NAR Settlement, A.I., and N+1 Order Consequences

A generated photo of a woman standing in front of a house looking into a dream-like neighborhood.

If you’ve been reading this rag, it’s likely not the first you’ve heard of the NAR settlement.

To jog your memory, In March, the world’s largest trade association, the National Association of Realtors (NAR), announced a historic $418 million settlement over accusations of "tying" broker commissions to create an artificially high agent pricing structure.

Many outlets have covered the NAR settlement news in-depth and the likely first-order consequences (the best one I’ve seen is from Brad Hargreaves at Thesis Driven).

The TL;DR of it is: massive changes to the economics of how homes are bought are looming. Incentives will likely overhaul commissions for agents on the buy side, reduce the number of agents in the U.S., and dismantle the monopoly of the MLS model.

These outcomes aren’t hard to imagine and I have nothing more to add to this conversation.

Instead, let’s speculate further (you are reading the blog of a VC – did you forget that?) and cover the second, third, and n+1 order effects of the settlement, and how a perfect storm of circumstances met by the convergence of A.I. might accelerate structural change in real estate brokerage faster than most are anticipating.

Second Order Effects

The NAR Settlement ruling is intended to create better transparency and eliminate commission negotiations from being obfuscated from buyers. Where previously, buy-side commissions would’ve been bundled into the mortgage, they are now just an expensive out-of-pocket transaction cost.

The interesting ripple effect is with interest rates at 7.5% and housing costs at all-time highs (both nominally and in real terms as a ratio of disposable income), no consumer wants to have higher out-of-pocket fees when purchasing a new home.

What the NAR settlement does is give buyers pemission to buyers to ask their agents for the first time in decades: "Can I pay less than 3% and receive the same services?" (Note: the average commission structure around most of the world is about 2%).

That simple permission to negotiate will cause other dominoes to fall:

  • Broker Consolidation: With over 100,000 brokerages in the U.S., as commissions shrink, many will face pressure on their margins. Smaller and less financially strong brokerages will struggle to survive, leading to consolidation. This could concentrate market power and reduce competition, altering the underlying economics of brokerages and potentially, the services bundle offered.
  • iBuyer Model As a Broker Channel: From Tyler Oakland, analyst covering Opendoor, “Opendoor offers a 1% commission for agents who bring sellers to the $OPEN platform, and this program in growing rapidly. 1% is lower commission than standard for agents, but meaningfully less work, and a great tool for agents to have in their pocket -- at least one cash offer for their clients no matter what. If commissions drop further, I believe agents will find this product increasingly attractive. Why set up a dozen open houses for weeks and weeks for 1.5-2% commission when you can have your client sell immediately to Opendoor for 1% cash, no contingencies?”
  • Reduced Aggregator Ad Spend: From Galleon Founder and Amanda Orson: “The universe of real estate agent ad dollars for ad-dependent aggregators will necessarily shrink as the number of real estate agents, and their relative compensation, does.”
  • New MLS/Premium Listing Services: With the MLS model now vulnerable for disruption, aggregators might attempt to build their own and start charging for basic listing services or offer premium placements for a fee, changing how property visibility is managed online. This mirrors international models where listing fees and premium tiers are common.
  • Buy Side Retainers: From Rob Hahn: “First, no buyer agent anywhere would take the kind of risk of not getting paid at all, without either (a) huge upside, or (b) minimized downside. Think contingency fees for lawyers, who take 30-40% of the recovery routinely because of the risk of not getting paid at all. Minimizing downside likely means […] some kind of a retainer up front to minimize risk, then a refund/net-out system if the seller does agree to compensation.”
  • Priority Listings from Aggregators: Another model path for aggregators: they could introduce fee-based or subscription services offering early access to priority listings or communication.

These are not trivial consequences. As you can see, the upstream impact of reduced broker income and ends up cascading down to brokerage economics, aggregator business models, and iBuyer growth.

And yet, we haven’t even introduced the fireworks of the fastest-growing technology wave since the internet into the conversation – hello artificial intelligence.

Third-Order Effects: Immediate A.I. Disruptive Cases

Today, LLMs excel at precisely the things needed to complete a successful buy-side real estate transaction.

Imagine an A.I. agent that could handle everything from meticulous property sourcing and virtual showings to accurate appraisal, provide analyst-level investment analysis, help with asset due diligence via computer vision, manage mortgage and insurance underwriting, and handle the complexities of the transaction. This digital agent could streamline the entire buying process, taking it fully cradle to crave. There’s no human presence, yes, but what’s the cost comparison? Simply the cost of compute, an infinitesimal number compared to the 3% fee we see today.

With A.I., the traditional barriers of time and availability disappear as well. Buyers and sellers could access a wealth of information and services 24/7, making the emotional and stressful process of buying a home more convenient, friendly, and faster than ever.

The crazy thing about this visualization is that it’s not a 5-10 year cycle to build this. There is currently no technical hurdle and teams like Tomo and others are actively building it.

Here are a few other immediate ways A.I. will likely impact RE brokerage:

  • The No Agent Experience – There’s a niche market of experienced buyers who prefer autonomy. These self-assured buyers are confident in their ability to manage the complexities of purchasing a home without an agent, relying instead on technology and their own expertise. Startups like Galleon are working on this trend, leveraging A.I. to fully automate the home listing process. The bet is that if you can reduce the friction to list a home to zero, buyers/sellers will be willing to try out a new model with obviously better economics.
    • All-In cost for the buyer? 0%
  • Verticalized Unbundling – Specialized A.I. tools could be developed for distinct stages of the real estate process. Whether it’s buying, securing mortgages, managing closing procedures, or arranging insurance, consumers can select and pay for only the services they need. This unbundling allows for greater customization and potentially lower costs. By focusing on specific aspects of the transaction, these tools can provide deeper, more tailored services that surpass the capabilities of broad, generalized solutions.
    • All-in cost for the buyer? Fee based, likely less than 1.5%
  • A.I.-Enabled BrokerageIs A.I. the missing piece to Redfin’s unprofitable model? A.I.-enabled brokerages could offer full-end-to-end services packages that combine human expertise with automation. By automating large components of the process, these brokerages could reduce operational costs and pass the savings on to buyers through lower commission rates. This model incentivizes buyers with lower costs while maintaining a level of human touch where it’s most needed. For example, A.I. can handle routine tasks and data analysis, while human agents step in for complex negotiations and personal advice.
    • All-in cost for the buyer? Likely less than 1.5%

N+ 1 Effects: A.I.'s Transformative Impact on Sales Personalization

Let’s take one step further into the future. Andrew Chen, GP at Andreessen Horowitz, in his essay "How A.I. Will Reinvent Marketing," takes the potential impact of A.I. further discussing A.I.’s transformative potential in sales and personalization. As Chen puts it, A.I. isn't just about automating tasks or crunching numbers; it’s about creating deeply personalized, scalable interactions that feel tailor-made for every individual consumer.

"When a marketer kicks off a new campaign, it might be more like spinning up an instance of millions of virtual A.I. salespeople -- or better yet, 'sales companions' -- that go out and engage consumers in the exact way they want to be engaged." – Andrew Chen

I can’t shake this idea applied to brokerage because it turns a current weakness of A.I. into a strength. Here’s some examples of how an A.I. product might enhance sales and personalization for real estate buyers:

  • Hyper-Personalized Engagement: A.I. can analyze vast amounts of data to understand individual buyer preferences and behaviors. This allows real estate platforms to offer recommendations and insights that are incredibly tailored to each user’s unique needs. As Chen suggests, these "sales companions" can interact in the exact manner each consumer prefers—whether through chat, personalized ads, emails, or calls. This level of customization was previously unimaginable and is now within reach.
  • Scalable, Human-Like Interactions: Traditional sales approaches struggle to scale while maintaining a personal touch. A.I. breaks this barrier. As Chen highlights, A.I. agents can handle millions of interactions simultaneously, each tailored to feel as personal as a one-on-one conversation. This scalability is particularly transformative in real estate, where each client typically requires significant personal attention and even, emotional hand-holding.
  • Predictive Insights and Recommendations: Leveraging predictive analytics, A.I. can forecast future buyer behaviors and market trends, offering proactive recommendations. This could mean suggesting properties that a buyer hasn’t yet considered but are likely to meet their needs, sharing analysis on asset value potential derived from a custom AVM, or advising sellers on the optimal time to list their property based on market dynamics.
  • Language and Cultural Adaptation: Chen notes that A.I. can transcend linguistic and cultural barriers, making it possible to engage with a diverse global audience. In real estate, this means A.I. agents could fluently communicate with buyers from different backgrounds, understanding and respecting cultural nuances that influence purchasing decisions.

Once you see A.I.’s potential to transform the full stack of the RE buying process, it’s hard to view it as speculation. It feels inevitable, simply because the cost will be so dramatically lower for consumers and eventually, the experience far superior. The only variable is time. And with that variable, we can speculate.


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