Increasingly, investors and property developers are throwing technology at the challenge of where to invest in real estate in the hope to be first to discover soon-to-be hot markets early in their ascension, or to avoid markets that are just beginning to cool. Machine learning-based cognitive computing is being applied with the expectation that the science of artificial intelligence will supplant the art of, old-fashioned site selection and yield better outcomes.
The results for the foreseeable future are likely to be a mixed bag, though. While elastic compute utilities like those available from large online retailers and technology companies enable tens of thousands of dimensions to be weighed when making a particular investment decision, the preponderance of available data are largely lagging indicators of the vibrancy of local real estate markets thus these super-computing systems are getting good at telling us what’s already happened, not what’s going to happen next.
To understand why, consider the number of “local” building permit applications as an input to an investment decision: deducing that the number of applications for building permits in an area has doubled month-to-month over the prior 180 days might be fundamental in determining that that particular market is about to take-off. However, because it may take more than 90 days for a local municipality to assemble and publish the information, by the time this insight is discovered, it’s potentially beyond its “use by date,” or at the very least, it’s only able to confirm that which the market has already come to know – that things in the area are heating up.
The challenge then is to find signals that are either leading indicators or super early lagging indicators, i.e. indicators that point – in near real-time –to a change in inertia or direction. Previously, a dearth of fast-moving signals made this difficult as evidenced above. But the advent of new signal types from sources like ride share services, search engine terms, payment networks, and wireless providers offer a transformational opportunity. Further, because the observation window for these signals is short – often, intraday – it is now possible to use these fast-moving signals to gain an unprecedented pulse on a market.
Take these one by one.
All ride sharing services track affiliate cars – their pick-up and drop-off times and locations. Some of them frequently publish or expose their data —through application program interfaces or other access methods.