In May, Airbnb revealed its “biggest change in a decade". This included, amongst other things, a new kind of search based on category. You're greeted with cute icons on the homepage representing categories like "vineyards", "beach", "surfing", and "amazing views". This new category search is a brilliant way to highlight one of Airbnb’s biggest moats – its unique and differentiated supply. As is typical for Airbnb, the launch appears a massive success. And of course it is: we can finally search for listings based on the reasons many of us travel in the first place: skiing, wine, and surfing.
Let's just reflect on this: until this very recent update, there was no easy way to search Airbnb based on characteristics of the place you wanted to visit! It's pretty bonkers. You could work around it by zooming and panning on a map to try to understand what's nearby, but what's more likely is that you went to Google, typed something like "ski areas in france train from paris", or "cool coffeeshops in LA", read a bunch of blogs and listicles, and then maybe went back to Airbnb once you had narrowed down a few cities/locations. This is good for neither Airbnb (your likelihood of booking goes down if you have to leave the site), nor the user (it's super inefficient).
In the words of Airbnb's CEO, “Now you can just click on vineyards". This type of experience is largely missing from travel and real estate sites because it's really hard to support. When I worked at Airbnb we did not even know what listings were by the beach, let alone which ones were by vineyards. They've come a long way. Whether you like searching via cute icons or not, this is a really huge advancement... and it's only possible when you have a sense of place alongside your listing data.
We'll share more examples of companies using place data in coming weeks and months –– stay tuned for more! And of course, if you're interested in building great products like category search, let me know!
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– convert more customers.
Zillow has a hard-to-find 'waterfront homes' filter. But it's not really accessible to home searchers.
Getting geospatial data into ML models is hard. One reason for this is that there are few “canonical” sources for geospatial data at scale. Open Street Map (OSM) is understood as one such source for rideshare, but its potential in real estate use cases is widely unexplored. In this post we discuss some of the quality control/QC work we do to improve off-the-shelf OSM data and measure the impact of that work via a real estate pricing model benchmarking test.
Virtually all companies are in the same boat: it's way too hard to innovate their products when it comes to location data. The few who can leverage geospatial data can innovate in truly game-changing ways.