Does a “one model fit all” approach applies to the econometric modeling of regional house price determination? To answer this question, we utilize a panel of 100 US Metropolitan Statistical Areas over the period 1980q1–2010q2. For each area we estimate a separate cointegrated VAR model, focusing on differences in the effect of subprime lending and lagged house price appreciation. Our results demonstrate substantial differences in the importance of subprime lending for house price determination across regional housing markets. Specifically, we find a greater impact of subprime lending in areas with a high degree of physical and regulatory restrictions on land supply. Likewise, lagged house price appreciation interpreted as capturing an adaptive expectation channel – is found to be more important in areas where the supply of dwellings is more constrained, in areas located in a state with non-recourse lending, and in more populous areas. Our results also suggest that disequilibrium constellations are restored more slowly in areas located in a state with non-recourse lending. The evolution of US house prices differed markedly across geographical regions over the recent house price cycle. For example, coastal areas experienced much greater house price volatility relative to inland areas (Huang and Tang, 2012; Cohen et al., 2012; Sinai, 2012; Anundsen and Heebøll, 2013). Higher house price volatility was also related to a more severe worsening of employment conditions and a higher rise in foreclosures during the financial crisis period (Rogers and Winter, 2013). Against this background, the objective of this paper is to understand what the drivers of regional US house prices are. For that purpose, we analyze individual time series models for the 100 largest Metropolitan Statistical Areas (MSAs) in the US, paying particular attention to regional differences in the effect of lagged house prices, the speed of equilibrium adjustment, and the role of subprime lending.