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The Journal of Real Estate Finance and Economics

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06-09-2019

Valuing Curb Appeal

Authors: Erik B Johnson, Alan Tidwell, Sriram V Villupuram

Published in: The Journal of Real Estate Finance and Economics | Issue 1-2/2020

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Abstract

We recover the value of curb appeal in residential housing by using photos obtained from Google Street View, a deep learning classification algorithm and a variety of hedonic controls. We show that own property curb appeal is worth about twice that of an across the street neighbor. Together, neighbor and own property curb appeal together may account for up to 7% of a house’s sale price. The curb appeal premium is more pronounced during times of housing market weakness and greater in neighborhoods with high average curb appeal. Results are robust to a variety of spatial controls and curb appeal specifications.

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100 images for every curb appeal category. The classified images, model, labels and python code are publicly available at: https://github.com/erikbjohn/curb_appeal/tree/master/Replication. This will enable replication of training and model creation used in this paper.

Importantly, although the web interface allows for time-series selection of photos, the API only returns the most recent photo from the location. In the results section, we show that our results are robust to changes in the photo/sale date time windows. Thus, it appears that curb appeal scores are relatively stationary. This obviates concerns about the most recent photo restriction. This is consistent with the findings of Glaeser et al. (2018).

For example, if the assigned probabilities for curb appeal categories 1, 2, 3 and 4 are 0.01, 0.01, 0.03 and 0.95 respectively, then the weighted average curb appeal score is calculated as: 0.01(1) + 0.01(2) + 0.03 (3) + 0.95(4) = 3.92

We do not use the interaction of neighborhood and sale year dummies owing to the limited number of observations after applying the filters

It could very well be the case that most of the houses were built to be in the mid-curb appeal range with a smaller volume of houses that have the best curb appeal. The houses in the mid-range of curb appeal transform into lower curb appeal houses for a variety of reasons.

Due to the annualized data, in the event of a repeated sale within a given year, only the first observation is used and any remaining repeat observation during the year dropped.

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Title: Valuing Curb Appeal
Authors: Erik B Johnson
Alan Tidwell
Sriram V Villupuram
Publication date: 06-09-2019
Publisher: Springer US
Published in: The Journal of Real Estate Finance and Economics / Issue 1-2/2020
Print ISSN: 0895-5638
Electronic ISSN: 1573-045X
DOI: https://doi.org/10.1007/s11146-019-09713-z

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