Investigations on deep learning techniques for property assessment
department or school: Applied Science, Faculty of
department or school: Engineering, School of (Okanagan)
data provider: DSpace
degree campus: UBCO
creator: Bin, Junchi
issued: 2019-01-16T17:25:09Z
issued: 2019
related degree: Master of Applied Science - MASc
degree grantor: University of British Columbia
description: Property assessment plays a vital role in not only people's daily life but also the establishment of policies in North America. Therefore, the robust and accurate assessment is thus required. However, there are several challenges to prevent improving performance in property assessment. For example, the to-be-valued property and its neighbors can influence their values mutually, which is named peer-dependence. Besides, the property values also depend on their topography around the house. Moreover, various urban data in the city, i.e., criminal activities and point of interests, also reflects the property values. The accuracy of property assessment can be further improved if the process can fuse the urban data for assessment. Nowadays, deep learning models become the most powerful computational models in the subjects of both electrical engineering and computer science. Therefore, the thesis aims to investigate deep learning techniques to address three specific challenges in property assessment, which are considering peer-dependence considering topography and considering urban data for improving property assessment. In the first study, a recurrent neural network (RNN), peer-dependence valuation model (PDVM), is proposed to converts the peer-dependence based valuation into sequence prediction. Firstly, K-Nearest similar house sampling (KNSHS) algorithm generates sequences from the to-be-valued property and nearby houses. Then, an RNN estimates the property values from given sequences. The experimental results indicate that PDVM outperforms the other novel computational models being used for property assessment. In the second study, a convolutional neural network (CNN) based method, attention-based multi-modal fusion (AMMF), is proposed to estimate property values with house attributes and corresponding street maps which contain features of topography. The experimental results indicate the proposed method outperforms the contemporary approaches. In the third study, to in…
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/68253
note: INVESTIGATIONS ON DEEP LEARNINGTECHNIQUES FOR PROPERTY ASSESSMENTbyJunchi BinB.Eng., Chang’an University, 2016B.Sc., Northern Arizona University, 2016A THESIS SUBMITTED IN PARTIAL FULFILLMENTOF THE REQUIREMENTS FOR THE DEGREE OFMASTER OF APPLIED SCIENCEinTHE COLLEGE OF GRADUATE STUDIES(Electrical Engineering)THE UNIVERSITY OF BRITISH COLUMBIA(Okanagan)January 2019© Junchi Bin, 2019The following individuals certify that they have read, and recommend to the Col-lege of Graduate Studies for acceptance, the thesis entitled:INVESTIGATIONS ON DEEP LEARNING TECHNIQUES FOR PROPERTYASSESSMENTsubmitted by Junchi Bin in partial fulfillment of the requirements ofthe degree of Master of Applied Science .Dr. Zheng Liu, School of EngineeringSupervisorDr. Eric Li, Faculty of ManagementCo-supervisorDr. Jian Liu, School of EngineeringSupervisory Committee MemberDr. Dimitry Sediako, School of EngineeringSupervisory Committee MemberDr. Nicolas Miguel Peleato, School of EngineeringUniversity ExamineriiAbstractProperty assessment plays a vital role in not only people’s daily life but also theestablishment of policies in North America. Therefore, the robust and accurateassessment is thus required. However, there are several challenges to prevent im-proving performance in property assessment. For example, the to-be-valued prop-erty and its neighbors can influence their values mutually, which is named peer-dependence. Besides, the property values also depend on their topography aroundthe house. Moreover, various urban data in the city, i.e., criminal activities andpoint of interests, also reflects the property values. The accuracy of property assess-ment can be further improved if the process can fuse the urban data for assessment.Nowadays, deep learning models become the most powerful computational modelsin the subjects of both electrical engineering and computer science. Therefore, thethesis aims to investigate deep learning techniques to address three specific chal-lenges in property ass…
has type: Thesis/Dissertation
date issued: 2019-02
is shown at: 10.14288/1.0376072
language: eng
degree discipline: Electrical Engineering
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
type: Text
identifier URI: http://hdl.handle.net/2429/68253