The best known housing bubble is probably the one that burst in the US in 2006-2007, eventually causing the Global Financial Crisis. China as the second world largest economy is experiencing a house price upsurge. If this is a bubble, at least it has not burst. This chapter aims to show the importance of disaggregating house price dynamics so that policy-makers can have better information about the sizes of bubbles to enhance corresponding policies. Furthermore, ignoring the importance of disaggregation of housing markets, according to their characteristics, and differences in time period, can cause ineffective policies in some cities while overshooting in others. This chapter discusses how to apply a regime shift model and Pooled Mean Group/Mean Group Estimations to both the US and the Chinese housing market bubbles. It pays particular attention to cities with high and low house price growth, coastal/inland cities, and high and low supply elasticities.

We analyze causes of the surge in defaults experienced by Fannie Mae and Freddie Mac during the Great Recession. Our data are consistent with the following: The two faced a trade-off between subsidized risk-taking (due to a guarantee implied by their charters) and franchise value (due to the scarcity of their charters). Around 2005 there was a change in the relative benefits of the two due to increased competition from "private label" securities. Along with declines in house prices, the increase in defaults is best explained by exercising an option to change strategy, before regulators fully caught on, toward newer, riskier loan types, after the decline in franchise value.

This paper studies the evolution of property values and the connections between shadow banking and property markets in China. We use Pooled Mean Group estimation to analyze Chinese house prices in 65 cities from 2007-2016, define the "fundamentals" of housing prices with the Gordon dividend discount model, and use lagged rents, prices, real and nominal interest rates, and shadow banking activity as short term explanatory factors. We find that the cities tend to share long run fundamentals and adjust relatively quickly to deviations from the fundamentals. We do not find bubbles; rather houses are like growth stocks with house prices rapidly chasing growing rents. More importantly, we find that house prices increase more quickly with the availability of shadow banking funds, which have grown rapidly.

Securitization provides borrowers with access to capital markets, most notably as an alternative to bank lending. However, it has also used complicated structures in order to attract investors. Complicated structures can provide, and have provided, vehicles for hiding risk and inducing moral hazard, which were at the center of the Great Crash. This paper provides descriptions of structures and increasing complexity over time. It suggests where moral hazard would have been expected to show up, empirically, and provides some evidence of moral hazard based on the timing of the changes in the structures.

The recent surge in property values in China has been similar to the surge in the U.S before the crash in 2007. This raises concerns about whether China is destined to have a crash as well. We estimate similar models of property values for the two countries, in order to compare price dynamics side by side. We find little in common between them. In the U.S. the adjustment process appears prone to “bubbles” in the sense of strong momentum, but Chinese prices have been generally mean reverting, without momentum. This suggests that the recent price rise in China has had more to do with scarcity than with irrational exuberance.

Imperfect competition has been an important branch of economic theory, at least since Cournot's (1838) model of duopoly. A more recent development is spatial competition, which serves as a foundation for models of imperfect competition. The concept of space as the groundwork for imperfect competition provides many useful insights into price determination and resource allocation. Our goal is to illustrate these insights. In pursuing this track, we ignore many traditional issues in location theory including issues revolving around the shape of market ares. We also bypass questions about the existence of equilibrium in spatial models, which are discussed along with many of the locational issues in a recent lengthy survey by Gabszewicz and Thisse (1984).

This paper studies U.S. house prices across 45 metropolitan areas from 1980 to 2012. It applies a version of the Gordon dividend discount model for long‐run “fundamentals” and uses Mean Group and Pooled Mean Group estimation to estimate long‐run and short‐run determinants of house prices. We find great similarity across cities in that the long‐run house prices are largely explained by the same fundamentals; the long‐run rent to price ratio is approximately 5% plus 0.75 times the real interest rate (which is on the order of 2%). However, adjustments to deviations from the fundamentals are slow, in the long‐run, closing the gap at a rate of around 10% per year. We find sharp differences in short‐run adjustments (momentum) away from the fundamentals across cities, and the differences are correlated with local supply elasticities (more momentum with lower elasticity). Analysis of residuals suggests strong cyclical deviations, which are mean‐reverting.

Financial Technology (Fintech) is evolving quickly within the financial system, giving rise to new forms of lending and opening up a version of shadow banking. This is particularly true in China. While Fintech finance and shadow banks can improve a banking system, they can also become new sources of fragility. This paper aims to provide a theoretical analysis of risks of Fintech finance to financial sectors, with particular examples for China. These risks are most likely to be important when Fintech moves beyond its technological focus and performs financial intermediation, particularly bank-like, functions. We provide implications for the evolution of Fintech finance as new sources of payments and funding. We propose "ring-fencing" as an approach to mitigate risks from contagion.

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The Federal Housing Administration (FHA) deserves considerable credit for helping support the housing market during the recent financial crisis by increasing its own market share. However, as the recovery continues, the FHA can gradually return to its "traditional" role as an insurer of low-down-payment home mortgages for low-tomoderate-income and first-time homebuyers. A major concern going forward is susceptibility to increased adverse selection if it continues in nontraditional markets. Indeed, the modest market share of the FHA going into the housing collapse was important both in limiting its losses and in allowing it to maintain the market when other traditional secondary market makers failed.

This paper models incentives for risk-taking by managers of banks or securitization deals. Of particular interest are risk-retention rules for producers of structured securitization deals, which have been mandated by the Dodd-Frank Act; the model can also be applied to bank managers. We show how incentives can be set up so that problems of asymmetric information can co-exist with socially optimal risk-taking. The role of holding an equity piece as an incentive tool has been over-emphasized; the best “skin in the game” incentive structure for management is to hold securities of all levels of risk, including the safest piece. As a device for protecting against bank runs, the best incentive tools require tilting the incentive structure toward the safest pieces, but not by much.

This paper analyzes the risk-taking behavior of financial intuitions that have guarantees and/or institutions that find it beneficial to develop a reputation for not taking risk. It focuses on two questions: Is it rational for them to take on less risk than they can get away with, and if it is rational, under what conditions will they shift strategies and increase their risk? To answer the question we allow for future benefits from survival in the form of ifranchise value.i With franchise value they might take less risk than they are allowed; however, if they experience large enough negative shocks, they reach a tipping point where they will change their strategy discontinuously, and igamble for resurrection.i For instance, a decline in franchise due to increased competition can lead to abrupt changes in risk-taking. This is a possible explanation for changes in quality of pools of securitized loans. Similarly a decline capital can lead financial institutions ramp up risk-taking.

This paper is about identification and endogeneity in models that estimate the effects of instrument choice on performance (or wealth or welfare) in situations where the instruments are chosen to optimize performance. Such models occur frequently in corporate finance, analysis of happiness and effectiveness of economic policy. The instruments are endogenous because of the optimization. We suggest an impossibility theorem: that identification is not possible for this class of models, and that in a well-specified model the expected values of estimated impacts of instruments choices will be exactly zero. This is a direct application of the envelope theorem. We follow with four applications to empirical work in economics and finance.

The recent financial turmoil has triggered a credit crunch whereby illiquid, but not necessarily insolvent, banks were not able to borrow money and were forced to be liquidated, bought or bailed out. A response to this problem has been contingent convertible bonds (or CoCo bonds), which are ordinary bonds that are converted into equity when certain financial triggers, such as capital ratios of banks, are reached. These have the potential of providing an automatic source of liquidity without having to go through bankruptcy or getting bailouts’ money. We present a model of liquidity with two types of investors who have different information about risk; one group has better information but a less elastic supply of funds, and the other produces liquidity via an elastic supply of funds but with much less information. The model generates a critical value of overall risk, above which there are “flights to quality” by liquidity suppliers. This leads to a sharp increase in borrowing costs for banks even though the underlying increase in risk is small. We next show that the existence of CoCo bonds can help to reduce the magnitude of a large and abrupt shift of credit dry-up from a relatively stable level.