POLICY AND MOBILITY IN DUTCH HOUSING MARKET CONTEXTS.

1984 ◽  
Vol 75 (4) ◽  
pp. 242-248 ◽  
Author(s):  
P. C. J. EVERAERS ◽  
W. A. V. CLARK
Keyword(s):  
2010 ◽  
Author(s):  
Erik R. de Wit ◽  
Peter Englund ◽  
Marc Francke
Keyword(s):  

1986 ◽  
Vol 77 (4) ◽  
pp. 243-250 ◽  
Author(s):  
M. A. J. LINDE ◽  
F. M. DIELEMAN ◽  
W. A. V. CLARK
Keyword(s):  

Author(s):  
Marc Francke ◽  
Alex van de Minne ◽  
Johan Verbruggen
Keyword(s):  

2020 ◽  
Vol 13 (2) ◽  
pp. 257-270
Author(s):  
Arvydas Jadevicius ◽  
Peter van Gool

Purpose This study is a practice undertaking examining three main concerns that currently dominate Dutch housing market debate: how long is the cycle, will the current house price inflation continue and is housing market in a bubble. With national house prices reaching record highs across all major cities, future market prospects became a topic of significant debate among policymakers, investors and the populace. Design/methodology/approach A triangulation of well-established academic methods is used to perform investigation. The models include Hodrick-Prescott (HP) filter, volatility autoregressive conditional heteroskedasticity (ARCH approximation) and right tail augmented Dickey–Fuller (Rtadf) test (bubble screening technique). Findings Interestingly, over the years from 1985 to 2019 research period, filtering extracts only one Dutch national housing cycle. This is a somewhat distinct characteristic compared to other advanced Western economies (inter alia the UK and the USA) where markets tend to experience 8- to 10-year gyrations. Volatility and Rtadf test suggest that current house prices in most Dutch cities are in excess of historical averages and statistical thresholds. House price levels in Almere, Amsterdam, The Hague, Groningen, Rotterdam and Utrecht are of particular concern. Originality/value Retail investors should therefore be cautious as they are entering the market at the time of elevated housing values. For institutional investors, those investing in long-term, housing in key Dutch metropolitan areas, even if values decline, is still an attractive investment conduit.


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.


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