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The purpose of this paper is the study of the causal relationship. The concept called “naive” causality can be stated more generally as the belief (or knowledge) that results follow actions, and that these results are not random, but are consistently linked with causes. The authors have thus formed a very general and precarious concept of causality, but one that appropriately reflects the meaning of causality at the level of common sense.

Mathematical and logical development of the causality in complex systems.

There are three aspects of rationality that give the human mind a unique vision of reality: quantification: reduction of phenomena to quantitative terms; cause and effect: causal relationship, which allows predicting; and the necessary and valid use of (deterministic) mechanical models. This work is dedicated to the second aspect, that of causality, but at present leaves aside the discussion of possibility-necessity, proposing a modification to philosophical synthesis of causality specified by Bunge (1959), with contributions made by Patten et al. (1976) and LeShan and Margenau (1982).

Causality is an epistemological category, because it concerns the experience and knowledge of the human subject, without being necessarily a property of reality.

The purpose of this study is to reveal the dynamics of house prices and sales in spatial and temporal dimensions across British regions.

This paper incorporates two empirical approaches to describe the behaviour of property prices across British regions. The models are applied to two different data sets. The first empirical approach is to apply the price diffusion model proposed by Holly et al. (2011) to the UK house price index data set. The second empirical approach is to apply a bivariate global vector autoregression model without a time trend to house prices and transaction volumes retrieved from the nationwide building society.

Identifying shocks to London house prices in the GVAR model, based on the generalized impulse response functions framework, I find some heterogeneity in responses to house price changes; for example, South East England responds stronger than the remaining provincial regions. The main pattern detected in responses and characteristic for each region is the fairly rapid fading of the shock. The spatial-temporal diffusion model demonstrates the presence of a ripple effect: a shock emanating from London is dispersed contemporaneously and spatially to other regions, affecting prices in nondominant regions with a delay.

The main contribution of this work is the betterment in understanding how house price changes move across regions and time within a UK context.

With the rapid-growing house market in the past decade, the purpose of this paper is to study the important issue of house price information flows among 12 major cities in China, including Shanghai, Beijing, Xiamen, Shenzhen, Guangzhou, Hangzhou, Ningbo, Nanjing, Zhuhai, Fuzhou, Suzhou and Dongguan, during the period of June 2010 to May 2019.

The authors approach this issue in both time and frequency domains, latter of which is facilitated through wavelet analysis and by exploring both linear and nonlinear causality under the vector autoregressive framework.

The main findings are threefold. First, in the long run of the time domain and for timescales beyond 16 months of the frequency domain, house prices of all cities significantly affect each other. For timescales up to 16 months, linear causality is weaker and is most often identified for the scale of four to eight months. Second, while nonlinear causality is seldom determined in the time domain and is never found for timescales up to four months, it is identified for scales beyond four months and particularly for those beyond 32 months. Third, nonlinear causality found in the frequency domain is partly explained by the volatility spillover effect.

Results here should be of use to policymakers in certain policy analysis.

The purpose of this paper is to apply the concept of “Interconnected Geoscience” to a disaster and risk reduction (DRR) case study at SECMOL College, near Leh, Ladakh, N. India. Interconnected geoscience is a model that advocates holistic approaches to geoscience for development. This paper reports research/practical work with Ladakhi students/staff, undertaking community-oriented DRR exercises in hazard awareness, DRR themed village/college mapping, vulnerability assessments and DRR management scenario development. The geoscientific hazard analysis work is published within a separate sister paper, with results feeding into this work. This work addresses aspects of, and contributes to, the DRR research(science)-policy-interface conversation.

Interconnected geoscience methodologies for DRR here are: the application of geoscience for hazard causality, spatial distribution, frequency and impact assessment, for earthquakes, floods and landslides, within the SECMOL area; the generation of community-developed DRR products and services of use to a range of end-users; the development of a contextual geoscience approach, informed by social-developmental-issues; and the active participation of SECMOL students/teachers and consequent integration of local world-views and wisdom within DRR research. Initial DRR awareness levels of students were assessed with respect to earthquakes/floods/landslides/droughts. Following hazard teaching sessions, students engaged in a range of DRR exercises, and produced DRR themed maps, data, tables and documented conversations of relevance to DRR management.

Students levels of hazard awareness were variable, generally low for low-frequency hazards (e.g. earthquakes) and higher for hazards such as floods/landslides which either are within recent memory, or have higher frequencies. The 2010 Ladakhi flood disaster has elevated aspects of flood-hazard knowledge. Landslides and drought hazards were moderately well understood. Spatial awareness was identified as a strength. The application of an interconnected geoscience approach immersed within a student+staff college community, proved to be effective, and can rapidly assess/build upon awareness levels and develop analytical tools for the further understanding of DRR management. This approach can assist Ladakhi regional DRR management in increasing the use of regional capability/resources, and reducing the need for external inputs.

A series of recommendations for the DRR geoscience/research-policy-practice area include: adopting an “interconnected geoscience” approach to DRR research, involving scientific inputs to DRR; using and developing local capability and resources for Ladakhi DRR policy and practice; using/further-developing DRR exercises presented in this paper, to integrate science with communities, and further-empower communities; taking account of the findings that hazard awareness is variable, and weak, for potentially catastrophic hazards, such as earthquakes, when designing policy and practice for raising DRR community awareness; ensuring that local values/world views/wisdom inform all DRR research, and encouraging external “experts” to carefully consider these aspects within Ladakh-based DRR work; and further-developing DRR networks across Ladakh that include pockets of expertise such as SECMOL.

The term “interconnected geoscience” is highly novel, further developing thinking within the research/science-policy-practice interface. This is the first time an exercise such as this has been undertaken in the Ladakh Himalaya.

To explore the state of this conditional Granger causality when other cities are not factors, we investigate housing market networks in China's major cities by using a combination of conditional Granger causality and network analysis.

Although housing market networks have been well discussed for different countries, the question of housing market networks in China's major cities based on the conditional causality perspective has yet to be answered.

We discover that second-tier cities are more influential than first-tier cities. Although the connectivity of the primary housing market is more complex than the diversified connectivity observed in the secondary housing market, both markets are scale-free networks that exhibit high stability. Moreover, we reveal that geographic conditions and economic development jointly determine the housing market's modular hierarchical structure. Our results provide meaningful information for both Chinese policymakers and investors.

By excluding the influence of other cities, our conditional Granger causality identifies the true casual relation between cities' housing markets. Moreover, it is the first paper to consider the primary housing market and secondary housing market separately. Specifically, Chinese prefer new house rather than second-hand house from both speculative and self-housing. Generally speaking, the new house price is lower than the second-hand house price since the new house is off-plan property. Therefore, understanding the difference between primary and secondary housing markets will provide useful information for both policymakers and speculators.

This paper focusses on analysing the impact of crime on the housing market in Los Angeles (LA) County. By looking at different types of crime instead of general crime measures and controlling by spatial dimension of prices and crime as well as endogeneity, a model is developed that allows for the understanding of how a specific crime impacts the housing market transaction price. To perform the analysis, the paper merges different data sets (crime, housing transaction and census data) and then computes the distances to crucial transport modes to control the accessibility features affecting housing prices. The latter allows estimating the association of housing prices and crime in the distance and estimating the impact on housing depending on it.

This paper focusses on the following crimes: aggravated assault, burglary (property crime), narcotics, non-aggravated assault and vandalism. The paper shows firstly how incidents of reported crime are distributed across space and how they are related to each other – thus highlighting crime models with spatial influences. Secondly, the research utilises instrumental variables within the methodology to estimate house prices using spatial analysis techniques while controlling for endogeneity. Thirdly, it estimates the direct impact of crime on house prices and explores the impact of housing and neighbourhood features.

Results suggest that house transaction prices and crime are closely correlated in two senses. Housing prices are endogenously negatively associated with the levels of narcotics and aggravated assaults. For narcotics, the impact of distance is shorter (1,000 m). However, for burglary, vandalism and non-aggravated assaults, the price reaction suggests a positive association: the further away the crime occurs, the higher the prices. The paper also shows the large spatial association of different crimes suggesting that they occur together and that their accumulation would make negative externalities appear affecting the whole neighbourhood.

The use of a huge database allows interesting findings, but one limitation can be to not have longer time observations to identify the crime evolution and its impact on housing prices.

Large implications as the relationship identified in this paper allow defining precise policies to avoid crime in different areas in LA. In addition, crime has significant but quantitative small effects on LA housing transaction prices suggesting that the effect depends on the spatial scale as well as lack on information about where the crimes are committed. Lack on information suggests low transparency in the market, affecting the transaction decision-taken process, affecting the risk perception and with relevant implications over household welfare.

This paper relates the spatial association among crimes defining the hotspots and their impacts on housing transaction prices.

This study aims to investigate dynamic relationships among residential housing price indices of ten major Chinese cities for the years 2005–2021.

Using monthly data, this study uses vector error correction modeling and the directed acyclic graph for characterization of contemporaneous causality among the ten indices.

The PC algorithm identifies the causal pattern and the Linear Non-Gaussian Acyclic Model algorithm further determines the causal path, from which this study conducts innovation accounting analysis. Sophisticated price dynamics are found in price adjustment processes following price shocks, which are generally dominated by the top tiers of cities.

This study suggests that policies on residential housing prices in the long run might need to be planned with particular attention paid to these top tiers of cities.

Accounting for locational effects in determining price is of fundamental importance. The demise of the mainstream property market has culminated in increasing appetite and investment activity within the private rental sector. The primary purpose of this paper aims to analyse the local variation and spatial heterogeneity in residential rental prices in a large urban market in the UK using various geo-statistical approaches.

Applying achieved price data derived from a leading internet-based rental agency for Belfast Northern Ireland is analysed in a number of spatially based modelling frameworks encompassing more traditional approaches such as hedonic regressive models to more complex spatial filtering methods to estimate rental values as a function of the properties implicit characteristics and spatial measures.

The principal findings show the efficacy of the geographically weighted regression (GWR) technique as it provides increased accuracy in predicting marginal price estimates relative to other spatial techniques. The results reveal complex spatial non-stationarity across the Belfast metropole emphasizing the premise of location in determining and understanding rental market performance. A key finding emanating from the research is that the high level of segmentation across localised pockets of the Belfast market, as a consequence of socio-political conflict and ethno-religious territoriality segregation, requires further analytical insight and model specification in order to understand the exogenous spatial and societal effects/implications for rental value.

This study is one of only a few investigations of spatial residential rent price variation applying the GWR methodology, spatial filtering and other spatial techniques within the confines of a UK housing market. In the context of residential rent prices, the research highlights that a soft segmentation modelling approaches are essential for understanding rental gradients in a polarised ethnocratic city.

This paper aims to examine the existence of the ripple effect from Amsterdam to the housing markets of other regions in The Netherlands. It identifies which regional housing markets are influenced by house price movements in Amsterdam.

The paper considers the ripple effect as a lead-lag effect and a long-run convergence between the Amsterdam and regional house prices. Using the real house prices for second-hand owner-occupied dwellings from 1995q1 to 2016q2, the paper adopts the Toda–Yamamoto Granger Causality approach to study the lead-lag effects. It uses the autoregressive distributed lags (ARDL)-Bounds cointegration techniques to examine the long-run convergence between the regional and the Amsterdam house prices. The paper controls for house price fundamentals to eliminate possible confounding effects of common shocks.

The cumulative evidence suggests that Amsterdam house prices have influence on (or ripple to) all the Dutch regions, except one. In particular, the Granger Causality test concludes that a lead-lag effect of house prices exists from Amsterdam to all the regions, apart from Zeeland. The cointegration test shows evidence of a long-convergence between Amsterdam house prices and six regions: Friesland, Groningen, Limburg, Overijssel, Utrecht and Zuid-Holland.

The paper adopts an econometric approach to examine the Amsterdam ripple effect. More sophisticated economic models that consider the asymmetric properties of house prices and the patterns of interregional socio-economic activities into the modelling approach are recommended for further investigation.

This paper focuses on The Netherlands for which the ripple effect has not yet been researched to the authors’ knowledge. Given the substantial wealth effects associated with house price changes that may shape economic activity through consumption, evidence for ripples may be helpful to policy makers for uncovering trends that have implications for the entire economy. Moreover, the analysis controls for common house price fundamentals which most previous papers ignored.