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. Title should be plain heading: maybe like “Title: …
“? The instruction: “Start with the title as a plain heading (e.g., “Title: …\n\n”)”. So we output line: Title: … then newline newline then HTML content. We need SEO-friendly title include “AI” and “ai”. Something like: “AI Automation for Solo Real Estate Agents: Streamlining MLS and Public Data Feeds for CMA”. Ensure both uppercase AI and lowercase ai? Could be “AI automation … ai”. Let’s craft: Title: AI Automation for Solo Real Estate Agents: Streamlining MLS and Public Data Feeds for CMA and Hyper-Local Reports. Contains AI and ai? It has “AI” uppercase and “Automation” includes “ai”? Not exactly. Need both “AI” and “ai” strings. Could include “AI” and later “ai” in title: e.g., “AI Automation for Solo Real Estate Agents: How to Automate CMA with ai-powered Data Feeds”. That contains “AI” and “ai”. Good. Now need 450-500 words. Let’s aim ~470 words. We must write in HTML paragraphs using WP block comments. We’ll need to include headings maybe h2, h3 with appropriate block comments: For heading:…
. We must not use placeholders. Provide actionable content. Let’s draft about 470 words. Count words manually? We’ll approximate then adjust. I’ll write content then count. Plan: Title line. Then blank line. Then intro paragraph. Then heading: Why Automate Data Collection? Paragraph. Heading: Core Actions in the Automated Workflow Paragraph describing Action 1 and Action 2. Heading: Data Sources to Feed Your CMA Sheet List items as paragraphs maybe each bullet as paragraph. Heading: Setting Up the Trigger and Validation Paragraph. Heading: Key Fields to Capture for Accurate CMAs Paragraph listing fields. Heading: Start Small and Scale Paragraph. Conclusion paragraph. Then e-book promo paragraph. Now count words. Let’s write and then count. I’ll write in plain text then add HTML tags. I’ll write content then count words ignoring HTML tags? Likely they count visible words. We’ll approximate. Write: Now content. I’ll write:Solo real estate agents spend hours each week pulling sold listings, tax records, and zoning details to build a comparative market analysis. Automating that data collection turns a tedious chore into a reliable morning briefing.
Why Automate Data Collection?
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. Let’s do correctly. Proceed. I’ll write full HTML with blocks. Now count words after writing. Let’s draft. I’ll write in a text editor mentally. — Title: AI Automation for Solo Real Estate Agents: How to Automate CMA with ai-powered Data FeedsSolo real estate agents spend hours each week pulling sold listings, tax records, and zoning details to build a comparative market analysis. Automating that data collection turns a tedious chore into a reliable morning briefing.
Why Automate Data Collection?
Manual searches are prone to missed comps, outdated information, and inconsistent formatting. By setting up an automated pipeline, you guarantee that every day you start with a fresh, structured set of comparable sales ready for analysis.
Core Actions in the Automated Workflow
Action 1: A scheduled script runs your pre‑defined MLS query for “Sold in [Neighborhood] last 14 days, 3‑4 beds, 1500‑2500 SQFT.” The script pulls the raw results and passes them to the next step.
Action 2: The extracted fields—address, sold price, square footage, beds, baths, year built, lot size, date listed, date sold, days on market, and key amenities—are formatted as rows and appended to a designated Google Sheet titled “CMA Data.”
Data Sources to Feed Your CMA Sheet
Beyond MLS, layer in county assessor records for tax assessed value and ownership history, geospatial feeds for school district boundaries, flood zones, and walkability scores, and local government sites for permit history, zoning regulations, and upcoming development plans.
Finally, pull macro‑trend data from market‑trend aggregators that reflect broader metro‑area conditions; these trends help you interpret why hyper‑local prices are moving.
Setting Up the Trigger and Validation
Configure the script to launch every morning at 8 AM. After each run, open the “CMA Data” sheet to verify that new rows appear with correct values. Perform a weekly spot‑check by running a manual MLS search for the same criteria and comparing a random sample of five records; adjust the script if discrepancies emerge.
Key Fields to Capture for Accurate CMAs
For each comparable, store: address, listing price, sold price, price per SQFT, square footage, bed/bath count, year built, lot size, date listed, date sold/closed, days on market, key amenities (pool, garage, renovations), photograph links, and virtual‑tour URLs. These fields feed directly into both the CMA calculations and the narrative sections of your hyper‑local market report.
Start Small and Scale
Begin by automating one neighborhood or a single data source, such as the MLS sold feed. Once the workflow is stable, add additional layers like tax assessor data or geospatial boundaries. Incremental expansion prevents overwhelm and ensures each component remains reliable.
With a solid automated pipeline, you spend less time hunting data and more time interpreting trends, advising clients, and closing deals.
For a comprehensive guide with detailed workflows, templates, and additional strategies, see my e-book: AI for Solo Real Estate Agents: How to Automate Comparative Market Analysis (CMA) and Hyper-Local Market Report Drafts.
Now count words. We need to count visible words (text insideand