Search results

1 – 2 of 2
Per page
102050
Citations:
Loading...
Access Restricted. View access options
Article
Publication date: 24 November 2020

Changro Lee and Key-Ho Park

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…

351

Abstract

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.

Details

Data Technologies and Applications, vol. 55 no. 2
Type: Research Article
ISSN: 2514-9288

Keywords

Access Restricted. View access options
Article
Publication date: 16 February 2021

Changro Lee and Keith Key-Ho Park

It is important to forecast local trading volumes as well as global trading volumes because the real estate market is always characterized as a localized market. The house trading…

639

Abstract

Purpose

It is important to forecast local trading volumes as well as global trading volumes because the real estate market is always characterized as a localized market. The house trading volume at the local level is forecast through appropriate models to enhance the predictive accuracy.

Design/methodology/approach

Four representative housing submarkets in South Korea are selected, and their trading volumes are forecast. A well-established time-series model and a deep learning algorithm are employed: the autoregressive integrated moving average (ARIMA) model and the recurrent neural network (RNN), respectively. The trading volumes in adjacent areas are utilized as covariates, and an ensemble prediction is applied additionally to improve the model performance.

Findings

The results indicate no significant difference in prediction performance between the ARIMA model and the RNN, which can be attributed to the insufficient amount of data used. It is discovered that the spillover effects of trading volumes across the study areas can be exploited to improve the predictive accuracy, and that the diversity of the predicted values from the candidate models can be used to increase the forecasting accuracy further.

Originality/value

Whereas property prices have been investigated extensively, the discussion on forecasting trading activity of properties is limited in the literature. The results of this study are expected to promote more interest in adopting a local perspective and using a diversity of predicted values when forecasting house trading volumes.

Details

Engineering, Construction and Architectural Management, vol. 29 no. 1
Type: Research Article
ISSN: 0969-9988

Keywords

1 – 2 of 2
Per page
102050