Estimation of mortgage financing reference values in Mexico using machine learning: model based on SNIIV administrative data (2016 to 2025)
DOI:
https://doi.org/10.29059/cienciauat.v20i2.2058Keywords:
machine learning, random forest, SNIIV, estimation, mortgage financingAbstract
Estimating mortgage financing reference values can provide valuable information to corroborate the results obtained in property valuation. The objective of this study was to estimate the average mortgage loan amount in Mexico for each unique combination of socioeconomic, geographic, and temporal variables, that is, aggregated reference values rather than individual loan amounts. Three machine learning (ML) models were used: random forests (RF), decision trees (DT), and linear regression (LR), based on administrative data from the National Housing Information and Indicators System (SNIIV) for the 2013 to 2025 period, comprising 4 515 891 records. The 2016–2025 period was analyzed, since the UMA (unit of measure and update), used to classify housing value, has been in operation since 2016. Data refinement included removing missing values, filtering by loan modality, percentile truncation, anomaly detection using Isolation Forest and filtering by UMA normative ranges, yielding 829 542 records which, grouped into unique attribute combinations, produced 139 069 observations. Model stability was verified through five-fold crossvalidation, and performance was evaluated on a test set using the root mean square error (RMSE), the mean absolute percentage error (MAPE), and the coefficient of determination (R2). The RF model showed the best performance, with an R2 of 0.970, an RMSE of 187 250 pesos, and a MAPE of 11.48 %, with minimal variability across folds. This study establishes that ML, especially RF, can complement traditional valuation methods, although its full implementation requires including physical property variables and its integration into the current regulatory framework.
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Accepted 2026-09-03
Published 2026-09-21
