Forecasting Australian Real House Price Index: A Comparison Study of Machine Learning and Time Series Methods

2019 ◽  
Author(s):  
George Milunovich
2017 ◽  
Vol 10 (3) ◽  
pp. 303-330 ◽  
Author(s):  
Laura Gabrielli ◽  
Paloma Taltavull de La Paz ◽  
Armando Ortuño Padilla

Purpose This paper aims to present the dynamics of housing prices in Italian cities based on unpublished data with regional details from the late 1960s, half-yearly base, for all main Italian cities measuring the average prices for three city dimensions: city centre, sub-centres and outskirts or suburbs. It estimates the Italian long-term house price index, city based in real terms, and shows a combination of methods to deal with large time-series data. Design/methodology/approach This paper builds long-term cycles based on the city (real) data by estimating the common components of cointegrated time series and extracting the unobservable signals to build real house price index for sub-regions in Italy. Three different econometric methodologies are used: Johansen cointegration test and VAR models to identify the long-term pattern of prices at the estimated aggregate level; principal components to obtain the common (permanent and transitory) components; and signal extraction in ARIMA time series–model-based approach method to extract the unobserved time signals. Findings Results show three long-term cycle-trends during the period and identify several one-direction causal non-permanent relationships among house prices from different Italian areas. There is no evidence of convergence among regional’s house prices suggesting that the Italian housing prices converge inside the local market with only short diffusion effects at larger regional level. Research limitations/implications Data are measured as the average price in squared meters, and the resulting index is not quality controlled. Practical implications The long-term trends on housing prices serve to implement further research and know deeply the evolution of Italian housing prices. Originality/value This paper contains new and unknown information about the evolution of housing prices in Italian regions and cities.


2019 ◽  
Author(s):  
Richard H. Stanton ◽  
Chris Strickland ◽  
Nancy E. Wallace

2017 ◽  
Vol 15 (4) ◽  
Author(s):  
Salina Hj Kassim ◽  
Nur Harena Redzuan ◽  
Nor Zalina Harun

The current practise of the Islamic banks to rely on market interest rate as pricing benchmark for their home financing products has been a subject of intense debate among many parties. Muslim scholars have warned that it is highly discouraged as it could lead to a possible convergence between the practices of the Islamic and conventional banks. This paper intends to address the financing issues in the discussion of human settlement or housing policy by presenting the determinants for house price index as well as looking into the possibility of adopting the House Price Index (HPI) to replace the market interest rate as a pricing benchmark for the Islamic home financing. The study applies Auto-Regressive Distributed Lag (ARDL) method on a model comprising HPI as the dependent variable and a set of independent variables consisting of economic, housing demand and housing supply factors. The findings lead to the formulation of recommendations as a way forward for the Islamic banking industry in particular, and the economy in general. This will require a paradigm shift from basic financing products to a more holistic approach which integrates supply of housing factors, as well as urban planning and urban finance, with human rights and recognizes the need to place and shelter people.


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