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Show HN: We made a photo search engine for homes for sale

news.ycombinator.com

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Re: Show HN: We made a photo search engine for homes for sale

#31

Post by joosters »

"Floor to ceiling libraries with a ladder"

Does that mean it excludes most of the results from "Floor to ceiling libraries without a ladder"?

You know, if I'm buying a house, I think I can supply my own ladder separately...

Less pedantically, what I'm trying to say is: are you really sure these are the kinds of searches that home buyers are really looking for? "Home in london, under £1m, with big beautiful windows" - I suspect that most London buyers are going to care an awful lot about where in London the house is, a city-wide search isn't going to be useful to most. Maybe your functionality (as presented) won't inspire actual buyers.

Speaking of which, that might be a way to improve it - combine with location & mapping data to figure out nearby transport, services, schools, etc...

Re: Show HN: We made a photo search engine for homes for sale

#32

Post by travisleestreet »

joosters wrote: post #31

"Floor to ceiling libraries with a ladder" Does that mean it excludes most of the results from "Floor to ceiling libraries without a ladder"? You know, if I'm buying a house, I think I can supply my own ladder separately... Less pedantically, what I'm trying to say is: are you really sure these are the kinds of searches that home buyers are really looking for? "Home in london, under £1m, with big beautiful windows" -…

For sure, those searches are more about showing what’s possible (and making browsing fun) than what a high-intent buyer would actually type in.

That said, from speaking to a lot of London buyers, people are often more flexible on location than you’d expect. The real criteria tend to look more like: “3-bed house under £700k, 30 min commute from [office], near a park, low crime, good schools” rather than “3-bed in Hackney.” Basically along the lines of the location / mapping data you're suggesting.

We’ve already built travel-time search and plan to layer in more of that other 'services' style data in the next few months.

Re: Show HN: We made a photo search engine for homes for sale

#35

Post by travisleestreet »

gardaani wrote: post #3

Does your crawler respect robots.txt? Does it request pages with nice delays so that it doesn't bring down servers? Related: https://news.ycombinator.com/item?id=43422413

We don’t respect robots.txt because estate agents often block listing pages - not necessarily to prevent indexing, but for SEO reasons (Google penalises large sites with transient pages).

That said, we do crawl responsibly i.e. we use reasonable request delays, respect rate limits etc. We want agents to like us, ultimately, and blowing up their servers doesn't help with that. If an agent prefers opt-out, we always honour it.

Re: Show HN: We made a photo search engine for homes for sale

#36

Post by itscoops »

sky_rw wrote: post #20
kureikain wrote: post #6

We did this in 2013, got serve with a lot of lawsuit. https://en.wikipedia.org/wiki/NeighborCity Be careful with the real estate industry

It seems like this site is more EU based. Does anybody here know if there is an MLS equivalent in this jurisdiction and/or if similar copyright holders over there are as litigious?

There’s not an MLS equivalent in the UK, property data is quite fragmented.

We’re not looking to display anything an estate agent doesn’t want us to - agents tell us they’re very happy for us to serve as an advertising channel. And we’d immediately action any sort of take down request.

Re: Show HN: We made a photo search engine for homes for sale

#39

Post by travisleestreet »

charliegoforit wrote: post #27

Hi, this looks really cool. What LLM’s are you using ? And how is this working “Parses every photo through an embeddings vector space so that people can search for whatever they want” Thank you

For the image search portion specifically, we use Google’s embedding model. We then use vector search (https://cloud.google.com/alloydb/docs/ai/run-vector-similari...) to calculate the distance between the search phrase and pre-calculated embeddings for each image.

Then there’s a bit of ranking and scoring magic to build a results set.

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