When most investors talk about comps (comparable sales), they’re usually trying to justify a purchase price or project an ARV (After Repair Value).

But there’s a more strategic way to use comps, one that flips the script entirely:

Instead of using comps after you find a property… Use comps to find the property.

In other words: reverse-engineer.

This guide will show you how to study the comps in your market like a detective, work backward from flip profits, and build a laser-targeted list of properties that match exactly what you’re looking for.

Why Reverse-Engineering Works

When you start with comps instead of random lead lists:

• You let real data guide your hunt

• You uncover patterns that wholesalers and casual investors miss

• You narrow your search to high-conversion zones

• You spend less time analyzing dead-end deals

It’s the difference between wandering the desert and using a GPS.

Step 1: Choose a Target Exit Scenario

To reverse-engineer comps, you need to be clear on one thing first:

What kind of flip are you trying to do?

Narrow it down by these factors:

• Price point: Are you aiming for sub-$300K? Mid-range? Luxury?

• Buyer avatar: First-time homebuyer? Landlord? Cash investor?

• Exit strategy: Retail flip? Rental resale? BRRRR?

• Timeline: Do you want to be in and out in 90 days or 9 months?

Start by studying flips that fit your exact target, not just any sale.

Step 2: Pull the Right Set of Comps

Use tools like:

• PropStream

• MLS access (direct or via agent)

• BatchLeads

• Privy

• Redfin or Zillow (for surface-level work)

Filter for:

• Closed sales in the last 6–12 months

• Renovated or turnkey properties

• Same zip code or neighborhood (not just radius)

• Similar square footage and bed/bath count

Pro tip: Don’t skip photos. You want to visually verify these were real flips, not just updated rentals.

Step 3: Study the Flip Footprint

Now look for flip clues:

• Was the home purchased recently and resold within 6–12 months?

• Was there a dramatic increase in price from the previous sale to ARV?

• Do the listing photos show modern renovations, staging, or landscaping upgrades?

You’re looking for:

• Purchase date vs. resale date

• Purchase price vs. resale price

• ARV spread

• Visible level of rehab

This tells you what the investor paid, how long they held it, and what kind of work they did.

Step 4: Find the Common Patterns

Here’s where the real detective work starts. Look at 10–20 flips and ask:

• What’s the average square footage and lot size?

• How old are the homes (year built)?

• What was the purchase price range?

• How much of a spread did they capture (gross margin)?

• How quickly did they sell (DOM = days on market)?

• Were they all within a few zip codes or school districts?

Write down every commonality. If most flips in your city are:

• 3 bed / 2 bath

• Built between 1950–1975

• Around 1,400–1,800 sqft

• Purchased for $130–170K

• Sold for $240–280K

… then that’s your ideal flip avatar.

Step 5: Work Backward From the Ideal Deal

Now that you know what “good” looks like, you can hunt for properties that match the pre-flip version.

Here’s how:

• Filter public records or PropStream for homes that haven’t sold in 10+ years

• Look for properties built in the same decades

• Narrow by square footage, lot size, and bedroom count

• Exclude homes that were recently renovated or listed

• Layer on distress indicators: code violations, tax liens, absentee owners, etc.

You’re not chasing random leads, you’re chasing lookalikes of past profits.

Step 6: Grade Neighborhood Flipability

Reverse-engineering comps also shows you where flips are working best.

Look at:

• How many flips have happened in a zip in the last year?

• What’s the average time on market post-renovation?

• Do ARVs support the construction cost plus margin?

• Are buyers showing up quickly, or do flips sit stale?

If one neighborhood has flips reselling in 12 days while another takes 65, guess where you should focus?

Heat + velocity = prime flip zone.

Step 7: Use Comps to Filter Lists (Not Just Validate)

Most investors pull a giant list of off-market leads… then start guessing which ones could be flips.

You’re going to flip the process:

1. Use comp analysis to create your flip criteria

2. Apply that filter to your list-building process

3. Only keep leads that match your ideal avatar

4. Reach out with messages tailored to that scenario

You’re not throwing spaghetti. You’re sniping.

Example: From Comp to Flip Target

Let’s say you pull 15 comps and discover:

• All flips were 3/2 homes built between 1965–1980

• Square footage ranged from 1,250–1,600 sqft

• ARVs were $285–310K

• They were purchased for $145–165K and sold within 5 months

• All had 2-car garages and were within 1.5 miles of a certain school

Now you can target:

• Homes in that same school zone

• Matching bed/bath and sqft

• Built 1960–1990

• Last sold before 2010

• No permits pulled in the last 5 years

That’s your hyper-focused flip list, and you’re way ahead of anyone buying “high equity + absentee” lists.

Track Flipper Activity and Competition

Comps also tell you who else is playing the game.

Track:

• Who is buying, flipping, and reselling

• Which LLCs or trust names appear repeatedly

• Which agents or contractors they use

• Whether flippers are still active (or went quiet)

You’ll quickly identify:

• Who’s dominating certain neighborhoods

• Which investors overpaid

• Who left profit on the table

Use that intel to go where they’re not, or to partner where it makes sense.

Reverse-Engineering Is a Power Move

Most investors go:

1. Buy list

2. Call leads

3. Hope it’s a flip

4. Check comps

5. Try to make it work

But you?

You’ll go:

1. Pull successful flip comps

2. Extract ideal criteria

3. Filter for match properties

4. Reach out with confidence

5. Stack the deck before you ever analyze a deal

This doesn’t just help you find flips, it helps you walk away from time-wasters faster, too.