Measuring the influence of space and time effects on time on the market

Abstract

This paper is concerned with spatial effects of time on the market for residential property and time varying relationships using a dataset of properties from the Adelaide metropolitan area, Australia, during the period 2002-2011. The analysis firstly considers the spatial dependence in time on the market and secondly extends the analysis to a space-time model using 2SLS regression. The findings demonstrate the complexity in spatial analysis with results indicating a random distribution of time on the market in 86% of observations a pattern that is consistent over time. Spatial autocorrelation is shown to increase time on the market in the subject property while spatial error decreases time on the market in the subject property suggesting a high level of market transparency and improved liquidity. The compensating or nullifying effects of both types of spatial association is shown to contribute to the random distribution observed in time on the market. A strong explanatory capacity of the business cycle suggests that economic drivers are leading time on the market rather than prices.

Impact of crime on spatial analysis of house prices: evidence from a UK city

“…Spatial patterns in house prices and crime variables arguably are best established using Moran's I and Local Indicator Spatial Association (LISA) models which enable variation of spatial dependence between two variables to be studied. More widely in the real estate sector, (McGreal et al, 2015) have the benefits of utilizing these techniques and specifically in the crime sector Zhang and McCord (2014) provide a useful illustration of the application of this type of spatial analysis in establishing relationships between foreclosures and crime rates in the Louisville Metro (Kentucky, USA). In the case of Seattle, Matthews et al (2013) utilizing Moran's I identified localized spatial effects for different types of crime.…”

Composition and context drivers of residential property location value as a socioeconomic status measure

“…This area was selected due to Adelaide’s urban character, 1.6 million (median age of 39 years) population in 2016 and 366,090 single detached residential properties on large land parcels within 2689 Statistical Area 1 (SA1) units (the smallest spatial data unit used to disseminate the Australian Population Census results) (Australian Bureau of Statistics, 2018). Adelaide was considered an appropriate geographic scale as it reflected a “contained” housing market characterised by sub-markets (McGreal et al., 2016). The majority of sale transactions were through private treaty with owner occupancy the main tenure, similar to many international jurisdictions.…”

Private land use for public housing projects: The Influence of a Government Announcement on Housing Markets in Hong Kong

“…In the literature, housing search behavior is often considered the relationship between TOM and housing transaction price, which has long been investigated by researchers (for example , Miller, 1978;Zuehlke, 1987;Haurin, 1988;Kalra and Chan, 1994;Forgey et al, 1996;Kramer, 1999;Ong and Koh, 2000;Knight, 2002;Merlo and Ortalo-Magne, 2004;Li, 2004;Hui et al, 2012;Haurin et al, 2013;Khezr, 2015;McGreal et al, 2016;Liang et al, 2018). In the following part of the review, the selected papers are from the perspective of the determinants of TOM, which is by no means exhaustive.…”

“…The exceptions are some recent studies (e.g. McGreal et al, 2016;. McGreal et al (2016) examined the spatial effects of TOM on property markets in Adelaide, Australia.…”

“…McGreal et al, 2016;. McGreal et al (2016) examined the spatial effects of TOM on property markets in Adelaide, Australia. By adopting the space-time model via a two-stage least square (2SLS) technique, they found that the cycle parameter has a greater influence on TOM than property price.…”