Agglomeration effects and housing market dynamics

Purpose This paper aims to investigate the dynamic relationship between economic policy uncertainty (EPU) and housing prices (HPI) in Germany. It focuses on identifying episodes in which uncertainty shocks shape housing market dynamics rather than reacting to them. The analysis pays particular attention to events such as Brexit, the COVID pandemic and recent shocks to energy prices and interest rates. Design/methodology/approach Using monthly German data from 2014:M01 to 2025:M06, the study estimates a vector autoregression for housing prices, EPU and the bank lending rate. Parameter stability tests afterwards assess whether these relationships can be treated as constant over time. As the tests indicate clear time variation, a bootstrap rolling window Granger causality approach is used to trace how the direction of influence between EPU and HPI changes around major shocks. Findings The results reveal a nuanced, context-dependent relationship between EPU and the housing market. In the full sample, Granger tests indicate that movements in house prices precede changes in EPU. Rapid housing price increases appear to have spillover effects, leading to policy debates rather than EPU guiding housing market developments over time. Research limitations/implications The analysis is limited to Germany, which restricts the generalizability of the findings to other countries with different housing market dynamics. Additionally, the study uses aggregate national-level data, which may obscure regional differences in housing market responses to EPU. Future research could explore regional heterogeneity and expand the analysis to multiple countries or use disaggregated data to understand local housing market behaviors more thoroughly. Practical implications Policymakers can use the findings to better understand the cyclical impacts of EPU on housing markets. By focusing on specific episodes where policy uncertainty interacts with house prices, the research highlights the need for clear and stable housing regulations, particularly during times of significant uncertainty. The results suggest that predictable housing market policies could help mitigate housing price volatility and reduce uncertainty. Social implications The study contributes to understanding how EPU influences social stability, particularly in housing affordability. As HPI are a major social concern, clear and stable policies could reduce public uncertainty, particularly in times of economic and political stress. This would help in ensuring that housing markets are more resilient to external shocks, benefiting both homeowners and renters in Germany. Originality/value The article brings together parameter stability tests and bootstrap rolling-window Granger causality, focusing on an event-based relationship between EPU and house prices in Germany. This research actively supports policymakers and market participants to evaluate episodes of uncertainty that either dampen housing activity or solely reflect worries about affordability.

ABSTRACT:Community development corporations (CDCs) and other large nonprofit housing organizations are major producers of housing for low-income households. One of the least recognized and examined set of challenges facing these groups relates to how the workings of the private housing market—both weak and strong market conditions—impact their ability to carry out their mission. After presenting examples of how housing market conditions create challenges for nonprofits, the article explores how organizational strategic planning can help the nonprofit to more explicitly focus on housing market dynamics and plan its activities taking into account these concerns. An analysis of the two major training programs for nonprofit housing organizations that were available during 2007 revealed that out of the 216 different courses offered by NeighborWorks America and the Local Initiatives Support Corporation, only 8% appear to relate closely to housing market dynamics and land-use issues and 3% relate to strategic planning. Initiatives that would assist nonprofits to both recognize the importance of the private housing market and to assist them in developing strategies for confronting market-based challenges are presented.

Brownfield redevelopment projects are often perceived as more risky than greenfield investment, and financing opportunities may be more limited and expensive. Different financial support projects have been developed to support regeneration projects, and empirical evidence has shown that all buildings near the intervention area may benefit from an increase in prices once the brownfield project is complete. The article considers the Connecticut market and evaluates the characteristics of the brownfield projects that had access to a financial support program (loan or grant), the impact of the regeneration process on the liquidity of the housing market, and the gap between the price and the appraisal value of the residential unit. Target areas for this type of financing program are mainly characterized by low income, a high density of population, a high incidence of homeowners, and a high crime rate. Once completed, the brownfield requalification has an impact on the housing market because the brownfield recovery reduces the number of house sales due to the increase in the average price in the surrounding area and makes the selling price more consistent with the appraisal valuation. The empirical evidence provided may be useful for public institutions that are suffering from budget constraints and have to prioritize areas for financial support solutions.

The state of development of rental markets as a genuine alternative to home-ownership stands out as a particularly relevant institutional factor shaping the outcome of the housing market and playing a balancing role and alleviating house price pressures. This is especially the case when it proves to be an affordable platform for young and low-income households, providing them with a viable alternative to a hasty first step into the property ladder. In order to help policymakers develop a sizeable private rental market acting as an attenuating factor of housing prices volatility, it is important to depict the relevant dimensions of the rental market regulation and assess their likely impact on the aggregate housing market. Against this background, this paper first develops a two-dimensional indicator on rental market regulation, covering for rent controls and the tenant-landlord relationship. The resulting indices are put to the test by assessing their impact on housing prices. According to this analysis, an efficient, fair and swift judicial system appears as a necessary step towards unlocking rental markets full potential. Moreover, rent controls appear to have a significant destabilizing impact on the aggregate housing market, increasing the volatility of house prices when confronted with different shocks. Finally, qualitative aspects of the tenancy contract negotiation do not have a first-hand impact on housing market dynamics.

This study examines how housing prices relate to cognitive functioning among older Chinese adults. Specifically, it (1) distinguishes relationships by homeownership status, (2) compares associations across different cognitive domains, and (3) evaluates heterogeneity by sociodemographic status to reveal how housing market dynamics may shape cognitive inequality in later life. Drawing on longitudinal data from the China Health and Retirement Longitudinal Study (2011-2018) linked to city-level housing price data, we used individual and time fixed-effects models to assess relationships. We performed mediation analyses to explore underlying mechanisms and conducted heterogeneous analyses to identify differential associations across sociodemographic groups. We find that positive housing price growth significantly enhances cognitive functioning among older homeowners, particularly improving mental intactness, whereas non-homeowners show no significant cognitive benefit. The positive relationship between housing price growth and cognitive functioning is notably stronger among socioeconomically disadvantaged groups. Exploratory mediation analyses suggest that the positive association between city-level housing price growth and cognition among homeowners may be partly explained by improved financial security, reduced depressive symptoms, and greater engagement in leisure activities. China's near-universal homeownership, rapid aging, and volatile housing markets offer early insight into how asset inequality shapes cognitive functioning. This study shows that housing wealth functions as a key social determinant of cognitive health in later life. Policies promoting affordable housing, stronger safety nets, and community-based care can help reduce cognitive disparities and support more equitable aging.

Urban regeneration efforts often face significant challenges due to the presence of brownfield sites, which pose financial, administrative, and policy issues. These sites can lead to negative environmental and social impacts, reducing the appeal and value of nearby properties. Due to the limited market evidence in Malaysia, this study investigates the impact of brownfield sites on house prices in Johor Bahru using a Multiple Regression Analysis (MRA) model. The research identifies key factors influencing house prices, including proximity to brownfield sites, and examines 691 housing observations over ten years. Results show no significant impact of an abandoned apartment building on house prices, suggesting that distance from certain brownfield sites may not deter buyers. These findings are valuable for property market stakeholders and urban planners, offering insights for future urban regeneration projects. Future research could explore other types of brownfield sites and employ more precise distance measures to understand their impact on the housing market further, supporting urban regeneration goals.

This paper presents a dynamic theory of housing market fluctuations. It develops a life-cycle model where households are heterogeneous with respect to income and preferences, and mortgage lending is restricted by a down-payment requirement. The market interaction of young credit-constrained households with older or richer unconstrained households generates the following results. (1) Current income of young credit-constrained households affects housing prices independently of aggregate income. (2) Housing prices and the number of housing transactions are positively correlated. (3) Housing prices over-react to income shocks. (4) A relaxation of the down-payment constraint triggers a boom-bust cycle. These results are consistent with patterns observed in the US and the UK.

While there has been considerable commentary and analysis on the origins and aftermath of the Asian Financial Crisis there have been few studies which focus on the housing markets of the countries most directly affected. This is surprising given the importance of housing investment in many Asian economies and that over-inflated real estate was deeply implicated in the crisis. This paper explores the particular dynamics of the Hong Kong housing market and its institutional structure during the Asian Financial Crisis. The paper focuses specifically on the relationship between the development structure and the shaping of the policy process and on the interconnections between the housing market and the wider economy. It explains the apparent resilience of the giant developers during the crisis. More generally, the paper emphasises the importance of endogenous institutional dynamics in mediating the impact of the crisis in Hong Kong

Purpose Traditional forecasting methods often struggle to capture the influence of a restaurant’s location and surrounding businesses on its demand. This study aims to address this gap by proposing a novel deep learning model for inter-restaurant spatial competition and intra-cluster agglomeration effects among restaurant groups to enhance restaurant demand forecasting accuracy. Design/methodology/approach A novel graph neural network architecture combines graph attention networks, which capture restaurant competition effects, and long short-term memory networks, which model temporal dynamics, to forecast daily demand. Findings Experimental evaluation using real-world restaurant data shows the benefits of modeling competition and agglomeration, outperforming baseline non-spatial models. Furthermore, the spatiotemporal forecasting model incorporating agglomeration effects demonstrates enhanced predictive performance relative to the spatiotemporal benchmark lacking cluster-induced externalities. Originality/value This study proposes constructing a restaurant-cluster graph based on distance ranges to capture the spatial agglomeration effect. A graph neural network equipped with an attention mechanism enables differentiating the influence of restaurants located at different distances on the target restaurant’s demand.

The latest global financial tsunami and its follow-up global economic recession has uncovered the crucial impact of housing markets on financial and economic systems. The Chinese stock market experienced a marked fall during the global financial tsunami and China’s economy has also slowed down by about 2%–3% when measured in GDP. Nevertheless, the housing markets in diverse Chinese cities seemed to continue the almost nonstop mania for more than 10 years. However, the structure and dynamics of the Chinese housing market are less studied. Here, we perform an extensive study of the Chinese housing market by analyzing 10 representative key cities based on both linear and nonlinear econophysical and econometric methods. We identify a common collective driving force which accounts for 96.5% of the house price growth, indicating very high systemic risk in the Chinese housing market. The 10 key cities can be categorized into clubs and the house prices of the cities in the same club exhibit an evident convergence. These findings from different methods are basically consistent with each other. The identified city clubs are also consistent with the conventional classification of city tiers. The house prices of the first-tier cities grow the fastest and those of the third- and fourth-tier cities rise the slowest, which illustrates the possible presence of a ripple effect in the diffusion of house prices among different cities.

This study introduces an integrative approach to forecasting urban morphogenesis and housing allocation dynamics in rapidly developing cities. Doha metropolitan is examined as the case study, with projections extending to the year 2035. Based on a robust theoretical framework, the study considers three factors: (i) residential land capacity and regulatory policies, (ii) housing supply and demand, and (iii) housing preferences. An integrative methodological framework was adopted, including content analysis of housing regulatory frameworks, a survey of housing preferences, and Machine Learning (ML). A Convolutional Long Short-Term Memory (ConvLSTM) network was utilized to simulate parametric urban morphogenesis. The integrated model was then employed to project housing allocation patterns for the year 2035. Spatial stratification in ArcGIS-Pro revises the predicted parametric urban morphogenesis, focusing on the residential growth within regulated growth boundary. The findings show that housing demand and supply patterns in Doha metropolitan are shaped by regulatory policies and socioeconomic factors. Predicted growth is concentrated in suburban areas, dominated by low-density villas, while the waterfront will require inclusionary zoning to balance housing needs. The study offers policymakers, urban planners, and developers a robust tool for regulatory adaptation that aligns urban morphology with housing market dynamics. By providing a nuanced prediction, the model supports sustainable urban growth by balancing housing needs for a diverse population and addressing supply and demand imbalances. This study provides a valuable reference for cities facing similar growth challenges worldwide.

In this article, we analyze the impact of high‐tech economic growth on the incidence of critical housing problems both among all households and among moderate‐income working families in major metropolitan areas. We rely on 1999 survey data, supplemented by other more recent data on housing market dynamics and building code enforcement. Overall, we found that the level of high‐tech activity affects, positively and significantly, the incidence of critical housing problems for all households and especially for moderate‐income working households, regardless of tenure. Consistent with anecdotal information about the problems of working families, we found a stronger impact on moderate‐income working households than on all households. We conclude that housing policy should be broadened to address the problems of working families as well as those of the poor, especially when the problems arise from rapid economic growth.

By developing a socio-anthropological approach to intra-urban residential (im)mobility, this paper examines the contribution of ethnographic methods to understanding of the multifaceted and intertwined motivations and practices shaping migrants’ integration and neighborhood choices. Drawing on evidence from Filipino migrants in Italy’s two main metropolitan areas, their experiences of intra-urban residential movement are analyzed in relation to other process of mobility and immobility occurring through national and transnational networks over the life course. Findings reveal that in Rome, propensities toward (im)mobility are primarily linked to domestic work, religious affiliation, family and friendship networks, and changes in household management, whereas in Milan, transport accessibility, housing market dynamics, kinship ties, and personal aspirations play a more central role. The study argues that a socio-anthropological lens challenges conventional quantitative approaches to intra-urban residential mobility, by integrating an ethnographic perspective that offers multiple levels of analysis. This perspective provides a more holistic understanding of the dynamics and challenges related to movement, relocation, and integration of migrants and their family groups within urban contexts of living.

Mortgage credit has been central in fuelling housing market dynamics in many countries. While mortgage debt and house price increases used to go hand in hand, we see an uncoupling post-global financial crisis, with relative mortgage debts decreasing while house prices are increasing at unprecedented rates. This article scrutinises this uncoupling, contributing in three ways. First, we argue it is representative of how debt-driven housing dynamics have been supplemented by wealth-driven ones: while mortgage debt remains crucial, it has stagnated, and increased wealth investments have fuelled a new round of price increases. Second, we explore key examples of wealth-driven housing dynamics: the increase in private landlordism (buy-to-let), older homeowners reinvesting accumulated housing wealth and intergenerational house purchases. Third, we demonstrate that these dynamics are associated with increasing fractures along the lines of class, age and place, particularly benefiting affluent and older insiders while advancing uneven development. We base our arguments on an empirical analysis of the Netherlands, a country with a highly financialised owner-occupied market boasting one of the highest mortgage-debt-to-GDP ratios in the world, but our arguments have wider relevance. We conclude that current dynamics point to a new era of house price rentierism.

Flood insurance reforms, housing market dynamics, and adaptation to climate risks