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Risk Integrated Applies Artificial Intelligence to CRE Risk

Risk Integrated has exploited the advances in machine learning (ML) to characterize the credit risk of commercial real estate lending. This has been achieved by using Risk Integrated’s comprehensive cashflow and risk simulation models in the Specialized Finance System (SFS). With the SFS we generate a massive dataset for pre-training the machine learning models and predict default events based on the characteristics of the underlying properties, leases and loans. A combination of Neural Networks and Fine Decision Trees were found to give the best characterization of the complex nonlinear, discontinuous risk profiles of commercial real estate transactions. This pre-training of the ML models has the advantage of improving the results with the finite real-world data.

In addition to pre-training ML models for credit risk data, Risk Integrated has adopted the machine learning approach to enhance the SFS by greatly reducing the time to get the results of the complex simulation models. Full simulation models have the great advantages of capturing all the details of complex commercial real estate structures, but on-the fly simulation of thousands of detailed scenarios can take several minutes. To speed the computation time, we have trained an ML network on the SFS to emulate the results. Emulation with machine learning has the great advantage that after training, evaluation of the risk requires a single pass through the neural net rather than running thousands of complex scenarios. This computation requires only milliseconds and greatly enhances the ability to run optimization analyses on large portfolios using the full sophistication of the underlying SFS analytics.

Based on these successes, Risk Integrated is now applying its AI tools to other aspects of transforming the commercial real estate market.

Dr. [...]

May 21st, 2018|

Dissecting CRE Loan Risks - Lease, Tenant, Interest Rate and Refinancing Risk

Introduction
This paper discusses an approach for dissecting CRE Loan Risks -- determining the relative contributions of lease risk, tenant risk, interest rate risk and refinancing risk for CRE assets. The approach applies to both individual loans and portfolios. The paper also discusses potential risk mitigation strategies based on this information.
Risk models typically give single numbers for the probability of default and loss given default. More advanced models also provide the annual risk profile, identifying spikes in the risk associated with the structure of the deal or portfolio. With cashflow simulation we can go a step further and cut apart the causes of risk, e.g., into lease risk, tenant default risk, interest rate risk and refinancing risk. This identification of the risk sources provides lenders with valuable information as to how they can mitigate the risks, rather than just accept the risk grade.
This analysis is used at two levels: for individual deals and for complete loan portfolios.

Individual report example

Portfolio report example

Examples of these reports can be downloaded with this article below.
Risks to an individual deal
This individual deal report shows that there is a spike in lease risk in 2017, and then there is ongoing moderate risk due to tenant default and finally a large refinancing risk. If this deal was under consideration for credit approval, the risk may be mitigated by for example changing the amortization rate. If the asset was already in the portfolio there are fewer options for changing the loan terms but it may still be possible to add a swap for example to reduce the risk of a poor exit yield if interest rates rise. If there is no obvious way to change [...]

May 26th, 2015|

Developing CRE Risk Models

Introduction
Risk Integrated is pleased to announce that it will allow clients open access to its proprietary cashflow simulation risk models in a transparent Excel/VBA form without the constraints and formalities of the full Specialized Finance System (SFS).

The models have been developed over twelve years and are currently being used for Basel capital, regulatory stress-testing and deal structuring across a wide variety of clients. The models use detailed Monte Carlo simulation to allow cashflows for IPRE and construction deals to be stressed in thousands of alternative scenarios including market movements, re-letting and tenant defaults. The results include a comprehensive suite of risk statistics including Probability of Default per Year, Loss Given Default, NPV of Loss, NPV of Income as well as all the financial ratios and line-by-line breakdown of expenses, income and debt service.

With the models in “raw” Excel form users can assess the risk of individual deals in a transparent flexible framework on their desktop, making adjustments and developing the models as they see fit. After any desired adjustments, clients have the options to use their models in perpetuity, implement them in their own systems, or implement them using the full SFS. The SFS is a web-based system providing control of the models and an interface for the concentration of comprehensive deal and portfolio data into a single SQL database. The full system allows access by hundreds of lending teams and allows portfolio users to run batch analyses without needing additional input from the lending teams.

By releasing these models in a “raw” Excel form, Risk Integrated is looking to provide analytics and services to clients who want models tailored to their business and to their experience but who [...]

March 3rd, 2015|