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A hybrid approach to optimize initial design strategies for pre-sale housing projects

Author(s):


Medium: journal article
Language(s): English
Published in: Engineering, Construction and Architectural Management, , n. 3, v. 26
Page(s): 515-534
DOI: 10.1108/ecam-04-2018-0187
Abstract:

Purpose

The purpose of this paper is to propose a hybrid decision-making model for optimizing the initial design strategies of pre-sales housing, identifying factors affecting the initial design of housing, and developing different initial design approaches and strategies based on buyers’ preferences.

Design/methodology/approach

Indicators and factors in line with the local initial planning and design are created according to the design quality indicator framework. The important indicators and factors are screened out preliminarily with the fuzzy Delphi method and decision-making trial and evaluation laboratory based analytic network process. The performances of two actual cases under similar site conditions are checked with regard to the overall residential sales rate and time on the market (TOM).

Findings

The result shows that the proposed model can effectively improve the sales rate, shorten the TOM and better complies with buyer design strategy demands, and thus positively correlating to economic value.

Originality/value

Pre-sales make possible the customized strategy of allowing future residents to participate in the housing design process. However, buyers’ participation in the design process is highly limited, and developers usually determine their planning and initial residential design strategies based on experience and intuition. With the proposed approach, the initial residential design of a project can be effectively intervened, so that home users can truly participate in the design, and the residential construction service can be provided in a unique, but non-universal way.

Structurae cannot make the full text of this publication available at this time. The full text can be accessed through the publisher via the DOI: 10.1108/ecam-04-2018-0187.
  • About this
    data sheet
  • Reference-ID
    10576717
  • Published on:
    26/02/2021
  • Last updated on:
    26/02/2021
 
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