Is There a Bubble in the Housing Market in Seoul?

Hong, Jengei; Kim, Hyeongjun; Ahn, Seryoong

doi:10.30902/jrea.2022.8.1.1

Cited by 7 publications

(6 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…During periods of high sentiment, higher expectations of future housing returns lead to an influx of home buyers into the property market (Dong et al, 2021), and a rapid increase in transaction volumes (Fischer and Stamos, 2013). This overreaction to price changes may lead to significant upward momentum in housing prices (Asal, 2019;Hong et al, 2022;Tsai and Peng, 2011), ultimately shaping price patterns.…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Persistent trends and inefficiencies in the Greek housing market: a sentiment based approach

Vasileiou,

Hadad,

Oikonomou

2024

JERER

View full text Add to dashboard Cite

PurposeWe examine the aggregate price trend of the Greek housing market from a behavioral perspective.Design/methodology/approachWe construct a behavioral real estate sentiment index, based on relevant real estate search terms from Google Trends and websites, and examine its association with real estate price distributions and trends. By employing EGARCH(1,1) on the New Apartments Index data from the Bank of Greece, we capture real estate price volatility and asymmetric effects resulting from changes in the real estate search index. Enhancing robustness, macroeconomic variables are added to the mean equation. Additionally, a run test assesses the efficiency of the Greek housing market.FindingsThe results show a significant relationship between the Greek housing market and our real estate sentiment index; an increase (decrease) in search activity, indicating a growing interest in the real estate market, is strongly linked to potential increases (decreases) in real estate prices. These results remain robust across various estimation procedures and control variables. These findings underscore the influential role of real estate sentiment on the Greek housing market and highlight the importance of considering behavioral factors when analyzing and predicting trends in the housing market.Originality/valueTo investigate the behavioral effect on the Greek housing market, we construct our behavioral pattern indexes using Google search-based sentiment data from Google Trends. Additionally, we incorporate the Google Trend index as an explanatory variable in the EGARCH mean equation to evaluate the influence of online search behavior on the dynamics and prices of the Greek housing market.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

“…, 2021), and a rapid increase in transaction volumes (Fischer and Stamos, 2013). This overreaction to price changes may lead to significant upward momentum in housing prices (Asal, 2019; Hong et al. , 2022; Tsai and Peng, 2011), ultimately shaping price patterns.…”

Section: Literature Reviewmentioning

confidence: 99%

Persistent trends and inefficiencies in the Greek housing market: a sentiment based approach

Vasileiou,

Hadad,

Oikonomou

2024

JERER

View full text Add to dashboard Cite

show abstract

“…Majorities concern about that whether the potential bubbles in housing market would burst in recent future. Some people even suggest that if China's policy makers don't attach importance to this problem, our housing market will be destroyed just like Japan [1]. This paper aims to better understand the housing price in China, and we employ various indicators to see whether there are bubbles in housing market with six cities of different tiers.…”

Section: Introductionmentioning

confidence: 99%

Housing Price Bubbles in China: An Investigation of Six Cities

2024

AJBM

View full text Add to dashboard Cite

Since the reform and opening up, China developed rapidly in almost all aspects, especially in economy. China's economy is characterized by a heaven reliance on the real estate sector. However, a contentious concern that the growth of Chinese housing marketing will lead to an enormous housing bubble has garnered significant attention, and people are keen for an objective evaluation of the housing market. This research paper aims investigate further in this field. In this research, we selected some typical cities across China, including 1 st tier super cities, 2 nd tier developed cities, and some still developing smaller cities. In every chosen city, we collected data of four categories, namely the median income level, the average price level of houses, and the average rent level. These data allowed us the so how high the housing price actually is when compared to people's income and to the rental price. Afterwards, we also estimated the inflation rate as well as the housing purchasing price and rental price for the next 10 years. We were able to use the DCF model to examine whether the houses are overvalued or not in the cities we selected. We concluded that bubbles do exist in major cities in China, but the situation is more optimistic in smaller cities.

show abstract

“…Notably, Google Trends has garnered attention among scholars for its reliability in measuring economic and financial uncertainty (Bilgin, Demir, Gozgor, Karabulut, & Kaya, 2019;Brodeur, Clark, Fleche, & Powdthavee, 2021;Choi & Varian, 2012;Preis, Moat, & Eugene Stanley, 2013;Vasileiou, 2021aVasileiou, , 2023, and is recognized as a leading sentiment indicator in financial analysis. However, while sentiment notably influences housing prices (Abildgren, Hansen, & Kuchler, 2018;Hong et al, 2022;Ling, Ooi, & Le, 2015;Muellbauer & Murphy, 2008;Tsai & Peng, 2011), especially in the Greek housing market (Anastasiou et al, 2021;Anastasiou & Kapopoulos, 2023), real estate research has not extensively utilized behavioral indicators based on Google Trends. The few exceptions include Dietzel (2016), who demonstrates that Google search volume data can act as a leading sentiment indicator and predict turning points in the US housing market, and Bulczak (2021), who employs Google Trends to predict the UK real estate market.…”

Section: Introductionmentioning

confidence: 99%

“…Recent studies highlight how optimistic outlooks and shifts in sentiment, detached from economic fundamentals, can instigate ‘bubble-bursting' phenomena in housing markets, leading to price fluctuations (Abraham & Hendershott, 1996; Muellbauer & Murphy, 2008; Shiller, 2008). Optimistic sentiment, driven by expectations of future housing returns, tends to attract more homebuyers into the market (Dong, Hui, & Yi, 2021), boosting transaction volumes (Fischer & Stamos, 2013) and driving up housing prices (Asal, 2019; Hong, Kim, & Ahn, 2022; Tsai & Peng, 2011). Residential property’s dual role as both a consumer good and an investment asset (Granziera & Kozicki, 2015; Marfatia, André, & Gupta, 2022), significantly influences demand for durable goods and shapes investors’ risk perceptions toward financial assets (Fuhrer, 1993; Mishkin, Hall, Shoven, Juster, & Lovell, 1978; Throop, 1992; Van Raaij & Gianotten, 1990).…”

Section: Introductionmentioning

confidence: 99%

What drives the real estate market? Could behavioral indicators be useful in house pricing models?

Vasileiou,

Hadad,

Melekos

2024

ECON

View full text Add to dashboard Cite

PurposeThe objective of this paper is to examine the determinants of the Greek house market during the period 2006–2022 using not only economic variables but also behavioral variables, taking advantage of available information on the volume of Google searches. In order to quantify the behavioral variables, we implement a Python code using the Pytrends 4.9.2 library.Design/methodology/approachIn our study, we assert that models relying solely on economic variables, such as GDP growth, mortgage interest rates and inflation, may lack precision compared to those that integrate behavioral indicators. Recognizing the importance of behavioral insights, we incorporate Google Trends data as a key behavioral indicator, aiming to enhance our understanding of market dynamics by capturing online interest in Greek real estate through searches related to house prices, sales and related topics. To quantify our behavioral indicators, we utilize a Python code leveraging Pytrends, enabling us to extract relevant queries for global and local searches. We employ the EGARCH(1,1) model on the Greek house price index, testing several macroeconomic variables alongside our Google Trends indexes to explain housing returns.FindingsOur findings show that in some cases the relationship between economic variables, such as inflation and mortgage rates, and house prices is not always consistent with the theory because we should highlight the special conditions of the examined country. The country of our sample, Greece, presents the special case of a country with severe sovereign debt issues, which at the same time has the privilege to have a strong currency and the support and the obligations of being an EU/EMU member.Practical implicationsThe results suggest that Google Trends can be a valuable tool for academics and practitioners in order to understand what drives house prices. However, further research should be carried out on this topic, for example, causality relationships, to gain deeper insight into the possibilities and limitations of using such tools in analyzing housing market trends.Originality/valueThis is the first paper, to the best of our knowledge, that examines the benefits of Google Trends in studying the Greek house market.

show abstract

Is There a Bubble in the Housing Market in Seoul?

Cited by 7 publications

References 21 publications

Persistent trends and inefficiencies in the Greek housing market: a sentiment based approach

Persistent trends and inefficiencies in the Greek housing market: a sentiment based approach

Housing Price Bubbles in China: An Investigation of Six Cities

What drives the real estate market? Could behavioral indicators be useful in house pricing models?

Contact Info

Product

Resources

About