Commercial Real Estate Mortgage Research Articles

Climate risk and commercial mortgage delinquency

AbstractNatural disasters such as hurricanes, floods, heatwaves, and wildfires are projected to become more prevalent in the foreseeable future. Climate risk is, therefore, increasingly recognized as an important factor by policy makers, the investment community, and financial markets. Due to the immobility of assets, the commercial real estate industry is especially vulnerable to climate risk, and there is an increasing interest to understand the impact of climate risk on the value of commercial real estate. For commercial real estate lenders, changes in collateral value are only of partial importance. The ability of borrowers to meet their payment obligations is equally, if not more important. By combining historical data on two major climate‐related disasters—Hurricanes Harvey and Sandy—with longitudinal information on commercial mortgage performance, this paper identifies the impact of climate risks on mortgage delinquency rates for commercial real estate mortgages. The results show that both Harvey and Sandy led to elevated levels of commercial mortgage delinquency, with significant heterogeneity based on the extent of damage in the Census block group. Information provided through FEMA 100‐year floodplain maps partially mitigates the effects, an indication that lenders incorporate flood risk information in the underwriting process. An analysis of potential mechanisms indicates a decrease in property income during the 2‐year period following the event for Hurricane Harvey, but no evidence of income effects for Sandy.

Safe Collateral, Arm's-Length Credit: Evidence from the Commercial Real Estate Mortgage Market

When collateral is safe, there are less opportunities for things to go wrong. We examine matching between collateral and creditors in the commercial real estate mortgage market by comparing loans in commercial mortgage backed securities (CMBS) conduits and bank portfolios. We model CMBS financing as lower cost but less informed, such that only safe collateral is funded by CMBS. This prediction is tested using the 2007-2009 shutdown of the CMBS market as a natural experiment. The loans funded by banks that would have been securitized are less likely to default or be renegotiated, indicating that the securitization channel, when available, funds safe collateral.

What’s in a Loan Yield?The Construction of a Hedonic Index of Yields for Commercial Real Estate Mortgages

In this article, the authors investigate the utility of using a hedonic model in constructing an index of yields on commercial mortgage loans. They use a large unique proprietary dataset of mortgage originations from 2000Q3–2012Q4 to construct a hedonic model of yields as a function of loan characteristics as well as time effects. The authors then use the hedonic model to construct a constant-quality index of mortgage yields and test whether the index performs better in uncovering underlying equilibrium relationships with other economy-wide yield variables than a simple average of yields. They find evidence that is strongly in favor of a hedonic index. Finally, the authors build a forecasting model of the hedonic index as a function of a widely available index of corporate bond yields and test the forecasting performance of the model. The major finding is that the index works well in a forecasting model and lends itself readily to forecasting applications.

What’s in a Loan Yield?The Construction of a Hedonic Index of Yields for Commercial Real Estate Mortgages

What’s in a Loan Yield?<i>The Construction of a Hedonic Index of Yields for Commercial Real Estate Mortgages</i>

Commercial Real Estate CLOs: Features That Make Them an Attractive Asset Class

In the world of securitization and structured finance, a new hybrid asset class—commercial real estate (CRE) collateralized loan obligations (CLOs)—has appeared on the scene, boasting the most attractive structural and underlying collateral features of its parents: the commercial mortgage-backed securities (CMBS) securitization transaction and the standard CLO transaction. The CRE CLO structure is one that can be used as a means of potentially averting financial loss in advance of the “maturity wave” (also known as the maturity wall, CMBS refinancing risk, and CMBS balloon risk), a lurking phenomenon wherein a number of CRE mortgage loans, including balloon loans and anticipated repayment date (ARD) loans, constituting more than one trillion dollars of the underlying collateral in CMBS securitizations from vintages of 5 to 10 years ago, are going to be coming due in 2015–2017. CRE CLOs may create appealing opportunities for lenders, because shorter-term financing can be used to refinance these legacy-maturity CRE mortgage loans into the more stabilized product that CRE CLOs offer. Also, CRE CLOs contain CLO structural features absent in a standard CMBS securitization, including a reinvestment period, a ramp-up period, various collateral quality tests, and an active management feature that empowers CRE CLO collateral managers with the ability and discretion to substitute poorly performing collateral in the CRE CLO’s underlying CRE mortgage loan collateral pool with “new” collateral that satisfies certain rigorous criteria. Moreover, today’s CRE CLO’s collateral is more resilient than CRE mortgage loan collateral from yesteryear because of the following: 1) more stringent lending and underwriting guidelines are in place today for CRE mortgage loans compared with those in place in 2007 at the height of the CMBS market and 2) today’s CRE CLOs do not contain split loans, mezzanine loans, or B-pieces; instead they contain only as collateral CRE senior mortgage whole loans with much shorter durations than the CRE mortgage loans from the pre-2008, CLO 1.0 era. Additionally, a CRE CLO may offer higher yield than other structured products because the collateral manager has “skin in the game” tied to the CRE CLO’s overall performance and payment waterfall, and therefore the collateral manager’s interests are more closely aligned with those of the investor. From a relative-value and performance perspective, investors looking to maximize their yield can thus find comfort investing in a lower tranche in the capital stack without potentially as high a risk exposure for non-payment as if they invested in a lower tranche in a CMBS securitization transaction. Finally, from a regulatory compliance perspective, the CRE CLO’s hybrid structure enables it to fall under certain exemptions under the Investment Company Act of 1940—including the “qualified purchasers” exemption under Section 3(c)(7) and the real estate mortgage loan exemption of Section 3(c)(5)(C)—and to fall under the Volcker Rule’s carve-out for securitization of loans, all of which enable a CRE CLO to escape “covered fund” status under the Volcker Rule.

Modeling of commercial real estate credit risks

Modeling the probability of default for commercial real estate mortgages is more complicated than that for non-commercial real estate loans. This is because borrowers will default only if both the net operating income and the property value fall below the threshold levels. To make modeling more complicated, the property value at the time of default will determine the loss-given default. In this paper, I derive closed-form solutions for the probability of default and the expected loss of commercial real estate mortgages in a Merton framework. The model is in its essence still a single risk factor model, although there is a sector risk factor that influences both the net operating income and the property value. I obtain analytically the economic capital for the corporate-wide commercial real estate portfolio, with granularity adjustments for name concentration and sector concentration.

Commercial Real Estate Loan Performance at Failed U.S. Banks

Exposure to commercial real estate (CRE) loans at regional and small banks and thrifts has soared over the last two decades. As banks’ balance sheets become more concentrated in these types of loans, banks have become more sensitive to swings in CRE fundamentals. The concentration in CRE loans peaked in 2007, just as commercial real estate prices started a historic free fall, declining more than 30 percent in just two years. Over this same time CRE concentration has been a significant factor in recent bank failures. Default and loss models of CRE mortgages have previously been estimated using loan data from large, income-generating properties financed by insurance companies and the commercial real estate mortgage (CMBS) market. Early research used data from insurance companies (Synderman (1991), Esaki et al (1999), Vandell et al (1993), Ciochetti et al (2003), while more recently researchers have used data from the CMBS market (Ambrose and Sanders (2003), Archer et al (2002), Deng et al (2004), Seslen and Wheaton (2010), An et al (2009). Black et al (2010) found that loans in CMBS pools that had been originated by portfolio lenders, such as insurance companies or commercial banks, were of a higher quality and outperformed loans originated by conduit lenders or investment banks. The CMBS and insurance company loans used in these studies differ in structure and underlying collateral from the loans backed by bank CRE loans. Roughly a third of bank CRE loans are backed by owner-occupied CRE and another 20 to 30 percent by land and construction loans. The owner-occupied properties, which lack an external and explicit rental stream, are usually not candidates for securitisation. The loans in bank portfolios backed by land acquisition, development, and construction (ADC) projects are even less similar to those in CMBS and in insurance company portfolios. Land and construction loans are short term and the collateral is the raw land or the partially completed construction project. Finally, the loans on banks’ books backed by existing income-generating commercial properties are likely to be different from those found in CMBS pools or in insurance company portfolios.Regional and small banks also make much smaller loans than those usually seen in CMBS pools or in insurance company portfolios. Clearly, each of these types of loans has performed differently during this recent financial crisis, yet we are still dependent on default and loss models estimated using data from only one type of loan.Ours is the first paper to estimate CRE default and loss models using a loan-level dataset drawn from bank portfolios. We develop a unique dataset consisting of loan-level information on CRE portfolios for a sample of banks entering FDIC receivership over the past several years. We use this dataset to estimate a series of default and loss models. We estimate these models on the loans backed by existing CRE properties and compare the results with those from other papers that estimate CRE default using data from the CMBS and insurance companies. We then extend our analysis to the performance of land and construction loans, providing the first loan-level analysis of the performance of such loans.Full publication: Property markets and financial stability

Commercial Mortgage Prepayment: Prepayment Fee, Yield Maintenance, or Defeasance

Commercial real estate mortgages are commonly paid off prior to their termination date. This may come about because of a sale, a refinancing, or an involuntary situation, such as foreclosure or bankruptcy. Early prepayment typically has an associated fee to compensate the lender for the change in income pattern. These include a lock-out, prepayment penalty, yield maintenance or a defeasance. This article explains the different options, their legality, and then presents models to calculate the cost of each. Such knowledge enables the participants in the mortgage situation to act so as to best meet their needs and minimize their costs.

Flushing Financial Corp.

AbstractOther Depository Banking (MIC: SIC: 6035 NAIC: 522120)Flushing Financial is the holding company for Flushing Savings Bank, a federally chartered stock savings bank. Co.'s principal business is accepting retail deposits from the public and investing those deposits together with funds from operations and borrowings primarily in: originations and purchases of one‐to‐four family, multi‐family residential and commercial real estate mortgage loans; construction loans; Small Business Administration loans and other small business loans; mortgage loan surrogates; and U.S. government securities, corporate fixed‐income securities and other marketable securities. As of Dec 31 2007, Co. had total assets of $3.35 billion and deposits of $2.03 billion.

Flushing Financial Corp.

AbstractOther Depository Banking (MIC: SIC: 6035 NAIC: 522120)Flushing Financial is the holding company for Flushing Savings Bank, a federally chartered stock savings bank. Co.'s principal business is accepting retail deposits from the public and investing those deposits together with funds from operations and borrowings primarily in: originations and purchases of one‐to‐four family, multi‐family residential and commercial real estate mortgage loans; construction loans; Small Business Administration loans and other small business loans; mortgage loan surrogates; and U.S. government securities, corporate fixed‐income securities and other marketable securities. As of Dec 31 2007, Co. had total assets of $3.35 billion and deposits of $2.03 billion.

Flushing Financial Corp.

AbstractOther Depository Banking (MIC: 8.5 SIC: 6035 NAIC: 522120)Flushing Financial is the holding company for Flushing Savings Bank, a federally chartered stock savings bank. Co.'s principal business is accepting retail deposits from the public and investing those deposits together with funds from operations and borrowings primarily in: originations and purchases of one‐to‐four family, multi‐family residential and commercial real estate mortgage loans; construction loans; Small Business Administration loans and other small business loans; mortgage loan surrogates; and U.S. government securities, corporate fixed‐income securities and other marketable securities. As of Dec 31 2007, Co. had total assets of $3.35 billion and deposits of $2.03 billion.

Strategic default, workout, and commercial mortgage valuation

This paper extends existing equilibrium commercial mortgage pricing models by endogenizing negotiated workout into the usual noncooperative lending game. Workout is a feasible subgame strategy for the lender to play whenever foreclosure transaction costs exist for either party to a loan transaction. In particular, negotiated workout solutions Pareto dominate the foreclosure alternative when default occurs. To obtain our results, we embed a cooperative bargaining game within a noncooperative mortgage loan/default game. We also address the valuation “wedge” problem that occurs when foreclosure transaction costs are introduced. Through the notion of replacement game equilibrium, we find symmetric mortgage pricing solutions that eliminate the valuation wedge and thus suggest that lending will occur in commercial real estate mortgage markets even when foreclosure transaction costs exist.

Commercial Mortgage Pricing with Unobservable Borrower Default Costs

This paper develops a pricing model for commercial real estate mortgage debt that recognizes the influence of default transaction costs on the borrower's default decision. These costs are heterogeneous across borrowers and largely un‐observable to the lender/investor at the time of origination or loan purchase. A recognition of these unobservable costs can explain why borrower default decisions may differ from those predicted by “ruthless” mortgage‐default pricing models. We address the determinants of default choice and timing by replacing sharp default boundaries found in the ruthless models with “fuzzy” boundaries that account for investor uncertainty with respect to evaluating borrower default decisions. To implement our model, we estimate probabilities of default as a junction of time and net equity in the property. Then, given that default occurs, loss severities are modeled based on expected property value recovery net of foreclosure costs and time until the asset is actually sold. Under reasonable parameter value choices, resulting Monte Carlo simulations produce numerical mortgage price estimates as well as component default frequency and severity levels that realistically reflect default premiums and loss levels observed in the marketplace.