# What Image Features Boost Housing Market Predictions?

arXiv: https://arxiv.org/abs/2107.07148 · HTML: https://mlsanity.com/paper/2107.07148/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 44 references that are in the map.
- First posted on arXiv 15 Jul 2021.

## Builds on (most cited first, 11 of 11)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [CatBoost: gradient boosting with categorical features support](https://mlsanity.com/paper/1810.11363/) (2018)
- [Places: An Image Database for Deep Scene Understanding](https://mlsanity.com/paper/1610.02055/) (2016)
- [Vision-based Real Estate Price Estimation](https://mlsanity.com/paper/1707.05489/) (2017)
- [Image Based Appraisal of Real Estate Properties](https://mlsanity.com/paper/1611.09180/) (2016)
- [CNN features are also great at unsupervised classification](https://mlsanity.com/paper/1707.01700/) (2017)
- [Beyond Spatial Auto-Regressive Models: Predicting Housing Prices with Satellite Imagery](https://mlsanity.com/paper/1610.04805/) (2016)
- [AI Blue Book: Vehicle Price Prediction using Visual Features](https://mlsanity.com/paper/1803.11227/) (2018)
- [Interpretable Selection and Visualization of Features and Interactions Using Bayesian Forests](https://mlsanity.com/paper/1506.02371/) (2015)

## Built on by (most cited first, 5 of 5)

- [Multimodal Machine Learning for Real Estate Appraisal: A Comprehensive Survey](https://mlsanity.com/paper/2503.22119/) (2025)
- [Measuring Product Quality Using Images: The CLIP Q-Score and an Application to Real Estate](https://mlsanity.com/paper/2608.01544/) (2026)
- [House Price Prediction Based On Deep Learning](https://mlsanity.com/paper/2204.09050/) (2022)
- [Real Estate Attribute Prediction from Multiple Visual Modalities with Missing Data](https://mlsanity.com/paper/2211.09018/) (2022)
- [Machine Learning Fairness in House Price Prediction: A Case Study of America's Expanding Metropolises](https://mlsanity.com/paper/2505.01591/) (2025)

## Abstract (arXiv)

The attractiveness of a property is one of the most interesting, yet challenging, categories to model. Image characteristics are used to describe certain attributes, and to examine the influence of visual factors on the price or timeframe of the listing. In this paper, we propose a set of techniques for the extraction of visual features for efficient numerical inclusion in modern-day predictive algorithms. We discuss techniques such as Shannon's entropy, calculating the center of gravity, employing image segmentation, and using Convolutional Neural Networks. After comparing these techniques as applied to a set of property-related images (indoor, outdoor, and satellite), we conclude the following: (i) the entropy is the most efficient single-digit visual measure for housing price prediction; (ii) image segmentation is the most important visual feature for the prediction of housing lifespan; and (iii) deep image features can be used to quantify interior characteristics and contribute to captivation modeling. The set of 40 image features selected here carries a significant amount of predictive power and outperforms some of the strongest metadata predictors. Without any need to replace a human expert in a real-estate appraisal process, we conclude that the techniques presented in this paper can efficiently describe visible characteristics, thus introducing perceived attractiveness as a quantitative measure into the predictive modeling of housing.