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Learning Based Comparative Market Analysis.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Learning Based Comparative Market Analysis./
Author:	Byreddy, Mounika Reddy.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	50 p.
Notes:	Source: Masters Abstracts International, Volume: 83-03.
Contained By:	Masters Abstracts International83-03.
Subject:	Computer science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28768209
ISBN:	9798544294320

Learning Based Comparative Market Analysis.
Byreddy, Mounika Reddy.

Learning Based Comparative Market Analysis. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 50 p.

Source: Masters Abstracts International, Volume: 83-03.

Thesis (M.S.)--San Diego State University, 2021.

This item must not be sold to any third party vendors.

Comparative Market Analysis (CMA) is standard process for real estate professional while assessing the market value of a property. A CMA report usually lists the target property and multiple recently sold houses in the same neighborhood and compares their features, e.g., built age, living size, number of bedrooms, etc. Professional realtors can then determine the appropriate price of the target property. However, without professional training, it is difficult to determine the importance of individual features and thus it remained challenging for buyers/sellers to appropriately use CMA reports. This dissertation aims to address this challenge by machine learning approach and automating the CMA based house appraisal procedure. The key idea is a self-supervised learning framework which includes two training stages. In the first stage, a set of nearly sold houses are identified for each target house in the training set and a contrastive loss function is introduced to encourage similarities or dissimilarities between the sold houses and the target house. This loss does not access the true market value of training samples and is used to pre-train the machine learning model. In the second stage, a set of supervised house-value pairs are used to fine-tune the pre-trained model for regression purposes. Experiments with various regression models are evaluated on a newly collected housing dataset. Results suggested the proposed self-training framework can consistently improve the appraisal accuracies of various regression models.

ISBN: 9798544294320Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

Comparative market analysis

Learning Based Comparative Market Analysis.
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245 1 0 $a Learning Based Comparative Market Analysis.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2021
300 $a 50 p.
500 $a Source: Masters Abstracts International, Volume: 83-03.
500 $a Advisor: Liu, Xiaobai.
502 $a Thesis (M.S.)--San Diego State University, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a Comparative Market Analysis (CMA) is standard process for real estate professional while assessing the market value of a property. A CMA report usually lists the target property and multiple recently sold houses in the same neighborhood and compares their features, e.g., built age, living size, number of bedrooms, etc. Professional realtors can then determine the appropriate price of the target property. However, without professional training, it is difficult to determine the importance of individual features and thus it remained challenging for buyers/sellers to appropriately use CMA reports. This dissertation aims to address this challenge by machine learning approach and automating the CMA based house appraisal procedure. The key idea is a self-supervised learning framework which includes two training stages. In the first stage, a set of nearly sold houses are identified for each target house in the training set and a contrastive loss function is introduced to encourage similarities or dissimilarities between the sold houses and the target house. This loss does not access the true market value of training samples and is used to pre-train the machine learning model. In the second stage, a set of supervised house-value pairs are used to fine-tune the pre-trained model for regression purposes. Experiments with various regression models are evaluated on a newly collected housing dataset. Results suggested the proposed self-training framework can consistently improve the appraisal accuracies of various regression models.
590 $a School code: 0220.
650 4 $a Computer science. $3 523869
650 4 $a Land use planning. $3 2122760
650 4 $a Artificial intelligence. $3 516317
650 4 $a Information science. $3 554358
653 $a Comparative market analysis
653 $a Real estate
653 $a Machine learning
690 $a 0984
690 $a 0800
690 $a 0536
690 $a 0723
710 2 $a San Diego State University. $b Computer Science. $3 2102764
773 0 $t Masters Abstracts International $g 83-03.
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791 $a M.S.
792 $a 2021
793 $a English
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