Ireland - House Price Index

Ireland: House Price Index

Mnemonic HPI.IIRL
Unit Index Jan2005=100, SA
Adjustments Seasonally Adjusted
Monthly 0.56 %
Data Mar 2019 107.92
Feb 2019 107.32

Series Information

Source Central Statistics Office (CSO)
Release Residential Property Price Index
Frequency Monthly
Start Date 3/31/1996
End Date 3/31/2019

Ireland: Real Estate

Reference Last Previous Units Frequency
House Price Index Mar 2019 107.92 107.32 Index Jan2005=100, SA Monthly
Building Permits 2018 Q4 6,300 7,440 #, NSA Quarterly
Residential Building Permits 2018 Q4 1,509 1,593 #, NSA Quarterly
Residential Building Completions Feb 2018 1,740 1,418 #, NSA Monthly
Dwelling Stocks 2017 2,018 2,008 Ths. # Annual
Building Completions 2017 Q1 3,896 4,425 #, NSA Quarterly
House Price Value for Existing Homes 2016 Q4 292,457 284,332 EUR, NSA Quarterly
House Price Value for New Homes 2016 Q4 323,875 305,300 EUR, NSA Quarterly

Release Information

The Residential Property Price Index (RPPI) is designed to measure the change in the average level of prices paid for residential properties sold in Ireland. The index is mix-adjusted to allow for the fact that different types of property are sold in different periods. Monthly from 2005.

The RPPI is compiled using data on mortgage drawdowns provided on a monthly basis by 8 of the main Mortgage Lending Institutions under Section 13 of the Housing Act (2002). This data provides details on the characteristics of properties bought (such as building type and size) as well as the price paid. It is transactions based; meaning that prices are recorded only where a sale occurs. Not all residential property transactions are funded by a mortgage (i.e. they are cash based) and these transactions are excluded from the scope of the index.

Calculating percentage changes in the index: The movement of the RPPI is expressed as percentage change, rather than a change in index points, because index point changes are affected by the level of the index in relation to its base period, whereas percentage changes are not.

Mix adjustment

Residential properties are heterogeneous, meaning that no two houses or apartments are exactly identical. This poses a challenge when trying to construct a price index as there is a need to separate pure price change from differences in the quality of the products being bought over time. Typically this is done by comparing the prices of exactly the same products, time after time. This is, for example, the method used in the Consumer Price Index. However, in the case of residential properties, price is determined by many characteristics (location, size, build type etc) which make direct price comparisons difficult. Furthermore, only a small portion of the total housing stock is sold in any given month. The combination of these factors means that the matching process that would typically be used to calculate a standard or typical price index cannot be used in the case of houses and apartments.

The hedonic method is the prevalent statistical process for the measurement of residential price change. In this method, a number of characteristics which influence prices are analyzed so that we can estimate and exclude the part of the price change that can be attributed to them. These characteristics are: location, property type, floor area, number of bedrooms, new or old and first time buyer or not. By excluding the price change determined by these characteristics we are left with an index of pure price change for a consistent set of characteristics - or more simply – a residential property price index. This index uses the rolling year hedonic regression model.


Weights are calculated at the beginning of each year based on the value of transactions during the previous year as given by the mortgage drawdown data. The index is an annual chain-linked Laspeyres-type index. It is calculated by updating the previous month’s weights by the estimated monthly changes in their average prices.


The index is compiled on a monthly basis. In order to smooth out short-term volatility in the series and highlight longer-term trends the published indices are based on a three-month rolling average, i.e. a simple average of the current month and the previous two months.

Data are not final until 12 months have passed following their initial release. As such data is subject to revisions.