Computer Vision and Real Estate: Do Looks Matter and Do Incentives Determine Looks / Edward L. Glaeser, Michael Scott Kincaid, Nikhil Naik.

Format:

Book

Author/Creator:

Glaeser, Edward L.

Contributor:

National Bureau of Economic Research.

Kincaid, Michael Scott.

Naik, Nikhil.

Series:

Working Paper Series (National Bureau of Economic Research) no. w25174.

NBER working paper series no. w25174

Language:

English

Physical Description:

1 online resource: illustrations (black and white);

Other Title:

Computer Vision and Real Estate

Place of Publication:

Cambridge, Mass. National Bureau of Economic Research 2018.

Summary:

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.

Notes:

Print version record

October 2018.