Testing the Temporal Stability of Accessibility Value in Residential Hedonic Prices

Purpose – This paper bridges the gap between, on the one hand, supply-driven (urban form and transportation networks) and demand-driven (action-based) accessibility to urban amenities and, on the other hand, house price dynamics as captured through panel hedonic modelling. It aims at assessing temporal changes in the valuation of accessibility, while ordering households’ priorities among access to labour market, schools and shopping outlets. Design/methodology/approach – Several indexes are built using a methodology developed by Thériault et al. (2005, published in Journal of Property Investment and Finance). They integrate car-based travel time on the road network (using GIS), distribution of opportunities (activity places) within the city, and willingness of persons to travel in order to reach specific types of activity places (mobility behaviour). While some measure centrality (potential attractiveness considering travel time, population and opportunities) others consist of action-based indexes using fuzzy logic and capture the willingness to travel in order to reach actual specific activity places (work places, schools, shopping centres, groceries). They summarise suitable opportunities available from each neighbourhood. Rescaled indices (worst - to 100 - best) are inserted simultaneously into a multiplicative hedonic model of single-family houses sold in Quebec City during years 1986, 1991 and 1996 (10,269 transactions). Manipulations of accessibility indexes are developed for ordering their relative impact on sale prices and isolate effects of each index on the variation of sale price, thus providing proxies of households’ priorities. Moreover, a panel-like modelling approach is used to control for changes in the valuation of each property-specific, taxation or accessibility attribute during the study period. Findings – This original approach proves efficient in isolating the cross-effects of urban centrality from accessibility to several types of amenities, while controlling for multicollinearity and heteroscedasticity. Results are in line with expectations. While only a few property-specific attributes experience a change in their marginal contribution to house value during the study period, all accessibility indexes do. Every single accessibility index has a much stronger effect on house values than centrality (which is still marginally significant). When buying their home, households put more emphasis on access to schools than they put on access to the labour market, which in turn, prevail over accessibility to either shopping centres or, finally, groceries. The ordering is rather stable but the actual valuation of a specific amenity may change over time. Practical implications – Better understanding the effect of accessibility to amenities on house values provides guidelines for choosing among a set of new neighbourhoods to develop in order to generate optimal fiscal effects for municipalities. It could also provide guidelines for decision making when improving transportation networks or locating new activity centres.

Cursive script recognition using wildcards and multiple experts

Variability in handwriting styles suggests that many letter recognition engines cannot correctly identify some hand-written letters of poor quality at reasonable computational cost. Methods that are capable of searching the resulting sparse graph of letter candidates are therefore required. The method presented here employs ‘wildcards’ to represent missing letter candidates. Multiple experts are used to represent different aspects of handwriting. Each expert evaluates closeness of match and indicates its confidence. Explanation experts determine the degree to which the word alternative under consideration explains extraneous letter candidates. Schemata for normalisation and combination of scores are investigated and their performance compared. Hill climbing yields near-optimal combination weights that outperform comparable methods on identical dynamic handwriting data

The impact of the fuzzy front end on new product development success in Japanese NPD projects

In a study of Japanese New Product Development (NPD) projects, the fuzzy front end of innovation is explored. Our conceptual model is based on the information-processing perspective. A structual equation model was fitted to data from 497 NPD projects from Japanese mechanical and electrical engineering firms to test the proposed model. The empirical analysis found support for all hypotheses except for one. Our study suggests that an early reduction of market and technical uncertainty and a draft initial planning prior to development have a positive impact on NPD project success. The model accounts for 17% of the variance of the efficiency and 24% of the variance of the effectiveness dependent variable. Thus, the front end phase is an important driver of NPD project success. Implications of the model are discussed. --

Applying the structural equation model rule-based fuzzy system with genetic algorithm for trading in currency market

The present study uses the structural equation model (SEM) to analyze the correlations between various economic indices pertaining to latent variables, such as the New Taiwan Dollar (NTD) value, the United States Dollar (USD) value, and USD index. In addition, a risk factor of volatility of currency returns is considered to develop a risk-controllable fuzzy inference system. The rational and linguistic knowledge-based fuzzy rules are established based on the SEM model and then optimized using the genetic algorithm. The empirical results reveal that the fuzzy logic trading system using the SEM indeed outperforms the buy-and-hold strategy. Moreover, when considering the risk factor of currency volatility, the performance appears significantly better. Remarkably, the trading strategy is apparently affected when the USD value or the volatility of currency returns shifts into either a higher or lower state.Knowledge-based Systems, Fuzzy Sets, Structural Equation Model (SEM), Genetic Algorithm (GA), Currency Volatility

Testing satistical hipotheses in fuzzy environment

In traditional statistics all parameters of the mathematical model and possible observations should be well defined. Sometimes such assumption appears too rigid for the real-life problems, especially while dealing with linguistic data or imprecise requirements. To relax this rigidity fuzzy methods are incorporated into statistics. We review hitherto existing achievements in testing statistical hypotheses in fuzzy environment, point out their advantages or disadvantages and practical problems. We propose also a formalization of that decision problem and indicate the directions of further investigations in order to construct a more general theory

Service Quality and Customer Loyalty in a Post-Crisis Context. Prediction-Oriented Modeling to Enhance the Particular Importance of a Social and Sustainable Approach

Research into the influence of service quality on customer loyalty has typically focused on confirming isolated direct causal influences regarding particular dimensions of quality, usually undertaken in the context of positive, firm-customer relations. The present study extends analysis of these factors through a new lens. First, the study was undertaken in a market context following a crisis that has had far-reaching consequences for customers’ relational behaviors. We explore the case of the Spanish banking industry, a sector that accurately reflects these new relational conditions, including a rising demand for more socially responsible banking. Second, we propose a holistic model that combines the effects of four key factors associated with service quality (outcome, personnel, servicescape and social qualities). We also apply an innovative predictive methodological technique using partial least squares (PLS) and qualitative comparative analysis (QCA) that enables us not only to determine the direct causal effects among variables, but also to consider different scenarios in which to predict customer loyalty. The results highlight the role of outcome and social qualities. The novelty of the social qualities factor helps to underscore the importance of social, ethical and sustainable practices to customer loyalty, although personnel and servicescape qualities must also be present to improve the predictive capability of service quality on loyalty

Modeling and Recognition of Smart Grid Faults by a Combined Approach of Dissimilarity Learning and One-Class Classification

Detecting faults in electrical power grids is of paramount importance, either from the electricity operator and consumer viewpoints. Modern electric power grids (smart grids) are equipped with smart sensors that allow to gather real-time information regarding the physical status of all the component elements belonging to the whole infrastructure (e.g., cables and related insulation, transformers, breakers and so on). In real-world smart grid systems, usually, additional information that are related to the operational status of the grid itself are collected such as meteorological information. Designing a suitable recognition (discrimination) model of faults in a real-world smart grid system is hence a challenging task. This follows from the heterogeneity of the information that actually determine a typical fault condition. The second point is that, for synthesizing a recognition model, in practice only the conditions of observed faults are usually meaningful. Therefore, a suitable recognition model should be synthesized by making use of the observed fault conditions only. In this paper, we deal with the problem of modeling and recognizing faults in a real-world smart grid system, which supplies the entire city of Rome, Italy. Recognition of faults is addressed by following a combined approach of multiple dissimilarity measures customization and one-class classification techniques. We provide here an in-depth study related to the available data and to the models synthesized by the proposed one-class classifier. We offer also a comprehensive analysis of the fault recognition results by exploiting a fuzzy set based reliability decision rule

A survey of cost-sensitive decision tree induction algorithms

The past decade has seen a significant interest on the problem of inducing decision trees that take account of costs of misclassification and costs of acquiring the features used for decision making. This survey identifies over 50 algorithms including approaches that are direct adaptations of accuracy based methods, use genetic algorithms, use anytime methods and utilize boosting and bagging. The survey brings together these different studies and novel approaches to cost-sensitive decision tree learning, provides a useful taxonomy, a historical timeline of how the field has developed and should provide a useful reference point for future research in this field

The probability approach in economic methodology: On the relation between Haavelmo's legacy and the methodology of economics (Revised version)

Economics