scholarly journals A Study on the Factors Affecting Commercial Property Price of ‘~Ridan-gil’ Side Streets in Seoul

2021 ◽  
Vol 7 (3) ◽  
pp. 119-134
Author(s):  
ChanIk Park ◽  
Hyunjung Kim ◽  
Chang Mu Jung
2019 ◽  
Vol 18 (1) ◽  
pp. 181-202
Author(s):  
Hyun Rim Ko ◽  
Jae Young Yu ◽  
Young Sik Youn

Author(s):  
Thomas A. Knetsch

Abstract The compilation of commercial property price indices (CPPIs) is challenging. Policymakers urge for timely, reliable and comprehensive data. In Germany, lack of data prevents the calculation of official figures by the national statistical authority. Different applications of price indices need different definitions of commercial real estate. CPPIs according to these definitions are constructed on the basis of existing data for 127 German towns and cities (that cover about one-third of German population). The overall price developments revealed by the various indices are rather similar in terms of central time series characteristics, while differences in detail can be explained by their specific compositions. Price increases for all definitions have been strongest in the seven largest cities. The definitions tend to lead to more marked differences for medium-sized towns.


2021 ◽  
Vol 29 (2) ◽  
pp. 16-28
Author(s):  
Hamza Usman ◽  
Mohd Lizam ◽  
Burhaida Burhan

Abstract The improvement of property price modelling accuracy using property market segmentation approaches is well documented in the housing market. However, that cannot be said of the commercial property market which is adjudged to be volatile, heterogeneous and thinly traded. This study, therefore, determines if the commercial property market in Malaysia is spatially segmented into submarkets and whether accounting for the submarkets improves the accuracy of price modelling. Using a 11,460 shop-offices transaction dataset, the commercial property submarkets are delineated by using submarket binary dummies in the market-wide model and estimating a separate hedonic model for each submarket. The former method improves the model fit and reduces error by 5.6% and 6.5% respectively. The commercial property submarkets are better delineated by estimating a separate hedonic model for each submarket as it improves the model fit by about 7% and reduces models’ error by more than 10%. This study concludes that the Malaysian commercial property market is spatially segmented into submarkets. Modelling the submarkets improves the accuracy and correctness of price modelling.


2018 ◽  
Author(s):  
Bogdan Marola ◽  
Oana Simene

2015 ◽  
Vol 7 (12) ◽  
pp. 200 ◽  
Author(s):  
Gholamreza Zandi ◽  
Mahadevan A/L Supramaniam ◽  
Ayesha Aslam ◽  
Lai Kin Theng

<p>The main purpose of this study is to investigate the economical factors which are effecting on the residence property price in the specific state of Malaysia called “Penang”. For this research, secondary data were collected from Bank Negara Malaysia, Department of Statistic Malaysia, Ministry of Finance Malaysia and Valuation and Property Service Department. All the economical factors are on a yearly basis from 2007 to 2014. The study was directed to verify the relationship between the economical factors and housing price in Penang. Both the individual effects and the interactive effects are analyzed. According to the analysis and calculations, the main factor Base Lending Rate (BLR) and second most effecting factor Gross Domestic Product (GDP) are the strong Factors which affect the property prices in Penang.</p>


2018 ◽  
Vol 2 (1) ◽  
pp. 4-27
Author(s):  
Luke Vella

Property prices have been on top of European governments’ agenda for decades as their contribution towards the whole economic system is imperative. Property prices in Malta have been on an upward trend and, lately, the upward trend has been larger than in previous years. Even though this is a sign of a strong and growing economy, it can have implications on residents due to affordability issues affecting their standard of living. This study seeks to determine the factors which have an influence on property price in Malta whilst also analysing the strength of the relationship each factor holds on house prices. This study examines the Gross Domestic Product, unemployment rate, population, inflation, the number of home loans within the Maltese economy, ageing population, the number of tourists, minimum wage, and development permits. Out of these nine variables tested, eight proved to be statistically significant. The variables which had the largest effect on house prices was the unemployment rate whilst the variable with the least effect on house prices was inflation.


2018 ◽  
Vol 90 ◽  
pp. 814-823 ◽  
Author(s):  
Iheanyichukwu Joachim Onuoha ◽  
Godwin Uche Aliagha ◽  
Mohd Shahril Abdul Rahman

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