Vision-based Real Estate Price Estimation

TL;DR

This paper introduces a deep learning-based approach that incorporates visual features from house photos to improve the accuracy of real estate price estimation, outperforming existing proprietary methods like Zillow.

Contribution

It presents a novel framework combining visual analysis with traditional home data for more precise market value predictions.

Findings

Visual features significantly impact house value estimates.

The proposed method outperforms Zillow's estimates on new datasets.

Deep convolutional neural networks effectively assess luxury levels from photos.

Abstract

Since the advent of online real estate database companies like Zillow, Trulia and Redfin, the problem of automatic estimation of market values for houses has received considerable attention. Several real estate websites provide such estimates using a proprietary formula. Although these estimates are often close to the actual sale prices, in some cases they are highly inaccurate. One of the key factors that affects the value of a house is its interior and exterior appearance, which is not considered in calculating automatic value estimates. In this paper, we evaluate the impact of visual characteristics of a house on its market value. Using deep convolutional neural networks on a large dataset of photos of home interiors and exteriors, we develop a method for estimating the luxury level of real estate photos. We also develop a novel framework for automated value assessment using the above photos in addition to home characteristics including size, offered price and number of bedrooms. Finally, by applying our proposed method for price estimation to a new dataset of real estate photos and metadata, we show that it outperforms Zillow's estimates.