Housing price prediction in Ireland: a machine learning framework integrating geospatial encodings and macroeconomic indicators

Purpose This study aims to develop and evaluate a machine learning framework for predicting residential property prices in Ireland using the complete Irish Property Price Register, a population-complete data set of 736,002 arm’s-length transactions spanning January 2010 to March 2026. It formally quantifies the marginal contribution of geospatial, structural and macroeconomic data modalities. Design/methodology/approach A multi-modal predictive framework integrates structural transaction data, address-derived geospatial location signals via Bayesian out-of-fold target encoding and macroeconomic indicators from the Central Statistics Office. Five models are trained and evaluated, including Ridge Regression, Random Forest and three HistGradientBoosting variants on an 80/20 train-test split. Findings Geospatial location features increase R² by 0.2552, from 0.3494 to 0.6046, reducing mean absolute error by €28,271 per property. Macroeconomic lag features covering the consumer price index, unemployment and earnings change R² by less than 0.0001. The best model, HistGradientBoosting v3, achieves R² = 0.6046 and MAE = €80,755 on 144,470 held-out observations. Research limitations/implications The study is constrained by the absence of floor area, bedroom count and BER information for most transactions in the Irish Property Price Register. Originality/value To the best of the authors’ knowledge, this is the first population-scale machine learning study applied to the complete Irish Property Price Register. It introduces Bayesian out-of-fold target encoding of raw address strings as a geocoding-free geospatial method generalisable to any national transaction register and delivers an ablation study isolating geospatial and macroeconomic modality contributions in the Irish residential market.