scholarly journals Economic Relationships Between Selling and Rental Prices in the Italian Housing Market

Author(s):  
Pierluigi Morano ◽  
Benedetto Manganelli ◽  
Francesco Tajani

In this paper the relationship between price and rent dynamics in the Italian housing market is studied. The aim is reached through the implementation of a multivariate autoregressive model (VAR), that makes it possible to explain the interdependencies of multiple time series. The analysis considers a series of macroeconomic variables in the model that in the deductive interpretation of the phenomenon and on the basis of other experiences in current literature, were evaluated as potential keys to understanding the relationship between prices and rents. The variables selected, along with residential realestate prices and real residential rents, were: the real short term interest rate, the time series of the annual differences between the actual and the expected Gross Domestic Product, real investments in housing. The data series cover the period from 1980 to 2008. The results obtained show some peculiarities of the Italian real estate market.

2003 ◽  
Vol 36 (20) ◽  
pp. 985-990 ◽  
Author(s):  
Jong-Man Cho ◽  
Jin-Hack Kim ◽  
Woo-Hyun Park ◽  
Yun-ho Lee ◽  
Jin-O Kim

Author(s):  
Monday Osagie Adenomon ◽  
Benjamin Agboola Oyejola

The goal of VAR or BVAR is the characterization of the dynamics and endogenous relationships among time series. Also the VAR models are known for their applications to forecasting and policy analysis. This paper compare the performance of VAR and Sims-Zha Bayesian VAR models when the multiple time series are jointly influenced by different levels of collinearity and autocorrelation in the short term (T=16, 32, 64 and 128). Five levels (-0.9,-0.5, 0,+0.5,+0.9) of collinearity and autocorrelation were considered and the results from the simulation study revealed that VAR(2) model dominated for no and moderate levels of autocorrelation (-0.5, 0, +0.5) irrespective of the collinearity level except in few cases when T=16. While the BVAR models dominated for high autocorrelation levels (-0.9 and +0.9) irrespective of the collinearity level except in few cases when T=128. The performance of the models varies at different levels of the collinearity and autocorrelated error, and also varies with the short term periods. Furthermore, the values of the RMSE and MAE criteria decrease as a result of increase in the time series length. In conclusion, the performance of the forecasting models depend on the time series data structure and the time series length. It is therefore recommended that the data structure and series length should be considered in using an appropriate model for forecasting.


2007 ◽  
Vol 191 (2) ◽  
pp. 106-112 ◽  
Author(s):  
Lisa A. Page ◽  
Shakoor Hajat ◽  
R. Sari Kovats

BackgroundSeasonal fluctuation in suicide has been observed in many populations. High temperature may contribute to this, but the effect of short-term fluctuations in temperature on suicide rates has not been studied.AimsTo assess the relationship between daily temperature and daily suicide counts in England and Wales between 1 January 1993 and 31 December 2003 and to establish whether heatwaves are associated with increased mortality from suicide.MethodTime-series regression analysis was used to explore and quantify the relationship between daily suicide counts and daily temperature. The impact of two heatwaves on suicide was estimated.ResultsNo spring or summer peak in suicide was found. Above 18 °, each 1 ° increase in mean temperature was associated with a 3.8 and 5.0% rise in suicide and violent suicide respectively. Suicide increased by 46.9% during the 1995 heatwave, whereas no change was seen during the 2003 heat wave.ConclusionsThere is increased risk of suicide during hot weather.


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