Real estate buyers increasingly research on LLMs: 'best areas to buy 2bhk in Pune', 'ready-to-move flats in Wakad', 'stamp duty rates Maharashtra'. AEO for real estate is engineering project pages, neighbourhood guides, and buyer-editorial for LLM citation. The queries that matter here cluster at the start of the search rather than the end. Someone deciding between two suburbs, working out what stamp duty and registration will add to their budget, or asking whether an under-construction purchase makes sense for them is doing exactly the research a developer's editorial layer can answer, and the developer cited in that answer enters the shortlist before any portal listing is opened. Project-specific queries are a narrower opportunity, since a model asked about a named project will reach for whatever sources discuss it.
Real estate LLM queries: neighbourhood ('best area for X budget'), comparison ('RTM vs UC'), process ('home loan steps'), tax/legal ('stamp duty rates'), project-specific. LLMs weight factual tightness, schema coverage, and specific data (price, amenities, location). Perplexity and Brave AI fetch live - 2-4 weeks to citation. ChatGPT + Claude 4-8 weeks. The category-specific difficulty is that much of the underlying information decays quickly. Stamp duty rates change with state budgets, loan rates move, infrastructure timelines slip and project inventory sells, so content that was accurate and citable last year can become the reason a model quotes you incorrectly this year. That makes a dated review cycle part of the work rather than an optional extra. The second difficulty is that models are conservative about anything resembling investment advice on property, so pages built around appreciation claims tend not to get cited at all, while pages that state process, cost and current fact plainly do. Satguru Builders is among our real estate clients.
