Publication Archives - Risk Integrated

ejinsight: Why commercial real estate sector needs proptech solutions now

Original article

When it comes to tech adoption, the commercial real estate (CRE) industry has typically lagged behind other sectors. However, over the past few years the industry has begun to take property technology - or proptech - seriously. Last year, proptech start-ups raised US$625.9 million in APAC, and in 2018 this figure was a record-breaking US$1 billion.

The industry has come to embrace technologies that include artificial intelligence (AI), augmented reality (AR) and the Internet of Things (IOT). And the applications of these solutions have also been far reaching - ranging from smart property management to office space design.

But the adoption of technology has been uneven across the sector. Some aspects, such as facilities management, have taken to innovations more easily. However, the investment and transactions side of commercial real estate have remained more resistant since leasing professionals prefer to meet in person to share data with clients, rather than using technology.
Solving the day-to-day pain points
For example, when making leasing and investment deals, CRE professionals are still largely reliant on physical visits.

Introducing OfficeBlocks - Zoom in to commercial real estate data

JLL and Risk Integrated launch new AI technology to revolutionise real estate leasing and investment
OfficeBlocks empowers decision-makers with rapid data access and insights to compare and analyse local and regional commercial real estate opportunities

This innovative and intuitive suite of tools brings together the power of AI and big data to provide actionable estimated insights into commercial properties across major cities in Asia Pacific, along with detailed portfolio analytics software

Hong Kong and Singapore, October 15, 2020 - Leading global real estate consultancy, JLL (NYSE: JLL), and risk management company, Risk Integrated, today unveiled OfficeBlocks, an industry-first property technology – or proptech – suite of tools set to transform commercial real estate leasing and investment.

OfficeBlocks gives investors, occupiers and commercial real estate (CRE) brokers rapid data access, insights and analytics for commercial real estate decision making. This innovative and intuitive suite of tools combines artificial intelligence (AI), big data and mobile communications, to provide immediate insights and analysis into commercial real estate across major cities in Asia Pacific.

To date, investors, occupiers and CRE brokers have often lacked real-time and actionable insights into commercial properties and decision making. They have instead been reliant on manual, time-consuming methods of information gathering - from physical visits of properties to analysis of complex data-filled reports. OfficeBlocks is set to transform real estate decision-making by taking millions of data points and processing market intelligence in a meaningful manner to uncover actionable insights - from rental performance to property availability and portfolio analytics.

By combining JLL’s comprehensive 25-year industry data set with the cutting-edge AI of Risk Integrated, OfficeBlocks provides unrivalled commercial real estate insights and analytical tools including:

AI estimated rent and floorspace for office properties across Asia Pacific.
Comparison and benchmarking [...]

October 15th, 2020|

The COVID-19 Stress on Commercial Real Estate

Introduction
We are living the stress test. In this crisis it is the responsibility of risk managers to avoid compounding the medical and economic crises with a financial one. For commercial real estate lenders this means understanding the potential impact on borrowers and getting ahead of the game to restructure their loans and avoid the costs and dislocations of bankruptcy. That restructuring needs to be economically sensible, robust to the range of different possible outcomes, and transparent so that investors do not need to have unfounded fears of financial difficulties.

The financial industry has been increasingly guided by financial risk models. However, normal credit models have been broken by the COVID-19 crisis because there is no historical default data for such an event. Now banks are turning to scenario analysis. That too has limitations, because it only tests a handful of possibilities. In this note we are looking at what can be learned from cashflow simulation. For those unfamiliar with scenario analysis and simulation, Appendix 1 gives a brief introduction.

The note is organized in four sections:

Qualitative discussion of the short and medium-term impacts on real estate
Converting those considerations into specific scenarios that will drive cashflows
Quantification of the increased risk of default for a typical deal
The effectiveness of restructuring alternatives

Of the restructuring alternatives tested, an 18-month interest roll-up and a sweep were effective in reducing the loss.
Qualitative Discussion of the Impacts on Commercial Real Estate
In the next few months there will be historic levels of unemployment, economic disruption and government interventions. These will play out unevenly across geographies and sectors.

Looking at the short term there will be lease defaults by “non-essential” retail businesses and many office-based businesses. It will be nearly impossible to [...]

April 8th, 2020|

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. Chris Marrison
CEO, Risk Integrated
Chris.Marrison@RiskIntegrated.com
About Risk Integrated
Risk Integrated delivers [...]

May 21st, 2018|

Risk Reporting for Equity Investors in Commercial Real Estate

Introduction
Risk Integrated has extended its well proven Specialized Finance System (SFS) to include equity risk metrics for commercial real estate. Over the last 16 years the SFS has been developed primarily to provide credit risk metrics such as probability of default and stress-loss for commercial mortgages using cashflow simulation. The credit metrics are based on the cashflows to the loans whereas the equity metrics are the “residual” cashflows to the property owners and include the effects of leveraging, interest rate swaps and currency effects for multi-currency portfolios.

For equity investors the SFS produces familiar metrics such as the internal rate of return and cash on cash return, in both the base-case and across thousands of possible market and tenant scenarios. The figures below illustrate the IRR for a deal without and with leverage.
Returns without Leverage

Returns with Leverage

The links below give the complete reports for the deals without and with leverage, and include the credit report on the debt for the leveraging loan.

Equity Report for Unleveraged Property Investment
Equity Report for Leveraged Property Investment
Credit Report for the Leveraging Loan

Dr. Chris Marrison
CEO, Risk Integrated
Chris.Marrison@RiskIntegrated.com

March 6th, 2018|

The Effect of Deal Structures on Current Expected Credit Loss

Introduction
In a recent paper, we looked at the effect of forecasts on IFRS-9 and Current Expected Credit Loss (CECL) deductions. This paper goes a step further to explore what lenders can do with deal structures to limit the expected losses. This can be viewed in three ways:

Approaches to reduce the CECL deductions
Approaches to reduce the actual risk
Approaches to arbitrage anyone who does not take these features into account

In the previous paper, we looked at several variations on a commercial real estate deal under three forecasts. In this paper, we take the most-risky combination and show the effect of changing the loan structure. The stylized "moderately negative" central forecast used was as follows:

Interest rates, inflation and vacancy rates increase 0.5% per year for three years and then flatten
Capital values and rental rates fall 5% per year for three years and then flatten

The loan is towards the risky end of the spectrum. It is for a Boston office with an LTV of 75%, initial DSCR of 1.35, floating-rate interest, 20-year amortization and a maturity of 5 years. There are four equal leases with a 1% probability of tenant default, one lease expiring in year three, the others expiring after year eight, and all leases repricing to the market annually.
Analysis
Using the Specialized Finance System's cashflow simulation, we quantified the effect of six variations in the financing structure:

Reduced loan amounts
Alternative amortization profiles
Interest rate caps
Sweep amortization (i.e., also taking all excess income to pay-down the loan)
Sweep triggered by covenants on Loan to Value (LTV)
Sweep triggered by covenants on Debt Service Coverage Ratio (DSCR)

The details of each variation are described later. Let us first look at the overall results. [...]

May 8th, 2017|

The Effect of Forecasts on IFRS-9 and Current Expected Credit Loss

Introduction
The Financial Accounting Standard Board's (FASB's) new IFRS-9 and the Current Expected Credit Loss (CECL) approach requires banks to deduct the Expected Loss from the balance-sheet value of a loan. Furthermore, it requires the Expected Loss to be estimated using a reasonable forecast of the future market. Now consider this recent quote on Bloomberg:
"Apartment rents in cities such as New York and San Francisco will need to fall as much as 15 percent for a glut of high-end developments to be absorbed, according to billionaire real estate investor Richard LeFrak"
This leads to questions as to how much difference do the forecasts make to the CECL and what kind of deals are most affected?

This short paper outlines the answers to those questions for commercial real estate by testing a range of mean-reverting forecasts against typical example deals. The approach used to assess the risk is the comprehensive cashflow simulation embedded in the Specialized Finance System (SFS). The simulation tests the deals under thousands of dispersed random scenarios, centered around forecasts for inflation, interest rates, capital values, rental rates and vacancy rates.
Analysis
Three simplified forecasts are used for illustration here:

Basecase:

Simulation is around a flat forecast relative to today

Mildly negative central forecast:

Interest rates, inflation and vacancy rates increase 0.25% per year for three years and then flatten
Capital values and rental rates fall 2.5% per year for three years and then flatten.

Moderately negative central forecast:

Interest rates, inflation and vacancy rates increase 0.5% per year for three years and then flatten
Capital values and rental rates fall 5% per year for three years and then flatten.

The loans are all for a Boston office with an LTV of 75%, initial DSCR of 1.35, 20-year [...]

April 5th, 2017|

How to Arbitrage Slotting Capital

Introduction
One of the options for allocating regulatory capital under Basel III and Solvency II is to use the "slotting" approach, whereby the capital for each asset is assigned according to a small number of slots, e.g., low, medium, and high risk. This creates specific behavior-incentives for the institutions under slotting and specific arbitrage opportunities for those institutions with more advanced approaches. This paper looks at the "game" set up by this set of incentives. The paper first summarizes the arbitrage opportunities and then explains the underlying rational. The opportunities are best explained on a graph.
Graph 1. Cost of Funds for Each Level of Measured Risk

In Graph 1 the x-axis is the riskiness as judged by the slotting criteria. The y-axis is the cost-of-funds. In Graph 1 the slotting cost-of-funds depends simply on the assigned capital per slot, multiplied by the required return on equity. For later discussion, the points on the steps are labeled as leading-edge and trailing-edge.
Graph 2. Range of Cost of Funds for Actual Risk

Graph 2 adds a range for the cost-of-funds. This range shows what the cost-of-funds (COF) would be if the risk was perfectly known. The true COF is different to the slotting COF for two reasons. Obviously the first reason is that the slots produce steps. The second reason is that the simplified risk assessments such as the typical slotting criteria do not capture all the possible risk variations within a deal: in a previous paper we demonstrated how the tenant, property and financial structures can produce great differences in risk for commercial property deals that seem to have identical risk according to conventional measures such as Loan to Value (LTV) and Debt Service Coverage Ratio (DSCR).
Graph 3. Areas [...]

June 22nd, 2016|

Risk Integrated Releases Quarterly CCAR Model for Commercial Real Estate

FOR IMMEDIATE RELEASE:
Risk Integrated Releases Quarterly CCAR Model for Commercial Real Estate
New York / London – May 19, 2016 – Risk Integrated, the leading international risk solution provider for commercial real estate, today announced the release of a quarterly cashflow simulation model (CFM) specifically tailored to the requirements of the US regulatory Comprehensive Capital Analysis and Review (CCAR) stress tests. In addition to breaking the risk into granular, quarterly time steps, this new model requires less input data and is easier to use and maintain.

The CCAR CFM is built on the same foundation as the comprehensive fully detailed CFM that clients are currently using in the Specialized Finance System (SFS) for grading and deal structuring as well as Basel III and Solvency II regulatory compliance. The full model takes into account the many features, such as covenants, lease terms, etc., which can change the risk of a commercial real estate financing, and are especially useful when structuring new credits. By building on the same foundation, the new CCAR CFM model leverages the proven track record of the full model (see Validation of the Specialized Finance System). However, the CCAR CFM has been optimized and greatly simplified such that it only requires data which is available from the CCAR dataset (the FR Y-14Q data).

At a portfolio level, the new model gives very similar results to the full model. The graph below illustrates a portfolio's cumulative loss, quarter by quarter, for the CCAR stress, comparing the quarterly losses with the average losses from annual time steps.

At this time there is increased regulatory pressure for institutions to only use capital models that they control and understand in full detail - i.e., not to use vendor models that [...]

May 19th, 2016|

Solvency II Compliance achieved with Risk Integrated’s Specialized Finance System

FOR IMMEDIATE RELEASE:
Solvency II Compliance achieved with Risk Integrated’s Specialized Finance System
New York / London – April 12, 2016 – Risk Integrated, the leading technology firm focused on risk measurement for commercial real estate, today announced that a leading global insurer has gained regulatory approval to use the Specialized Finance System as their internal model to calculate regulatory capital requirements under Solvency II. Risk Integrated’s Specialized Finance System (SFS) is also being used for gathering detailed data on its commercial real estate loans, reporting on the assets’ risk profile and structuring new loans.

At a time of increasing regulatory and risk management pressures, the client is using the SFS to strengthen the reporting of its commercial real estate (CRE) assets to give management, the board and regulators an increased understanding of the portfolio. They are also using the SFS to provide a competitive advantage when originating new transactions by gaining deeper insights into the sources of risk. A senior executive risk manager in the organisation commented that "We see the SFS as providing both regulatory compliance and a competitive advantage. This tool is a key factor for integrating all the aspects of risk management across the business".

At the core of the SFS there is a set of highly detailed cashflow simulation models. The models are transparent and under the control of the client's analysts to incorporate its business expertise. The resulting reports allow users to see the interaction of the risk factors within each deal's structure. Examples of detailed cashflow risk reports are shown at this link: Dissecting CRE Loan Risks

Dr. Yusuf Jafry, Risk Integrated's CTO, commented that "I am delighted that they have selected the SFS. From a technical point of view, our system's [...]

April 12th, 2016|

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