House price history, biased expectations, and credit cycles: The role of housing investors

Stefani, Alessia De

doi:10.1111/1540-6229.12328

Cited by 30 publications

(7 citation statements)

References 32 publications

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“…The results from Table 3 below reveal that the during the first cycle, the recovery phase (a) (Remoy et al , 2019; Canepa et al , 2020; Huang, 2019; Alkali et al , 2018; Clayton, 2020; Roulac, 2020, Barras, 2007; Locurcio et al , 2020; Wang and Croucher, 2021; Wisniewski and Brzezicka, 2021; Harnos, 2018; De Stefani, 2021; Pai and Wang, 2020; Jakimowicz and Kulesza, 2017; Grum and Govekar, 2015; Bednarek et al , 2021; Duca et al , 2021; Toledano, 2019) had a total of 3,215 properties transferred, and valuing to a total of P620,708,121.6. Expansion phase (a) had a total of 3,557 properties transferred and with a total value of P2,312,546,275; oversupply phase (a) consisted of a total count of 2,261 properties transferred and a total value of P4,161,698,812.00.…”

Section: Resultsmentioning

confidence: 99%

An assessment of real estate cycles and their impact on property values in Gaborone, Botswana

Kampamba

Kachepa

Lesobea

2022

IJHMA

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Purpose The purpose of this study was to assess real estate cycles and their impact on property values in Gaborone, Botswana. Investors and real estate professionals in Botswana rarely assess property cycles when purchasing property. This study therefore, aims to assess whether real estate cycles do exist, their duration and the type of real estate cycle that Botswana experiences. Design/methodology/approach Data was collected from primary and secondary sources. This included sourcing out information at the Deeds Registry Office in Gaborone on residential property sales and a questionnaire to 100 property investors. A record was made of properties that were sold for the period of 16 years starting from the year 2000 to 2016. Secondary data on the other hand was also collected from published and unpublished books, academic journals, professional journals, magazines, reports and monographs. A quantitative approach was used in this study. Data was analysed using Microsoft Excel and subsequently presented in form of tables and graphs. Findings The findings from the literature review revealed that there are four phases in the real estate cycles (recovery, expansion, oversupply and recession) and each has distinct features that an investor must be aware of to avoid consequences in the property market. The results from the data analysis revealed that real estate cycles do exist in Botswana as identified during the past 16 years. The cycle that Botswana experiences is called the kitchen cycle. It was also evident that Botswana experienced three cycles lasting five to six years each. Furthermore, it was discovered that all phases in the real estate cycles affect property values. Research limitations/implications There is relatively little information about property cycles and their timing in Botswana. Therefore, this study may assist valuation surveyors to make promptly informed decisions on property investment through cycle assessment and hence positively inform the public and financial stakeholders. Society might find this beneficial in as far as decision-making is concerned when thinking of investing in real estate. The current system at the deeds office is cumbersome and time consuming, thus making it difficult for the researchers and possibly the public to analyse the property market. This study therefore, may encourage the Deeds Registry Office to computerize their records. Practical implications There is relatively little information about property cycles and their timing in Botswana. Therefore, this study may assist valuation surveyors to make promptly informed decisions on property investment through cycle assessment and hence positively inform the public and financial stakeholders. Social implications Society might find this beneficial in as far as decision-making is concerned when thinking of investing in real estate. Originality/value To the best of the authors’ knowledge, this paper is the first of its kind in Botswana to extend the knowledge of real estate cycles and their impact on property cycles in Botswana.

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Section: Resultsmentioning

confidence: 99%

An assessment of real estate cycles and their impact on property values in Gaborone, Botswana

Kampamba

Kachepa

Lesobea

2022

IJHMA

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“…Some researchers note that people are accustomed to adjusting their expectations based on past experience, so sellers will raise the selling prices of their property if they observe a market increase [10]. A Michigan survey shows that buyers' housing price expectations are closely related to the historical housing prices of the cities in which they reside [11]. Another study shows that even if behavioral biases are controlled for, housing prices are nevertheless closely related to investors' extrapolative expectations [12].…”

Section: Literature Review 21 Effect Of Expectations On Housing Price...mentioning

confidence: 99%

“…Substituting Equations ( 9) and (11) into Equation (2) leads to the optimal decision function of buyer i as follows:…”

Section: Equilibrium Analysis Of Individual Housing Purchase Decisionsmentioning

confidence: 99%

Are Housing Prices Sustainable in 35 Large and Medium-Sized Chinese Cities? A Study Based on the Cheap Talk Game and Dynamic GMM

et al. 2021

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Focusing on the rapid rise of China’s housing prices in recent years, in this paper, we construct a model using the cheap talk game that centers on how information receivers in a market make inferences about the true state of the information based on the cost-free signals they receive and then make decisions based on these inferences, which in turn affect market equilibrium. By constructing a cheap talk game model of house prices, we examine the correlation between market expectations, economic fundamentals, and individual housing purchase decisions. Then, we conduct an empirical analysis using the dynamic GMM method based on the panel data of 35 large and medium-sized cities in China. The analysis reveals that: (1) when the whole of society expects housing prices to rise, housing prices will rise regardless of the true state of economic fundamentals; (2) when information on economic fundamentals is accurate and reliable, home buyers tend to make purchase decisions based on such information, thus maximizing the utility of society as a whole; but when information on economic fundamentals is unreliable, home buyers tend to make purchase decisions according to their own housing price expectations, which is not conducive to the optimization of the utility of society as a whole; (3) the empirical results also reveal that expectations are a non-negligible factor in the rise of housing prices, and the coefficient of expectations exceeds that of each economic fundamental; this demonstrates the investment-driven attribute of the housing market in China. At the same time, economic fundamentals have a definite effect on housing prices, which implies that the rise of housing prices in China is supported by objective conditions.

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“…The characteristics of large movements in house prices presented in Sun and Tsang (2019) also contradict a GBM process as a data-generating process for house prices. Furthermore, people systematically underestimate momentum and they overlook mean reversion in house prices, staying over-optimistic for several quarters after a sustained increasing house price trend (Stefani, 2021).…”

Section: Introductionmentioning

confidence: 99%

On Non-Negative Equity Guarantee Calculations with Macroeconomic Variables Related to House Prices

Badescu

Quaye²,

Tunaru

2021

SSRN Journal

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House price history, biased expectations, and credit cycles: The role of housing investors

Cited by 30 publications

References 32 publications

An assessment of real estate cycles and their impact on property values in Gaborone, Botswana

An assessment of real estate cycles and their impact on property values in Gaborone, Botswana

Are Housing Prices Sustainable in 35 Large and Medium-Sized Chinese Cities? A Study Based on the Cheap Talk Game and Dynamic GMM

On Non-Negative Equity Guarantee Calculations with Macroeconomic Variables Related to House Prices

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