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This chapter investigates a shock transmission path between a home country (a country where globalized banks’ headquarters are located) and a host country (Indonesia as the emerging market) through the lending channel of global banks’ local branches (i.e., the internal transfer channel). Using novel data of monthly individual foreign bank’s balance sheet in Indonesia, the author finds the evidence that shocks to a parent bank and a home economy are transmitted to a host economy through the foreign banks’ internal capital market. With the Indonesia banks’ capital injections and their difficulty in financing dollar funds without risk premiums since the 1998s crisis, the foreign banks’ dollar lending in Indonesia is a good showcase of internal capital markets. A change in a home stock market index and industrial production appears to have a negative effect on growth rates in foreign currency loans of foreign banks in the host market. On the other hand, high growth rates in the parent bank’s stock price in the home market lead to an increase in foreign banks’ US dollar lending in the host country. This effect does not appear in local currency lending because limited hedging instruments against foreign exchange risk results in immobility of bank capital in the local currency.

The 2007–2009 financial crisis initially appeared to have destroyed a huge amount of wealth in the United States. Housing prices dropped about 21% across the country and as much as 50% in some places, and the stock market dropped by nearly 50% as well. This chapter examines how the financial crisis differentially affected households at different parts of the income and wealth distributions. Our results show that all households lost about the same percentage of their wealth in that period. But because households in the top 10% of the wealth distribution owned many different kinds of assets, their wealth soon recovered. The bottom 80% of the wealth distribution had more of their wealth tied up in housing. We show that financial distress, indexed by foreclosures, being behind in mortgage payments, and changes in house prices were particularly concentrated in households in the bottom 80% of the wealth distribution. These households lost a large part of their wealth and have not yet recovered. Households that were most deeply affected were those who entered the housing market late and took out subprime loans. African American and Hispanic households were particularly susceptible as they bought houses late in the price bubble often with subprime loans.

The global slack hypothesis is central to the discussion of the trade-offs that monetary policy faces in an increasingly more integrated world. The workhorse New Open Economy Macro (NOEM) model of Martínez-García and Wynne (2010), which fleshes out this hypothesis, shows how expected future local inflation and global slack affect current local inflation. In this chapter, I propose the use of the orthogonalization method of Aoki (1981) and Fukuda (1993) on the workhorse NOEM model to further decompose local inflation into a global component and an inflation differential component. I find that the log-linearized rational expectations model of Martínez-García and Wynne (2010) can be solved with two separate subsystems to describe each of these two components of inflation.

I estimate the full NOEM model with Bayesian techniques using data for the United States and an aggregate of its 38 largest trading partners from 1980Q1 until 2011Q4. The Bayesian estimation recognizes the parameter uncertainty surrounding the model and calls on the data (inflation and output) to discipline the parameterization. My findings show that the strength of the international spillovers through trade – even in the absence of common shocks – is reflected in the response of global inflation and is incorporated into local inflation dynamics. Furthermore, I find that key features of the economy can have different impacts on global and local inflation – in particular, I show that the parameters that determine the import share and the price-elasticity of trade matter in explaining the inflation differential component but not the global component of inflation.

In this paper we investigate the relationship between loose monetary policy, low inflation, and easy bank credit and house price booms.

Using a panel of 11 OECD countries from 1920 to 2011 we estimate a panel VAR in order to identify loose monetary policy shocks, low inflation shocks, bank credit shocks, and house price shocks.

We show that during boom periods there is a heightened impact of all three “policy” shocks with the bank credit shock playing an important role. However, when we look at individual house price boom episodes the cause of the price boom is not so clear. The evidence suggests that the house price boom that occurred in the United States during the 1990s and 2000s was not due to easy bank credit.

Shocks from the shadow banking system are not separately identified. These are incorporated into the fourth “catch-all” shock.

Our evidence on housing price booms that expansionary monetary policy is a significant trigger buttresses the case for central banks following stable monetary policies based on well understood and credible rules.

This paper uses historical evidence to evaluate the relative importance of three main causes of house price booms. Our results bring into question the commonly held view that loose bank credit was to blame for the U.S. house price bubble of the later 1990s.

Portfolios of mortgage loans played an important role in the Great Recession and continue to compose a material part of bank assets. This chapter investigates how cross-sectional dependence in the underlying properties flows through to the loan returns, and thus, the risk of the portfolio. At one extreme, a portfolio of foreclosed mortgage loans becomes a portfolio of real estate whose returns exhibit substantial cross-sectional and spatial dependence. Near the other extreme, almost all loans perform and yield constant returns, which do not correlate with other performing loan returns. This suggests that loan performance effectively censors the random returns of the underlying properties. Following the statistical properties of the correlations among censored variables, the authors build off this foundation and show how the loan return correlations will rise as economic conditions deteriorate and the defaulting loans reveal the underlying housing correlations. In this chapter, the authors (1) adapt tools from spatial statistics to document substantial cross-sectional dependence across house price returns and examine the spatial structure of this dependence, (2) investigate the nonlinear nature of correlations among loan returns as a function of the default rate and the underlying house price correlations, and (3) conduct a simulation exercise using parameters from the empirical data to show the implications for holding a portfolio of mortgages.

Why did the United States experience a housing and mortgage market boom and bust in the 2000s, while analogous Canadian markets were relatively stable? Both US and Canadian markets are replete with government interventions. In this paper, I account for the US and Canada’s different experiences by arguing that government interventions are not created equal. Some government interventions prevent market participants from pursuing actions that ex ante are reckoned beneficial. Alternatively, other interventions lead to the pursuit of actions that turn out to be costly ex post. It is the latter type that we expect to manifest in crises. The US case is one where government interventions in the mortgage markets led to actions that appeared ex ante beneficial but were revealed to be costly ex post. Alternatively, Canada’s mortgage market was and remains essentially a regulated oligopoly. Regulatory capture makes for a sclerotic market that likely imposes costs on Canadian borrowers in the forms of limited financing options and higher interest rates. However, this sclerosis also lends itself to stability. This market structure made the Canadian mortgage market relatively insusceptible to a bubble.

The idea that a bank's overall performance is influenced by the regional economy in which it operates is intuitive and broadly consistent with historical bank performance. Yet, micro-level research on the topic has borne mixed results, failing to find a consistent link between various measures of bank performance and regional economic variables. This chapter attempts to reconcile the intuition with the micro-level data by aggregating bank performance, as measured by nonperforming loans, up to the state level. This level of aggregation reduces the influence of idiosyncratic bank effects sufficiently so as to examine more clearly the influence of state-level economic variables. We show that regional variables, such as employment growth and changes in real estate prices, are not particularly useful for predicting changes in bank performance, but that coincident indicators developed to track a state's gross output are quite useful. We find that these coincident indicators have a statistically significant and economically important influence on state-level, aggregate bank performance. In addition, the coincident indicators potentially contribute to the out-of-sample forecasts of the relative riskiness of state-level bank portfolios, which should be of interest to bankers and bank supervisors.

As a sustainability initiative with the backing of civil society, business, or government interests, urban agriculture can drive green gentrification even when advocates of these initiatives have good intentions and are aware of their exclusionary potential for urban farmers and residents. I investigate this more general pattern with the case of how urban agriculture became used for green gentrification in Denver, Colorado. This is a city with many urban farmers that gained access to land after the Great Recession but faced the contradiction of being a force for displacement and at risk of displacement as the city adopted new sustainability and food system goals, the housing market recovered, and green gentrification spread. I argue that to understand this outcome, it is necessary to explain how political economy and cultural forces create neighborhood disinvestment and economic marginalization and compel the entrance of urban agriculture initiatives due to their low-profit mode of production and potential economic, environmental, and social benefits. Central to how urban agriculture initiatives contribute to green gentrification is the process of revalorization, which is how green growth machines repurpose such initiatives by drawing on their cultural cachet to exploit rent gaps. I conclude with a set of hypotheses to help other scholars test the conditions under which urban agriculture is more or less likely to contribute to green gentrification. Doing so may help nuance convictions about the benefits of urban agriculture within the context of entrenched inequalities in rapidly changing cities.