House Price Prediction using Satellite Imagery
arXiv:2105.06060v11 citations
Originality Synthesis-oriented
AI Analysis
This work addresses property valuation for real estate stakeholders, but it is incremental as it applies an existing method to a new data type.
The paper tackled house price prediction by incorporating satellite imagery, achieving a ~10% improvement in R-squared score compared to baseline models using only non-image features.
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.