Computer Vision and Real Estate: Do Looks Matter and Do Incentives Determine Looks

Edward Glaeser, Michael Scott Kincaid and Nikhil Naik

No 25174, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: How much does the appearance of a house, or its neighbors, impact its price? Do events that impact the incentives facing homeowners, like foreclosure, impact the maintenance and appearance of a home? Using computer vision techniques, we find that a one standard deviation improvement in the appearance of a home in Boston is associated with a .16 log point increase in the home’s value, or about $68,000 at the sample mean. The additional predictive power created by images is small relative to location and basic home variables, but external images do outperform variables collected by in-person home assessors. A home’s value increases by .4 log points, when its neighbor’s visually predicted value increases by one log point, and more visible neighbors have a larger price impact than less visible neighbors. Homes that went through foreclosure during the 2008-09 financial crisis experienced a .04 log point decline in their appearance-related value, relative to comparable homes, suggesting that foreclosures reduced the incentives to maintain the housing stock. We do not find more depreciation of appearance in rental properties, or more upgrading of appearance by owners before resale.

JEL-codes: C40 G21 R30 Z11 (search for similar items in EconPapers)
Date: 2018-10
New Economics Papers: this item is included in nep-ure
Note: PE
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Citations: View citations in EconPapers (12)

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