But I recently saw Refine promoting partnerships with journals. The stated goal: reduce reviewer workload in an era of rising submission volumes. If AI can help screen papers, summarize contributions, identify weaknesses, and streamline referee reports, many editors would understandably be interested.

Yet this raises a deeper question. What happens when both sides of the process rely on the same class of tools?

If authors use LLMs to preemptively polish papers, address likely criticisms, and improve exposition, then journals may receive submissions that are increasingly optimized for AI-based review criteria. In turn, review tools may be forced to search for smaller and smaller flaws in already polished manuscripts. The equilibrium could become strange: authors using AI to satisfy reviewers, reviewers using AI to uncover issues generated by authors anticipating AI reviewers. We may be entering a recursive loop in which machines are increasingly evaluating work prepared for machines.

Another possibility is that these tools eventually stop behaving like harsh gatekeepers and instead become validators. If a paper meets certain methodological, statistical, and presentation standards, the review system may simply certify it as technically sound. In that world, peer review shifts away from subjective judgments of novelty or style and toward verification.

This leads to an even more provocative implication. If journals can use agentic review tools, why can authors not use agentic response tools? Imagine submitting a paper alongside an expert AI agent trained on the manuscript, data, and code. Reviewers request robustness checks, additional tables, alternative specifications, or clarifications. The author’s agent runs the analysis, produces the output, and responds instantly. After several rounds of machine-to-machine interaction between review agents and author agents, the paper emerges revised and publication-ready.

Push the idea one step further, and the role of journals themselves becomes less obvious. Researchers could upload papers to open repositories such as arXiv or SSRN, accompanied by a transparent AI-generated review report evaluating correctness, assumptions, robustness, and contribution. Readers would then observe both the paper and the audit trail. Rather than waiting months for editorial gatekeeping, the market for ideas could operate in real time.

Under this scenario, the central scarcity is no longer correctness. AI systems may make it easier to detect coding errors, flawed identification strategies, missing citations, or weak robustness checks. Technical quality becomes cheaper to verify. The scarce resource instead becomes attention. If many papers are methodologically sound, then the key question is not whether a paper is “correct,” but whether it is important, useful, original, or worth reading.

That may be the real future of research publishing. Journals historically bundled multiple functions: quality control, certification, filtering, and distribution. AI may unbundle them. Verification can be automated. Distribution is already open. What remains hardest to automate is judgment about relevance.

If so, LLM review tools are not just changing peer review. They may be quietly redefining why peer review exists at all.

This post takes stock of where AI stands in residential real estate today. We focus on three areas: how consumers search for and discover homes to buy, how AI-driven valuation models hold up under stress, and the fact that automated decision-making in mortgage underwriting and tenant screening decisions may not be compliant with existing regulations. We draw on industry data, academic research, and recent developments to bring these dynamics into focus.

Subscribe now

Conversational AI Search: A New Interface for Home Discovery

Redfin launched its conversational search in November 2025, built in partnership with Sierra AI. Rather than selecting filters for bedrooms, price range, and zip code, users describe what they are looking for in their own words (and in a language of their choosing). The system interacts with the user and refines results through back-and-forth exchanges. The early results are notable: in Redfin’s initial testing, conversational search users viewed nearly twice as many listings as users using standard search. Users using conversational search were also 47% more likely to request home tours.

Zillow responded with “Zillow AI Mode” in March 2026. Beyond offering help with search, this conversational assistant provides guidance throughout the transaction. Users can ask questions like "How has this home's Zestimate changed over time?" or "What would a fair offer be?" The system remembers preferences across sessions, adapts to user behavior, and draws on Zillow's proprietary data and valuation models. Zillow's CEO has called generative AI a "bigger platform shift than mobile" for the company.

Both Zillow and Redfin also launched apps within OpenAI’s ChatGPT (Zillow in October 2025, Redfin in February 2026 ). These apps allow users to search for homes directly inside the chatbot. This degree of integration has raised legal questions about IDX licensing agreements, which govern how data from Multiple Listing Services (real estate brokers databases) can be displayed. Critics argue that transmitting listing data to a third-party AI platform may fall outside the scope of existing agreements.

An important backdrop to the developments discussed above is industry consolidation. Rocket Companies acquired Redfin for $1.75 billion in July 2025, creating a vertically integrated platform that spans home search, brokerage, and mortgage lending. The combined entity now controls over 14 petabytes (million gigabytes) of data across 100 million properties. Meanwhile, Zillow generated $2.6 billion in revenue in 2025, up 16% year over year, and is pursuing what it calls a “housing super app” strategy. The implication is clear: the AI arms race in real estate favors large, data-rich incumbents.

AI-Powered Home Valuations: The Limits of the Algorithm

Automated valuation models (AVMs) are now ubiquitous. Zillow’s Neural Zestimate covers over 100 million U.S. homes using deep learning, and similar models are embedded in mortgage underwriting, portfolio management, and consumer-facing apps. But AVMs remain subject to important limitations, particularly in markets with heterogeneous housing stock and exposure to catastrophic risk.

Zillow reports a median error rate of 1.9% for on-market homes and 7.1% for off-market properties. On a home priced at $1 million, the off-market error translates to approximately $71,000. These are median figures; the distribution has a long right tail, meaning a substantial share of estimates are considerably further off. Recent research has shown that these valuation errors have played a key role in the downfall of Zillow’s iBuyer business.

In a previous R.E.A.L. post, we documented that trust in algorithmic valuations has real market consequences. Following Zillow’s shutdown of its iBuyer program in November 2021, the absolute gap between listing prices and Zestimates increased by 26.8%, sellers more frequently priced above the Zestimate, and homes actually sold faster and at higher premiums. Diminished algorithmic trust did not reduce market activity; it shifted how participants used the information.

The LA Wildfire Stress Test

The January 2025 Los Angeles wildfires provided the most severe real-world test that consumer-facing AVMs have ever faced in a major U.S. metro. According to Zillow’s own analysis, approximately $46 billion in residential housing value was located within the fire perimeters, encompassing 19,605 units with a median value of nearly $1.95 million.

The impact on actual transaction prices was dramatic. In the Palisades fire burn zone, average sale prices dropped 33%, from approximately $3.6 million to $2.4 million. In the Eaton fire zone, the decline was 62%: from $1.8 million to roughly $684,000. As we documented in a recent R.E.A.L. post, this drop in valuations coincided with a wave of damaged homes coming to market.

AVMs are fundamentally unable to account for sudden physical destruction. They rely on comparable sales data, tax assessments, and historical transaction records. None of these can update instantaneously after a disaster. An algorithm cannot detect that a house has burned down. This is an inherent limitation, not a design flaw, but it underscores why algorithmic estimates should not be treated as substitutes for professional appraisals, especially in volatile or disaster-affected markets.

AI in Mortgage Underwriting: Faster, but for Whom?

AI-driven mortgage underwriting has moved from experimental to standard practice. Lenders using AI models report a 90% increase in processing speed. For standard approval cases, end-to-end origination (from application submission to fund disbursement) can be reduced from 3–5 days to under 60 minutes at some institutions. Approximately 85% of mortgage lenders now use AI for fraud detection, and industry data imply that AI has helped reduce mortgage application fraud by roughly half.

The emerging frontier is what the industry calls “agentic AI”. These are systems that autonomously retrieve documents, query data sources, run risk models, resolve exceptions, and generate underwriting memos without requiring human instruction at each step. This represents a shift from AI as a tool that assists underwriters to AI as an agent that manages routine workflows end-to-end, with human oversight reserved for complex cases and quality control.

The market for AI-powered lending was valued at $109.7 billion in 2024 and is projected to reach $2.01 trillion by 2037, growing at a 25.1% compound annual growth rate. These figures reflect an industry-wide bet that AI may become the default infrastructure for mortgage origination.

Speed and efficiency gains are real. But they raise two important questions: Do the benefits accrue equally across borrower populations? And are algorithmic decisions compliant with existing regulations? The Massachusetts Attorney General settled a fair lending action in July 2025 against a lender whose AI underwriting model produced disparate impact along racial and immigration-status lines. This case is unlikely to be the last. AI systems can likely infer restricted variables, which should be excluded from credit decisions (such as race and gender), from combinations of legally permissible variables.

Tenant Screening

Landlords increasingly rely on AI-powered tenant screening programs that evaluate applicants based on credit scores, eviction records, and criminal background checks. Evidence from multiple studies and lawsuits indicates that these programs disproportionately deny applications from Black and Latino renters. This is partly due to the usage of data that are incorrect or outdated.

In one notable case, Harbor Group Management was found to have deployed an AI leasing assistant that issued blanket denials to Housing Choice Voucher holders. A growing body of psychological research further suggests that bias introduced by an AI system can persist in human decision-making even after the AI is no longer being used.

Looking Ahead

The integration of AI into the housing market is proceeding rapidly and on multiple fronts. Conversational search is changing how buyers discover homes. Automated underwriting is accelerating mortgage approvals. AI-driven risk models are providing information that traditional assessments miss. Industry consolidation is concentrating data and market power in a small number of large platforms.

For researchers, the priority is clear: we need rigorous, independent measurement of how AI tools perform across demographic groups, property types, and market conditions, including in disaster-affected and historically underserved areas. For policymakers, the challenge is crafting regulations that preserves the genuine efficiency gains of AI while ensuring that automated systems meet the same standards of fairness and accountability that we expect of human decision-makers.

Share