This article develops and compares residential property price models required for banking and insurance applications including pricing, risk management and portfolio management. Our study is based on postcode‐level house prices for Sydney over the period 01–1979 to 03–2011. The estimation results of single‐factor panel data models show that the market‐wide house price index explains between 42 per cent and 44 per cent of the longitudinal and cross‐sectional variation in postcode‐level house price growth rates. Macroeconomic and financial variables, as well as geographic and sociodemographic postcode characteristics are confirmed as important factors for pricing and risk management of products exposed to house price risk.