Paper: arXiv 2402.15498

Authors: Ying Wu, Garvit Arora, Xuan Mei

Abstract

Forecasting the loss given default (LGD) for defaulted Commercial Real Estate (CRE) loans poses a significant challenge due to the extended resolution and workout time associated with such defaults, particularly in CCAR and CECL framework where the utilization of post-default information, including macroeconomic variables (MEVs) such as unemployment (UER) and various rates, is restricted. The current environment of persistent inflation and resultant elevated rates further compounds the uncertainty surrounding predictive LGD models. In this paper, we leverage both internal and public data sources, including observations from the COVID-19 period, to present a list of evidence indicating that the growth rates of the Consumer Price, such as Year-over-Year (YoY) growth and logarithmic growth, are good leading indicators for various CRE related rates and indices. These include the Federal Funds Effective Rate and CRE market sales price indices in key locations such as Los Angeles, New York, and nationwide, encompassing both apartment and office segments. Furthermore, with CRE LGD data we demonstrate how incorporating CPI at the time of default can improve the accuracy of predicting CRE workout LGD. This is particularly helpful in addressing the common issue of early downturn underestimation encountered in CRE LGD models.

Complexity vs Empirical Score

  • Math Complexity: 3.5/10
  • Empirical Rigor: 6.8/10
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper provides a practical empirical solution to a common regulatory challenge (CECL/CCAR) by using CPI as a leading indicator for Commercial Real Estate LGD forecasting. While mathematically straightforward, the empirical approach is solid and addresses the well-known issue of early downturn underestimation.

Research Flowchart

  flowchart TD
    A["Research Goal: Improve CRE Workout LGD Forecasting & Reduce Early Downturn Underestimation"] --> B["Data Collection: Internal CRE LGD Data + Public MEVs & Price Indices"]
    B --> C["Methodology: Analyze CPI Growth Rates as Leading Indicators for Rates & CRE Price Indices"]
    C --> D["Computational Process: Evaluate LGD Models Incorporating Default-Date CPI vs Standard Restricted MEVs"]
    D --> E["Key Finding 1: CPI YoY/Log Growth Strongly Leads Fed Funds Rate & CRE Price Indices"]
    D --> F["Key Finding 2: Adding Default-Date CPI Significantly Improves LGD Forecast Accuracy"]

Cited References (20)

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  3. COSTAR COMMERICAL REPEAT-SALE INDICES PRESS RELEASE (2023) — link
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  6. Using the censored gamma distribution for modeling fractional response variables with an application to loss given default (2011)
  7. Estimation of relationships for limited dependent variables (1958)
  8. Sample selection bias as a specification error (1979)
and 12 more
  1. Enhancing two-stage modelling methodology for loss given default with support vector machines (2017)
  2. Comparison of modeling methods for loss given default (2011)
  3. Loss given default of high loan-to-value residential mortgages (2009)
  4. Loss given default models incorporating macroeconomic variables for credit cards (2012)
  5. The determinants of recovery rates in the US corporate bond market (2014)
  6. Predicting loss given default of unsecured consumer loans with time-varying survival scores (2023)
  7. Systematic effects among loss given defaults and their implications on downturn estimation (2018)
  8. The rate of interest (1907)
  9. Note on the uniqueness of the maximum likelihood estimator for the Tobit model (1978)
  10. The elements of statistical learning: data mining, inference, and prediction (2009)
  11. Multivariate adaptive regression splines (1991)
  12. An Exploration to the Correlation Structure and Clustering of Macroeconomic Variables (MEV) — → 2401.10162

1 of 20 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.