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The growth of lending from nontraditional lenders may pose challenges for official US Department of Agriculture (USDA) farm sector debt estimates, but it is difficult to find data to assess official estimates. The purpose of this study is to examine whether debt provided by nontraditional lenders is accurately accounted for in official estimates.

We compare traditional and nontraditional lending data from farm equipment lien collateral values and the USDA Agricultural Resource Management Survey (ARMS). After analyzing trends in equipment lending implied by farm equipment lien data and ARMS, we estimate whether changes in farm equipment lien values predict changes in equipment debt reported in ARMS and whether lender type influences that relationship.

We find that credit provided by nontraditional lenders is likely underreported in ARMS. Our econometric model shows that equipment debt volumes for nontraditional lenders are consistently lower than traditional loan volumes in ARMS across a variety of model specifications. We also find that an increase in lien values for nontraditional lenders is less likely to predict an increase in ARMS equipment debt volumes than an increase for traditional lenders.

Official farm sector debt estimates may not fully account for nontraditional lenders.

This study demonstrates how the growth of nontraditional lending poses challenges for estimating US farm sector debt. We evaluate farm sector debt estimates and advance knowledge of the role of nontraditional lenders in farm equipment credit provision. The farm equipment lien dataset provides a rich source of novel data for research on local and national equipment debt and investment.

Carbon management in the construction industry plays a vital role as carbon emissions have a significant impact on the environment. Current emphasis on reducing operational carbon through passive designs, zero carbon buildings and so forth has resulted in losing focus on embodied carbon (EC) reduction. Though there are various databases and tools to estimate EC in construction, these estimates are lacking in accuracy and consistency. A Blockchain-based Embodied Carbon (BEC) Estimator was developed as a solution to accurately estimate EC using a supply chain value addition concept as a methodology.

This study focused on developing, testing and validating the blockchain-based prototype system identified as BEC Estimator. The system was developed using Hyperledger Fabric following a waterfall model. Case studies and an expert forum were used to test and validate BEC Estimator.

The system architecture, development process and the user interface of BEC Estimator are presented in this paper. The comparative evaluation with existing EC databases/tools and the expert forum validated the functioning of BEC Estimator and proved it to be an accurate, secure and trustworthy EC estimating system. SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis identified the strengths and opportunities that will benefit the industry as well as the weaknesses and threats in the system that could be mitigated in future.

BEC Estimator accurately accounts for EC additions happening at each supply chain node for any product that gets incorporated in a building, thereby facilitating EC-related decision-making for all relevant stakeholders.

Valuers should independently assess market value. The purpose of this article is to analyze whether the valuation behavior remains independent when commissioned by publicly listed companies in Taiwan.

This study used both quantitative and qualitative methods. Quantitative data analysis was used to examine the estimated premium ratio and estimated divergent ratio with the independent sample t test and Wilcoxon-Mann-Whitney test. To complement and validate the quantitative analysis, open-ended questionnaires were conducted, providing additional insights into the research findings.

The results showed that there is a significant difference in estimated valuations commissioned by representatives of buyers and sellers, and the estimated premium ratios commissioned by representatives of buyers were higher than those of sellers. Furthermore, the open-ended questionnaires results indicate that these findings may be influenced by clients for less experienced appraisers. However, for senior appraisers, this is seen as an action to gain a better understanding of the valuation purpose and always within a reasonable price range. In addition, client influence is not a static factor; it may transform into the valuer's behavior as the appraiser's experience grows and deepens.

It is difficult to obtain valuation reports commissioned by representatives of both buyers and sellers for the same property transactions. In this study, data were obtained from the Market Observation Post-System (MOPS) in Taiwan. As valuation reports could not be obtained, estimated valuations and transaction prices are used to calculate estimated premium ratio and estimated divergent ratios.

Previous investigations of the client effect have been conducted using qualitative methods including questionnaire surveys, in-depth interviews and experimental design. However, these studies are subject to moral hazard. This study may be the first study that has access to data on valuations for both buyers and sellers in such a formal setting.

This study is carried out to demonstrate the computational practicalities of environmental construction economics necessary to offer early-stage cost advice. A case study of a private sector client’s development proposal is used. This is for the acquisition of a vacant freehold land of 1.2 acres brownfield site to develop a Grade A office complex with plans to achieve the BREEAM Excellent rating green building certification.

A three-stage methodology was deployed: Order of cost estimating, before life cycle costing and then development appraisal. The Order of Cost Estimate is generated using the BCIS online database, following the procedural guideline of the New Rules Measurement (NRM). The life cycle costing was carried out from an environmental perspective to explore two design options – Design A and Design B, in terms of which would offer the best value for money whilst reducing carbon emissions.

Based on the outcome of the life cycle costing computations, Design B was chosen as the advised development due to minimal differences in net present values and annual equivalents. Further evaluation of Design B, using the residual method of developmental appraisal was carried out, with all necessary assumptions made. From the extensive computations carried out, the project is considered unviable, as it reports a loss. Alternative use of the site or an alternative site is thus recommended to check if a greater return on investment is tenable.

The study narratively interweaves the application of three computational techniques that are core to offering early-stage cost advice.

The purpose of this paper is to propose a new vulnerability to income and multidimensional poverty estimation index (VIMPI). This index is designed to measure the likelihood of individuals falling into and remaining in poverty.

The paper introduces a new methodology that integrates the concepts of the well-being gap, individual and indicator-specific weighting, and vulnerability. This approach is simple to apply and accurately measures vulnerability with less susceptibility to measurement error and outliers. The index satisfies all poverty and vulnerability axioms, including transferability and monotonicity. The newly proposed method has been applied to Namibian and Ghanaian data and compared with similar techniques.

The results showed that Ghana's vulnerability to income and multidimensional poverty was 37.9% and 56%, respectively. Of the 37.9% of vulnerable individuals, 23.4% were at risk of falling into poverty, while 14.57% were at risk of remaining in poverty. These findings demonstrate the effectiveness of VIMPI in accurately estimating vulnerability to poverty and its potential to inform targeted policies to alleviate poverty.

This paper proposes a new methodology to estimate vulnerability to income and multidimensional poverty.

While much of the literature testing for shirking by professional athletes have used performance metrics, some works have quantified shirking in dollar terms by comparing salary to estimated marginal revenue product (MRP). However, Ordinary Least Squares (OLS) approaches to measuring shirking by comparing salary to MRP have an endogeneity problem, as salary and contract length are determined simultaneously. We test for shirking in Major League Baseball (MLB) using an MRP approach, addressing this potential endogeneity.

This paper uses instrumental variables regression to address potential endogeneity using MLB season-level player and team data from 2010 to 2017.

Using OLS regression, the impact of an additional year of guaranteed contract on shirking is estimated at approximately $1m in 2010 US dollars, and the impact of having a long-term contract is estimated at $5m, estimates comparable to those in the literature. Using instrumental variables regression, these impacts increase to $1.6m and over $9m in 2010 dollars.

Given large, causal shirking estimates, profit maximizing sports organizations should take caution when negotiating long-term contracts. These findings also have important implications for other labor market settings where workers feel job security.

To our knowledge, this is the first work testing for shirking in sports using an MRP approach which uses instrumental variables regression to address potential endogeneity.

We model the relation between excess returns, fund size and industry size for active equity funds.

We study and contrast four markets – global equities, emerging markets, Australia core and Australia small caps – and use the results to investigate the extent to which funds deviate from estimated capacity.

We uncover a significantly negative relation between returns and both fund size and industry size across all markets. The estimated percentage of funds operating above versus below capacity varies both across markets and over time, as does the role played by fund size versus industry size. We find a greater prevalence of funds operating significantly below than above capacity, in contrast to findings for US equity mutual funds. Significant deviations from estimated capacity persist for a median of between two and six quarters.

Our main contribution is to show that the dynamics governing deviations from capacity for active equity funds vary across markets.

Audit trail cost management is crucial for ensuring accountability and enhancing quality assurance in construction management. Despite limited practical studies on audit trail management from a cost perspective; this study developed a lifecycle-based audit trail cost management framework. It used synchronized Building Information Modeling (BIM) cost models and Bills of Quantities (BoQs) to address the existing gap.

This study employed a descriptive case study approach of a real-life hospital project in China. Data triangulation was achieved through interviews, observations, documents, and relevant artifacts.

The study identified three key factors contributing to cost variances between BIM cost models and BoQs: differences in measurement rules, model precision, and professional errors, particularly evident during the preliminary estimate stage. Notably, significant cost savings of approximately RMB 5.811 million were achieved during the detailed estimate stage. During the construction phase, a synchronized approach was deployed to improve precise payment verification and modifications to the BIM model. In the post-construction phase, the synchronized as-built BIM models and BoQs served as primary references to facilitate the resolution of operational discrepancies.

The research contributes to the literature by proposing a synchronized approach of BIM cost models and BoQs. This approach enhances traceability and accountability of project information, catering to the digitalization needs of the construction industry.

This study unveils a pragmatic approach to enhancing transparency and accountability in audit-trail cost management by synchronizing BIM cost models and BoQs at various project stages. The synchronized approach offers a promising direction for future research and implementation of audit trail frameworks to enhance cost management in construction.

We propose an economic model of housing markets. The model incorporates the macroeconomic relationships between prices, demand and supply. Since vacancy rates are not observable, the demand-supply mismatches are identified using a microeconomic model of search, matching and price formation. The model is applied to data on regional housing markets in England and Wales.

Economic theory combining macroeconomics and microeconomics together with new generation econometric methods for empirical analysis.

The empirical model, estimated for the ten government office regions of England and Wales, validates the economic model. We find that there is substantial heterogeneity across the regions, which is useful in informing housing and land-use policies. In addition to heterogeneity, the model enables us to better understand unrestricted inter-regional spatial relationships. The estimated spatial autocorrelations imply different drivers of spatial diffusion in different regions.

In the nature of other empirical work, the findings are subject to specificities of the data considered here. The understanding of spatial diffusion can also be further developed in future work.

This paper develops a nice way of closing macroeconomic models of housing markets when complete demand, supply and pricing data are not available. The model may also be useful when data are available but with large measurement errors. The model comes together with corresponding empirical methods.

Implications for the housing market and other regional policies are important. These are context-specific, but some implications for housing policy in the UK are provided in the paper as an example.

Unique housing market paper combining both macroeconomic and microeconomic theory as well as both theory and empirics. The rich framework so developed can be extended to much future work.

The use of statistical methods in the field of real estate appraisals presents a trade-off between the efficiency of the estimates (that would require the use of sophisticate econometric models) and the ease of the economic interpretation of the outcomes (that characterises the hedonic pricing models). This paper shows that multilevel modelling (MLM) can represent a suitable solution to this trade-off.

This paper uses the so-called “multilevel modelling” (henceforth MLM). MLM can represent a further step forward in the use of statistical methods in property appraisals. MLM is easy to implement and the MLM estimates have a clear economic meaning. Furthermore, MLM provides more efficient estimates of hedonic prices (the prices of housing attributes) with respect to standard hedonic pricing models. Finally, MLM is particularly suitable for the housing market analysis, where the feature “location” plays a key role.

For the Italian context, characterised by many “benchmark locations” (small municipalities that share similar geographic, historic, and socioeconomic characteristics), the paper finds that multilevel modelling (MLM) is needed to correctly estimate the hedonic prices also in a micro-area.

MLM allows to further enhance the key role of “location”. Location is indeed used as the “grouping variable” in MLM, instead of being treated as a generic housing attribute in hedonic pricing models. When the benchmark locations are many, therefore, MLM represents a very effective compromise between the estimates’ efficiency and the ease of outcomes’ economic interpretation.

Unlike the related literature that, basically, use MLM to investigate what are the main determinants (levels) of housing prices, this paper uses MLM to make more efficient the estimation of hedonic prices.