Year: 2025
Type: article
Abstract: Leading digits in mortgage collateral value estimates are expected to approximate a logarithmic distribution, consistent with the Newcomb–Benford Law (NBL), when such values are modeled as geometric sequences or random samples drawn from heterogeneous price distributions. Using representative US datasets, we document substantial discrepancies between the observed and expected distributions of leading digits in appraisal values. The magnitude of this deviation increases with loan-to-value ratios and interest rates, while it declines with higher credit scores and safer credit classifications. We also find a systematic underrepresentation of high digits (e.g., 8 and 9) and overrepresentation of low digits (e.g., 0 and 1) in the second leading positions, and that this pattern becomes more pronounced with greater ex-ante loan risk. These findings suggest that nonconformance with NBL may stem from upward bias in appraised values, particularly in riskier lending environments.
Source: Empirical Economics
Authors: Hong Lee, Joseph R. Mason
Institutions: Wright State University, Louisiana State University
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,490
Funder: European Commission
OpenAlex ID: W4415526817