Place data in the websites we use and love: Spotlight on Realtor.com

This is the 3rd post in our ongoing series on place data in various websites. Last time we focused on Zillow which we discovered has an inadvertent "waterfront" filter courtesy of its SEO strategy. This time we're turning the spotlight to Realtor.com.

A highlight of Realtor.com's site is the layers enabled on the map. Beyond schools data, they also support noise, flood, wildfire, amenities and transit-- quite an array. What's interesting to me though is not what they support, but how they support it: all of these layers are secondary to a place search. These filters suggest that home seekers are price and place sensitive first, and once those bases are covered, they get more precise. And realtor is not alone-- all of the housing marketplaces I've come across support this same user flow.

What this place and price first approach doesn't encourage is discovery. There is a of course a workaround- you can click on a layer, zoom in and out on the map, and open a bunch of tabs for houses that look good. But what happens when folks want to lead with the filters? As an example, I'd like a cabin in the woods with a low chance of wildfire. I know California is bad for wildfires, and so is southern Oregon, but I'd love some help here! Why is the map the way the only way I have to navigate this data? Why can't I actually search based on the wildfire data they clearly have? Is it a data limitation or a product decision? If you happen to know, reach out!

About iggy

Without Iggy, building innovative user-facing products and tools with neighborhood and geographic data requires sourcing and buying fragmented and unwieldy datasets, hiring specialized geospatial analysts/data scientists to work with them, and engineers to bring what they build to prod. It’s complicated, expensive, and slow.

Iggy brings data about neighborhoods to your product development stack and lets you build innovative products and experiences in a fraction of the time by completely eliminating the need to source, preprocess, analyze, and aggregate individual and incomplete spatial datasets so you can do what you do best.

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