Just closed my biggest deal ever using AI — $8.4M luxury waterfront 🏆
6 weeks ago I started using the Negotiation Assistant on a really tough seller. Multiple offers, emotional attachment, cash buyer trying to lowball. The psychology analysis told me exactly what to lean on. Ended up $340k over asking with a 21-day close. Happy to share the exact scenario setup if anyone wants — this thing is unreal for luxury deals.
The 'Neighborhood Storyteller' prompt that 3x'd my listing engagement
I've been testing this one for 30 days. Instead of describing the house, I have GPT describe what daily life FEELS like in the neighborhood. Coffee shops the sellers actually go to. Where kids ride bikes. Which restaurants are worth the wait. Saves in my sharing → showings → offers all jumped noticeably. Drop your email if you want the full prompt.
Neighborhood Storyteller v3
Listing Descriptions · Free with sign-up
How are you using AI for buyer follow-up right now?
Curious what's actually working for people. Trying to cut the 'ghost after 2nd showing' problem.
357 votes · Ends in 2 days
Ultra-luxury clients don't want AI-written copy — they want AI-informed copy
Been in luxury 10 years. My $10M+ buyers can smell templated language from three counties away. What actually works: use AI for research (market comps, buyer psychology, neighborhood facts) then WRITE THE COPY YOURSELF using that research. Curious if other luxury folks are doing the same or if I'm missing something.
Brand new agent (3 months in) — where should I actually start with AI?
I'm drowning trying to figure out what to prioritize. Should I focus on the listing generator first? The social tool? The prompt vault? What did you wish you had focused on when you were new?
Rolled AI Realtor 101 to my 28-agent team last month — here's what happened
TL;DR: 42% average time saved on listings, 3 agents went from 1-2 deals/quarter to 4+, team NPS score jumped from 32 to 71. Happy to answer questions from other broker owners considering rolling this out. The single biggest thing was the shared Prompt Vault so agents could learn from each other's best-performing prompts.
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