Automated Valuation Model based on fuzzy and rough set theory for real estate market with insufficient source data

2019 ◽  
Vol 87 ◽  
pp. 104021 ◽  
Author(s):  
Malgorzata Renigier-Biłozor ◽  
Artur Janowski ◽  
Maurizio d’Amato
Keyword(s):  
Real Estate ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Real Estate Market ◽  
Model Based ◽  
Valuation Model ◽  
Source Data ◽  
Automated Valuation

scholarly journals International Real Estate Review

2021 ◽  
Vol 24 (2) ◽  
pp. 139-183
Author(s):  
Kristoffer B. Birkeland ◽  
◽  
Allan D. D’Silva ◽  
Roland Füss ◽  
Are Oust ◽  
...  
Keyword(s):  
Real Estate ◽  
Real Estate Market ◽  
Percentage Error ◽  
Residential Real Estate ◽  
Valuation Model ◽  
Stacked Generalization ◽  
Novel Approach ◽  
Out Of Sample ◽  
Selection For ◽  
Automated Valuation

We develop an automated valuation model (AVM) for the residential real estate market by leveraging stacked generalization and a comparable market analysis. Specifically, we combine four novel ensemble learning methods with a repeat sales method and tailor the data selection for each value estimate. We calibrate and evaluate the model for the residential real estate market in Oslo by producing out-of-sample estimates for the value of 1,979 dwellings sold in the first quarter of 2018. Our novel approach of using stacked generalization achieves a median absolute percentage error of 5.4%, and more than 96% of the dwellings are estimated within 20% of their actual sales price. A comparison of the valuation accuracy of our AVM to that of the local estate agents in Oslo generally demonstrates its viability as a valuation tool. However, in stable market phases, the machine falls short of human capability.

scholarly journals Support or Risk? Software Project Risk Assessment Model Based on Rough Set Theory and Backpropagation Neural Network

2019 ◽  
Vol 11 (17) ◽  
pp. 4513 ◽  
Author(s):  
Xiaoqing Li ◽  
Qingquan Jiang ◽  
Maxwell K. Hsu ◽  
Qinglan Chen
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Attribute Reduction ◽  
Assessment Model ◽  
Software Project ◽  
Risk Assessment Model ◽  
Backpropagation Neural Network ◽  
Model Based ◽  
Sample Data

Software supports continuous economic growth but has risks of uncertainty. In order to improve the risk-assessing accuracy of software project development, this paper proposes an assessment model based on the combination of backpropagation neural network (BPNN) and rough set theory (RST). First, a risk list with 35 risk factors were grouped into six risk categories via the brainstorming method and the original sample data set was constructed according to the initial risk list. Subsequently, an attribute reduction algorithm of the rough set was used to eliminate the redundancy attributes from the original sample dataset. The input factors of the software project risk assessment model could be reduced from thirty-five to twelve by the attribute reduction. Finally, the refined sample data subset was used to train the BPNN and the test sample data subset was used to verify the trained BPNN. The test results showed that the proposed joint model could achieve a better assessment than the model based only on the BPNN.

The Applications Based on the Rough Set Theory Algorithms to Determine the Weight

2014 ◽  
Vol 989-994 ◽  
pp. 1736-1738
Author(s):  
Ning Ning Chen
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Model Based

This paper puts forward on the Rough Set theory algorithms, and combined it with the method of determining the index weights, then established a weight determined algorithm model based on rough set, Thereby reducing the influence of subjective factors on the weight determination.

scholarly journals Structure of a decision support subsystem in real estate management

2013 ◽  
Vol 13 (1) ◽  
pp. 56-75 ◽  
Author(s):  
Małgorzata Renigier-Biłozor
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Real Estate ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
The Real ◽  
The Real Estate ◽  
Estate Management ◽  
Real Estate Management

Abstract This study proposes a decision support subsystem in real estate management. Owing to the complex and multi-layered character of the discussed problem, only selected aspects of real estate management are discussed in this paper. The described system will play the role of a relatively simple and effective “assistant” which is expected to maximize the effectiveness of a decision and shorten decision-making time. The author has made an attempt to develop a subsystem as an adviser to subjects operating in the real estate management. This system was developed accounting for and combining the classical economic and real estate market theories with the implementation of non-classical methods in the data mining category in an effort to increase its effectiveness. The rough set theory has been proposed as a tool that supports analytical processes. Fuzzy logic best reproduces expert knowledge, and it is one of the most effective tools for solving “vaguely defined” problems. The given work is an attempt to prove the hypothesis that: the reduction of uncertainty in the real estate management decision-making process is possible by the development of the advisory system based on the rough set theory. The main aim of this work is to increase the efficiency and efficacy of entities operating in the real estate management, thus influencing the effectivness of the entity and management.

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