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Real estate sales agents

Also called Real estate agent, Realtor, Buyer agent, Buyer's agent, Listing agent

In brief

In May 2025, BLS counted 193,370 US real estate sales agents, with median pay of $52,830 a year. Of the BLS counts since 2003, the highest was 197,720, in May 2023, close to the May 2025 count. The earliest figure here is an estimate of 75,000 from 1920, not measured the same way as today's count. The last full technology era for real estate sales agents: iBuyer platforms, 2012 to 2022.

What the sources say

Official outlook

BLS projects employment to grow 1.7% from 2025 to 2035.

BLS Employment Projections 2025-35

AI exposure studies

  • 41.1% of this job's tasks are exposed to large language models. Not a forecast of job losses. Eloundou and others, 2023
  • 0.86 estimated probability that the whole occupation could be computerised, over an unspecified number of years. Not a forecast of job losses. Frey and Osborne, 2013

Since the 2013 prediction

In 2013, Frey and Osborne put this job's probability of computerisation at 0.86. BLS counted 158,850 of these jobs in May 2013 and 193,370 in May 2025 (+22%, a change derived by Future History).

Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35. BLS publishes these counts as yearly survey snapshots and cautions against reading them as a precise trend. In May 2021 BLS changed how it estimates these counts (a model-based method using three years of survey data) and moved fully to the 2018 occupation codes, so part of a change across 2021 can come from the method rather than the work.

Last updated .

Cite this page
APA

Future History. (2026, October 11). Real estate sales agents: jobs and pay since 1920 [Data set; data as of BLS OEWS May 2025; BLS Employment Projections 2025-35]. https://futurehistory.earth/timeline/41-9022

Chicago

Future History. "Real estate sales agents: jobs and pay since 1920." Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35. Last modified October 11, 2026. https://futurehistory.earth/timeline/41-9022.

BibTeX

@misc{futurehistory_41_9022, author = {{Future History}}, title = {Real estate sales agents: jobs and pay since 1920}, year = {2026}, month = oct, howpublished = {\url{https://futurehistory.earth/timeline/41-9022}}, note = {Last updated 2026-10-11. Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35}, }

The access date is today's date on your device.

drag to travel through time
1925195019752000now
Country
2026
Known then as Real Estate Exchange member / broker (NAR founding era)
Known then as REALTOR® / Real Estate Agent (NAR member, Code of Ethics adopted 1913)
Known then as Licensed Real Estate Salesperson / REALTOR® (state licensing era)
Known then as Real Estate Agent / Sales Associate (MLS computerization era)
Known then as Real Estate Sales Agent (BLS SOC 41-9022)
Known today as Real Estate Sales Agent (BLS SOC 41-9022)
Projected to still be called Real Estate Sales Agent (BLS SOC 41-9022)
Data from the May 2025 survey
US Employment
193K
Est.

Stated in source: BLS OEWS

Official statistic
Report an error
Median Annual Wage
$52,830
Est.

≈ $51,476 in 2024 prices (derived by us, CPI-adjusted)

Official statistic
Report an error
Beat · 1908

May 12, 1908: 120 delegates from 19 local real estate boards meet in Chicago and found the National Association of Real Estate Exchanges, the organization that will become NAR. The founding motivation is professional credibility: the pre-1908 real estate market is populated by unlicensed speculators and informal brokers with no code of conduct, no standard contracts, and no disclosure obligations. The founders want a distinction between professional agents and land speculators. They will adopt a Code of Ethics in 1913, coin the REALTOR® trademark in 1916, and eventually become the largest trade association in the United States with 1.5 million members.

Beat · 1934

The Federal Housing Administration is created as part of the National Housing Act of 1934, establishing the 30-year amortizing mortgage as the American standard and insuring lenders against borrower default. For real estate agents, the FHA creates the modern mortgage-backed residential transaction: buyers now purchase homes with 10-20% down and 30 years of payments rather than cash or short-term balloon notes. Every FHA transaction requires an appraisal, a standard contract, and documented disclosure: a paperwork complexity that positions the professional agent as a necessary guide through the process. The suburban homeownership society of the post-WWII era is built on FHA/VA financing, and real estate agents are its front-line intermediaries.

Beat · 1974

December 22, 1974: President Gerald Ford signs the Real Estate Settlement Procedures Act (RESPA). The law ends a widespread practice of undisclosed kickbacks among real estate service providers: lenders, title companies, and agents had routinely shared referral fees without disclosing them to buyers. RESPA requires disclosure of all settlement costs, prohibits undisclosed referral fees, and establishes that agents can only receive compensation for services actually performed. The law does not eliminate agent commissions; it requires they be disclosed. RESPA also establishes the Good Faith Estimate, giving buyers a standardized view of transaction costs before closing.

Beat · 2006

NAR membership reaches its all-time record of approximately 1.37 million in 2006: the largest the trade association has ever been, corresponding to the peak of the US housing bubble. By this point Zillow has launched (February 2006), Trulia (2005) is growing rapidly, and home prices have risen 50-100% in major markets from 2000. The membership peak represents the maximum number of agents ever in the US workforce. The subsequent bust will eliminate approximately 30% of agents over the next five years as transaction volumes collapse, prices fall 30%+, and commission-based income becomes insufficient to sustain a full-time practice.

Beat · 2006

February 8, 2006: Zillow launches its public website. The site crashes from overwhelming traffic. Co-founders Rich Barton and Lloyd Frink had built a free, public database of estimated home values (the "Zestimate") and searchable listings for every address in America. The incumbent theory: if listing search and home valuation were freely available online, the information monopoly that justified agent commissions had been destroyed. The Zestimate tool eventually covers 160 million homes. And yet NAR membership is simultaneously at its all-time high. The technology that was supposed to disintermediate the agent instead becomes the tool buyers use to identify which agent to call.

Beat · 2021

November 2021: Zillow announces the shutdown of Zillow Offers, its iBuyer division, after losing $420 million in Q3 2021 alone. Zillow owned approximately 7,000 homes at shutdown: properties it had purchased at algorithmically-determined prices that turned out to be too high when the market peaked. CEO Rich Barton acknowledged the algorithm had failed to predict price movements accurately. The shutdown represented the definitive end of the thesis that algorithmic home pricing could disintermediate the human agent at scale. Opendoor continued but would lose $662 million in 2021 and $1.4 billion in 2022 as the market turned. The iBuyer experiment's net result: hundreds of millions in losses at scale, no permanent displacement of agents, and a demonstration that local real estate knowledge is harder to algorithmize than tech investors assumed.

Beat · 2024

March 2024: NAR settles the Sitzer/Burnett antitrust case for $418 million, agreeing to eliminate its MLS commission rules. The original jury verdict (October 2023) was $1.78 billion. Starting August 17, 2024, two structural changes take effect: buyer agents can no longer have their compensation advertised on MLS listings (ending the standard 2.5-3% seller-paid buyer commission), and buyers must sign written compensation agreements with their agents before viewing properties together. The changes represent the most significant restructuring of real estate agent compensation since the modern MLS was established. Industry observers estimate the settlement may reduce commissions on both sides of a transaction and accelerate the exit of agents who cannot demonstrate clear value independent of the transaction infrastructure. Early membership data suggests NAR membership declining from its post-pandemic levels.

How to read this number

OEWS is a point-in-time survey snapshot, not a continuous time series; BLS advises against using it for year-over-year trend comparison.

Each dot is a cited figure over time; the dotted line only links them (values between aren't measured). Hollow dots are estimates.

Official statistic: Copied by a script from a statistics agency table or a published study's table, not retyped by hand. BLS figures and the forecast and exposure tables are also re-checked against that table on every data run. Researched context: Researched from the cited source rather than copied from it by a script, so it is not machine-checked yet. Spot a mistake? Each figure has a “Report an error” link.

Cited forecasts · present → 2035

Will AI replace real estate sales agents? What the research says

Each figure is stated by the named source; we publish no forecasts of our own. Where sources disagree, we show the disagreement rather than an average.

Each figure is stated by the named source; we publish no forecasts of our own. Where sources disagree, the spread is the honest signal.

What published forecasts say
Forecast change in the number of people doing this work, as published by each named source. Bars share one scale on every role, centred on zero and reaching the end at ±30%. Grey marks a small change, 3% or less either way.
BLS Employment Projections 2025-35
→ 2035
+1.7%
BLS National Employment Matrix, 2025-35 cycle, line for SOC 41-9022 (Real estate sales agents): 427.0 thousand jobs in 2025, projected 434.5 thousand in 2035, a change of +7.4 thousand (+1.7%). Matrix employment counts all jobs in the occupation, including self-employed workers (54.3% of this occupation in 2025). The average for all occupations in this cycle is +3.5%.
Official statisticReport an error
How exposed is this work to AI (research measures)
Scores as published by each study. Each row says what it measures. These measure what AI could technically do, not forecasts of jobs lost.
Frey & Osborne (2013), "The Future of Employment"
2013 study · Probability of computerisation (2013 model)
86%
probability
What this measures

Published appendix value for SOC 2010 41-9022 Real Estate Sales Agents: probability of computerisation 0.86, rank 497 of 702 (rank 1 is least computerisable). Frey and Osborne estimated in 2013, with a Gaussian-process classifier on O*NET variables, how likely the whole occupation is to be technically automatable over "some unspecified number of years, perhaps a decade or two". It is a probability for the occupation, not a share of tasks and not a forecast of jobs lost.

Official statisticReport an error
Eloundou et al. (2023), "GPTs are GPTs"
2023 study · Share of tasks exposed to LLMs (β, human-rated)
41.1%
of tasks
What this measures

Published occupation-level exposure from the paper's occ_level.csv for O*NET-SOC 41-9022.00 Real Estate Sales Agents. Shown: human-rated β = 0.411, the share of the occupation's tasks an LLM could do in at least half the time at equal quality, counting tasks that need LLM-powered software at half weight (β = E1 + 0.5 × E2). Other published measures for this occupation: human-rated α (LLM alone) 0.286 and γ (all LLM-assisted) 0.536; GPT-4-rated β 0.438. This measures how much of the work an LLM could touch, not how many jobs disappear.

Official statisticReport an error
Today, in this role

What's changing day to day for real estate sales agents

Three parts of your work where AI is already doing real lifting, and what stays yours.

AI is taking this onSchedule and manage showings, confirm appointments, and collect post-showing buyer feedback: use Zillow ShowingTime+ AI to auto-schedule showing requests, send automated confirmation and reminder texts to buyers and sellers, and gather structured feedback from buyer agents, all without agent involvement until a feedback review or offer conversation is needed.

Schedule and manage showings, confirm appointments, and collect post-showing buyer feedback: use Zillow ShowingTime+ AI to auto-schedule showing requests, send automated confirmation and reminder texts to buyers and sellers, and gather structured feedback from buyer agents, all without agent involvement until a feedback review or offer conversation is needed.[8],[7]

Tools picking this up
Where your edge is

Showing logistics is one of the most fully automated tasks in the listing agent workflow. Use the time you recover to focus on what the AI cannot do: call the buyer agent directly after a showing to understand what their client thought, build the relationship, and surface objections early. That real-time intel from a personal call still beats automated feedback surveys when you are deciding whether to reduce price.

AI is sitting alongside you hereWrite listing descriptions and prepare marketing packages: direct ListingAI or ChatGPT to generate SEO-optimized MLS copy from property spec inputs, review and edit for accuracy and local voice, then coordinate with Restb.ai-tagged property photos and Virtual Staging AI-rendered room visuals, reducing listing prep from a half-day to under an hour.

Write listing descriptions and prepare marketing packages: direct ListingAI or ChatGPT to generate SEO-optimized MLS copy from property spec inputs, review and edit for accuracy and local voice, then coordinate with Restb.ai-tagged property photos and Virtual Staging AI-rendered room visuals, reducing listing prep from a half-day to under an hour.[9],[7],[10]

Where your edge is

AI-generated listing copy is now table stakes: every listing agent in your market has access to the same tools. Your edge is editing for local voice and micro-neighborhood storytelling that the AI cannot source: the specific school boundaries, the walking-distance coffee shop, the HOA board dynamics. Write the first sentence yourself. Let AI draft paragraphs 2–4. Review for factual accuracy and local specificity before publishing. Pair with professional photography; virtual staging is a supplement, not a replacement for quality images in competitive markets.

Get started with these tools
AI is taking this onManage lead routing, follow-up, and pipeline nurturing: configure BoldTrail or Follow Up Boss AI to auto-assign inbound leads by geography and buyer stage, set automated SMS and email follow-up sequences triggered by listing views and form fills, and review AI-generated lead-score dashboards to prioritize which contacts warrant a personal call.

Manage lead routing, follow-up, and pipeline nurturing: configure BoldTrail or Follow Up Boss AI to auto-assign inbound leads by geography and buyer stage, set automated SMS and email follow-up sequences triggered by listing views and form fills, and review AI-generated lead-score dashboards to prioritize which contacts warrant a personal call.[6],[11],[12]

Where your edge is

AI CRM follow-up has made the first several automated touches essentially free. Agents who close more transactions with AI CRMs (RISMedia, Jan 2026) are not sending fewer messages; they are spending the time saved on higher-value activities: listing consultations, showing feedback calls, and referral-partner lunches. Configure your AI sequences and then get off the dashboard.

Where real estate sales agents go next

Natural next steps for someone with your foundation: not exits, evolutions.

What each move takes

Property managers share most of the foundational skills with real estate agents (landlord-tenant law, lease documentation, vendor coordination, property marketing) but generate recurring management-fee income rather than commission-per-transaction income. The income model is meaningfully more stable: a portfolio of many managed properties produces predictable monthly revenue regardless of whether any individual home sells. BLS projects growth for Property Managers through 2034. AI automates maintenance-request routing and rent-payment processing, but the tenant relationship, owner reporting, and physical property inspections remain human. For agents who want more predictable income while staying in real estate, property management is the clearest lateral pivot with a low licensure barrier (most states allow sales agents to manage property under an active license).

What you'd add
  • · Property management software: AppFolio, Buildium, Yardi Breeze, maintenance ticketing, rent ledgers, owner portals
  • · Landlord-tenant law for the applicable state(s): eviction procedures, habitability standards, security deposit rules
  • · Lease drafting and lease-renewal negotiation for residential and small commercial properties
  • · Vendor management: soliciting bids, approving repairs, managing contractor relationships
  • · HOA governance: CC&Rs, board meeting facilitation, reserve study interpretation (for community association management)
What it takesMost of your skills carry over
Tools of the era

How has the work of real estate sales agents changed since 1880?

The tools that defined the work, one line per era. Select an era to see how it reshaped the work.

  • Word of mouth, courthouse records, and newspaper classified listings

    Before the NAR, before the MLS, before state licensing, real estate transactions were brokered through personal knowledge, courthouse deed records, and classified newspaper advertisements. A broker who knew what properties were available and who owned them had a genuine information advantage: one maintained through physical visits to county recorders, relationships with banks and estate administrators, and word-of-mouth networks in neighborhoods. The newspaper classified section (developed by urban papers through the 1880s-1890s) was the first mass-market listing medium: sellers could announce properties to a broader audience, but the broker was still needed to negotiate access and complete documentation. The lack of standardization meant every transaction was a one-off negotiation, with no standard contract forms and no established disclosure practices.

    Effect on the work

    The pre-professional broker's core value was holding exclusive information about available properties. The entire subsequent century of real estate technology, from the MLS to Zillow, was an attack on that information monopoly.

  • NAR Code of Ethics (1913) + first local MLS systems (1910s-1950s)

    The National Association of Real Estate Exchanges was founded May 12, 1908 in Chicago, with more than a hundred founding members across several local boards. Its first Code of Ethics was adopted in 1913, establishing the principle that an agent represents a client's interests rather than simply facilitating a transaction for a fee. The "REALTOR®" trademark was created in 1916 by NAR member Charles N. Chadbourn to distinguish code-subscribing members from unaffiliated brokers. State licensing laws began with California in 1917 and spread through the 1920s, by 1930, most states required some form of examination and registration to act as a broker. The Multiple Listing Service concept emerged from informal broker cooperation in the late 1800s ("brokers regularly gathered at the offices of their local associations to share information about properties they were trying to sell," as NAR describes it) and formalized into structured systems through the 1910s-1950s. The first formal MLS, operated by a local board, enabled cooperating brokers to share listings and split commissions: the structural foundation of the modern two-agent transaction (listing agent plus buyer's agent) that would define the profession for a century.

    Effect on the work

    Licensing requirements raised the barrier to entry, reduced fly-by-night operators, and formalized commission structures. The Code of Ethics and REALTOR® brand created a two-tier market: licensed, professional agents versus anyone who could technically sell a property. The MLS made cooperation between agents in the same market the default, which expanded each individual agent's ability to represent clients on both sides of the transaction.

  • FHA / VA loans (1934 / 1944) + first national MLS (1953) + RESPA (1974)

    Three federal interventions reshaped real estate agency between 1934 and 1974. The Federal Housing Administration (1934) standardized mortgage underwriting and made amortizing mortgages the American default, creating a paperwork and appraisal process that agents were uniquely positioned to navigate on behalf of buyers. The GI Bill (1944) put homeownership in reach for millions of veterans, generating the suburban expansion of the 1950s-1960s and an enormous sustained demand for agents who could show, negotiate, and close residential transactions. NAR launched a nationwide MLS system in 1953, formalizing the cooperative listing structure that had existed locally since the early 1900s into a national framework. The Real Estate Settlement Procedures Act (RESPA, 1974) prohibited undisclosed kickbacks and required disclosure of settlement costs, establishing that agents could only receive compensation for services actually rendered. RESPA also standardized the Good Faith Estimate, giving buyers a clearer picture of transaction costs. These federal interventions, taken together, transformed real estate agency from a local information brokerage into a regulated, federally-touched professional service.

    Effect on the work

    The FHA/VA mortgage paperwork burden increased the practical value of an agent: few buyers in 1950 understood the underwriting standards, disclosure requirements, and appraisal processes of federally-backed loans. Agents who could navigate this became indispensable to the transaction rather than optional facilitators.

  • Computerized MLS + fax machines + lockbox systems

    The 1980s brought the first major information-technology wave to real estate: computerized MLS databases replaced handwritten and mimeographed listing sheets, fax machines allowed offer documents to be transmitted across cities in minutes rather than days, and electronic lockbox systems (first deployed by the Supra brand in the 1980s) let agents schedule showings without the listing agent physically present. Computerized MLS did not displace agents: it made cooperation between agents faster and more systematic. An agent in 1990 could search listings by bedroom count, price range, and school district in seconds; the same search in 1975 required manually flipping through paper listing books. The productivity gain was real but it expanded what agents could do rather than reducing how many were needed. The internet was still emerging as a listing medium; the first online real estate listings (early Realtor.com, launched 1996 as a partnership between NAR and Move Inc.) appeared in the mid-1990s, before the wave that Zillow would eventually bring.

    Effect on the work

    Computerized MLS reduced the time-per-search dramatically but increased agent productivity without reducing agent headcount: the efficiency gains were absorbed by serving more clients and maintaining larger territories rather than by reducing total agents.

  • Internet listings: Realtor.com (1996) + Zillow (February 2006) + Trulia (2005)

    Zillow was incorporated in December 2004 and launched its public website on February 8, 2006: the site crashed on launch day from overwhelming traffic. Co-founders Rich Barton and Lloyd Frink, both former Microsoft and Expedia executives, built Zillow explicitly to democratize real estate information: the Zestimate tool put automated property valuations on 160 million homes for anyone to see for free. Trulia launched in 2005. For the first time, a buyer could search every listed property in a city, see estimated values, view satellite photos, and read neighborhood statistics: all without talking to an agent. The incumbent theory was that disintermediation was inevitable: if the MLS information monopoly was the agent's core value, free internet search had just destroyed it. The reality was more complicated. The housing bubble of 2004-2006 (NAR membership peaked at a record 1.37 million in 2006) masked the structural change. The crash of 2007-2011 showed that agents who survived were the ones whose value was in negotiation, local knowledge, transaction management, and relationship trust, not in search. Buyers found listings on Zillow; they still called agents to get inside.

    Effect on the work

    Internet listing platforms did not reduce agent headcount during the bubble years: NAR membership was at its all-time high in 2006. What they did was eliminate the information-monopoly justification for agent commissions, setting up the commission-restructuring story that would play out in the 2024 settlement. The bubble masked the structural change; the bust revealed it.

  • iBuyer platforms: Opendoor (2014) + Zillow Offers (2018-2021) + Redfin Now

    If internet search could not disintermediate the agent, perhaps algorithmic buying could. Opendoor launched in March 2014 with a direct offer model: homeowners could request an instant cash offer, skip the showings and negotiations, and close in days. The company bought 37,000 homes in 2021 at the peak of its scale. Zillow launched Zillow Offers in 2018, extending its own brand to the iBuyer model. Redfin, which had launched in 2004 with flat-fee commission refunds and a salaried-agent model, launched Redfin Now as its iBuyer product. The theory: if an algorithm could price homes accurately enough to buy and resell at a profit, or even at break-even while generating ancillary mortgage and title revenue, the listing and buyer agent became irrelevant to the transaction. The theory failed at scale. In November 2021, Zillow CEO Rich Barton announced the shutdown of Zillow Offers, citing $420 million in losses in Q3 2021 alone (Zillow owned approximately 7,000 homes at shutdown). The algorithm had consistently over-paid for homes in a rising market and could not adjust fast enough when prices peaked. Opendoor lost $662 million in 2021 and $1.4 billion in 2022. Redfin shuttered Redfin Now and laid off a small share of staff in November 2022. The iBuyer experiment demonstrated that algorithmic home valuation at scale, under volatile market conditions, consistently mispriced assets in ways that individual local agents, with their granular neighborhood knowledge, did not.

    Effect on the work

    iBuying, at its 2021 peak, handled a small share of US home transactions. Even at that volume it generated hundreds of millions in losses. The agents who worked iBuyer-adjacent transactions (helping buyers purchase Opendoor homes, helping sellers understand offers) retained employment throughout the experiment. The iBuyer moment showed that the agent's value is in local judgment under uncertainty, not in the paper transaction.

  • NAR v. Sitzer/Burnett settlement (2024) + AI property tools + rate-shock market

    On October 31, 2023, a federal jury found NAR and co-defendants (Keller Williams, HomeServices of America, Anywhere Real Estate, Re/Max) liable for conspiring to inflate real estate commissions, awarding nearly $1.8 billion in damages in the Sitzer/Burnett case. In March 2024, NAR settled for $418 million, agreeing to eliminate its MLS commission rules and waive the right to appeal. (2) Buyers must sign a written compensation agreement with their agent before viewing properties together. These changes structurally restructure how buyer agents are compensated: for the first time in the MLS era, buyers and buyer agents must explicitly negotiate and agree on compensation rather than assuming it will flow automatically from the seller. Early post-settlement data suggested NAR membership declining, some buyer agents exiting the market, and commission rates compressing on both sides. Simultaneously, AI property tools (Zillow's natural-language search, AI-powered comparative market analysis, automated disclosure review) have automated the research and documentation tasks that previously justified significant agent time, raising the question of what an agent's hour is worth when software can produce a CMA in seconds.

    Effect on the work

    BLS projects +3.1% employment growth for real estate sales agents 2024-2034 (National Employment Matrix). The settlement's net employment effect is uncertain: it may reduce the number of buyer agents (whose compensation model changed most dramatically) while concentrating the remaining agents on higher-value transactions requiring more relationship management. The self-employment rate means most agents absorb market changes directly rather than through employer layoffs.

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Real Estate Sales Agents employment and pay by year

On record since1908
Latest US employment193,370 (May 2025)Source: BLS OEWS May 2025Official statisticReport an error
Latest US median pay$52,830 (May 2025)Source: BLS OEWS May 2025Official statisticReport an error
Outlook+1.7% by 2035Source: BLS Employment Projections 2025-35Official statisticReport an error

In other countries

The latest figure each country's statistics agency publishes for this job. Each uses its own occupation definition, survey basis and currency, so the numbers are not directly comparable with each other or with the US figures.

  • Canada (NOC 63101)

    Employment, 2020
    97,705, Statistics Canada Census 2021 Official statistic Report an errorStatistics Canada Census of Population 2021: number of persons with employment income in NOC 63101 Real estate agents and salespersons, Canada, reference year 2020. A recipients basis (anyone with employment income during the year), slightly broader than a point-in-time employed count. Statistics Canada Open Licence.
    Median pay, 2020
    C$54,800, Statistics Canada Census 2021 Official statistic Report an errorStatistics Canada Census of Population 2021 (Table 98-10-0586): median employment income for NOC 63101 Real estate agents and salespersons, Canada, reference year 2020, full-year full-time workers (the basis comparable to the US OEWS full-time median). Statistics Canada Open Licence.
View all 27 years of cited US data, each linked to its source
YearUS employmentAnnual pay (median unless marked)SourceReport
192075,000n/aEst., NAR Researched contextReport
1950140,000n/aEst., US Department of Housing and Urban… Researched contextReport
1975290,000n/aEst., Consumer Financial Protection Bureau Researched contextReport
2000408,000$30,000Est., BLS Occupational Outlook Handbook Researched contextReport
2003123,490$32,610BLS OEWS May 2003 Official statisticReport
2004126,470$35,670BLS OEWS May 2004 Official statisticReport
2005150,200$39,240BLS OEWS May 2005 Official statisticReport
2006168,400$39,760BLS OEWS May 2006 Official statisticReport
2007172,030$40,600BLS OEWS May 2007 Official statisticReport
2008164,080$40,150BLS OEWS May 2008 Official statisticReport
2009151,550$40,100BLS OEWS May 2009 Official statisticReport
2010153,740$40,030BLS OEWS May 2010 Official statisticReport
2011158,340$39,070BLS OEWS May 2011 Official statisticReport
2012162,560$39,140BLS OEWS May 2012 Official statisticReport
2013158,850$39,800BLS OEWS May 2013 Official statisticReport
2014157,660$40,990BLS OEWS May 2014 Official statisticReport
2015151,700$43,370BLS OEWS May 2015 Official statisticReport
2016151,840$44,090BLS OEWS May 2016 Official statisticReport
2017147,960$45,990BLS OEWS May 2017 Official statisticReport
2018156,760$48,690BLS OEWS May 2018 Official statisticReport
2019162,330$48,930BLS OEWS May 2019 (employment) Official statisticBLS OEWS May 2019 (pay) Official statisticReport
2020168,740$49,040BLS OEWS May 2020 Official statisticReport
2021175,920$48,340BLS OEWS May 2021 Official statisticReport
2022193,010$49,980BLS OEWS May 2022 Official statisticReport
2023197,720$54,300BLS OEWS May 2023 (employment) Official statisticBLS OEWS May 2023 (pay) Official statisticReport
2024190,600$56,320BLS OEWS May 2024 Official statisticReport
2025193,370$52,830BLS OEWS May 2025 Official statisticReport
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