# Extracting real estate values of rental apartment floor plans using graph convolutional networks

arXiv: https://arxiv.org/abs/2303.13568 · HTML: https://mlsanity.com/paper/2303.13568/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 52 references that are in the map.
- First posted on arXiv 23 Mar 2023.

## Builds on (most cited first, 12 of 12)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [Convolutional Networks on Graphs for Learning Molecular Fingerprints](https://mlsanity.com/paper/1509.09292/) (2015)
- [Residual Gated Graph ConvNets](https://mlsanity.com/paper/1711.07553/) (2017)
- [Learning Convolutional Neural Networks for Graphs](https://mlsanity.com/paper/1605.05273/) (2016)
- [Vision-based Real Estate Price Estimation](https://mlsanity.com/paper/1707.05489/) (2017)
- [House price estimation from visual and textual features](https://mlsanity.com/paper/1609.08399/) (2016)
- [Image Based Appraisal of Real Estate Properties](https://mlsanity.com/paper/1611.09180/) (2016)
- [When Work Matters: Transforming Classical Network Structures to Graph CNN](https://mlsanity.com/paper/1807.02653/) (2018)
- [Integrating Floor Plans into Hedonic Models for Rent Price Appraisal](https://mlsanity.com/paper/2102.08162/) (2021)
- [Subjective Functionality and Comfort Prediction for Apartment Floor Plans and Its Application to Intuitive Searches](https://mlsanity.com/paper/2202.12799/) (2022)

## Abstract (arXiv)

Access graphs that indicate adjacency relationships from the perspective of flow lines of rooms are extracted automatically from a large number of floor plan images of a family-oriented rental apartment complex in Osaka Prefecture, Japan, based on a recently proposed access graph extraction method with slight modifications. We define and implement a graph convolutional network (GCN) for access graphs and propose a model to estimate the real estate value of access graphs as the floor plan value. The model, which includes the floor plan value and hedonic method using other general explanatory variables, is used to estimate rents and their estimation accuracies are compared. In addition, the features of the floor plan that explain the rent are analyzed from the learned convolution network. Therefore, a new model for comprehensively estimating the value of real estate floor plans is proposed and validated. The results show that the proposed method significantly improves the accuracy of rent estimation compared to that of conventional models, and it is possible to understand the specific spatial configuration rules that influence the value of a floor plan by analyzing the learned GCN.