Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11861/7253
Title: Predicting car park prices indices in Hong Kong using autoML
Authors: Dr. LI Yi Man, Rita 
Song, Lingxi 
Li, Bo 
Crabbe, M. James C. 
Yue, Xiao-Guang 
Issue Date: 2022
Source: Computer Modeling in Engineering & Sciences, 2022, vol. 134(3), pp. 2247-2282.
Journal: CMES - Computer Modeling in Engineering and Sciences 
Abstract: The aims of this study were threefold: 1) study the research gap in carpark and price index via big data and natural language processing, 2) examine the research gap of carpark indices, and 3) construct carpark price indices via repeat sales methods and predict carpark indices via the AutoML. By researching the keyword “carpark” in Google Scholar, the largest electronic academic database that coversWeb of Science and Scopus indexed articles, this study obtained 999 articles and book chapters from 1910 to 2019. It confirmed that most carpark research threw light on multi-storey carparks, management and ventilation systems, and reinforced concrete carparks. The most common research method was case studies. Regarding price index research, many previous studies focused on consumer, stock, press and futures, with many keywords being related to finance and economics. These indicated that there is no research predicting carpark price indices based on an AutoML approach. This study constructed repeat sales indices for 18 districts in Hong Kong by using 34,562 carpark transaction records from December 2009 to June 2019.Wanchai’s carpark price was about four times that of Yuen Long’s carpark price, indicating the considerable carpark price differences in Hong Kong. This research evidenced the features that affected the carpark price indices models most: gold price ranked the first in all 19 models; oil price or Link stock price ranked second depending on the district, and carpark affordability ranked third.
Type: Peer Reviewed Journal Article
URI: http://hdl.handle.net/20.500.11861/7253
ISSN: 1526-1492
1526-1506
DOI: 10.32604/cmes.2022.020930
Appears in Collections:Economics and Finance - Publication

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