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    CRE CECL Methodologies

    The second in our CECL Quantification webinar series, this webinar discussed how commercial real estate (CRE) models and methodologies can be leveraged to fulfill CECL requirements, and key considerations in transitioning these models.

    Implementation of the new financial instruments impairment standard (CECL), may take between twelve months to two years and over 62% of banks surveyed by Moody’s Analytics expect CECL compliance to increase their overall provisions.

    Successful implementation requires understanding the impact of the accounting standard on provisions and identification of appropriate methodologies to incorporate the forward-looking information and life-of-loan horizon required for CECL.

    Moody’s Analytics has designed a series of CECL Methodology webinars to help firms of all sizes with the tactical and strategic considerations when selecting the best modeling approach.

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    Managing the Shock to Commercial Real Estate: A Practical Approach to Quantify Loan Performance

    Join us for an in-depth analysis of CRE loan performance and credit risks under Moody’s latest economic and real estate scenarios.

    July 2020 WebPage Laurent Birade, Sumit Grover, Christian Henkel, James Partridge

    Accounting for Credit Losses in CRE A Case Study

    Credit loss forecasting models are only as effective as the data on which they were built, and few, if any, were designed to capture the effects a sudden pandemic would unleash on the U.S. economy. In times like this, how are financial institutions determining the right amount to set aside for future credit losses?

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    Qualitative Overlay Factors for CRE Credit Risk Models in the Context of COVID-19

    COVID-19 has impacted several industries. We examine qualitative overlays to CRE loans that can be made no matter your CRE model.

    May 2020 WebPage Eric Bao, Jake Carr, Dr. Jun Chen

    Coronavirus (COVID-19): Credit Risk Impact on Commercial Real Estate Loan Portfolios

    Join us for a comprehensive presentation on the state of the U.S. CRE market, and the impact on measures of credit quality.


    Coronavirus (COVID-19): Credit Risk Impact on Commercial Real Estate Loan Portfolios

    This study takes a scenario analysis approach and dissects the credit risk impact on financial institutions' commercial real estate (CRE) loan portfolios under various COVID-19 scenarios.

    March 2020 Pdf Eric Bao, Dr. Jun Chen, Wenjing Wang, Arka Kundu, Junrong Liu

    Panic, Volatility, and CRE Finance & Transaction Markets

    Volatility has risen significantly in financial markets, driven by COVID-19. How might this affect US multifamily and commercial real estate (CRE) transaction markets? What are the mechanisms through which panic and a flight to safety will hurt some markets but benefit some players?


    ‍The CRE Equivalent of Zestimate: Combining Machine Learning and Spatial Modeling to Mine Big Data ‍

    With an increasing demand for faster and more objective estimates of fair market rent and pricing for commercial properties, we explore bringing the automated valuation model (AVM) to CRE.

    November 2019 Pdf Dr. Jun Chen, Wenjing Wang

    The Evolution of Risk Ratings: Lessons Learned and Where We Go From Here

    Employing a data-driven approach to risk rating commercial loans has gone from a nascent idea to an established practice, allowing financial institutions to make informed decisions, improve profitability, and better identify trends in risk. Join us for an in-depth discussion on leading practices and lessons learned from a decade of enhancing the process of risk rating commercial loans.

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    Invest with the Best (in Commercial Real Estate)

    Learn how data and technology are being used to improve CRE lending and investment decisions…and how to motivate your underwriting staff!

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    CECL – Using a Reasonable and Supportable Forecast (Presentation Slides)

    The new CECL accounting standard requires institutions to incorporate forward-looking information in their estimate of expected lifetime losses.

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