Search results

This paper examines the price setting behaviour over time and space in the Athens residential market. In periods of house price inflation asking prices are often based upon the last observed highest selling price achieved for a similar property in the same micro-location. However, in a falling market, prices may be rigid downwards and less sensitive to the most recent transaction prices, weakening spatial effects. Furthermore, the paper considers whether future price expectations affect price setting behaviour.

The paper employs a dataset of approximately 24,500 property values from 2007 until 2014 in Athens incorporating characteristics and locational variables. The authors begin by estimating a baseline hedonic price model using property characteristics, neighbourhood amenities and location effects. Following this, a spatio-temporal autoregressive (STAR) model is estimated. Running separate models, the authors account for spatial dependence from historic valuations, contemporaneous peer effects and expectations effects.

The initial STAR model shows significant spatial and temporal effects, the former remaining important in a falling market contrasting with previous literature findings. In the second STAR model, whilst past sales effects remain significant although smaller, contemporaneous and price expectations effects are also found to be significant, the latter capturing anchoring and slow adjustment heuristics in price setting behaviour.

As valuations used in the database are based upon comparable sales, then in the recessionary periods covered in the dataset, finding comparables may have become more difficult, and hence this, in turn, may have impacted on valuation accuracy.

In addition to past effects, contemporaneous transactions and expected future values need to be taken in consideration in analysing spatial interactions in housing markets. These factors will influence housing markets in different cities and countries.

The information content of property valuations should more carefully consider the relative importance of different components of asking prices.

This is the first paper to use transactions data over a period of falling house prices in Athens and to consider current and future values in addition to past values in a spatio-temporal context.

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.

WeChat is the largest acquaintance social networking platform in China, in which users can view and reshare web pages shared by friends. This paper aims to analyze the spatio-temporal dynamics of web pages diffused in WeChat and advice on commercials.

A large number of web pages diffused in WeChat are collected and exclusively divided into four categories according to their titles, including advertisements, news bulletins, holiday greetings and emotional essays. For each web page, an information cascade (tree structure) is constructed to describe the diffusion trace. Based on the categories, the spatio-temporal popularity is characterized; the topological, temporal and spatial properties are examined; and the spatio-temporal diffusion velocity is explored.

Through comparative analysis, different categories of pages show diversity. For spatio-temporal popularity, there is no significant difference in cascade size; holiday greetings usually last for a relatively short time on average; emotional essays are more likely to spread to more provinces. For topological, temporal and spatial characteristics, the diffusion process of advertisements is more likely to be broadcasting than other categories; news bulletins and holiday greetings have an obvious bursty; the number of viewing behavior decreases from east to west in general. For spatio-temporal diffusion velocity, emotional essays diffuse the fastest in topological and spatio-temporal dimensions.

These findings contribute to promoting products and providing support for data driven modeling of information diffusion and human activity in spatio-temporal dimensions.

Recent research into enterprise performance has focussed on the importance of firm proximity to total productivity. Using spatial correlation of firm performance as a proxy for knowledge transfers and diffusion, the purpose of this paper is to examine the evidence for these spatial effects in non-farm enterprise performance in Uganda, across space and time.

The author uses data from the geo-referenced Uganda National Panel Survey from 2010 to 2012, and employs explicit spatial techniques in the analysis of rural non-farm enterprise performance. Spatial autocorrelation of firm performance are used as proxies for knowledge transfers and information flows among enterprises across space and over time.

The study finds evidence of spatial spillover effects across space and time in Uganda. This implies that, as existing studies of developed countries have found, social infrastructure and firm proximity contribute significantly to the performance of rural economies, through information exchange and knowledge transfers.

Given the communal nature of rural households in the African setting, knowledge exchange and transfers among neighbouring firms should be encouraged as studies have found they have strong effects on business performance. Additionally, business “leaders” could also be useful in disseminating useful new technologies and applications to neighbouring enterprises in order to boost performance and productivity.

There should be better targeting of policy interventions to clusters of particularly needy enterprises.

To the best of the author’s knowledge, this is the first time that spatio-temporal effects of business performance have been explored. While spatial analyses of business performance have been carried out in developed countries, studies using explicit spatial techniques in the developing country setting have been conspicuously absent.

The purpose of this study is to develop a spatio-temporal neighbourhood-level house price index (STNL-HPI) incorporating a geographic information system (GIS) functionality that can be used to improve the house price indexation system.

By using the Malaysian house price index (MHPI) and application of geographically weighted regression (GWR), GIS-based analysis of STNL-HPI through an application called LHPI Viewer v.1.0.0, the stand-alone GIS-statistical application for STNL-HPI was successfully developed in this study.

The overall results have shown that the modelling and GIS application were able to help users understand the visual variation of house prices across a particular neighbourhood.

This research was only able to acquire data from the federal government over the period 1999 to 2006 because of budget limitations. Data purchase was extremely costly. Because of financial constraints, data with lower levels of accuracy have been obtained from other sources. As a consequence, a major portion of data was mismatched because of the absence of a common parcel identifier, which also affected the comparison of this system to other comparable systems.

Neighbourhood-level HPI is needed for a better understanding of the local housing market.

Universities claim to provide many benefits to their context. What remains less clear is what is meant by context. Whatever it is, context is fundamental to decision-making. Understanding what context means is crucial to understanding leadership in higher education.

Theoretically informed by Eacott's relational approach, this study is based on interview data from a purposive sample of ten English vice-chancellors and nine Canadian university presidents. Transcripts were analysed for the assumptions participants held regarding the work of universities and how that played out in practice.

Context is not an external variable engaged with or acted upon. It is not separate to leadership and the work of universities but is constitutive of and emergent from activities. There is no single definition of context, and this has major implications for university activities.

Context(s) is based on assumptions. Making explicit the assumptions of participants, without pre-defining them, is a key task of research on leadership in higher education.

Leaders need to explicitly articulate their assumptions regarding the work of universities. Assessment should be based on the coherence between the espoused position and activities undertaken.

Through the emerging resources of relational scholarship, this paper demonstrates how context is constitutive of and emergent from the activities of universities. More than novel vocabulary, the paper makes a fundamental point about the generative nature of context. De-centring entities (e.g. university, leader, context) and focusing on relations our approach provide a path forward by encouraging the articulation of intended purpose(s) and perspective on the work of universities.

This study aims to empirically examine the impact of digital finance on spatial urbanization and elucidate its underlying mechanisms.

Using panel data of Chinese prefecture-level cities from 2011 to 2021, and using a spatial dynamic panel model, the authors analyzed the effects of digital finance on spatial urbanization and the mechanism of its action.

The findings of the study reveal that digital finance, along with its sub-dimensions, namely coverage breadth, usage depth and digitization degree, all contribute to the enhancement of spatial urbanization. The information channel effect generated by the development of postal and telecommunication businesses, the goods delivery effect generated by the development of retail businesses and the wealth accumulation effect generated by the accumulation of household wealth are all important channels through which digital finance promotes spatial urbanization. Digital finance exerts a significant promotional effect on spatial urbanization in second-tier cities, third-tier cities and their subsequent tiers. This observation alludes to the regionally inclusive nature of spatial urbanization promotion facilitated by digital finance.

The present study endeavors to fill this void by employing empirical analysis to investigate the ramifications of digital finance on spatial urbanization, thereby shedding light on the pivotal role played by digital finance in expediting the progression of spatial urbanization. This study undertakes an examination of the spatial spillover effects, thus providing a comprehensive exposition of the influence of digital finance on spatial urbanization. This study introduces this crucial dimension, and the empirical findings elucidate that digital finance fosters the evolution of spatial urbanization by broadening the coverage of information channels, augmenting the efficiency of goods distribution and enhancing wealth accumulation efficacy.

By discussing the spatial spillover effect and regional heterogeneity of digital economy and green agricultural development level, this paper aims to provide countermeasures and suggestions for the better development of green agriculture in the contemporary era when digital economy is universally developed and at the same time provide development suggestions suitable for green agriculture's development characteristics and initial conditions for different regions.

This paper discusses the theoretical foundation of the digital economy and green agriculture development and utilizes panel data from 30 provinces in China from 2011 to 2018. By employing the Super-Efficiency Slack-based Measure and Malmquist-Luenberger (SBM-ML) model based on unexpected output to measure the total factor productivity of green agriculture and employing the spatial panel Durbin model to empirically test the spatiotemporal effects of the digital economy on green agriculture development from both temporal and spatial dimensions. Finally, the model is tested for robustness as well as heterogeneity.

The research findings are as follows: First, from the perspective of time effect, digital economy has a continuous driving effect on the development of green agriculture and with the passage of time, this effect becomes more and more prominent; second, from the perspective of spatial effect, digital economy has a significant positive impact on the development of local green agriculture, while digital economy has a significant negative impact on the development of surrounding green agriculture. Finally, the impact of digital economy on the development of green agriculture shows significant differences in different dimensions and regions.

As an important driver of economic growth, the digital economy has injected new impetus into agricultural and rural development. Along with the intensifying environmental pollution problems, how to influence the green development of agriculture through the digital economy is a proposition worthy of attention nowadays. This paper analyzes the relationship between the digital economy and agricultural green development in multiple dimensions by exploring the temporal and spatial spillover effects of the digital economy on agricultural green development, as well as the heterogeneity in different dimensions and in different regions and derives policy insights accordingly in order to improve relevant policies.

This study aims to analyze the impact of COVID-19 on housing price within four major metropolitan areas in Texas: Austin, Dallas, Houston and San Antonio. The analysis intends to understand economic and mobility drivers behind the housing market under the inclusion of fixed and random effects.

This study used a linear mixed effects model to assess the socioeconomic and housing and transport-related factors contributing to median home prices in four major cities in Texas and to capture unobserved factors operating at spatial and temporal level during the COVID-19 pandemic.

The regression results indicated that an increase in new COVID-19 cases resulted in an increase in housing price. Additionally, housing price had a significant and negative relationship with the following variables: business cycle index, mortgage rate, percent of single-family homes, population density and foot traffic. Interestingly, unemployment claims did not have a significant impact on housing price, contrary to previous COVID-19 housing market related literature.

Previous literature analyzed the housing market within the first phase of COVID-19, whereas this study analyzed the effects of the COVID-19 throughout the entirety of 2020. The mixed model includes spatial and temporal analyses as well as provides insight into how quantitative-based mobility behavior impacted housing price, rather than relying on qualitative indicators such as shutdown order implementation.

The purpose of this paper is to provide a detailed analysis of the trends in temperature and rainfall over the period 1967–2016 (50 years) for the Kingdom of Saudi Arabia and estimate the effect of these climatic changes on major crop production.

To set up an empirical association between crop yields and climatic variables, the study uses a fixed effect regression framework. This approach makes it possible to capture the effects of time-invariant indicators and farmers' independent adaptation strategies in reaction to year-to-year variations in precipitation and temperature.

The study observes a significant increase in average temperature by 1.9 degrees Celsius in the last 50 years and the greatest increase is noted in the summer. However, there is no significant change in rainfall. The results indicate that a one-degree Celsius increase in temperature reduces crop yields by 7–25%. The results also indicate that rainfall has a positive effect on all the crops. But, rainfall could not offset much of the adverse effects of temperature.

Future research can focus on the analysis of the climate change impact assessment for different regions in the Kingdom of Saudi Arabia and develop a place-based policy.

The recent initiative to phase out crop production makes the Kingdom of Saudi Arabia entirely rely on imports. This may have little or no impact presently. However, in the future, it is possible that any global shocks on agriculture due to climate change or geopolitical instability will make the situation worse off. It will threaten both food and nutrition security in the Kingdom of Saudi Arabia. Therefore, it is important to study these in the present context to prepare a road map for future food, water and nutrition security.