Same Bones, Smaller Mortgage: I Ran 6 'Find Similar Homes' Tools Against One $1.2M Listing

I have a hobby that costs me nothing and ruins my mood: opening Zillow at 11 p.m. and looking at houses I will never own. Last week I found The One. A 1928 bungalow with the wide front porch, the built-in bookcases flanking a brick fireplace, the little breakfast nook with the original glass knobs. Three bed, two bath, 1,840 square feet. Asking $1.19 million in a walkable neighborhood where the coffee costs eight dollars.

Obviously I cannot buy that house. But here's the thing about the modern listing site — it keeps insisting there's more where that came from. "Similar homes." "Recommended for you." "You may also like." Every major portal has some flavor of this button, and a few smaller tools now lean on computer vision to match on style rather than just square footage and ZIP code.

So I tested them. One aspirational listing, six tools, one question: can any of these actually find me the same house in a neighborhood where my budget survives?

How I set up the test

I saved the bungalow as my reference point and wrote down what made it it — not the price, the DNA:

• Exterior: low-pitched roof, deep porch, brick and clapboard mixed
• Layout: fireplace as the living room's focal point, kitchen open to a small dining nook, bedrooms clustered on one side
• Vibe: old-house charm with a renovated kitchen, nothing flipped-to-death and gray

Then I gave each tool the same job: surface lookalikes in the $600K–$850K range, which is roughly what my life can handle. For each round I scored two separate things — vibe match (does it feel like my house?) and budget match (could I actually do this?). A tool that nails the look but only in $1.5M listings gets a shrug, not a gold star.

The lineup

I ran the same search through Zillow's similar-homes carousel, Redfin's recommended listings, Realtor.com's similar properties module, Homes.com, Compass, and then, because I wanted to see what pure image matching does, Google Lens on the listing's exterior photo.

The portal-native tools are all variations on the same idea: they look at beds, baths, square footage, lot size, school zone, and proximity, then hand you what's nearby and close in size. Some of them clearly weight price band; others weight geography harder.

Google Lens doesn't know or care about any of that. It just looks at the picture.

Where image search genuinely surprised me

Lens was the tool I expected to be a party trick, and it was the one that consistently pulled the strangest — but most visually correct — results. Feeding it the exterior photo returned a stack of houses with the same roofline and porch depth, including a few Craftsman-style builds two states over that no portal would ever have shown me, because no portal was going to suggest a house 900 miles from my search.

That's the real value here: image similarity ignores your map. When I re-ran Lens with a tighter region and the price filter bolted on afterward, I got a shortlist where about half the homes made me do the little eyebrow-raise. The porches matched. The window proportions matched. The brick did too.

What the portal tools got right

Zillow's carousel was the most practical of the six. Its picks weren't as stylish as Lens's, but they were plausibly buyable — same metro, similar size, and it respected my price band once I set one. It also surfaced a couple of ranch-era homes with the same footprint and fireplace placement, which is not the same house, but it's the same life. That distinction matters and I don't think the machine understands it yet.

Redfin ran a close second. Its recommendations leaned a bit more toward recency of the listing than toward similarity of style, which meant fresher options but less of the "wait, that's my house's cousin" feeling.

Realtor.com and Homes.com both clustered in the middle — solid on size and location, indifferent to personality. Compass surfaced the smallest pool, but the picks felt editorially curated rather than computed, which I'll admit I found charming even if it's less reproducible.

Where plain old filters still win

Here's my honest takeaway: for the stuff that actually determines whether you'd want to live somewhere, filters beat matching. Every time.

A similarity engine will not know that you need a garage because you have a bike problem, or that you want a south-facing kitchen, or that the school you care about is on a specific side of a specific road. It also can't tell you that a "similar" house is on a loud corner. When I looked up the addresses the tools had suggested, the price-per-square-foot math, the lot shape, the HOA, the commute — none of that came from the similarity model. It came from me sitting there with a spreadsheet at midnight like a very sad accountant.

The best workflow I found was an unglamorous two-step: use the visual tools to generate candidates you'd never have thought to search for, then take those candidates and run them through hard filters in a spreadsheet. The tools are a way to widen the funnel. They are not a way to narrow it.

What I'd actually do with this

If I were seriously house hunting — and not just doomscrolling a porch I can't afford — I'd save three or four listings I love rather than one. Similarity engines do better with a pattern than with a single example, and I noticed that when I fed Lens a second exterior in the same style, the results tightened up noticeably.

I'd also stop expecting any of these tools to understand budget as a style. Six tools, six chances to show me something lovely in my range, and not one of them reliably did the thing I actually wanted, which was: find me this exact feeling, smaller, for less, twenty minutes closer to work.

That problem is still a human one. For now the machines are just really good at finding porches.