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In metropolitan areas experiencing rapid urbanization and housing production, the size of housing units emerges as a crucial factor to consider in housing policy formulation. This study aims to focus on Turkey, a developing country undergoing rapid urbanization and a construction boom in recent years, to examine households’ housing size preferences. Through a detailed analysis, this research delves into the causal relationships between income, education and housing size preferences.

This study uses the Family Structure Survey in Turkey 2016 data set collected nationwide by the Turkish Statistical Institute (TurkStat). To address potential endogeneity issues related to income and education levels in households’ choice of house size, an extended regression model is used. In addition, survey weights are applied to the statistical model to generalize the results of the study.

The study demonstrates that household income correlates with an increase in house size, while household education is associated with a decrease in house size. Variables such as household age, composition and vehicle ownership are found to impact the choice of house size. Particularly, one-person and couple-only households tend to prefer smaller dwellings compared to others. Lastly, the results reveal that the influence of household composition on dwelling size varies according to household age.

This study presents a comprehensive analysis of the determinants influencing households’ housing size preferences within the framework of a developing country context, focusing on Turkey. It specifically offers insights into the causal impact of education and income levels on housing size preferences, as well as the intricate interplay between household characteristics in shaping these preferences.

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.

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.

The primary aim of this research is to examine the effects of the Renovation, Conversion, and Extension (ROT) tax deduction for renovations on the scope and quality of renovations and its subsequent impact on house prices across various Swedish municipalities.

This study utilises a two-way fixed effect instrument variable (IV) spatial Manski approach, analysing balanced panel data from 2004 to 2020 at the municipal level (290 municipalities) in Sweden. The methodology is designed to assess the impact of the ROT subsidy on the housing market.

The study reveals that the ROT subsidy has significantly influenced house prices, with noticeable variations between municipalities. These differences are attributed to the varying amounts of tax reductions for renovations and the extent to which property owners utilise these subsidies.

The research is limited to the context of Sweden and may not be generalisable to other countries with different housing and subsidy policies. The findings are crucial for understanding the specific impacts of government subsidies on the housing market within this context.

For policymakers and stakeholders in the housing market, this study highlights the tangible effects of renovation subsidies on property values. It provides insights into how such financial incentives can shape the housing market dynamics.

The research underscores the role of government policies in potentially influencing equitable access to housing. It suggests that subsidies like ROT can have broader social implications, including the distribution of housing benefits among different income groups and regions.

This study contributes original insights into the field of applied real estate economics by quantitatively analysing the impact of a specific government subsidy on the housing market. It offers a unique perspective on how fiscal policies can affect property values and renovation activities at the municipal level in Sweden.

The present study aims to investigate the relationship between building regulations, urban planning, and perceptions of housing affordability and prices in Prishtina, Kosovo.

A self-report survey with 1,000 respondents, selected through stratified probability sampling, provided the necessary data. Principal component analysis was applied to the questionnaire's internal structure, while regression analysis helped uncover housing affordability and housing prices perception predictors.

The study found that building regulation standards and zoning/land-use regulations reveal positive relationships with housing prices and housing affordability perception. Among these components, building regulations and standards show a stronger connection with housing affordability and price perception in comparison to urban planning and development.

By investigating the relationship between building regulations, urban planning, and housing affordability and price perception in Prishtina, the present research makes a valuable contribution to the existing literature. The findings of this research hold significant implications for policymakers, urban planners, and developers, highlighting the relevance of adopting a well-balanced approach to building regulations and urban planning in order to uphold and maintain housing affordability and understand housing price dynamics.

The novelty of this research originates from the investigation of these relationships within a rapidly urbanizing city context, contributing to a deeper understanding of the complex dynamics between regulatory policies and outcomes in the housing market. Further research should examine additional dimensions and employ longitudinal designs to gain a deeper understanding of the components predicting housing affordability and price perception in Prishtina and similar urban contexts.

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.

This study aims to quantify the influence of urbanization on housing prices at the district-based level, while also investigating the heterogeneous impacts across different quantiles of housing prices.

The study uses remote-sensed spectral images from the Landsat 7 ETM+ satellite to measure urbanization, replacing prior reliance solely on urban population metrics. Subsequently, the two-step system generalized method of moments is used to evaluate how urbanization influences district-based housing prices through three spectrometries: Urban Index (UI), Normalized Difference Built-up Index (NDBI) and Built-Up Index (BUI). Finally, this study examines the heterogeneous impacts across various housing price quantiles through Dynamic Panel Quantile Regression with non-additive fixed effects under Markov Chain Monte Carlo simulation.

The study demonstrates that urbanization leads to an increase in regional housing prices. However, these impact magnitudes vary across housing price quantiles. Specifically, the impact exhibits an inverse V-shaped curve, with urbanization exerting a more pronounced influence on the 60th percentile of housing prices, while its effect on the 10th and 90th percentiles is comparatively weaker.

This study uses a novel method of remote sensing to measure urbanization and investigates its effects on housing prices. Furthermore, it provides an empirical application of non-additive fixed effect quantile regression for analyzing heterogeneity.

This research explores the role of concrete 3D printing (C3DP) in the development of culturally appropriate housing in Indigenous Reserves in Canada through the design, building and evaluation of the Star Lodge project located in the Siksika Nation of Alberta, Canada. The project aims to assess the potential of C3DP in addressing the escalating housing demands in Indigenous communities in Canada.

The research involved a collaborative and multidisciplinary approach, engaging Blackfoot Elders, Knowledge Keepers from the Siksika Nation, Siksika Housing and Nidus3D. Central to this was the design, build and documentation of the Star Lodge project to analyse the advantages and challenges, guided by weekly meetings and site visits.

The project harnessed C3DP to streamline construction, enhance durability, reduce maintenance costs and enhance the energy performance of the homes. Notable time savings were achieved compared to conventional construction methods. Challenges included developing strategies to overcome extreme cold weather conditions, achieving a consistent concrete mix and integrating conventional construction elements such as drywall construction in interiors. The project served as a platform for collaboration and community participation, shaping the design and construction process while raising awareness of innovative construction techniques in the community.

This study provides an evidence-based framework for the evaluation of C3DP technology by analysing the Star Lodge Project, the first C3DP project in Alberta and the largest of its kind in Canada. By addressing housing challenges in Indigenous communities, the research holds broader implications for sustainable development and Indigenous empowerment across Canada.

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.

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.