We need to write a concise 400-500 word article. Must count words. Let's target ~440 words.

Structure:

Title: something like "AI-Powered Automation: Streamlining MLS and Public Data Feeds for Solo Real Estate Agents"

Then sections: Intro, Core, Mini-scenario, Implementation, Conclusion.

We need subheadings: maybe ## Introduction, ## Core Principle, ## Mini-Scenario, ## Implementation Steps, ## Conclusion.

We need to include one specific tool name and its purpose from facts. Facts mention: automated script runs pre-defined MLS search, extracted data formatted and appended to designated Google Sheet. So tool could be "Google Sheets" or "Zapier" or "Integromat". But we need from facts: maybe "Google Sheets" as the destination. Or "MLS API" as source. Use "Google Sheets" as tool.

We need to include facts: Action 1, Action 2, data sources, trigger every morning at 8 AM, validation, etc. Must embed them.

We must not reveal exact prompts or detailed configurations. So we can say: set up an automated script that queries MLS for sold comps, etc.

Implementation: 3 high-level steps.

Conclusion: summarize key takeaways only.

Word count: need to count.

Let's draft ~440 words.

We'll write in markdown.

Now count words manually.

I'll draft then count.

Draft:

Introduction

Solo agents spend hours pulling comparable sales and neighborhood data, time that could be spent closing deals. Manual searches are tedious, error‑prone, and eat into prime prospecting hours. Automating the data feed turns that chore into a background process, giving you fresh comps every morning.

Core Principle: Trigger‑Driven Data Pipeline

The key is to build a trigger‑driven pipeline that pulls, normalizes, and stores data on a schedule. A morning trigger (e.g., 8 AM) launches a script that runs your pre‑defined MLS search for recent sales, extracts the needed fields, and appends them to a Google Sheet. By separating the trigger, the extraction logic, and the storage destination, you create a repeatable system that can be expanded to other data sources without rewriting the whole workflow.

Mini‑Scenario

At 8 AM the script fires, queries the MLS for “Sold in Maple Grove last 14 days, 3‑4 beds, 1500‑2500 SQFT,” and pulls address, price, SQFT, beds/baths, year built, lot size, and days on market. Within seconds the new rows appear in your “CMA Data” sheet, ready for analysis while you sip your coffee.

Implementation Steps

1. Define the trigger and source – Choose a scheduling tool (e.g., cron, Zapier, or a cloud function) to run at 8 AM and point it to your MLS API or web scraper with the saved search criteria.
2. Extract and normalize – Write a lightweight script (Python or JavaScript) that receives the raw MLS response, pulls the fields listed in the facts (address, price, SQFT, bed/bath count, year built, lot size, date listed/sold, days on market, key amenities, photograph links), formats them as CSV rows, and prepares them for upload.
3. Store and validate – Append the rows to a designated Google Sheet (the specific tool) and set up a weekly spot‑check: compare a random sample of automated entries against a manual MLS search to catch any drift or failures.

Conclusion

Automating MLS and public‑data feeds eliminates repetitive tasks that provides fresh, structured data feeds gives solo agents a reliable, up‑to‑date comps foundation without daily manual labor. Start with one neighborhood or data source, use a morning trigger to feed a Google Sheet, and validate weekly. The result is faster CMAs, hyper‑local reports and more time for hyper‑local reports, and more focus on client relationships.

Now count words.

I'll count manually.

Title line: "AI-Powered Automation: Streamlining MLS and Public Data Feeds for Solo Real Estate Agents"

Words: AI-Powered(1) Automation:(2) Streamlining(3) MLS(4) and(5) Public(6) Data(7) Feeds(8) for(9) Solo(10) Real(11) Estate(12) Agents(13) => 13.

Introduction heading not counted? Usually headings count as words? We'll count everything after markdown as words. Safer to count all words in the article (excluding markdown symbols?). We'll count words in the content, ignoring markdown symbols like #, ##. But safer to count everything except the symbols. Let's count manually.

I'll rewrite the article with line breaks and count.

Article:

AI-Powered Automation: Streamlining MLS and Public Data Feeds for Solo Real Estate Agents

Introduction

Solo agents spend hours pulling comparable sales and neighborhood data, time that could be spent closing deals. Manual searches are tedious, error‑prone, and eat into prime prospecting hours. Automating the data feed turns that chore into a background process, giving you fresh comps every morning.

Core Principle: Trigger‑Driven Data Pipeline

The key is to build a trigger‑driven pipeline that pulls, normalizes, and stores data on a schedule. A morning trigger (e.g., 8 AM) launches a script that runs your pre‑defined MLS search for recent sales, extracts the needed fields, and appends them to a Google Sheet. By separating the trigger, the extraction logic, and the storage destination, you create a repeatable system that can be expanded to other data sources without rewriting the whole workflow.

Mini‑Scenario

At 8 AM the script fires, queries the MLS for “Sold in Maple Grove last 14 days, 3‑4 beds, 1500‑2500 SQFT,” and pulls address, price, SQFT, beds/baths, year built, lot size, and days on market. Within seconds the new rows appear in your “CMA Data” sheet, ready for analysis while you sip your coffee.

Implementation Steps

1. Define the trigger and source – Choose a scheduling tool (e.g., cron, Zapier, or a cloud function) to run at 8 AM and point it to your MLS API or web scraper with the saved search criteria.
2. Extract and normalize – Write a lightweight script (Python or JavaScript) that receives the raw MLS response, pulls the fields listed in the facts (address, price, SQFT, bed/bath count, year built, lot size, date listed/sold, days on market, key amenities, photograph links), formats them as CSV rows, and prepares them for upload.
3. Store and validate – Append the rows to a designated Google Sheet (the specific tool) and set up a weekly spot‑check: compare a random sample of automated entries against a manual MLS search to catch any drift or failures.

Conclusion

Automating MLS public‑data feeds eliminates repetitive manual work, delivers fresh comps each morning, and frees solo agents to focus on client relationships. Start small, validate regularly, and let the pipeline scale as your business grows.

Now count words.

I'll count line by line.

First line: "# AI-Powered Automation: Streamlining MLS and Public Data Feeds for Solo Real Estate Agents"

Ignore "#". Words: AI-Powered(1) Automation:(2) Streamlining(3) MLS(4) and(5) Public(6) Data(7) Feeds(8) for(9) Solo(10) Real(11) Estate(12) Agents(13) =>13.

Next line blank? ignore.

"## Introduction" => words: Introduction(1) (ignore ##) =>1.

"Solo agents spend hours pulling comparable sales and neighborhood data, time that could be spent closing deals." Count:

Solo1 agents2 spend3 hours4 pulling5 comparable6 sales7 and8 neighborhood9 data,10 time11 that12 could13 be14 spent15 closing16 deals17. =>17.

"Manual searches are tedious, error‑prone, and eat into prime prospecting hours." Count:

Manual1 searches2 are3 tedious,4 error‑prone,5 and6 eat7 into8 prime9 prospecting10 hours11. =>11.

"Automating the data feed turns that chore into a background process, giving you fresh comps every morning." Count:

Automating1 the2 data3 feed4 turns5 that6 chore7 into8 a9 background10 process,11 giving12 you13 fresh14 comps15 every16 morning17. =>17.

Now "## Core Principle: Trigger‑Driven Data Pipeline"

Words: Core1 Principle:2 Trigger‑Driven3 Data4 Pipeline5 =>5.

"The key is to build a trigger‑driven pipeline that pulls, normalizes, and stores data on a schedule." Count:

The1 key2 is3 to4 build5 a6 trigger‑driven7 pipeline8 that9 pulls,10 normalizes,11 and12 stores13 data14 on15 a16 schedule17. =>17.

"A morning trigger (e.g., 8 AM) launches a script that runs your pre‑defined MLS search for recent sales, extracts the needed fields, and appends them to a Google Sheet." Count:

A1 morning2 trigger3 (e.g.,4 8 AM)5 launches6 a7 script8 that9 runs10 your11 pre‑defined12 MLS13 search14 for15 recent16 sales,17 extracts18 the19 needed20 fields,21 and22 appends23 them24 to25 a26 Google27 Sheet28. =>28.

"By separating the trigger, the extraction logic, and the storage destination, you create a repeatable system that can be expanded to other data sources without rewriting the whole workflow." Count:

By1 separating2 the3 trigger,4 the5 extraction6 logic,7 and8 the9 storage10 destination,11 you12 create13 a14 repeatable15 system16 that17 can18 be19 expanded20 to21 other22 data23 sources24 without25 rewriting26 the27 whole28 workflow29. =>29.

Now "## Mini-Scenario"

Words: Mini-Scenario1 =>1.

"At 8 AM the script fires, queries the MLS for “Sold in Maple Grove last 14 days, 3‑4 beds, 1500‑2500 SQFT,” and pulls address, price, SQFT, beds/baths, year built, lot size, and days on market." Count:

At1 8 AM2 the3 script4 fires,5 queries6 the7 MLS8 for9 “Sold10 in11 Maple12 Grove1