東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁 到查詢結果 [ null ]

切換: 標籤 | MARC模式 | ISBD

FindBook

Google Book

Amazon

博客來

Real Estate Housing Price Prediction Based on the Effect of the COVID-19 Pandemic on U.S. Population Migration.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Real Estate Housing Price Prediction Based on the Effect of the COVID-19 Pandemic on U.S. Population Migration./
作者:	Chun, Bo Won.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	40 p.
附註:	Source: Masters Abstracts International, Volume: 83-03.
Contained By:	Masters Abstracts International83-03.
標題:	Statistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28720554
ISBN:	9798538168507

Real Estate Housing Price Prediction Based on the Effect of the COVID-19 Pandemic on U.S. Population Migration.
Chun, Bo Won.

Real Estate Housing Price Prediction Based on the Effect of the COVID-19 Pandemic on U.S. Population Migration. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 40 p.

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

Thesis (M.S.)--Utica College, 2021.

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

With the U.S. real estate house prices rising throughout the COVID-19 Pandemic, this report analyzes potential causes for the continual rise in prices. The research initially hypothesizes that the migration of people from county to county affected the real estate market prices. To test the hypothesis, machine learning models were employed to assess whether the migrations of people living in densely populated areas moving to less densely populated areas in the United States affected the real estate listing prices. More specifically, the Linear Regression model, Decision Tree model, Random Forest model, and the Support Vector Machine model were used to in the testing of this hypothesis. While the Random Forest model proved to be the most accurate in predictive power compared to the other models, the accuracy statistics of the Random Forest model were still low to be considered reliably accurate. With additional data, the accuracy of the models employed in this research could rise. The novelty of the Pandemic and the lack of 2021 Census Bureau county population estimates (U.S. Census Bureau, 2021) only allowed for a limited analysis of the data.

ISBN: 9798538168507Subjects--Topical Terms:

517247
Statistics.
Subjects--Index Terms:

Data science

Real Estate Housing Price Prediction Based on the Effect of the COVID-19 Pandemic on U.S. Population Migration.
LDR:02359nmm a2200385 4500 001 2351110
005 20221107085257.5
008 241004s2021 ||||||||||||||||| ||eng d
020 $a 9798538168507
035 $a (MiAaPQ)AAI28720554
035 $a AAI28720554
040 $a MiAaPQ $c MiAaPQ
100 1 $a Chun, Bo Won. $3 3690661
245 1 0 $a Real Estate Housing Price Prediction Based on the Effect of the COVID-19 Pandemic on U.S. Population Migration.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2021
300 $a 40 p.
500 $a Source: Masters Abstracts International, Volume: 83-03.
500 $a Advisor: McCarthy, Michael.
502 $a Thesis (M.S.)--Utica College, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a With the U.S. real estate house prices rising throughout the COVID-19 Pandemic, this report analyzes potential causes for the continual rise in prices. The research initially hypothesizes that the migration of people from county to county affected the real estate market prices. To test the hypothesis, machine learning models were employed to assess whether the migrations of people living in densely populated areas moving to less densely populated areas in the United States affected the real estate listing prices. More specifically, the Linear Regression model, Decision Tree model, Random Forest model, and the Support Vector Machine model were used to in the testing of this hypothesis. While the Random Forest model proved to be the most accurate in predictive power compared to the other models, the accuracy statistics of the Random Forest model were still low to be considered reliably accurate. With additional data, the accuracy of the models employed in this research could rise. The novelty of the Pandemic and the lack of 2021 Census Bureau county population estimates (U.S. Census Bureau, 2021) only allowed for a limited analysis of the data.
590 $a School code: 1754.
650 4 $a Statistics. $3 517247
650 4 $a Datasets. $3 3541416
650 4 $a Disease control. $3 3562653
650 4 $a Epidemics. $3 588395
650 4 $a COVID-19. $3 3554449
650 4 $a Bias. $2 gtt $3 1374837
650 4 $a Information science. $3 554358
653 $a Data science
653 $a Economic health
653 $a House listing price
653 $a Modifiable areal unit problem
653 $a Mortgage
653 $a Machine learning
690 $a 0501
690 $a 0463
690 $a 0723
710 2 $a Utica College. $b Data Science. $3 3433220
773 0 $t Masters Abstracts International $g 83-03.
790 $a 1754
791 $a M.S.
792 $a 2021
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28720554