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.