scholarly journals Artificial Neural Network, Quantile and Semi-Log Regression Modelling of Mass Appraisal in Housing

Mathematics ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 783
Author(s):  
Jose Torres-Pruñonosa ◽  
Pablo García-Estévez ◽  
Camilo Prado-Román
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Real Estate ◽  
Housing Stock ◽  
Hedonic Prices ◽  
Quantile Regressions ◽  
Regression Modelling ◽  
Real Estate Valuation ◽  
Artificial Neural ◽  
Better Than

We used a large sample of 188,652 properties, which represented 4.88% of the total housing stock in Catalonia from 1994 to 2013, to make a comparison between different real estate valuation methods based on artificial neural networks (ANNs), quantile regressions (QRs) and semi-log regressions (SLRs). A literature gap in regard to the comparison between ANN and QR modelling of hedonic prices in housing was identified, with this article being the first paper to include this comparison. Therefore, this study aimed to answer (1) whether QR valuation modelling of hedonic prices in the housing market is an alternative to ANNs, (2) whether it is confirmed that ANNs produce better results than SLRs when assessing housing in Catalonia, and (3) which of the three mass appraisal models should be used by Spanish banks to assess real estate. The results suggested that the ANNs and SLRs obtained similar and better performances than the QRs and that the SLRs performed better when the datasets were smaller. Therefore, (1) QRs were not found to be an alternative to ANNs, (2) it could not be confirmed whether ANNs performed better than SLRs when assessing properties in Catalonia and (3) whereas small and medium banks should use SLRs, large banks should use either SLRs or ANNs in real estate mass appraisal.

scholarly journals USE OF ARTIFICIAL NEURAL NETWORKS IN THE STATE CADASTRAL VALUATION OF LAND PLOTS

2021 ◽  
Vol 26 (5) ◽  
pp. 180-190
Author(s):  
Ekaterina N. Loseva ◽  
◽  
Natalia O. Mitrofanova ◽  
◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Real Estate ◽  
Individual Characteristics ◽  
Affecting Factors ◽  
High Rise ◽  
Increase Productivity ◽  
Artificial Neural ◽  
The Individual

At present, real estate cadastral valuation is carried out in large volumes and at regular intervals, which may reduce the objectivity and relevance of the results of such a valuation. In other words, the traditional cadastral valuations do not meet the current needs of society. The solution may be to use new techniques and technologies, such as neural networks. The automation of the cadastral valuation will reduce the estimation time, increase productivity and quality, and take into account all the individual characteristics of the property being evaluated. The subject of research is the calculation of the cadastral value of land plots using an artificial neural network. The object of this research is residential area land plots within the Novosibirsk boundaries which were divided into two segments: segment 2 "Residential constructions (mid-rise and high-rise)", segment 13 "Horticulture, low-rise residential con-structions". The tasks of the research: the determination of factors which influence the cadastral value of real estate and their differentiation; accumulation of up-to-date information about real estate; preparation of data for creation of artificial neural network. And as a result were revealed the basic and additional cost-affecting factors, which then were differentiated for further development of artifitial neural network capable of calculating the cadastral value in automated mode.

scholarly journals Artificial Neural Networks and the Mass Appraisal of Real Estate

2018 ◽  
Vol 14 (03) ◽  
pp. 180 ◽  
Author(s):  
Gang Zhou ◽  
Yicheng Ji ◽  
Xiding Chen ◽  
Fangfang Zhang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Real Estate ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Support Vector ◽  
The Real ◽  
The Real Estate ◽  
Artificial Neural

<p>With the rapid development of computer, artificial intelligence and big data technology, artificial neural networks have become one of the most powerful machine learning algorithms. In the practice, most of the applications of artificial neural networks use back propagation neural network and its variation. Besides the back propagation neural network, various neural networks have been developing in order to improve the performance of standard models. Though neural networks are well known method in the research of real estate, there is enormous space for future research in order to enhance their function. Some scholars combine genetic algorithm, geospatial information, support vector machine model, particle swarm optimization with artificial neural networks to appraise the real estate, which is helpful for the existing appraisal technology. The mass appraisal of real estate in this paper includes the real estate valuation in the transaction and the tax base valuation in the real estate holding. In this study we focus on the theoretical development of artificial neural networks and mass appraisal of real estate, artificial neural networks model evolution and algorithm improvement, artificial neural networks practice and application, and review the existing literature about artificial neural networks and mass appraisal of real estate. Finally, we provide some suggestions for the mass appraisal of China's real estate.</p>

scholarly journals ϕ-Divergence Loss-Based Artificial Neural Network

2019 ◽  
Vol 17 (2) ◽  
Author(s):  
R. L. Salamwade ◽  
D. M. Sakate ◽  
S. K. Mathur
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Linear Functions ◽  
Loss Functions ◽  
Likelihood Estimator ◽  
Artificial Neural ◽  
Standard Techniques ◽  
Better Than

Artificial Neural Networks (ANNs) can fit non-linear functions and recognize patterns better than several standard techniques. Performance of ANNs is measured by using loss functions. Phi-divergence estimator is generalization of maximum likelihood estimator and it possesses all its properties. A neural network is proposed which is trained using phi-divergence loss.

scholarly journals AGCS Technique to Improve the Performance of Neural Networks

2019 ◽  
Vol 29 (1) ◽  
pp. 1235-1245
Author(s):  
Kishor Kumar Katha ◽  
Suresh Pabboju
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Cuckoo Search ◽  
Overall Performance ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
In The Beginning ◽  
Better Than

Abstract In this paper, a fresh method is offered regarding training of particular neural networks. This technique is a combination of the adaptive genetic (AG) and cuckoo search (CS) algorithms, called the AGCS method. The intention of training a particular artificial neural network (ANN) is to obtain the finest weight load. With this protocol, a particular weight is taken into account as feedback, which is optimized by means of the hybrid AGCS protocol. In the beginning, a collection of weights is initialized and the similar miscalculation is discovered. Finally, during training of an ANN, we can easily overcome the training complications involving ANNs and also gain the finest overall performance with training of the ANN. We have implemented the proposed system in MATLAB, and the overall accuracy is about 93%, which is much better than that of the genetic algorithm (86%) and CS (88%) algorithm.

scholarly journals The Budapest Amyloid Predictor and Its Applications

Biomolecules ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 500
Author(s):  
László Keresztes ◽  
Evelin Szögi ◽  
Bálint Varga ◽  
Viktor Farkas ◽  
András Perczel ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Support Vector ◽  
User Needs ◽  
Computational Technique ◽  
Linear Support Vector Machine ◽  
Β Sheets ◽  
Artificial Neural ◽  
Amorphous Aggregation

The amyloid state of proteins is widely studied with relevance to neurology, biochemistry, and biotechnology. In contrast with nearly amorphous aggregation, the amyloid state has a well-defined structure, consisting of parallel and antiparallel β-sheets in a periodically repeated formation. The understanding of the amyloid state is growing with the development of novel molecular imaging tools, like cryogenic electron microscopy. Sequence-based amyloid predictors were developed, mainly using artificial neural networks (ANNs) as the underlying computational technique. From a good neural-network-based predictor, it is a very difficult task to identify the attributes of the input amino acid sequence, which imply the decision of the network. Here, we present a linear Support Vector Machine (SVM)-based predictor for hexapeptides with correctness higher than 84%, i.e., it is at least as good as the best published ANN-based tools. Unlike artificial neural networks, the decisions of the linear SVMs are much easier to analyze and, from a good predictor, we can infer rich biochemical knowledge. In the Budapest Amyloid Predictor webserver the user needs to input a hexapeptide, and the server outputs a prediction for the input plus the 6 × 19 = 114 distance-1 neighbors of the input hexapeptide.

scholarly journals Neural Networks: Low‐Power Self‐Rectifying Memristive Artificial Neural Network for Near Internet‐of‐Things Sensor Computing (Adv. Electron. Mater. 6/2021)

2021 ◽  
Vol 7 (6) ◽  
pp. 2170017
Author(s):  
Seok Choi ◽  
Yong Kim ◽  
Tien Van Nguyen ◽  
Won Hee Jeong ◽  
Kyeong‐Sik Min ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Internet Of Things ◽  
Low Power ◽  
Artificial Neural

scholarly journals Non-Destructive Micromagnetic Determination of Hardness and Case Hardening Depth Using Linear Regression Analysis and Artificial Neural Networks

Metals ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 18
Author(s):  
Rahel Jedamski ◽  
Jérémy Epp
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Regression Analysis ◽  
Artificial Neural Networks ◽  
Linear Regression ◽  
Linear Regression Analysis ◽  
Case Hardening ◽  
Artificial Neural ◽  
Non Destructive

Non-destructive determination of workpiece properties after heat treatment is of great interest in the context of quality control in production but also for prevention of damage in subsequent grinding process. Micromagnetic methods offer good possibilities, but must first be calibrated with reference analyses on known states. This work compares the accuracy and reliability of different calibration methods for non-destructive evaluation of carburizing depth and surface hardness of carburized steel. Linear regression analysis is used in comparison with new methods based on artificial neural networks. The comparison shows a slight advantage of neural network method and potential for further optimization of both approaches. The quality of the results can be influenced, among others, by the number of teaching steps for the neural network, whereas more teaching steps does not always lead to an improvement of accuracy for conditions not included in the initial calibration.

scholarly journals Optimal Artificial Neural Network Type Selection Method for Usage in Smart House Systems

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 47
Author(s):  
Vasyl Teslyuk ◽  
Artem Kazarian ◽  
Natalia Kryvinska ◽  
Ivan Tsmots
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Input Data ◽  
Optimization Criterion ◽  
Fuzzy Input ◽  
Different Types ◽  
Smart House ◽  
Artificial Neural

In the process of the “smart” house systems work, there is a need to process fuzzy input data. The models based on the artificial neural networks are used to process fuzzy input data from the sensors. However, each artificial neural network has a certain advantage and, with a different accuracy, allows one to process different types of data and generate control signals. To solve this problem, a method of choosing the optimal type of artificial neural network has been proposed. It is based on solving an optimization problem, where the optimization criterion is an error of a certain type of artificial neural network determined to control the corresponding subsystem of a “smart” house. In the process of learning different types of artificial neural networks, the same historical input data are used. The research presents the dependencies between the types of neural networks, the number of inner layers of the artificial neural network, the number of neurons on each inner layer, the error of the settings parameters calculation of the relative expected results.

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