Purpose

Most prior attempts at real estate valuation have focused on the use of metadata such as size and property age, neglecting the fact that the building workmanship in the construction of a house is also a key factor for the estimation of house prices. Building workmanship, such as exterior walls and floor tiling correspond to the visual attributes of a house, and it is difficult to capture and evaluate such attributes efficiently through classical models like regression analysis. Deep learning approach is taken in the valuation process to utilize this visual information.

Design/methodology/approach

The authors propose a two-input neural network comprising a multilayer perceptron and a convolutional neural network that can utilize both metadata and the visual information from images of the front view of the house.

Findings

The authors applied the two-input neural network to Guri City in Gyeonggi Province, South Korea, as a case study and found that the accuracy of house price estimations can be improved by employing image information along with metadata.

Originality/value

Few studies considered the impact of the building workmanship in the valuation process. The authors revealed that it is useful to use both photographs and metadata for enhancing the accuracy of house price estimation.

中文翻译：

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使用照片和元数据估算韩国的房价

目的

先前的大多数房地产评估尝试都集中在元数据的使用上，例如规模和房屋使用年限，而忽略了房屋建造过程中的建筑工艺也是估计房屋价格的关键因素这一事实。建筑工艺（例如外墙和地板砖）与房屋的视觉属性相对应，并且很难通过经典模型（例如回归分析）有效地捕获和评估这些属性。在评估过程中采用深度学习方法来利用这些视觉信息。

设计/方法/方法

作者提出了一种包含多层感知器和卷积神经网络的双输入神经网络，该神经网络可以利用元数据和来自房屋正视图的图像中的视觉信息。

发现

作者将两输入神经网络应用到韩国京畿道九里市的案例研究中，发现可以通过将图像信息和元数据一起使用来提高房价估算的准确性。

创意/价值

很少有研究考虑建筑工艺在评估过程中的影响。作者透露，使用照片和元数据来提高房价估算的准确性是有用的。