About the Author / Organisation

Ryan O’Connor developed HouseVal, a free, independent online tool that provides information on typical house and apartment prices across each of Ireland’s 3,420 electoral divisions.

HouseVal began as Ryan’s final-year college project and was subsequently rebuilt and expanded in 2026. The service is free to use, with no advertising or sign-up required.

About the Project

HouseVal is an interactive map that allows users to explore typical residential property prices and related housing information at local level across Ireland.

The platform is primarily built using residential property sales from the Property Price Register dating from 2010. The data is mapped and processed to improve its usefulness, including removing bulk sales, identifying apartments and adjusting new-build prices for VAT. Historical sales are then adjusted to current price levels using the CSO Residential Property Price Index.

For areas with relatively few property transactions, HouseVal uses a model incorporating Census 2022 data to estimate typical prices. Where sufficient local sales data is available, actual sales in the area are used to refine these estimates. In testing on 2025 – 2026 sales the model hadn’t seen, an area’s estimated typical price was generally within about 5% of what homes there sold for in Dublin, 7% in other towns and cities and 11% in rural areas. Individual homes vary more, as the Property Price Register doesn’t record size or bedrooms.

Testing against sales from 2025 and 2026 that were held back from the model found that estimated typical area prices were generally within approximately 7% of actual sale prices.

Users can explore a range of information for individual areas, including:

• Property Price Register sales displayed on a map
• Modelled property price history since 2012 and five-year price growth
• Indicative household income required to purchase, using CSO household income data
• Purchaser profiles, including first-time buyers, movers and investors
• RTB rental data and gross rental yields
• Pobal HP Deprivation Index information
• EPA radon risk data
• Bus and rail service frequency based on NTA timetable data
• Residential planning applications

HouseVal is designed to show typical prices at area level rather than provide valuations for individual properties. The Property Price Register does not include information such as floor area or number of bedrooms, which limits the ability to estimate the value of a particular home.

Why It’s a Showcase

HouseVal demonstrates how a range of open and publicly available datasets can be combined to create a practical, accessible public-facing service. Rather than presenting property prices in isolation, the project brings together data on property transactions, incomes, rents, purchaser types, transport, planning, deprivation and environmental factors. This allows users to explore housing and property markets within a broader local context. It also demonstrates the potential for open data to support innovation outside government. What began as a student project has developed into a nationwide tool covering all 3,420 electoral divisions and presenting complex datasets through an accessible map-based interface.

Impact of the Showcase

HouseVal makes housing and property data easier for members of the public to explore and understand at a local level. By bringing together information that would otherwise need to be sourced from several different organisations and datasets, the platform provides users with a more comprehensive picture of individual areas. It can help people explore not only typical property prices and how they have changed over time, but also factors such as affordability, rental yields, purchaser activity, public transport availability and local socioeconomic and environmental characteristics.

The project highlights how open data can be reused, combined and transformed into accessible digital tools that provide additional value to the public, while demonstrating the importance of clearly communicating the limitations of the underlying data.