Conceptual framework for real estate transactions

2020 ◽  
Vol 38 (3) ◽  
pp. 245-262
Author(s):  
Werner Gleißner ◽  
Cay Oertel
Keyword(s):  
Risk Management ◽  
Decision Support ◽  
Real Estate ◽  
Decision Support System ◽  
Conceptual Framework ◽  
Support System ◽  
Portfolio Management ◽  
Management Practices ◽  
Content Type ◽  
Legal Obligations

PurposeThe purpose of this paper is the development for a conceptual framework with regard to the risk management of real estate positions as foundation for transaction decisions. In this context, the current market environment and legal obligations are the main drivers for market participants to improve their risk management practices. Based on this environment, a practical but science backed model is outlined.Design/methodology/approachThe paper uses a conceptual approach based on the existing literature to develop a practical decision support system. In addition, the current risk management best practices are outlined to illustrate the corporate and methodological foundation for the decision support system.FindingsThe conceptual model development reveals a clear necessity for the supplementation of price to value measures. Additional measures are derived from theoretic considerations based on Monte Carlo Simulation approaches to the risk management of property investments. These additional risk metrics support investors in order make risk-appropriate decisions.Practical implicationsThe resulting decision support system can be applied to the risk management of transaction decisions. Here, the model can be applied in any investment decision to support portfolio management considerations from a comprehensive risk management perspective. Investors can implement the system as part of their transaction procedure.Originality/valueThe existing body of literature mainly focuses on macroeconomic ratios in the context of decision support. In contrast, the present paper reveals a corporate decision support system, which is supposed to foster decisions of market agents especially with regard to potential price and value divergences and tightening legal obligations.

A decision support system for retail assortment planning

2017 ◽  
Vol 45 (7/8) ◽  
pp. 808-825 ◽  
Author(s):  
Alexander Hübner
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Solution Structure ◽  
Product Variety ◽  
Substitution Effects ◽  
Assortment Planning ◽  
Shelf Space ◽  
Content Type ◽  
Solution Approach

Purpose Because increasing product variety in retail conflicts with limited shelf space, managing assortment and shelf quantities is a core decision in this sector. A retailer needs to define the assortment size and then assign shelf space to meet consumer demand. However, the current literature lacks not only information on the comprehensive structure of the decision problem, but also a decision support system that can be directly applied to practice in a straightforward manner. The paper aims to discuss these issues. Design/methodology/approach The findings were developed and evaluated by means of explorative interviews with grocery retail experts. An optimization model is proposed to solve the problem of assortment planning with limited shelf space for data sets of a size relevant in real retail practice. Findings The author identifies the underlying planning problems based on a qualitative survey of retailers and relates the problems to each other. This paper develops a pragmatic approach to the capacitated assortment problem with stochastic demand and substitution effects. The numerical examples reveal that substitution demand has a significant impact on total profit and solution structure. Practical implications The author shows that the model and solution approach are scalable to problem sizes relevant in practice. Furthermore, the planning architecture structures the related planning questions and forms a foundation for further research on decision support systems. Originality/value The planning framework structures the associated decision problems in assortment planning. An efficient solution approach for assortment planning is proposed.

Network-aware credit scoring system for telecom subscribers using machine learning and network analysis

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Hongming Gao ◽  
Hongwei Liu ◽  
Haiying Ma ◽  
Cunjun Ye ◽  
Mingjun Zhan
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Scoring System ◽  
Credit Scoring ◽  
Prediction Performance ◽  
Content Type ◽  
Connected Subgroup ◽  
Source Data ◽  
Credit Scores

PurposeA good decision support system for credit scoring enables telecom operators to measure the subscribers' creditworthiness in a fine-grained manner. This paper aims to propose a robust credit scoring system by leveraging latent information embedded in the telecom subscriber relation network based on multi-source data sources, including telecom inner data, online app usage, and offline consumption footprint.Design/methodology/approachRooting from network science, the relation network model and singular value decomposition are integrated to infer different subscriber subgroups. Employing the results of network inference, the paper proposed a network-aware credit scoring system to predict the continuous credit scores by implementing several state-of-art techniques, i.e. multivariate linear regression, random forest regression, support vector regression, multilayer perceptron, and a deep learning algorithm. The authors use a data set consisting of 926 users of a Chinese major telecom operator within one month of 2018 to verify the proposed approach.FindingsThe distribution of telecom subscriber relation network follows a power-law function instead of the Gaussian function previously thought. This network-aware inference divides the subscriber population into a connected subgroup and a discrete subgroup. Besides, the findings demonstrate that the network-aware decision support system achieves better and more accurate prediction performance. In particular, the results show that our approach considering stochastic equivalence reveals that the forecasting error of the connected-subgroup model is significantly reduced by 7.89–25.64% as compared to the benchmark. Deep learning performs the best which might indicate that a non-linear relationship exists between telecom subscribers' credit scores and their multi-channel behaviours.Originality/valueThis paper contributes to the existing literature on business intelligence analytics and continuous credit scoring by incorporating latent information of the relation network and external information from multi-source data (e.g. online app usage and offline consumption footprint). Also, the authors have proposed a power-law distribution-based network-aware decision support system to reinforce the prediction performance of individual telecom subscribers' credit scoring for the telecom marketing domain.

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