Investigation of principal characteristics of the real estate by the wavel-transformation of time ranks
DOI:
https://doi.org/10.32347/2707-501x.2020.44.3-16Keywords:
time series, discrete Haar wavelet transform, scaling factor, approximation coefficient, detail factor, filter, trend, cost of housing, activation of economic processes.Abstract
An approach to housing cost forecasting using modified time series wavelet transforms is proposed, which can be used to compile optimistic, neutral and pessimistic scenarios for the implementation of housing investment projects.
The article proposes a method of modifying a discrete Haar wavelet transform, the use of which allows to obtain results that are suitable for meaningful economic interpretation. In order to provide an economic interpretation of the detailing coefficients of the time series, a modification for the wavelet coefficients is proposed, which will consist of dividing the sets of detailing wavelet coefficients by the indicators inverted by the square of the normalizing factors. Modification of the wavelet made it possible to formulate an economic interpretation of the transformation: for detailing the quarterly average it is necessary to know how much the values of the first and second counts (offsets by 0 and 1 from the beginning) deviate from their average, which shows the activation of the economic process at the beginning of the reference three. The upward trend will be curtailed in the positive results of the wavelet transformation, while speculative economic growth at the end of the quarter will correspond to the negative results.
The proposed modification of Haar wavelet transform was applied to time series of cost of homes in the Kiev region for the period from 2014 to 2018. unexpected rise in real estate prices.
It is revealed that the cost of housing in the Zgurov district of Kiev region. characterized by a steady downward trend in price. This is indicated by the positive values of all wavelet transformations for each of the following quarters. The modified wavelet analysis identifies warning indicators of destabilization of supply and demand in the rural housing market. The expediency of applying regression analysis to the results of wavelet transformations of time series of economic indicators is proved.
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