Quantifying curb appeal

Zachary Bessinger, Nathan Jacobs

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Scopus citations

Abstract

The curb appeal of a home, which refers to how attractive it is when viewed from the street, is an important decisionmaking factor for many home buyers. Existing models for automatically estimating the price of a home ignore this factor, instead focusing exclusively on objective attributes, such as number of bedrooms, the square footage, and the age. We propose to use street-level imagery of a home, in addition to the objective attributes, to estimate the price of the home, thereby quantifying curb appeal. Our method uses deep convolutional neural networks to extract informative image features. We introduce a large dataset to support an extensive evaluation of several approaches. We find that using images and objective attributes together results in more accurate home price estimates than using either in isolation. We also find that representations learned for scene classification tasks are more discriminative for home price estimation than those learned for other tasks.

Original languageEnglish
Title of host publication2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings
Pages4388-4392
Number of pages5
ISBN (Electronic)9781467399616
DOIs
StatePublished - Aug 3 2016
Event23rd IEEE International Conference on Image Processing, ICIP 2016 - Phoenix, United States
Duration: Sep 25 2016Sep 28 2016

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume2016-August
ISSN (Print)1522-4880

Conference

Conference23rd IEEE International Conference on Image Processing, ICIP 2016
Country/TerritoryUnited States
CityPhoenix
Period9/25/169/28/16

Keywords

  • Computational aesthetics
  • Image feature representation
  • Image understanding
  • Neural networks

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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