@article {Jensen43, author = {Rod E. Jensen and Jonathan Reifler}, title = {Automated Valuation Models and Non-Agency RMBS Property Evaluation and Analysis}, volume = {15}, number = {4}, pages = {43--57}, year = {2010}, doi = {10.3905/JSF.2010.15.4.043}, publisher = {Institutional Investor Journals Umbrella}, abstract = {This article explores various applications of automated valuation models (AVMs) in the evaluation and analysis of both loans and associated properties underlying non-agency residential mortgage-backed transactions and securities. In relation to available information only a few years ago, structured finance investors are now equipped to take a deeper and increasingly granular look into non-agency RMBS pools. In particular, the article discusses AVM property valuations in conjunction with ZIP indices as they relate to current and forward-looking surveillance activities on active, securitized loans. And it introduces the idea of a dealspecific home price time series, where such an index would track and aggregate property prices of active loans in a given transaction and could be used as a quantitative gauge of dealproperty over-, under-, or fair-valuation. Another analysis compares the results of current property valuations using ABSNet Loan HomeVal AVMs with those based on the FHFA and S\&P/Case-Shiller indexes to show how substantial differences can be between the approaches. Finally, the article examines the relative statistical efficiency of the relationship between loan-level delinquency performance by ZIP code aggregation and state- and CBSA-level home price index time-adjusted valuations versus AVM-generated valuations.TOPICS: MBS and residential mortgage loans, big data/machine learning, statistical methods}, issn = {1551-9783}, URL = {https://jsf.pm-research.com/content/15/4/43}, eprint = {https://jsf.pm-research.com/content/15/4/43.full.pdf}, journal = {The Journal of Structured Finance} }