Impact of Outlier Detection on Neural Networks Based Property Value Prediction

Sandbhor, Sayali; Chaphalkar, N. B.

doi:10.1007/978-981-13-3329-3_45

Cited by 18 publications

(15 citation statements)

References 19 publications

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“…Use of the deep learning approach has become widespread in the valuation industry since the AlphaGo versus Lee Sedol match in 2016, and novel attempts have been made to apply the deep learning technique to the valuation process. The most representative method for implementing deep learning is the neural network, and it has been established as a de facto standard model in a wide range of property valuation projects (Abidoye and Chan, 2017;Sandbhor and Chaphalkar, 2019;Talaga et al, 2019). Actually, the neural network model started to appear in valuation literature since the 1990s (Lenk et al, 1997;McGreal et al, 1998), but it soon displayed unstable convergence and poor performance, and was almost completely shunned by data scientists around 2010 (Chollet, 2018).…”

Section: Literature Review 21 Methodology For House Price Estimationmentioning

confidence: 99%

Using photographs and metadata to estimate house prices in South Korea

Lee

Park

2020

DTA

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PurposeMost prior attempts at real estate valuation have focused on the use of metadata such as size and property age, neglecting the fact that the building workmanship in the construction of a house is also a key factor for the estimation of house prices. Building workmanship, such as exterior walls and floor tiling correspond to the visual attributes of a house, and it is difficult to capture and evaluate such attributes efficiently through classical models like regression analysis. Deep learning approach is taken in the valuation process to utilize this visual information.Design/methodology/approachThe authors propose a two-input neural network comprising a multilayer perceptron and a convolutional neural network that can utilize both metadata and the visual information from images of the front view of the house.FindingsThe authors applied the two-input neural network to Guri City in Gyeonggi Province, South Korea, as a case study and found that the accuracy of house price estimations can be improved by employing image information along with metadata.Originality/valueFew studies considered the impact of the building workmanship in the valuation process. The authors revealed that it is useful to use both photographs and metadata for enhancing the accuracy of house price estimation.

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Section: Literature Review 21 Methodology For House Price Estimationmentioning

confidence: 99%

Using photographs and metadata to estimate house prices in South Korea

Lee

Park

2020

DTA

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“…As said above, a characteristic feature of the real estate market is a large amount of unreliable data called statistical outliers [33]. To search for such outliers, we used the median method recommended by the authors of [33] and the factual-graphical search method [38]. e results were compared to show that the neural network method proposed in [34] is the most effective one.…”

Section: Methodsmentioning

confidence: 99%

“…Analyzing the papers devoted to neural network modeling of estate markets, it can be noted that few researchers (e.g., [33]) have paid attention to the specific problems of modeling this subject area and to the issues of overcoming these problems. When constructing a neural network system for assessing real estate, the authors in [33] faced the challenge of overcoming the negative impact of statistical outliers on the accuracy of the created models. For the real estate market, they tested a number of methods for detecting outliers such as Tukey's method, standard deviation method, median method, Z-score method, MAD method, and modified Z-score method.…”

Section: Introductionmentioning

confidence: 99%

The Complex Neural Network Model for Mass Appraisal and Scenario Forecasting of the Urban Real Estate Market Value That Adapts Itself to Space and Time

Yasnitsky

Alekseev

2021

Complexity

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In the modern scientific literature, there are many reports about the successful application of neural network technologies for solving complex applied problems, in particular, for modeling the urban real estate market. There are neural network models that can perform mass assessment of real estate objects taking into account their construction and operational characteristics. However, these models are static because they do not take into account the changing economic situation over time. Therefore, they quickly become outdated and need frequent updates. In addition, if they are designed for a specific city, they are not suitable for other cities. On the other hand, there are several dynamic models taking into account the overall state of the economy and designed to predict and study the overall price situation in real estate markets. Such dynamic models are not intended for mass real estate appraisals. The aim of this article is to develop a methodology and create a complex model that has the properties of both static and dynamic models. Moreover, our comprehensive model should be suitable for evaluating real estate in many cities at once. This aim is achieved since our model is based on a neural network trained on examples considering both construction and operational characteristics, as well as geographical and environmental characteristics, along with time-changing macroeconomic parameters that describe the economic state of a specific region, country, and the world. A set of examples for training and testing the neural network were formed on the basis of statistical data of real estate markets in a number of Russian cities for the period from 2006 to 2020. Thus, many examples included the data relating to the periods of the economic calm for Russia, along with the periods of crisis, recovery, and growth of the Russian and global economy. Due to this, the model remains relevant with the changes of the international economic situation and it takes into account the specifics of regions. The model proved to be suitable for solving the following tasks: industrial economic analysis, company strategic and operational management, analytical and consulting support of investment, and construction activities of professional market participants. The model can also be used by government agencies authorized to conduct public cadastral assessment for calculating property taxes.

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“…Although outliers are part of a data set, they are significantly different from other observations. In this study, Tukey's method, which utilizes the median, upper, and lower quartiles of a data set, was applied as an outlier detection method [50]. Since quartiles are resistant to farthest data of the data set, Tukey's method is less sensitive, compared to methods using mean and standard variance [50].…”

Section: Clp Data Preparationmentioning

confidence: 99%

Hybrid Artificial Intelligence HFS-RF-PSO Model for Construction Labor Productivity Prediction and Optimization

2021

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This paper presents a novel approach, using hybrid feature selection (HFS), machine learning (ML), and particle swarm optimization (PSO) to predict and optimize construction labor productivity (CLP). HFS selects factors that are most predictive of CLP to reduce the complexity of CLP data. Selected factors are used as inputs for four ML models for CLP prediction. The study results showed that random forest (RF) obtains better performance in mapping the relationship between CLP and selected factors affecting CLP, compared with the other three models. Finally, the integration of RF and PSO is developed to identify the maximum CLP value and the optimum value of each selected factor. This paper introduces a new hybrid model named HFS-RF-PSO that addresses the main limitation of existing CLP prediction studies, which is the lack of capacity to optimize CLP and its most predictive factors with respect to a construction company’s preferences, such as a targeted CLP. The major contribution of this paper is the development of the hybrid HFS-RF-PSO model as a novel approach for optimizing factors that influence CLP and identifying the maximum CLP value.

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Impact of Outlier Detection on Neural Networks Based Property Value Prediction

Cited by 18 publications

References 19 publications

Using photographs and metadata to estimate house prices in South Korea

Using photographs and metadata to estimate house prices in South Korea

The Complex Neural Network Model for Mass Appraisal and Scenario Forecasting of the Urban Real Estate Market Value That Adapts Itself to Space and Time

Hybrid Artificial Intelligence HFS-RF-PSO Model for Construction Labor Productivity Prediction and Optimization

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