@inproceedings{c13ba965eb4d4cb68057ba7c3ec06e65,
title = "SPATIAL ANALYTICS OF HOUSING PRICES WITH USER-GENERATED POI DATA, A CASE STUDY IN SHENZHEN",
abstract = "Housing is among the most pressing issues in China. Researchers are eager to identify housing property's internal and geographic factors influencing residential property prices. However, few studies have examined the relationship between social media users' point of interest (POI) data and house prices using big data. This paper presents a machine learning model for regression analysis to reveal the relationship between housing prices and check-in POI density in Futian District, Shenzhen. The results show that our proposed price prediction model using additional features based on POI data proved to provide higher prediction accuracy. Our results indicate that incorporating POI features based on current feeds from location-based social networks can provide more up-to-date estimates of housing market price trends.",
keywords = "Check-in POI, Hedonic Pricing Method, Kernel Density Estimation, SVR Model",
author = "Muxin Jia and Taro Narahara",
note = "Publisher Copyright: {\textcopyright} 2023 and published by the Association for Computer-Aided Architectural Design Research in Asia (CAADRIA), Hong Kong.; 28th International Conference on Computer-Aided Architectural Design Research in Asia, CAADRIA 2023 ; Conference date: 21-03-2023 Through 23-03-2023",
year = "2023",
doi = "10.52842/conf.caadria.2023.1.635",
language = "English (US)",
isbn = "9789887891796",
series = "Proceedings of the International Conference on Computer-Aided Architectural Design Research in Asia",
publisher = "The Association for Computer-Aided Architectural Design Research in Asia",
pages = "635--644",
editor = "Immanuel Koh and Dagmar Reinhardt and Mohammed Makki and Mona Khakhar and Nic Bao",
booktitle = "HUMAN-CENTRIC - Proceedings of the 28th International Conference on Computer-Aided Architectural Design Research in Asia, CAADRIA 2023",
}