# House Price Prediction using Satellite Imagery

arXiv: https://arxiv.org/abs/2105.06060 · HTML: https://mlsanity.com/paper/2105.06060/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 10 references that are in the map.
- First posted on arXiv 13 May 2021.

## Builds on (most cited first, 3 of 3)

- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Beyond Spatial Auto-Regressive Models: Predicting Housing Prices with Satellite Imagery](https://mlsanity.com/paper/1610.04805/) (2016)

## Built on by (most cited first, 1 of 1)

- [Thailand Asset Value Estimation Using Aerial or Satellite Imagery](https://mlsanity.com/paper/2307.08650/) (2023)

## Abstract (arXiv)

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.