Solving Old Puzzles with New Tricks: Addressing Endogeneity and Nonlinearity in Time-on-Market Research

Benefield, Justin D.; Cain, Christopher L.; Maynard, Norman

doi:10.1080/08965803.2020.1844476

Cited by 3 publications

(1 citation statement)

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“…To understand the influence of these features on the TOM, we use CHPM, as it allows us to consider sold and unsold records (Benefield et al , 2020) and, therefore, reduce the survivorship bias. In this context, it is essential to mention that each value of the categorical features is treated as an independent covariate in the analysis.…”

Section: Methodsmentioning

confidence: 99%

The influence of design-related features on houses time-on-market: a statistical analysis

BuHamdan

Minayhashemi

Alwisy

et al. 2021

IJHMA

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Purpose Researchers have not widely explored design-based factors that govern buildings’ physical properties and human–building interactions. This paper aims to understand the influence of design-related factors on the time-on-market (TOM) of listed houses and, consequently, study the effect of design features on the desirability of a given house. Design/methodology/approach This research analyzes a dataset of listed houses, provided by the REALTORS® Association of Edmonton (RAE) and covers a period extending from January 2009 to August 2019, using Cox proportional-hazards regression model to identify building features that influence people purchasing decisions. Findings The research findings affirm the statistical insignificance of the price on the TOM compared to other design features, such as the construction method, the installed mechanical systems and cladding materials. Research limitations/implications The data used in the analysis comes from a single North American region, i.e. Edmonton, Alberta, Canada. Also, the data provided by the RAE includes only records that involve a realtor. Practical implications The observations of the research presented in this paper influence the housing market players’ decisions about housing designs, mainly those concerned with building new residential dwellings such as speculative builders and designers. Originality/value The research novelty stems from two aspects: the medium used for analysis, i.e. Cox proportional-hazards regression model, which allows considering the listed-but-not-sold units and helps to eliminate the survivorship bias that leads to over-optimistic outcomes; and the assessment of design-related features which allows to understand people’s preferences in design alternatives.

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Section: Methodsmentioning

confidence: 99%