Deep-Learning Approach for an Analysis of Real-Estate Prices and Transactions
Research on the study of houses, condominiums and buildings in Taiwan’s metropolitan areas continues to be an important area of research. In real estate forecasting and analysis, methods such as statistical analysis and questionnaire data collection are widely used. However, when multidim...
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10994762/ |
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| author | Cheng-Hong Yang Borcy Lee Yu-Da Lin |
| author_facet | Cheng-Hong Yang Borcy Lee Yu-Da Lin |
| author_sort | Cheng-Hong Yang |
| collection | DOAJ |
| description | Research on the study of houses, condominiums and buildings in Taiwan’s metropolitan areas continues to be an important area of research. In real estate forecasting and analysis, methods such as statistical analysis and questionnaire data collection are widely used. However, when multidimensional data is considered, these methods are time-consuming and inadequate. This study aimed to build a real estate forecasting model that can adapt to a changing environment. Data were collected from public government databases, the collected data were standardized for accurate clustering, an appropriate data clustering algorithm was applied to the standardized data, and cross-statistical analysis was performed to verify the adopted algorithm. We used a deep learning based on autoencoder algorithm to increase the accuracy of the clustering analysis. A double-bottom map particle swarm optimization (DBM-PSO) clustering algorithm was then used to determine the optimal clustering solution. Cluster analysis and deep learning were conducted on data collected from public websites to understand the factors that led to the sustained increase in housing prices in Taiwan over the past decade. The results of this study indicate that three key factors—the number of real estate transactions, the average unit price of real estate transactions, and the building material and construction index—significantly affected real estate prices in Taiwan. Our results could help researchers and governments to focus on specific aspects of real estate development without being influenced by other related factors. In addition, the relationships between real estate trends and the aforementioned three key factors were determined to obtain valuable information that can enable the Taiwanese government to regulate the property market and prevent excessive growth. The framework proposed in this paper allows researchers and governments to focus on specific aspects of real estate development without being influenced by other related factors, and provides a new mechanism for approaching real estate-related forecasting. |
| format | Article |
| id | doaj-art-b829afde6d684e86967a96b3dce612e1 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| spelling | doaj-art-b829afde6d684e86967a96b3dce612e12025-08-20T03:48:24ZengIEEEIEEE Access2169-35362025-01-0113892488926510.1109/ACCESS.2025.356879810994762Deep-Learning Approach for an Analysis of Real-Estate Prices and TransactionsCheng-Hong Yang0https://orcid.org/0000-0002-2741-0072Borcy Lee1Yu-Da Lin2https://orcid.org/0000-0001-5100-6072Department of Information Management, Tainan University of Technology, Tainan, TaiwanDepartment of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, TaiwanDepartment of Computer Science and Information Engineering, National Penghu University of Science and Technology, Penghu, TaiwanResearch on the study of houses, condominiums and buildings in Taiwan’s metropolitan areas continues to be an important area of research. In real estate forecasting and analysis, methods such as statistical analysis and questionnaire data collection are widely used. However, when multidimensional data is considered, these methods are time-consuming and inadequate. This study aimed to build a real estate forecasting model that can adapt to a changing environment. Data were collected from public government databases, the collected data were standardized for accurate clustering, an appropriate data clustering algorithm was applied to the standardized data, and cross-statistical analysis was performed to verify the adopted algorithm. We used a deep learning based on autoencoder algorithm to increase the accuracy of the clustering analysis. A double-bottom map particle swarm optimization (DBM-PSO) clustering algorithm was then used to determine the optimal clustering solution. Cluster analysis and deep learning were conducted on data collected from public websites to understand the factors that led to the sustained increase in housing prices in Taiwan over the past decade. The results of this study indicate that three key factors—the number of real estate transactions, the average unit price of real estate transactions, and the building material and construction index—significantly affected real estate prices in Taiwan. Our results could help researchers and governments to focus on specific aspects of real estate development without being influenced by other related factors. In addition, the relationships between real estate trends and the aforementioned three key factors were determined to obtain valuable information that can enable the Taiwanese government to regulate the property market and prevent excessive growth. The framework proposed in this paper allows researchers and governments to focus on specific aspects of real estate development without being influenced by other related factors, and provides a new mechanism for approaching real estate-related forecasting.https://ieeexplore.ieee.org/document/10994762/Machine learningreal estateparticle swarm optimization algorithmeconomyautoencoderdeep learning |
| spellingShingle | Cheng-Hong Yang Borcy Lee Yu-Da Lin Deep-Learning Approach for an Analysis of Real-Estate Prices and Transactions IEEE Access Machine learning real estate particle swarm optimization algorithm economy autoencoder deep learning |
| title | Deep-Learning Approach for an Analysis of Real-Estate Prices and Transactions |
| title_full | Deep-Learning Approach for an Analysis of Real-Estate Prices and Transactions |
| title_fullStr | Deep-Learning Approach for an Analysis of Real-Estate Prices and Transactions |
| title_full_unstemmed | Deep-Learning Approach for an Analysis of Real-Estate Prices and Transactions |
| title_short | Deep-Learning Approach for an Analysis of Real-Estate Prices and Transactions |
| title_sort | deep learning approach for an analysis of real estate prices and transactions |
| topic | Machine learning real estate particle swarm optimization algorithm economy autoencoder deep learning |
| url | https://ieeexplore.ieee.org/document/10994762/ |
| work_keys_str_mv | AT chenghongyang deeplearningapproachforananalysisofrealestatepricesandtransactions AT borcylee deeplearningapproachforananalysisofrealestatepricesandtransactions AT yudalin deeplearningapproachforananalysisofrealestatepricesandtransactions |