40% of owners are mortgage-free as AI moves into housing

Forty percent of U.S. homeowners own their homes without a mortgage, according to Census Bureau statistics cited by Real-REMAX Group CEO Tamir Poleg. That equity cushion helps explain why today’s strained housing market does not automatically point to another foreclosure crisis—even if sales remain slow and prices soften.

At the RISE conference in Las Vegas on October 20, Poleg warned agents to prepare for several more years of sluggish existing-home sales in the United States and Canada. He also predicted that U.S. prices could decline over the coming months and years and argued that artificial intelligence will eventually run much of housing’s transaction infrastructure. For sellers, the practical issue is not whether that sweeping AI forecast comes true exactly as stated. It is how a slower market and increasingly automated systems could affect pricing, timing and the amount left after closing.

Home equity may limit distress without preventing price cuts

Poleg said the housing recovery anticipated for 2026 has not arrived. Affordability remains the central constraint: mortgage rates are elevated, ownership costs consume a large share of household income, and consumers are carrying substantial debt. He was also skeptical that mortgage rates would return to 4.5% soon.

At the same time, he distinguished current conditions from 2008. Foreclosures have increased somewhat but remain well below their historical average, he said, while bankruptcies have been easing rather than climbing in tandem. Mortgage-free owners and borrowers with substantial accumulated equity have more options than deeply underwater owners did during the financial crisis.

That distinction matters to anyone preparing to sell. A market can avoid widespread forced sales and still produce longer listing periods, more buyer negotiation and selective price declines. Equity protects an owner’s balance sheet, but it does not guarantee that buyers will accept a price based on a neighbor’s peak-era sale.

Sellers should therefore separate two questions: whether they are financially secure and whether their asking price is competitive. A mortgage-free owner may have room to wait, but holding out has a cost in taxes, insurance, maintenance and missed opportunities. An owner with a loan needs to compare the likely sale proceeds with the payoff amount and every expected transaction expense before choosing a price.

AI is likely to change transaction work before home values

Poleg’s larger claim was that AI is well suited to industries built around extensive records, detailed rules and repetitive paperwork. In housing, he expects the technology to become more influential across brokerage transactions, lending and other ownership services. HousingWire reported that he sees the Real-REMAX combination as a way to expand the pool of listing, transaction and conversation data available to the companies’ technology.

The combined organizations represent a substantial flow of information. RealTrends Verified data based on 2025 production ranked The Real Brokerage fifth by transaction sides and sales volume, with 131,047 sides. REMAX ranked sixth among brands by sides, with 378,961. Poleg said the companies’ scale could give them greater control over how their data is used and monetized.

For sellers, AI’s near-term effect is more likely to appear in the mechanics of a sale than in a machine independently setting the home’s value. Automated systems can organize records, flag missing documents, compare prior transactions, draft marketing materials and help agents respond faster. Those tools may shorten administrative delays, but they cannot repair bad source information.

A wrong square-footage entry, unrecorded permit, inaccurate room count or outdated property description can be repeated across valuation and marketing systems. Before listing, sellers should review the public record, prior listing history, tax data, disclosures and basic property facts. Automation makes clean data more useful—and errors more scalable.

Pricing should reflect the buyer’s monthly payment ceiling

If Poleg is right that sales will remain slow and prices may weaken, pricing from the market’s current edge becomes more important. Buyers generally shop according to a monthly payment limit, not the seller’s equity position or original purchase price. Higher borrowing costs reduce what the same household can comfortably offer.

A seller should examine recent closed sales, competing active listings, pending properties and listings that were withdrawn or expired. Closed sales show what buyers accepted in the past; active and failed listings reveal what they are rejecting now. In a shifting market, relying only on the highest nearby comparable can lead to an extended listing and later reductions.

Timeline should be part of the initial pricing decision. A seller who must move by a fixed date may benefit from pricing to attract qualified buyers early. Someone with flexibility can test a narrower range, but should establish in advance when to reassess based on showings, offers and competing inventory. Waiting without a defined review point can turn a manageable adjustment into months of carrying costs.

AI-generated valuations can be useful as reference points, not final answers. They may miss condition, noise, views, renovation quality, insurance complications or a rapidly changing block-level supply picture. Sellers should ask what comparable properties support a recommended price and which differences require human judgment.

Seller net depends on concessions, delays and verified documents

The highest offer is not always the offer that produces the strongest net. In an affordability-constrained market, buyers may request closing-cost credits, mortgage-rate assistance, repairs or price reductions after inspection. Sellers should compare offers using estimated proceeds after concessions, commissions, taxes, liens, payoff amounts and expected repair obligations.

Financing risk also matters. A higher financed offer with weak contingencies can be less dependable than a slightly lower offer with stronger documentation and a realistic appraisal position. AI may help screen files or accelerate underwriting, but sellers should still confirm deadlines, deposit terms, financing conditions and responsibility for requested credits.

Data use deserves attention as brokerages build larger AI systems. Listing photos, property records, disclosures and transaction communications can all become inputs. Sellers should ask who receives their information, whether marketing materials were machine-generated and how factual claims were verified. A polished description is not a substitute for an accurate disclosure.

The immediate seller takeaway is straightforward: do not plan around a sudden return of cheap mortgages, and do not assume technology will rescue an overpriced listing. Build the asking price around current buyer capacity, calculate the cost of extra time and verify every important property fact before automated systems distribute it. AI may change who processes the paperwork, but the seller still bears the financial consequences of the final contract.