# Multimodal Machine Learning for Real Estate Appraisal: A Comprehensive Survey

arXiv: https://arxiv.org/abs/2503.22119 · HTML: https://mlsanity.com/paper/2503.22119/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 50 references that are in the map.
- First posted on arXiv 28 Mar 2025.

## Builds on (most cited first, 14 of 14)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Multimodal Machine Learning: A Survey and Taxonomy](https://mlsanity.com/paper/1705.09406/) (2017)
- [Deep Neural Networks and Tabular Data: A Survey](https://mlsanity.com/paper/2110.01889/) (2021)
- [Multimodal Deep Learning](https://mlsanity.com/paper/2301.04856/) (2023)
- [MultiBench: Multiscale Benchmarks for Multimodal Representation Learning](https://mlsanity.com/paper/2107.07502/) (2021)
- [Foundations and Trends in Multimodal Machine Learning: Principles, Challenges, and Open Questions](https://mlsanity.com/paper/2209.03430/) (2022)
- [A Survey on Multi-modal Summarization](https://mlsanity.com/paper/2109.05199/) (2021)
- [MugRep: A Multi-Task Hierarchical Graph Representation Learning Framework for Real Estate Appraisal](https://mlsanity.com/paper/2107.05180/) (2021)
- [Vision-based Real Estate Price Estimation](https://mlsanity.com/paper/1707.05489/) (2017)
- [The economic value of neighborhoods: Predicting real estate prices from the urban environment](https://mlsanity.com/paper/1808.02547/) (2018)
- [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)
- [PATE: Property, Amenities, Traffic and Emotions Coming Together for Real Estate Price Prediction](https://mlsanity.com/paper/2209.05471/) (2022)
- [MONOPOLY: Learning to Price Public Facilities for Revaluing Private Properties with Large-Scale Urban Data](https://mlsanity.com/paper/2411.18085/) (2024)

## Built on by (most cited first, 1 of 1)

- [MARCUS: Missing-Aware Region Representation with Contextual Urban Signals for Rent Prediction](https://mlsanity.com/paper/2608.18546/) (2026)

## Abstract (arXiv)

Real estate appraisal has undergone a significant transition from manual to automated valuation and is entering a new phase of evolution. Leveraging comprehensive attention to various data sources, a novel approach to automated valuation, multimodal machine learning, has taken shape. This approach integrates multimodal data to deeply explore the diverse factors influencing housing prices. Furthermore, multimodal machine learning significantly outperforms single-modality or fewer-modality approaches in terms of prediction accuracy, with enhanced interpretability. However, systematic and comprehensive survey work on the application in the real estate domain is still lacking. In this survey, we aim to bridge this gap by reviewing the research efforts. We begin by reviewing the background of real estate appraisal and propose two research questions from the perspecve of performance and fusion aimed at improving the accuracy of appraisal results. Subsequently, we explain the concept of multimodal machine learning and provide a comprehensive classification and definition of modalities used in real estate appraisal for the first time. To ensure clarity, we explore works related to data and techniques, along with their evaluation methods, under the framework of these two research questions. Furthermore, specific application domains are summarized. Finally, we present insights into future research directions including multimodal complementarity, technology and modality contribution.