Enhancement of parcel valuation with adaptive artificial neural network modeling

Yalpır, Şükran

doi:10.1007/s10462-016-9531-5

Cited by 16 publications

(11 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…They concluded that ANN outperformed OLS. Similarly, Yalpır (2018) and Selim (2009) compared ANN with OLS and suggested that the former performed better. Yalpır (2018) used 98 observations, whereas Selim (2009) used a relatively larger sample (5,741).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Similarly, Yalpır (2018) and Selim (2009) compared ANN with OLS and suggested that the former performed better. Yalpır (2018) used 98 observations, whereas Selim (2009) used a relatively larger sample (5,741). Yalpır (2018) used three activation functions (the sigmoid, tangent hyperbolic, and adaptive activation functions) to build an ANN.…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

A Comparison of Residential Apartment Rent Price Predictions Using a Large Data Set: Kriging Versus Deep Neural Network

Seya

Shiroi²

2021

Geographical Analysis

View full text Add to dashboard Cite

The hedonic approach based on a regression model has been widely adopted for the prediction of real estate property price and rent. In particular, a spatial regression technique called Kriging, a method of interpolation that was advanced in the field of spatial statistics, are known to enable high accuracy prediction in light of the spatial dependence of real estate property data. Meanwhile, there has been a rapid increase in machine learning-based prediction using a large (big) dataset and its effectiveness has been demonstrated in previous studies. However, no studies have ever shown the extent to which predictive accuracy differs for Kriging and machine learning techniques using big data. Thus, this study compares the predictive accuracy of apartment rent price in Japan between the nearest neighbor Gaussian processes (NNGP) model, which enables application of Kriging to big data, and the deep neural network (DNN), a representative machine learning technique, with a particular focus on the data sample size (n = 10 4 , 10 5 , 10 6 ) and differences in predictive performance. Our analysis showed that, with an increase in sample size, the out-of-sample predictive accuracy of DNN approached that of NNGP and they were nearly equal on the order of n = 10 6 . Furthermore, it is suggested that, for both higher and lower end properties whose rent price deviates from the median, DNN may have a higher predictive accuracy than that of NNGP.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

A Comparison of Residential Apartment Rent Price Predictions Using a Large Data Set: Kriging Versus Deep Neural Network

Seya

Shiroi²

2021

Geographical Analysis

View full text Add to dashboard Cite

show abstract

“…Used a modified regression and genetic algorithm model to predict real estate valuations and validated it with real estate data from South Korea as stated in [19]. Compared the three artificial neural network models based on different algorithms, and concluded that the prediction accuracy of the adaptive neural network is the highest as stated in [20]. Aimed at BP neural network can lead to poor generalization and slow convergence.…”

Section: Artificial Neural Network Model Evolution and Algorithm Improvementmentioning

confidence: 99%

Artificial Neural Networks and the Mass Appraisal of Real Estate

Zhou

Chen

et al. 2018

Int. J. Onl. Eng.

View full text Add to dashboard Cite

<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>

show abstract

“…It requires up-to-date data, clear consistency and transparency [9][10][11]. Therefore, in most countries of the world mass valuation of land using geoinformation technologies [12][13][14][15][16][17][18][19] is performed, which allows to automate the collection of factors influencing the assessment, to analyze them and the necessary calculations. The use of automatic estimation models has been popular for more than ten years in developed countries such as Sweden, Canada and the United States, and is becoming popular around the world [20].…”

Section: Introductionmentioning

confidence: 99%

Modern development trend of normative monetary valuation of non-agricultural land plots in Ukraine

2021

View full text Add to dashboard Cite

The article identifies the main types of land valuation – expert monetary valuation for individual evaluation of particular plots and normative monetary valuation for systematic valuation of land for tax purposes. To conduct the last one, a mass assessment of land plots considering geoinformation technologies is used. It is established that at the present stage of normative monetary valuation of non-agricultural land plots in Ukraine two methodologies are used: within the settlement and outside it. The project of the unified methodology of normative monetary valuation of land plots by combining the existing methods submitted for discussion by the Ministry of Economy, Trade and Agriculture of Ukraine is considered. The values of land plots according to the existing and unified methodologies are studied on the example of four land plots under gas stations, which are located in the city of Kharkiv and outside it. It is established that the values of land plots, determined by existing and unified methods, differ both in the direction of increase and decrease, the difference varies from 9 % to 97 %. It is necessary to conduct additional analysis of initial data and indicators, which are the basis for calculating and improve the offer unified methodology.

show abstract

Enhancement of parcel valuation with adaptive artificial neural network modeling

Cited by 16 publications

References 14 publications

A Comparison of Residential Apartment Rent Price Predictions Using a Large Data Set: Kriging Versus Deep Neural Network

A Comparison of Residential Apartment Rent Price Predictions Using a Large Data Set: Kriging Versus Deep Neural Network

Artificial Neural Networks and the Mass Appraisal of Real Estate

Modern development trend of normative monetary valuation of non-agricultural land plots in Ukraine

Contact Info

Product

Resources

About