Multi-source urban data fusion for property value assessment: A case study in Philadelphia

Bin, Junchi; Gardiner, Bryan; Li, Eric; Zheng, Liu

doi:10.1016/j.neucom.2020.05.013

Cited by 42 publications

(41 citation statements)

References 29 publications

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“…Lai et al [40] controlled the traffic lights by fusing the signals of traffic lights on different roads, to improve the congestion of the whole road network. Bin et al [41] proposed using two deep neural networks to extract the features of urban structured numerical data and housing property structured data, respectively, and then fuse the two type features to achieve more accurate property value assessment for the real estate industry. Ma et al [42] proposed an unsupervised framework based on generative adversarial network (GAN) [43] to realize the fusion of panchromatic images and low-resolution multispectral images, to obtain high-resolution multispectral images.…”

Section: Feature Fusionmentioning

confidence: 99%

A Feature Fusion Method with Guided Training for Classification Tasks

Zhang

Fan

et al. 2021

Computational Intelligence and Neuroscience

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In this paper, a feature fusion method with guiding training (FGT-Net) is constructed to fuse image data and numerical data for some specific recognition tasks which cannot be classified accurately only according to images. The proposed structure is divided into the shared weight network part, the feature fused layer part, and the classification layer part. First, the guided training method is proposed to optimize the training process, the representative images and training images are input into the shared weight network to learn the ability that extracts the image features better, and then the image features and numerical features are fused together in the feature fused layer to input into the classification layer for the classification task. Experiments are carried out to verify the effectiveness of the proposed model. Loss is calculated by the output of both the shared weight network and classification layer. The results of experiments show that the proposed FGT-Net achieves the accuracy of 87.8%, which is 15% higher than the CNN model of ShuffleNetv2 (which can process image data only) and 9.8% higher than the DNN method (which processes structured data only).

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Section: Feature Fusionmentioning

confidence: 99%

A Feature Fusion Method with Guided Training for Classification Tasks

Zhang

Fan

et al. 2021

Computational Intelligence and Neuroscience

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“…This is mainly the size of the built-up floor area in m 2 (CZK / m 2 ) or the number of rooms (CZK / pc). However, today's technologies allow the use of other parameters of buildings as units of measure which distinguish individual buildings very precisely from other buildings [2]. The development of new technologies also allows, provided the cooperation of all parts of the real estate industry (construction, marketing, work of real estate agencies, etc.)…”

Section: Literary Researchmentioning

confidence: 99%

Methodology for Calculating the Reproduction Value of Buildings

et al. 2021

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The article deals with the description of the methodology for calculating the reproduction value of buildings. It is a mathematical model that uses data obtained from a statistical survey and calculates the total reproduction value of buildings from a large data set. The model works with many variable inputs that describe the valued building. These include, for example, the use of the building, the enclosed space of the building, the floor area of the building, the building material of the supporting structures of the building, the age of the building, the technical condition of the building, the year the building was acquired and others. Based on the results, the model was calibrated and its parameters optimized to achieve the most accurate values.

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“…In the era of big data and machine intelligence, deep-learning methods have been more frequently utilized in research and engineering problems, due to their superior fitting abilities and powerful generalization performance. The house selling and rental prices can also be strongly modeled by deep learning, and they can be automatically provided to assess prices in the housing market with higher accuracy and reliability [8,9]. The currently adopted deep-learning models for housing prices include the multilayer perceptron regressors [10][11][12], convolutional neural networks (CNN) [2,9,13,14], and their variants.…”

Section: Introductionmentioning

confidence: 99%

“…The house selling and rental prices can also be strongly modeled by deep learning, and they can be automatically provided to assess prices in the housing market with higher accuracy and reliability [8,9]. The currently adopted deep-learning models for housing prices include the multilayer perceptron regressors [10][11][12], convolutional neural networks (CNN) [2,9,13,14], and their variants. These methods can explain the nonlinear and complex relationships but do not explicitly consider the spatial heterogeneity of the houses in an area.…”

Section: Introductionmentioning

confidence: 99%

Exploring a Pricing Model for Urban Rental Houses from a Geographical Perspective

Shen

Lin

Zhu

et al. 2021

Land

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Models for estimating urban rental house prices in the real estate market continue to pose a challenging problem due to the insufficiency of algorithms and comprehensive perspectives. Existing rental house price models based on either the geographically weighted regression (GWR) or deep-learning methods can hardly predict very satisfactory prices, since the rental house prices involve both complicated nonlinear characteristics and spatial heterogeneity. The linear-based GWR model cannot characterize the nonlinear complexity of rental house prices, while existing deep-learning methods cannot explicitly model the spatial heterogeneity. This paper proposes a fully connected neural network–geographically weighted regression (FCNN–GWR) model that combines deep learning with GWR and can handle both of the problems above. In addition, when calculating the geographical location of a house, we propose a set of locational and neighborhood variables based on the quantities of nearby points of interests (POIs). Compared with traditional locational and neighborhood variables, the proposed “quantity-based” locational and neighborhood variables can cover more geographic objects and reflect the locational characteristics of a house from a comprehensive geographical perspective. Taking four major Chinese cities (Wuhan, Nanjing, Beijing, and Xi’an) as study areas, we compare the proposed method with other commonly used methods, and this paper presents a more precise estimation model for rental house prices. The method proposed in this paper may serve as a useful reference for individuals and enterprises in their transactions relevant to rental houses, and for the government in terms of the policies and positions of public rental housing.

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Multi-source urban data fusion for property value assessment: A case study in Philadelphia

Cited by 42 publications

References 29 publications

A Feature Fusion Method with Guided Training for Classification Tasks

A Feature Fusion Method with Guided Training for Classification Tasks

Methodology for Calculating the Reproduction Value of Buildings

Exploring a Pricing Model for Urban Rental Houses from a Geographical Perspective

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