Modernizing Real Estate Brokerage: Confronting Legacy Inefficiencies with AI and Transparency

Brokerage at a Crossroads: Data Transparency, AI, and the Shift from Product to Platform

This white paper provides a critical examination of the traditional brokerage model’s inefficiencies, supported by data and real-world examples. It analyzes regulatory pressures for transparency, highlights emerging AI-driven trends reshaping real estate processes, and contrasts incumbent models with agile, data-forward firms. The findings indicate that a shift from product-centric mindsets (selling one-off services and software) to distribution-centric models (leveraging platforms and network effects to deliver services at scale) is underway. The paper also discusses how democratizing access to information and embracing transparency can unlock market-wide efficiencies and better outcomes for consumers. The tone is neutral and evidence-based, sounding a cautionary note: if the industry fails to adapt to these trends, it risks growing regulatory intervention, loss of consumer confidence, and competitive irrelevance (medium.com).

Finally, we illustrate how companies like Novus are leveraging AI to modernize real estate workflows and eliminate legacy frictions, providing an example of the path forward. The goal is to inform regulators, the general public, and industry stakeholders about the urgent need for change and the benefits of a more transparent, efficient brokerage ecosystem.

Introduction: Inefficiencies in the Traditional Brokerage Model

Traditional real estate brokerages have long been the intermediaries connecting buyers and sellers, but the ways in which they operate have changed little over decades. The result is a system laden with friction and outdated practices. From bloated commission structures that can total 5–6% of a home’s sale price to outdated marketing tactics and fragmented service delivery, the cracks in the system are apparent (medium.com).

These legacy inefficiencies are not just minor inconveniences – they carry real costs in time and money and are increasingly misaligned with consumer expectations and technological possibilities. Key pain points include:

• Legacy Systems & Fragmented Technology: TL;DR: legacy technology is a drag on the entire brokerage operation.
  
  Many brokerages still rely on legacy software and siloed systems (or only basic tools like email and spreadsheets) for critical tasks (linkedin.com). These outdated systems lack integration, resulting in repetitive data entry, inconsistent records, and organizational silos. Remarkably, real estate firms spend an estimated 60–70% of their IT budgets maintaining legacy systems, leaving little capacity to innovate (linkedin.com). This heavy upkeep burden lowers efficiency and increases costs, impeding firms’ ability to adopt modern solutions.
  
  Fragmented tech stacks also mean agents often juggle multiple platforms (MLS systems, CRMs, transaction management apps, document signing tools, etc) that don’t communicate with each other, further reducing productivity. Outdated or unsupported software has broader impacts as well: it frustrates employees, consumes excessive resources, and ultimately holds companies back from growth (ascendixtech.comascendixtech.com).
  
  
  

• Manual Processes & Settlement Delays: TL;DR: the traditional closing process is rife with friction, and the industry’s failure to streamline these steps contributes to frequent holdups.
  
  Real estate transactions remain notoriously slow. Even after a buyer and seller reach agreement, the closing process averages about 44 days to complete, largely due to manual steps, lender procedures, and third-party coordination (bankrate.com). In a world of instant digital services, a month-and-a-half to finalize a home purchase “can feel like an eternity” (bankrate.com).
  
  Moreover, approximately 17% of home sale contracts face delayed settlements (as of 2022 data) (nar.realtor). These delays have tangible costs: when closings are postponed, buyers may incur additional loan interest and sellers continue paying taxes and insurance on a property they expected to transfer (explore.fednow.org).
  
  A Federal Reserve analysis notes that many real estate transactions still involve paper-based, manual processes, which “lead to inefficiencies and costly delays” (explore.fednow.org). For example, waiting on a check to clear or a wire transfer to post can hold up the closing, forcing last-minute extensions that trigger per diem interest charges or penalty fees (explore.fednow.org). Each extra day in limbo is an added expense and inconvenience for all parties.
  
  Multiple intermediaries must coordinate (agents, mortgage underwriters, appraisers, title companies, inspectors, county recorders, etc.), and a breakdown with any one of them can cascade into delays. The complexity of having so many participants itself breeds inefficiency – the real estate transaction involves “numerous complexities and participants across development, construction, financing, sales, leasing, and management” (explore.fednow.org).
  
  

• Opacity in Operations: TL;DR: Opaque commissions baked into home prices face extinction as lawsuits force full brokerage fee disclosure.
  
  The brokerage process has historically lacked transparency in several respects. One major issue is the opacity of commission structures. Traditionally, home sellers agreed to a total commission (often 5%–6% of the sale price) which would then be split between the listing agent and the buyer’s agent (urban.org).
  
  Buyers, however, seldom saw this number or understood that the buyer’s agent’s fee was effectively baked into the home price. For decades, most buyers had no input into the commission and were often unaware it was even negotiable (urban.org). This opaqueness has bred mistrust and misaligned incentives – for example, buyer agents might steer clients away from lower-commission listings, and consumers have been kept in the dark about how their agents are paid.
  
  Beyond commissions, there is also operational opacity in how offers are presented and handled (in some markets, buyers have had limited visibility into whether there were competing offers or how negotiations were conducted). An insider culture persisted where information asymmetry benefited brokers at the expense of clients’ full understanding. Such practices have drawn increasing scrutiny from regulators and the public. In fact, a series of class-action lawsuits alleged that the dominant commission system – reinforced by National Association of Realtors (NAR) rules – reduced competition and allowed buyer brokers to mislead buyers regarding commissions (gmlaw.com). This regulatory pressure underscores that the status quo of opacity is no longer tenable. The industry is being pushed toward clearer disclosure of who is paying whom, and for what, in each transaction.
  
  

• Outdated SaaS Models: TL;DR: outdated SaaS tools bleed brokerages of efficiency and cash, blocking the gains of integrated, AI-driven platforms.
  
  Many incumbent brokerages rely on software-as-a-service (SaaS) products that, while once cutting-edge, have not kept pace with modern user expectations or integration capabilities. For instance, a brokerage might use one vendor’s platform for transaction management, another for customer relationship management, and yet another for e-signatures – none of which seamlessly share data.
  
  This patchwork of disjointed tools creates duplicate work and the need for manual reconciliation between systems. In some cases, the software itself is antiquated: it may have a clunky interface and lack mobile access, reflecting design philosophies from the early 2000s. The cost structure of these tools can also be inefficient: brokerages pay hefty licensing fees per agent for software that delivers marginal gains. Agents, in turn, sometimes avoid fully using the systems because of poor usability or partial overlap with other tools, defeating the purpose of the investment.
  
  In sum, the industry’s technology vendors have often offered “products” rather than solutions – standalone applications rather than truly interconnected platforms. As one analysis notes, legacy products in real estate “decrease efficiency, skyrocket costs, and reduce competitive opportunities” (ascendixtech.com). By clinging to dated SaaS models, brokerages not only waste money, but also miss out on the productivity benefits that modern, cloud-based and AI-enabled platforms could provide.
  
  
  

• High Fees and Commission Structures: TL;DR: U.S. sellers still pay about 5–6% (≈ $100 B/year) in commissions—far above the 1.5–3 % common abroad—revealing a legacy inefficiency that tech, competition, and lawsuits are now squeezing.
  
  The cumulative effect of the above inefficiencies is perhaps best exemplified in the high fees consumers pay. American home sellers (indirectly, buyers as well) have historically paid among the highest real estate brokerage commissions in the world – roughly 5%–6% of the sale price, amounting to about $100 billion annually in fees in recent years (urban.org).
  
  This is a significant transaction cost in an era when digital platforms in other industries have driven distribution costs down. Critics argue that such fees are outsized relative to the value provided, especially as online listings and DIY tools become prevalent. Indeed, virtually all buyers now use the internet in their home search, with 41% of recent buyers reporting that their very first step was looking at properties online (crosscountrymortgage.com).
  
  The traditional agent-centric model, where information was the agent’s primary value-add, is being eroded by this democratization of data. Yet the fee structure hasn’t meaningfully adjusted until recently. Many consumers and economists see the 5–6% commission as an inefficiency – essentially a legacy artifact of a less transparent era.
  
  High fees also create barriers to entry for some buyers and reduce sellers’ proceeds, potentially dampening overall market activity. In markets like the U.K. or Australia, total commissions are often much lower (on the order of 1.5%–3%), suggesting that the U.S. brokerage model has room to become more cost-efficient.
  
  The persistence of elevated fees despite technology’s promise of streamlining is a clear sign of inefficiency – one that is now under direct threat from both market competition and legal challenges to the traditional pricing model.

Table 1: Key Inefficiencies Across the Traditional Brokerage Lifecycle

Table 1: A summary of major inefficiencies in the legacy brokerage model, with examples and data on their impact.

As Table 1 highlights, these inefficiencies span the entire brokerage lifecycle – from initial client engagement and search (e.g. reliance on outdated tools, lack of data integration), to the transaction phase (lengthy escrow and closing due to manual steps), through to post-sale follow-ups (often fragmented or lacking value-add). Cumulatively, the cost of these inefficiencies is enormous.

They manifest in longer transaction times, higher expenses, and lost opportunities to serve clients better. Perhaps most importantly, they indicate a mismatch between how traditional brokerages operate and what is possible (and expected) in 2025. The next sections will explore how external pressures and new technologies are poised to force a transformation of this model.

Regulatory Landscape: The Push for Transparency and Fairness

Regulators and courts have increasingly taken notice of the inefficiencies and consumer-unfriendly practices in the real estate brokerage industry, particularly around transparency and competition. In the past, industry rules – often backed by powerful trade groups like NAR – allowed opaque practices to persist, effectively shielding the traditional brokerage model from price competition. That paradigm is now shifting under legal and regulatory pressure.

Landmark Settlements and Rule Changes: A watershed moment came in March 2024, when the NAR reached a settlement to end several class-action antitrust lawsuits challenging its commission policies (gmlaw.comgmlaw.com). The lawsuits had alleged that NAR’s longstanding rules (such as requiring listing brokers to offer a blanket commission to buyer brokers via the MLS) were anticompetitive, inflating costs for consumers (gmlaw.com). Rather than continuing to fight in court, NAR agreed to a settlement with major implications for transparency. The settlement (which took effect in August 2024) required NAR to pay $400+ million in damages and, importantly, to rewrite its rules to promote transparency (urban.orggmlaw.com). Under the new rules:

• No More Hidden Buyer Agent Commissions on MLS: Listing agents can no longer advertise or offer compensation to buyers’ agents on the MLS (bankrate.com). Previously, a typical MLS listing would openly state something like “Buyer’s Agent Commission: 2.5%.” That practice is now prohibited on any NAR-affiliated MLS (bankrate.com). The intent is to break the expectation that a buyer’s agent is automatically paid out of the seller’s proceeds without the buyer’s explicit consent or awareness.
  
  
  

• Mandatory Buyer-Agent Agreements: Buyers and their agents must enter into a written, binding representation agreement before the agent can start showing homes to the buyer (gmlaw.com). This forces a conversation up front about how the buyer’s agent will be compensated (whether through a commission paid by the seller, a fee paid by the buyer, or some combination). In short, the buyer must acknowledge and agree to their agent’s compensation arrangement, rather than it being a behind-the-scenes item.
  
  
  

These changes aim to “promote healthy trade practices and transparency” and create a fairer marketplace for consumers (gmlaw.comgmlaw.com). For the first time in decades, buyers will directly see and negotiate the fees for their representation. Meanwhile, sellers are no longer tacitly locked into offering a standard buyer-side commission to get their home sold (since such offers cannot be posted on the MLS). The immediate implication is that real estate commissions could become more price-competitive. In essence, brokers will have to justify their fees more explicitly to their own clients.

Early Reactions and Ongoing Debates: The rollout of these new rules in August 2024 was met with a mix of optimism and concern. Consumer advocates hailed the potential for cost savings; some analysts predicted a “near-nirvana” for homebuyers where a price war among agents drives commissions down significantly (bankrate.com). Indeed, one industry analyst estimated that the $100 billion in annual commissions could be cut by roughly 30%, which if realized, would wipe out tens of billions in fees and potentially half of the 1.6 million U.S. agents (as less-efficient or part-time agents find it unprofitable to continue) (linkedin.comreddit.com). While such an extreme outcome remains speculative, the direction is clear: easier comparison of service offerings and fees is expected to put downward pressure on commission rates, especially for higher-priced properties and repeat customers who have leverage to negotiate urban.org.

On the other hand, industry voices have raised concerns about unintended consequences. One worry is a cost shift: if sellers no longer feel obligated to pay a traditional buyer agent commission, buyers might have to pay their agents out-of-pocket, which could reduce affordability (gmlaw.com). For example, a first-time buyer who previously “paid” their agent via the home price (rolled into a mortgage) might now be asked to write a check for, say, $5,000 – an expense they may not have budgeted for. This has led to speculation that some buyers will forego agent representation or try to handle more of the process themselves if they cannot finance these fees. Lenders currently do not typically allow buyer agent fees to be financed as part of the loan (since traditionally it was a seller-side cost), so new mechanisms may be needed if the model shifts.

Another concern is the impact on small brokerages and part-time agents. A transparent, competitive fee environment will likely reward agents who can clearly articulate their value and possibly undercut others on price. Large, efficient brokerages or tech-enabled platforms may be able to sustain profitability at lower per-unit commissions (through higher volume or ancillary services), whereas smaller traditional offices might struggle. There is speculation that many marginal players – those who close only a few deals a year – could exit the industry if commissions compress. In short, transparency is a double-edged sword: great for consumers and efficient markets, but challenging for those who relied on information asymmetry or industry norms to maintain higher fees.

Global and Broader Regulatory Trends: The push for transparency in real estate isn’t limited to the United States. Globally, transparency is recognized as “an essential ingredient of a well-functioning economy and society” in real estate, leading to better outcomes for governments, investors, and the public (weforum.org). The Global Real Estate Transparency Index, published by consulting firms like JLL, shows improvements in many countries, but also highlights that progress must accelerate to meet public demands (weforum.orgweforum.org). Key transparency initiatives worldwide include open data on transaction prices and ownership, stricter enforcement of anti-money-laundering rules in property transactions, and consumer protection laws requiring upfront disclosure of fees.

In the U.K., for instance, there has long been a norm of lower commissions (often 1–2% paid by the seller, and buyer agents are rare), which some attribute to higher transparency and consumer price-sensitivity. In Australia and New Zealand, auctions are a common method of sale, which inherently adds transparency to the price discovery process (although agents there still collect commissions, they tend to be more openly negotiated). These examples suggest that increased transparency often correlates with more efficient, lower-cost markets. According to the World Economic Forum, a transparent real estate market “helps governments, public bodies and the private sector make smarter decisions…boosting business efficiency” (weforum.org). When stakeholders have ready access to data and consistent rules, competition tends to flourish and costs tend to come down (weforum.orgweforum.org).

The regulatory trajectory is clear: whether through formal rules or the court of public opinion, brokerages are being driven toward greater openness. The era of hidden fees or proprietary control of basic market information is ending. Regulators want an environment where consumers can compare services and costs, and where agents compete on value rather than relying on enforced norms. For the industry, this means that inefficiencies like opaque operations are not just a business weakness – they are now liabilities that could invite sanctions or loss of business to more transparent competitors.

In summary, the regulatory landscape is applying pressure on traditional brokerage inefficiencies, especially regarding transparency and fairness. Those brokerages that proactively adapt – by embracing clearer disclosure, unbundling their services, and perhaps adjusting pricing models – may not only avoid penalties but could gain trust and market share. Those that resist change, conversely, risk both regulatory penalties and consumer defection. The next section turns to another major force compelling change: technology, and in particular, AI and automation, which offer tools to directly address many of the inefficiencies outlined earlier.

Trends in AI and Automation Reshaping Workflows

While regulation pushes from one side, technology – especially artificial intelligence – is pulling from the other, offering new ways to streamline and modernize real estate workflows. The past few years have seen an acceleration in proptech innovation, with AI at the forefront of many solutions. Real estate, often seen as a late adopter of digital transformation, is now catching up as AI tools become more accessible and tailored to industry needs. This section highlights key trends in AI and automation that are reshaping how real estate business is done, addressing inefficiencies and unlocking new capabilities.

1. Intelligent Data Processing and Integration: One of the most impactful applications of automation is in handling the mountain of paperwork and data in a typical transaction. AI-powered document processing can now extract and validate information from contracts, title deeds, loan applications, and disclosures far faster than humans. For example, instead of an employee manually checking that all fields in a 50-page closing package are correctly filled, an AI system can scan for omissions or errors in seconds. This reduces errors and speeds up the closing timeline. Robotic process automation (RPA) is being used to bridge legacy systems – for instance, automatically taking data from an agent’s CRM and inputting it into an MLS listing or a marketing flyer template, eliminating duplicate data entry. Integration is also improving: Modern platforms use APIs and AI to connect formerly siloed systems, so that when a price change is made in one database, all marketing materials and portals update automatically. The net result is less manual labor, fewer mistakes, and more cohesive data across the brokerage. Studies have quantified these benefits: businesses leveraging AI in real estate have seen a 7.3% increase in productivity on average, along with a 5.6% improvement in operational efficiency (brainvire.com). These gains directly attack the inefficiencies of legacy systems and fragmented data noted earlier.

2. AI-Enhanced Property Valuation and Market Analysis: Valuing a property accurately is both crucial and challenging – traditionally done via comparative market analysis (CMA) by agents, which can be as much art as science. AI has revolutionized this area through automated valuation models (AVMs) that analyze vast datasets (recent sales, property characteristics, neighborhood trends, school ratings, crime statistics, etc.) to estimate home values in a more objective and data-backed way. What once took an experienced broker many hours of research can now be done in seconds by a machine learning model, with surprisingly high accuracy. These models continue to learn and improve as more transaction data becomes available. The benefit is twofold: (a) consumers get more transparency and confidence (many listing sites now publish AI-derived value estimates alongside listings), and (b) agents can advise clients with better data, reducing the risk of mispricing a property (an efficiency gain – correctly priced homes sell faster and with fewer complications). Predictive analytics, another AI tool, helps identify trends such as which neighborhoods are heating up or which types of buyers to target for a property, enabling more efficient marketing spend. In a data-forward brokerage, decisions like setting list prices or rental rates are increasingly guided by these AI insights rather than solely gut instinct.

3. Virtual Assistants and Chatbots: Customer service and lead management in real estate can benefit greatly from AI-driven virtual assistants. Brokerages are starting to deploy AI chatbots on their websites or messaging platforms to handle common inquiries – 24/7. These bots can answer questions about listings, schedule showings, provide mortgage rate information, and even pre-qualify leads by asking basic questions. As a result, potential clients get immediate responses at any hour, while human agents are freed up from fielding repetitive queries (novuscatalyst.com). A well-designed chatbot can nurture leads until a human agent steps in for more complex discussions. Similarly, AI virtual assistants can help agents stay organized: for instance, by automatically reminding them of tasks (inspection deadlines, contingency expirations) or suggesting which leads to follow up with based on predictive scoring. The improvements in natural language processing (NLP) mean these virtual assistants are increasingly adept at understanding nuanced questions and providing helpful answers, making them a valuable extension of the team. According to industry surveys, 52% of companies plan to integrate AI assistants to streamline processes and improve outcomes (brainvire.com). Real estate is no exception, as firms see opportunities to reduce response times and handle higher volumes of customer interactions without proportional increases in staff.

4. Streamlined Transactions and Smart Contracts: Startups and forward-thinking brokerages are experimenting with end-to-end digital transaction platforms that incorporate AI for efficiency and even blockchain for trust and security. For example, AI can auto-populate contract forms with known data and flag any unusual clauses that deviate from standard (acting as a digital assistant to the broker during offer preparation). Machine learning models can predict the likelihood of a deal closing based on various factors (buyer’s financing strength, local market conditions, etc.), which helps brokers triage transactions that need extra attention. Some firms are exploring smart contracts on blockchain to handle earnest money and escrow – the idea being that funds and title transfer can be automated when predefined conditions are met, potentially reducing the role of intermediaries. While widespread adoption of blockchain in property deals is still nascent, the concept aligns with the push for more secure, transparent, and quick settlements (e.g., automatically releasing deposit money back to a buyer if inspection contingency isn’t waived by a deadline). Moreover, the FedNow instant payment service introduced by the U.S. Federal Reserve in 2023 is being touted as a way to modernize real estate payments – allowing things like instantaneous earnest money deposits and real-time disbursement of closing funds (explore.fednow.orgexplore.fednow.org). An always-on payment network means a buyer isn’t waiting for banking hours to send a wire, and a seller can receive sale proceeds immediately at closing (explore.fednow.org). This kind of innovation, combined with AI to coordinate the timing, could significantly compress the settlement timeline and reduce the “idle” days waiting for checks to clear. By leveraging these tools, agile firms can turn the lengthy closing process into a swift, largely automated sequence – mitigating the inefficiencies of traditional escrow.

5. Personalized Client Experiences Through AI: In a world where consumers are used to Amazon and Netflix personalizing recommendations, real estate is moving in that direction too. AI algorithms can learn a buyer’s preferences (from search behavior, saved homes, and even aesthetic choices) and then surface listings that are a strong match, even ones that might have been overlooked. For sellers, AI can target the right audience for marketing their property – for example, identifying likely move-up buyers for a family home and ensuring ads reach them. This makes marketing spend more efficient and often reduces time on market. AI can also enhance the transaction experience: some brokerages have introduced client-facing dashboards powered by AI that keep buyers and sellers updated on transaction progress, automatically explain next steps in plain language, and answer FAQs about the process. This level of transparency and hand-holding was traditionally only possible with significant manual effort by the agent; now it can be done at scale with software. By improving communication and setting expectations, these tools help avoid miscommunication-related delays or dissatisfaction. The end result is higher client satisfaction and potentially higher throughput (an agent enabled by AI can handle more clients at once without dropping the ball).

Overall, the AI revolution in real estate is turning what were once qualitative, labor-intensive aspects of brokerage into data-driven, automated processes. The industry’s AI market is growing explosively – one analysis projected the real estate AI sector to expand from about $165 billion in 2023 to $226+ billion in 2024 (brainvire.com), reflecting the massive investment and interest in these technologies. Anecdotally, we see that tech-enabled brokerages with significant AI capabilities have been able to scale faster and operate at lower cost. For example, tech-forward brokerage models (such as Redfin’s) used AI for things like optimizing agent routing to showings and dynamically adjusting listing prices, enabling one agent to do more transactions than they might have under a traditional model (though Redfin’s model also involved salaried agents and other innovations).

Importantly, AI is not positioned as a replacement for human agents, but as an augmentation. By taking over routine tasks and providing deeper insights, AI allows brokers and agents to focus on the human-centric aspects of the job – negotiating deals, providing local expertise, and building relationships. However, as AI takes on more of the heavy lifting, it inevitably shifts the skill set required of successful agents and brokerages. Data analysis and comfort with technology become as important as sales acumen. Brokerages that invest in training their staff to leverage AI tools (and perhaps hiring more data analysts and software engineers, as some leading firms have done) will likely outperform those that stick to business-as-usual. According to industry strategist Mike DelPrete, the defining trait of tech-enabled brokerages is their ability to scale faster and at lower cost by leveraging technology (mikedp.com). This leads us to the next topic: how the business models in real estate are evolving from the old product-centric mindset to new distribution-centric platforms, and how incumbent players compare to newer, agile entrants on this front.

(Potential visual aids: a chart or table illustrating AI use-cases across the real estate value chain – e.g., a table with columns for “Traditional Process” vs “AI-Augmented Process” for tasks like property valuation, lead screening, customer service, and closing, showing time/cost saved; also perhaps a diagram of an “AI-powered real estate ecosystem” depicting how various tools (AVMs, chatbots, transaction management) connect to streamline the workflow.)

From Product-Centric to Distribution-Centric Models

In parallel with technological change, the real estate brokerage industry is experiencing a strategic shift in business models. Historically, many firms in real estate and proptech have been product-centric – meaning they focused on building and selling a particular product or service (for example, a customer relationship management software for agents, or a marketing toolkit, or just the brokerage service of helping buy/sell homes one transaction at a time). Success was often measured by the volume of that product sold and its market share. However, the new generation of companies is increasingly distribution-centric, concentrating on controlling the platform or network through which real estate services are delivered and monetized. This shift has profound implications for efficiency and competitive dynamics.

To clarify the terms: a product-centric real estate business might be a brokerage that prides itself on its bespoke client service (the “product” being the transaction experience it provides), or a software vendor that sells subscriptions to an MLS system or contract management tool (the product being the software itself). A distribution-centric business, on the other hand, is one that prioritizes building a network or marketplace – essentially owning the pipeline of customers and data, and then monetizing that pipeline in flexible ways.

Rise of Platforms and Marketplaces: Online real estate portals like Zillow, Redfin, Realtor.com, and others exemplify the distribution-centric approach. Zillow didn’t set out to be just another brokerage with agents selling houses (at least originally); it became a platform where virtually all listings are visible and attracted millions of consumers to search there. By doing so, Zillow captured the distribution of attention and leads in the homebuying process. It then monetized that distribution by selling leads to agents and later by providing ancillary services (and even briefly doing direct buying via Zillow Offers). The key point is that Zillow’s power came not from owning the homes being sold (the product) but from owning the eyeballs and data (the distribution channel). A network effect developed: more users on the site made it more imperative for agents and sellers to be on the site, which in turn brought more users – a self-reinforcing cycle (mikedp.com). Similar dynamics are seen with other platforms; for example, Airbnb in rentals (though not exactly a brokerage, it’s a parallel in lodging: it doesn’t own properties, it owns the network that connects hosts and guests).

In a distribution-centric model, scale and reach are king. The economics often shift to winner-takes-most, because the platform that all clients gravitate to becomes immensely valuable. This is a contrast to a traditional brokerage office that might compete only on local reputation and individual agent skill – a more fragmented, product-by-product competition. For real estate brokerages, becoming more distribution-centric might mean creating an ecosystem where consumers can do more than just buy or sell a home – perhaps a one-stop-shop where they can also get mortgage quotes, title services, home insurance, and even post-purchase services (renovation, moving, utilities setup) in a seamless way. The brokerage, in this case, isn’t just selling a service; it’s facilitating a multitude of services and taking a cut or fee from each (or using a low-cost service to draw customers in and then monetizing via another). This distribution focus tends to reduce friction and cost for the consumer by bundling and streamlining, which in turn can increase volume and loyalty.

Implications for Efficiency: Distribution-centric firms can achieve efficiencies that product-centric ones struggle with. First, they leverage network effects – as noted, which can drastically lower the marginal cost of customer acquisition over time. Traditional brokers might spend large portions of commissions on marketing and prospecting for each new client (direct mail, local ads, etc.), whereas a platform with strong brand recognition and traffic (like a top website or app) attracts clients organically or at a much lower cost per client. Second, distribution-centric models often employ modern, scalable tech infrastructure from the ground up, because their business is inherently digital. This means cloud-based systems, microservices, and data analytics are in their DNA, making it easier to integrate AI and adapt quickly. By contrast, an older brokerage firm that only dabbles in tech (using a few off-the-shelf products) can’t easily pivot to become a platform – it lacks the architecture and culture.

Another efficiency gain is through specialization and partnerships. A distribution-focused business doesn’t need to do everything itself; it can partner for various pieces. For example, consider a company that builds a popular home search app (distribution) and then partners with a network of local real estate agents who actually service the transactions, taking a referral fee for each client handed off. This is essentially the model of some newer “digital brokerages” or referral platforms. They focus on the front-end user experience and lead aggregation, and outsource the fulfillment (the actual brokerage work) to partner agents or firms, maintaining an asset-light approach. The benefit is flexibility and low overhead: if one partner doesn’t perform, they can be replaced; if demand spikes, more partners can be onboarded. The end consumer might not even distinguish between the platform and the agent – they see it as one service – but behind the scenes the distribution company has avoided heavy fixed costs by not hiring all those agents as employees.

Product-Centric vs Distribution-Centric: A Comparison: It may help to illustrate the differences in a direct comparison:

Table 2: Contrasting traditional product-centric approaches vs modern distribution-centric platforms in real estate.

It’s worth noting that incumbent firms are not blind to this shift. Many are trying to pivot or at least hybridize their models. Big brokerages now talk about their “platforms” and aim to offer end-to-end services. For instance, some have launched in-house mortgage or title services (capturing more of the distribution chain) or heavily invested in consumer-facing apps to retain clients in their ecosystem. However, genuine transformation is challenging – it requires not just new software, but a new mindset about where value comes from.

A cautionary tale can be seen in how travel agencies or stock brokerage firms evolved. Travel agencies that were product-centric (selling airline tickets and vacation packages from a catalogue) were largely displaced by distribution-centric models like Expedia or Booking.com that aggregate options and empower consumers to self-serve. In stock trading, product-centric brokerage (full-service, high commission per trade) was disrupted by distribution-centric platforms like Robinhood or E*Trade that amassed users and monetized order flow or ancillary services with zero commissions. Real estate has unique elements, but it may follow some analogous patterns. The writing on the wall is that simply offering a quality service is not enough if someone else owns the customer’s journey.

For consumers and the market as a whole, the shift to distribution-centric models can bring greater efficiency: easier access to information (all listings in one place), more competition (platforms often allow upstarts to reach customers, like a new brokerage can compete on a level field on Zillow’s site next to established ones), and often lower fees (platforms tend to drive pricing towards equilibrium by transparency). However, there are risks too – if one platform becomes too dominant, it could potentially exploit that position (e.g., charging high referral fees to agents or selling ancillary services at a premium). Regulators and industry stakeholders will need to balance these considerations, possibly ensuring that distribution power doesn’t simply shift gatekeeping from old brokers to new tech giants.

In summary, the distribution-centric approach prioritizes reaching the customer through integrated, data-rich channels, whereas the product-centric approach focuses on the discrete service or tool being offered. The trend in real estate is clearly moving toward the former, as evidenced by the success of platforms and the changing strategies of incumbents. Embracing a distribution mindset often goes hand in hand with being data-forward and tech-driven – which leads to the next comparison: how incumbent models stack up against new agile, data-focused firms.

Incumbents vs. Agile Data-Forward Firms: A Comparative Analysis

The contrast between traditional incumbent brokerages and newer agile, tech-driven firms is becoming increasingly stark. This section compares their operating models, strengths, and weaknesses, particularly in the context of efficiency, adaptability, and use of data. While there is a spectrum (not all incumbents are technophobic dinosaurs, and not all startups are efficient paragons), it’s useful to generalize to understand broad trends.

• Technology Adoption: Incumbent brokerages often operate on legacy tech stacks, as discussed, and may have a patchwork of vendor-provided tools. Many established firms only allocate a small percentage of budget to R&D or have small IT teams relative to their size. In contrast, agile data-forward firms (think of venture-funded proptech companies or forward-leaning brokerages) invest heavily in engineering and data science talent. For example, successful tech-enabled brokerages worldwide tend to have on the order of 10% of their staff in technical roles (mikedp.com), whereas a traditional brokerage of the same size might have just a few IT support staff (far less than 10%). These new entrants build custom software for their workflows and leverage cloud infrastructure for scalability. This means they can iterate faster – deploying updates weekly or daily – whereas an incumbent might be stuck waiting for annual updates from a software vendor or reluctant to change systems due to training overhead. The outcome is that agile firms can respond quickly to user feedback, fix inefficiencies in their processes with code, and in general continuously improve their platform. A telling metric is operational efficiency: technology allows some newer firms to handle more transactions per employee than traditional firms. For instance, in the past, one agent might close ~8 deals per year on average (as hinted by RealTrends data comparing flat-fee vs traditional brokers) (realtrends.com), but a tech-augmented agent could handle significantly more by automating tasks (Redfin reported higher transactions per agent when it first scaled up, due in part to its software ecosystem).
  
  
  

• Business Agility and Innovation: Incumbents often have established ways of doing business and layers of hierarchy, which can make radical changes slow. They also have to consider the sentiments of thousands of agents used to a certain commission split or process. Introducing a new technology or altering the model (say, shifting to a lower commission structure or new compensation plan) can face internal resistance. Agile data-forward companies, typically being newer, lack this legacy baggage. They can pivot their business model more readily. A good example is how some proptech firms quickly adapted to opportunities: when the market slowed, several iBuyer startups added services for online home tours or financing to diversify. Traditional brokers in the same situation often just tightened belts rather than innovate, because they are not structured to experiment. Moreover, data-forward firms use analytics for decision-making – they track metrics on everything (website conversion, cost per lead, time to close, customer satisfaction) and adjust strategies based on what the data tells them. This scientific approach to improving efficiency is less common in old-school brokerages that might rely on the intuition and experience of a few senior managers.
  
  
  

• Cost Structure and Fees: As noted earlier, incumbents generally maintain the 5-6% commission model and a high-cost structure (brick-and-mortar offices, franchise fees, multiple middle managers, etc.). Data-forward challengers have tried alternative fee structures: for instance, some offer flat-fee or lower-percentage commissions with the bet that technology lets them operate profitably at those levels. Others, like certain 100% commission brokerages (flat monthly fee for agents) or transaction coordinators, unbundle services and charge only for specific tasks. These new models often undercut incumbents on price, which is attractive to consumers but forces the new companies to be hyper-efficient to make money. Agile firms might automate marketing (saving on expensive ad campaigns) or use remote online notarization to cut down on closing costs, passing savings to clients. Incumbents, to defend their pricing, are increasingly highlighting quality and brand trust – but if their cost base is fundamentally higher due to inefficiencies, it becomes hard to justify to savvy consumers in the long run. The pressure from agile competitors may eventually force incumbents to trim fat (we’re already seeing some big brokerages experimenting with 4% total commission offerings in limited markets, essentially price matching discounters to retain business).
  
  
  

• Use of Data as an Asset: Traditional brokerages, to the extent they collected data (past sales, client preferences, etc.), often did not fully harness it. Maybe they’d run occasional reports or market stats, but the data largely stayed in silo (an agent’s Rolodex, an MLS sales history file, etc.). Agile data-forward firms treat data as a core asset. They mine it for patterns – for example, identifying that homes with certain features sell faster, or that a certain zip code has many renters who could be first-time buyers, then acting on those insights. They might also share data openly with consumers to add value. A case in point: some newer firms provide dashboards that show real-time data like how many people viewed your listing, what the feedback is, where your home’s price stands vs others – information incumbents might not readily provide. This transparency through data builds trust and engages clients. It also reflects an efficiency: there’s less back-and-forth when a client can self-serve much of the info (e.g., instead of calling the agent for an update, the seller logs into their portal and sees exactly how many showings were scheduled this week, etc.). Agile firms lean on data not just for external use but also internal optimization: routing leads, matching the best agent to a given client based on performance stats, or even predicting which agents might leave for a competitor and addressing that proactively. In short, data-forward companies continuously learn and improve, whereas many incumbents stagnated by doing things “the way we’ve always done.”
  
  
  

• Culture and Talent: Efficient tech-driven firms often recruit talent from outside traditional real estate – software engineers, data scientists, UX designers – creating a culture of solving problems with technology. Incumbents historically recruited mainly salespeople (agents) and operated more like franchise sales organizations than tech companies. The culture of an incumbent might emphasize relationships and sales quotas, whereas a data-forward firm emphasizes user experience, innovation, and rapid development. This cultural difference can be felt in how quickly issues get resolved or new ideas implemented. For example, if customers express frustration with a step in an app, a tech brokerage might push a fix within days. In a traditional firm, if clients hate some paperwork process, it could take years and possibly external regulation to force a change. The talent factor is significant: a company with dozens of skilled programmers will naturally automate and streamline tasks that in another company are still done manually by office admins.
  
  
  

To illustrate these differences, consider the following scenario: scheduling home showings. In a traditional model, a buyer sees a home on a website, calls their agent, who calls the listing brokerage’s office, which coordinates with the seller and perhaps tenant, then confirms back to the buyer’s agent, who then informs the buyer – a chain of calls and emails that can take hours or days. An agile approach: the buyer clicks a “Schedule Tour” button on an app, an AI assistant finds an open slot that works for buyer, seller, and an available showing agent, and confirms instantly, sending all parties a notification. The latter is clearly more efficient. It requires an integrated platform (the app connected to scheduling software and availability data) – something an agile tech brokerage would have but a typical incumbent wouldn’t. Multiply such differences across dozens of micro-processes in a transaction, and the time and cost savings add up.

It’s not all black-and-white: incumbents have strengths like brand recognition, deep local expertise, and often a human touch honed over years. Agile newcomers can sometimes stumble by relying too much on tech and not enough on personal service (some early online brokerages found that buyers still wanted hand-holding at various points). The optimal model might blend the two – maintaining high-touch service while using AI and automation to remove the drudgery and inefficiency.

However, the trajectory in market competition is clear: the firms that are “agile” and “data-forward” are capturing a disproportionate share of growth. In recent years, much of the industry’s transaction volume growth (when the market was strong) went to either the very large platforms (Zillow’s advertising, etc.) or innovative brokers (like eXp Realty scaling via cloud-based operations, or Compass attracting agents with its tech and capital). Traditional independent brokerages and smaller franchises saw shrinking market share or had to consolidate. Consumers are voting with their business – they gravitate toward solutions that are convenient, transparent, and cost-effective, which tends to favor the more tech-driven offerings.

In conclusion, incumbents must learn from their agile counterparts. This might mean investing in their own technology, forging partnerships with proptech firms, or fundamentally rethinking their workflows. The cautionary tone is warranted: those who fail to adapt could lose relevance, as “the industry must evolve or risk irrelevance” (medium.com). The next and final section will discuss the broader implications of embracing transparency and democratized access, and how doing so can create efficiencies at a market-wide level – essentially, the upside of all these changes if harnessed properly, with a brief look at how a neutral, AI-driven approach (HI, I'M NOVUS!) fits into this vision.

Democratized Access, Transparency, and Market-Wide Efficiency

One of the recurring themes in this analysis is that greater transparency and access to information tend to drive efficiency and benefit the market as a whole. When data that was once siloed or restricted becomes openly available, it empowers consumers, reduces asymmetry, and fosters competition. Similarly, when processes that were opaque are made clear (through better communication or regulatory mandates), participants can make more informed decisions, leading to better outcomes and fewer costly mistakes. This section explores how democratizing access and increasing transparency can yield broad efficiencies, and what the real estate industry stands to gain by embracing these principles – as well as what it stands to lose if it doesn’t.

Empowering Consumers and Reducing Friction: In the past, a homebuyer might have been largely dependent on their agent for information – which homes are on the market, what a fair price is, how the process works step by step. This information dependency created friction; if the agent was busy or if there was mistrust, the transaction could stall or fall apart. Today, thanks to democratized data (all listings online, public sales records, extensive how-to guides), consumers come to the table more informed. Far from making agents obsolete, this can actually streamline interactions: agents spend less time on basic questions and can focus on higher-level guidance and negotiation. A transparent environment also means fewer surprises – for example, if the buyer can easily see estimates of closing costs, they are less likely to balk at the closing table or delay while scrambling for cash. By providing “self-service” information (via portals, dashboards, or disclosures written in plain language), modern firms reduce the back-and-forth that characterized old transactions. The result is often faster decision-making and a smoother process, which is an efficiency gain for all involved. As noted by JLL’s CEO, transparent markets boost business efficiency and decision-making quality (weforum.org) – when everyone knows the rules and has the data, things move quicker and with more confidence.

Enhancing Competition and Innovation: Transparency is a great equalizer. When commission rates, service offerings, and performance metrics are out in the open, brokers have to compete on true value. This can spur innovation – for instance, an agency might develop a novel service bundle or a tech feature to differentiate themselves if they can’t just rely on hidden fees. Over time, inefficient players (those who cannot justify their cost) will be squeezed out, and more efficient models will gain market share. This Darwinian competition drives the industry to a more efficient equilibrium. It’s similar to how, in financial markets, greater transparency leads to tighter spreads and more liquidity. In real estate, we might see lower average commissions, faster transaction times, and higher service quality as firms vie for educated consumers. The market-wide efficiency includes not just cost reduction but optimal allocation of resources – for example, fewer unnecessary listings (homes priced unrealistically that sit for months) because data transparency helps calibrate pricing right from the start.

Reducing Systemic Risks and Issues: Some inefficiencies in real estate have broader societal costs – think about deals that fall through, causing chain reactions (a buyer’s sale falls through so they can’t buy someone else’s house, etc.), or about fraud and money laundering risks in opaque markets. Transparency and digital processes can mitigate these. If every step of a deal is logged and visible to relevant parties, it’s harder for fraud to go undetected (like someone misusing escrow funds). If ownership records and liens are easily searchable, title issues can be resolved faster. If agent behavior is transparent (e.g., all offers must be presented and perhaps even logged in a system visible to sellers), ethical breaches diminish. These improvements reduce the waste and loss from bad transactions or legal disputes. In a transparent system, regulators can also monitor the market health in real time (through data on transactions, prices, etc.), potentially addressing bubbles or abuses sooner. All of this contributes to a more stable and efficient housing market, which has positive ripple effects on the economy.

Market Liquidity and Participation: When inefficiencies are removed, more people are likely to participate in the market. Some potential home sellers might have held back because of the hassle and cost of selling a home (dealing with showings, paying a big commission, etc.). If new models make selling easier (e.g., an online platform that can line up instant offers, or an AI tool that values the home accurately so it sells quickly) and cheaper (lower fees), those owners might decide to list after all. Similarly, buyers who were discouraged by opaque bidding wars or confusing processes might be more inclined to jump in if they know exactly what to expect and see a fair playing field. Increased participation means higher transaction volumes – which is a sign of efficiency in an economic sense, as assets (homes) change hands to those who value them most, more readily. The liquidity of the housing market improves, which can even lead to more economic mobility (people aren’t stuck in a home for fear of transaction costs). In 2023, only about 4 million existing homes changed hands (a little under 3% of housing stock) (explore.fednow.org), which is a relatively low turnover partly due to market conditions but also frictions. If efficiencies remove some friction, turnover could increase in normal times, helping right-size housing choices for families (empty nesters downsizing, etc., which they might delay if transactions are too costly or slow).

Cautionary Note – If Change Is Not Adopted: It’s important to underscore the cautionary tone requested: if the traditional players do not embrace transparency and efficiency, the likely outcome is disruption. We have already seen outsiders and new entrants step in to offer what incumbents would not. If legacy brokerages continue with business-as-usual – high fees, minimal transparency, clunky processes – they could face a sharp decline. Consumers may bypass traditional agents entirely if viable alternatives arise (for example, some sellers are trying “Power Buyer” programs or selling to institutional buyers to avoid the listing process; some buyers are using services that refund a portion of the buyer agent commission or platforms that handle transactions for a flat fee). Regulators could also impose changes that are less favorable to incumbents if the industry doesn’t police itself. For instance, there have been calls for outright bans on dual agency (an agent representing both sides, which is seen as a conflict of interest) or for mandatory posting of all offers in a transparent system to prevent whisper sales. If such rules come without industry initiative, they might be more rigid. It’s in the enlightened self-interest of the industry to adapt and lead in these changes rather than resist until they are forced.

Another risk of not adapting is talent drain: new generations of real estate professionals might prefer to work at companies that use modern tools and offer innovative models. Traditional brokerages could struggle to recruit the best agents if those agents gravitate toward firms that give them AI-powered CRMs, quality leads from a popular platform, and a more future-proof value proposition to clients. Already, we see many top-producing agents joining tech-enabled brokerage brands or starting their own teams leveraging proptech solutions, rather than relying solely on the old brokerage brand name for success.

Novus: A Neutral, Evidence-Driven Modernizer (Case Example)

NOVUS is build the push toward AI-driven, transparent workflows. We present not as a traditional brokerage or a mere software vendor, but as a neutral platform and service provider that uses data and AI to eliminate legacy frictions in real estate transactions. In practical terms, Novus offers an integrated system where every step of a transaction – from lead generation to listing to closing – is facilitated or monitored by AI for efficiency, clarity and compliance. For example, AI algorithms auto-generate comprehensive property profiles (using public records, market comps, and even image recognition on photos to highlight features), which helps ensure that listings are accurate and informative, reducing the back-and-forth of correcting information. During negotiations, Novus’s platform might provide real-time data to both parties about local market trends to ground discussions in facts, increasing transparency about whether an offer is fair.

What will set Novus apart is its neutral, evidence-driven approach: it is not affiliated with a major brokerage brand and thus positions itself as an honest broker of information and process management. It leverages AI for tasks like scheduling, document verification, and progress tracking, which speeds up the deal and minimizes human error. All stakeholders (agents, buyers, sellers, lenders) can log into Novus’s portal to see exactly where things stand – whether an appraisal is scheduled, if the title search is complete, what items remain for closing – a level of operational transparency that preempts the uncertainty that often plagues deals. By serving as the central spine of the transaction, Novus will reduce the need for dozens of status update calls/texts/emails. In essence, it’s an example of moving from the fragmented, product-centric model (where each provider does its part in silo) to a distribution-centric, platform model where information flows freely and the entire value chain is orchestrated intelligently.

Crucially, Novus will use data analytics to identify inefficiency hotspots. If certain types of deals are seeing delays, Novus will analyze the cause (say, a particular lender consistently closing late or a certain contract clause causing confusion) and then proactively suggests solutions – perhaps flagging that lender to users or updating its contract templates to be clearer. This evidence-driven continuous improvement loop means the platform gets smarter and more efficient with each transaction. For regulators or auditors, a platform like Novus could even provide aggregated, anonymized data showing industry trends (e.g., average closing times, average fees) which contribute to a more informed oversight and policy-making environment.

By modernizing workflows in this neutral manner, Novus aims to benefit all parties without taking on the adversarial role that some disruptors have (it’s not trying to eliminate agents, for instance, but to empower all sides to work together more effectively). It represents how embracing AI and transparency can maintain or even boost the value of human professionals by freeing them from busywork and building trust with clients through openness. Companies like Novus indicate that the future of real estate might not belong exclusively to old brokers or new disruptors, but to those who can blend deep industry knowledge with cutting-edge technology and an unbiased commitment to efficiency and fairness.

Conclusion: Embracing Change for a Sustainable Future

The traditional real estate brokerage space, as we have dissected, is laden with inefficiencies that are no longer possible to ignore. Legacy systems, slow settlements, opaque operations, outdated business models, and high fees have cumulatively created a system that serves neither consumers nor the industry as well as it could. The status quo is unsustainable (medium.com) in the face of evolving consumer expectations, regulatory scrutiny, and technological progress.

However, within this challenge lies a tremendous opportunity. By identifying the pain points – and we have, from **17% of deals being delayed (nar.realtor) to $100 billion in commissions at stake (urban.org) – stakeholders can target reforms and innovations that yield outsized returns in efficiency. The push for transparency, exemplified by the recent NAR settlement, is injecting much-needed sunlight into the brokerage process. In parallel, AI and automation offer the tools to fundamentally re-engineer workflows, slashing delays and errors. The industry is witnessing a shift from old guard to new guard: those firms that are agile, data-driven, and distribution-focused are demonstrating how it’s possible to do more with less – more transactions, more customer satisfaction, with less wasted time, less ambiguity, and ultimately lower costs. The comparison between incumbents and tech-forward entrants is not just academic; it’s a preview of a potential realignment of market leadership.

For regulators and the public, the findings here should be encouraging: a more efficient real estate sector means lower costs for consumers, faster transactions, and arguably a more dynamic economy (as people can move and invest with less friction). It also means fewer contentious surprises – if fees are transparent and processes clear, there will be fewer disputes and litigation. Regulators should continue to encourage transparency and interoperability of data (for instance, ensuring MLS data can be accessible in consumer-friendly ways, or that fintech innovations like instant payments are adopted in real estate). At the same time, they should monitor the new models to ensure that in solving old inefficiencies we don’t create new ones (for example, if one platform dominates, guarding against anti-competitive behavior).

Industry providers – from the largest brokerage franchises to the smallest SaaS vendors – must recognize that clinging to legacy advantages (like proprietary data or non-negotiable fee structures) is a losing proposition. Instead, they should pivot to competing on value, experience, and efficiency. This could involve partnering with or adopting platforms like Novus that can enhance their service, investing in in-house tech development, or re-training staff to use data-driven approaches. It also means rethinking revenue models: in a distribution-centric world, there may be other ways to earn income beyond the standard commission – perhaps subscription models for premium service, or volume-based pricing, or referral fees from an ecosystem of services. Flexibility and openness to change will be key.

Finally, it’s worth emphasizing the human element even in this tech-driven discussion. Real estate is, at its core, about homes and thus about people’s lives and emotions. The inefficiencies we target are not about removing the human touch, but removing the unnecessary hurdles between the humans. A cautionary scenario if change is not adopted is not just an abstract loss of efficiency, but real human frustration: families losing out on dream homes because of sluggish processes, buyers overpaying because they lacked transparency, or honest agents losing clients because the overall experience was poor. Conversely, the vision of a modernized brokerage industry is one where agents, empowered by AI, can focus on advising and advocating for their clients, and where clients feel confident and informed throughout. It is an industry where trust is rebuilt through transparency and where deals close in days or weeks, not months, because the system itself propels momentum rather than impedes it.

In conclusion, the call to action for all stakeholders – be it a veteran broker, a policymaker, a tech entrepreneur, or a consumer – is to embrace the wave of modernization. The inefficiencies of the past are increasingly exposed, and the tools to eliminate them are at hand. The transition may be uncomfortable for some, but the reward is a healthier, more equitable, and more efficient real estate marketplace. Change is coming; indeed, it is already here in pockets. The question that remains is who will adapt and thrive, and who will be left behind. The hope is that through neutral, evidence-driven efforts and a shared commitment to improvement, the real estate industry will evolve into a model of efficiency and transparency that other sectors might even seek to emulate. The next few years will be critical in determining this outcome – a true inflection point where those willing to innovate and prioritize consumers can lead the industry into a new era of prosperity and trust.

Sources:

• Bobby Bryant, Medium.com (Dec 2024) – on outdated brokerage models medium.commedium.com.

• Federal Reserve FedNow briefing (2024) – on manual processes causing costly delays explore.fednow.orgexplore.fednow.org.

• Olena Kaplan, LinkedIn (May 2023) – on legacy IT costs (60–70% budgets) and siloed systems linkedin.comlinkedin.com.

• Urban Institute report (Mar 2024) – on $100B annual commissions and NAR settlement terms urban.orgurban.org.

• Greenspoon Marder legal summary (Sep 2024) – on NAR settlement rules for transparencygmlaw.comgmlaw.com.

• Bankrate (Aug 2024) – on new commission rules effect (no buyer agent fee on MLS, etc.)bankrate.combankrate.com.

• JLL/World Economic Forum (2020) – on benefits of transparency for efficiencyweforum.orgweforum.org.

• Brainvire Insights (2025) – on AI benefits (productivity +7.3%, etc.)brainvire.com.

• Mike DelPrete analysis (2019) – on tech-enabled brokerage scaling faster at lower costmikedp.com.

• RealTrends data (2023) – on flat-fee vs traditional agent productivityrealtrends.com.

• NAR Profile of Home Buyers (2024) – on nearly all buyers using internet for searchcrosscountrymortgage.com.

• Bankrate (2022) – on 17% of transactions delayednar.realtor and ICE Mortgage data (~44 days to close)bankrate.com.

• Ascendix Tech (Feb 2025) – on outdated products causing high costs and frustrationascendixtech.comascendixtech.com.

• Novus (case context) – illustrative of AI-driven workflow improvements (company literature, 2025).