Image Based Appraisal of Real Estate Properties

TL;DR

This paper explores using advanced computer vision and RNNs to predict real estate prices from online property images, offering an alternative to traditional economic models.

Contribution

It introduces a novel approach employing RNNs with visual features to estimate property prices, outperforming existing baseline algorithms.

Findings

RNN-based model achieves lower MAE and MAPE than baseline methods.

Visual features from property images significantly improve price prediction accuracy.

The approach demonstrates the potential of computer vision in real estate valuation.

Abstract

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).