# Vision-based Real Estate Price Estimation

arXiv: https://arxiv.org/abs/1707.05489 · HTML: https://mlsanity.com/paper/1707.05489/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jul 2017.

## Built on by (most cited first, 15 of 15)

- [MugRep: A Multi-Task Hierarchical Graph Representation Learning Framework for Real Estate Appraisal](https://mlsanity.com/paper/2107.05180/) (2021)
- [What Image Features Boost Housing Market Predictions?](https://mlsanity.com/paper/2107.07148/) (2021)
- [ST-RAP: A Spatio-Temporal Framework for Real Estate Appraisal](https://mlsanity.com/paper/2308.10609/) (2023)
- [Computer vision-based analysis of buildings and built environments: A systematic review of current approaches](https://mlsanity.com/paper/2208.00881/) (2022)
- [A Multi-Modal Deep Learning Based Approach for House Price Prediction](https://mlsanity.com/paper/2409.05335/) (2024)
- [A Comparison of Multi-View Learning Strategies for Satellite Image-Based Real Estate Appraisal](https://mlsanity.com/paper/2105.04984/) (2021)
- [Real Estate Property Valuation using Self-Supervised Vision Transformers](https://mlsanity.com/paper/2302.00117/) (2023)
- [Multimodal Machine Learning for Real Estate Appraisal: A Comprehensive Survey](https://mlsanity.com/paper/2503.22119/) (2025)
- [Real Estate Attribute Prediction from Multiple Visual Modalities with Missing Data](https://mlsanity.com/paper/2211.09018/) (2022)
- [Extracting real estate values of rental apartment floor plans using graph convolutional networks](https://mlsanity.com/paper/2303.13568/) (2023)
- [Designing Explainable Predictive Machine Learning Artifacts: Methodology and Practical Demonstration](https://mlsanity.com/paper/2306.11771/) (2023)
- [ARED: Argentina Real Estate Dataset](https://mlsanity.com/paper/2403.00273/) (2024)
- [Scalable Property Valuation Models via Graph-based Deep Learning](https://mlsanity.com/paper/2405.06553/) (2024)
- [Using deep learning to enhance electronic service quality: Application to real estate websites](https://mlsanity.com/paper/2408.06364/) (2024)
- [Fake Advertisements Detection Using Automated Multimodal Learning: A Case Study for Vietnamese Real Estate Data](https://mlsanity.com/paper/2501.10848/) (2025)

## Abstract (arXiv)

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.