Proceedings of the 11th International Conference on Emerging Challenges: Smart Business and Digital Economy 2023 (ICECH 2023)

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House Price Forecast Model: Case of Vietnam Housing Market

Authors
Nguyen Tai Quang Dinh1, Dang Bich Ngoc2, Ngo Thu Giang3, *
1School of Applied Mathematics and Informatics, Hanoi University of Science and Technology, Hanoi, Vietnam
2School of Business, University of Lincoln, Lincoln, UK
3School of Economics and Management, Hanoi University of Science and Technology, Hanoi, Vietnam
*Corresponding author. Email: giang.ngothu@hust.edu.vn
Corresponding Author
Ngo Thu Giang
Available Online 5 February 2024.
DOI
10.2991/978-94-6463-348-1_26How to use a DOI?
Keywords
– Housing market; House Price Forecast; forecast models; forecast techniques
Abstract

Purpose – The purpose of this study is to build up a comprehensive model for house price forecast in Vietnam Housing market.

Design/methodology/approach – The data related to house price; macro economic indicators and housing industry were gathered from www.euromonitor.com which is source of data for market and industrial researches. The study reviewed house price forecasting models with different forecast techniques from pass research papers. In results, a new house price forecasting model with a suitable forecasting technique is proposed.

Findings – The results are obtained from the application of different forecasting techniques. The final model is confirmed based on the models’ error consideration. The forecast value is also determined basing on the selected model and forecasting techniques. Two proposed models are ARIMA and VAR. With the ARIMA model, the study comes up the conclusion that the housing price index has a dependent relationship depending on the value of that index over the past 2 years. With VAR model, the research found out that Urban population Ratio has an impact on housing price value for 3 consecutive years; and Housing Completions Index has a strong impact on the housing price index. Remarkably, the use of VAR models can bring positive results as well as more accurate forecasts in forecasting housing prices.

Practical implications – The project of building a housing price forecast model in the real estate market in Vietnam. Therefore, the research results are important for investors in the housing market, suppliers, and policy makers in all fields from economics, finance and specifically in the real estate market in Vietnam.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 11th International Conference on Emerging Challenges: Smart Business and Digital Economy 2023 (ICECH 2023)
Series
Advances in Economics, Business and Management Research
Publication Date
5 February 2024
ISBN
10.2991/978-94-6463-348-1_26
ISSN
2352-5428
DOI
10.2991/978-94-6463-348-1_26How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Nguyen Tai Quang Dinh
AU  - Dang Bich Ngoc
AU  - Ngo Thu Giang
PY  - 2024
DA  - 2024/02/05
TI  - House Price Forecast Model: Case of Vietnam Housing Market
BT  - Proceedings of the 11th International Conference on Emerging Challenges: Smart Business and Digital Economy 2023 (ICECH 2023)
PB  - Atlantis Press
SP  - 345
EP  - 358
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-348-1_26
DO  - 10.2991/978-94-6463-348-1_26
ID  - Dinh2024
ER  -