Factors affecting commercial housing prices in Vietnam

Abstract

Purpose The study aims to identify the affecting factors and their impact rates on the commercial housing prices. The study also aims to suggest implications related to commercial housing prices to develop the commercial housing market. Design/methodology/approach The study investigates housing investors, real estate agents and buyers to identify factors that might affect commercial housing prices. The proposed research model has 7 latent factors and is tested by Cronbach' alpha and exploratory factor analysis by SPSS20.0 software. Findings There are 7 groups with 24 factors affecting commercial housing prices. The neighboring factor group has the greatest impact rate (18.54%); the housing service group has the lowest impact rate (11.48%). Research limitations/implications The study has only determined the affecting factors and their impact rates on commercial housing prices in Bac Ninh city. Therefore, it is necessary to conduct research on factors affecting commercial housing prices in other provinces and cities of Vietnam in the coming time. In addition, the proposed research method can also be consulted when it is necessary to determine the factors affecting commercial housing prices in other countries around the world. Practical implications The study proposes some implications related to commercial housing prices such as commercial housing valuation; housing selection with suitable prices for people intending to buy houses; state support policies for commercial housing investors to develop commercial housing with reasonable prices. Social implications The implementing the implications proposed in the study will facilitate people's easier access to commercial housing; real estate investors do business more efficiently. Originality/value To the best of the authors’ knowledge, this paper presents for the first time a method to determine the affecting factors and their impact rates on commercial housing prices in Vietnam. The paper also points out a number of specific factors affecting commercial housing prices that are different from those shown in previous studies.

The effect of proximity and spatial dependence on the house price index for Dar es Salaam

“…Examples include Singla and Bendigiri (2019) in India and Özsoy and Şahin (2022) in Izmir, Turkey. Nam and Phuong (2022) argue that such a positive relationship could be due to the perception that hospitals are places with unhealthy environmental conditions. Findings by Peng (2020), however, are categorical, as he reports that proximity to hospitals is not desirable for low priced neighbourhoods on account of ambulance’ disamenity yet desirable for high priced neighbourhoods.…”

Analysing Factors Influencing Land Use Planning for Sustainable Land Resource Management in Vietnam: A Case Study of Dan Phuong District in Hanoi City

“…The exploratory factor analysis (EFA) method has been used by many researchers around the world in many different fields, most commonly in natural resource and environmental management research [27,28]. In the field of land management, EFA has been used to focus on aspects such as the factors affecting land use planning, the factors affecting land prices, and the factors affecting efficient land use [14,22,29,30]. This shows that EFA is a popular and effective tool for investigating factors affecting the land use planning process on a global scale.…”