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The purpose of this paper is to present an algorithm of real estate mass appraisal in which the impact of attributes (real estate features) is estimated by inequality restricted least squares (IRLS) model.

This paper presents the algorithm of real estate mass appraisal, which was also presented in the form of an econometric model. Vital problem related to econometric models of mass appraisal is multicollinearity. In this paper, a priori knowledge about parameters is used by imposing restrictions in the form of inequalities. IRLS model is therefore used to limit negative consequences of multicollinearity. In ordinary least squares (OLS) models, estimator variances might be inflated by multicollinearity, which could lead to wrong signs of estimates. In IRLS models, estimators efficiency is higher (estimator variances are lower), which could result in better appraisals.

The final effect of the analysis is a vector of the impact of real estate attributes on their value in the mass appraisal algorithm. After making expert corrections, the algorithm was used to evaluate 318 properties from the test set. Valuation errors were also discussed.

Restrictions in the form of inequalities were imposed on the parameters of the econometric model, ensuring the non-negativity and monotonicity of real estate attribute impact. In case of real estate, variables are usually correlated. OLS estimators are then inflated and inefficient. Imposing restrictions in form of inequalities could improve results because IRLS estimators are more efficient. In the case of results inconsistent with theoretical assumptions, the real estate mass appraisal algorithm enables having the obtained results adjusted by an expert. This can be important for low quality databases, which is often the case in underdeveloped real estate markets. Another reason for expert correction may be the low efficiency of a given real estate market.

This paper seeks to provide an exploratory empirical study of the variables that are part of the return on trade show information (RTSI) concept, which is based on the use and value of information gathered at a trade show.

The research is designed to explore relationships and identify those variables that are a particularly important part of the RTSI concept. The paper provides an exploratory test of the relationship between a series of variables that are related to the value of information gathered at trade shows. Data were collected from trade show attendees approximately 60 days after the trade show. A multiple regression model was developed that explores the relationship between the dependent variable that focuses on information value and the independent variables on various aspects of information acquisition, information dissemination, and information use.

The final multiple regression model found a significant relationship for several variables and has an adjusted R² value of 0.552. Four significant independent variables were identified – one each in the information use and the shared information categories and two in the information acquisition category. These findings present an interesting picture of how information is used within an organization after it is acquired at a trade show.

The research is limited by the multiple regression model used to explore the relationships in the data. Also, data from only one trade show were used in the model.

This paper focuses on the intangible, longer‐term benefits as important considerations when determining the value of new trade show information to the firm. The evaluation of trade show information also should include these intangible benefits, such as improved interdepartmental relations or interactions as well as discussions with other trade show participants in finding new uses for information that impacts the company's future success, as well as shorter‐term benefits such as booth activity.

The paper offers a unique approach for determining the value of information acquired at trade shows. Though information gathering has been included as an outcome variable in previous trade show studies, no other research has studied the value of this new trade show information to the company.

This study aims to apply a systematic statistical approach, including several plot indexes, to diagnose the goodness of fit of a logistic regression model, and then to detect the outliers and influential observations of the data from experimental data.

The proposed statistical approach is applied to analyze some experimental data on internal solitary wave propagation.

A suitable logistic regression model in which the relationship between the response variable and the explanatory variables is found. The problem of multicollinearity is tested. It was found that certain observations would not have the problem of multicollinearity. The P‐values for both the Pearson and deviance χ² tests are greater than 0.05. However, the Pearson χ² value is larger than the degrees of freedom. This finding indicates that although this model fits the data, it has a slight overdispersion. After three outliers and influential observations (cases 11, 27, and 49) are removed from the data, and the remaining observations are refitted the goodness‐of‐fit of the revised model to the data is improved.

A comparison of the four predictive powers: R², max‐rescaled R², the Somers' D, and the concordance index c, shows that the revised model has better predictive abilities than the original model.

The goodness‐of‐fit and prediction ability of the revised logistic regression model are more appropriate than those of the original model.

The purpose of this paper is to find out the factors affecting rentals of residential apartments in Pune, India.

Four regression models are developed, i.e. basic ordinary least square (OLS) regression model, OLS regression model with robust estimates, OLS regression model with clustered robust estimates and generalized least square (GLS) regression model with maximum likelihood (ML) robust estimates. Based on the Akaike information criterion and Bayesian information criterion criteria, OLS regression model with clustered robust estimates and GLS regression model with robust estimates are best fit. The data are tested for multicollinearity and the models are tested for heteroscedasticity. The study uses the expected rent value data collected from Web portals and the data on factors affecting the rental value of residential property are collected through the study of land use maps, Google earth software and field visits.

Total floor area and number of rooms are structure related factors that positively affect the rental value, i.e. more the area and number of rooms, higher the rental value. The distances from the nearest police station and fire station are security and safety factors. The results suggest that higher distance from these factors leads to lower rental values, as safety and security is the top priority of residents seeking residential property on rental basis. The distance from employment zones, distance from nearest school/college and the distance from the nearest public transport terminal are convenience related factors that negatively affect the rental value, as greater the distance, lesser the rental value and vice versa. The distance from Central Business District and hospitals has a positive effect on the rental values of a residential property implying that higher distances from these places command higher rental value.

The study relies on rental data that owner is expecting for a particular property, it is not certain that the property would be actually rented for the same value. Second, researchers had to drop certain important drivers of rental value because of the issue of multicollinearity.

This is one of the rare studies conducted in Indian context, and the findings of the study are useful from the owner, tenants, urban bodies and developers’ point of view. Knowing that India is one of the fastest growing markets and need for housing is increasing day by day (including housing facility on rental basis), the stakeholders need to take care of the factors that affect the rental values of a residential property.

The authors suggest the governments and the municipal bodies in India to come up with a public rental housing policy that separately caters to the needs of the lower income group, middle and upper income group in at least metros, tier I and tier II cities that are witnessing unprecedented growth in job seeking immigrants, who are seeking properties on rental basis. While developing a public rental policy, they must keep in mind the factors that are driving the rental values, such as proximity to employment zones, proximity to proper school and college, efficient public transport system as well as all safety and security measures. Creation of such a public rental policy is a win–win situation for immigrants, property owners and government/urban development bodies.

This paper is the first empirical study about the factors affecting rental values in Pune, India. The study will help property owners, immigrant and local tenants, government and urban development bodies to develop an understanding about the important factors affecting rental value and come up with their respective plans. Advanced econometric regression models are used based on the data that is collected through actual field visits, study of maps and secondary information rather than use of survey method or creation of dummy variables.

The purpose of this paper is to propose an analysis method based on a hybrid model, which combines principal component regression (PCR) model and general regression neural network (GRNN) to solve both multicollinearity problems and non‐linear problems at the same time.

First, the financial ratio data of companies with stocks listed in regular stock market and over‐the‐counter stock market in Taiwan and Mainland China are collected and used as sample data. Grey relational analysis is used to rank the enterprises' operation performance, and the enterprises in Taiwan and Mainland China with business operation performance in the first place are selected and their stock information collected to perform the prediction of stock closing price.

Five indices such as the root mean square error, revision Theil inequality coefficient, mean absolute error, mean absolute percentage error and coefficient of efficiency of the test result are calculated; the empirical results show that the prediction power of the hybrid model of PCR+genetic algorithm general regression neural network is obviously better than the model of PCR, GRNN and PCR+GRNN.

The paper adopts a hybrid model and parameter adjustment to increase prediction capability.

The purpose of this paper is to find out the best method for forecasting European Union Allowance (EUA) returns and determine its price determinants. The previous studies in this area have focused on a particular subset of EUA data and do not take care of the multicollinearities. The authors take EUA data from all three phases and the continuous series, adopt the principal component analysis (PCA) to eliminate multicollinearities and fit seven different homoscedastic models for a comprehensive analysis.

PCA is adopted to extract independent factors. Seven different linear regression and auto regressive integrated moving average (ARIMA) models are employed for forecasting EUA returns and isolating their price determinants. The seven models are then compared and the one with minimum (root mean square error is adjudged as the best model.

The best model for forecasting the EUA returns of all three phases is dynamic linear regression with lagged predictors and that for forecasting EUA continuous series is ARIMA errors. The latent factors such as switch to gas (STG) and clean spread (capturing the effects of the clean dark spread, clean spark spread, switching price and natural gas price), National Allocation Plan announcements events, energy variables, German Stock Exchange index and extreme temperature events have been isolated as the price determinants of EUA returns.

The current study contributes to effective carbon management by providing a quantitative framework for analyzing cap-and-trade schemes.

This study differs from earlier studies mainly in three aspects. First, instead of focusing on a particular subset of EUA data, it comprehensively analyses the data of all the three phases of EUA along with the EUA continuous series. Second, it expressly adopts PCA to eliminate multicollinearities, thereby reducing the error variance. Finally, it evaluates both linear and non-linear homoscedastic models incorporating lags of predictor variables to isolate the price determinants of EUA.

The aim of this paper is to develop a new analysis method, named dynamic extreme bounds analysis (DEBA), and to determine decisive determinants of foreign direct investment (FDI) by using this new method.

In econometrics, the extreme bounds analysis (EBA) method is a convincing way of examining the strength of independent variables. However, the results obtained when using the EBA method contain little information, since each variable is only either strong or fragile, and some strong variables may be omitted because their significance could be undermined by just one unreasonable regression. Therefore, in order to overcome these limitations, this paper proposes DEBA, a new analysis method.

The authors employ the DEBA method to determine the factors which impact FDI in 86 countries. The authors note that in developing countries, the level of previous FDI, a high degree of openness, large market size and development of infrastructure help to attract FDI, whereas the development of domestic industry deters it. In developed countries, FDI is lured by the level of previous FDI stock, a high degree of openness, large market size, macroeconomic instability and availability of energy.

Although this study is expected to contribute a new methodological approach and define the strong determinants of FDI, the study is not without limitations, such as the unavailability of data. Further studies should improve the DEBA method by developing DEBA packages for use in popular statistical software, enhancing methods for other types of data and more accurately determining the estimation order of variables. In addition, further research should expand the study's FDI model, providing more potential variables for an in-depth overview of this model.

This study is to contribute a new methodological approach (DEBA method) for data analysis and defining of strong determinants of FDI. The study findings are useful for governments, policy-makers and economists in formulating more attractive FDI policies.

This paper considers the determinants of residential values from a hierarchical approach based on market experience using a system of Likert Scaling. The approach is tested initially by statistical methods based on a stepwise selection in an ordinary least squares model. The results, however, seem to indicate the presence of multicollinearity, and ridge regression is used to screen the variables and to help select the appropriate model. The study was carried out in Singapore in relation to the rental value of a cluster of 114 high rise apartments in the prime Orchard Road area. The data were collected by Yeo Swee Ching, and both he and Lee Hin Tak used them as a basis for their student dissertations under the author's guidance. This paper sets out some of the methodology and findings of the investigations.