Are Real Estate Cycle Lengths and Magnitudes Changing?

2016 ◽  
Author(s):  
Glenn Mueller ◽  
Andrew Mueller
Keyword(s):  
Real Estate ◽  
Cycle Lengths ◽  
Real Estate Cycle

scholarly journals What sells in a crisis? Determinants of sale probability over a cycle and through a crash

2017 ◽  
Vol 35 (6) ◽  
pp. 619-637 ◽  
Author(s):  
David Scofield ◽  
Steven Devaney
Keyword(s):  
Real Estate ◽  
Sample Selection ◽  
Sampling Technique ◽  
Real Estate Market ◽  
Commercial Real Estate ◽  
Content Type ◽  
Asset Selection ◽  
The Uk ◽  
Real Estate Cycle ◽  
Over Time

Purpose The purpose of this paper is to understand what affects the liquidity of individual commercial real estate assets over the course of the economic cycle by exploring a range of variables and a number of time periods to identify key determinants of sale probability. Design/methodology/approach Analyzing 12,000 UK commercial real estate transactions (2003 to 2013) the authors use an innovative sampling technique akin to a perpetual inventory approach to generate a sample of held assets for each 12 month interval. Next, the authors use probit models to test how market, owner and property factors affect sale probability in different market environments. Findings The types of properties that are most likely to sell changes between strong and weak markets. Office and retail assets were more likely to sell than industrial both overall and in better market conditions, but were less likely to sell than industrial properties during the downturn from mid-2007 to mid-2009. Assets located in the City of London more likely to sell in both strong and weak markets. The behavior of different groups of owners changed over time, and this indicates that the type of owner might have implications for the liquidity of individual assets over and above their physical and locational attributes. Practical implications Variation in sale probability over time and across assets has implications for real estate investment management both in terms of asset selection and the ability to rebalance portfolios over the course of the cycle. Results also suggest that sample selection may be an issue for commercial real estate price indices around the globe and imply that indices based on a limited group of owners/sellers might be susceptible to further biases when tracking market performance through time. Originality/value The study differs from the existing literature on sale probability as the authors analyzed samples of transactions drawn from all investor types, a significant advantage over studies based on data restricted to samples of domestic institutional investors. As well, information on country of origin for buyers and sellers allows us to explore the influence of foreign ownership on the probability of sale. Finally, the authors not only analyze all transactions together, but the authors also look at transactions in five distinct periods that correspond with different phases of the UK commercial real estate cycle. This paper considers the UK real estate market, but it is likely that many of the findings hold for other major commercial real estate markets.

The Real Estate Industry and Economic Development Issues Discussed

2014 ◽  
Vol 1065-1069 ◽  
pp. 2542-2544
Author(s):  
Zhi Neng Tong
Keyword(s):  
Economic Development ◽  
Real Estate ◽  
Process Development ◽  
Real Estate Industry ◽  
Macroeconomic Volatility ◽  
The Real ◽  
The Real Estate ◽  
Analysis Process ◽  
Depth Study ◽  
Real Estate Cycle

Through the course of the economic cycle and the development of urban real estate industry analysis process, economic development, in-depth study of the real estate cycle fluctuations and macroeconomic volatility relationship, trying to figure out the development of the real estate cycle and links between the current economic city fluctuations in the real estate cycle process development law issues to try to do some qualitative research.

Real estate market cyclical dynamics

2014 ◽  
Vol 10 (2) ◽  
pp. 241-262 ◽  
Author(s):  
Kim Hin/David Ho ◽  
Kwame Addae-Dapaah
Keyword(s):  
Hong Kong ◽  
Real Estate ◽  
Real Estate Market ◽  
Var Model ◽  
Kuala Lumpur ◽  
Content Type ◽  
The Real ◽  
The Real Estate ◽  
Price Response ◽  
Real Estate Cycle

Purpose – The purpose of this paper is to help us understand the real estate cycle and offers an analysis using a vector auto regression (VAR) model. The authors study the key international cities of Hong Kong, Kuala Lumpur and Singapore. The authors find four key outcomes. One, the real estate cycle is generally different from the underlying business cycle in local markets for the cities studies. Two, the real estate cycle is more exaggerated in the construction and development areas than in rents and vacancies. Three, the vacancy cycle tends to lead the rental cycle. And four, new construction completions tend to peak when vacancy is also peaking. The authors believe that future research should try to help understand the linkages that drive these outcomes. For example, are rigidities in the local permit and construction markets responsible for the link between construction peaks and vacancy peaks? Design/methodology/approach – Real estate market cyclical dynamics and its estimation via VAR model offers an insightful set of practical and empirical models. It affirms a comprehensive theoretical underpinning for analysing the prime office and residential sectors of the capitol cities of Kuala Lumpur, Singapore and Hong Kong in the fast developing Asia region. Its unrestricted form also provides an effective and insightful way of modelling real estate market cyclical dynamics utilising only real estate market indicators, furnished by real estate market data providers. Findings – The office rental VAR model for Singapore (SOR), KL (KOR) and HK (HOR) show good fits. In the HOR model, rents and vacancies are negatively signed and significant for certain lagged relationships with other variables and with rents themselves. The office CV VAR model for Singapore (SOCV), KL (KOCV) and HK (HOCV) show good fits. In the HOCV model, capital values (CVs) and initial yields are negatively signed and significant for certain lagged relationships with other variables and with CVs themselves. Impulse response functions specified for seven years to mirror a medium-term real estate market cycle “die out” to zero for the stationary VAR models that are estimated for the endogenous variables. The accumulated responses asymptote to some non-zero constant. Practical implications – The VAR model offers a complete and meaningful dynamic system of solely real estate variables for international real estate investors and policy makers in decision making. Its unrestricted form offers an effective and insightful way of modelling real estate market cyclical dynamics utilising only real estate market indicators, which can be reliably provided by a dedicated real estate information and consultancy provider of international standing. Originality/value – The theoretical model offers a complete dynamic model system of the real estate space market, comprising a unique system of six linked equations that denote the relationship among supply, demand, construction, vacancy and rent over time, inclusive of price response slopes and lags. The VAR model enables the investigation of the effect of the lagged values of all the variables concerned. It also enables the explicit and rigorous quantitative forecasts of say rents and CVs when the rest of the variable can be forecasted beforehand.

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