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The commercial property market is complex, but the literature suggests that simple models can forecast it. To confirm the claim, the purpose of this paper is to assess a set of models to forecast UK commercial property market.

The employs five modelling techniques, including Autoregressive Integrated Moving Average (ARIMA), ARIMA with a vector of an explanatory variable(s) (ARIMAX), Simple Regression (SR), Multiple Regression, and Vector Autoregression (VAR) to model IPD UK All Property Rents Index. The Bank Rate, Construction Orders, Employment, Expenditure, FTSE AS Index, Gross Domestic Product (GDP), and Inflation are all explanatory variables selected for the research.

The modelling results confirm that increased model complexity does not necessarily yield greater forecasting accuracy. The analysis shows that although the more complex VAR specification is amongst the best fitting models, its accuracy in producing out-of-sample forecasts is poorer than of some less complex specifications. The average Theil’s U-value for VAR model is around 0.65, which is higher than that of less complex SR with Expenditure (0.176) or ARIMAX (3,0,3) with GDP (0.31) as an explanatory variable models.

The paper calls analysts to make forecasts more user-friendly, which are easy to use or understand, and for researchers to pay greater attention to the development and improvement of simpler forecasting techniques or simplification of more complex structures.

The paper addresses the issue of complexity in modelling commercial property market. It advocates for simplicity in modelling and forecasting.

The housing market in Kenya continues to experience an excessive imbalance between supply and demand. This imbalance renders the housing market volatile, and stakeholders lose repeatedly. The purpose of the study was to forecast housing prices (HPs) in Kenya using simple and complex regression models to assess the best model for projecting the HPs in Kenya.

The study used time series data from 1975 to 2020 of the selected macroeconomic factors sourced from Kenya National Bureau of Statistics, Central Bank of Kenya and Hass Consult Limited. Linear regression, multiple regression, autoregressive integrated moving average (ARIMA) and autoregressive distributed lag (ARDL) models regression techniques were used to model HPs.

The study concludes that the performance of the housing market is very sensitive to changes in the economic indicators, and therefore, the key players in the housing market should consider the performance of the economy during the project feasibility studies and appraisals. From the results, it can be deduced that complex models outperform simple models in forecasting HPs in Kenya. The vector autoregressive (VAR) model performs the best in forecasting HPs considering its lowest root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and bias proportion coefficient. ARIMA models perform dismally in forecasting HPs, and therefore, we conclude that HP is not a self-projecting variable.

A model for projecting HPs could be a game changer if applied during the project appraisal stage by the developers and project managers. The study thoroughly compared the various regression models to ascertain the best model for forecasting the prices and revealed that complex models perform better than simple models in forecasting HPs. The study recommends a VAR model in forecasting HPs considering its lowest RMSE, MAE, MAPE and bias proportion coefficient compared to other models. The model, if used in collaboration with the already existing hedonic models, will ensure that the investments in the housing markets are well-informed, and hence, a reduction in economic losses arising from poor market forecasting techniques. However, these study findings are only applicable to the commercial housing market i.e. houses for sale and rent.

While more research has been done on HP projections, this study was based on a comparison of simple and complex regression models of projecting HPs. A total of five models were compared in the study: the simple regression model, multiple regression model, ARIMA model, ARDL model and VAR model. The findings reveal that complex models outperform simple models in projecting HPs. Nonetheless, the study also used nine macroeconomic indicators in the model-building process. Granger causality test reveals that only household income (HHI), gross domestic product, interest rate, exchange rates (EXCR) and private capital inflows have a significant effect on the changes in HPs. Nonetheless, the study adds two little-known indicators in the projection of HPs, which are the EXCR and HHI.

The financial crisis has led the Basel Committee to improve the system of capital requirements for market risks. This paper aims to investigate the effects of different models to estimate the market risk in the management of the trading book. The study takes into account the events occurring in the financial markets and the new prudential rules.

The author compares different models and proposes an opportunity cost function able to evaluate the cost related to capital requirements. He identified seven asset classes and studies the effects of different models for estimating VaR simulating financial portfolios with increasing risk. The series consists of the daily return from 01/01/2002 to 06/30/2012.

The results show that it is possible to identify a wide area between aggressive and conservative approach where the bank management must choose. The regulation does not encourage intermediaries to the use of complex models that could better evaluate the risk in financial markets. The revision of the market risk framework increases the capital requirement and reduces the incentive to use models with more predictive power for regulatory purposes.

The work differs from previous contributions for three characteristics: first, it uses a set of extended data and more consistent with the actual operation. Secondly, the author presents an opportunity cost function in order to evaluate the estimation models. Third, he calculates the effect of stressed‐VaR after a year and a half of adoption.

The purpose of this paper is to simulate possoble scenarios of São Paulo’s office market recovering. In 2006, a previous paper that dealt with the same issue was published which the authors propose to analyse here. After eight years, the São Paulo office market is starting a new phase within its cycle. Then, the first part of this paper, as in Rocha-Lima and Alencar (2006), describes the economic scenario in which investment decisions are made for developing office buildings in the Brazilian market. Afterward, the authors simulated both the necessary period of time for investments in the São Paulo office market to recover attractiveness and time for the increase in the occupation rate to absorb the current vacant spaces.

These simulations were carried out using simple linear regressions models using the Brazilian gross domestic product (GDP) as explanatory variable to prices and vacancy rates dependent ones.

The authors have found that the vacant space can be fully re-occupied in the beginning of 2021 or mid of 2022, according to the GDP growth rate, and, from this moment on, the demand for new spaces may grow, and, moreover around 2019, investments may become attractive again in this market.

This paper offers an alternative approach for estimating office building scenarios, especially when the database of the market is scarce. It also permits to evaluate an investment strategy for emerging markets within next years, particularly in São Paulo, Brazil.

In this chapter, we describe how random utility maximization (RUM) discrete choice models are used to estimate the demand for commodity attributes in quality-differentiated goods. After presenting a conceptual overview, we focus specifically on the conditional logit model. We examine technical issues related to specification, interpretation, estimation, and policy use. We also discuss identification strategies for estimating the role of price and non-price attributes in preferences when product attributes are incompletely observed. We illustrate these concepts via a stylized application to new car purchases, in which our objective is to measure preferences for fuel economy.

The recent 2008–2009 financial crisis has led international financial authorities to review the existing regulation; the Basel Committee on Banking Supervision has been thus induced to review the pillars of the Basel Accord (Basel II) in order to strengthen the risk coverage of capital framework (Basel 2.5 and III). These reforms will help to raise capital requirements for the trading book, which represents a major source of losses for internationally financial institutions, especially during crisis periods. In particular, the Committee has introduced a Stressed Value-at-Risk (SVaR) capital requirement, as a new methodology to evaluate market risk.

This chapter aims to shed some lights on the issues major banks have to face when calculating SVaR in the context of emerging markets, pointing out the differences in adopting an estimation model with respect to another one. Our results show a considerable increase in capital requirements especially when new rules are applied to financial markets with high-risk parameters, such as emerging markets are. The increased cost due to higher capital requirements could be a disincentive to investment in markets with higher risk profiles than the developed markets, taking also into account that diversification benefits deriving from investing in emerging economies have shown a decrease over time. The reduction of institutional investors can thus represent a brake on the process of innovation and evolution of emerging markets.

The purpose of this paper is to describe the tool and procedure developed in order to design the control laws of several UAV (Unmanned Aerial Vehicle) sub‐systems. The authors designed and developed the logics governing: landing gear, nose wheel steering, wheel braking, and fuel system.

This procedure is based on a general purpose, object‐oriented, simulation tool. The development method used is based on three‐steps. The main structure of the control laws is defined through flow charts; then the logics are ported to ANSI‐C programming language; finally the code is implemented inside the status model. The status model is a Matlab‐Simulink model, which uses an embedded Matlab‐function to model the FCC (Flight Control Computer). The core block is linked with the components, but cannot access their internal model. Interfaces between FCCs and system components in the model reflect real system ones.

The user verifies systems' reactions in real time, through the status model. Using block‐oriented approach, development of the control laws and integration of several systems is faster.

The tool aims to test and validate the control laws dynamically, helping specialists to find out odd logics or undesired responses, during the pre‐design.

The development team can test and verify the control laws in various failure scenarios. This tool allows more reliable and effective logics to be produced, which can be directly used on the system.

The paper aimed to explore the underlying work engagement role in transformational leadership and employees' job performance relationships. Moreover, this study also looked at the moderation of leaders' managerial skills in the transformational leadership and work engagement nexus.

The time-lagged data of 360 followers — leader dyads nested in 71 teams were collected from star-rated hotels in Italy and structural equation modeling (SEM) analysis was executed.

Based on social learning theory and idiosyncrasy credit theory, the SEM results demonstrate that transformational leadership is significantly and positively linked with job performance and work engagement mediated this relationship. The results also confirmed that leaders' managerial skills strengthen the transformational leadership and work engagement nexus.

Hotel managers should consider hiring individuals with transformational leadership skills and provide training to Polish their managerial skills in order to enhance employee engagement at work, which may result in job performance.

With this study, the researchers emphasize the significance of transformational leadership and work engagement for better job performance in the Italian hospitality industry. Our analysis also provides new evidence that leaders' managerial skills strengthen the transformative leadership and work engagement nexus. The study is one of the first to investigate the boundary conditions of leaders' managerial skills in the transformational leadership and work engagement relationship. Based on the findings, the practical and theoretical contributions are also discussed.

Millennial generation is fashion inclined, interactive and informative social beings. They are very conscious of the brands they wear. Millennia seek, share, inform and exchange fashion brand-related information on social networking sites (SNS). Marketers are subsequently engaging the young prospects and customers to keep up or improve enthusiasm and participation. The study attempts to investigate the role of customer-brand engagement (CBE) of millennials with fashion brands on SNS. The study simultaneously tests the moderating role of involvement levels affecting participation and CBE leading to brand loyalty.

The study followed a purposive sample by collecting 466 respondents from the graduate students of a university. The study adopted structural equation modelling (SEM) and Hayes process macros in SPSS 20.0 to test the moderated-mediation model.

The study confirms the mediating role of CBE in the relationship between participation and brand loyalty. Different degrees of involvement moderate the mediating role of CBE. Higher levels of involvement enhance the positive effect of participation on CBE.

The study is first of its kind to investigate the role of CBE and involvement among the millennial group. It also contributes to the related theories like service-dominant logic, social exchange theory and consumer culture theory regarding a unique population group, which is promising and profitable.