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The fundamental role of housing in community care has long been acknowledged. However, progress in achieving any real integration of housing and social care has been slow. This article reports the findings from the Crossing the Housing and Care Divide programme, which was jointly sponsored by the Housing Corporation and Anchor Trust. The programme aimed to stimulate developments in services for older people that would enable housing to become part of community care, lead to greater inter‐agency working, enhance the involvement of users in the planning, monitoring and delivery of services and deliver a high quality of service more cost‐effectively. The programme offers many practical lessons for the effectiveness of services.

This paper aims to, using a unique loan-level data set, show the extent to which negative equity in Ireland is concentrated in younger age groups. The sharp decline in house prices since 2007 has led to the emergence of widespread negative equity in Ireland. However, little is known about the type of borrower experiencing negative equity.

This paper uses a unique data set that, for a large sample of mortgages, provides details on both the characteristics of the borrowers and their mortgages. Using this data set, the paper estimates the incidence of negative equity by analysing loans taken out to purchase a primary residence in the period 2005-2012.

The analysis finds the situation in Ireland to be much more severe than that being experienced in other housing market downturns at present, with 64 per cent of borrowers in the period 2005-2012 experiencing negative equity. Analysis by age gives rise to concern, with the majority of those in negative equity aged under 40 years. The paper also points to the large wealth loss experienced by Irish households, in the order of 43 billion, as a result of the fall in property values.

The paper is one of the first using loan-level time-series data in Ireland. It highlights the growth in negative equity during the crisis and the extent to which it is concentrated in the younger age groups. It also provides an estimate of the loss in wealth suffered by all households due to the fall in Irish house prices.

This paper aims to use Markov switching vector auto regression (MSVAR) methods to examine UK house price cycles in UK regions at NUTS1 level. There is extensive literature on UK regional house price dynamics, yet empirical work focusing on the duration and magnitude of regional housing cycles has received little attention. The research findings indicate that the regional structure of UK exhibits that UK house price changes are best described as two large groups of regions with marked differences in the amplitude and duration of the cyclical regimes between the two groups.

MSVAR principal component analysis NUTS1 data are used.

The housing cycles can be divided into two super regions based on magnitude, duration and the way they behave during recession, boom and sluggish periods. A north-south divide, a uniform housing policy and a monetary policy increase the diversion among the regions.

Markov switching needs high-frequency data and long time spans.

Questions a uniform housing policy in a heterogeneous housing market. Questions the impact of monetary policy on a heterogeneous housing market. The way the recovery of the housing market varies among regions depends on regional economic performance, housing market structure and the labour market. House price convergence, beta-convergence.

No such work has been done looking at duration and magnitude of regional housing cycles. A new econometric method was used.

This article aims to study the dual search problem using data on the Norwegian housing market during the financial crisis of 2008 and begin the detailed mapping of the elements in the transmission mechanism from policy to the housing market. Moving owner occupiers face a simultaneous dual search and matching problem, as they must locate both a buyer and a seller with whom to transact. Individual agents solve this optimization under uncertainty by planning to make their bids for a new house partially conditional upon the sale of the old house.

Norway may function as a window into a policy quasi-laboratory since the housing market was turned around in December 2008 in the midst of a worldwide financial crisis and after a year and a half of price decreases. The article proposes that one key dimension in the recovery was the reduced frequency of households with conditional demand involving sell-first strategies and acquires data to shed light on this proposition.

Empirical evidence on the sell-first–buy-first differential, for-sale stock and stock-to-volume supports this proposition, and results indicate that the housing market is affected by sell-first strategies. The article discusses policy alternatives.

The article introduces a miniature model of housing trade sequences and a simple apparatus with which to analyze the consequences of sell-first behavior. It also acquires and combines new data on sell-first–buy-first differential, for-sale stock and stock-to-volume ratio. The article analyzes the co-movement between these time series and the house price index.

This paper aims to examine the main characteristics of the housing taxation and subsidy systems in six European Union countries. The structure of this support over the past two decades, before and after the global financial crisis has been investigated and its total effective dimensions have been approximated.

Official national data and existing literature on housing policy expenses have been analysed and the authors add their own estimations of missing data, where possible. Latest changes in housing policy guidelines and expenses were interpreted.

It was found that state support for housing is heavily underestimated by official data in most countries, mainly due to missing estimates for the value of imputed rents tax relief, reduced VAT rates and low real estate and capital gains taxation. Our estimates suggest that total public support for the housing sector reaches more than 3 per cent of the gross domestic product in three of the six countries, and about 2 per cent in the others. State support to the housing sector has developed quite differently in the investigated countries over the past decades. In particular, there was no universal downward trend.

This is the first attempt to provide a more comprehensive analysis of national housing policy expenses applying a very broad definition of state support for housing. In particular, we consider indirect tax advantages to the housing sector that are generally not taken into account. Furthermore, we apply a discounted present value approach of current housing policy expenses to facilitate international comparison.

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.

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.

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.

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.

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.

The article aims to examine why residential property price indices (RPPI) are important, particularly in the European Union (EU) with its highly integrated financial system and examines the problems in developing a pan-European price index that aggregates the indices of different countries.

The reasons why RPPI are important is explored through a review of the literature on residential price bubbles and the issues with the indices through studies of individual examples.

Financial integration in the EU has taken place without adequate consideration having been given to diversity in residential property markets. The development of means of monitoring them has lagged behind integration with the national price indices using a variety of methods and approaches to data that limit the extent to which they can be aggregated.

The article shows the need for better quality data about house price trends in Europe if the consequences of future bubbles are to be avoided. Current initiatives are unlikely to satisfy this, as they leave too many choices about methodology and data in the hands of individual countries.

Currently, in the education data use literature, there is a lack of research and examples that consider the early steps of filtering, organizing and visualizing data to inform decision-making. The purpose of this study is to describe how school leaders and researchers visualized and jointly made sense of data from a common learning management system (LMS) used by students across multiple schools and grades in a charter management organization operating in the USA. To make sense of LMS data, researchers and practitioners formed a partnership to organize complex data sets, create data visualizations and engage in joint sensemaking around data visualizations to begin to launch continuous improvement cycles.

The authors analyzed LMS data for n = 476 students in Algebra I using hierarchical cluster analysis heatmaps. The authors also engaged in a qualitative case study that examined the ways in which school leaders made sense of the data visualization to inform improvement efforts.

The outcome of this study is a framework for informing evidence-based improvement cycles using large, complex data sets. Central to moving through the various steps in the proposed framework are collaborations between researchers and practitioners who each bring expertise that is necessary for organizing, filtering and visualizing data from digital learning environments and administrative data systems.

The authors propose an integrated cycle of data use in schools that builds on collaborations between researchers and school leaders to inform evidence-based improvement cycles.