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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.

If the only difference between cities lies in their initial housing prices, the initially lower-price cities should eventually catch up with the initially higher-price ones, i.e., “absolute convergence.” Alternatively, if the major determinants of housing prices are city-specific, cities will converge to parallel growth paths of housing prices, i.e., “conditional convergence.” This study tests for the existence of absolute and conditional convergence in house prices among cities in China. The strong evidence for conditional convergence suggests that each city possesses its own steady-state housing price to which it is converging, which depends on the city's own socio-economic characteristics. In other words, differences in these socio-economic characteristics among cities can create permanent differences in housing price among them. The differences in steady-states house price across cities reflect differences in the level of socio-economic development among them. The findings inform the kinds of interventions and resources that are most likely to be effective in reducing income disparity.

The twenty-first century is noted globally as the urban century because more than half of the world's population lives in cities. This population is projected to increase to 70% by 2050. South Africa is no exception to this phenomenal increase in urban population. More than 60% of South African population lives in urban areas, and this figure is projected to increase to 71.3% and 80% by 2030 and 2050, respectively. Access to human settlement by this teaming population remains a challenge. The problem of access to human settlement is compounded by historical apartheid's spatial geography characterized by racial segregation, fragmentation of urban space and separate development. During the apartheid period, settlement patterns were designed and planned in accordance with racial differentiation. This resulted in fragmented, segregated and dysfunctional residential settlements pattern that forced many people to travel long distances between place of work and home. Since 1994, the various housing policies, programmes and legislations have not been able to find solution to the spatial challenges that South Africa faces. The objective of this book is to investigate and unravel mixed-income housing development planning strategy and how this housing typology with a new framework can bring about spatial integration, inclusive cities, improved access to services and the promotion of social cohesion and economic inclusion. This book utilized the case study research design and employed the Delphi method for the investigation. The findings reveal that proper coordination across all sectors of government and good working relationship between the private and public sectors will increase the sustainability of mixed-income housing development. This study also supports existing theory that mixed-income housing might not be able to bring about the overall social integration, and solve all housing problems but has the unique tendency and potential in the South African case to address spatial imbalances by increasing the affordability of low-cost housing. This book concludes that there is need for both inter-sectoral and intergovernmental collaboration as well as proper coordination/adequate urban planning policies to address the increasing human settlement challenges in South Africa and for effective implementation of mixed-income housing development.

Housing activity is an important indicator of general economic activity, and house price movements are an important variable in international financial markets. In this chapter we utilise vector autoregressive models to examine how the interrelationship between housing activity and general economic activity has evolved in four OECD countries. Our results provide support for the hypothesis that the relationship between housing activity and general economic activity has changed in many OECD countries. For Australia, however, no such evidence was found. These results suggest that caution needs to be exercised when using previous experience to forecast both housing cycles and general economic activity.

Using a sample of developed and developing nations (including China and Hong Kong), this study examines the financial market and housing wealth effects on consumption. Housing performs the dual functions as both a commodity providing a flow of housing services and an investment providing a flow of capital income. With an empirical framework based on the permanent income hypothesis, this study's findings suggest that a rise in housing price has both a positive wealth effect and a negative price effect on consumption. While the positive wealth effect is caused by an increase in capital income from housing investment, the negative price effect is caused by an increase in the cost of consuming housing services. Moreover, the sensitivity of consumption to unanticipated changes in housing price is related to the level of financial and institutional development.

The objective of this paper is to examine the determinants of the Greek house market during the period 2006–2022 using not only economic variables but also behavioral variables, taking advantage of available information on the volume of Google searches. In order to quantify the behavioral variables, we implement a Python code using the Pytrends 4.9.2 library.

In our study, we assert that models relying solely on economic variables, such as GDP growth, mortgage interest rates and inflation, may lack precision compared to those that integrate behavioral indicators. Recognizing the importance of behavioral insights, we incorporate Google Trends data as a key behavioral indicator, aiming to enhance our understanding of market dynamics by capturing online interest in Greek real estate through searches related to house prices, sales and related topics. To quantify our behavioral indicators, we utilize a Python code leveraging Pytrends, enabling us to extract relevant queries for global and local searches. We employ the EGARCH(1,1) model on the Greek house price index, testing several macroeconomic variables alongside our Google Trends indexes to explain housing returns.

Our findings show that in some cases the relationship between economic variables, such as inflation and mortgage rates, and house prices is not always consistent with the theory because we should highlight the special conditions of the examined country. The country of our sample, Greece, presents the special case of a country with severe sovereign debt issues, which at the same time has the privilege to have a strong currency and the support and the obligations of being an EU/EMU member.

The results suggest that Google Trends can be a valuable tool for academics and practitioners in order to understand what drives house prices. However, further research should be carried out on this topic, for example, causality relationships, to gain deeper insight into the possibilities and limitations of using such tools in analyzing housing market trends.

This is the first paper, to the best of our knowledge, that examines the benefits of Google Trends in studying the Greek house market.

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 purpose of this study is to examine the buyers’ preferences influencing the purchase of privately developed affordable housing in Kolkata and to determine whether unsold houses result from misalignment with these preferences.

The literature review and user-opinion survey identified 119 independent variables that indicate buyers’ preferences. A questionnaire survey of 383 households in affordable housing units from 32 housing complexes in Kolkata recorded buyers’ preferences and satisfaction against the independent variables grouped under five levels of characteristics. The product weights of variables derived from the rank sum method and percentage satisfaction give the Utility Score. Multivariate regression and univariate linear regressions were conducted to determine the significance of each Level of characteristics and each variable, identifying the significant variables that would affect the sale of affordable houses.

The multivariate regression analysis has indicated that 68.56% of the variation in the percentage of unsold houses was explained by the five utility scores, which affirms that misalignment with buyers’ preferences significantly affects the sale of privately developed affordable houses. Furthermore, building and neighbourhood-level utility show the highest significance as predictors, while city-level and miscellaneous utility have moderate significance, but housing complex-level utility lacks statistical significance.

This study addresses a research gap in privately developed affordable housing in Kolkata, enhancing understanding of buyer preferences in this segment.