The Behavioral Mechanism and Forecasting of Beijing Housing Prices from a Multiscale Perspective
The first-hand house price in Beijing, the capital of China, has skyrocketed with 43 percent annual growth from 2005 to 2017, exerting tremendous adverse effects on people’s livelihood and the development of real estate. Thus, exploring the behavioral mechanism and accurate forecasts of house prices...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2020/5375206 |
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| author | Yan Li Zhaoyang Xiang Tao Xiong |
| author_facet | Yan Li Zhaoyang Xiang Tao Xiong |
| author_sort | Yan Li |
| collection | DOAJ |
| description | The first-hand house price in Beijing, the capital of China, has skyrocketed with 43 percent annual growth from 2005 to 2017, exerting tremendous adverse effects on people’s livelihood and the development of real estate. Thus, exploring the behavioral mechanism and accurate forecasts of house prices is a critical element in making decisions under uncertain conditions and is of great practical significance for both participants and policymakers in real estate. According to the complex features of house price, including nonlinear, nonstationary, and multiscale, and considering the remarkable time and frequency discrimination capability of multiscale analysis in dealing with house price problems, we develop an ensemble empirical mode decomposition- (EEMD-) based multiscale analysis paradigm to investigate the behavioral mechanism and then obtain accurate forecasts of house prices. Specifically, the monthly house price in Beijing over the period January 2005 to November 2018 is first decomposed into several different time-scale intrinsic-mode functions (IMFs) and a residual via EEMD, revealing some interesting characteristics in house price volatility. Then, we compose the IMFs and residual into three components caused by normal market disequilibrium, extreme events, and the economic environment using the fine-to-coarse reconstruction algorithm. Finally, we propose an improved hybrid prediction model for forecasting house prices. Our experimental results show that the proposed multiscale analysis paradigm is able to clearly reveal the behavioral mechanism hidden in the original house price. More importantly, the mean absolute percentage errors (MAPEs) of the proposed EEMD-based hybrid approach are 5.62%, 7.24%, and 8.63% for one-, three-, and six-step-ahead prediction, respectively, consistently lower than the MAPE of the three competitors. |
| format | Article |
| id | doaj-art-cd87206f0bd848b493102010aaf7385b |
| institution | Kabale University |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-cd87206f0bd848b493102010aaf7385b2025-02-03T01:05:00ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2020-01-01202010.1155/2020/53752065375206The Behavioral Mechanism and Forecasting of Beijing Housing Prices from a Multiscale PerspectiveYan Li0Zhaoyang Xiang1Tao Xiong2College of Cultural Management, Wuhan University of Communication, Wuhan 430205, ChinaCollege of Economics and Management, Huazhong Agricultural University, Wuhan 430070, ChinaCollege of Economics and Management, Huazhong Agricultural University, Wuhan 430070, ChinaThe first-hand house price in Beijing, the capital of China, has skyrocketed with 43 percent annual growth from 2005 to 2017, exerting tremendous adverse effects on people’s livelihood and the development of real estate. Thus, exploring the behavioral mechanism and accurate forecasts of house prices is a critical element in making decisions under uncertain conditions and is of great practical significance for both participants and policymakers in real estate. According to the complex features of house price, including nonlinear, nonstationary, and multiscale, and considering the remarkable time and frequency discrimination capability of multiscale analysis in dealing with house price problems, we develop an ensemble empirical mode decomposition- (EEMD-) based multiscale analysis paradigm to investigate the behavioral mechanism and then obtain accurate forecasts of house prices. Specifically, the monthly house price in Beijing over the period January 2005 to November 2018 is first decomposed into several different time-scale intrinsic-mode functions (IMFs) and a residual via EEMD, revealing some interesting characteristics in house price volatility. Then, we compose the IMFs and residual into three components caused by normal market disequilibrium, extreme events, and the economic environment using the fine-to-coarse reconstruction algorithm. Finally, we propose an improved hybrid prediction model for forecasting house prices. Our experimental results show that the proposed multiscale analysis paradigm is able to clearly reveal the behavioral mechanism hidden in the original house price. More importantly, the mean absolute percentage errors (MAPEs) of the proposed EEMD-based hybrid approach are 5.62%, 7.24%, and 8.63% for one-, three-, and six-step-ahead prediction, respectively, consistently lower than the MAPE of the three competitors.http://dx.doi.org/10.1155/2020/5375206 |
| spellingShingle | Yan Li Zhaoyang Xiang Tao Xiong The Behavioral Mechanism and Forecasting of Beijing Housing Prices from a Multiscale Perspective Discrete Dynamics in Nature and Society |
| title | The Behavioral Mechanism and Forecasting of Beijing Housing Prices from a Multiscale Perspective |
| title_full | The Behavioral Mechanism and Forecasting of Beijing Housing Prices from a Multiscale Perspective |
| title_fullStr | The Behavioral Mechanism and Forecasting of Beijing Housing Prices from a Multiscale Perspective |
| title_full_unstemmed | The Behavioral Mechanism and Forecasting of Beijing Housing Prices from a Multiscale Perspective |
| title_short | The Behavioral Mechanism and Forecasting of Beijing Housing Prices from a Multiscale Perspective |
| title_sort | behavioral mechanism and forecasting of beijing housing prices from a multiscale perspective |
| url | http://dx.doi.org/10.1155/2020/5375206 |
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