Time on the market and price change: the case of Sydney housing market

Khezr, Peyman

doi:10.1080/00036846.2014.972549

Cited by 18 publications

(28 citation statements)

References 15 publications

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“…Another variable that is worth discussing is location. In following research papers by Rosiers et al [ 37 ], Owusu-Edusei et al [ 34 ], Benefield et al [ 6 ], Khezr [ 27 ], Verbrugge et al [ 40 ], Baldominos et al [ 5 ], Du et al [ 18 ], Bogin et al [ 11 ], Metzner and Kindt [ 32 ] and Oust et al [ 33 ], location was found to affect the price of an asset significantly in one direction or the other. The connotation behind this covariate is simply that some areas of the city have better infrastructure or perhaps higher traffic and crime rates, thus, prices are higher or lower in certain zones.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Unfortunately, only a handful of papers have investigated the latter issue. The papers that attempted to estimate the probability of the price change were written by Knight [ 28 ], Khezr [ 27 ] and Verbrugge et al [ 40 ]. However, while Verbrugge et al [ 40 ] noted that the initial rent price, TOM and location were the most important variables in predicting rent price changes, the authors regrettably did not analyse the sales price.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Further, the modelling of price change in most cases was either a by-product of the models, meaning that the dependent variable was not the price change but the final transaction or listing price. Although it is easy to miss some studies in the sea of real estate literature, the ones using price change as a dependent variable were carried out by Knight [ 28 ], Khezr [ 27 ], Verbrugge et al [ 40 ], but only probit and regression models were employed. Additionally, Pérez-Rave et al [ 36 ] argued that the predictive power of hedonic regression is not mature and is more suited to inference, simultaneously admitting that machine learning (ML) models possess drawbacks in explaining predictive power.…”

Section: Introductionmentioning

confidence: 99%

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Predictive analytics using Big Data for the real estate market during the COVID-19 pandemic

2021

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As the COVID-19 pandemic came unexpectedly, many real estate experts claimed that the property values would fall like the 2007 crash. However, this study raises the question of what attributes of an apartment are most likely to influence a price revision during the pandemic. The findings in prior studies have lacked consensus, especially regarding the time-on-the-market variable, which exhibits an omnidirectional effect. However, with the rise of Big Data, this study used a web-scraping algorithm and collected a total of 18,992 property listings in the city of Vilnius during the first wave of the COVID-19 pandemic. Afterwards, 15 different machine learning models were applied to forecast apartment revisions, and the SHAP values for interpretability were used. The findings in this study coincide with the previous literature results, affirming that real estate is quite resilient to pandemics, as the price drops were not as dramatic as first believed. Out of the 15 different models tested, extreme gradient boosting was the most accurate, although the difference was negligible. The retrieved SHAP values conclude that the time-on-the-market variable was by far the most dominant and consistent variable for price revision forecasting. Additionally, the time-on-the-market variable exhibited an inverse U-shaped behaviour.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Predictive analytics using Big Data for the real estate market during the COVID-19 pandemic

2021

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“…Belief in these superstitious ideas are often linked to the community's ethno-religious backgrounds; Sinhala-Buddhist, Sinhala-Catholic, Tamil-Hindu, Tamil-Catholic and Muslims. Buying a house for a household is a lifetime investment (Khezr, 2015;Grønhaug et al, 1987) and an important household decision, thus it demonstrates a peculiar buying process that might comprise both so-called rational and irrational factors (Grønhaug et al, 1987;Koklic & Vida, 2011). This is particularly relevant with the Sri Lankan housing market where households have a very strong bond with their house and in most cases buying or building houses to live in their lifetime (Ariyawansa, 2009;Dayaratne & Kellett, 2008).…”

Section: 10 Introductionmentioning

confidence: 99%

Superstitions and Residential Property Buyer Decision Making in Sri Lanka

Poologasingam

Perera

2021

INTREST

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The purpose of this study is to identify the influence of superstitious beliefs on the residential property buyer’s decision-making in Sri Lanka. Despite plethora of research devoted to study superstitious beliefs affecting residential property prices, limited studies are available discussing the effects of superstitious beliefs on the entire buyer decision-making process. Besides, no studies are dedicated to discuss the issue pertaining to the Sri Lankan residential market. Rooted to Vastu, Almanac, and Islamic discipline, superstitious beliefs on the residential property exist in Sri Lanka. These superstitious beliefs, on the whole, concentrate on the design, shape, alignment, size, location, and structure of residential properties. Superstitious beliefs of buyers become a factor affecting their problem recognition, an insight for the information search, a criterion for evaluation of alternatives, a critical factor to make a purchase or purchase intention decision, and measurement of satisfaction of the residential property purchased. These findings are based on in-depth interviews with twenty (20) residential and community experts and thirty (30) residential buyers. John Dewey's five-stage buyer decision-making process is employed as a theoretical framework for data analysis. This examination provides useful insights on the behavioural aspect of the residential market in Sri Lanka for its market actors including real estate developers, agents, businesses, and real estate planners.

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“… The potential impact of duration on the final selling prices is well documented in the literature. For example, see Khezr () for a study of the Sydney housing market. …”

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confidence: 99%

Dynamic and Static Asking Prices in the Sydney Housing Market

Khezr

Menezes

2016

Economic Record

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This paper investigates the impact of two commonly used asking price strategies on house sales prices. In particular, we compare a dynamic asking price, where the seller adjusts her asking price over time as she fails to sell her property, with a static asking price, where the seller sets an asking price and sticks to it until the property is sold. While we show this comparison to be ambiguous from a theoretical perspective, our empirical study, using a comprehensive dataset of the properties sold in the greater Sydney region in 2011, indicates that properties with a dynamic asking price sold, on average, for approximately 1.9 per cent less than properties with a static asking price. In addition, we show that, controlling for the asking price strategy, the duration of sale has a significant impact on sales prices.

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Time on the market and price change: the case of Sydney housing market

Cited by 18 publications

References 15 publications

Predictive analytics using Big Data for the real estate market during the COVID-19 pandemic

Predictive analytics using Big Data for the real estate market during the COVID-19 pandemic

Superstitions and Residential Property Buyer Decision Making in Sri Lanka

Dynamic and Static Asking Prices in the Sydney Housing Market

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