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This study aims to contribute to the literature on migration by examining the nexus between migration-related fears and housing affordability in France, Germany, the UK and the USA using new datasets for migration-related fears.

This study adopts the feasible quasi-generalized least squares approach wherein a predictor can be isolated in the estimation process. Thus, rather than specifying a multi-predictor model that may also lead to parameter proliferation, a single-predictor model (for the predictor of interest) is formulated while also accounting for other salient features resulting from suppressing other important factors that may not be of interest to the current study. Such salient features include persistence, endogeneity and conditional heteroscedasticity issues.

Overall, the results show heterogeneous responses of housing affordability to migration fears across the four developed countries, as the latter deteriorates housing affordability in Germany and the USA and improves it in France and the UK. Similarly, the GFC makes housing less affordable in all four countries as low interest rate passes the mediation test in the nexus. The results, especially for low interest rates, are robust to different uncertainty measures.

As is often the case with economic phenomena, no single model can capture all the factors influencing an economic variable. Thus, besides examining the nexus between migration fears and housing affordability, the authors also account for the role of GDP per capita, given the influence of population and income dynamics on housing affordability. However, incorporating GDP per capita alone does not substantially enhance the model’s ability to predict housing affordability. Future research should explore additional macroeconomic and social factors, such as human capital development, to further enhance this subject.

The findings have significant implications for policymakers regarding the use of low interest rates to counteract the adverse effects of migration-related fear on housing affordability. Specifically, to mitigate the potential negative impact of migration and the associated fear on housing affordability, monetary authorities could adopt a more accommodative stance on mortgages. By allowing real estate investors to obtain loans at lower rates, this approach would help increase housing supply and reduce the housing gap exacerbated by migration influx.

The values of this study lie in its examination of housing affordability in relation to migration fears from both the demand and supply sides of the market. Furthermore, the analyses are conducted to cover out-of-sample forecast evaluation as in-sample predictability may not guarantee out-of-sample prediction.

The study aims to estimate the short- and long-run effects of monetary policy on residential property prices in South Africa. Over the past decades, there has been a monetary policy shift, from targeting money supply and exchange rate to inflation. The shifts have affected residential property market dynamics.

The Johansen cointegration approach was used to estimate the effects of changes in monetary policy proxies on residential property prices using quarterly data from 1980 to 2022.

Mortgage finance and economic growth have a significant positive long-run effect on residential property prices. The consumer price index, the inflation targeting framework, interest rates and exchange rates have a significant negative long-run effect on residential property prices. The Granger causality test has depicted that exchange rate significantly influences residential property prices in the short run, and interest rates, inflation targeting framework, gross domestic product, money supply consumer price index and exchange rate can quickly return to equilibrium when they are in disequilibrium.

There are limited arguments whether the inflation targeting monetary policy framework in South Africa has prevented residential property market boom and bust scenarios. The study has found that the implementation of inflation targeting framework has successfully reduced booms in residential property prices in South Africa.

This study aims to develop a modelling framework of housing supply dynamics within the context of urban microsimulation systems. Housing markets have witnessed substantial investigation over recent decades, predominantly concerning residential demand. However, comparatively limited attention has been directed towards comprehending the housing supply dynamics. Housing policy disconnects with the developers’ market behaviours, which leads to significant mismatch between the housing construction and affordable housing needs of the population. Research attention should be made in comprehending the residential construction market activities. To address this gap, this study developed an autoregressive distributed lag (ARDL) model and analyzed the temporal evolution of housing construction.

An ARDL model was developed to address the issue of temporal modelling of the housing supply. An empirical study was conducted in the Greater Toronto and Hamilton Area (GTHA) based on a longitudinal housing starts data set from 1998 to 2020. The model integrates diverse variables, including macroeconomic conditions, property development costs, dwelling prices and opportunity costs. Notably, the model captures both the path-dependent effects stemming from supply market fluctuations and the temporal lag effect of influential factors.

The findings reveal that the supply-side’s responsiveness to market condition alterations may span up to 18 months. The model has reasonable and satisfying performance in fitting the observed starts. The methodological foundations laid will facilitate future modelling of housing supply dynamics.

This study innovatively separated the modelling of housing supply within the context of urban microsimulation, into two parts, the modelling of housing starts and completion. The housing starts are determined in a complex and regressive process influenced by both the micro-economic environment and the construction cost and housing market trends. Through the temporal modelling method, this study captures how long it would take for the housing supply to respond to multiple factors and provides insight for urban planners in regulating the housing market and leveraging various policies to influence the housing supply.

The study analyzes the relationship between homeownership and financial investment of households in the context of emerging markets like India. It also examines how homeownership affects the portfolio decisions of Indian households.

Using the nationally representative All-India Debt and Investment Survey of 2019 and employing an instrumental variable approach, the authors analyze the relationship between homeownership and the share of financial assets held by Indian households. The study also employs several sensitivity checks, including alternate estimation techniques and alternative definitions of the housing variables, and accounts for additional factors to ensure that the authors are able to capture the effect of homeownership on the outcome variable.

The analysis suggests homeownership crowds out financial investment in India due to high repair and maintenance costs. The negative effect is mainly observed in urban households. Further, the findings imply that homeownership leads households to reallocate their asset portfolio. Homeowners have a lower share in liquid short term deposits, indicating the high liquidity risk of their portfolios. On the other hand, homeownership increases the share of long term retirement funds along with no effect on risky asset share. The authors observe that the crowding out effect is more striking for younger households and poorer households with low income, and the effect is lower for indebted households.

The findings underscore the need for financial awareness programs so that housing does not crowd out liquid investments of households. Additionally, the results highlight that policies should first focus on young and poor households as the negative effect is more prominent for these groups. Finally, there is scope for policies to support repair and maintenance costs incurred by vulnerable households to reduce the negative effect of housing on liquid financial investments.

This paper is among the few studies that provide insights into how homeownership relates to financial investment and portfolio decisions in the context of an emerging economy. Furthermore, the heterogeneous effects based on poor economic status and age underscore the need for complementary policies.

Property guardianship is increasingly being viewed as an alternative and, in many cases, a last resort to the unaffordable private rental market. This upsurge in the incidence of guardianship necessarily amplifies the existing legal grey areas and the inherent insecurity and precarity in the sector for guardians. Drawing on interviews with property guardians and archival research, the purpose of this study is to explore the background to the guardianship occupation model; highlight the key problems guardianship generates and, building on this, propose recommendations for reform to the regulatory landscape of guardianship. This study argues that a culture change in property guardianship is needed so that guardians can be better protected, and local authorities empowered to be more proactive in overseeing standards of guardian properties in their areas.

This study draws on qualitative semi-structured interviews with 46 property guardians and archival research.

The author argues that property guardians routinely enter the sector largely as a matter of last resort based on financial considerations or following difficult life experiences. Insecure and precarious, guardianship operates under licence agreements which provide less protection for guardians. Coupled with ambiguity around the application of existing housing legislation to guardianship and research showing non-engagement by local authorities with guardianship, this study suggests regulatory reform is urgently needed.

With traditional residential tenancies in the private rental sector increasingly unaffordable for many and guardianship becoming a viable alternative, this study argues for significant regulatory reform to the guardianship sector to ensure guardians are adequately protected under the law. This study presents a series of proposals to deliver a culture change in the sector.

This study aims to explore the relationship between house prices and time-on-market (TOM) in Silicon Valley. Previous findings have been inconclusive due to variations in property characteristics. This paper highlights the discrepancy between listing and selling prices and identifies differences among housing types such as condominiums, detached houses and townhouses based on housing orientations and customer groups. Additionally, this study considers the impact of the COVID-19 pandemic and the Fed’s interest rate policies on the housing market.

The authors analyze 63,853 transactions from the Bay East Board of Realtors’ Multiple Listing Service during 2018 to 2022. The study uses a multiple-stage methodology, including a nonlinear hedonic pricing model, search theory and two-stage least squares method to address concerns relating to endogeneity.

The Silicon Valley housing market shows resilience, with low-end properties giving buyers more bargaining power without significant price drops. High-end properties, on the other hand, attract more attention over time, leading to aggressive bidding and higher final sale prices. The pandemic, despite reducing housing supply, did not dampen demand, leading to price surges. Post-COVID, price correlations with TOM changed, indicating a more cautious buyer approach toward high premiums. The Fed’s stringent monetary policies post-2022 intensified these effects, with longer listing times leading to greater price disparities due to financial pressures on buyers and shifting dynamics in buyer interest.

Results reveal a nonlinear positive correlation between TOM and the price formation process, indicating that the longer a listed property is on the market, the greater the price changes. For low-end properties, TOM becomes significantly negative, while for high-end properties, the coefficient becomes significantly positive, with effects and magnitudes varying by type of dwelling. Moreover, external environmental factors, especially those leading to financial strain, can significantly impact the housing market.

The experience of Silicon Valley is valuable for cities using it as a development model. The demand for talent in the tech industry will stimulate the housing market, especially as the housing supply will not improve in the short term. It is important for government entities to plan for this proactively.

House price fluctuations send vital signals to many parts of the economy, and long-term predictions of house prices are of great interest to governments and property developers. Although predictive models based on economic fundamentals are widely used, the common requirement for such studies is that underlying data are stationary. This paper aims to demonstrate the usefulness of alternative filtering methods for forecasting house prices.

We specifically focus on exponential smoothing with trend adjustment and multiplicative decomposition using median house prices for Sydney from Q3 1994 to Q1 2017. The model performance is evaluated using out-of-sample forecasting techniques and a robustness check against secondary data sources.

Multiplicative decomposition outperforms exponential smoothing at forecasting accuracy. The superior decomposition model suggests that seasonal and cyclical components provide important additional information for predicting house prices. The forecasts for 2017–2028 suggest that prices will slowly increase, going past 2016 levels by 2020 in the apartment market and by 2022/2023 in the detached housing market.

We demonstrate that filtering models are simple (univariate models that only require historical house prices), easy to implement (with no condition of stationarity) and widely used in financial trading, sports betting and other fields where producing accurate forecasts is more important than explaining the drivers of change. The paper puts forward a case for the inclusion of filtering models within the forecasting toolkit as a useful reference point for comparing forecasts from alternative models.

To the best of the authors’ knowledge, this paper undertakes the first systematic comparison of two filtering models for the Sydney housing market.

This study aims to explore a novel framework for housing price bubbles in the Turkish economy during the pandemic. It examines the probability of housing bubble formation relative to the pre-pandemic period and identifies possible determinants of housing bubbles in the Turkish economy.

In this study, a two-stage novel estimation method is applied. In the first stage, bubble periods are identified through the right-tailed supremum augmented Dickey–Fuller test. In the second stage, the determinants of these bubbles are identified, and the housing bubble determinants during the COVID-19 pandemic are compared to the pre-pandemic period.

The findings indicate that there is an asset price bubble in the housing market during the pandemic period. Furthermore, mortgage credit expansion, mortgage credit rates and the depreciation of the Turkish Lira against the USD could increase housing bubble formation. However, housing sector sales to foreign investors do not contribute to housing bubble formation during the pandemic in the Turkish housing market.

To the best of the author’s knowledge, this is the first study to address the relative determinants of housing bubbles in an emerging market context during the pandemic.

Our aim in this study is to investigate the relative importance of the economic policy uncertainty and of the geopolitical risk on U.S. REITs (Real Estate Investment Trusts) returns with a special focus on the different real estate sectors.

We use an augmented Fama-French (1993)’s asset pricing model, including economic policy uncertainty indices (EPU), introduced by Baker et al. (2016), and geopolitical risk indices (GPR) recently developed by Caldara and Iacoviello (2022), to price the potential risk factors for U.S. Nareit indices returns. To obtain robust economic results, we correct for the problems of errors-in-variables in linear asset pricing models; we advocate the use of higher moments estimators as instruments in a generalized method of moments (GMM) framework.

Our results report that economic policy uncertainty (EPU), and geopolitical risk (GPR) are priced for the different Nareit sectors for the last three decades. The GPR index stands as a relevant risk factor. The coefficient estimates are low compared to Fama-French risk factors. They are higher for Shopping Centers, Retail and Region Malls and lower for Health Care and Lodging/Resorts. EPU indices are also priced and less statistically significant. Health Care sector, followed by Shopping Centers and Retail are the most policy-sensitive sectors.

In their “2023–2024 Top Ten Issues Affecting Real Estate” “political unrest and global economic health” is ranked 1 issue by the Counselors of Real Estate. Our results report that economic policy uncertainty and geopolitical risk are priced for the different Nareit sectors. They suggest implications for investors, insurers, bankers, policymakers and other stakeholders. The geopolitical risk index (GPR) stands as a relevant and significant risk factor for REITs returns.

Based on parsimonious robust asset pricing models, the results shed a new light on the relative importance of geopolitical risk and economic policy uncertainty in the real estate sector, with a special focus on the different U.S. REITs sectors. They suggest possible implications for investors, insurers, bankers, policymakers and other stakeholders in a context marked by higher uncertainty shocks and geopolitical risks.

The primary objective of this study was to evaluate the influence of greenery on residential mobility within purpose-built student housing facilities in Northern Ghana.

This research employed a structured questionnaire and utilized an experimental block design, encompassing 124 comparative greened and non-greened student housing facilities, with a total of 995 resident participants. The impact of greenery on residential mobility was analyzed using a repeated sales model and t-test analysis.

Results revealed that residential mobility was significantly higher in non-greened student housing facilities than their greened counterparts. The study further indicated that the presence of greenery had a substantial effect on residential mobility, attributed to residents' preferences for the ecological, social and economic benefits associated with greenery, rather than merely infrastructure considerations.

Enhancing the aesthetic appeal, economic viability, safety, security and health benefits of greened student housing facilities while managing the influence of greenery on infrastructure was found to affect residential mobility. The findings suggest that improving occupancy rates in these facilities through the incorporation of greenery could yield higher rental income and better cash flows for investors involved in student housing operations.

This study highlights the ecological, social and economic advantages of greenery for residents. While the benefits of greenery in residential contexts are increasingly recognized, the specific impact of greenery on residential mobility within the Sub-Saharan African context represents a novel contribution. The application of neighborhood effects theory to the examination of greenery benefits and residential mobility in this region adds a new dimension to existing research.