Does a "one model fits all" approach apply 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.