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House Price Prediction using Satellite Imagery

Machine Learning 2021-05-14 v1 Machine Learning

Abstract

In this paper we show how using satellite images can improve the accuracy of housing price estimation models. Using Los Angeles County's property assessment dataset, by transferring learning from an Inception-v3 model pretrained on ImageNet, we could achieve an improvement of ~10% in R-squared score compared to two baseline models that only use non-image features of the house.

Cite

@article{arxiv.2105.06060,
  title  = {House Price Prediction using Satellite Imagery},
  author = {Sina Jandaghi Semnani and Hoormazd Rezaei},
  journal= {arXiv preprint arXiv:2105.06060},
  year   = {2021}
}

Comments

Stanford CS230 Deep Learning, Winter 2018

R2 v1 2026-06-24T02:03:51.858Z