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Sviatlana Anop Royal Instritute of Technology (KTH), Stockholm, Sweden

Determinants of foreign direct investment in Real estate in European countries – panel data analysis. Sviatlana Anop Royal Instritute of Technology (KTH), Stockholm, Sweden. Introduction.

josiah-mills
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Sviatlana Anop Royal Instritute of Technology (KTH), Stockholm, Sweden

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  1. Determinants of foreign direct investment in Real estate in European countries – panel data analysis Sviatlana Anop Royal Instritute of Technology (KTH), Stockholm, Sweden

  2. Introduction The goal of this study is to highlight theoretical and empirical findings about determinants of foreign direct investment in real estate in developed European countries-members of OECD.

  3. Introduction Figure 1. FDI Inflows and outflows, 1999-2007, and 2008 forecast Source: OECD

  4. Introduction

  5. Literature review • Market-size hypothesis: international investments are “attracted by both the size of the host country and by the purchasing power of its inhabitants.” Sader (1993) • Nonnemberg and Cardoso de Medonco (2004) and Mottaleb (2007) have shown that such factors as the size and rate of growth of the GDP, the availability of skilled labor, modern communication facilities significantly affect the inflow of FDI in developed countries

  6. Contribution • However almost no research was done regarding investments in particular industries and assets, and more specifically regarding FDI in real estate. • Moshirian F., Pham T. (2000) in their study of US FDI show that US financial wealth, US FDI in manufacturing and banking, US bilateral trade, foreign current account balance and US foreign financial liabilities contribute positively to the expansion of US FDI in real estate. • This study investigates determinants of FDI in real estate in 15 OECD Countries of European area for 1996-2007

  7. Data Open data source: http://stats.oecd.org/index.aspx The annual panel data consists of 15 OECD countries of European area and runs from 1996-2007 both are inclusive.

  8. List of countries • Austria • Czech republic • Denmark • Finland • France • Germany • Greece • Hungary • Netherlands • Norway • Poland • Slovak republic • Spain • Sweden • United Kingdom

  9. General dynamic model FDIREi,t= ai +β1GYi,t-1+β2yi,t-1+β5h i,t-1+β6road i,t-1+εit where FDIRE - FDI in real estate, GY - real GDP growth, y - GDP, h - human capital, road – road infrastructure.

  10. Descriptive statistics Main variables in the model: • FDI in RE is measured in millions US Dollars. • The Gross Domestic Product (GDP) measured in billion US Dollars is the way of measuring the size of country’s economy. • Real GDP growth is measured in percentage • Human capital is measured as a tertiary rate of the country. This is a percentage of population age 24-65 that enrolled in the tertiary schools. • Road reflects road infrastructure in the country and is measured as road fatalities per million inhabitants.

  11. Descriptive statistics

  12. First-difference model ΔFDIREi,t=β1ΔGYi,t-1+β2Δyi,t-1+β5Δhi,t-1+ +β6Δroadi,t-1+Δεit where FDIRE - FDI in real estate, GY - real GDP growth, y - GDP, h - human capital, road – road infrastructure.

  13. Fixed effect model FDIREi,t = ai+β1GYi,t-1+β2yi,t-1+β5hi,t-1+β6roadi,t-1+ εit where FDIRE - FDI in real estate, GY - real GDP growth, y - GDP, h - human capital, road – road infrastructure.

  14. Results

  15. Dinamic model • Wooldridge test: no serial autocorrelation • Fist-difference model: no significant estimators • Fixed effect model: GDP growth is not signiciant, GDP size is significant, positive effect, human capital and road infrastructure are significant, negative effect • Random effects model: only GDP size is significant, positive effect

  16. Sensitivity analysis

  17. Thank you!

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