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Industry Report

Homebuilders New Homes

Content Phrases
Dominant · Search Engine Appearances ρ=0.538

AI recommendation signal analysis across 1,503 domains and 11 buyer personas in Homebuilders New Homes, led by lennar.com as the most LLM-recommended domain.

1,503Domains Tracked
11Buyer Personas
56.1MReddit Posts
281KWikipedia Articles
31.7MOpen Web Matches
content_phrases.report
TermScore
new home construction
24.3
new construction homes
18.1
new homes
15.5
This is a shortened preview of the Homebuilders New Homes report

Many tables and charts on this page show only the top few results; the full data behind them runs far deeper. The complete report unlocks every row, chart, and download for this industry.

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About This Report

How to use this page

Industry view: this page aggregates every persona's data for the whole industry, plus industry-wide queries. Pick a persona from the sidebar to narrow the lens to one segment.
Use Case

Speak the language LLMs expect

These are the phrases AI models associate with this market. Work them into your pages and positioning so you describe your offer in the same vocabulary the models use when matching brands to buyer questions.

How It's Calculated

Where the numbers come from

We ask multiple LLMs which content phrases they associate with this industry or persona, then aggregate across models and runs. Score is a rank-weighted sum; count and % are plain tallies.

Overview

What's on this page

Phrase rankings with per-model filtering and a side-by-side comparison of any two models.

Keyword Data

Content Phrases

Phrases and terms LLMs associate with Homebuilders New Homes across all personas, ranked by recommendation strength. Score is a rank-weighted sum across every model (higher-ranked appearances count for more); count and % are plain appearance tallies.

Filter By Model

Content Phrases by model

Same ranking as above, scoped to whichever models you select below: pick one, several, or none. Updates automatically as you check/uncheck.

Model Comparison

Compare two models

Pick a model for each column to compare their recommendation patterns side by side.