Essays on Credit Supply, Government Policy, and the Housing Bubble

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This dissertation consists of two essays that study how credit expansion and government policy on mortgages shaped the 1999-2010 U.S. housing cycle and business cycle. Chapter 1 studies whether credit expansion or speculation mainly causes the housing cycle. During the

This dissertation consists of two essays that study how credit expansion and government policy on mortgages shaped the 1999-2010 U.S. housing cycle and business cycle. Chapter 1 studies whether credit expansion or speculation mainly causes the housing cycle. During the 1999-2009 U.S. housing cycle, two opposing empirical facts present a puzzle: the correlation between income growth and mortgage growth is negative across ZIP codes within metropolitan areas (some argue for the credit expansion view) but positive across metropolitan areas (others argue for the speculation view). First, this paper shows that the cross-metropolitan phenomenon, in fact, supports the credit expansion view: by an instrumental variable approach, this paper shows that net export growth across metropolitan areas causes income growth and credit expansion in mortgage growth, which eventually leads to the housing cycle. Second, this chapter builds a simple model to illustrate how credit expansion can reconcile the above two empirical facts. This model generates new predictions of double differences: the differential stronger boom and bust cycle in mortgages and house prices in low-income ZIP codes than in high-income ZIP codes within the same metropolitan area is more pronounced in high net-export-growth metropolitan areas. This paper provides empirical causal evidence for these predictions, further supporting the credit expansion view. Chapter 2 uses the causal framework in Chapter 1 to test which business cycle theory can explain the 1999-2010 U.S. business cycle. This chapter shows that credit expansion in private-label mortgages causes a differentially stronger boom (2000-2006) and bust (2007-2010) cycle in the house-related industries in the high net-export-growth areas. Thus, these results are consistent with the credit-driven household demand hypothesis. Most importantly, the unique research design enables a set of tests on theories (hypotheses) regarding the business cycle. This paper shows that the following theories (hypotheses) cannot explain the cause of the 1999-2010 U.S. business cycle: the speculative euphoria hypothesis, the real business cycle theory, the collateral-driven credit cycle theory, the business uncertainty theory, and the extrapolative expectation theory.
Date Created
2024
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MADM-Based Smart Parking Guidance Algorithm

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In smart parking environments, how to choose suitable parking facilities with various attributes to satisfy certain criteria is an important decision issue. Based on the multiple attributes decision making (MADM) theory, this study proposed a smart parking guidance algorithm by

In smart parking environments, how to choose suitable parking facilities with various attributes to satisfy certain criteria is an important decision issue. Based on the multiple attributes decision making (MADM) theory, this study proposed a smart parking guidance algorithm by considering three representative decision factors (i.e., walk duration, parking fee, and the number of vacant parking spaces) and various preferences of drivers. In this paper, the expected number of vacant parking spaces is regarded as an important attribute to reflect the difficulty degree of finding available parking spaces, and a queueing theory-based theoretical method was proposed to estimate this expected number for candidate parking facilities with different capacities, arrival rates, and service rates. The effectiveness of the MADM-based parking guidance algorithm was investigated and compared with a blind search-based approach in comprehensive scenarios with various distributions of parking facilities, traffic intensities, and user preferences. Experimental results show that the proposed MADM-based algorithm is effective to choose suitable parking resources to satisfy users’ preferences. Furthermore, it has also been observed that this newly proposed Markov Chain-based availability attribute is more effective to represent the availability of parking spaces than the arrival rate-based availability attribute proposed in existing research.

Date Created
2017-12-13
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