Modelling the housing market in OECD countries

Abstract

Recent episodes of housing bubbles, which occurred in several economies after the burst of the United States housing market, suggest studying the evolution of housing prices from a global perspective. We utilise a theoretical model for the purposes of this contribution, which identifies the main drivers of housing price appreciation, such as, for example, income, residential investment, financial elements, fiscal policy and demographics. In a second stage of our analysis, we test our theoretical hypothesis by means of a sample of 18 OECD countries from 1970 to 2011. We employ the vector error correction econometric technique in terms of our empirical analysis, which permits us to model the long-run equilibrium relationship and the short-run dynamics, which also helps to account for endogeneity and reverse causality problems.JEL Classifications: C22, R31 IntroductionThe occurrence of several episodes of housing bubbles in a number of developed economies in the second half of the 2000s, suggests the necessity of identifying the main drivers of this market.In order to contribute to the existing debate, we begin by proposing a theoretical explanation of housing prices, which puts forward the role of the basic fundamentals of this market, for example, disposable income, real residential investment, mortgage rates and demographics. Our model also accounts for the behaviour of monetary authorities by including credit standards. Moreover, in our specification there is room for the public sector to perform its role by means of taxation. We then proceed to test the validity of our theoretical model by applying cointegration analysis (Johansen 1995) on a sample of 18 OECD economies during the period 1970-2011. Our econometric results assure the existence of a long-run relationship among the variables and capture the adjustment in the short run by means of a Vector Error Correction Model (VECM). This technique also accounts for endogeneity and reverse causality problems.In terms of economic policy our analysis upgrades the role of the prudential and fiscal policies in relation to monetary policy. Moreover, our results also point to disposable income as the cornerstone of the model.

Macroeconomic drivers of London house prices

“…Some of the most commonly examined demand drivers in studies of house price movements in countries other than the UK include income per capita (Quigley, 1999) or GDP/capita (Sivitanides, 2015;Pashardes and Savva, 2009;Posedel and Vizek, 2009;Hossain and Latif, 2009;Egert and Mihaljek, 2007), the number of households or population size (Quigley, 1999;Pashardes and Savva, 2009;Arestis and Gonzalez, 2014;Sivitanides, 2015), interest rates (Arestis and Gonzalez, 2014;Pashardes and Savva, 2009;Egert and Mihaljek, 2007;Apergis and Rezitis, 2003), inflation (Kearl, 1979;Poterba, 1992) and credit availability or money supply (Egert and Mihaljek, 2007). The main factors that have been included in house price models from the supply side include construction permits (Quigley, 1999), residential investment (Arestis and Gonzalez, 2014), construction costs (Pashardes and Savva, 2009;Sivitanides, 2015) and interest rates (Sivitanides, 2015). Egert and Mihaljek (2007) examined the determinants of house prices in Central and Eastern Europe (CEE) countries and OECD countries.…”

Persistence and cyclical dynamics of US and UK house prices: Evidence from over 150 years of data

“…Muñoz (2004) and Clark and Coggin (2011) fail to reject the unit-root hypothesis of house prices in the USA. Arestis and González (2014) confirm the presence of a unit root in house prices of 18 OECD countries. In contrast, Cook and Vougas (2009) support the stationarity of UK housing prices but with structural change.…”

An Assessment of House Loans Cointegration with Macro Variables in Selected Euro Zone Countries

“…There are a number of authors (Anundsen et al, 2016;Arestis et al 2014;Davis and Zhu, 2011;Hoffman, 2001) who performed cross country analyses for a large group of countries, to find out private credit interaction with aggregated macro variables. Panel vector error correction model for cross-country credit empirical analyses is advantageous of the single country analyses due to ability to aggregate the data when single country yearly data are too short for multivariate regression analyses.…”