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In this chapter, we aim to analyze the housing market development in Czechia, in particular the development of housing prices over the last 25 years. We quantify and discuss three distinct periods of excessive growth of regional Czech housing prices, identified through the formation of large positive GAPs – (1) before the entrance of Czechia to the European Union (EU), (2) at the onset of the Global Financial Crisis GFC, (3) in 2021. In all these periods, we identify significant differences among regions. We find that GAPs above 15% may be considered an indication of unsustainable long-term housing price growth that will be followed by a correction.

We then employ fixed effect panel data model to determine the drivers of flat and house prices in 14 Czech regions. Our results show that wage growth, migration and crime rate are significant factors affecting the prices of both flats and houses. Nevertheless, the impact of GDP per capita and job market indicators differs between flats and houses. Moreover, we find that higher migration into the region increases the difference between the prices of houses and flats, while increasing GDP per capita growth and crime rate mitigate this difference significantly.

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.

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.

This study aims to explore the gendered nature of housing insecurity by investigating how gender affects women’s experience moving from transitional to market housing. By describing women’s pathways out of supportive or transitional housing support, the authors show how patriarchal forces in housing policies and practices affect women’s efforts to find secure housing. The authors argue that gender-neutral approaches to housing will fail to meet women’s needs.

This study explores the narratives from women accessing support services in Halifax, Canada. The first author conducted deep narrative interviews with women seeking to move from transition to market housing.

This research sheds light on the effects of gendered barriers to safe, suitable and affordable housing; how women’s experiences and expectations are shaped by these barriers; and, how housing-based supports must address the uniquely gendered experiences women face as they access market housing. In addition, this research reveals the importance of gender-responsive services that empower women facing a sexist housing market.

Little research has explored questions related to gender and housing among those seeking to move from transitional to marker housing, and existing research focuses on women’s housing insecurity as it relates to domestic violence. The sample of women included those having housing insecurity for a variety of reasons, including substance use and young motherhood.

The lack of a proper register to store, match and display information on the adapted property has led to a waste of resources and prolonged delays in matching the disabled and elderly people with appropriate properties. This paper presents the development of a Housing Adaptations Register with user-matching functionalities for different mobility categories. The developed system accurately captures and documents adapted home information to facilitate the automated matching of disabled/aged applicants needing an adapted home with suitable property using banding, mobility and suitability index.

A theoretical review was conducted to identify parameters and develop adaptations register construct. A survey questionnaire approach to rate the 111 parameters in the register as either moderate, desirable or essential before system development and application. The system development relied on DSS modelling to support data-driven decision-making based on the decision table method to represent property information for implementing the decision process. The system is validated through a workshop, four brainstorming sessions and three focus group exercises.

Development of a choice-based system that enables the housing officers or the Housing Adaptations Register coordinators to know the level of adaptation to properties and match properties quickly with the applicants based on their mobility status. The merits of the automated system include the development of a register to capture in real-time adapted home information to facilitate the automated matching of disabled/aged applicants. A “choice-based” system that can map and suggest a property that can easily be adapted and upgraded from one mobility band to the other.

The development of a housing adaptation register helps social housing landlords to have a real-time register to match, map and upgrade properties for the most vulnerable people in our society. It saves time and money for the housing associations and the local authorities through stable tenancy for adapted homes. Potentially, it will promote the independence of aged and disabled people and can reduce their dependence on social and healthcare services.

This system provides the local authorities with objective and practical tools that may be used to assess, score, prioritise and select qualified people for appropriate accommodation based on their needs and mobility status. It will provide a record of properties adapted with their features and ensure that matching and eligibility decisions are consistent and uniform.

The purpose of this paper is to investigate millennials’ purchase behaviours towards green housing in India. This paper also examines the mediating effect of purchase intention between determinants of buying green housing and purchase behaviour in the real estate industry.

A cross-sectional research design was applied to collect data from 393 rural and 388 urban millennials. This study used “partial least squares structural equation modelling” to verify the framed hypotheses.

The outcomes indicate that attitude, environmental concern and green trust substantially influence the purchase intention and purchase behaviour towards green housing in rural and urban studies. However, perceived risk has an insignificant effect on purchase intention and purchase behaviour towards green housing in both studies. Likewise, innovativeness insignificantly impacts the purchase intention in study rural while substantially impacting the purchase behaviour in both studies. Additionally, a favourable relationship between purchase intention and purchase behaviour towards green housing in both rural and urban contexts.

This study provides fruitful evidence for practitioners, marketers and academicians about the drivers of purchase behaviour toward green housing. The results of this study also enable regulatory bodies to design appropriate strategies and tactics to foster the sustainable growth of nations.

This paper is a preliminary attempt to explore the decision to buy green housing in India. Furthermore, the authors targeted a specific age group, especially millennials, to gain a valuable understanding of how different factors affect green housing decisions in different areas, that is, rural and urban areas.

This study aims to evaluate sustainable investment policies for housing in Qatar.

This paper proposes a new model for analyzing sustainable investment policies for housing demand in Qatar via a hybrid quantum fuzzy decision-making model. The study processed the criteria with the facial expression-based Quantum Spherical fuzzy DEMATEL and ranked the alternatives with the facial expressions-based quantum spherical fuzzy TOPSIS. Four factors were determined due to a comprehensive literature review (Environment, Housing Design, Building Design, and Surrounding the building), with five sustainable investment policy alternatives (Electricity production with renewable energies, Recycling systems and materials in construction, Transport with less carbon emission, Biodiversity for residents, and Resilience to natural disasters).

The analysis indicates that the design of the building is the most important factor (0.254), while the environment is the most influencing factor (0.253) regarding housing demand in Qatar. Transport with less carbon emission and electricity production with renewable energies are the most critical alternative investment policies.

This study provides useful insights for regulators, policymakers, and stakeholders in Qatar’s sustainable investment policies for housing demand. The main motivation of this study is that there is a need for a novel model to evaluate the sustainable investment policies for housing demand. The main reason is that existing models in the literature are criticized due to some issues. In most of these models, emotions of the experts are not taken into consideration. However, this situation has a negative impact on the appropriateness of the findings. Because of this situation, in this proposed model, facial expressions of the experts are considered. With the help of this issue, uncertainties in the decision-making process can be handled more effectively.

Globally, hepatitis C treatment uptake is lower among people who are homeless or unstably housed compared to those who are housed. Understanding and addressing this is essential to ensure no one is left behind in hepatitis C elimination efforts. This study aims to explore peoples’ experiences of unstable housing and health care, and how these experiences influenced engagement in hepatitis C treatment.

Purposive sampling was used to recruit people with lived experience of injection drug use, hepatitis C and unstable housing in Melbourne, Australia. In-depth semistructured interviews were conducted and a case study approach with interpretative phenomenological analysis was used to identify personal experiential themes and group experiential themes.

Four people were interviewed. The precarious nature of housing for women who inject drugs was a group experiential theme, however, this did not appear to be a direct barrier to hepatitis C treatment. Rather, competing priorities, including caregiving, were personal experiential themes and these created barriers to treatment. Another group experiential theme was “right place, right time, right people” with these three elements required to facilitate hepatitis C treatment.

There is limited research providing in-depth insight into how personal experiences with unstable housing and health care shape engagement with hepatitis C treatment. The analyses indicate there is a need to move beyond a “one size fits-all” approach to hepatitis C care. Instead, care should be tailored to the needs of individuals and their personal circumstances and regularly facilitated. This includes giving greater attention to gender in intervention design and evaluation, and research more broadly.

The purpose of this study is to examine the effects of the Odd-Even Road Rationing Policy (RRP) on housing prices in Jakarta, Indonesia. It aims to evaluate the net effect of the RRP on housing prices.

The study uses the monocentric model and employs the difference-in-differences (DD) method. Annual neighborhood-level housing price data is analyzed to assess the impact of the RRP on housing prices. Additionally, propensity score matching is used to address potential biases resulting from non-random policy assignments.

The results demonstrate that houses located within the RRP-restricted area experience a decrease in price that is relative to those in the control group. The findings indicate a decrease in housing prices ranging from 7.59% to 14.7% within the RRP-restricted area. This suggests that the positive impacts resulting from the RRP have not fully compensated for the restricted accessibility experienced by individuals who have limited behavioral changes. The study also confirms the significance of commuting costs in individuals' location decisions, aligning with predictions from urban economics models.

This study contributes to the literature by providing insights into the effects of a RRP on housing prices. It expands understanding beyond the immediate effects on traffic conditions and air pollution, which previous studies have primarily focused on. Furthermore, to the best of the authors’ knowledge, this research will be the first conducted to identify the impacts of RRP on housing prices in Indonesia.

The purpose of this study is to reveal the dynamics of house prices and sales in spatial and temporal dimensions across British regions.

This paper incorporates two empirical approaches to describe the behaviour of property prices across British regions. The models are applied to two different data sets. The first empirical approach is to apply the price diffusion model proposed by Holly et al. (2011) to the UK house price index data set. The second empirical approach is to apply a bivariate global vector autoregression model without a time trend to house prices and transaction volumes retrieved from the nationwide building society.

Identifying shocks to London house prices in the GVAR model, based on the generalized impulse response functions framework, I find some heterogeneity in responses to house price changes; for example, South East England responds stronger than the remaining provincial regions. The main pattern detected in responses and characteristic for each region is the fairly rapid fading of the shock. The spatial-temporal diffusion model demonstrates the presence of a ripple effect: a shock emanating from London is dispersed contemporaneously and spatially to other regions, affecting prices in nondominant regions with a delay.

The main contribution of this work is the betterment in understanding how house price changes move across regions and time within a UK context.