Volume 5, Issue 18 (12-2014)
jemr 2014, 5(18): 7-46 |
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Shahab Lavasani K, Abbasi Nejad H. Forecasting the Hosing Booms or Busts Using Wavelet Decomposition and Artificial Neural Networks. jemr 2014; 5 (18) :7-46
URL: http://jemr.khu.ac.ir/article-1-1121-en.html
URL: http://jemr.khu.ac.ir/article-1-1121-en.html
1- , keyvanshahab@ut.ac.ir
2- Faculty Of Economics
2- Faculty Of Economics
Abstract: (7503 Views)
Generally,some booms in housing prices are followed by busts. One common phenomenon relating these changes is that the house price cycle is generally believed to the product of the short-run deviations from the long-run upward trends. The long-term cyclical fluctuation in Iran’s housing market was periodically occurred about every 6 years.
Furthermore, Movements in house prices have significant impact on household welfare, financial stability and business cycles. Being able to forecast housing price booms is therefore of central importance for central banks, financial supervision authorities as well as for other economic agents. However, forecasting house prices using only a single or a few selected variables at a time intuitively appears efficient because only a single variable almost contain all of the pertinent investigative information about the past behavior of the variable. In this study, wavelet decomposition has been used to extract the cyclical components of house price, and then using the cyclical components and neural network methodwe start to forecast the booms in housing prices in 2013.
Furthermore, Movements in house prices have significant impact on household welfare, financial stability and business cycles. Being able to forecast housing price booms is therefore of central importance for central banks, financial supervision authorities as well as for other economic agents. However, forecasting house prices using only a single or a few selected variables at a time intuitively appears efficient because only a single variable almost contain all of the pertinent investigative information about the past behavior of the variable. In this study, wavelet decomposition has been used to extract the cyclical components of house price, and then using the cyclical components and neural network methodwe start to forecast the booms in housing prices in 2013.
Type of Study: Applicable |
Subject:
شهری و منطقه ای
Received: 2014/10/7 | Accepted: 2014/12/20 | Published: 2015/03/1
Received: 2014/10/7 | Accepted: 2014/12/20 | Published: 2015/03/1
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