Using deep learning to enhance electronic service quality: Application to real estate websites

TL;DR

This paper proposes a deep learning-based method to incorporate visual damage assessment into real estate websites, enhancing electronic service quality by making property searches more tangible and user-friendly.

Contribution

It introduces a novel visual feature, Damage Level, using Mask-RCNN, to improve the tangibility and efficiency of real estate e-services.

Findings

Damage Level estimation improves property search relevance.

Visual features enhance user experience on real estate platforms.

Deep learning effectively adds tangible attributes to electronic services.

Abstract

Electronic service quality (E-SQ) is a strategic metric for successful e-services.Among the service quality dimensions, tangibility is overlooked. However, by incorporating visuals or tangible tools, the intangible nature of e-services can be balanced. Thanks to advancements in Deep Learning for computer vision, tangible visual features can now be leveraged to enhance the browsing and searching experience of electronic services. Users usually have specific search criteria to meet, but most services will not offer flexible search filters. This research emphasizes the importance of integrating visual and descriptive features to improve the tangibility and efficiency of e-services. A prime example of an electronic service that can benefit from this is real-estate websites. Searching for real estate properties that match user preferences is usually demanding and lacks visual filters, such as the Damage Level to the property. The research introduces a novel visual descriptive feature, the Damage Level, which utilizes a deep learning network known as Mask-RCNN to estimate damage in real estate images. Additionally, a model is developed to incorporate the Damage Level as a tangible feature in electronic real estate services, with the aim of enhancing the tangible customer experience.