Housing attributes and relative house prices in Ghana

Owusu‐Manu, De‐Graft; Edwards, David John; Donkor-Hyiaman, Ken A.; Asiedu, Richard Ohene; Hosseini, M. Reza; Obiri-Yeboah, Emmanuel

doi:10.1108/ijbpa-01-2019-0003

Cited by 14 publications

(12 citation statements)

References 44 publications

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“…According to [13], efficiency of public education, community social status and proximity to shopping malls typically improve the worth of a property. There is a substantial rise in house prices from the fifth-class suburban community to affluent neighborhood as predicted [16]. Nonetheless, [13] study found that these qualities tend to be cultural based, as they are not similarly relevant in all cultures.…”

Section: Neighbourhoodmentioning

confidence: 87%

“…In [14] in his earlier study, structural attributes would be the key consideration for house hunters in determining what to purchase as such attributes represent their market value. In their earlier study, [15] stated that all these attributes have a positive relationship to rising house prices [16].…”

Section: B Structuralmentioning

confidence: 99%

“…In fact, [16] described the location as an important house price predictor. The contribution of location attribute to house price drops as predicted from first class residential districts to fifth class residential districts.…”

Section: Fig1 Types Of Attributes Used In Previous Studymentioning

confidence: 99%

See 2 more Smart Citations

House Price Prediction using a Machine Learning Model: A Survey of Literature

Zulkifley¹,

Rahman²,

Ubaidullah³

et al. 2020

IJMECS

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Section: Neighbourhoodmentioning

confidence: 87%

Section: B Structuralmentioning

confidence: 99%

See 1 more Smart Citation

House Price Prediction using a Machine Learning Model: A Survey of Literature

Zulkifley¹,

Rahman²,

Ubaidullah³

et al. 2020

IJMECS

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“…Edwards et al , 1998; Burton et al , 2021; Posillico et al , 2021), which revealed that a quantitative research strategy was commonly used in previous similar studies (cf. Owusu-Manu et al , 2019; Spellacy et al , 2020; Owusu-Manu et al , 2021). Deductive reasoning (cf.…”

Section: Research Frameworkmentioning

confidence: 99%

Energy conservation literacy among households in Sub-Sahara Africa

Owusu‐Manu

Quaigrain

Edwards

et al. 2022

IJESM

Self Cite

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Purpose Energy conservation literacy within households is a contemporary and topical issue globally. However, scant research has been conducted on energy-saving literacy amongst Ghanaian households. To substantiate the problem, this paper aims to examine energy conservation literacy and behaviours among Ghanaian households in the Greater Accra Region. Design/methodology/approach The study assessed household electricity use and explored determinants of household energy conservation behaviours. Data was collected through a survey administered to households within the target region and analysed using descriptive statistics and Spearmen’s rank correlation. Findings Results showed electricity conservation among households is greatly influenced by the number of household occupants, household income levels, and the quality and quantity of appliances. The study also found that conservation behaviours are positively correlated to the number of occupants, household income levels, the quantity of electrical appliances, age of household members, number of rooms and level of urbanization within the home’s geographical region. Cumulatively, the findings suggest households held positive attitudes towards efficient energy practices. Enigmatically, the use of energy-conserving alternative technologies was not widely used by households; hence, this factor does not significantly affect household energy conservation. Research limitations/implications Although limited to Ghana’s capital region, the findings can be used to inform policy and regulations at the regional and national levels in designing an efficient and effective mechanism to reduce the country’s overall energy use. Practical implications Premised upon the findings, the study recommends an intensification of education and awareness-creation on various energy-saving regulations and initiatives and thorough education on the usage of standardized (approved) refrigerators to promote the consistent adoption of energy conservation measures among households. Originality/value This study pioneers investigations into the influence of household demographic variables on overall electricity conservation behaviours exhibited by Ghanaian households

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“…Technical condition, often represented by age, is a significant price-setting factor in several research studies. However, the method of evaluating a specific condition is not usually described (Ventolo and Williams, 2005; Owusu-Manu et al , 2019; Rong and Sun, 2014; Ooi et al , 2014). Based on the market knowledge, a large number of sold houses were built in the past and repeatedly renovated and rebuilt.…”

Section: Introductionmentioning

confidence: 99%

Technical condition of houses: a framework for the Czech market

Jandásková

Hrdlička

Cupal

et al. 2022

IJHMA

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Purpose This study aims to provide a framework for assessing the technical condition of a house to determine its market value, including the identification of other price-setting factors and their statistical significance. Time on market (TOM) in relation to the technical condition of a house is also addressed. Design/methodology/approach The primary database contains 631 houses, and the initial asking price and selling price are examined. All the houses are located in the Brno–venkov district in the Czech Republic. Regression analysis was used to test the influence of price-setting factors. The standard ordinary least squares estimator and the maximum likelihood estimator were used in the frame of generalized linear models. Findings Using envelope components of houses separately, such as the façade condition, windows, roof, condition of interior and year of construction, brings better results than using a single factor for the technical condition. TOM was found to be 67 days lower for houses intended for demolition – as compared to new houses – and 18 days lower for houses to refurbishment. Originality/value To the best of the authors’ knowledge, this paper is original in the substitution of specific price-setting factors for factors relating to the technical condition of houses as well as in proposing the framework for professionals in the Czech Republic.

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Housing attributes and relative house prices in Ghana

Cited by 14 publications

References 44 publications

House Price Prediction using a Machine Learning Model: A Survey of Literature

House Price Prediction using a Machine Learning Model: A Survey of Literature

Energy conservation literacy among households in Sub-Sahara Africa

Technical condition of houses: a framework for the Czech market

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