@Article{cmes.2022.020930,
AUTHOR = {Rita Yi Man Li, Lingxi Song, Bo Li, M. James C. Crabbe, Xiao-Guang Yue},
TITLE = {Predicting Carpark Prices Indices in Hong Kong Using AutoML},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {134},
YEAR = {2023},
NUMBER = {3},
PAGES = {2247--2282},
URL = {http://www.techscience.com/CMES/v134n3/49757},
ISSN = {1526-1506},
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 covers Web 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.},
DOI = {10.32604/cmes.2022.020930}
}