In just ONE HOUR we built the below product: Here it is if you want it: https://github.com/mikepans1013/MarketResearch @Shawn Dwyer @Dylan Stewart thanks for joining and participating One of the biggest mistakes investors make is underwriting the property but barely underwriting the market. So we built a simple market research dashboard to help evaluate new MHP markets faster. You enter a property address, and the tool starts pulling together market-level data around that location, including: • State, county, city/place, ZIP/ZCTA, and census tract data • Population • Median household income • Median home value • Renter percentage • Age demographics • Saved market reports so you can revisit old searches • Manual update button so you only refresh data when you want to A few important notes: The goal is not to replace your underwriting. The goal is to quickly answer: “Is this a market I should spend more time on?” Next up, we’ll be adding rent data, Section 8/FMR values, population growth trends, major employers, crime data, and eventually a map layer showing nearby Walmart/grocery access and employers. This is still early, but the direction is simple: Less guessing. Faster market screening. Better questions before going deep on a deal.

Within THE FIRST HOUR after Setup TODAY....@Blake Bandeff and @Reagan Thomson had already used OpenClaw to create a staff presentation they had been procrastinating on.... Blake opened a voice note and SPOKE to his bot Aria, prompting her with just stream-of-conscious for what he wanted to create and the key points he wanted inside that presentation.... and VOILA! OpenClaw opened a Google Slides deck and created the whole presentation for them in seconds.... You don't have to be a power user to see immediate benefit. OUR NEW SLOGAN THEREFORE IS: JUST TALK TO IT!

What It Does Researches local lenders for a real estate deal, finds commercial lending contacts via LinkedIn, and executes outreach campaigns. Step-by-Step Workflow Step 1: Gather Deal Parameters • Ask the user for: property location, lot count, deal name • MUST ask for spreadsheet URL - never assume which spreadsheet to use Step 2: Research Lenders via Google Places API • Search "banks near [location]", "credit unions near [location]", "community banks near [location]" • Categorize results (Community Bank, Regional, National, Credit Union) • Cross-reference existing spreadsheet to avoid duplicates • Only add new banks; update "Deal" column for existing banks that serve the area Step 3: Research Commercial Lending Websites • Find each bank's commercial lending page (usually under Business → Loans) • Add URL to spreadsheet Website column • Extract any contacts (names, phones, emails) from "Meet Our Team" or "Contact Us" sections • Add contacts to Contacts tab with Source = "Bank Website" Step 4: Find LinkedIn Contacts via Apify • Run Google search via Apify: "[Bank] commercial lending officer [STATE] site:linkedin.com/in" • Score contacts by title relevance: • A-tier: VP, SVP, Chief Lending Officer • B-tier: Commercial RM, Lending Specialist • C-tier: Branch Manager Step 5: Update Spreadsheet • NEVER overwrite existing data - only add new rows or fill empty cells • Financing Leads tab: banks with addresses, types, status • Contacts tab: names, titles, emails, phones, LinkedIn URLs, quality scores • Highlight top-priority contacts in yellow Step 6: LinkedIn Outreach via Browser • Log into LinkedIn • Send connection requests with 10-15 second delays • Use personalized notes for top 2-3 contacts (LinkedIn limits ~3-5/month) • Template: "Hi [NAME], I'm a MHP investor looking for financing on a small [LOT_COUNT]-lot park [LOCATION_DESCRIPTION]..." Step 7: Extract Contact Info • Check profiles for public contact info (About section, Contact Info overlay)

Tenant screening is one of the most important parts of real estate investment. Oftentimes, we receive a bunch of jumbled documents with very little context. Even using RentManager to have our tenants fill out their applications, it's sometimes a process to get those applications into a go/no-go status. I've started using OpenClaw to compile these documents, rename them, put them in the right folders, and develop a risk analysis for each prospective tenant.

Don't know if anyone has posted this already or not. Don't have one continuous convo on telegram with your bot. Break topics into their own threads.