…
and headings:…
etc. We need to use facts from e-book: output nearly finished market report, first draft of written analysis, AI & Tool Integration bullet points, Your Actionable Checklist, Your Framework & Prompt Example, Your Framework, Your Monthly Automation Script, and pillars. We need to write actionable content, no placeholders. Let’s draft about 470 words. We’ll count words manually. Plan: Title line: Title: Building Your AI-Powered CMA Engine: The Core Framework Then blank line. Then start HTML. We’ll need headings: maybe h2 for each pillar, and intro. Let’s draft content then count. I’ll write then count. Draft:Solo agents can now turn raw MLS data into a polished comparative market analysis (CMA) in minutes by assembling an AI‑powered engine that handles comp selection, adjustments, narrative, visuals, and hyper‑local reporting.
The output is a nearly finished market report you can review, brand, and email to your sphere in minutes, and you already have the first draft of the written analysis that accompanies your data grids and charts.
The AI & Tool Integration
Apply logical adjustments and synthesize a value range.
Go beyond basic filters (bed/bath, square footage, zip code); instruct your AI to perform a nuanced comparative analysis that weighs lot size, condition, and recent renovations.
Transform the broader neighborhood data you’re already collecting into a digestible, one‑page hyper‑local market report.
Write clear, persuasive sections of the CMA draft, letting the AI handle tone and structure while you focus on final polish.
Your Actionable Checklist
[ ] Update Market Report Template: Feed the latest month’s data into your Hyper‑Local Report script and generate a draft for review.
[ ] Verify Data Feeds: Confirm your automated MLS data pulls (from Chapter 4) are running without errors.
Pillar 1: Intelligent Comp Selection & Data Enrichment
Use AI to pull comparable sales within a dynamic radius, then enrich each record with school scores, walk‑ability indices, and recent permit activity.
Pillar 2: Automated Adjustment & Valuation Modeling
Let the model apply logical adjustments for differences in square footage, age, and condition, then synthesize a value range with confidence intervals.
Pillar 3: Narrative & Insight Generation
Prompt the AI to draft the executive summary, key trends, and pricing strategy sections, ensuring the language matches your brand voice.
Pillar 4: Visualization & Report Assembly
Automatically generate charts (price per sqft trend, days on market distribution) and embed them into a pre‑styled template that outputs a PDF ready for branding.
Pillar 5: Hyper‑Local Market Report Drafting
Feed aggregated neighborhood metrics (inventory levels, median price change, new listings) into a second script that creates a one‑page market snapshot you can attach to the CMA or send separately.
Your Framework & Prompt Example
Framework: Define data inputs, AI tasks, review checkpoints, and delivery format. Example prompt: “Given the last 12 months of sales in ZIP 90210, adjust for ±15% square footage variance, condition score, and lot size, then provide a three‑point value range and a 150‑word neighborhood summary.”
Your Monthly Automation Script
1. Trigger MLS pull → 2. Run comp selection script → 3. Enrich data → 4. Execute adjustment model → 5. Generate narrative via AI → 6. Assemble visuals → 7. Produce CMA draft and hyper‑local report → 8. Notify you for final review.
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 we need to count words. We need to count words in the HTML content (excluding the title line and the HTML comments? Probably count visible text words. Safer to count all words visible (including inside). We’ll count manually. Let’s extract visible text: Paragraph1: “Solo agents can now turn raw MLS data into a polished comparative market analysis (CMA) in minutes by assembling an AI‑powered engine that handles comp selection, adjustments, narrative, visuals, and hyper‑local reporting.” Count words: Solo(1) agents2 can3 now4 turn5 raw6 MLS7 data8 into9 a10 polished11 comparative12 market13 analysis14 (CMA)15 in16 minutes17 by18 assembling19 an20 AI‑powered21 engine22 that23 handles24 comp25 selection,26 adjustments,27 narrative,28 visuals,29 and30 hyper‑local31 reporting32. So 32 words. Paragraph2: “The output is a nearly finished market report you can review, brand, and email to your sphere in minutes, and you already have the first draft of the written analysis that accompanies your data grids and charts.” Count: The1 output2 is3 a4 nearly5 finished6 market7 report8 you9 can10 review,11 brand,12 and13 email14 to15 your16 sphere17 in18 minutes,19 and20 you21 already22 have23 the24 first25 draft26 of27 the28 written29 analysis30 that31 accompanies32 your33 data34 grids35 and36 charts37. 37 words. Heading “The AI & Tool Integration” – heading words maybe count? Usually headings count as words. We’ll count them. “The AI & Tool Integration” words: The1 AI2 &3 Tool4 Integration5 =>5. Paragraph after heading: “Apply logical adjustments and synthesize a value range.” Count: Apply1 logical2 adjustments3 and4 synthesize5 a6 value7 range8 =>8. Next paragraph: “Go beyond basic filters (bed/bath, square footage, zip code); instruct your AI to perform a nuanced comparative analysis that weighs lot size, condition, and recent renovations.” Count: Go1 beyond2 basic3 filters4 (bed/bath,5 square6 footage,7 zip8 code);9 instruct10 your11 AI12 to13 perform14 a15 nuanced16 comparative17 analysis18 that19 weighs20 lot21 size,22 condition,23 and24 recent25 renovations26. 26 words. Next paragraph: “Transform the broader neighborhood data you’re already collecting into a digestible, one‑page hyper‑local market report.” Count: Transform1 the2 broader3 neighborhood4 data5 you’re6 already7 collecting8 into9 a10 digestible,11 one‑page12 hyper‑local13 market14 report15. 15 words. Next paragraph: “Write clear, persuasive sections of the CMA draft, letting the AI handle tone and structure while you focus on final polish.” Count: Write1 clear,2 persuasive3 sections4 of5 the6 CMA7 draft,8 letting9 the10 AI11 handle12 tone13 and14 structure15 while16 you17 focus18 on19 final20 polish21. 21 words. Heading “Your Actionable Checklist”: words: Your1 Actionable2 Checklist3 =>3. Checkbox paragraph1: “[ ] Update Market Report Template: Feed the latest month’s data into your Hyper‑Local Report script and generate a draft for review.” We need to count words ignoring the bracket? Probably count as words. “[ ]” maybe not a word. We’ll count after. Update1 Market2 Report3 Template:4 Feed5 the6 latest7 month’s8 data9 into10 your11 Hyper‑Local12 Report13 script14 and15 generate16 a17 draft18 for19 review20. 20 words. Checkbox paragraph2: “[ ] Verify Data Feeds: Confirm your automated MLS data pulls (from Chapter 4) are running without errors.” Count: Verify1 Data2 Feeds:3 Confirm4 your5 automated6 MLS7 data8 pulls9 (from10 Chapter11 4)12 are13 running14 without15 errors16. 16 words. Heading “Pillar 1: Intelligent Comp Selection & Data Enrichment”: words: Pillar1 1:2 Intelligent3 Comp4 Selection5 &6 Data7 Enrichment8 =>8. Paragraph: “Use AI to pull comparable sales within a dynamic radius, then enrich each record with school scores, walk‑ability indices, and recent permit activity.” Count: Use1 AI2 to3 pull4 comparable5 sales6 within7 a8 dynamic9 radius,10 then11 enrich12 each13 record14 with15 school16 scores,17 walk‑ability18 indices,19 and20 recent21 permit22 activity23. 23 words. Heading “Pillar 2: Automated Adjustment & Valuation Modeling”: words: Pillar1 1:2 Automated3 Adjustment4 &5 Valuation6 Modeling7 =>7. Paragraph: “Let the model apply logical adjustments for differences in square footage, age, and condition, then synthesize a value range with confidence intervals.” Count: Let1 the2 model3 apply4 logical5 adjustments6 for7 differences8 in9 square10 footage,11 age,12 and13 condition,14 then15 synthesize16 a17 value18 range19 with20 confidence21 intervals22. 22 words. Heading “Pillar 3: Narrative & Insight Generation”: words: Pillar1 1:2 Narrative3 &4 Insight5 Generation6 =>6. Paragraph: “Prompt the AI to draft the executive summary, key trends, and pricing strategy sections, ensuring the language matches your brand voice.” Count: Prompt1 the2 AI3 to4 draft5 the6 executive7 summary,8 key9 trends,10 and11 pricing12 strategy13 sections,14 ensuring15 the16 language17 matches18 your19 brand20 voice21. 21 words. Heading