The price of a view: estimating the impact of view on house prices

Jayasekare, Ajith S.; Herath, Shanaka; Wickramasuriya, Rohan; Perez, Pascal

doi:10.1080/14445921.2019.1626543

Cited by 17 publications

(10 citation statements)

References 27 publications

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“…is research found that disposable income, the market housing ratio, the commercial housing ratio, the purchase of commercial housing (which has its own ratio), and self-built housing [26] are not significant, except that public housing has its own correlation coefficient at the p < 0.01 level. Housing property rights are related not only to income but also to housing prices [27].…”

Section: Literaturementioning

confidence: 89%

Housing Sources of Second‐Tier Urban Residents Based on Multivariate Correspondence

Zhang

2020

Advances in Civil Engineering

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To explore the relationship between different housing sources and individual attributes in second-tier cities, finding the key point of contradiction between supply and demand in the real estate market is important. Multivariate correspondence analysis (MCA) is a powerful method to solve the complex problem of the relationship between classified variables. Also known as corresponding analysis, it is a multivariate statistical analysis method that aims to describe the relationship between two classification variables in a corresponding table in a low-dimensional space. MCA has unique advantages when dealing with categorical variable data. It combines the advantages of factor analysis and multidimensional scaling. On the basis of combining and analysing the existing literature, this paper makes an analysis of the data obtained from a housing questionnaire survey collected in Jinan, Shandong Province, in 2017. The research results show that the housing sources of second-tier urban residents are closely related to age, annual family income, marriage, education level, and individual characteristics of the permanent family population but not to gender.

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Section: Literaturementioning

confidence: 89%

Housing Sources of Second‐Tier Urban Residents Based on Multivariate Correspondence

Zhang

2020

Advances in Civil Engineering

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“…To what extent has the knowledge of the importance of viewing, living amongst, and using green spaces informed policies, guidelines, and standards for greening programs and for urban planning at large? The aspect of visible green spaces has probably received the least attention, although views of green spaces are a well-known positive factor in real estate markets (Jayasekare et al 2019). The City of Frederiksberg in Denmark has included a specific target in its tree strategy program that says that it should be possible to see at least one tree from every residence (Frederiksberg Kommune 2018).…”

Section: Implementation Of Research Findingsmentioning

confidence: 99%

Evidence-based guidelines for greener, healthier, more resilient neighbourhoods: Introducing the 3–30–300 rule

Konijnendijk

2022

J. For. Res.

127

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The important contributions of urban trees and green spaces to for example, climate moderation and public health have been recognized. This paper discusses guidelines and norms that promote the benefits of viewing green, living amongst green, and having easy access to green spaces for recreational use. Having trees and other vegetation in sight from one’s home, place of work, or school has important mental health and performance benefits. Local tree canopy cover is positively associated with cooling and other aspects of climate moderation. With public green spaces in proximity to one’s home stimulates regular use of these areas and results in positive impacts on mental, physical, and social health. After analyzing existing guidelines and rules for urban green space planning and provision, a new, comprehensive guideline is presented, known as the ‘3–30–300 rule’ for urban forestry. This guideline aims to provide equitable access to trees and green spaces and their benefits by setting the thresholds of having at least 3 well-established trees in view from every home, school, and place of work, no less than a 30% tree canopy in every neighbourhood; and no more than 300 m to the nearest public green space from every residence. Current implementation of this new guideline is discussed, as well as the advantages and disadvantages of using this evidence-based but also clear and simple rules.

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“…are also used. Abidoye and Chan (2016), Abidoye and Chan (2017), Deng et al (2018), Ecer (2014), Jayasekare et al (2019), Pai andWang (2020), andYilmazel et al (2018) have tried to estimate housing prices using the microvariability listed above.…”

Section: Literature Researchmentioning

confidence: 99%

Forecasting housing prices in Turkey by machine learning methods

2022

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In this study, decision tree regression, artificial neural networks (ANN) and support vector machines (SVM) methods are applied by using monthly data for the period 2013-2020 in the estimation of housing sales in Turkey. In the analysis, the volume of individual mortgage loans offered by banks, the average annual interest rate of mortgage loans from macroeconomic and market variables, the consumer price index (CPI), the BIST 100 index, the benchmark bond interest rate, gold prices and the values of the US dollar and Euro Turkish lira and the housing sales price per square meter in Turkey are used. As a result of the analysis carried out on the model created house sales prices in the Turkish housing market have been successfully estimated and in the light of these estimates, it is determined that banks can guide banks in the creation of various credit packages and appropriate loan targets to support the housing sector.

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The price of a view: estimating the impact of view on house prices

Cited by 17 publications

References 27 publications

Housing Sources of Second‐Tier Urban Residents Based on Multivariate Correspondence

Housing Sources of Second‐Tier Urban Residents Based on Multivariate Correspondence

Evidence-based guidelines for greener, healthier, more resilient neighbourhoods: Introducing the 3–30–300 rule

Forecasting housing prices in Turkey by machine learning methods

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