Abstract
We make two contributions to the study of house price index and mortgage credit modeling accuracy. First, we assess the predictive power of house price indices calculated at different levels of geographic aggregation. Lower levels of aggregation offer superior fit when appreciation rates vary substantially across submarkets and the indices are based on a sufficient number of transactions. Second, we estimate a competing options credit model using 15 years of mortgage performance data in the United States. Model accuracy is highest when using indices at a city or lower level of aggregation to construct current loan-to-value ratios. Fit is weaker when using state or national price indices. Overall, this research highlights the benefits of using more localized house price indices when predicting property values and mortgage performance.
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