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Summary This paper, which was originally presented as the inaugural lecture of the Centre for Studies in Propety Valuation and Management at the City University in March 1987 analyses and explains the various options, both currently available and shortly to become available, which are open to a developer faced with the task of funding a particular scheme. To fulfil these objectives, a hypothetical retail mega‐development has been invented, realistic figures used and the various funding scenarios then applied to it, each on their own merit, with a view to selecting that which most suited the scheme. By way of introduction, the paper looks at the financial resources of the typical property company and then at the parameters which determine the commercial viability of a scheme. The main text then moves on to analyse each of the funding options available starting with short term borrowing, then looking at forward funding and commitment, then the mortgage alternative and finally a look towards the future with an explanation and application of unitisation to the hypothetical scheme. The paper concludes by comparing the various options set out above, giving the would be developer an indication of the merits and drawbacks of each.

The recent coronavirus disease 2019 (COVID-19) pandemic poses numerous challenges to supply chains. This pandemic is quite unique when compared to previous epidemic disruptions and has had a severe impact on supply chains. As a result, the operational challenges (OCs) caused by COVID-19 are still unknown among practitioners and academics. It is critical to comprehensively document current OCs so that firms can plan and implement strategies to overcome them. Consequently, this study systematically identifies and ranks COVID-19-related OCs.

This study uses an integrated methodology combining expert interviews and the best-worst method (BWM) to analyze the results. The data have been collected from the electronics industry of Bangladesh, an emerging economy. This study also conducts a sensitivity analysis to check the robustness of the results.

The results reveal 23 COVID-19-related OCs under five categories: sourcing, production and inventory management, demand management and distribution, return management and after-sales service, and supply chain-wide challenges. The quantitative investigation reveals that overstock in finished goods inventory, low end-customer demands, order cancellations from dealers and retailers, high inventory holding costs and lack of transportation are the top five OCs.

The findings will help practitioners to understand the OCs and allow them to prepare for future major disruptions and formulate long-term strategies for operations during and after the COVID-19 pandemic.

This study contributes to the literature on supply chain complexity and challenges by considering a major pandemic outbreak. Moreover, the study also contributes to the knowledge on emerging economies, which have been largely neglected in the current literature.

Before we consider the subject matter of this paper we should briefly remind ourselves as to what investment is! Basically, this involves a sacrifice of something now for the prospect of a future gain. This will inevitably cause the individual or company to give up the purchase of goods or services today in order to achieve greater consumption in the future, despite the uncertainty of the return of the investment made.

This chapter addresses the related questions of how to assess housing redevelopment and what constitutes a successful redevelopment project, based on the HOPE VI transformation of Boston’s Orchard Park from one of the city’s most notorious, crime-ridden public housing projects into a mixed-income community that remained overwhelmingly composed of low-income residents.

The analysis is based on a unique set of interviews with a sample of residents before and after housing redevelopment occurred. In addition, we draw upon interviews with housing authority staff, official agency file documents, and archival materials.

We find increased residential satisfaction after redevelopment but lingering concerns about safety and security despite marked declines in crime. Although the redevelopment process displaced some households, residents attributed improvements in living conditions to changes in tenant composition prompted by the housing transformation.

The results suggest an alternative model of public housing redevelopment that accommodates a majority of poor, subsidized households with some displacement. Still, loss of housing units, tenant selection, and social problems complicate notions of successful redevelopment.

This chapter contributes to the literature by showing how some low-income families may benefit from housing displacement induced by the redevelopment process. We analyze an overlooked but frequently implemented approach to housing redevelopment under the HOPE VI program to keep the majority of redeveloped units for low-income residents. It is the only study of which we are aware that has collected public housing resident opinions both before and after HOPE VI redevelopment occurred.

This study aims to examine the technical efficiency of the chemical-free farming system in India using a hybrid combination of data envelopment analysis (DEA) and machine learning (ML) approaches.

The study used a two-stage approach. In the first stage, the efficiency scores of decision-making units’ efficiency (DMUs) are obtained using an input-oriented DEA model under the assumption of a variable return to scale. Based on these scores, the DMUs are classified into efficient and inefficient categories. The 2nd stage of analysis involves the identification of the most important predictors of efficiency using a random forest model and a generalized logistic regression model.

The results show that by using their resources efficiently, growers can reduce their inputs by 34 percent without affecting the output. Orchard's size, the proportion of land, grower's age, orchard's age and family labor are the most important determinants of efficiency. Besides, growers' main occupation and footfall of intermediaries at the farm gate also demonstrate significant influence on efficiency.

The study used only one output and a limited set of input variables. Incorporating additional variables or dimensions like fertility of the land, climatic conditions, altitude of the land, output quality (size/taste/appearance) and per acre profitability could yield more robust results. Although pineapple is cultivated in all eight northeastern states, the data for the study has been collected from only two states. The production and marketing practices followed by the growers in the remaining six northeastern states and other parts of the country might be different. As the growers do not maintain farm records, their data might suffer from selective retrieval bias.

Given the rising demand for organic food, improving the efficiency of chemical-free growers will be a win-win situation for both growers and consumers. The results will aid policymakers in bringing necessary interventions to make chemical-free farming more remunerative for the growers. The business managers can act as a bridge to connect these remote growers with the market by sharing customer feedback and global best practices.

Although many developments have happened to the DEA technique, the present study used a traditional form of DEA. Therefore, future research should combine ML techniques with more advanced versions like bootstrap and fuzzy DEA. Upcoming research should include more input and output variables to predict the efficiency of the chemical-free farming system. For instance, environmental variables, like climatic conditions, degree of competition, government support and consumers' attitude towards chemical-free food, can be examined along with farm and grower-specific variables. Future studies should also incorporate chemical-free growers from a wider geographic area. Lastly, future studies can also undertake a longitudinal estimation of efficiency and its determinants for the chemical-free farming system.

No prior study has used a hybrid framework to examine the performance of a chemical-free farming system.

The existing literature on linking growers directly with the market mostly overlooks the case of smallholders. They grow commercial–perishable crops and have to rely on the efficacy of the marketing system. The present paper intends to fill this void.

The paper studies the performance of two local markets among 216 pineapple producers and 50 traders using the structure–conduct–performance framework. Following which the authors attempt to unravel the determinants of growers' direct participation in the market and the impact of such involvement on the farm income using the Heckman two-stage treatment effect model.

The study analysis shows that the likelihood of growers’ direct participation in markets, found oligopolistic, increases with education, price information and family labor unit, while decreases with the growers' age, distance from market and the footfall of intermediaries at the farm gate. The second stage of the model has established a positive impact of participation on farm income.

The small sample size could restrict generalization. The authors used only operating efficiency as an indicator of the performance of the marketing system due to the unavailability of district-level time series data on pineapple pricing.

This study shows that local food markets are oligopolistic. Growers fetch very less share in consumers' price and become vulnerable to food insecurity. The study highlights the determinants of growers' direct participation in the local market and the impact of such involvement on farm income.