Purpose With the boom of shale oil and gas in the USA as the context, the quick expansion of pipeline networks has garnered public concern regarding the economic consequences of pipeline failures. Design/methodology/approach The survey examined 426 gas pipeline accidents in the USA from 2010 to 2020 and examined their impact on housing prices. Findings It was found that only a very serious accident on the ground pipeline (for example, an explosion, a fire and someone else was injured, which we called a “high exposure events”), reduce the price of a neighboring house, and other types of accidents did not cause any major changes in housing prices. Further analysis also showed that high exposure events would reduce housing prices within 1 billion meters of the pipeline by 8.2%, and this poor effect lasted an average of about 8 years. Originality/value In addition, we found that at the beginning of the business, the volume of the real estate transaction fell slightly, which shows that the power side of demand responds quickly to such things. Compared to the apparent impact of the pipeline accident, the impact of the pipeline installation on housing prices is not great, and statistically little meaning. The study also showed that different types of things are very different, so it is impossible to summarize all the situations with the results of the same thing.

Purpose This study aims to examine how the dynamics of housing prices and population aging jointly affect banks’ real estate industry loan risk in China. This study focuses on whether population aging affects the association between housing price changes and the credit risk of banks’ real estate industry loans. Design/methodology/approach This paper uses the panel data of 49 commercial banks in China from 2011 to 2020 covering about 69% of total financial assets. It adopts the individual fixed-effects model with bank-clustered standard errors. The study ensures the robustness of the conclusion by using the natural logarithm of housing prices, rebasing the housing price index to 2015, replacing the aging indicator and conducting heterogeneity analysis. Findings The empirical results show that housing price changes are negatively associated with banks’ real estate industry non-performing loan (NPL) ratios. However, population aging plays a significant positive moderating role between the two. The marginal effects of housing price changes are weak at lower aging levels, but become positive and statistically significant around the median and upper quartiles of the aging distribution. This mechanism is particularly prominent in the non-eastern region and non-state-owned banks. Originality/value This study not only reveals the aspect of population aging as a risk amplifier but also documents its dual role: population aging is associated with a lower baseline level of banks’ real estate industry NPL ratios, yet it strengthens the risk response to housing price changes at higher aging levels, providing key evidence for macro-prudential supervision and differentiated credit policies in an aging society.

After more than two decades of rapid growth, China's real estate market has gradually contracted in recent years amid economic weakness, with property prices beginning to fall sharply. Since new home sales in China primarily operate under a pre-sale system, where developers secure government land through auctions, mortgage that land to banks for development funding, and rely on bank capital alongside buyers' down payments and mortgages, the price decline not only adversely impacts banks' real estate loan risks but also leads to buyers defaulting due to insufficient collateral value, thereby amplifying systemic financial risks. Against this backdrop, our study examines the mediating role of educational attainment, measuring whether regional housing price fluctuations influence the non-performing loan ratio of commercial banks' real estate loans through educational levels. Using a bank-level panel matched to provincial socio-economic indicators, we estimate fixed effects models and implement a three-step mediation design with bootstrap inference. The evidence reveals a clear mechanism: changes in housing prices are associated with higher regional educational attainment; in turn, education is linked to greater credit expansion and higher non-performing loan ratios. Once this channel is taken into account, the direct link between housing prices and bank risk markedly attenuates, with the bulk of the overall effect operating indirectly through education. These patterns are economically meaningful, robust to alternative education proxies, and extensive resampling. The findings highlight a macroprudential paradox: while better-educated borrowers are typically safer at the micro level, improvements in human capital can, at scale, amplify risk-taking and balance-sheet exposure. Supervisors should therefore monitor human capital trends alongside collateral dynamics and incorporate them into early warning systems and the calibration of borrower and capital-based tools, so that bank risk assessments remain informative even in a housing market downturn.

The housing price index is one of the key economic indicators reflecting changes in housing prices in a country or region. This index is of critical importance, particularly for investors and financial institutions, in making short- and long-term investment decisions. Various macroeconomic variables affect the housing price index. Therefore, examining the effects of macroeconomic factors on the housing price index is of great importance. In this study, using data from the period 2020:4–2024:12, the effects of macroeconomic variables such as the consumer price index (CPI) and the industrial production index (IPI) on the TR3 region housing price index (HPI3) were analyzed using the ARDL bounds test method. According to the long-term analysis results, the CPI variable has a negative effect on HPI3 at a 10% significance level, and a 1% increase in CPI reduces HPI3 by 0.49%. The IPI was found to have a positive effect on the HPI3 at the 5% significance level, with a 1% increase in the IPI increasing the HPI3 by 7.98%. The error correction coefficient was calculated as -0.17, indicating that short-term imbalances will return to long-term equilibrium within approximately 6 months. In the short-term analysis, the current, one-period, and three-period lagged values of CPI were found to have positive and significant effects on HPI3. These findings reveal that inflation may have a positive effect on housing prices in the short term.

Abstract This article examines the implications of urban spatial models for estimating the long‐run own‐price elasticity of housing supply. It demonstrates theoretically that housing supply elasticity varies inversely with city size, the cost of structure inputs, and rural land price in both classical and neoclassical models. Notably, planning regulations and topographic features that reduce the share of land available for development do not affect supply elasticity, if they apply uniformly. The effect of transportation costs on supply elasticity is complex. In addition, empirical estimates of supply elasticity depend on the location within the city where housing price changes are measured. These relations can confound direct empirical estimates of housing supply elasticity, but are less problematic for estimates obtained from numerical simulation models or inferred from urban wage premiums.

Abstract Background Housing property is a major part of family assets, and its value changes affect financial conditions and mental health. In recent years, falling urban housing prices have become common, leaving many families facing asset shrinkage and financial pressure. Studies show that major property loss greatly increases depression risk, yet most research focuses on acute crises like unemployment or bankruptcy. Little work examines depressive emotions triggered by falling housing prices as a chronic stressor. From a psychological perspective, property loss influences self-worth, security, and expectations. Cognitive appraisal theory holds that subjective evaluation of stressors shapes emotional reactions. This study examines how falling housing prices affect depressive emotions among family financial managers and explores the roles of cognitive appraisal, coping styles, and social support. Methods The study uses a cross-sectional and longitudinal design. Participants are primary financial decision-makers from 800 families who purchased homes in a first-tier city from 2021 to 2023 and experienced at least a 15% price drop. Participants are 25–55 years old and are recruited through the housing transaction center and community committees. Baseline assessment uses a self-developed questionnaire that collects purchase time, price drop level, and loan pressure. The Beck Depression Inventory II (BDI-II) assesses depressive symptoms. A cognitive appraisal scale measures threat appraisal and controllability appraisal. A brief coping questionnaire measures positive and negative coping. A social support scale assesses objective support, subjective support, and support utilization. Confounders include income, marital status, and psychiatric history. Follow-up assessments occur at 6 and 12 months to track depressive symptom trajectories. Data analysis uses multiple regression, structural equation modeling, and mediation tests. Results Baseline results showed that 28.7% of financial managers experiencing falling housing prices had mild to moderate depressive symptoms (BDI-II ≥ 14), higher than the 12.3% in the general population (p<.001). Housing price decline correlated with depression scores (r = 0.42, p<.001). After controlling for age, gender, and income, housing price decline, mortgage-to-income ratio, and purchase time remained significant predictors (β = 0.35, 0.28, 0.19, p<.01). Mediation analysis showed that falling prices increased perceived financial threat (β = 0.51, p<.001), which raised depressive emotions. Positive coping reduced the impact (β = −0.23, p=.006), whereas negative coping intensified it (β = 0.31, p=.002). Longitudinal follow-up found that 56.3% of high-risk individuals developed clinically significant depression (BDI-II ≥ 20) at 12 months without intervention. Discussion The study confirmed that falling housing prices were a major risk factor for depressive emotions among family financial managers. Cognitive appraisal linked financial loss with emotional reactions. When individuals saw falling prices as an uncontrollable threat, they were more likely to feel helpless and depressed. These findings supported cognitive behavioral theory and pointed toward cognitive restructuring as a key intervention. Coping styles and social support moderated this process, with stronger psychological resources helping maintain better mental health under stress. The study offered a framework for identifying high-risk groups during housing price fluctuations and highlighted targets such as cognitive reframing, coping training, and social support enhancement. Future research should track long-term effects of housing price changes, design targeted interventions, examine demographic differences, and explore asymmetry between the impacts of rising versus falling prices.

As a pillar industry of the national economy, real estate price fluctuations affect residents' living quality, macroeconomic stability and urban development. Housing prices in Shanxi Province have also fluctuated in recent years, affecting people's living standards. However, there is no clear formula to reveal the essential reasons for changes in housing prices. To explore the law of housing price changes in Shanxi Province, this paper uses SPSS to build a multiple linear regression model, and analyzes the impact mechanism of various factors on housing prices from the perspective of supply and demand based on relevant data of Shanxi Province from 2013 to 2024. With regional GDP, per capita disposable income of urban residents, residential sales area, population, residential completion investment and residential completed area as independent variables, and the average sales price of commercial housing as the dependent variable, the study shows that residential commodity investment, regional GDP and residential commercial housing completion area have a great impact on housing prices in Shanxi Province, and the real estate industry is highly correlated with economic development and residents' income. Relevant suggestions are put forward to promote the healthy and steady development of Shanxi's real estate industry and keep housing prices in a reasonable range.

ABSTRACT The stability of housing prices is crucial to both macroeconomic stability and people's livelihoods. In order to explore the root causes of the decline in China's real estate prices and formulate targeted policies for stabilizing housing prices, this study adopts a configuration perspective and employs the fuzzy set Qualitative Comparative Analysis (fsQCA) method. Taking the housing price changes of 267 prefecture‐level cities in China from 2021 to 2023 as the research object, this paper identifies the key combined paths influencing the downward trend of housing prices. The research findings reveal four core configuration paths for housing price decline: cost relief type, cost reduction adaptation type, supply‐demand contraction type, and supply‐demand recession type. All four paths include a high decline rate of land prices, indicating that the decrease in land prices serves as a critical foundational condition for the decline in housing prices. The interaction between the supply and demand sides determines the market mechanism underlying the housing price decline: when demand is strong, the supply side proactively adjusts its structure and reduces costs by leveraging the decrease in land costs to achieve active price adjustment; when demand is weak, the supply side is forced into passive price discounts due to inventory backlogs and capital chain pressures. Financial support, functioning as a lubricant, amplifies elasticity on the demand side and mitigates risks on the supply side, yet it cannot reverse the fundamental trends of demand recession and supply clearance.

Urban residents' consumption serves as the core of the national economic cycle and a stabilizer for economic growth, while expanding domestic demand to promote consumption is a key driver of high-quality economic development. As the core wealth of urban households in China, housing value fluctuations profoundly influence consumption decisions. Against this backdrop, studying the impact of housing wealth changes on urban residents' consumption holds both theoretical and practical significance. Based on microdata and within the framework of the Life Cycle Hypothesis, this paper systematically explores the impact and mechanisms of housing wealth on consumption through empirical tests, robustness checks, heterogeneity analysis, and panel vector autoregression (PVAR) models. The results indicate that housing wealth appreciation significantly promotes total consumption and development-oriented consumption, with heterogeneous effects across groups, differential impacts from price fluctuations of different housing types, as well as asymmetric consumption effects of housing price changes. The wealth effect and credit constraint mitigation effect are identified as the core transmission mechanisms. Based on these findings, policy recommendations are proposed, including optimizing housing asset structures, reconstructing supply systems, and implementing regionally targeted regulation.

Purpose As global warming intensifies natural disasters, this study aims to investigate the socioeconomic impacts of extreme rainfall. Using the “720” rainstorm that struck Henan in 2021 as a quasi-natural experiment, this research seeks to address gaps in the existing analysis concerning developing regions and the underlying transmission mechanisms. Design/methodology/approach Using 36 months of housing price data from 287 Chinese cities, the author uses a Difference-in-Differences (DiD) design. Areas affected by extreme rainfall are designated as the treatment group, while unaffected areas serve as the control. The DiD design compares changes in housing prices before and after the event between these two groups. Findings A key finding is that extreme rainfall caused a 2.78% relative drop in the housing prices of affected areas, a result robust to various tests. Notably, the provincial capital Zhengzhou saw a smaller decline, reflecting its stronger market resilience. The negative impact peaked shortly after the disaster and then eased as confidence rebounded. This price depression was driven by a drop in demand, due to the twin channels of physical property damage and diminished buyer sentiment. Originality/value This study enriches developing economy research, fills literature gaps on extreme rainfall and housing markets and identifies transmission channels, offering empirical insights for climate risk mitigation in real estate.

This study aims to overcome the limitations of previous research by distinguishing between terminations due to death and those due to voluntary choice and by addressing the insufficient reflection of micro-level price changes in regional housing markets. We construct quarterly panel data at the subscriber level, utilizing the complete dataset of reverse mortgage subscribers for apartments up to the end of June 2025 and incorporating sales price indices at the district level. Furthermore, we aim to identify the pure effect of economic factors on voluntary termination by controlling for the possibility of attrition due to death by setting age-specific mortality probabilities as an offset in the model. Using a discrete-time hazard model, first, the analysis confirmed that both long-term cumulative housing price changes in enrollment and short-term price changes from the immediately preceding quarter were key drivers that increased the risk of voluntary termination. Second, the impact of housing price appreciation on termination was heterogeneous across age groups, showing a tendency to be most sensitive among the younger group and weaken with increasing age. This suggests the need for customized risk management that considers both regional and age-specific characteristics to ensure the sustainability of the system.

In recent years, with the significant impact of housing price changes on economic and social stability, scientifically predicting housing price trends can prompt local governments to formulate policies related to housing prices, and investors can make investments based on the corresponding housing prices. At the same time, homebuyers can also make housing purchase plans according to housing prices. Traditional methods for predicting housing prices are primarily based on experience or simple statistical models, which cannot reasonably consider complex factors. This paper investigates the efficacy of machine learning methodologies in the prediction of housing market valuations. Firstly, it discusses the characteristics and value of supervised, unsupervised, and decision tree algorithms in the machine learning field when applied to housing price prediction. Secondly, it explores the reasons affecting housing prices, such as economic characteristics like national income, regional features, and the influence of supply and demand. Then, it applies a public housing price dataset to establish a decision tree for predicting housing prices. Based on basic features, it expands the feature library. It seeks suitable feature combinations to analyze housing prices better and draw conclusions on the value of machine learning methods in housing price prediction. This article summarizes successful experiences from existing application cases and studies the problems and deficiencies in the application process.

Abstract Using a monthly panel data set of individuals' debt, we show that house price changes can explain a significant fraction of personal debt composition dynamics. We exploit the variation in local house price growth as shocks to homeowners' housing wealth to study the consequential adjustment of debt portfolio. We present direct evidence that homeowners reoptimize their debt structure by using parts of withdrawn home equity to pay down comparatively expensive nonmortgage debt during a housing boom. The effect is strongest for homeowners that have a high debt‐to‐income ratio and live in a municipality with a high literacy level. We find evidence that macroprudential policy and interest rates are important for consumer debt decisions.

In developing countries such as Türkiye, the housing sector plays a crucial role in shaping both the macroeconomic balance and the quality of life of individuals. Changes in house prices have a significant impact on household consumption and investment decisions. Therefore, understanding the dynamics that affect house prices is critical for shaping economic policies effectively. In this study, the factors affecting house prices in Türkiye are analyzed using econometric models based on the Fourier approach. The main objective of the study is to determine the effects of variables such as BIST Construction Sector Index, exchange rate and Construction Cost Index on house prices. In the analysis using the data set covering the period between December 2016 and September 2024 for Türkiye, the Fourier ADL (Autoregressive Distributed Lag) cointegration test, which is an effective tool to detect cointegration relationships in time series data and to examine possible break points, was applied. To determine the stationarity level of the variables used in the model, the Augmented Dickey-Fuller (ADF) test, one of the traditional unit root tests, and the Fourier ADF unit root test, which accounts for structural breaks, were applied. The results of both tests show that the variables are non-stationary at level values, but become stationary when first differences are taken. To examine the long-run relationship between the variables, Banerjee, Arcabic and Lee (2017) Fourier ADL cointegration test was applied and a significant cointegration relationship was found in the long run. Finally, the long-run cointegration coefficients are estimated using the FMOLS (Fully Modified Least Squares) method. The results show that house prices are significantly affected by macroeconomic factors such as construction sector indices and exchange rates. The Fourier approach offers a more flexible analysis compared to traditional cointegration models. The study provides important findings on the dynamics of the housing market in Türkiye and provides valuable information for policymakers and investors that can be taken into account when making strategic decisions.

This study investigates the impact of shopping malls on the price of residential housing in the neighborhood, focusing on how the distance of shopping malls to residential housing affects the price of housing. This study uses statistical regression analysis via STATA 17 data from six selected urban districts and finds that housing prices of neighborhoods with higher accessibility to shopping malls and convenience stores improve significantly. The findings reveal that proximity to shopping centers significantly increases housing values, with homes closer to these centers becoming more expensive, primarily due to increased accessibility and improved amenities. It was also established that the quality of school districts has a significant effect on House prices, especially in high-demand income areas. Subsequently, neighborhoods in top-ranked districts possess higher prices because of the value that families attribute to educational assets. Further studies should generalize the research area, use wider sets of control variables, and make use of long-run models to reflect the dynamic tendencies of housing price changes.

Fluctuations in real estate prices are closely linked to the macro-economy, exerting a profound influence on social investment and consumption levels. As a key source of funding for the real estate market, bank credit significantly affects housing price changes in major Chinese cities. This paper explores the transmission mechanisms and pathways of bank credit on real estate prices through theoretical analysis and empirical research. It constructs a panel regression model to empirically analyze the relationship between bank credit scale and housing prices in 35 large and medium-sized Chinese cities from 2012 to 2022, assess the impact of credit on housing price fluctuations, and compare differences between first-tier and second-tier cities. Based on these findings, the paper proposes suggestions for regulating housing prices by controlling credit scale, aiming to deepen the understanding of the relationship between bank credit and housing prices and support the stable development of China’s macro-economy and real estate market.

To address the issue of spatial mismatch of real estate resources at its source, we incorporate the multidimensional urbanization speeds into the real estate market stock-flow model, simulate the regional real estate resource allocation process from a dynamic perspective, and explore the impact of urbanization on housing prices, as well as the characteristics that a coordinated multidimensional urbanization should possess. Utilizing data on population flow, economic development, and the relative increment of newly built housing units that meet delivery standards from 2008 to 2022 in 35 large- and medium-sized cities in China. We employ the dynamic panel system GMM approach to estimate the direct effect of single-dimensional urbanization on housing prices, and utilize the threshold effect model to examine the comprehensive effect of multidimensional urbanization on housing prices. The findings reveal that population, economic, and spatial urbanization influence housing prices by altering the flow of real estate supply and demand, with their effects being significantly shaped by the scarcity of stock real estate resources. The dynamic coordination of multidimensional urbanization ρ has a significant threshold effect on housing price changes. When Vsu and Vpu reach the optimal match, the real estate market achieves dynamic equilibrium, and housing prices remain relatively stable. This not only underscores the significance of multidimensional urbanization as a driver of urban housing price variations but also provides valuable insights for cities on how to adjust the quantity of new residential construction (or land supply) during the dynamic urbanization process, thereby enhancing the spatial allocation rationality of real estate resources from the source.

Abstract This paper examines the revealed preferences for the composition of local public expenditures. It analyzes how the combined system of local governments in US cities may attract people to move to their jurisdiction, captured by change in population and housing prices. The paper examines the complete local public fiscal composition, considering the constrained choice set, in which each expenditure decision affects the rest of the fiscal composition. Furthermore, it makes an explicit distinction between capital outlay and current operations expenditures. The results show that change in population and housing prices are complementary in capturing revealed preferences. Although they both suggest a preference for lower spending/taxes, and allocating spending towards parking facilities and investments in highways, they also reveal different preferences for the allocation of the expenditures. Expenditures on higher education are associated with population growth, whereas expenditures on airports are associated with higher housing prices.

As global climate change intensifies, extreme weather events, sea level rise and persistent droughts have become major challenges to the economic development of countries. Climate change not only affects ecosystems, but also has far-reaching impacts on urban economies, real estate markets and asset values. Real estate, as a typical long-term asset, is highly sensitive to environmental changes, so it is of great academic value and practical significance to study how climate change affects the real estate market, especially the volatility of house prices in different regions. This thesis provides an overview of the mechanisms by which climate change affects real estate market prices, explores how different types of climate risks act on real estate markets globally, and analyses the impacts of regional differences, policy interventions and market psychology on house price changes. In addition, the paper points out the gaps and deficiencies in the current research and proposes directions for future research.

This study aims topresent a financial model for quantitatively estimating the value of default risk in jeonse contracts. The jeonse system, inwhich tenants pay a lump-sumdeposit to the landlord and receive it back in full at the end of the contract, has long been one of the primarymeans of housing stability. However, the expansion of housing price volatility, rapid interest rate hikes, and widening regional disparities in the jeonse-to-price ratio have elevated the default risk of jeonse deposits to a significant social issue. Previous studies have identified risk factors using probability-based approaches such as logistic regressionmodels, Merton’s default model, and Cox’s proportional hazardmodel. Nonetheless, these approaches have limitations in reflecting the economic structure of the contract or the option value under uncertainty in valuing the risk itself. To address these limitations, this study models the default process of jeonse deposits as the exercise of a European put option, combiningMerton’s(1974) default model with the binomial option pricingmodel proposed by Cox, Ross, and Rubinstein(1979). In this model, the housing price is set as the underlyingasset, the jeonsedeposit as the strikeprice, and factors suchas volatility, risk-free interest rate, and contract maturity are incorporated to estimate the default risk value. Subsequently, a sensitivity analysis is conducted to examine how changes in housing price, jeonse-to-price ratio, volatility, and interest rates affect the default risk. Based on the results, the study provides practical and policy implications applicable to the design of guarantee insurance premiums, risk-based guarantee limits, anddifferentiated regulationsby region.