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Purpose The purpose of this study is to make property price forecasts for the Chinese housing market that has grown rapidly in the last 10 years, which is an important concern for both government and investors. Design/methodology/approach This study examines Gaussian process regressions with different kernels and basis functions for monthly pre-owned housing price index estimates for ten major Chinese cities from March 2012 to May 2020. The authors do this by using Bayesian optimizations and cross-validation. Findings The ten price indices from June 2019 to May 2020 are accurately predicted out-of-sample by the established models, which have relative root mean square errors ranging from 0.0458% to 0.3035% and correlation coefficients ranging from 93.9160% to 99.9653%. Originality/value The results might be applied separately or in conjunction with other forecasts to develop hypotheses regarding the patterns in the pre-owned residential real estate price index and conduct further policy research.

Housing price comovements are an important issue in economics. This study focuses on monthly housing prices of 99 major cities in China for the years 2010–2019 by using correlation-based hierarchical analysis and synchronisation analysis, through which one could determine interactions and interdependence among the prices, heterogeneous patterns in price synchronisations and their changing paths over time. Empirical results show that the degree of comovements is slightly lower after March 2017 but no persistent drop is found. Several groups of cities are identified, each of which has its members showing relatively strong but volatile price synchronisations. Certain cities show potential of serving as price leaders within a group. Results here could be useful to policy analysis regarding housing price comovements.

This study embarks on an exploration of the intricate relationship and housing prices within the dynamic urban landscape of Santiago, from 2008 to 2019. Amidst an escalating housing affordability crisis prevalence of informal settlements, this research seeks to elucidate tors contributing to housing price dynamics, with a particular prices. Utilizing a robust dataset encompassing over 556,400 compiled from the Santiago Real Estate Registrar, this investigation multi-criteria evaluation methodology, incorporating Granger the complex interplay between various economic indicators. At the sis lies the innovative application of weekly data transformations Dicky-Fuller test to ensure the stationarity of variables, thereby for the Granger causality assessment. The study’s findings scape where, contrary to prevailing assumptions, land prices do significant impact on housing prices. Instead, the influence of land fordability is intricately linked to other pivotal factors, inflation, and market indices such as the Santiago Stock Exchange not only challenges conventional wisdom regarding the primacy of ing market dynamics but also offers valuable insights into the estate economics in Santiago. By unraveling the limited causality ing prices, this study contributes a critical perspective to the development and housing policy in Chile. It underscores the to adopt a more holistic approach, considering a broader spectrum in addressing the housing affordability crisis and fostering Highlights for public administration, management and planning:

While wealth inequality is increasing worldwide, social interaction quality among communities is simultaneously decreasing. Although inequality is primarily correlated with the income gap, inequality is also significantly associated with the housing price gap, especially in South Korea. This study explores the correlation between housing price inequality and social interaction levels in Seoul. For this analysis, the housing price Gini coefficient was utilized through the housing transaction price, and social interaction was measured using the Korea Housing Survey. The results of this study indicate that the social interaction level was low in regions with large housing price inequality. Moreover, the social interaction level was low in regions where housing price inequality increased for 10 years. Furthermore, the negative correlation between housing price inequality and social interaction was significant only in the lower-asset class. The fact that inequality negatively influences social interactions only in the low-asset class is another aspect of inequality.

Background Rising housing prices are becoming a top public health priority and are an emerging concern for policy makers and community leaders. This report reviews and synthesizes evidence examining the association between changes in housing price and health outcomes. Methods We conducted a systematic literature review by searching the SCOPUS and PubMed databases for keywords related to housing price and health. Articles were screened by two reviewers for eligibility, which restricted inclusion to original research articles measuring changes in housing prices and health outcomes, published prior to June 31st, 2022. Results Among 23 eligible studies, we found that changes in housing prices were heterogeneously associated with physical and mental health outcomes, with multiple mechanisms contributing to both positive and negative health outcomes. Income-level and home-ownership status were identified as key moderators, with lower-income individuals and renters experience negative health consequences from rising housing prices. This may have resulted from increased stress and financial strain among these groups. Meanwhile, the economic benefits of rising housing prices were seen to support health for higher-income individuals and homeowners – potentially due to increased wealth or perception of wealth. Conclusions Based on the associations identified in this review, it appears that potential gains to health associated with rising housing prices are inequitably distributed. Housing policies should consider the health inequities born by renters and low-income individuals. Further research should explore mechanisms and interventions to reduce uneven economic impacts on health.

This study explores dynamic relationships among Chinese housing prices for the years 2010–2019. With monthly data from 99 major cities in China, we use the vector error correction model and directed acyclic graph to characterize contemporaneous causality among housing prices from different tiers of cities. The PC algorithm identifies the causal pattern and the LiNGAM algorithm further identifies the causal path, from which we perform innovation accounting analysis. Complex housing price dynamics are found in the price adjustment process following price shocks, which is not only dominated by the top tiers of cities. This suggests that policies on housing prices in the long run might need to be planned from a national perspective.

While supply elasticity can explain why housing prices appreciate by different amounts across cities, it may play a lesser role in smaller geographic units, such as neighbourhoods within a city. This is because of location substitution: a city cannot be easily substituted by another city, but neighbourhoods of the same city can be close substitutes. This paper revisits the question of whether supply elasticity can differentiate housing price appreciation rates within a city by carefully accounting for substitution effects at the neighbourhood level. From a Hong Kong housing boom (2003—2018), we have found that the impact of supply elasticity on another neighbourhood on average is about one-tenth of the impact on its own neighbourhood. It rejects the notion of perfect substitution, under which this magnitude difference should not have been identified. The contribution of this paper is threefold: 1) It clarifies the theoretical relationship between supply elasticity and substitution in shaping housing price movements. 2) It proposes two novel ways to account for neighbourhoods’ substitution using the spatial spillover of land availability and price co-movement. 3) It delivers a clear answer that supply elasticity can shape the housing price movements within a city.

This study investigated the extent to which subjectively and objectively measured street-level perceptions complement or conflict with each other in explaining property value. Street-scene perceptions can be subjectively assessed from self-reported survey questions, or objectively quantified from land use data or pixel ratios of physical features extracted from street-view imagery. Prior studies mainly relied on objective indicators to describe perceptions and found that a better street environment is associated with a price premium. While very few studies have addressed the impact of subjectively-assessed perceptions. We hypothesized that human perceptions have a subtle relationship to physical features that cannot be comprehensively captured with objective indicators. Subjective measures could be more effective to describe human perceptions, thus might explain more housing price variations. To test the hypothesis, we both subjectively and objectively measured six pairwise eye-level perceptions (i.e., Greenness, Walkability, Safety, Imageability, Enclosure, and Complexity). We then investigated their coherence and divergence for each perception respectively. Moreover, we revealed their similar or opposite effects in explaining house prices in Shanghai using the hedonic price model (HPM). Our intention was not to make causal statements. Instead, we set to address the coherent and conflicting effects of the two measures in explaining people’s behaviors and preferences. Our method is high-throughput by extending classical urban design measurement protocols with current artificial intelligence (AI) frameworks for urban-scene understanding. First, we found the percentage increases in housing prices attributable to street-view perceptions were significant for both subjective and objective measures. While subjective scores explained more variance over objective scores. Second, the two measures exhibited opposite signs in explaining house prices for Greenness and Imageability perceptions. Our results indicated that objective measures which simply extract or recombine individual streetscape pixels cannot fully capture human perceptions. For perceptual qualities that were not familiar to the average person (e.g., Imageability), a subjective framework exhibits better performance. Conversely, for perceptions whose connotation are self-evident (e.g., Greenness), objective measures could outperform the subjective counterparts. This study demonstrates a more holistic understanding for street-scene perceptions and their relations to property values. It also sheds light on future studies where the coherence and divergence of the two measures could be further stressed.

This study examines the impact of housing price increases on income inequality using the dynamic system GMM for OECD countries (2010–2021). We test the hypothesis that housing price appreciation affects income distribution differently based on economic development levels and homeownership patterns. The analysis is conducted both for the entire sample and by dividing countries into two groups based on per capita income, Group 1 (16 countries) with below-median per capita GDP and Group 2 (17 countries) with above-median per capita GDP, to account to account for structural differences in housing markets, financial systems, and wealth accumulation mechanisms. The findings show that rising housing prices help reduce income inequality, especially in countries that are relatively low-income and where more low-income households own their homes. Specifically, our estimates indicate that a one-point increase in the housing price index leads to a statistically significant (p < 0.05) 0.21 percentage point reduction in the Gini change rate in lower-income countries. However, in higher-income countries, the effect of housing prices on inequality is statistically insignificant, suggesting that the relationship between housing markets and income inequality varies across different economic contexts. This insignificance likely stems from countervailing forces: while housing appreciation increases wealth for homeowners, higher housing costs may disproportionately burden lower-income households through rental markets in these economies. The findings highlight the importance of country-specific housing programs that consider homeownership patterns and financial market access in tackling inequality, along with comprehensive public social policies. Our study has implications for policymakers seeking to address inequality through housing market interventions, particularly during the post-2008 recovery period and into the early pandemic phase.

Modelling spatial heterogeneity (SH) is a controversial subject in real estate economics. Single-family-home prices in Austria are explored to investigate the capability of global and locally weighted hedonic models. Even if regional indicators are not fully capable to model SH and technical amendments are required to account for unmodelled SH, the results emphasise their importance to achieve a well-specified model. Due to SH beyond the level of regional indicators, locally weighted regressions are proposed. Mixed geographically weighted regression (MGWR) prevents the limitations of fixed effects by exploring spatially stationary and non-stationary price effects. Besides reducing prediction errors, it is concluded that global model misspecifications arise from improper selected fixed effects. Reported findings provide evidence that the SH of implicit prices is more complex than can be modelled by regional indicators or purely local models. The existence of both stationary and non-stationary effects implies that the Austrian housing market is economically connected.