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ARTICLE

Predicting Carpark Prices Indices in Hong Kong Using AutoML

Rita Yi Man Li1, Lingxi Song2, Bo Li2,3, M. James C. Crabbe4,5,6, Xiao-Guang Yue7,*
1 Sustainable Real Estate Research Center, Hong Kong Shue Yan University, Hong Kong, 999077, China
2 Chakrabongse Bhuvanarth International Institute for Interdisciplinary Studies, Rajamangala University of Technology Tawan-Ok, Bangkok, 10400, Thailand
3 Zhongyuan Region Company, Jinke Property Group Co., Ltd., Zhengzhou, 450000, China
4 Wolfson College, Oxford University, Oxford, OX2 6UD, UK
5 Institute of Biomedical and Environmental Science & Technology, University of Bedfordshire, Luton, LU1 3JU, UK
6 School of Life Sciences, Shanxi University, Taiyuan, 030006, China
7 Department of Computer Science and Engineering, European University Cyprus, Nicosia, 1516, Cyprus
* Corresponding Author: Xiao-Guang Yue. Email:
(This article belongs to this Special Issue: Computer Modelling for Safer Built Environment and Smart Cities)

Computer Modeling in Engineering & Sciences 2023, 134(3), 2247-2282. https://doi.org/10.32604/cmes.2022.020930

Received 20 December 2021; Accepted 13 May 2022; Issue published 20 September 2022

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.

Graphical Abstract

Predicting Carpark Prices Indices in Hong Kong Using AutoML

Keywords

Carpark; repeat sales index; AutoML; Hong Kong; natural language processing; tokenization

Cite This Article

Yi, R., Song, L., Li, B., James, M., Yue, X. (2023). Predicting Carpark Prices Indices in Hong Kong Using AutoML. CMES-Computer Modeling in Engineering & Sciences, 134(3), 2247–2282.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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