You’ve just picked up a lead in a suburb you don’t normally work. Maybe it’s a referral, maybe it’s a seller who liked you at a dinner party - either way, you’re about to walk into a listing presentation in an area where you don’t yet know who the players are, what’s actually selling versus sitting, or why the incumbent agent has held the mandate for three years running.

The old way to fix that gap is a weekend of manual portal-hopping: scrolling Property24 and Private Property suburb by suburb, screenshotting listings, building a spreadsheet by hand. The new way is to hand the whole job to an agent that does it for you - and hands back a deck, not a data dump. That’s what a Manus Task is built for.

A Quick Refresher on What Makes This a “Task” and Not a Chat

We’ve covered Manus’s mechanics elsewhere in this series, so the short version: it isn’t a chatbot you go back and forth with. You give it a goal, and it runs that goal inside its own cloud computer - opening browser tabs, reading pages, pulling numbers, writing the odd bit of code to total things up - until it comes back with a finished result. No babysitting, no “now do step two.” One brief in, one deliverable out.

That distinction matters here because competitor analysis is exactly the kind of job that’s tedious for a human and tailor-made for an agent: repetitive, spread across multiple websites, and entirely mechanical until the very last step, where it suddenly needs judgement. Manus does the mechanical part; the prompt below is what tells it where the judgement should go.

The Prompt: Analyse the residential property sales market in {Insert Area, City, Country}. Provide a concise 4-part competitor analysis: Part 1 - Total active listings & average asking vs. sale price gap. Part 2- Top 10 agencies by listing volume. Part 3 - Top 5 individual agents & their specific niches. Part 4 - Four actionable gaps I can exploit to win sole mandates. Put the results into a Powerpoint presentation - no more than five slides.

What Manus Is Actually Doing With Each Part

  • Part 1 - Listings and the price gap. Manus browses the major listing portals for the area you’ve named, counts what’s currently active, and cross-references recently sold prices against original asking prices to calculate the average gap. That single number tells you more about a market than almost anything else: a wide gap usually means agents are overpricing to win mandates, then negotiating sellers down later - which is a credibility opening for you if you pitch realistic pricing from day one.
  • Part 2 - Agency listing volume. This is a straightforward count-and-rank across the portals, but it’s the fastest way to see who actually dominates the area versus who just has a sign board on the main road. Volume tells you who you’re really competing against.
  • Part 3 - Individual agents and niches. Here Manus has to do more than count - it has to read. It looks at what each top agent is actually listing (price band, property type, street, sectional title vs freehold) and infers their specialisation. This is the part a basic scrape can’t do; it requires the model to synthesise a pattern across dozens of individual listings.
  • Part 4 - The actionable gaps. This is the genuinely valuable step, and it’s also the one that needs the most scepticism from you. Manus is being asked to take everything above and propose three specific openings - an underserved price band, a marketing angle nobody’s using, a niche with no clear specialist - that you could use to win a sole mandate. It’s a recommendation, not a fact, and it should be read that way.

Why Five Slides and Not a Twelve-Page Report

The “no more than 5” instruction is doing real work in this prompt. Left unconstrained, an agent like Manus will happily hand you eight pages of methodology and caveats. Telling it the output format and the ceiling up front forces it to prioritise - which is exactly what you want when the real use case is opening your laptop in the driveway before a listing presentation, not settling in for an evening of reading. A tight prompt about format is as important as a tight prompt about content.

Why This Matters for Agents Breaking Into a New Market

Walking into an unfamiliar suburb with this deck instead of a gut feeling changes the conversation with a seller in a specific way: you’re not selling enthusiasm, you’re showing them you already understand their market better than the agent who’s been farming it for years. A seller deciding on a sole mandate is, at that moment, choosing between agents who all sound confident - data is what separates “I’ll get you a great price” from “here’s the gap between what agents in this suburb are asking and what’s actually selling, and here’s where you’re underserved.” One is a pitch. The other is evidence. It also compresses the timeline that usually keeps agents out of new areas altogether. Cross-suburb expansion has traditionally required either months of slow organic relationship-building or a research budget most individual agents don’t have. A same-day, AI-built briefing removes that barrier - you can credibly walk into a new market on a Tuesday for a Thursday listing appointment.

The Caveat Worth Building Into Your Process

Treat the output as a strong first draft, not a final answer. Listing portals aren’t always complete or current, agent niches can be misread from a small sample of listings, and the “actionable gaps” section is the model’s inference, not verified market intelligence. Before any of this goes in front of a seller, sanity-check the headline numbers against what you already know about the area, and be ready to say “based on current listing data” rather than presenting it as gospel. The value of this prompt is speed to a credible starting point - your local knowledge is still what turns it into a winning pitch.