Joseph B. Nichols

Swings in commercial and residential land prices in the United States

We use a large dataset of land sales dating back to the mid-1990s to construct land price indexes for 23 MSAs in the United States and for the aggregate of those MSAs. The price indexes show a dramatic increase in both commercial and residential land prices over several years prior to their peak in 2006-07 and a steep descent since then. These fluctuations have exceeded those in well-known indexes of home prices and commercial real estate prices. Because those indexes price a bundle of land and structures, this comparison implies that land prices have been more volatile than structures prices over this period. This result is a key element of the land leverage hypothesis, which holds that home prices and commercial property prices will be more volatile, all else equal, in areas where land represents a larger share of real estate value.

Commercial and Residential Land Prices Across the United States

We use a national dataset of land sales to construct land price indexes for 23 MSAs in the United States and for the aggregate of those MSAs. We construct the price indexes by estimating hedonic regressions with a large sample of land transactions dating back to the mid-1990s. The regressions feature a flexible method of controlling for spatial price patterns within an MSA. The resulting price indexes show a dramatic increase in both commercial and residential land prices over several years prior to their peak in 2006-07 and a steep descent since then. These fluctuations in land prices are considerably larger than those in well-known indexes of commercial real estate and house prices. Because those existing indexes price a bundle of land and structures, this comparison implies that land prices have been more volatile than structures prices over this period.

Differences Across Originators in CMBS Loan Underwriting

There is considerable heterogeneity in the organizational structures of CMBS loan originators and in their incentives for underwriting risky loans. We treat an originators organizational type - commercial bank, investment bank, insurance company, finance company, conduit lender, or foreign-owned entity - as a proxy for incentives related to warehousing risk and balance-sheet lending. After controlling for observable credit characteristics of over 30,000 loans securitized into CMBS since 1999, we find considerable differences in loan performance across originator types. The results suggest that moral hazard - captured by lack of warehousing risk - negatively affected the quality of loans underwritten by conduit lenders. On the other hand, despite the potential for engaging in adverse selection, balance-sheet lenders - commercial banks, insurance companies and finance companies - actually underwrote higher-quality loans.

What is Subordination About? Credit Risk and Subordination Levels in Commercial Mortgage-backed Securities (CMBS)

Subordination is designed to provide credit risk protection for senior CMBS tranches by allocating the initial credit losses to the more junior tranches. Subordination level should in theory reflect the underlying credit risk of the CMBS pool. In this paper, we test the hypothesis that subordination is purely about credit risk as intended. We find a very weak relation between subordination levels and both the ex post and ex ante measures of credit risk, rejecting our null-hypothesis. Alternatively, we find that subordination levels were driven by non-credit risk factors, including supply and demand factors, deal complexity, issuer incentive and a general time trend. We conclude that contrary to the traditional view, the subordination level is not just a function of credit risk. Instead it also reflects the market need of a certain deal structure and is influenced by the balance of power among issuers, CRAs and investors.

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

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.

Nominal Mortgage Contracts and the Effects of Inflation on Portfolio Allocation

Households who wish to extract home equity through refinancing their mortgage face a hidden transaction cost. The real value of the fixed nominal mortgage payment declines over time with inflation. The change in the real value of the mortgage payments from taking on a new mortgage is positive and an increasing function of inflation; higher inflation thus discourages households from re-balancing their portfolio as frequently as they would otherwise. The life cycle model developed in this paper demonstrates how the share of total wealth held in housing is sensitive to the rate of inflation, even when perfectly anticipated. Households hold larger positions in home equity earlier in the life cycle and smaller positions later in the life cycle as the rate of inflation increases.