Impact of Syrian refugees on house prices in Istanbul

ABSTRACT

Turkey is the world’s biggest refugee hosting nation, and Istanbul, Turkey’s commercial capital and Europe’s largest metropolitan city by population, is home to almost 1,500,000 Syrians. We investigate the short-run impact of refugees on house prices in Istanbul’s districts. We find a negative impact of massive immigration on house prices.

KEYWORDS:

• Migrants
• house prices
• Istanbul
• Syrian refugees

JEL CLASSIFICATION:

• F22
• R21
• R23
• R31

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

¹ https://www.unhcr.org/tr/en/refugees-and-asylum-seekers-in-turkey.

² https://www.unhcr.org/refugee-statistics/; https://www.hurriyetdailynews.com/over-1-million-foreigners-including-refugees-live-in-istanbul-governor-169080.

³ https://reliefweb.int/report/turkey/analysis-baseline-assessment-stanbul-province-october-december-2018.

⁴ Mean equality test confirms selection of control and treatment districts. Slightly expanding or contracting the control group does not alter results. Heckman (1976) does find a selection bias for the model. The balance test (McKenzie 2015) fails to reject the significance of coefficients, individually/jointly. Results available.

⁵ Alternatively, we allow the exogenous shock (treatment) to occur in 2014 and the pre-treatment and post-treatment period to cover 2011m1-2013m12 and 2014m1-2016m12, respectively, assuming that refugees were allowed to move in 2014 because of insufficient camp capacity. For robustness, we try different pre/post-treatment periods, including (2011–2015 vs 2016), (2011–2015 vs 2016–17), and (2010–2014 vs 2015). Overall, we consistently observe that house prices increase less in the treatment districts.

⁶ All variables are real adjusted for monthly local inflation. We also experiment with log (×) instead of X, and random effects. Qualitatively identical results are available.

⁷ There are limited data on sale transactions, only annual as of 2015. Both region prices exhibit a similar declining pattern in 2016; slightly more so in control districts.

⁸ Models with supply-side variables (number of new residential flats or residential square metres built) show the higher the number (or size) of new residential buildings in a district, the lower the house prices. The main findings do not change.