Search Results (104)

Housing prices have recently risen sharply in many countries, primarily linked to the global credit cycle. Although various factors play a role, the ability of developing countries to navigate this cycle and maintain autonomous monetary policies is crucial. This paper introduces a dynamic macroeconomic model featuring a housing production sector within an imperfect banking framework. It captures key housing and economic dynamics in advanced and emerging economies. The analysis shows domestic liquidity policies, such as bank capital requirements, reserve ratios, and currency devaluation, can stabilize investment and production. However, their effectiveness depends on foreign interest rates and liquidity. Stabilizing housing prices and risk-free bonds is more effective in high-interest environments, while foreign liquidity shocks have asymmetric impacts. They can boost or lower the effectiveness of domestic policy, depending on the country’s level of financial development. These findings have several policy implications. For example, foreign capital controls would be adequate in the short term but not in the long term. Instead, governments would try to promote the development of local financial markets. Controlling debt should be a target for macroprudential policy as well as promoting saving instruments other than real estate, especially during low interest rates. Full article

This paper investigates the dynamics of intra-urban real estate cycles by examining the segmentation of real estate markets and their spatial heterogeneity. Despite extensive literature on real estate cycles, insights into intra-urban cycles remain scarce. Utilizing a dataset of over 350,000 apartment sales from 2007 to 2022, first we apply the SKATER (Spatial K’luster Analysis by Edge Tree Removal) algorithm to delineate the city into six distinct clusters, each containing at least 3000 observations, and then analyze the six generated time series of real estate prices. Our findings confirm the hypothesis of market segmentation, revealing significant cyclical differences among the identified submarkets. Analysis indicates that real estate cycles are not uniform across the city. This approach contributes a novel perspective to the existing literature on real estate cycles, emphasizing the need to consider spatial endogenous regimes. Full article

This study investigated the intangible value transferred from urban megaprojects to surrounding residential property markets, focusing on Brisbane’s transformative urban regeneration projects currently in the development pipeline. The research objectives were twofold: first, to empirically investigate the dynamics of property markets influenced by urban megaprojects and second, to assess the impact of a specific case study on these markets through a longitudinal analysis of residential sales data. Drawing from environmental economics, the concept of willingness to pay (WTP) is used to quantify externalities associated with urban megaprojects. The research constructs a comprehensive dataset integrating geospatial and property-specific data. Through revealed preference methods, the intangible value transferred from mixed-use developments is identified and quantified via residential transaction prices. Utilising hedonic price modelling, this study systematically analysed residential transaction data to estimate implicit prices associated with spatial proximity to megaprojects. A comprehensive dataset integrating property-specific attributes, geospatial proximity measures, and temporal dynamics of project development phases underpins this analysis. This research and its findings advance the existing literature in several important dimensions. That is, this research represents the first microeconomic assessment of the property market’s impacts resulting from mixed-use megaprojects in Brisbane, offering novel empirical insights for both academic and practical applications, how urban megaprojects shape residential property values, and informing stakeholders involved in urban planning, policymaking, and real estate investment decisions. Practitioners and policymakers can leverage these insights to inform policy frameworks and strategic decisions. At the governmental level, the results offer applicable insights for urban revitalisation strategies, particularly relevant to central business districts undergoing similar developments. Private sector stakeholders can utilise these outcomes to anticipate market adjustments, managing supply and demand fluctuations more effectively. Full article

This paper employs a Time-Varying Parameter Vector Autoregression Directional–Spillover (TVP-VAR-DY) model to investigate the dynamic spillover effects among China’s energy, real estate, and stock markets from 2013 to 2023, with a focus on the impact of extreme events. The findings show that the total conditional spillover index (TCI) typically remains below 40% in the absence of extreme events, but significantly increases during such events, reaching 51.09% during the 2015 stock market crisis and nearing 60% during the COVID-19 pandemic in 2020. Specifically, the oil and gas market exhibited a net spillover index of 4.61%, emerging as a major source of risk transmission. In contrast, the real estate market, which had a net spillover index of −9.38%, became a net risk absorber. The net spillover index indicates that the risk transmission role of different markets towards other markets is dynamically changing over time and is closely related to significant global or domestic economic events. These results indicate that extreme events not only directly impact specific markets but also rapidly propagate risks through complex inter-market linkages, exacerbating systemic risks. Therefore, it is recommended to enhance market monitoring, improve transparency, and optimize risk management strategies to cope with uncertainties in the global economy and financial markets. Full article

With regard to the Italian context, the present research aims to empirically assess whether and to what extent real estate market dynamics (prices and vibrancy levels) are influenced by the life quality in a specific reference area. In particular, the study compares parameters related to the residential real estate market—such as the Real Estate Market Observatory quotations and the real estate market intensity index (used as a proxy for market dynamism)—with the Life Quality index developed by the study center of the Italian newspaper “Il Sole 24 Ore” for the selected provincial capitals. Furthermore, by breaking down the Life Quality index into the individual indicators used for its elaboration, the research identifies those most closely linked to real estate market mechanisms to explore these relationships within each context. This approach allows for the identification of potential local differences, providing insights into the degree of geographical heterogeneity. Finally, a GIS-based analysis is employed to graphically represent the various indicators, capturing the potential spatial correlations related to phenomena where the geographic component plays a significant role. Full article

This study examines housing affordability trends in Central Europe, focusing on the Czech Republic, Slovakia, Austria, and Poland, in the wake of recent global disruptions including the COVID-19 pandemic, the 2021–2022 energy crisis, and the war in Ukraine. These events have intensified housing affordability challenges by driving up property prices, rental costs, and energy expenses. Using data from December 2022 to March 2023, the paper analyzes wage levels relative to housing costs in major cities—Prague, Brno, Bratislava, Vienna, Graz, Warsaw, and Kraków—through price-to-income and rent-to-income ratios. The findings reveal that affordability is most strained in Czech cities, particularly Prague, where property prices outpace wages, while Vienna demonstrates better affordability due to higher average incomes. The study integrates real estate platform data with official statistics and employs spatial mapping and exploratory econometric testing to identify affordability patterns and disparities. It concludes that affordability outcomes are shaped by wage dynamics, housing supply constraints, migration pressures, and policy responses. The study underscores the importance of targeted housing policies and wage interventions to address these challenges and highlights the need for cross-country policy learning and regional coordination to improve housing affordability and market resilience across Central Europe. Full article

This study investigates the time-varying causal relationship between China’s economic policy uncertainty (EPU) and housing prices (HP) at the macroeconomic level. Using sub-sample rolling-window techniques on monthly nationwide data spanning January 2000 to January 2025, we systematically analyze the dynamic interactions and structural shifts between these variables. It finds that EPU has both positive and negative impacts on HP, which are consistent with the general equilibrium model (GEM). Additionally, the study identifies a feedback effect of HP on EPU. The findings offer objective evidence and recommendations for the Chinese government to pay close attention to the intricate dynamic interactions between EPU and HP. Furthermore, the study provides insights into real estate market reactions in a developing country, which can be valuable for other market participants. Full article

While the influence of digital technologies on firms’ innovation performance has been examined in the digital transformation literature, the mechanism by which digital transformation affects innovation quality has remained largely unexplored. By analyzing a longitudinal sample of 17,216 China’s A-share listed companies from 2009 to 2021 (excluding real estate and financial firms), we employed a fixed-effects regression model to investigate the impact of digital transformation on strategic risk-taking behavior. The findings indicate that digital transformation significantly enhances innovation quality. Market competition enhances the positive effect of digital transformation on innovation quality. Further analysis reveals that digital transformation has a positive impact on dynamic capability, which in turn mediates the relationship between digital transformation and innovation quality. Furthermore, digital transformation breaks down the barriers to innovation quality by reducing financing costs and financing constraints. These findings have implications for firms’ digital strategy in emerging economies. Full article

The transition to energy-efficient buildings is a priority of the European EPBD (Energy Performance Building Directive) and requires deep retrofits to reduce consumption and emissions. However, their financial viability remains underexplored. This research assesses the financial feasibility of deep retrofit interventions through innovative business models, focusing on the Managed Energy Services Agreement (MESA), which is considered the most effective for residential buildings. Additionally, we integrate off-site production from the Energiesprong model, which optimizes costs and time through long-term contracts and industrialized retrofit technologies. The analysis targets two investment profiles—owner/tenant and developer/entrepreneur—in Italian urban contexts with different market dynamics. A static analysis evaluates retrofits based on existing costs and technologies, while a dynamic analysis considers future profitability improvements because of cost reductions enabled by off-site production. The results indicate that, under current conditions, residential retrofitting is not financially sustainable without public subsidies. However, cost reductions driven by off-site technologies improve profitability, making large-scale retrofits feasible. Moreover, real estate market characteristics affect financial sustainability: in smaller cities, deeper cost reductions are necessary for retrofit interventions to become viable. Full article

This paper constructs a theoretical framework integrating health risk transmission, remote work constraints, and spatial equilibrium to analyze the impact mechanisms of major public health events on the real estate market. This study finds that pandemics affect market equilibrium through multiple channels, including changes in residents’ utility functions, the reshaping of labor market structures, and adjustments in location choices. The model combines the SIR model from epidemiology with spatial economics to depict the endogenous decision mechanism of health risks. By constructing a two-sector general equilibrium model that includes remote work sectors, this study reveals the impact of technological change on the spatial structure of the real estate market. Based on the mobility preference theory, an asset pricing framework incorporating health risk premiums is established. Comparative static analysis shows that the health risk transmission coefficient influences housing prices through two channels: directly lowering willingness to pay and indirectly affecting the distribution of population density. Dynamic analysis indicates that, under specific parameter conditions, the market exhibits asymptotic stability. Policy simulation results show that the transmission effects of monetary and fiscal policies exhibit significant spatial heterogeneity, requiring policymakers to pay more attention to regional differences. This study not only enriches the analytical tools of real estate economics but also provides theoretical support for relevant policy formulation. Full article

As a pillar industry of China’s economy, the real estate sector has been challenged by the increasing prevalence of housing vacancies, which negatively impacts market stability. Traditional vacancy rate estimation methods, relying on labor-intensive surveys and lacking official statistical support, are limited in accuracy and scalability. To address these challenges, this study proposes a novel framework for assessing residential community-level housing vacancy rates through the integration of multi-source data. Its core is based on night-time lighting data, supplemented by other multi-source big data, for housing vacancy rate (HVR) estimation and practical validation. In the case study of Longquanyi District in Chengdu City, the main conclusions are as follows: (1) with low data resolution, the model estimates a root mean square error (RMSE) of 0.14, which is highly accurate; (2) the average housing vacancy rate (HVR) of houses in Longquanyi District’s residential community is 46%; (3) the HVR rises progressively with the increase in the distance from the city center; (4) the correlation between the HVR of Longquanyi District and the house prices of the area is not obvious; (5) the correlation between the HVR of Longquanyi District and the time of completion of the communities in the region is not obvious, but the newly built communities have extremely high HVR. Compared to the existing literature, this study innovatively leverages multi-source big data to provide a scalable and accurate solution for HVR estimation. The framework enhances understanding of urban real estate dynamics and supports sustainable city development. Full article

This research delves into the causal relationship between government expenditure (GE) and housing price (HP) in China, aiming to validate the dynamic equilibrium model. Given the potential for structural shifts, an analysis of the long-term nexus using the full-sample data casts doubt on the reliability of conventional causality tests. To address this, a time-varying rolling-window methodology is utilized to reassess the dynamic causal interplay between the two variables. The findings subsequently underscore a mutual causal association between HP and GE, indicating that changes in one variable can influence and be influenced by changes in the other. Specifically, HP fluctuations positively and negatively impact GE throughout various sub-periods. Conversely, GE has a beneficial influence on HP, aligning with the dynamic equilibrium model. To maintain a relatively consistent HP level, particularly during structural economic transformations, it is crucial to reinforce preventive controls associated with GE and ensure a rational and stable flow of GE into the real estate market. This strategy contributes to obtaining a more accurate understanding of the real estate market’s condition and development, thereby providing a basis for governmental policy-making and adjustments to preserve market stability and promote healthy growth. Full article

The dynamic fluctuations in the real estate market significantly impact the development of the national economy. Investigating the spatiotemporal characteristics of housing prices can assist the government in formulating rational regulatory policies. Taking Zhengzhou City as the research subject, this study analyzed the spatiotemporal characteristics of housing prices based on housing price data and POI (Point of Interest) data from January 2022 to March 2024, utilizing a spatial scale of 500 m × 500 m grids. A hedonic price model and a geographically weighted regression (GWR) model were constructed to examine the mechanisms of 12 influencing factors on housing prices. The results indicate that housing prices in the eastern part of Zhengzhou are higher than those in the west, with an overall declining trend observed in Zhengzhou’s housing prices. Among the influencing factors, the age of the house exerts the greatest impact on housing prices, while finance has the least influence. The GWR model demonstrates superior fitting performance compared to the hedonic price model. The mechanisms of the influencing factors exhibit spatial heterogeneity. This study provides valuable insights for relevant government departments in Zhengzhou City, contributing to the optimization of urban planning and the regulation of the real estate market. Full article

With the new phase of urbanization in China, enhancing urban spatial quality has become a key task in urban development. As an important indicator of residents’ willingness to live, housing prices provide valuable feedback from their perspective for improving spatial quality. Taking Wuhan as a case study, this paper constructs an indicator system with 12 explanatory variables, including a subjective evaluation of buildings generated using deep learning techniques. Using OLS and GWR models, the study analyzes the factors influencing housing prices and their spatiotemporal dynamics in Wuhan’s core urban areas from 2016 to 2024, encompassing the full cycle of housing price fluctuations from an upward to a downward trend. The findings reveal that, as housing prices return to more rational levels, the impact of location factors diminishes, while the influence of community quality factors—such as property fees, green space ratio, and building quality—significantly increases. Factors such as proximity to hospitals also exhibit a certain degree of spatiotemporal complexity. This trend highlights residents’ growing attention to housing quality and living environments, marking a fundamental shift in the behavior of homebuyers. The results of this study provide crucial insights into the evolution of residential preferences and the spatiotemporal dynamics of the housing market. They offer significant theoretical and practical references for understanding residents’ housing needs from their perspective, thereby promoting the healthy development of the real estate market and improving urban spatial quality. Full article

In the realm of real estate, each property owns a unique set of characteristics that distinguish it from others. While each property has its own distinctive features, the appraisal process prioritises only those qualities that meaningfully affect the value in the given market context. However, in the dynamically evolving market situation, expectations of real estate buyers can also transform. This study aims to explore how the surrounding environment and micro-location aspects affect the property value, which can deliver valuable outcomes for real estate market participants and researchers. For that purpose, the authors selected nine factors, called non-market attributes, that may affect the estimated value: air quality, noise emissions, green areas, rivers and water reservoirs, kindergartens and primary schools, universities, medical facilities, shopping centres and religious buildings. Moreover, apart from non-market attributes, the authors selected six market attributes usually used for the determination of residential real estate values according to the Polish regulations in this field. The detailed analysis of factors influencing the property value has been conducted based on the residential apartments in the district Zwięczyca in Rzeszów. Specifically, with the use of Pearson’s total correlation coefficients, authors explored market and non-market attributes and examined their relationships with unit transaction prices, attempting to answer the research question on whether non-market attributes can differentiate market values of residential apartments, when local real estate markets are considered. The results demonstrate that all selected market factors have a visible effect on analysed real estate prices and might be adopted for appraisal. Among nine non-market factors, only three of them have a pronounced effect on prices and might be used for the valuation of residential properties on the local market. The combined database of market and non-market factors reveals eight attributes (five market and three non-market) affecting prices of residential apartments. Full article