# Improving Real Estate Appraisal with POI Integration and Areal Embedding

arXiv: https://arxiv.org/abs/2311.11812 · HTML: https://mlsanity.com/paper/2311.11812/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 16 references that are in the map.
- First posted on arXiv 20 Nov 2023.

## Builds on (most cited first, 7 of 7)

- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [Hex2vec -- Context-Aware Embedding H3 Hexagons with OpenStreetMap Tags](https://mlsanity.com/paper/2111.00970/) (2021)
- [MugRep: A Multi-Task Hierarchical Graph Representation Learning Framework for Real Estate Appraisal](https://mlsanity.com/paper/2107.05180/) (2021)
- [The economic value of neighborhoods: Predicting real estate prices from the urban environment](https://mlsanity.com/paper/1808.02547/) (2018)
- [Look Around! A Neighbor Relation Graph Learning Framework for Real Estate Appraisal](https://mlsanity.com/paper/2212.12190/) (2022)
- [ST-RAP: A Spatio-Temporal Framework for Real Estate Appraisal](https://mlsanity.com/paper/2308.10609/) (2023)
- [DoRA: Domain-Based Self-Supervised Learning Framework for Low-Resource Real Estate Appraisal](https://mlsanity.com/paper/2309.00855/) (2023)

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

- [Retrieval-Enhanced Real Estate Appraisal](https://mlsanity.com/paper/2603.12986/) (2026)

## Abstract (arXiv)

Despite advancements in real estate appraisal methods, this study primarily focuses on two pivotal challenges. Firstly, we explore the often-underestimated impact of Points of Interest (POI) on property values, emphasizing the necessity for a comprehensive, data-driven approach to feature selection. Secondly, we integrate road-network-based Areal Embedding to enhance spatial understanding for real estate appraisal. We first propose a revised method for POI feature extraction, and discuss the impact of each POI for house price appraisal. Then we present the Areal embedding-enabled Masked Multihead Attention-based Spatial Interpolation for House Price Prediction (AMMASI) model, an improvement upon the existing ASI model, which leverages masked multi-head attention on geographic neighbor houses and similar-featured houses. Our model outperforms current baselines and also offers promising avenues for future optimization in real estate appraisal methodologies.