Abstract
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.
Highlights
Reforms in China’s system of urban housing, an important part of the “reform and opening up” policy initiated in 1978, led to a general and significant improvement in accommodations for most of the urban population in the country
This study develops a multiscale analysis framework based on ensemble empirical mode decomposition (EEMD) [31] and a fine-to-coarse reconstruction algorithm [22] to explore the behavioral mechanism of house prices in Beijing
From the perspective of multiscale analysis, we further propose a four-step modeling framework, integrating EEMD, fine-to-coarse reconstruction algorithm, autoregressive integrated moving average (ARIMA), polynomial function, and support vector regression (SVR), for forecasting short-term house prices
Summary
Reforms in China’s system of urban housing, an important part of the “reform and opening up” policy initiated in 1978, led to a general and significant improvement in accommodations for most of the urban population in the country. This study develops a multiscale analysis framework based on EEMD [31] and a fine-to-coarse reconstruction algorithm [22] to explore the behavioral mechanism of house prices in Beijing. E monthly price of a house in Beijing from January 2005 to November 2018 is used as experimental data for the purpose of validation In this multiscale analysis paradigm, the original house price series is first decomposed using EEMD, which is a substantial improvement over the original EMD, into several IMFs and a residual. From the perspective of multiscale analysis, we further propose a four-step modeling framework, integrating EEMD, fine-to-coarse reconstruction algorithm, autoregressive integrated moving average (ARIMA), polynomial function, and support vector regression (SVR), for forecasting short-term house prices.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
