Modelling residential prices in Spain under the light of cointegrating techniques and automatic selection algorithms
||Modelling residential prices in Spain under the light of cointegrating techniques and automatic selection algorithms
||21st Annual European Real Estate Society Conference in Bucharest, Romania
||In this paper we have developed a VEC model to capture long term equilibrium trends as well as short term dynamics of the residential prices of the Spanish market. A parsimonious model has been selected, based on economic fundamental variables, explaining supply and demand interplay in the period 1995-2012 with quarterly observations. GDP per-capita, mortgage rate, Gross capital formation in dwellings and building starts have proxied demand, supply and opportunity costs. Insights on the impact of these variables on residential prices have been brought to light as well as the speed of adjustment once the price deviates from the long term equilibrium. Our model suggests that the Spanish residential prices adjust relatively fast, with around 22% of the deviation of the long term trend corrected each quarter. Furthermore, house prices are mainly driven by income (GDP per-capita) while impacts on prices are less important if come from variations in mortgage rates or stock changes.The time span used has conveniently allowed us to analyze the market in the recent residential price bubble context. As expected, during this period market rationale drifted from economic fundamentals and shock conditions sprang. Therefore, in our model we successfully identify structural break conditions since early 2008, instance when the residential prices busted in Spain. For this study a comprehensive database of real estate variables has been constructed for the Spanish market. 52 variables (six of them correspond to different definitions of housing prices) have been collected offering a pool of 46 candidate regressors to explain residential prices. In this context we have tried methods of automatic modelling selection using Genetic Algorithms (GA). Preliminary results point to similar results to the structural modelling. Different specifications obtained tend to render the same variables set, including purchasing capacity, opportunity costs measures and housing stock. With some definitions of prices, demographic and credit conditions are added to our structural specification.
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||PHD: Doctoral Session
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