Consumer Credit & the Housing Market: An Examination of Trends in Home Equity Lines of Credit

Author(s):  
Norbert J. Michel ◽  
John Phillip Lajaunie ◽  
Shari Lawrence ◽  
Ronnie Fanguy
2012 ◽  
Author(s):  
John Phillip Lajaunie ◽  
Norbert J. Michel ◽  
Shari Lawrence ◽  
Ronnie Fanguy

2014 ◽  
Vol 7 (3) ◽  
pp. 307-326 ◽  
Author(s):  
M.K. Francke ◽  
F.P.W. Schilder

Purpose – This paper aims to study the data on losses on mortgage insurance in the Dutch housing market to find the key drivers of the probability of loss. In 2013, 25 per cent of all Dutch homeowners were “under water”: selling the property will not cover the outstanding mortgage debt. The double-trigger theory predicts that being under water is a necessary but not sufficient condition to predict mortgage default. A loss for the mortgage insurer is the result of a default where the proceedings of sale and the accumulated savings for postponed repayment of the principal associated to the loan are not sufficient to repay the loan. Design/methodology/approach – For this study, the authors use a data set on losses on mortgage insurance at a national aggregate level covering the period from 1976 to 2012. They apply a discrete time hazard model with calendar time- and duration-varying covariates to analyze the relationship between year of issue of the insurance, duration, equity, unfortunate events like unemployment and divorce and affordability measures to identify the main drivers of the probability of loss. Findings – Although the number of losses increases over time, the number of losses relative to the active insurance is still low, despite the fact that the Dutch housing market is the world’s most strongly leveraged housing market. On average, the peak in loss probability lies around a duration of four years. The average loss probability is virtually zero for durations larger than 10 years. Mortgages initiated just prior to the beginning of the financial crisis have an increased loss probability. The most important drivers of the loss probability are home equity, unemployment and divorce. Affordability measures are less important. Research limitations/implications – Mortgage insurance is available for the lower end of the market only and is intended to decrease the impact of risk selection by banks. The analysis is based on aggregate data; no information on individual households, like initial loan-to-value and price-to-income ratios; current home equity; and unfortunate events, like unemployment and divorce, is available. The research uses averages of these variables per calendar year and/or duration. Information on repayments of insured mortgages is missing. Originality/value – This paper is the first to describe the main drivers of losses on insured mortgages in The Netherlands by using loss data covering two housing market crises, one in the early 1980s and the current crisis that started in 2008. Much has changed between the two crises. For instance, prices have risen steeply as has household indebtedness. Furthermore, alternative mortgage products have increased in popularity. Focusing a study on the drivers of mortgage losses exclusively on the current crisis could therefore be biased, given the time-specific circumstances on the housing market.


10.28945/3947 ◽  
2018 ◽  
Vol 2 ◽  
pp. 001-019

What triggered the crash of the U.S. housing market? This analysis looks at the economic and industry forces that led to an economic downturn that put as many as half of all U.S. residential builders out of business. Since the Great Depression, the U.S. housing market has significantly influenced economic production and employment levels. Direct and indirect investments in the housing industry, along with the induced economic activities such as real estate transactions and construction as well as other factors, accounted for an estimated 15-20% of GDP during boom years (CBPP, 2012). The burst of the $8 trillion housing bubble in 2007 and the subsequent collapse of the financial markets in 2008 created massive disarray in homebuilding (Bivens, 2011). As many as 50% of homebuilders closed their doors, either voluntarily or through bankruptcy filings (Quint, 2015). Concurrently, from 2006 through 2012, the Great Recession resulted in the loss of over $7 trillion of home equity (Gould Ellen, 2012). Over 24 percent of home mortgages went “underwater” with balances exceeding home values (Carter & Gottschalck, n.d.). For some homeowners, the unfortunate thought of losing their homes through foreclosure and incurring disruption to family life became a reality. The stress from threats of the loss of a home, unemployment, and depletion of savings exacted a great toll on many. Not since the Great Depression has the U.S. economy faced forces so devastating to the housing market and personal wealth.


Author(s):  
Stuart Aveyard ◽  
Paul Corthorn ◽  
Sean O’Connell

A decade on from the ‘credit crunch’, with repeated warnings of the potential for a repeat of that financial trauma, this is an appropriate moment to offer a historical investigation of the politics of consumer credit in modern Britain. By the 1980s, Britain had the most diverse and liberalized consumer credit sector in Europe. From one perspective, the element of risk in the British credit market was a strength, but it made the nation more vulnerable to severe economic jolts caused by events such as the housing market correction of the early 1990s and the credit crunch of 2008. ...


2014 ◽  
Vol 7 (2) ◽  
pp. 204-217 ◽  
Author(s):  
Trond-Arne Borgersen

Purpose – The purpose of this paper is to highlight the importance of home equity and the interplay between market segments for housing market developments. The intention is to show that it is not only the aggregate equity gain but also the distribution of equity gains between segments that matter for how shocks to income impact house prices. Design/methodology/approach – The paper sets out a linear housing market model with three segments. Households trade up a housing ladder and link the three segments for owner-occupied housing. The up-trading is equity-induced. An expression for the house price index, which is related to the market segment prices both directly through the segment size and indirectly through a segment position on the housing ladder is derived. The author considers the price effects of shocks to income in four housing market regimes. Findings – The heterogeneous housing market model shows how the interplay between segments impacts housing markets. When considering shocks to income, short-run deviations in the price-to-income (PTI) ratio compared to their long-run equilibrium due to equity-induced up-trading were found. The extent of PTI overshooting is related to the intensity of equity-induced up-trading between different segments. The market structure necessary to eliminate such overshooting is contingent on the distribution of equity gains between segments. Finally, the paper shows how the price effects of macroprudential interventions might be non-negligible when indirect effects are taken into account. Originality/value – The linear housing market model with three market segments introduces a framework where the intensity of equity-induced up-trading in different market segments can be analyzed. This distributional aspect is, to the best of the author's knowledge, novel. The context-specific relation between housing market structure, equity-induced up-trading and short-run deviations in the PTI ratio provides a foundation for future research.


2016 ◽  
Vol 106 (5) ◽  
pp. 636-640 ◽  
Author(s):  
Andreas Fuster ◽  
Basit Zafar

We use a strategic household survey to study the sensitivity of intended homeownership decisions to financing constraints. We find that the average stated likelihood of buying a home is strongly sensitive to the size of the required down payment, which we vary exogenously across three scenarios. This sensitivity is particularly high for respondents that appear more liquidity constrained based on observable characteristics (including current renters, or owners with low savings or low home equity). For renters, expectations of future rent inflation and of improvements to their personal financial situation also predict intention to buy.


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