Isn't the main issue with the register that it's not market rent?
@patfert1 stating the available data is a normal distribution is not the same as saying this is of use for setting market rent.
Right, two different things entirely.
Regarding market rent and the RTB register...
A "supply and demand" driven market rent for one specific property and one specific tenant, cannot be definition be represented by any register of rent amounts for different properties and different tenants. Plus, the RTB search query is relatively crude in that it does not take into account condition of the property, location advantages unique to the property, etc.
According to the legislation (from Google AI, I believe it is accurate):
Under the Residential Tenancies (Miscellaneous Provisions) Act 2026, effective from 1 March 2026, market rent is defined as the amount a tenant would reasonably pay for a similar home in a comparable area at the time the tenancy begins, determined by using the RTB rent register. Landlords must justify new rents based on comparable properties (similar BER, floor area, and bedrooms) and provide this evidence to the tenant and RTB.
These two things are not really compatible. I mean "the amount a tenant would reasonably pay for a similar home in a comparable area at the time the tenancy begins" suggests normal rules of supply and demand, whereas "determined by using the RTB rent register" refers to other properties which may be under RPZ, rents set years ago and not updated, new tenancies 24 months ago, etc.
Having read the RTB rent register algorithm kindly obtained by
@Sr. Tayto, at first read I don't find anything particularly objectionable or blatantly biased in the algorithm, as I noted earlier. I could be wrong, but this is my working hypothesis. But since I could be wrong, I'd like to challenge this hypothesis. What I would like to do is the following:
1) Scrape a new set of very comprehensive data for a local electoral area (and later other LEAs). It should include a wide range of all the search criteria and represent all of the EDs in the LEA. I have the tool for this.
2) Implement the RTB algorithm and run it on this data set. First, see if I can get in-or-around the same 10 matches for a reference property, as the RTB register query tool. If yes, then...
3) Generate the match score for all of the other properties in the data set. Examine this data set to see if there are some or many which arguably should be in the top 10 matches, e.g. higher rent levels not in the original 10 which were left out due to the RTB algorithm, but which under a slightly different algorithm might have appeared. To be honest though, even if I find that for one reference property, it doesn't mean much unless I can get the same result for many other references properties, and in other parts of Ireland too. And I'm unlikely to have bandwidth for such a task. But anyway this is what I am working toward.
I'm working on item 2...
Thoughts and suggestions welcome!