Progressively, investors and property developers are tossing technology at the challenge of where to invest in real estate in the optimism to be first to ferret out soon-to-be hot markets early in their upswing or to neglect the markets that are just losing their relevance. Machine learning-based levelheaded computing is being applied here with the hope that the science of artificial intelligence will supersede the art of, old-fashioned site selection and reap better fruits.
The results for the foreseeable future seem to be a miscellany of everything, though. While resilient compute utilities like those available from online vendors and technology enterprises enable tens of thousands of dimensions to be weighed while going ahead with a particular investment decision, the preponderance of open data are largely dawdling indicators of the vibrancy of local real- estate markets thus these super-computing systems are getting better at telling us what's already occurred, rather than saying what's going to happen next.
To understand why to consider the infinite number of "local" building permit applications as crucial factors while making investment decision: deducing that the number of requests for building permits in an area has doubled month-to-month over the previous 180 days might be rudimentary in determining that the particular market is about to be quashed. However because of the awareness that a local municipality will take more than 90 days to assemble and publish the provided information, by the time this insight is figured out, it's potentially farther than its "date of expiration," or at least, it's only able to corroborate that which the market has already aware of – that things in the area are getting warmer.
The challenge then is to discover signals that are either leading indicators or super early dilatory pointers, i.e., signs that point – in near real-time –to a change in inertia or pathway. Previously, a lack of fast-moving signals made this problematic as pointed out above. However, the advent of new signal types from sources like rideshare services, payment networks, search engine terms, and wireless providers offer a transformational opportunity. Further, due to the observation window for the aforementioned signals is short – often, intra-day – it is now feasible to use these fast-moving signals to gain a single pulse on the market.
Take this one by one.
All ride-sharing services track affiliate cars – their pick-up and drop-off times and locations.