An Analysis of the Relationship Between Housing Prices and Business Survey Index

2021 ◽  
Vol 22 (3) ◽  
pp. 67-87
Author(s):  
Cha Soon Choi
Keyword(s):  
Housing Prices ◽  
Business Survey ◽  
The Relationship

scholarly journals Growing Value: Describing The Nature Of The Relationship Between Street-Level Greenery and Housing Prices in Toronto

2021 ◽  
Author(s):  
Spencer Bridgwater
Keyword(s):  
Urban Environment ◽  
Housing Prices ◽  
Urban Forestry ◽  
Demographic Variables ◽  
Urban Environments ◽  
Developmental Context ◽  
Street Level ◽  
The Relationship ◽  
Aerial Perspective

The role of urban forestry has become increasingly important in the context of sustainability, both from an environmental context, and from a developmental context. Greenery in an urban environment has demonstrable implications for health, air quality, aesthetics, and land value, as described broadly across the literature. Until recently, studies on green urban canopies and housing prices have been limited in their methodology by using aerial-perspective data. The MIT Senseable City Lab in 2015 developed the Treepedia project, which uses Google Street View images to quantify greenery levels in urban environments. Using the green view index (GVI) data from the Treepedia project, street-level greenery densities were compared against housing prices across Toronto. Models for different property types, accounting for characteristic, locational, and demographic variables, were estimated. It was determined that a statistically significant relationship between street-level greenery and housing prices exists in Toronto for detached homes, semi-detached homes, row/townhouse units, condo apartments, and condo townhouses.

scholarly journals Urban Structure, Subway Systemand Housing Price: Evidence from Beijing and Hangzhou, China

2019 ◽  
Vol 11 (3) ◽  
pp. 669 ◽  
Author(s):  
Xiaoqi Zhang ◽  
Yanqiao Zheng ◽  
Lei Sun ◽  
Qiwen Dai
Keyword(s):  
Housing Market ◽  
Housing Prices ◽  
Housing Price ◽  
Structural Features ◽  
Urban Structure ◽  
Constrained Clustering ◽  
The Core ◽  
Urban Core ◽  
Subway System ◽  
The Relationship

Using housing market data of Beijing and Hangzhou, China, we conduct a case study to detect how the difference of urban structure can affect the relationship between the subway system and housing prices. To quantify the characteristics of urban structure, we propose a constrained clustering method, which can not only reveal the spatial heterogeneity of the housing market, but also provides a link between heterogeneity and the underlying urban structure. Applying constrained clustering to Beijing and Hangzhou, we find that the relationship between accessibility to metro stations and housing prices is weak and vulnerable, while the improvement of commuting efficiency, measured by a key variable, the metro index, does have a robust connection to metro premium on housing units. In particular, only a large metro index can be associated with a positive metro premium. Structural features, such as the size of urban core and the existence of multiple sub-centers, influence the metro premium by affecting the value and spatial distribution of the metro index. The evidence from Beijing and Hangzhou supports that in a mono-centric city, the size of the urban core is positively associated with the metro index and the metro premium, while in a poly-centric city with a small urban core, the metro index tends to be lower in the core region and higher in the satellite regions, which enforces the metro premium to be negative in the core while positive outside of the core.

The role of market imperfections in the relationship between housing prices and household credit: Evidence from Taiwan

2013 ◽  
Vol 27 (2) ◽  
pp. 131-143 ◽  
Author(s):  
An-Pin Wei ◽  
Wei-Ling Huang ◽  
Chih-Yuan Yang ◽  
Ming-Chi Chen
Keyword(s):  
Housing Prices ◽  
Market Imperfections ◽  
Household Credit ◽  
The Relationship

scholarly journals Housing Prices and Multiple Employment Nodes: Is the Relationship Nonmonotonic?

2012 ◽  
Vol 27 (8) ◽  
pp. 1182-1208 ◽  
Author(s):  
Liv Osland ◽  
Gwilym Pryce
Keyword(s):  
Housing Prices ◽  
The Relationship

scholarly journals HUBUNGAN INDEKS HARGA PROPERTI RESIDENSIAL DAN KREDIT PERBANKAN

2019 ◽  
Vol 3 (1) ◽  
pp. 95-111
Author(s):  
Njo Anastasia ◽  
Fabian Hidayat
Keyword(s):  
Gross Domestic Product ◽  
Housing Market ◽  
Housing Prices ◽  
Housing Price ◽  
Domestic Product ◽  
Test Model ◽  
Mortgage Interest ◽  
House Type ◽  
Banking Credit ◽  
The Relationship

Demand and supply in housing market depends on macroeconomic conditions such as Gross Domestic Product, interest rates, and housing prices. Changes to these variables are related to changes in housing market. This study aims to examine the relationship of housing prices, Gross Domestic Product, mortgage interest rate to Banking Credit. Knowing the relationship will be useful in making strategic decisions related to property investment and portfolio management. Housing price using Residential Price Index in primary market will be grouped into three parts based on land area of residential property consist of small house type, medium house type, and big house type. Data processing using Auto Regressive Distribution Lag (ARDL) bound test model to test the relationship between variables. The result of the research shows that there is a significant long run cointegration on the variable of housing price, Gross Domestic Product, and mortgage interest to banking credit. Furthermore, in testing each housing price group, the test results also show the relationship between these variables.

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