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Homeowner preferences after September 11th, a microdata approach

Adam Nowak and Sayago-Gomez, Juan
Authors registered in the RePEc Author Service: Juan Tomas Sayago Gomez

Regional Science and Urban Economics, 2018, vol. 70, issue C, 330-351

Abstract: The existence of homeowner preferences – specifically homeowner preferences for neighbors – is fundamental to economic models of sorting. This paper investigates whether or not the terrorist attacks of September 11, 2001 (9/11) impacted local preferences for Arab neighbors. We test for changes in preferences using a differences-in-differences approach in a hedonic pricing model. Relative to sales before 9/11, we find properties within 0.1miles of an Arab homeowner sold at a 1.4% discount (approximately $4133) in the 180 days after 9/11. The results are robust to a number of specifications including time horizon, event date, distance, time, alternative ethnic groups, and the presence of nearby mosques. Previous research has shown price effects at neighborhood levels but has not identified effects at the micro or individual property level, and for good reason: most transaction level data sets do not include ethnic identifiers. Applying methods from the machine learning and biostatistics literature, we develop a binomial classifier using a supervised learning algorithm and identify Arab homeowners based on the name of the buyer. We train the binomial classifier using names from Summer Olympic Rosters for 221 countries during the years 1948–2012. We demonstrate the flexibility of our methodology and perform an interesting counterfactual by identifying Hispanic and Asian homeowners in the data; unlike the statistically significant results for Arab homeowners, we find no meaningful results for Hispanic and Asian homeowners following 9/11.

Keywords: House prices; Ethnicity; Homeowner preferences; Terrorism; September 11th (search for similar items in EconPapers)
JEL-codes: R21 R23 R31 J15 (search for similar items in EconPapers)
Date: 2018
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Working Paper: Homeowner Preferences after September 11th, a Microdata Approach (2017) Downloads
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