How to Find Distressed Properties with AI

In this guide, I’ll walk you through exactly how to set this up, the prompts that work, what the output actually looks like, and the mistakes to avoid so you don’t waste credits or get useless results.

Why Traditional Distressed Property Research Is Broken

Finding distressed properties has always been a grind. Here’s what the manual process typically looks like:

• You open your city’s housing department foreclosure registry.

• Browse through pages of records.

• Cross-reference addresses against bankruptcy court filings.

• Check REO listing sites.

• Maybe call a few brokers.

• Copy property data into a spreadsheet by hand.

If you’re thorough, this takes 2-4 hours. If you’re honest, you do it once a month at best. And by the time you find something, there’s a solid chance someone else already has. The real problem is consistency. Distressed opportunities appear and disappear quickly. Foreclosure notices get filed daily. Bankruptcy auctions get scheduled weekly. If you’re only checking monthly, you’re missing the majority of what comes to market. That’s where AI agents come in. They can run this research for you on a schedule — daily if you want — and compile results into a structured report any time.

What You’ll Need Before You Start

Before you set anything up, here’s what you need:

• A Manus account — Manus is the AI agent I used for this workflow. It’s one of the best autonomous agents out there for web research. You’ll need credits (each run uses some)

• A research prompt — I’ll give you the exact one I used, but you’ll want to customize it for your target market

• A target geography — Be specific. “Los Angeles city” worked for me. “Southern California” would be too broad and produce messy results

• Your distress categories are defined as foreclosures, bankruptcies, REOs, probate sales, tax liens, and code violations. Know what you’re looking for

• About 5 minutes for setup — After that, it runs on autopilot

Optional but recommended: have Claude or ChatGPT help you write your prompt. I built mine in Claude first, then pasted it into Manus. AI writing prompts for other AI. Funny, but it works (shoutout to my guy Claude for prompt engineering).

Step 1: Choose Your AI Agent

I used Manus for this workflow because its research capabilities are genuinely impressive. It browses the web autonomously, navigates websites, extracts data, and compiles reports without you guiding it through each step. But this approach works with any autonomous AI agent that can browse the web. The key capabilities you need:

Key Capabilities

• Web browsing — the agent needs to visit websites and extract data

• Task planning — good agents create their own to-do lists and work through them

• Data compilation — should output structured data, not just text summaries

• Scheduled execution — must support recurring runs (daily, weekly)

Manus checks all four boxes. If you’re using a different agent, make sure it can do these things before you invest time building prompts.

What About Cost?

Each Manus run uses credits. The exact cost depends on how much browsing the agent does, which varies by the complexity of your search. My LA distressed property search used roughly one task’s worth of credits. If you’re running daily, factor that into your budget. For most people, the time savings is more than that.

Step 2: Build Your Distressed Property Prompt

This is the most important step. Your prompt determines the quality of everything that follows. Here’s the exact prompt I used:

“Research and compile a comprehensive list of apartment buildings in LA city that are currently available as distressed opportunities due to ownership changes, foreclosures, or corporate bankruptcies. Create a spreadsheet with property addresses, date registered, type of distress, key details, and the source.”

Why Each Part Matters

Geographic Specificity

“LA city” — not “LA area” or “Southern California.” The tighter your geography, the more focused your results.

Distress Categories

“Ownership changes, foreclosures, or corporate bankruptcies” — I named specific distress types so Manus knew exactly what to search for.

Other distress categories worth adding:

• Tax lien sales

• Code violation accumulation

• Receivership filings

• Estate/probate sales

• Loan maturity defaults

Output Format

“Create a spreadsheet with property addresses, date registered, type of distress, key details, and the source.” — Tell the AI exactly what columns you want.

Improved Prompt Example

“Research and compile a list of apartment buildings (5+ units) in Los Angeles city currently available as distressed opportunities. Include foreclosures with notices filed, bankruptcy court liquidations, bank-owned REO listings, probate sales, and properties with significant code violations. Create a spreadsheet with:  property address, unit count (if available), date of filing/listing, type of distress, key details, and source URL. Only include properties not found in previous runs. Prioritize properties with actionable next steps (active listings, upcoming auction dates, contact information for sellers/trustees).”

Step 3: Set Up the Scheduled Task

This is the easy part. In Manus:

1. Go to Settings → Scheduled Tasks → New Schedule

2. Name your task (I used “Distressed Property Finder”)

3. Paste your prompt

4. Connect integrations (I connected Gmail for email results)

5. Set the cadence: daily, weekly, or monthly

6. Pick a run time

7. Save

Daily vs. Weekly Cadence

I run daily because foreclosure notices get filed constantly. Weekly might work in slower markets. Monthly is too infrequent.

Step 4: Run It and Review the Output

When Manus runs, it goes through a multi-step process:

1. Search current foreclosure properties and REO listings

2. Research bankruptcy filings and court records

3. Identify distressed property sales and off-market opportunities

4. Access public records

5. Compile and organize findings

6. Deliver the final research report

About 8 minutes later, the run was complete.

My First Run Results: 88 Properties

• 84 properties in the LAHD foreclosure registry with notices filed

• 2 properties in bankruptcy court liquidation

• 1 bank-owned REO for sale

• 1 probate distress sale

The output came in two formats: Markdown Research Report and Structured Spreadsheet.

Step 5: Iterate Your Prompt for Better Results

Improvements to consider:

• Add de-duplication: “Only surface new properties not included in prior reports.”

• Request source URLs

• Add unit count filters

• Request auction dates and contact info

• Specify freshness: only include recent filings

What the Full Output Looks Like

1. Markdown Research Report — narrative overview by location, council districts, lenders, immediate opportunities, data sources

2. Structured Spreadsheet — property addresses, registration dates, distress types, key details, sources

Distressed Property Data Sources AI Agents Use

Data Source	What AI Looks For	Why It Matters
Foreclosure Registries	Notice of default, auction filings	Identifies properties entering foreclosure before auctions occur
Bankruptcy Court Records	Liquidation filings, distressed ownership	Finds owners forced to sell assets quickly
REO Listings	Bank-owned property sales	Banks often sell these properties below market value
Probate Sales	Estate property transfers	Estates sometimes sell quickly to settle an inheritance
Tax Lien Records	Delinquent property taxes	Indicates financial distress that may lead to a sale
Code Violation Databases	Repeated building violations	Signals poorly managed or distressed assets

What It Does Well vs. Where It Falls Short

Strengths

• Speed and consistency — 88 properties in 8 minutes daily

• Breadth of search — multiple public sources

• Structured output — ready for use

• Prioritization — identifies immediate opportunities

Limitations

• Public data only

• Data freshness varies

• No verification of accuracy

• Credit cost for daily runs

Time Comparison: AI vs. Manual Research

Tips from Running This Daily

1. Start with one city and one asset type

2. Run your first search manually

3. Keep a running list of properties already investigated

4. Connect your email integration

5. Pair with follow-up workflows for owners, values, and contacts.

Use Cases by Role

• Brokers: Be first to know about new foreclosure or bankruptcy auctions

• Acquisitions analysts: Create daily deal pipelines for your firm

• Investors: Monitor multiple markets with scheduled tasks

• Property managers: Track neighboring ownership changes and distress signals

Start Using This Today

I’ve shared the full demo video, exact prompt, and sample output at the AI for CRE Collective. 540+ CRE professionals are testing workflows like this every week and sharing what actually works. Join the Collective for full access, or subscribe to the newsletter for weekly AI playbooks and CRE automation workflows.