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This paper aims to analyze the median price for existing single-family housing units in Las Cruces, New Mexico. The proposed theoretical model accounts for the interplay between supply and demand sides of a metropolitan housing market.

This study analyzes the median price for existing single-family housing units in Las Cruces, New Mexico. The proposed theoretical model accounts for the interplay between supply and demand sides of a metropolitan housing market. Explanatory variables used in the analysis are real per capita income, the housing stock, real mortgage rates, real apartment rents and the median real price of single-family units in the USA. Annual frequency data are collected for a 1971–2017 sample period. Parameter estimation is completed using two-stage generalized least squares. Empirical results confirm several, but not all, of the hypotheses associated with the underlying analytical model. In particular, Las Cruces housing prices are found to be reliably correlated with local income and national housing prices.

Empirical results confirm several of the hypotheses associated with the underlying analytical model. In particular, Las Cruces housing prices are found to be reliably correlated with local income and national housing prices.

Results obtained support only a subset of the hypothetical relationships associated with the theoretical model. Additional testing for other small and/or medium sized is required to clarify whether these outcomes are unique to Las Cruces.

Local income fluctuations and national housing price fluctuations appear to be reliably related to housing price fluctuations for this metropolitan economy.

Comparatively little housing market research has been conducted for small and medium size urban economies. There is no guarantee that results obtained for large metropolitan housing markets are representative of smaller regional housing markets. The model developed has fairly moderate data requirements and may be applicable to other small and medium size economies such as Las Cruces.

This paper aims to examine the dynamics of house prices in metropolitan cities in an emerging economy. The purpose of this study is to characterise the house price dynamics and the spatial heterogeneity in the dynamics.

The author explores spatial heterogeneity in house price dynamics, using data for 35 Indian cities with a million-plus population. The research methodology uses panel econometrics allowing for spatial heterogeneity, cross-sectional dependence and non-stationary data. The author tests for spatial differences and analyses the income elasticity of prices, the role of construction costs and lending to the real estate industry by commercial banks.

Long-term fundamentals drive the Indian housing markets, where wealth parameters are stronger than supply-side parameters such as construction costs or availability of financing for housing projects. The long-term elasticity of house prices to aggregate household deposits (wealth proxy) varies considerably across cities. However, the elasticity estimated at 0.39 is low. The highest coefficient is for Ludhiana (1.14), followed by Bhubaneswar (0.78). The short-term dynamics are robust and show spatial heterogeneity. Short-term momentum (lagged housing price changes) has a parameter value of 0.307. The momentum factor is the crucial dynamic in the short term. The second driver, the reversion rate to long-term equilibrium (estimated at −0.18), is higher than rates reported from developed markets.

This research applies to markets that require some home equity contributions from buyers of housing services.

Stakeholders can characterise stable housing markets based on long-term fundamental value and short-run house price dynamics. Because stable housing markets benefit all stakeholders, weak or non-existent mean reversion dynamics may prompt the intervention of policymakers. The role of urban planners, and local and regional governance, is essential to remove the bottlenecks from the demand side or supply side factors that can lead to runaway prices.

Existing literature is concerned about the risk of a housing bubble due to relaxed credit norms. To prevent housing market bubbles, some regulators require higher contributions from home buyers in the form of equity. The dynamics of house prices in markets with higher owner equity requirements vary from high-leverage markets. The influence of wealth effects is examined using novel data sets. This research, documents in an emerging market context, the observations cited in low-leverage developed markets such as Germany and Japan.

The paper aims to disentangle the factors behind territorial disparities in the coronavirus disease 2019 (COVID-19) case fatality ratio, focusing on the pressure put by the pandemic on healthcare services and adopting a spatial perspective.

Multiscale geographically weighted regression (MGWR) models have been used for uncovering the spatial variability in the impact of healthcare services on COVID-19 case fatality ratio, allowing authors to better capture the real spatial patterns at local level. The authors proved that this approach yields better results, and the MGWR model outperforms traditional regression methods. The selected case studies are two of the biggest UE countries, among the first affected by a high incidence of COVID-19 cases, namely Italy and Germany.

The authors found sizeable regional differences in COVID-19 mortality rates within each of the analysed countries, and the stress borne by local healthcare systems seems to be the most powerful factor in explaining them. In line with other studies, the authors found additional factors of influence, such as age distribution, gender ratio, population density and regional development.

This research clearly indicated that COVID-19 related deaths are strongly associated with the degree of resilience of the local healthcare systems. The authors supply localized results on the factors of influence, useful for assisting the decision-makers in prioritizing limited healthcare resources. The authors provide a scientific argument in favour of the decentralization of the pandemic management towards local authorities not neglecting, however, the necessary regional or national coordination.

We are interested in modeling the impact of spatial and interindustry dependence on firm-level innovation of Chinese firms The existence of network ties between cities imply that changes taking place in one city could influence innovation by firms in nearby cities (local spatial spillovers), or set in motion a series of spatial diffusion and feedback impacts across multiple cities (global spatial spillovers). We use the term local spatial spillovers to reflect a scenario where only immediately neighboring cities are impacted, whereas the term global spatial spillovers represent a situation where impacts fall on neighboring cities, as well as higher order neighbors (neighbors to the neighboring cities, neighbors to the neighbors of the neighbors, and so on). Global spatial spillovers also involve feedback impacts from neighboring cities, and imply the existence of a wider diffusion of impacts over space (higher order neighbors).

Similarly, the existence of national interindustry input-output ties implies that changes occurring in one industry could influence innovation by firms operating in directly related industries (local interindustry spillovers), or set in motion a series of in interindustry diffusion and feedback impacts across multiple industries (global interindustry spillovers).

Typical linear models of firm-level innovation based on knowledge production functions would rely on city- and industry-specific fixed effects to allow for differences in the level of innovation by firms located in different cities and operating in different industries. This approach however ignores the fact that, spatial dependence between cities and interindustry dependence arising from input-output relationships, may imply interaction, not simply heterogeneity across cities and industries.

We construct a Bayesian hierarchical model that allows for both city- and industry-level interaction (global spillovers) and subsumes other innovation scenarios such as: (1) heterogeneity that implies level differences (fixed effects) and (2) contextual effects that imply local spillovers as special cases.

This paper revisits the joint and conditional Lagrange multiplier tests derived by Debarsy and Ertur (2010) for a fixed effects spatial lag regression model with spatial autoregressive error, and derives these tests using artificial double length regressions (DLR). These DLR tests and their corresponding LM tests are compared using an empirical example and a Monte Carlo simulation.

The results of recent macroeconomic studies have consistently reflected economic convergence among the Economic and Monetary Union (EMU) countries. In this paper, we propose to analyse financial measures to discover whether or not the business structures of the EMU countries have grown any closer together. The study is based on a non‐linear principal components analysis in order to achieve a double objective. In the first place, the aim is to find out which factors have been significant for the joint evolution of financial variables over a ten‐year period (1990 to 1999). In the second place, it is to examine the performance of firms in each of the EMU countries in order to assess business convergence between them. The results of the study indicate high levels of convergence in the profitability vs. leverage dimension, while structural differences between countries in the labour productivity vs. sales efficiency dimension have hindered convergence in business practices.

Amidst compounding crises and increasing global population’s nutritional needs, food supply chains are called to address the “diet–environment–health” trilemma in a sustainable and resilient manner. However, food system stakeholders are reluctant to act upon established protein sources such as meat to avoid potential public and industry-driven repercussions. To this effect, this study aims to understand the meat supply chain (SC) through systems thinking and propose innovative interventions to break this “cycle of inertia”.

This research uses an interdisciplinary approach to investigate the meat supply network system. Data was gathered through a critical literature synthesis, domain-expert interviews and a focus group engagement to understand the system’s underlying structure and inspire innovative interventions for sustainability.

The analysis revealed that six main sub-systems dictate the “cycle of inertia” in the meat food SC system, namely: (i) cultural, (ii) social, (iii) institutional, (iv) economic, (v) value chain and (vi) environmental. The Internet of Things and innovative strategies help promote sustainability and resilience across all the sub-systems.

The study findings demystify the structure of the meat food SC system and unveil the root causes of the “cycle of inertia” to suggest pertinent, innovative intervention strategies.

This research contributes to the SC management field by capitalising on interdisciplinary scientific evidence to address a food system challenge with significant socioeconomic and environmental implications.

This study aims to examine the price and income elasticities of short- and long-run industrial electricity demand in Brazil between 2003 and 2020. The research also examines the impacts of COVID-19 in Brazil’s industrial electricity sector, including an analysis in states more and less industrialized.

Dynamic adjustments models in panel data are used to present robust estimates and analyze the impact of different methodologies on reported elasticities.

The short-run price elasticity is estimated at −0.448, while the long-run values are around −1.60. Regarding income elasticity, the value is 0.069 in the short-run and is concentrated in 0.25 in the long-run. The inelastic results of income show that the industrial demand for electric energy follows the trend of loss of competitiveness of the Brazilian industry in the past years. In addition, the price of natural gas, the level of employment, and, in specific cases, the level of imports also influence industrial electricity demand.

The research is a pioneer in the investigation of the industrial behavior of electricity of the Brazilian industrial branch, using as control variables, the average temperature, and the level of rainfall, this one, so important for a country whose main source is hydroelectric. In addition, to the best of the authors’ knowledge, it is the first study, which is prepared to analyze the effects of COVID-19 on electric consumption in the industrial sector, investigating these impacts, including in the states considered more and less industrialized. The estimates generated may help in the design of the Brazilian energy policy.

The development and testing of the hedonic methods for property valuation require statistical analysis and professional preparation of relevant databases. As a first step, the presumable relevant influencing variables (parameters) have to be determined. Previous studies have shown a large variety of parameters which overlap or deviate from each other. This study aims to collect, systematise and structure different parameters for the further development and testing of hedonic models.

The study comprises a detailed research and deeper analysis of previous studies regarding the hedonic valuation (mainly for residential properties). Flanking areas of examination serve, if they are appropriately suitable, as supplements (e.g. performance analysis and regression). Parameters are extracted from a wide range of literature, compared and integrated into an overall presentation of the results.

In total, 407 parameters were extracted from previous studies on hedonic valuation and performance analysis. Because of various definitions of some parameters in the literature, the current paper combined them in one meaning to avoid misunderstandings in further analysis. Higher-level (global) and lower-level (specific) parameters are contained/described in the final list. The result of this study identifies up to five levels of parameters (within the relevant hierarchy).

The parameters have not yet been statistically tested. The relevance of individual parameters has to be tested with relevant corresponding databases and statistical methods (e.g. correlation and regression).

To manage larger real estate portfolios, there is a need for regular property valuations. From this perspective, there is a great interest related to the optimisation of the valuation costs, valuation quality and valuation duration. Hedonic methods are considered as an efficient way of performing these valuation tasks. However, further suitable models and parameters are needed. The study describes parameters that can be appropriate for the development of relevant models and creates a structured parameters list, providing the technical basis for the latter. This structured parameter list is substantiated by the evaluation of the existing research.

Property values represent a significant asset for the national economy and for the individual wealth/welfare. The development of property value in a national economy is also relevant for politics, economy and society. The use of hedonic methods and the knowledge of important individual parameters can contribute towards assessing and substantiating the effect of political decisions on the value of real estate.

For the first time, a comprehensive and structured analysis on the value of the relevant parameters used in hedonic methods is performed. Thereby a large number of parameters were identified which question the stability of the results in respective individual studies. In addition, the newly developed hierarchy of parameters can serve as the basis for further research.

The purpose of this paper is to collectively measure and compare the efficiency of Brazilian and American soybean transport corridors, from farmers to export ports, using the data envelopment analysis (DEA).

This paper aims to determine routes from main producing micro-regions to main export ports, specifically using slack-based measure and variables that represent the three pillars of sustainability (economic, social, and environmental). The choice of variables was guided by literature review and analyzed through the principal component analysis. After the application of the model, the quantitative tiebreaking method of the composite index is applied.

The findings are coherent with a global report that compares soybean transportation in both countries (Brazil and USA). Efficient routes and corridors tend to present short distance truck trips and long distance train or barge trips. The efficiency of the inland waterway trips depends on how many barges are used in the same expedition. Routes with more than three modes tend to be inefficient which suggest that there is a limit for multimodality.

Corridor benchmarking is a rare topic in the literature and previous works normally focus on some specific and limited corridor performance characteristics, such as cost. The main contribution of this research is that it expands the discussion regarding corridor benchmarking and it focuses on efficiency as a whole. The paper also proposes a method that can be applied in different logistics contexts, like expanding the study to different countries. More specifically, this method could be used in infrastructure investments programs.