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Article: Sustainable development scale of housing estates: An economic assessment using machine learning approach

TitleSustainable development scale of housing estates: An economic assessment using machine learning approach
Authors
Keywordsdensity
housing
machine learning
threshold
urban planning
Issue Date2021
PublisherJohn Wiley & Sons Ltd. The Journal's web site is located at http://www.interscience.wiley.com/journal/sd
Citation
Sustainable Development, 2021, Epub 2021-01-17 How to Cite?
AbstractEconomic sustainability is often addressed from the perspective of economic growth and at the national level. In contrast, this research attempts to examine the question of economic sustainability of human settlement at a local project level. Urban planners need to strike a balance between dispersal and over-concentration of population in cities. The existing theories suggest that either excessively low or extremely high levels of household concentration is undesirable to a neighborhood. In this study, an economic assessment using machine learning (ML) techniques is used to identify the threshold scale of a housing estate, which comprises many privately owned residential units (like condos) with shared amenities. Using two decades of property transaction data in Hong Kong as our evidence, this study has found that a tipping point exists in the development scale of these housing estates. Housing values initially rise with the number of residential units in a housing estate but gradually fall when it increases beyond a critical limit. This nonlinear economic relationship is attributed to the per household share of common facilities, which does not increase sufficiently to match with the growing population density of the housing estates. The policy implication is that, to optimize housing supply, urban planning should not just focus on increasing the development bulk of housing but should also pay attention to the possible bottlenecks in the provision of shared amenities in the neighborhood.
Persistent Identifierhttp://hdl.handle.net/10722/295548
ISSN
2020 Impact Factor: 6.159
2020 SCImago Journal Rankings: 1.115
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorTang, BS-
dc.contributor.authorHo, WKO-
dc.contributor.authorWong, SW-
dc.date.accessioned2021-01-25T11:16:26Z-
dc.date.available2021-01-25T11:16:26Z-
dc.date.issued2021-
dc.identifier.citationSustainable Development, 2021, Epub 2021-01-17-
dc.identifier.issn0968-0802-
dc.identifier.urihttp://hdl.handle.net/10722/295548-
dc.description.abstractEconomic sustainability is often addressed from the perspective of economic growth and at the national level. In contrast, this research attempts to examine the question of economic sustainability of human settlement at a local project level. Urban planners need to strike a balance between dispersal and over-concentration of population in cities. The existing theories suggest that either excessively low or extremely high levels of household concentration is undesirable to a neighborhood. In this study, an economic assessment using machine learning (ML) techniques is used to identify the threshold scale of a housing estate, which comprises many privately owned residential units (like condos) with shared amenities. Using two decades of property transaction data in Hong Kong as our evidence, this study has found that a tipping point exists in the development scale of these housing estates. Housing values initially rise with the number of residential units in a housing estate but gradually fall when it increases beyond a critical limit. This nonlinear economic relationship is attributed to the per household share of common facilities, which does not increase sufficiently to match with the growing population density of the housing estates. The policy implication is that, to optimize housing supply, urban planning should not just focus on increasing the development bulk of housing but should also pay attention to the possible bottlenecks in the provision of shared amenities in the neighborhood.-
dc.languageeng-
dc.publisherJohn Wiley & Sons Ltd. The Journal's web site is located at http://www.interscience.wiley.com/journal/sd-
dc.relation.ispartofSustainable Development-
dc.rightsSubmitted (preprint) Version This is the pre-peer reviewed version of the following article: [FULL CITE], which has been published in final form at [Link to final article using the DOI]. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. Accepted (peer-reviewed) Version This is the peer reviewed version of the following article: [FULL CITE], which has been published in final form at [Link to final article using the DOI]. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.-
dc.subjectdensity-
dc.subjecthousing-
dc.subjectmachine learning-
dc.subjectthreshold-
dc.subjecturban planning-
dc.titleSustainable development scale of housing estates: An economic assessment using machine learning approach-
dc.typeArticle-
dc.identifier.emailTang, BS: bsbstang@hku.hk-
dc.identifier.emailHo, WKO: winkyh@HKUCC-COM.hku.hk-
dc.identifier.authorityTang, BS=rp01646-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1002/sd.2168-
dc.identifier.hkuros321055-
dc.identifier.volumeEpub 2021-01-17-
dc.identifier.isiWOS:000608171400001-
dc.publisher.placeUnited Kingdom-

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