The Irish government’s decision to spend €160 million annually on tax cuts for landlords is under scrutiny, as doubts emerge over the so-called “exodus” of landlords from the rental market.
According to The Journal, the tax relief, introduced to keep landlords from leaving and to stabilize rent prices, will cost taxpayers €160 million a year from 2026 onwards. The move was largely justified by claims that landlords were fleeing the market, leading to fewer rental properties and higher prices for tenants.
However, recent data from the Residential Tenancies Board (RTB) raises questions about the validity of these claims. Contrary to fears of a landlord exodus, the number of registered landlords and rental properties has actually increased slightly. From mid-2023 to the end of March 2024, registered tenancies rose from 213,000 to 230,000, while the number of landlords grew from 97,000 to 103,000.
Yet, interpreting these figures isn’t straightforward. The rise could be attributed to landlords catching up with new, stricter registration requirements rather than an actual increase in rental properties. Moreover, the RTB has adjusted its data collection methods to eliminate duplicates and inactive tenancies, complicating comparisons with previous years.
Amid this data confusion, some experts argue that the tax cut is misguided. Critics, including former ESRI economist Barra Roantree, argue that the majority of landlords were never considering leaving the market, making the tax break ineffective and a potential waste of taxpayer money. They suggest that many small landlords, especially those who became landlords by accident during the financial crash, were likely to sell their properties regardless of the tax incentive.
While the intention behind the tax relief—to increase the supply of rental properties and ease pressure on tenants—is sound, the lack of clear evidence supporting its effectiveness raises serious concerns. As it stands, the government is investing hundreds of millions of euros in a policy based on uncertain data, with no guarantee that it will achieve its intended goals.