Home Equity Loans: 2026 AI Market Discovery Index

Answer Capsule

In the home equity loans category for May 2026, AI recommendation power is heavily concentrated. Rocket Mortgage leads with 67 valid recommendations and a modeled monthly captured value of $90,369, more than double the next competitor. Bank of America holds a strong second position with 93 valid recommendations and $38,943 in captured value. Several lenders including Discover Home Loans and Bethpage Federal Credit Union register zero AI recommendations, creating a clear two-tier market.

Executive Summary

AI search platforms are reshaping how borrowers discover and compare home equity lenders, and the data shows a market already splitting into clear winners and invisible players. Rocket Mortgage dominates AI recommendations with 67 valid recommendations across 297 observations, capturing an estimated $90,369 in monthly recommendation value. That single figure exceeds all other measured lenders combined.

Bank of America holds a strong second position with 93 valid recommendations and $38,943 in monthly captured value, though its average rank of 1.89 trails Rocket Mortgage's 1.42. Figure, a fintech lender, appears in 46 observations with a perfect net sentiment score of 1.0 but captures only $9,270 in monthly value, suggesting it is mentioned positively but rarely placed in top recommendation slots.

The most striking finding is the visibility gap. Several well-known lenders appear in AI responses but fail to convert that presence into recommendation credit. TD Bank appears in 57 observations but earns only 27 valid recommendations. LendingTree appears in 35 observations but earns just 6. Discover Home Loans and Bethpage Federal Credit Union register zero presence across all platforms tested.

Recommendation power in home equity lending is not about brand awareness. It is about which lenders have the citation architecture, source visibility, and structured content that AI systems use to build shortlists. The lenders that solve for this will capture the growing share of borrowers who start their search with AI.

The AI Discovery Shift in Home Equity Loans

Traditional home equity marketing relies on brand recognition, paid search, and comparison site placement. AI platforms change this fundamentally. When a borrower asks an AI assistant for the best home equity loan options, the system returns a curated shortlist built from publicly available evidence, not a list of familiar brand names.

Being a known lender is no longer sufficient. The AI must find, verify, and trust the information it retrieves about each lender. Lenders with strong official content, positive review signals, clear rate transparency, and consistent citation across authoritative sources are more likely to earn recommendation credit. Lenders that rely solely on brand equity or paid placement risk invisibility.

The data confirms this pattern. Rocket Mortgage and Bank of America dominate not because they are the only well-known lenders in the category, but because their digital footprint aligns with what AI systems prioritize. Meanwhile, lenders like Discover Home Loans and Bethpage Federal Credit Union have no AI presence at all, despite being legitimate options for borrowers.

Ranked recommendations matter far more than simple appearances. A lender that appears in 57 observations but earns a top-3 rate of 3.7% is commercially weaker than a lender that appears in half as many observations and consistently earns the first position. The shortlist slot is the commercial outcome. Everything else is background noise.

Directional Category Leaders

1. Rocket Mortgage

Rocket Mortgage appears in 116 of 297 observations, a raw presence rate of 39.1%. It earns 67 valid recommendations, a coverage rate of 22.6%, with a top-3 rate of 17.5% and a rank-1 rate of 12.1%, the highest in the category. Its average recommended rank of 1.42 means it is typically the first or second lender listed when it appears. Modeled monthly captured recommendation value: $90,369.

The public interpretation: Rocket Mortgage is the clear AI shortlist leader in home equity loans, consistently ranked first across multiple platforms.

2. Bank of America

Bank of America appears in 138 of 297 observations, a raw presence rate of 46.5%, the highest in the category. It earns 93 valid recommendations, a coverage rate of 31.3%, with a top-3 rate of 20.9% and a rank-1 rate of 8.4%. Its average recommended rank of 1.89 is solid but trails Rocket Mortgage. Modeled monthly captured recommendation value: $38,943.

The public interpretation: Bank of America has the broadest AI visibility in home equity loans but is more likely to appear as a secondary recommendation rather than the top pick.

3. Figure

Figure appears in 46 of 297 observations with a raw presence rate of 15.5%. It earns 45 valid recommendations, a coverage rate of 15.2%, with a top-3 rate of 8.8% and a rank-1 rate of 1.0%. Its average recommended rank is 2.35. Figure achieves a perfect net sentiment score of 1.0, meaning every mention is positive. Modeled monthly captured recommendation value: $9,270.

The public interpretation: Figure is consistently mentioned positively but rarely earns the top recommendation slot, limiting its commercial impact despite strong sentiment.

4. TD Bank

TD Bank appears in 57 of 297 observations with a raw presence rate of 19.2%. It earns 27 valid recommendations, a coverage rate of 9.1%, with a top-3 rate of 3.7% and a rank-1 rate of 1.0%. Its average recommended rank is 2.36. Notably, 20 of its 57 mentions are neutral, suggesting it is often listed without strong endorsement. Modeled monthly captured recommendation value: $1,099.

The public interpretation: TD Bank has decent AI visibility but struggles to convert mentions into high-value recommendation positions.

5. Connexus Credit Union

Connexus Credit Union appears in 50 of 297 observations with a raw presence rate of 16.8%. It earns 26 valid recommendations, a coverage rate of 8.8%, with a top-3 rate of 3.0% and a rank-1 rate of 0.7%. Its average recommended rank is 2.22. Its net sentiment score of 0.66 is the lowest among lenders with meaningful presence. Modeled monthly captured recommendation value: $406.

The public interpretation: Connexus Credit Union appears regularly but with mixed sentiment and low rank positions, limiting its shortlist power.

6. LendingTree

LendingTree appears in 35 of 297 observations with a raw presence rate of 11.8%. It earns only 6 valid recommendations, a coverage rate of 2.0%, with a top-3 rate of 1.4% and a rank-1 rate of 1.0%. Modeled monthly captured recommendation value: $408. Despite being a widely recognized marketplace, LendingTree is mentioned more as a reference than a recommended option.

The public interpretation: LendingTree has strong brand recognition but weak AI recommendation power, appearing in the background rather than on shortlists.

7. Achieve

Achieve appears in 11 of 297 observations with a raw presence rate of 3.7%. It earns 9 valid recommendations, a coverage rate of 3.0%, with a top-3 rate of 2.0% and a rank-1 rate of 0.3%. Its net sentiment score of 0.91 is strong, but its low presence limits its reach. Modeled monthly captured recommendation value: $144.

The public interpretation: Achieve is positively framed when mentioned but has insufficient AI presence to compete for shortlist positions at scale.

8. Spring EQ

Spring EQ appears in only 3 of 297 observations with a raw presence rate of 1.0%. It earns 3 valid recommendations with a coverage rate of 1.0%. Modeled monthly captured recommendation value: $41. While its net sentiment is perfect, its presence is negligible.

The public interpretation: Spring EQ has near-zero AI visibility and is effectively absent from AI-driven borrower discovery.

9. Discover Home Loans and Bethpage Federal Credit Union

Both lenders register zero presence across all 297 observations. They appear in no AI responses, earn no recommendations, and capture no recommendation value.

The public interpretation: Both lenders are invisible to AI systems and are missing entirely from AI-driven borrower discovery.

The Buying Moments That Now Decide the Category

Best Home Equity Loans: Discovery and Ranking

This cluster accounts for 242 of 297 observations and represents the moment borrowers search for the best available options. It is the highest-value cluster with a modeled monthly opportunity of $140,519. Rocket Mortgage leads with $90,282 in captured value, followed by Bank of America at $38,910 and Figure at $9,270. All remaining lenders capture less than $1,100 combined. This cluster determines who appears on the initial shortlist, making it the most commercially critical moment in the borrower journey. Winning here shapes every subsequent stage of the decision.

Home Equity Loan Comparisons: Head-to-Head Evaluation

This cluster accounts for 22 observations and represents borrowers comparing specific lenders directly. Only LendingTree and Rocket Mortgage earn any recommendation value here. The low observation count suggests AI platforms generate fewer direct comparison responses in this category, but the cluster still matters for borrowers deep in evaluation. LendingTree's marginal value here, despite its comparison-platform identity, underscores how poorly it is performing relative to brand expectation.

Home Equity Loan Pricing: Cost and Plan Evaluation

This cluster accounts for 33 observations and represents borrowers evaluating rates and terms. Rocket Mortgage leads with the highest captured value, followed by Bank of America. LendingTree appears in 5 observations but earns no recommendation credit, confirming a pattern of reference mentions without shortlist conversion. Pricing queries are less dominated by a single lender than discovery queries, but Rocket Mortgage retains a consistent advantage even at the cost-evaluation stage.

Why Recommendation Power Is Concentrating

Recommendation power in home equity lending follows a clear pattern tied to the evidence layer that AI systems use to build responses. AI platforms retrieve information from official lender websites, comparison articles, review platforms, financial news, and community discussions. Lenders that appear consistently across these sources with positive framing are more likely to earn recommendation credit.

Rocket Mortgage benefits from extensive official content, strong review signals, and frequent citation in third-party comparison articles. Bank of America benefits from its scale as a national institution with broad media coverage and official rate pages that AI systems can retrieve cleanly. Figure, despite excellent sentiment, lacks the citation depth to earn top positions consistently, which explains the gap between its sentiment score and its commercial value.

The concentration effect is self-reinforcing. Lenders that earn recommendation credit appear in more AI responses. More appearances create more citation opportunities across third-party sources, which strengthens their position in future responses. Lenders that are absent or weakly cited fall further behind as AI systems refine their retrieval patterns.

Citation architecture matters here in a specific way. This does not mean citation count equals endorsement. It means that publicly available evidence helps AI systems retrieve, compare, trust, and recommend brands. The quality, structure, and consistency of that evidence layer is what separates a top-1 recommendation from a neutral mention.

The Category's Most Visible Warning Sign

The complete absence of Discover Home Loans from all 297 observations is the most commercially significant warning sign in this dataset.

Discover is a nationally recognized financial brand with a broad product portfolio. In home equity lending specifically, it has historically been an active lender. Yet across six AI platforms and 297 observations, it appears nowhere. No mentions, no sentiment, no recommendation credit.

This is not weak performance. It is total invisibility. Every borrower who uses AI to start their home equity search will never see Discover as an option. The brand does not exist in this discovery channel.

The commercial implication is direct. A lender with national advertising spend, a real product, and genuine borrower eligibility is capturing zero AI-driven discovery value while competitors with smaller brand footprints earn thousands of dollars in monthly recommendation value. For Discover, this represents a structural gap between its market presence and its AI authority, one that will not close without deliberate intervention.

What This Means for the Category

Shortlist compression is already the defining feature of this market. Two lenders capture 92% of the modeled monthly recommendation value. The remaining eight lenders share 8%. This ratio will not improve naturally for the underperforming group. It will worsen as AI platforms become more confident in their shortlist patterns and borrowers increasingly trust AI-generated recommendations.

Competitor displacement is happening in real time. Lenders that appear in AI responses but fail to earn recommendation credit are being displaced by lenders that earn top positions consistently. TD Bank appears in nearly 20% of observations but captures less than 1% of the recommendation value. Its AI presence is real. Its AI authority is not.

Trust-source dependency is becoming a structural competitive factor. AI systems rely on publicly available evidence to justify recommendations. Lenders that invest in official content, rate transparency, structured data, and authoritative third-party citation will have a compounding advantage. Lenders that treat AI discovery as a passive outcome rather than an active strategy will lose share to competitors who do not.

AI discovery is now part of the buyer choice architecture. Borrowers who start their search with AI receive a shortlist that frames their entire decision journey. A lender that is not on that shortlist is not in the consideration set, regardless of brand spend, product quality, or rate competitiveness. Stronger entity, content, source, and citation architecture is the requirement for participation.

What This Public Benchmark Does Not Include

- Full cluster dataset for all 10 measured clusters

- Prompt-level response tables showing exactly which lenders appear in which responses

- Citation-source failure maps identifying which sources are missing or underperforming

- Platform-by-platform recovery priorities for each lender

- Entity and schema diagnostics for structured data readiness

- Source-layer gap analysis identifying which content types are absent

- Company-specific content recommendations for improving AI visibility

- Exact competitor threat profiles for each lender

- Full paid opportunity model with platform-specific value breakdown

This page shows the market shape. The paid report shows the repair map.

Methodology and Disclaimers

Market studied: Home equity loans, including lenders offering home equity loans, home equity lines of credit, and related products.

Brands included: Figure, Achieve, Bank of America, Bethpage Federal Credit Union, Connexus Credit Union, Discover Home Loans, LendingTree, Rocket Mortgage, Spring EQ, and TD Bank. This is not a full market census.

Data collection window: May 2026.

AI platforms tested: Gemini, ChatGPT, Copilot, Perplexity, Google AI Mode, and Google AI Overviews.

Observations analyzed: 297 total observations across all platforms and clusters.

Prompt categories: Discovery and ranking (consideration), head-to-head comparisons (evaluation), and pricing and plan evaluation (decision). Full report includes 10 clusters; this public version covers 3.

Definition of a mention: A mention means the company appeared in an AI-generated response, regardless of sentiment or position.

Definition of a valid recommendation: A valid recommendation is a positive, shortlist-quality or ranked recommendation that earns recommendation credit. Visibility is not the same as recommendation credit.

Scoring metrics used: Valid recommendation coverage, top-3 rate, rank-1 rate, average recommended rank, net sentiment score, and modeled monthly captured recommendation value.

Limitations: This is a point-in-time benchmark. AI outputs can vary across sessions and change over time. Modeled values are estimates and do not represent guaranteed or actual revenue. This report is not a full audit and does not represent a complete market census.

For a Company-Specific Authority Index Report

For a company-specific Authority Index report, the deeper analysis would show which prompts each company wins or loses, which AI platforms are under-recognizing the brand, which source layers are shaping recommendations, and what changes may improve AI shortlist eligibility.