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

Vasileiou, Evangelos; Hadad, Elroi; Melekos, Georgios Article What drives the real estate market? Could behavioral indicators be useful in house pricing models? EconomiA Provided in Cooperation with: The Brazilian Association of Postgraduate Programs in Economics (ANPEC), Rio de Janeiro Suggested Citation: Vasileiou, Evangelos; Hadad, Elroi; Melekos, Georgios (2024) : What drives the real estate market? Could behavioral indicators be useful in house pricing models?, EconomiA, ISSN 2358-2820, Emerald, Bingley, Vol. 25, Iss. 1, pp. 157-174, https://doi.org/10.1108/ECON-10-2023-0166 This Version is available at: https://hdl.handle.net/10419/329561 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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Evangelos Vasileiou Department of Financial and Management Engineering, University of the Aegean, Chios, Greece Elroi Hadad Department of Industrial Engineering and Management, Shamoon College of Engineering, Beer-Sheva, Israel, and Georgios Melekos Department of Financial Management Engineer, University of the Aegean, Chios, Greece and Faculty of Turkish Studies and Modern Asian Studies, National and Kapodistrian University of Athens, Athens, Greece Abstract Purpose –The 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/approach –In 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. Findings –Our 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 implications –The 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/value –This is the first paper, to the best of our knowledge, that examines the benefits of Google Trends in studying the Greek house market. Keywords Inflation, Google Trends, Real estate, Behavioral indicators Paper type Research paper Behavioral house pricing models 157 © Evangelos Vasileiou, Elroi Hadad and Georgios Melekos. Published in EconomiA. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode The current issue and full text archive of this journal is available on Emerald Insight at: https://www.emerald.com/insight/1517-7580.htm Received 10 October 2023 Revised 29 January 2024 Accepted 4 March 2024 EconomiA Vol. 25 No. 1, 2024 pp. 157-174 Emerald Publishing Limited e-ISSN: 2358-2820 p-ISSN: 1517-7580 DOI 10.1108/ECON-10-2023-0166 1. Introduction The dynamics of the real estate markets have puzzled financial economists and scholars for decades due to the substantial impact of house prices on economic activity. Previous literature has predominantly linked housing prices with traditional economic markers, such as GDP, income levels, population trends and interest rates (Bjørnland & Jacobsen, 2010; Case, Shiller, & Thompson, 2012;Gupta, Jurgilas, Miller, & Van Wyk, 2011;Plakandaras, Gupta, Katrakilidis, & Wohar, 2020;Simo-Kengne, Miller, Gupta, & Balcilar, 2016). Major economic shifts, like alterations in monetary policies or fluctuations in interest rates, have been pinpointed as significant drivers of housing prices (De Santis & Surico, 2013;Demary, 2010;Everett, de Haan, Jansen, McQuade, & Samarina, 2021;Plakandaras et al., 2020;Rahal, 2016;Simo-Kengne et al., 2016). Economic growth and house prices present a positive correlation (Leung, 2003;Simo-Kengne et al., 2012,2016), as economic growth raises wages and increases consumption, ultimately raising house prices as well (Kishor, 2007;Lettau & Ludvigson, 2004). A positive change in interest rates, in particular, which leads to higher mortgage rates and housing costs, has a negative impact on housing demand and exerts downward pressure on house prices (De Santis & Surico, 2013;Demary, 2010;Everett et al., 2021;Rahal, 2016). Additionally, the inflation channel suggests a nuanced impact; while inflation may stimulate residential investment due to real estate’s role as an inflation hedge (Fama & Schwert, 1977), it could also lead to higher interest rates, potentially suppressing real estate demand and adversely affecting house prices (Demary, 2010). However, more recent studies show contradictory findings, highlighting the limited significance of Macroeconomic, Monetary, and Banking (MMB) fundamentals on housing prices (Alkay, Watkins, & Keskin, 2018). Hoesli, Lizieri, and MacGregor (2008) indicate that real estate offers a minimal hedge against high inflation. Additionally, other research suggests that changes in interest rates have a limited impact on housing prices (Glaeser, Gottlieb, & Gyourko, 2015;Shi, Jou, & Tripe, 2014;Taylor, 2009), implying that increases in the policy rate may not effectively depress real housing prices, particularly during periods of high inflation. Real estate can serve as a hedge against inflation (Fama & Schwert, 1977), leading to increased demand for housing to mitigate inflation risks, consequently driving up house prices (Shi et al., 2014). One potential explanation for the limited impact of MMB factors on housing prices lies in the psychological dimension of real estate investment, where consumer behavior plays a pivotal role (Beracha, Lang, & Hausler, 2019;Clayton, Ling, & Naranjo, 2009;Hausler, Ruscheinsky, & Lang, 2018). 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, Andr e, & 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). Given that housing sentiment impacts housing prices, especially during periods of economic uncertainty (Anastasiou, Kapopoulos, & Zekente, 2021;De Bandt, Barhoumi, & Bruneau, 2010), it is essential to consider the behavioral effect on housing markets. In this context, prior studies reveal that Greek homeowners perceive houses as investment assets rather than purely consumption goods (Gounopoulos, Merikas, Merika, & Triantafyllou, 2012) and view housing as a crucial investment decision for the average Greek citizen (Papageorgiou, Loulis, Efstathiades, & Ness, 2020). This stands in contrast to ECON 25,1 158 the home ownership rate in the Euro area, which experienced a decline over the last decade, primarily attributed to diminishing ownership rates among young adults and low-income groups (Calabria & Calder, 2019) or escalating housing prices (  Cerm akov a & Hromada, 2022). Furthermore, Greece has faced severe economic challenges, including a notable financial crisis (Lekkos, Staggel, Kefalas, & Vlachou, 2014), high level of debt (Leschinski & Bertram, 2017), financial instability (Anastasiou & Kapopoulos, 2023), deflation pressures (Lekkos et al., 2014) and high volatility in housing prices (Gounopoulos et al., 2012;Petropoulos, Liapis, & Thalassinos, 2023). These economic challenges potentially influence investor behavior, contributing to the impact on housing prices beyond market fundamentals, as noted by Marfatia et al. (2022). Given that the Greek housing market stands out with homeownership rates (Gounopoulos et al., 2012), high impact from changes in consumer behavior (Petropoulos et al., 2023) and is highly sensitive to changes in financial stress conditions (Anastasiou & Kapopoulos, 2023), it is intriguing to study how these unique characteristics interact with the dynamics of the Greek housing market, providing valuable insights for investors, policymakers and researchers alike. In this study, we examine the influence of behavioral sentiment variables and macroeconomic fundamentals on the variability of Greek house prices. Employing EGARC(1,1) model (Nelson, 1991), we explore how behavioral and MMB fundamentals impact housing market volatility, using the House Prices Index (HPI) data spanning from 2006 to 2022. Our MMB fundamentals encompass changes in GDP, inflation and mortgage interest (MI) rates, factors known to significantly affect housing demand and prices (Bjørnland & Jacobsen, 2010;Case et al., 2012;Everett et al., 2021;Leung, 2003;Plakandaras et al., 2020;Simo-Kengne et al., 2012,2016), especially in the Greek market (Apergis & Rezitis, 2003;Gounopoulos et al., 2012). For behavioral sentiment variables, we integrate Google search-based sentiment data from Google Trends. 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, 2021a,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. While some studies on the Greek housing market utilize survey-based sentiment indexes to elucidate house price variation (Anastasiou et al., 2021;Anastasiou & Kapopoulos, 2023), we find no evidence of studies utilizing Google Trends to capture behavioral sentiment. For robustness, we introduced our sentiment measure in addition to our MMB fundamentals individually. Our main findings show that media-based information from Google Trends is highly significant in elucidating variations in Greek housing prices, beyond the traditional MMB fundamentals. Our analysis demonstrates the potency of our sentiment measure in capturing the dynamics of the Greek housing market, thereby enhancing the understanding of the interplay between consumer sentiment and traditional economic indicators. These findings underscore the pivotal role of media-based information and sentiment in shaping housing market dynamics. Our study makes significant contributions to the existing literature in several key ways. First, we pioneer the use of Google Trends data in conjunction with macroeconomic variables to elucidate variations in house prices. By leveraging Google Trends indices, which serve as Behavioral house pricing models 159 behavioral indicators of public interest and sentiment (Vasileiou, 2021a,b), we argue that investigating media-based information and sentiment is paramount for achieving a more nuanced understanding of Greek house price volatility. This innovative approach not only enriches the current body of research but also offers a novel perspective on the drivers of housing market dynamics. Second, against the backdrop of economic uncertainty in Greece and the pronounced sensitivity of housing prices to financial instability, our study provides comprehensive insights into the impact of both behavioral patterns and economic stress conditions. By employing a novel model-based approach, we offer a detailed examination of the interplay between consumer sentiment and MMB fundaments. Our findings not only enhance the understanding of the Greek housing market dynamics but also serve as a valuable template for policymakers, regulators and researchers grappling with economic challenges elsewhere. By adapting our methodology, policymakers and regulators can gain deeper insights into the factors shaping housing markets and develop more effective strategies to mitigate risks and promote stability in housing sectors across various economic contexts. Thus, our study not only advances economic literature but also holds significant implications for policymakers and stakeholders seeking to navigate turbulent economic landscapes and foster sustainable housing markets. The rest of this paper goes as following: Section 2 presents the variables and the preliminary data of our study. Section 3 analyses the econometric model and presents the empirical results, and Section 4 concludes the study, discusses the findings and suggests some ideas for further research. 2. Data and variables In this paper, we use data from the Bank of Greece for the HPI and the MI, and we gather the GDP year on year change (GDP_yoy) and Inflation (I) from the Hellenic Statistical Authority. These variables will be the MMB variables that are usually used in similar studies (Apergis & Rezitis, 2003). From behavioral standpoint, the easy part is that if somebody is interested in buying a house, he/she searches the internet for properties and prices. Internet searches are a very useful tool for scholars because they enable us to incorporate what people are interested in and this may be an indication for their actions (Vasileiou, 2021b). The difficult part is to find which are the most representative terms taking into consideration specific factors that could influence each market. For example, during the last decade, there has been considerable discussion in Greece about the interest of foreigners in buying Greek properties, as well as the impact of such transactions on the domestic real estate market and the economy as a whole [1]. Foreign buyers are looking to purchase vacation homes or searching for investment opportunities, whereby they buy houses to convert them into Airbnb units. Non-EU nationals are also seeking to obtain a Golden Visa, which they can do by buying property (Lekkos et al., 2014;Papageorgiou et al., 2020). The challenging aspect of the behavioral analysis lies in the identification and quantification of pertinent behavioral indices. Pytrends facilitates the creation of behavioral indicators by leveraging its functionalities for extracting related topics, queries and suggestions. To construct these indicators, we explore various search terms, as Pytrends allows the use of up to five terms simultaneously. The efficacy of different term combinations is tested to ascertain which combination best encapsulates the sought-after interest. The results are not presented in raw volumes; rather, they are normalized and indexed on a scale from 0 to 100. To gauge international interest, English terms such as “Greece Golden Visa,”“House Sales in Greece,”etc., are employed. In contrast, for domestic interest originating from Greek users, terms like «Π ω λή σεις Σ πιτι ώ ν »and «E ν o ι κ ι ά σεις Σ πιτι ώ ν » (translated as “House Sales”and “Homes to Rent”in Greek, respectively) are tested. ECON 25,1 160 Additionally, the act of visiting real estate websites, spanning from real estate agents to platforms for house rentals and sales, serves as an indicator of a prospective willingness to purchase property. Given the abundance of suggested and related terms, and the myriad possible combinations, a new code is implemented to determine the most representative term combination for the specified objective. Consequently, a Local Search (LS) index is introduced to encapsulate the intention of Greek individuals to acquire a property. World Searches Index is devised to quantify global interest in Greek real estate. Figure 1 visually represents the relationship between each variable, including MMB and the behavioral indices, with the Housing Price Index (HPI). This graphical representation aids in comprehending the interplay between these variables. We should note that house prices in Greece decreased from the end of 2008 up until the end of 2017, which closely coincides with the Greek sovereign debt crisis and GDP decline that lasted from 2009 to 2017. These years constitute a large period of our sample, but since 2017 house prices have risen. We present the relationship of the explanatory MMB variables (MI, I, GDP_yoy) and the behavioral indices (World_Searches, LSs) with the dependent variable (HPI) in Figure 1, and we clarify the following points: (1) Inflation presents a negative relationship to the HPI, which means that investments in house property were not a way to hedge against inflation during the entire examined period. This runs counter to what international theory suggests in the case of a small, emerging and open economies that do not have a very strong currency (AssibeyYeboah & Mohsin, 2014;Thornton & Vasilakis, 2016). A possible explanation for these preliminary results could be that Greece may be a small and open economy, but it belongs to the European Union (EU) and the European Monetary Union (EMU), and it has strong currency. If Greece weren’t a member of the EU/EMU, its currency would have probably depreciated during the sovereign crisis and inflation issues would have emerged (e.g., cost inflation due to price increases in oil, imported goods, etc.). In such a case, house prices in a soft currency would be higher. The fact that Greek house prices decreased during the debt crisis, and inflation was not so high due to EU/ EMU and to the Euro. (2) GDP has positive behavior to the house prices, as the theory suggests. One easily observed exception is during the COVID-19 period when the GDP falls, but the house prices remain almost the same. This can be attributed to the stimulus packages that gave liquidity and income during the isolation and not very productive years (Vasileiou, 2023). (3) MI rates seem to be higher when the house prices were higher and lower when the prices declined. This positive relationship could be explained by the fact that bank interests followed the European rates when Greece suffered from its sovereign crisis (2009–2017), due to its strong currency (Euro) and the assistance of EU/EMU. When house prices fell, MI rates were as low as those in the Euro area. Low interests reduce the cost of a house and make the investment case more tantalizing, and usually the lead to an increase of the demand and of the prices. However, in Greece, local and foreign interest in buying property in the country increased when prices and mortgage rates increased. (4) World interest in Greek properties seems to have a positive relationship with house prices in the last HPI rise period, but it has a seasonality [2]. (5) The LSs for Greek property seem to have a smoother time series (without seasonality) and a positive relationship with the HPI. Behavioral house pricing models 161 Table 1 depicts the correlation matrix, revealing pivotal insights into the intricate relationships among key variables within our dataset. Notably, the HPI exhibits a robust positive correlation with interest rates (MI) (0.881), underscoring the significant influence of interest rates on housing prices. Conversely, the HPI demonstrates a moderate negative correlation with inflation (I) (0.504), suggesting a potential inverse relationship between inflation and housing prices. Inflation also manifests strong negative correlations with MI (0.566) and LSs (0.744), hinting at the potential impact of inflation on mortgage rates and (continued) Figure 1. House Price index and the Explanatory Variables ECON 25,1 162 (continued)Figure 1. Behavioral house pricing models 163 local interest in housing. Considering the behavioral variables, Google Trends Worldwide Searches (World_Searches) demonstrate a weak positive correlation with HPI; however, the LSs (Local_Searches) exhibit strong positive correlations with HPI (0.818) and MI (0.823), highlighting the substantial influence of local sentiment and interest on housing market dynamics. These correlations underscore the complex interplay between macroeconomic factors, consumer sentiment and housing market trends. Lastly, Table 2 presents the descriptive statistics of our study. We use the first differences of these variables because many of them are not stationary when we test them at level [3]. Notable observations include marginal decreases in HPI returns, juxtaposed with moderately positive inflation rates, hinting at potential shifts in market dynamics. However, the high standard deviations of these variables indicate significant variations in housing prices and inflation, possibly attributed to recent economic challenges faced by Greece. The GDP change hovers marginally above 0.00, indicating stable economic growth over the period, albeit with potential outliers that underscore economic resilience. The negative mean of MI signifies a HPI I GDP_yoy MI World_Searches Local_Searches HPI 1.000 0.504 0.156 0.881 0.189 0.818 I0.504 1.000 0.145 0.566 0.256 0.744 GDP_yoy 0.156 0.145 1.000 0.081 0.352 0.302 MI 0.881 0.566 0.081 1.000 0.328 0.823 World_Searches 0.189 0.256 0.352 0.328 1.000 0.126 Local_Searches 0.818 0.744 0.302 0.823 0.126 1.000 Source(s): Table by authors Figure 1. Table 1. Correlation matrix of our variables ECON 25,1 164 6. If somebody has the money to buy a house, s/he can buy it quickly, but interested parties that reside abroad will need to at least visit the property on site and this takes longer. 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