Search results
1 – 10 of over 4000Arnab Bhattacharjee, Jan Ditzen and Sean Holly
The authors provide a way to represent spatial and temporal equilibria in terms of error correction models in a panel setting. This requires potentially two different processes…
Abstract
The authors provide a way to represent spatial and temporal equilibria in terms of error correction models in a panel setting. This requires potentially two different processes for spatial or network dynamics, both of which can be expressed in terms of spatial weights matrices. The first captures strong cross-sectional dependence, so that a spatial difference, suitably defined, is weakly cross-section dependent (granular) but can be non-stationary. The second is a conventional weights matrix that captures short-run spatio-temporal dynamics as stationary and granular processes. In large samples, cross-section averages serve the first purpose and the authors propose the mean group, common correlated effects estimator together with multiple testing of cross-correlations to provide the short-run spatial weights. The authors apply this model to the 324 local authorities of England, and show that our approach is useful for modeling weak and strong cross-section dependence, together with partial adjustments to two long-run equilibrium relationships and short-run spatio-temporal dynamics. This exercise provides new insights on the (spatial) long-run relationship between house prices and income in the UK.
Details
Keywords
Yu Zhang, Arnab Rahman and Eric Miller
The purpose of this paper is to model housing price temporal variations and to predict price trends within the context of land use–transportation interactions using machine…
Abstract
Purpose
The purpose of this paper is to model housing price temporal variations and to predict price trends within the context of land use–transportation interactions using machine learning methods based on longitudinal observation of housing transaction prices.
Design/methodology/approach
This paper examines three machine learning algorithms (linear regression machine learning (ML), random forest and decision trees) applied to housing price trends from 2001 to 2016 in the Greater Toronto and Hamilton Area, with particular interests in the role of accessibility in modelling housing price. It compares the performance of the ML algorithms with traditional temporal lagged regression models.
Findings
The empirical results show that the ML algorithms achieve good accuracy (R2 of 0.873 after cross-validation), and the temporal regression produces competitive results (R2 of 0.876). Temporal lag effects are found to play a key role in housing price modelling, along with physical conditions and socio-economic factors. Differences in accessibility effects on housing prices differ by mode and activity type.
Originality/value
Housing prices have been extensively modelled through hedonic-based spatio-temporal regression and ML approaches. However, the mutually dependent relationship between transportation and land use makes price determination a complex process, and the comparison of different longitudinal analysis methods is rarely considered. The finding presents the longitudinal dynamics of housing market variation to housing planners.
Details
Keywords
The purpose of this paper is to analyse, through a temporal lens and from a managerial perspective, the role played by intellectual capital (IC) and intellectual liabilities (ILs…
Abstract
Purpose
The purpose of this paper is to analyse, through a temporal lens and from a managerial perspective, the role played by intellectual capital (IC) and intellectual liabilities (ILs) “in practice” within the value creation and value destruction processes. In particular, this study is based on the following research question: to what extent are time and its attributes considered, measured, and discussed with reference to IC and ILs and their influence on financial capital (FC)? In order to achieve this purpose, the author has carried out a field study.
Design/methodology/approach
A field study method is adopted in order to understand IC and ILs “in action” from a temporal perspective.
Findings
This study highlights the relevance of time when IC and ILs are analysed from a dynamic perspective. In particular, the main findings are the following. First, it emerges that the time dimension of IC tends not to be measured due to the complexity of IC itself and to the lack of adequate accounting practices. Second, IC time is generally considered to be non-cyclical and random. Third, even if time is not measured, some companies talk about it and when this is done with regularity, time perceptions move from an individual sphere to a collective one and they become more and more reliable. Moreover, IC performance is perceived to be “distant” from FC performance: the succession of events and the time lags are difficult to define and quantify as the influence of IC on FC is mediated by several resources and events. Lastly, the value destruction process related to ILs tends to generate negative effects faster than the value creation one, especially with reference to the impacts of IC on FC.
Research limitations/implications
The main limitations of this study are twofold. The first is related to the methodology adopted and the related risks that the results may be subject to both interviewee and interviewer bias and interpretation. The second is related to the fact that the constructs to be discussed were not proposed by the firms but by the author, in order to make the results comparable.
Practical implications
This study contributes to the literature on IC and ILs “in action” and “in practice”. Moreover, this study enriches the extant IC and ILs literature focusing on time, a variable that is generally assumed to be a natural unchangeable phenomenon that does not deserve attention. In particular, the findings highlight the different behaviours and perceptions that occur when IC and ILs are looked at through a temporal lens. Finally, this study pinpoints that value creation and value destruction processes seem to have different timings as it takes more time to create value than to destroy it.
Originality/value
In comparison to previous studies, this study does not focus on the positive and negative effects of IC separately, but combines the two issues, also comparing the value creation and the value destruction processes in order to offer a complete picture. Moreover, it adopts a temporal lens, which has been applied only with reference to IC but not to ILs as well. Finally, while the extant literature on ILs tends to investigate them from a theoretical perspective and adopting a static approach, this research investigates ILs empirically from a dynamic perspective.
Details
Keywords
Le Ma and Chunlu Liu
Studies into ripple effects have previously focused on the interconnections between house price movements across cities over space and time. These interconnections were widely…
Abstract
Purpose
Studies into ripple effects have previously focused on the interconnections between house price movements across cities over space and time. These interconnections were widely investigated in previous research using vector autoregression models. However, the effects generated from spatial information could not be captured by conventional vector autoregression models. This research aimed to incorporate spatial lags into a vector autoregression model to illustrate spatial‐temporal interconnections between house price movements across the Australian capital cities.
Design/methodology/approach
Geographic and demographic correlations were captured by assessing geographic distances and demographic structures between each pair of cities, respectively. Development scales of the housing market were also used to adjust spatial weights. Impulse response functions based on the estimated SpVAR model were further carried out to illustrate the ripple effects.
Findings
The results confirmed spatial correlations exist in housing price dynamics in the Australian capital cities. The spatial correlations are dependent more on the geographic rather than the demographic information.
Originality/value
This research investigated the spatial heterogeneity and autocorrelations of regional house prices within the context of demographic and geographic information. A spatial vector autoregression model was developed based on the demographic and geographic distance. The temporal and spatial effects on house prices in Australian capital cities were then depicted.
Details
Keywords
Jan de Graaff and Joachim Zietz
The purpose of this study is to examine the impact of crime on apartment prices for Hamburg, Germany, for the years 2012 to 2017.
Abstract
Purpose
The purpose of this study is to examine the impact of crime on apartment prices for Hamburg, Germany, for the years 2012 to 2017.
Design/methodology/approach
The authors use a panel data setting with fixed effects estimators and temporal lags to moderate the endogeneity concerns related to crime. The authors consider the effect of total crime, violent and property crime and some sub-categories of crime.
Findings
The estimates show that it takes two to three years for prices to react, with the longer run elasticity reaching −0.12 for total crime, −0.15 for property crime and −0.06 for violent crime. The elasticities are much larger in high-crime areas (−0.22 for total crime, −0.28 and −0.09 for property and violent crime) and elevated also in low-income areas.
Social implications
The finding that property crime matters more in terms of quantitative impact for housing values than violent crime provides reasonable grounds for rethinking the resource allocation of public spending on crime clearance and prevention in Germany. Far more emphasis on preventing property crime appears in order and especially so in the lower income or higher crime areas, which are significantly more affected by crime and in particular property crime than those in high income or low crime areas.
Originality/value
The estimates for Hamburg provide the first detailed results of the impact of crime on real estate prices in Germany. It is also the first study for Continental Europe using panel data.
Details
Keywords
Anja Danner-Schröder and Gordon Müller-Seitz
Tensions arising from temporary versus permanent forms of organising are a managerially relevant and commonplace phenomenon. How ensuing tensions unfold and what implications this…
Abstract
Tensions arising from temporary versus permanent forms of organising are a managerially relevant and commonplace phenomenon. How ensuing tensions unfold and what implications this has for organising responses across different levels of organising is the key concern of our inquiry. The authors draw upon a case study of what has been dubbed the German refugee crisis to make three contributions to the literature on managing temporary organisational phenomena: First, the authors offer a temporal continuum along which one can distinguish between comparatively fast responses of emergent temporary organisations on the micro-level and relatively slow responses by macro-level institutions that are predominantly engaged in permanent organising. The authors built upon this continuum to highlight the role of temporal lags, which arise from the different reaction times of micro- and macro-level organisations and which is filled by the respective other organisational form, a phenomenon the authors label temporal co-dependence. Second, the authors offer a distinction between deliberate and emergent forms of temporal organising. Third, the authors unearth boundary conditions that make the likelihood of this interplay between different levels possible.
Details
Keywords
Xavier de Luna and Marc G. Genton
We analyze spatio-temporal data on U.S. unemployment rates. For this purpose, we present a family of models designed for the analysis and time-forward prediction of spatio-temporal…
Abstract
We analyze spatio-temporal data on U.S. unemployment rates. For this purpose, we present a family of models designed for the analysis and time-forward prediction of spatio-temporal econometric data. Our model is aimed at applications with spatially sparse but temporally rich data, i.e. for observations collected at few spatial regions, but at many regular time intervals. The family of models utilized does not make spatial stationarity assumptions and consists in a vector autoregressive (VAR) specification, where there are as many time series as spatial regions. A model building strategy is used that takes into account the spatial dependence structure of the data. Model building may be performed either by displaying sample partial correlation functions, or automatically with an information criterion. Monthly data on unemployment rates in the nine census divisions of the U.S. are analyzed. We show with a residual analysis that our autoregressive model captures the dependence structure of the data better than with univariate time series modeling.
Agrata Pandey, Ranjeet Nambudiri, Patturaja Selvaraj and Ashish Sadh
The literature on destructive leadership has largely ignored the perspective of the subordinate, especially in terms of conflict coping mechanisms. This study aims to integrate…
Abstract
Purpose
The literature on destructive leadership has largely ignored the perspective of the subordinate, especially in terms of conflict coping mechanisms. This study aims to integrate research on destructive leadership and subordinates’ voice behaviour as a conflict coping mechanism. Drawing on the social exchange, conservation of resources and social identity theories, it argues that destructive leadership negatively affects employees’ voice behaviour and that this relationship is moderated by subordinate personality and organization climate.
Design/methodology/approach
The proposed model was tested on a sample of 275 professionals working in the banking and insurance sector in India using a temporal research design with data collected in two phases six months apart. Partial least squares structural equation modelling was used for data analysis.
Findings
The results support the main effect relationship between destructive leadership and subordinates’ voice behaviour and the moderation of subordinates’ personality and organizational climate. Temporal analysis indicates that the nature of some relationships changed across the two time periods.
Practical implications
A greater understanding of destructive leader behaviour and resultant coping strategies of subordinates is likely to provide insights for managers facing such situations. The findings of this study will inform the creation of redressal and voice mechanisms in organizations.
Originality/value
This is among the first studies to examine the impact of negative forms of leadership on subordinates’ conflict coping mechanisms using a temporal lag design across two time periods.
Details
Keywords
This paper aims to propose a new valuation method for income producing properties. The model originally called cyclical dividend discount models (d’Amato, 2003) has been recently…
Abstract
Purpose
This paper aims to propose a new valuation method for income producing properties. The model originally called cyclical dividend discount models (d’Amato, 2003) has been recently proposed as a family of income approach methodologies called cyclical capitalization (d’Amato, 2013; d’Amato, 2015; d’Amato, 2017).
Design/methodology/approach
The proposed methodology tries to integrate real estate market cycle analysis and forecast inside the valuation process allowing the appraiser to deal with real estate market phases analysis and their consequence in the local real estate market.
Findings
The findings consist in the creation of a methodology proposed for market value and in particular for mortgage lending determination, as the model may have the capability to reach prudent opinion of value in all the real estate market phase.
Research limitations/implications
Research limitation consists mainly in a limited number of sample of time series of rent and in the forecast of more than a cap rate or yield rate even if it is quite commonly accepted the cyclical nature of the real estate market.
Practical implications
The implication of the proposed methodology is a modified approach to direct capitalization finding more flexible approaches to appraise income producing properties sensitive to the upturn and downturn of the real estate market.
Social implications
The model proposed can be considered useful for the valuation process of those property affected by the property market cycle, both in the mortgage lending and market value determination.
Originality/value
These methodologies try to integrate in the appraisal process the role of property market cycles. Cyclical capitalization modelling includes in the traditional dividend discount model more than one g-factor to plot property market cycle dealing with the future in a different way. It must be stressed the countercyclical nature of the cyclical capitalization that may be helpful in the determination of mortgage lending value. This is a very important characteristic of such models.
Details