When in May of this year a US real estate investment company bought all 112 properties in a new housing development outside Dublin, Ireland in order to rent them out, there was public uproar. Previously, the same company had acquired 4 developments tailored for student accommodation with a total of 1,200 bedrooms and hundreds of apartments in its buy-to-rent strategy, all of this in a country where countless people are priced out of home ownership or even rental, and the housing crisis is so acute that it has contributed to a new wave of emigration.
This lack of affordability when it comes to housing is widespread and unfortunately growing: in most markets we are observing a widening of house price-to-income ratios; this in turn drives demand for housing rentals, thus creating a vicious circle where both rents and house prices increase and more and more people are left with ever diminishing prospects of both home-ownership and affordable rents – and the creeping sense of hopelessness and despair closely associated with housing insecurity.
In the Irish case, the Government responded to the outcry by saying that there wasn’t much it could have done to prevent this gobbling up of residential inventory, as it had very limited data on which type of institutional investors were buying whole housing estates and apartment blocks, or how many. The Department of Finance went further and admitted that: “While there is CSO [Central Statistics Office] data on purchases by institutions it does not provide any real colour around the specific type of institution purchasing in the market.”
Housing data is outdated, inconsistent, fragmented; it is also not easily accessible, patchy, unreliable, and in siloes, all of which hinders any effort to get a cohesive view of cleansed, trustworthy, and enriched multi-source data, facilitating instead inconsistencies in data availability, collection, usability, and integrity. The result is a limited understanding of the housing ecosystem as well as of potential solutions.
An AI-assisted data platform like iReal can help by providing data cleansing, enrichment, augmentation, and standardization. This in turn promotes data clarity, eliminates fragmentation, dismantles existing silos, and generates real-time data that can be trusted. By gaining more access to data and increasing its usability, we can successfully remove some of the barriers that today limits solutions towards increasing supply and securing housing for all. If we want to be able to respond to this growing crisis, we must succeed at making better, data-driven decisions as well as reaching those decisions faster.
An affordable place to live is much more than just having a roof over one’s head. It is a way to attain a sense of security, autonomy, emancipation, and control over our lives, so we can flourish and establish social connections, the foundation of vibrant, vital communities. Identifying the improvement tools and steps required on the journey towards affordable housing and fairer cities is one of today’s emergencies. The alternative to doing so is one we cannot afford.