# Image Based Appraisal of Real Estate Properties

arXiv: https://arxiv.org/abs/1611.09180 · HTML: https://mlsanity.com/paper/1611.09180/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Nov 2016.

## Built on by (most cited first, 10 of 10)

- [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)
- [Boosting House Price Estimations with Multi-Head Gated Attention](https://mlsanity.com/paper/2405.07456/) (2024)
- [LLP: LLM-Based Product Pricing in E-commerce](https://mlsanity.com/paper/2510.09347/) (2025)
- [Integrating Floor Plans into Hedonic Models for Rent Price Appraisal](https://mlsanity.com/paper/2102.08162/) (2021)
- [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)
- [A novel decision fusion approach for sale price prediction using Elastic Net and MOPSO](https://mlsanity.com/paper/2403.20033/) (2024)
- [Using deep learning to enhance electronic service quality: Application to real estate websites](https://mlsanity.com/paper/2408.06364/) (2024)
- [Meta-Transfer Learning Powered Temporal Graph Networks for Cross-City Real Estate Appraisal](https://mlsanity.com/paper/2410.08947/) (2024)

## Abstract (arXiv)

Real estate appraisal, which is the process of estimating the price for real estate properties, is crucial for both buys and sellers as the basis for negotiation and transaction. Traditionally, the repeat sales model has been widely adopted to estimate real estate price. However, it depends the design and calculation of a complex economic related index, which is challenging to estimate accurately. Today, real estate brokers provide easy access to detailed online information on real estate properties to their clients. We are interested in estimating the real estate price from these large amounts of easily accessed data. In particular, we analyze the prediction power of online house pictures, which is one of the key factors for online users to make a potential visiting decision. The development of robust computer vision algorithms makes the analysis of visual content possible. In this work, we employ a Recurrent Neural Network (RNN) to predict real estate price using the state-of-the-art visual features. The experimental results indicate that our model outperforms several of other state-of-the-art baseline algorithms in terms of both mean absolute error (MAE) and mean absolute percentage error (MAPE).