Which mortgage lenders does AI cite most?

Bottom line

Rocket Mortgage leads mortgages on AI search visibility with MVI 80, sitting firmly in the default-choice tier, ahead of the field, with Chase second at MVI 65.

• Who AI cites most

  Rocket Mortgage is cited in 75 of 100 prompt-engine pairs (75%). 95% confidence interval 71-86.

• Concentration

  The top 3 brands (Rocket Mortgage, Chase, Better) capture 48% of all citations in this segment. 8 of 20 tracked brands are cited in fewer than 1 in 10 prompt-engine pairs.

• Where the field sits

  Of 20 brands tested: 1 in default-choice, 3 in repeat-use, 3 in first-encounter, 13 not yet cited. Overall, AI cites a brand from this segment in 20% of buyer-intent prompt-engine pairs.

• Engine asymmetry

  Better is cited in 78% of Claude prompts but only 33% on Perplexity, visibility is engine-specific, not universal.

• Notable absence

  Mr. Cooper is not yet cited, 0 prompts across all 100 prompt-engine pairs. A recognizable brand AI is not yet surfacing.

Analyst note

Rocket Mortgage leads with an 80 MVI, far ahead of Chase at 65.

Rocket Mortgage has an 80 MVI, leading Chase by 15 points. Its strength is in discovery prompts with a score of 95, but it drops to 65 in the filtered phase. Chase scores 87 in discovery but falls to 48 in evaluation. There are 13 brands that are not visible in this segment.

Risk:LoanDepot is strong in discovery at 55 but nearly invisible in the filtered phase at 7, which poses an engine concentration risk.

Headline finding

Rocket Mortgage leads mortgages on AI search visibility with MVI 80, sitting firmly in the default-choice tier.

Citation rate per engine

How often each engine cites a brand from this category as a recommendation, averaged across all 20 brands tested.

Phase strength across the category

Which buyer-intent phases are easiest vs hardest to win in mortgages. Citation rate averaged across all brands tested. Phase weights are part of the MVI formula.

Who wins which buyer phase

Top 12brands by MVI mapped against the four buyer-intent phases. Each cell shows the brand's citation rate for that phase, color-coded so the visual pattern tells the story: a brand strong across all four phases reads as a horizontal orange band; a brand strong only at Discovery but weak at Evaluation reads as a left-heavy gradient. This is the segment's findings against the panel.

How to read it. Strong horizontal band = durable brand, AI cites it across the entire funnel. Left-heavy gradient = brand with awareness (Discovery) but weak recommendation (Evaluation), the demand-leak pattern from Finding 03. Right-heavy gradient = brand AI considers in Comparison and Evaluation but does not surface in initial Discovery, the anti-leak pattern. Color steps: dark orange ≥75%, orange ≥50%, peach ≥30%, light ≥10%, beige >0%, neutral 0%.

For CMOs in mortgages

What this report means for your mortgages portfolio

Each bullet is a category-specific decision derived from this month's data, with the mapou service that operationalizes it.

The mapou Visibility Index

Ranked by MVI score (Wilson 95% CI shown). The Spread column shows the gap between each brand's best and worst engine, under 15pp is durable, 50pp+ is engine-dependent. Per-engine columns show the count of prompts where each engine cited the brand as a recommendation (out of 20). Read each column as a signal: when ChatGPT cites you but Gemini doesn't, your gap is engine-specific. When all five miss you, the gap is foundational.

Strategic insights for mortgages

Five derived metrics computed from the same data, surfacing how this segment behaves on AI search. See the State of AI Search for cross-segment comparison.

Brands cited most across the category

Aggregated across every (brand × prompt × engine) combination tested. The most-cited brands here are the names AI consistently surfaces when buyers ask about mortgages.

Emerging brands AI is citing in mortgages

Brand names AI engines surfaced for mortgages prompts that are not currently on the mapou tracked panel. Ranked by mention count and engine breadth. These are panel candidates, brands AI considers part of the category even though we are not yet measuring them.

Method. Aggregated across the canonical run for mortgages. For every (panel brand × prompt × engine) we record the brand names the analyzer extracted (capped at 6 per response), then drop names that match the tracked panel or its aliases, plus a denylist of generic category terms. Threshold to qualify: at least 3 mentions across at least 2 of 5 engines. Click any row to see the AI quote that surfaced the brand. Some entries may be tracked elsewhere on mapou but not in this segment, in which case AI considers them cross-category competitors. Reviewed monthly to inform panel additions.

How mortgages rankings shift by buyer persona

The MVI score is calibrated to a generic shopping-assistant prompt. But buyers don't arrive generically. We re-ran the same 20 canonical prompts five more times, each with a different buyer-persona signal in the system prompt: budget-conscious, premium, working professional, first-time, values-driven. Top-3 overlap with baseline: 87%. Leader holds across all personas: yes.

Each cell is the citation rate (out of 20 canonical prompts) for that brand under that persona, ChatGPT only. Cells are tinted green when a brand gains 5+ percentage points vs baseline, orange when it loses 5+. Strong tints flag a 20+ percentage-point swing. Top 8 baseline brands shown; full per-persona data is in data/research/persona-robustness/2026-05-07-1625/. The full methodology is on the State of AI Search page.

The 20-prompt taxonomy

Every brand in this report is tested against the same 20 canonical prompts, spanning the four MVI dimensions (Discovery, Filtered Discovery, Comparison, Evaluation). The prompt set is fixed at methodology v1.0 and reused every monthly run, so MVI deltas are paired comparisons not noise.

Methodology v1.0. MVI is mapou's proprietary 0-100 visibility score across 5 AI engines and 4 buyer-intent dimensions. 95% Wilson confidence intervals. Equal engine weighting. See the framework →