Using tech tools
Share and housing market crashes are terrible events that can rip through society like chainsaws.
The difference, of course, is that while trading stocks is optional, we end up being housing-market captives whether we want it or not.
Another difference is that because nobody wants even little NZX to crash, only to correct itself when madness like the recent “Gamestonks” shorting of shares exuberance occurs, lots of data is being collected for traders.
Automated systems and humans use data to stop anomalies from snowballing into full-blown crashes in which entire economies go under. It doesn’t always work, but at least there’s a concerted effort to help people make informed choices and, we hope, issue a warning before that Black Swan descends on the market.
Housing, a basic human need, is traded too in various ways but where are the early-warning IT systems that could’ve gone “ahem, this is starting to look a bit dangerous”? Because house-price inflation hitting 20-30 per cent in a year isn’t healthy.
Already, a lot of housing-market data is collected. Just about all of it focuses on sales-related information like price, appreciation, days to sell, and the rental appraisal and mortgages to pay for properties.
It’s often presented by real estate firms who have a vested interest in boosting sales, and banks who want to make money out of house loans.
Government agencies also collect and present data, like Tenancy Services, but it’s very much after-thefact. A glance through Trade Me confirms that even Tenancy Services’ $875 a week upper-end figure for Herne Bay rents is woefully out of date, ditto the median of $600.
Independent organisations collect and analyse housing data but if the intention of that was to warn of, and maybe even stop, an enormous asset bubble forming, it didn’t work.
Centrist politicians don’t want to be painted into a corner by a housing market gone mad. Any intervention then will be blunderbuss-style with consequences for the less wealthy, and potentially election-losing outrage from powerful property owners. Luckily, it’s no longer 1987. We can collect plenty of fresh data, process and present it transparently, to put the brakes on a runaway housing market.
Think about how organisations monitor IT systems and networks for security and performance, by collecting data and looking for anomalies. The key is to catch dangerous activities in parts of the system before things snowball and become hard and expensive to fix.
A “Housing Market Intelligence System” that monitors property activity more accurately and regularly could be used to dampen hotspots in specific areas, understand what’s creating them, and generate an appropriate, small-scale correction.
With more fine-grained and timely data, we could identify when gaming the housing market begins. Like the person who managed to buy 20 properties in a year, with no deposits, only existing equity, and then wrote a book about it with hints, tips and financials for others to follow.
We could perhaps use the data to query the system, asking how such 100 per cent leveraged housing investment affects the rental market in a particular area, especially if other speculators are active there. With such high leverage, owners with multiple properties have to keep rents at a level that covers mortgage repayments.
In that model, and especially if accommodation supplements come into play, is there really a free market? Can the problem be fixed by increased housing supply if there are people able to buy houses in large numbers without deposits, an option that’s simply not available to firsttime buyers?
Joining up existing data repositories with new information sources could be an interesting project, if only because it’s tedious and painful to repeat old mistakes when we have the tools not to.