Application of Artificial Neural Networks to Predict the Selling Price in the Real Estate Valuation Process

2011 ◽  
Author(s):  
Youness El Hamzaoui ◽  
Jose Alfredo Hernandez Perez
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Real Estate ◽  
Selling Price ◽  
The Real ◽  
The Real Estate ◽  
Real Estate Valuation ◽  
Valuation Process ◽  
Artificial Neural

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 Principles and Criteria for using Statistical Parametric Models and Conditional Models for Valuation of Multi-Component Real Estate

2019 ◽  
Vol 27 (2) ◽  
pp. 33-43
Author(s):  
Tomasz Adamczyk ◽  
Agnieszka Bieda ◽  
Piotr Parzych
Keyword(s):  
Real Estate ◽  
Statistical Models ◽  
Parametric Model ◽  
Real Estate Market ◽  
Comparative Approach ◽  
The Real ◽  
The Real Estate ◽  
Real Estate Valuation ◽  
Conditional Models ◽  
Valuation Process

Abstract The complexity of multi-component real properties results from the possibility of identifying various components in legal, physical or functional terms. The possibility of distinguishing various functional elements of real properties, combined with the specificity resulting from their market properties, is problematic when applying the comparative approach to real estate valuation. In this case, the valuation procedure can be implemented using statistical models: the parametric model or the conditional one. This research paper demonstrates the construction of the parametric and conditional models taking into account the geometric and pricing attributes of multi-component real estate. The authors paid attention to adjusting the models to the available market data. They also specified the conditions for the use of statistical models in the real estate valuation process. Based on the analytical and accounting considerations, the estimation criteria for the parametric model and the conditional model were defined, which allow the correct application of these models at the stages of the real estate market analysis and the real estate valuation process.

scholarly journals Research on Factors Affecting Real Estate Values by Data Mining

2018 ◽  
Vol 6 (1) ◽  
pp. 220-239 ◽  
Author(s):  
Filiz Ersoz ◽  
Taner Ersoz ◽  
Muhammet Soydan
Keyword(s):  
Data Mining ◽  
Real Estate ◽  
Statistical Modelling ◽  
Unit Price ◽  
City Centre ◽  
Important Place ◽  
Factors Affecting ◽  
The Real ◽  
The Real Estate ◽  
Real Estate Valuation

Abstract Construction sector has an important place in Turkey’s economy. Real estate sales for the sector are increasing in parallel. However, the purchase cost is also important for those who are willing to buy a real estate. In the acquisition of real estate, factors such as size, location and age of the house are taken into consideration. The aim of the article is to conduct research on factors affecting real estate values by data mining. In this study, the most important variables that determine the value of the real estate have been investigated by data mining methods. The research has been carried out in Karabük and the variables determined according to the opinions of real estate experts. As classification methods, CHAID and C&RT algorithms have been used. It has been evaluated that both algorithm estimation results can be used. Within the framework of the study, the variables that have the most impact on the unit price have been determined, such as the size of the real estate, the distance to the city centre, the popularity, and the age of the building. The use of advanced technologies, such as statistical modelling and machine learning in real estate valuation and automatic value estimation, is of importance in determining the real value of the real estate.

scholarly journals Optimization of a Steam Reforming Plant Modeled with Artificial Neural Networks

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1923
Author(s):  
Eduardo G. Pardo ◽  
Jaime Blanco-Linares ◽  
David Velázquez ◽  
Francisco Serradilla
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Steam Reforming ◽  
Real Data ◽  
Memetic Algorithms ◽  
Black Box ◽  
Optimization Techniques ◽  
Ann Model ◽  
The Real ◽  
Artificial Neural

The objective of this research is to improve the hydrogen production and total profit of a real Steam Reforming plant. Given the impossibility of tuning the real factory to optimize its operation, we propose modelling the plant using Artificial Neural Networks (ANNs). Particularly, we combine a set of independent ANNs into a single model. Each ANN uses different sets of inputs depending on the physical processes simulated. The model is then optimized as a black-box system using metaheuristics (Genetic and Memetic Algorithms). We demonstrate that the proposed ANN model presents a high correlation between the real output and the predicted one. Additionally, the performance of the proposed optimization techniques has been validated by the engineers of the plant, who reported a significant increase in the benefit that was obtained after optimization. Furthermore, this approach has been favorably compared with the results that were provided by a general black-box solver. All methods were tested over real data that were provided by the factory.

scholarly journals Artificial Neural Networks and Linear Regression Reduce Sample Intensity to Predict the Commercial Volume of Eucalyptus Clones

Forests ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 268 ◽  
Author(s):  
Ivaldo Tavares Júnior ◽  
Jonas Rocha ◽  
Ângelo Ebling ◽  
Antônio Chaves ◽  
José Zanuncio ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Linear Regression ◽  
Model Fit ◽  
P Value ◽  
Diameter Class ◽  
The Real ◽  
Total Data ◽  
Artificial Neural ◽  
Clone Group

Equations to predict Eucalyptus timber volume are continuously updated, but most of them cannot be used for certain locations. Thus, equations of similar strata are applied to clonal plantations where trees cannot be felled to fit volumetric models. The objective of this study was to use linear regression and artificial neural networks (ANN) to reduce the number of trees sampled while maintaining the accuracy of commercial volume predictions with bark up to 4 cm in diameter at the top (v) of Eucalyptus clones. Two methods were evaluated in two scenarios: (a) regression model fit and ANN training with 80% of the data (533 trees) and per clone group with 80% of the trees in each group; and (b) model fit and ANN training with trees of only one clone group at ages two and three, with sample intensities of six, five, four, three, two, and one tree per diameter class. The real and predicted v averages did not differ in sample intensities from six to two trees per diameter class with different methods. The frequency distribution of individuals by volume class by the two methods (regression and ANN) compared to the real values were similar in scenarios (a) and (b) by the Kolmogorov–Smirnov test (p-value > 0.01). The application of ANN was more effective for total data analysis with non-linear behavior, without sampled environment stratification. The Prodan model also generates estimates with accuracy, and, among the regression models, is the best fit to the data. The volume with bark up to 4 cm in diameter at the top of Eucalyptus clones can be predicted with at least three trees per diameter class with regression (root mean square error in percentage, RMSE = 12.32%), and at least four trees per class with ANN (RMSE = 11.73%).

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