exposure

  • Data source: BLSPublished: '26.08

    Very high· relative

    LowFour relative bandsVery high

    Group-level value

    Scale, basis and source

    Four relative bands (Low / Moderate / High / Very high)

    831 detailed occupations in the BLS Employment Projections table. Assigned per National Employment Matrix (NEM) code, so occupations sharing a NEM code carry the same band

    Source dataset (XLSX download)

  • Data source: AnthropicPublished: '26.03

    0.283

    0.000Range of values carried here0.745
    Scale, basis and source

    Observed exposure index, 0–1 as published

    Mapped onto O*NET tasks

    Source dataset

  • Data source: ILOPublished: '25

    0.35

    0.09Range of values carried here0.70

    Group-level value

    Scale, basis and source

    Generative AI exposure index, 0–1 as published

    ISCO-08 unit group — every occupation sharing the code gets this value

    Computed by this site, not published by the ILO: of the 1,012 occupations this site links to the ILO dataset, 61% score at or above this value.

    Source dataset

What kind of figure this source publishes

The BLS category is a relative rank, not an absolute level, and it is not a first-hand measurement: it groups an occupation's percentile ranks across several published studies into four bands. It is not an employment or wage forecast, not a probability of adoption, and it does not separate automation from augmentation.

Task-level exposure

Exposed tasks only

Values in this tab are predicted labels, not observations. Eloundou et al. (2023) published two rating regimes — human raters and GPT-4 — and the β shown here is derived from the GPT-4 rater basis alone; the same task can take a different value under the other regime. The unit and the meaning differ from the observed shares (%) in the other tabs, so do not place them on the same axis.

TaskβE1 + 0.5 × E2
Present purchase offers to sellers for consideration.

O*NET Task ID 2440

0.5
Confer with escrow companies, lenders, home inspectors, and pest control operators to ensure that terms and conditions of purchase agreements are met before closing dates.

O*NET Task ID 2441

0.5
Interview clients to determine what kinds of properties they are seeking.

O*NET Task ID 2442

0.5
Prepare documents such as representation contracts, purchase agreements, closing statements, deeds, and leases.

O*NET Task ID 2443

0.5
Coordinate property closings, overseeing signing of documents and disbursement of funds.

O*NET Task ID 2444

0.5
Act as an intermediary in negotiations between buyers and sellers, generally representing one or the other.

O*NET Task ID 2445

0.5
Promote sales of properties through advertisements, open houses, and participation in multiple listing services.

O*NET Task ID 2446

0.5
Compare a property with similar properties that have recently sold to determine its competitive market price.

O*NET Task ID 2447

0.5
Coordinate appointments to show homes to prospective buyers.

O*NET Task ID 2448

0.5
Generate lists of properties that are compatible with buyers' needs and financial resources.

O*NET Task ID 2449

0.5
Display commercial, industrial, agricultural, and residential properties to clients and explain their features.

O*NET Task ID 2450

0.0
Inspect condition of premises, and arrange for necessary maintenance or notify owners of maintenance needs.

O*NET Task ID 2454

0.0
Accompany buyers during visits to and inspections of property, advising them on the suitability and value of the homes they are visiting.

O*NET Task ID 2455

0.0
Conduct seminars and training sessions for sales agents to improve sales techniques.

O*NET Task ID 2466

0.0

β = E1 + 0.5 × E2 · E1 = tasks where direct LLM access alone cuts time by at least 50%, E2 = tasks where software built on top of an LLM cuts time by at least 50%. Values take only 0 / 0.5 / 1.0.

Data sources & licenses — O*NET®, Eloundou et al. (2023): see full notices on the Credits page

Occupation information