Real Estate Property Valuation using Self-Supervised Vision Transformers

TL;DR

This paper introduces a novel property valuation method using self-supervised vision transformers combined with traditional features, demonstrating improved accuracy over conventional appraisal techniques in real estate.

Contribution

The paper presents a new AI-based approach that integrates vision transformers with hedonic models for more accurate property valuation.

Findings

Model achieves low RMSE in property value prediction

Outperforms traditional appraisal methods

Effective use of images and quantitative data

Abstract

The use of Artificial Intelligence (AI) in the real estate market has been growing in recent years. In this paper, we propose a new method for property valuation that utilizes self-supervised vision transformers, a recent breakthrough in computer vision and deep learning. Our proposed algorithm uses a combination of machine learning, computer vision and hedonic pricing models trained on real estate data to estimate the value of a given property. We collected and pre-processed a data set of real estate properties in the city of Boulder, Colorado and used it to train, validate and test our algorithm. Our data set consisted of qualitative images (including house interiors, exteriors, and street views) as well as quantitative features such as the number of bedrooms, bathrooms, square footage, lot square footage, property age, crime rates, and proximity to amenities. We evaluated the performance of our model using metrics such as Root Mean Squared Error (RMSE). Our findings indicate that these techniques are able to accurately predict the value of properties, with a low RMSE. The proposed algorithm outperforms traditional appraisal methods that do not leverage property images and has the potential to be used in real-world applications.