What Image Features Boost Housing Market Predictions?

TL;DR

This paper explores various image feature extraction techniques, demonstrating their effectiveness in enhancing housing market prediction models by quantifying visual attractiveness without replacing human appraisal.

Contribution

It introduces and compares multiple visual feature extraction methods, highlighting entropy and segmentation as key predictors for housing prices and lifespan.

Findings

Entropy is the most efficient single visual predictor for housing prices.

Image segmentation significantly improves housing lifespan prediction.

Deep image features help quantify interior characteristics for predictive modeling.

Abstract

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.