Housing markets play an integral role in economies, and real estate often represents households’ most significant investment. This thesis aims to develop a short-term forecasting model for real house prices in Iceland’s capital area and identify key predictors from a set of economic, demographic, and housing market indicators. Motivated by the delayed response of the real estate market to changes in economic conditions, as modelled in the DiPasquale-Wheaton model, this study employs the autoregressive distributed lag (ARDL) framework and forecast combination techniques. The results indicate that housing market indicators, along with measures of consumer confidence, income, and the cost of capital, are among the most important predictors of house prices in Iceland. Moreover, the results suggest that a multivariate ARDL model delivers the most comprehensive and accurate short-term forecasts. These findings may offer useful insights for policymakers, real estate professionals, homeowners, and housing market analysts, and the forecasting models developed may serve as practical monitoring tools for the Icelandic housing market.