Integrating Floor Plans into Hedonic Models for Rent Price Appraisal

Abstract

Online real estate platforms have become significant marketplaces facilitating users' search for an apartment or a house. Yet it remains challenging to accurately appraise a property's value. Prior works have primarily studied real estate valuation based on hedonic price models that take structured data into account while accompanying unstructured data is typically ignored. In this study, we investigate to what extent an automated visual analysis of apartment floor plans on online real estate platforms can enhance hedonic rent price appraisal. We propose a tailored two-staged deep learning approach to learn price-relevant designs of floor plans from historical price data. Subsequently, we integrate the floor plan predictions into hedonic rent price models that account for both structural and locational characteristics of an apartment. Our empirical analysis based on a unique dataset of 9,174 real estate listings suggests that current hedonic models underutilize the available data. We find that (1) the visual design of floor plans has significant explanatory power regarding rent prices -even after controlling for structural and locational apartment characteristics, and (2) harnessing floor plans results in an up to 10.56% lower out-of-sample prediction error. We further find that floor plans yield a particularly high gain in prediction performance for older and smaller apartments. Altogether, our empirical findings contribute to the existing research body by establishing the link between the visual design of floor plans and real estate prices. Moreover, our approach has important implications for online real estate platforms, which can use our findings to enhance user experience in their real estate listings.

Subjective Functionality and Comfort Prediction for Apartment Floor Plans and Its Application to Intuitive Online Property Search

“…Floor plan images have also proved to be useful for rent price prediction [12]- [14]. In [12], [13], it was demonstrated that conventional bag-of-features (BoFs) [15] has the potential to achieve lower-error prediction with smaller variance, although the contribution of BoFs was smaller than that of other apartment attribute information.…”

“…In [12], [13], it was demonstrated that conventional bag-of-features (BoFs) [15] has the potential to achieve lower-error prediction with smaller variance, although the contribution of BoFs was smaller than that of other apartment attribute information. In [14], image features were applied to hedonic price models [16] to predict the apartment rent price after controlling for locational and structural characteristics of an apartment.…”

Leveraging supplementary modalities in automated real estate valuation using comparative judgments and deep learning

“…, 2016). In the real estate field, Solovev and Pröllochs (2021) choose a pretrained ConvNet to predict apartment rent prices using pictures of the floor plans as the input. Shen et al.…”

A Framework to Integrate Bim With Artificial Intelligence and Machine Learning-Based Property Valuation Methods

“…Many authors have used them as additional factors to textual features for the development of different types of AVMs based on HPM, ANN, and different ML-based techniques. Some of them have also evaluated the efficacy of adding such visual features to other textual value-related factors and indicated the capabilities of visual features in increasing the accuracy of the AVMs (De Nadai and Lepri, 2018;Helbich et al, 2013;Kostic and Jevremovic, 2020;Law et al, 2019;Lee and Park, 2020;Liu et al, 2018;Muhr et al, 2017;Nouriani and Lemke, 2022;Poursaeed et al, 2018;Solovev and Pröllochs, 2021).…”