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The challenges of achieving housing stability are examinable from a variety of locations and perspectives, resulting in a range of solutions and recommendations for practice. Attending to the experiences and understandings of both service users and service providers within a broad environmental scan, one can obtain a more complete picture of how housing stability can be supported and sustained. The purpose of this research is to better understand the practical landscape, human relationships, interdisciplinary understandings and everyday activities of housing stability.

The paper draws on thematic analysis from expert informant interviews conducted during an environmental scan of housing options and facilities for those who have been labelled “hard to house” in a large, ethnically‐diverse urban city in western Canada. Interviews of selected service users yielded information on their perspectives of the barriers and facilitators of housing stability. Parallel interviews of service providers and/or policy makers were also conducted, and responses compared and contrasted with those of service users.

Three interconnected patterns on the path to housing stability were experienced by the expert informants, and are explored herein. The overlapping and developmental themes “more than a house,” “finding support,” and “connecting multiple supports” are discussed. Conditions and realities of coordinated support need, particularly for those deemed “hard to house” because of addictions or mental health problems were revealed by the expert informants in this study.

Thematic analysis of the parallel interviews brought the landscape of housing stability into clearer focus and contributed to practice recommendations. This qualitative research approach was not intended to provide generalizable findings, but rather sheds light on particular experiences and understandings in ways that may contribute to further research. The themes identified may resonate in differing circumstances having arisen from the more generalized practical realities and social conditions which warrant an ongoing analysis.

The analysis revealed an ongoing gap of support for the management and coordination of the often complex requirements of support for housing stability for service users. Various service agencies, providing houses, providing financial aid, providing health and social support need to “connect multiple levels of support” something identified as an all too often missing link in achieving housing stability. Recommendations include recognizing the necessity of multiple sectors working together with multiple sites and layers of support, in particular for those who experience addictions or mental health problems. More tailored support, follow up and recognition of the potential for instability is a particular practical implication of the study. Invigorating a coordinative, case management role, with a view to bridging and bringing together seemingly disparate sectors and service providers are relevant practical implications of this study.

The research is novel in approach, drawing on a parallel expert informant interview process, and findings are of relevance to practical activities in housing, social and health services and policy as well as for scholarly dialogue and discussion.

The purpose of this paper is to study the effect of the 2008 global financial crisis on housing market dynamics in an emerging economy like India using quarterly data (Q4 2008–2009 to Q1 2018–2019). The study explores the extent of linkages between housing prices, monetary policy and financial stability by explaining the nature of the shocks to the housing sector and the degree of impact of those shocks; the possibility of adverse feedback loop which is beyond the natural levels; and the usefulness of explicit and direct role of monetary policy for the housing market stability, which was the loudest demand immediately after the crisis.

The paper follows a three-step methodology: data transformations, a variable selection process “general-to-specific modelling” with the help of OxMetrics 6 Package, and vector autoregressive modelling with the help of EViews 10. F-test was used to describe the short-term relationships between the variables. Impulse response and variance decomposition were used to explain the type of relationship (negative or positive) and the period of the relationships, respectively.

The study finds that the housing sector is sensitive to the monetary policy shocks, whereas the contribution of the housing market shocks to the fluctuations in other market variables is not substantial, though not negligible. As far as the nature of the shocks is concerned, the observed dynamics in the real house prices are diverging from their fundamental levels. The housing market shocks are more or less static; it rules out the chances for a self-reinforcing feedback loop with the existing setup.

The study concludes that the observed dynamics in the real house prices are diverging from their fundamental levels. Given the limitation, the researchers could extend this study by decomposing the part of the risk to the sector contributed by the other drivers, which may be inherent imperfections in housing markets, weak and unreliable wealth effect, and the presence of behavioural biases.

The present study finds countercyclical measures to be more useful for this sector as compared to the forward-looking monetary policy reforms in this sector. The central bank in India should continue to refrain from responding directly to the housing sector fluctuations. Investors can enjoy investing in the housing sector without any fear of the crisis as of now. The effect of speculation is small but not negligible, which enjoins the investors and the policy-makers to remain watchful. Interest rate, money supply and inflation lead (Granger-cause) the housing prices. This information is relevant for spending and investment decisions.

The study feels that banks should avoid using monetary policy to balance the house prices. This will be beneficial both for the economy and the society, as any change in monetary policy to especially curb out surging housing prices may adversely affect the output, and finally, may lead to the deflation. The fear of deflation may cause devastating economic, financial and social effects.

The study contributes to the literature by shedding some new insights about the interrelationship between macroeconomic variables, housing prices and financial stability in the aftermath of the 2008–2009 financial crisis. Such types of studies are absent from emerging markets, particularly from India.

The neighborhoods north and northwest of downtown St Louis are blighted by their abundance of substandard, abandoned, and demolished housing. Crime, poverty, and unemployment are high while family stability, educational achievement, and health outcomes are low. These conditions are not unique to St Louis, but can be found in neighborhoods in every city in America. How did this happen? What factors led to the demise of these neighborhoods? This chapter examines the history of St Louis along with theories of neighborhood succession to identify possible explanations for the city's collapse.

By considering the rapid and continuous increase of housing prices in Turkey recently, this study aims to examine the determinants of the residential property price index (RPPI). In this context, a total of 12 explanatory (3 macroeconomic, 8 markets and 1 pandemic) variables are included in the analysis. Moreover, the residential property price index for new dwellings (NRPPI) and the residential property price index for old dwellings (ORPPI) are considered for robustness checks.

A quantile regression (QR) model is used to examine the main determinants of RPPI in Turkey. A monthly time series data set for the period between January 2010 and October 2020 is included. Moreover, NRPPI and ORPPI are examined for robustness.

Predictions for RPPI, NRPPI and ORPPI are carried out separately at the country (Turkey) level. The results show that market variables are more important than macroeconomic variables; the pandemic and rent have the highest effect on the indices; The effects of the explanatory variables on housing prices do not change much from low to high levels, the COVID-19 pandemic and weighted average cost of funding have a decreasing effect on indices while other variables have an increasing effect in low quantiles; the pandemic and monetary policy indicators have a negative and significant effect in low quantiles whereas they are not effective in high quantiles; the results for RPPI, NRPPI and ORPPI are consistent and robust.

The results of the study emphasize the importance of the pandemic, rent, monetary policy indicators and interest rates on the indices, respectively. On the other hand, focusing solely on Turkey and excluding global variables is the main limitation of this study. Therefore, the authors encourage researchers to work on other emerging countries by considering global variables. Hence, future studies may extend this study.

The COVID-19 pandemic and market variables are determined as influential variables on housing prices in Turkey whereas macroeconomic variables are not effective, which does not mean that macroeconomic variables can be fully ignored. Hence, the main priority should be on focusing on market variables by also considering the development in macroeconomic variables.

Emerging countries can make housing prices stable and affordable, which will increase homeownership. Hence, they can benefit from stability in housing markets.

The QR method is performed for the first time to examine housing prices in Turkey at the country level according to the existing literature. The results obtained from the QR analysis and policy implications can also be used by other emerging countries that would like to increase homeownership to provide better living conditions to citizens by making housing prices stable and keeping them under control. Hence, countries can control housing prices and stimulate housing affordability for citizens.

Studies have shown positive housing retention and quality of life outcomes in moving on initiatives (MOIs). However, less is known about how movers’ health service use changes post-move. This paper aims to explore physical and mental health service use over time for participants in New York City’s MOI program.

This paper uses data collected at baseline, 12-months post move and 24-months post move to explore patterns in mental and physical health service use and their association with mental and physical health status for participants (N = 41). Health status was measured with the Medical Outcomes Study Short Form Survey Instrument.

Three mental health service use patterns emerged: service use at all time points, inconsistent service use across time points and no service use at any time point. Significant group differences in mental health were found at baseline and 12 months. Two physical health service use patterns emerged: service use at all three time points and inconsistent service use across time points. Significant group differences were found in mental health at 12  and 24 months.

This study showed that physical and mental health service use varied slightly over time for participants, with the majority of service use being for outpatient/non-acute care. The findings also point to possible relationships between service use and mental health status. Positive and negative implications of these findings are framed within the broader context of PSH and MOI goals.

This study aims to investigate the effect of monetary policy on housing prices for US economy. It specifically examines whether nominal or real interest rates are the key drivers behind fluctuations in housing prices in US.

Monthly data from January 1991 to July 2023 and various appropriate analytical tools such as unit root tests, Johansen’s cointegration test, vector error correction model (VECM), impulse response function and Granger causality test were applied for the data analysis.

The Johansen cointegration findings reveal the presence of a long-term relationship among the variables. VECM results indicate a negative correlation between nominal and real interest rates and housing prices in both the short and long terms, suggesting that a strict monetary policy can help in controlling the housing price increase in the USA. However, housing prices are more responsive to changes in nominal interest rates than to real interest rates. Additionally, the study reveals that the COVID-19 pandemic contributed to the upsurge in housing prices in the USA.

This study contributes by examining the role that nominal or real interest rates play in shaping housing prices in the USA. Moreover, given the recent significant upsurge in housing prices, this study presents a unique opportunity to investigate whether these price increases are influenced by the Federal Reserve's monetary policy decisions regarding nominal or real interest rates. Additionally, using monthly data, this study provides a deeper understanding of the fluctuations in housing prices and their connection to monetary policy tools.

The purpose of this paper is to assess the long-run and short-run drivers of real house prices in Nigeria from 1991Q1 to 2020Q4.

Vector autoregression and cointegration tests were used to assess the key drivers of Nigeria’s real house prices in the long run and short run.

The empirical findings revealed that household disposable income is the most important determinant of house prices in Nigeria. House prices increased by 1.6% and 60.8% in response to a 1% increase in disposable income in the long run and short run, respectively, while real mortgage credits pushed up house prices by 5% and have no long-run effects, suggesting that most Nigerians depend on their money income rather than credits in securing a home. In addition, prices of oil sector products and real interest rates had negative and significant relationship with house prices, while positive correlations were found for real effective exchange rate and real housing investments regardless of the time horizon. The impact of construction costs and cement prices was also documented.

This is likely a pioneering study of its kind to focus on the determinants of real house prices in Nigeria. It is probably the first study, the best of the author’s knowledge, to empirically examine the impact of the oil sector on house prices in the country.

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 chapter addresses housing policy in England since 2007 and changes in housing opportunities and inequalities. The credit crunch and its aftermath were experienced across the United Kingdom, and speeded the established trend to greater inequality. Many problems identified in England are relevant elsewhere, but the distinctive housing policies adopted in Scotland, Wales and Northern Ireland are not discussed here. The chapter argues that the policy direction adopted since 2010 failed in its ambition to increase housing supply and home ownership and further increased social and spatial inequalities.

The COVID-19 pandemic, a sudden and disruptive external shock to the USA and global economy, profoundly affected various operations. Thus, it becomes imperative to investigate the repercussions of this pandemic on the US housing market. This study investigates the impact of the COVID-19 pandemic on a crucial facet of the real estate market: the Time on the Market (TOM). Therefore, this study aims to ascertain the net effect of this unprecedented event after controlling for economic influences and real estate market variations.

Monthly time series data were collected for the period of January 2010 through December 2022 for statistical analysis. Given the temporal nature of the data, we conducted the Durbin–Watson test on the OLS residuals to ascertain the presence of autocorrelation. Subsequently, we used the generalized regression model to mitigate any identified issues of autocorrelation. However, it is important to note that the response variable derived from count data (specifically, the median number of months), which may not conform to the normality assumption associated with standard regression models. To better accommodate this, we opted to use Poisson regression as an alternative approach. Additionally, recognizing the possibility of overdispersion in the count data, we also explored the application of the negative binomial model as a means to address this concern, if present.

This study’s findings offer an insightful perspective on the housing market’s resilience in the face of COVID-19 external shock, aligning with previous research outcomes. Although TOM showed a decrease of around 10 days with standard regression and 27% with Poisson regression during the COVID-19 pandemic, it is noteworthy that this reduction lacked statistical significance in both models. As such, the impact of COVID-19 on TOM, and consequently on the housing market, appears less dramatic than initially anticipated.

This research deepens our understanding of the complex lead–lag relationships between key factors, ultimately facilitating an early indication of housing price movements. It extends the existing literature by scrutinizing the impact of the COVID-19 pandemic on the TOM. From a pragmatic viewpoint, this research carries valuable implications for real estate professionals and policymakers. It equips them with the tools to assess the prevailing conditions of the real estate market and to prepare for potential shifts in market dynamics. Specifically, both investors and policymakers are urged to remain vigilant in monitoring changes in the inventory of houses for sale. This vigilant approach can serve as an early warning system for upcoming market changes, helping stakeholders make well-informed decisions.