The Anatomy of a Housing Bubble: Overconfidence, Media and Politics

Bucchianeri, Grace Wong

doi:10.2139/ssrn.1877204

Cited by 6 publications

(4 citation statements)

References 27 publications

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“…Bütün bunların yanı sıra temel olarak fiyat köpüklerinin varlığına ilişkin farklı bir çok coğrafyada geniş bir akademik araştırma mevcuttur. Dorling (2016), Bucchianeri (2011), Byun (2010), Ratajczak (2006), Miles (2014), Caspi (2016), Cadıl ( 2009 2017)'nin araştırmalarında da mevcuttur. Bölgesel konut piyasaları örnekleminde gerçekleştirilen araştırmaların sonucuna göre araştırılan mahalli piyasalarda yüksek fiyatlara rastlansa da köpük olarak değerlendirilemeyeceği ve buna bağlı sert geri çekilmelere bağlı sönmelerin beklenmeyeceği ortaya konulmuştur.…”

Section: Li̇teratür Araştirmasiunclassified

Default Ri̇ski̇ne Dayali Olarak Türki̇ye’de Konut Fi̇yat Köpükleri̇ni̇n Varliğinin Anali̇zi̇: Tra 1 Bölgesi̇nden Kanitlar

Ece

2022

Erzincan Binali Yıldırım Üniversitesi İktisadi Ve İdari Bilimler Fakültesi Dergisi

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Bu çalışma, konut piyasasında fiyat köpüklerinin varlığını default riski ölçeğinde belirleme amacına sahiptir. Araştırma, Türkiye'de bölgesel sınıflandırma da Kuzeydoğu Anadolu Bölgesi yer alan ve Erzurum, Erzincan ve Bayburt illerini kapsayan TRA1 bölgesi örnekleminde gerçekleştirilmiştir. 2013-2020 inceleme dönemine sahip araştırmada aylık veriler kullanılmıştır. İki aşamalı bir araştırma içeriğine sahip olan araştırmada öncelikle şehirler özelinde konut piyasası arz ve talep dengesi incelenmiştir. Araştırma döneminde Erzincan ilinde ciddi arz fazlalığı göze çarparken, Erzurum ve Bayburt illerin talep fazlalığının yaşandığı görülmüştür. Araştırmanın ikinci aşamasında TRA1 bölgesindeki iller için konut varlık köpüğünün olup olmadığı ARDL test istatistiğine dayalı olarak araştırılmıştır. Bölge için yapılan ekonometrik ve istatistiksel analizler sonucunda 96 aylık dönem için fiyat köpüğü gözlenmemiştir. Buna göre, araştırma dönemi için konut fiyatlamasının piyasa şartlarına göre belirlendiğini söylemek mümkündür.

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Section: Li̇teratür Araştirmasiunclassified

Default Ri̇ski̇ne Dayali Olarak Türki̇ye’de Konut Fi̇yat Köpükleri̇ni̇n Varliğinin Anali̇zi̇: Tra 1 Bölgesi̇nden Kanitlar

Ece

2022

Erzincan Binali Yıldırım Üniversitesi İktisadi Ve İdari Bilimler Fakültesi Dergisi

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“…Duca et al (2021) suggested that house price increases are not only because of the low-interest-rate environment and government efforts to support firms and households but also because of behavioral factors. It is known that increases in market overconfidence are associated with increases in average price and trading activities, contributing to speculative asset bubbles (Scheinkman and Xiong, 2003; Bucchianeri, 2011; Bogan and Jankovic, 2018), and that overconfidence changes with time (Aghazadeh et al , 2018). The same effect occurs with herd behavior (Avery and Zemsky, 1998; Bikhchandani and Sharma, 2000; Wang et al , 2018).…”

Section: Literature Reviewmentioning

confidence: 99%

Heuristics and cognitive biases in the housing investment market

Cascão

Quelhas²,

Cunha³

2022

IJHMA

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Purpose This paper aims to analyze the heuristics and cognitive biases described by behavioral finance in the investment decision-making process of Portugal’s housing market. Design/methodology/approach In a first step, the authors applied an exploratory factor analysis (EFA) to assess the impact of heuristics and cognitive biases on investors’ decision-making. In a second step, the authors run a structural equation model (SEM) diagram path to assess if the sociodemographic characteristics of housing market investors determine the identified heuristics and if the heuristics condition the investors’ investment criteria. Findings Herd behavior and the heuristics of representativeness, availability and anchoring influence the housing market’s investors’ behavior in their decision-making process. Investors with above-average income show higher levels of overconfidence. Investors showing higher levels of overconfidence also tend to be more sensitive to the house price under analysis for investment. Women tend to show higher levels of the availability and anchoring heuristic. In turn, housing market investors showing higher levels of availability and anchoring heuristic tend to be more sensitive to the price and location of the house under analysis for investment. Research limitations/implications The explained variance of the EFA is below 50%, and the root mean square of approximation of the SEM is above the threshold of 0.05. These indicators are evidence of the models’ fragility. Practical implications Governments and regulators can better prevent real estate bubbles if they monitor behavioral biases and heuristics of housing investors together with quantitative indicators. Realtors can profit from adapting their marketing strategy and commercial communication to investors of sociodemographic groups more prone to a specific type of heuristics. Originality/value To the best of the authors’ knowledge, this is the first study that combines the contributions of behavioral finance with Portugal’s housing investment market and the first study connecting heuristics to investment criteria.

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“…When looking at the variables affecting homeownership; population growth, per capita income, unemployment rate, housing stock, construction costs, real wage, interest rate (Bucchianeri, 2008). As of 2003, construction-based economic policies were preferred for growth in Turkey.…”

Section: Introductionmentioning

confidence: 99%

The Effect of Selected Macroeconomic Variables on the Housing Price Index in Turkey

YAZAR ÖZTÜRK,

AKÇA

2023

Finans Ekonomi Ve Sosyal Araştırmalar Dergisi

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This study investigates how the house price is affected by macroeconomic variables such as interest rate, exchange rate, money supply and inflation rate. Housing supply and housing demand determines house price. On the one hand, housing supply dimensions covers contruction cost, existing land, housing stock. On the other hand, housing demand dimension involves interest rate, consumer price index as the indicator of inflation, income, population growth and employment rate. In order to measure the effects of macroeconomic variables more accurately, the housing price index variable determined by the Central Bank was used. Depending upon monthly data for the period 2010-2023 in Turkey, VAR models are used. In this way it is intended to find the direction and degree of the effect of selected macroeconomic variables on the house price index by performing impulse response function and variance decomposition analysis. According to this analysis findings, interest rate and exchange rate had a significant effect on the house price index. In line with the results obtained after the variance decomposition analysis, it was determined that the interest rate had a 19% effect and the exchange rate had a 14% effect on the house price index at the end of the 10th period.

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The Anatomy of a Housing Bubble: Overconfidence, Media and Politics

Cited by 6 publications

References 27 publications

Default Ri̇ski̇ne Dayali Olarak Türki̇ye’de Konut Fi̇yat Köpükleri̇ni̇n Varliğinin Anali̇zi̇: Tra 1 Bölgesi̇nden Kanitlar

Default Ri̇ski̇ne Dayali Olarak Türki̇ye’de Konut Fi̇yat Köpükleri̇ni̇n Varliğinin Anali̇zi̇: Tra 1 Bölgesi̇nden Kanitlar

Heuristics and cognitive biases in the housing investment market

The Effect of Selected Macroeconomic Variables on the Housing Price Index in Turkey

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