Home Bias and the Real Estate Prices

This paper argues that the Home Bias phenomenon prevails in the real estate market, which is inferred from psychology, economic, and financial literature. Utilizing the trait of the Home bias behavior, which can reduce the risk of information asymmetry, I modify the classical pure trading model and employ the parameter of relative risk aversion as the proxy variable of Home Bias to translate the relationship among Home Bias phenomenon, the property prices, and the expected returns. The comparative static analyses indicate that Home Bias behavior is negatively related to the property prices and positively related to the property returns. The marginal effects on property prices are heightened in situations of high time preference and relative low Home Bias. Conversely, the marginal effects on property returns are larger if the time preference parameter is smaller. As a household buyer with high time preference is located far away from a property, his bargaining power is easily affected by home bias behavior. Further, this paper focuses on the home bias elasticity of property prices and returns for the sake of unit-free property. Inelastic coefficients of elasticity of prices and returns indicate that the capability of households to lower property overvalued prices (i.e. increase investment returns) from reducing information asymmetry by using Home Bias behavior is still limited.


Introduction
There are two main objectives of the study. The first objective of this paper is to announce the argument of Home Bias phenomena prevalent in real estate market. This argument can be manifested from interdisciplinary research which can be generalized into two major academic tributaries about housing location decisions. First is emotional attachment, social ties, family ties, social network debated by sociologists, geographers, demographers and psychologists; second is the information asymmetry explored by economist and financial scholar. The second objective of this paper is to modify the Lucas (1978) model, to re-derive closed-form solution for equilibrium local property prices, and furthermore to explore how Home Bias phenomenon affect equilibrium prices and expected returns of local properties.
The remainder of this paper is organized as follows. Section 2 reviews psychological and financial literature to derive and demonstrate that the real estate market is awash with Home bias phenomenon. Section 3 constructs the home bias model, derives the closed-form solutions for the property prices and expected returns, and outlines the theoretical propositions. Section 4 calibrates the results of comparative static analyses and elasticities in section 3. Section 5 Concludes.

Social Psychology and Home Bias
Focusing on the social psychology perspective, housing location decisions would be affected by emotional attachment, social ties, family ties, the behavior of family members, the traditional Chinese concept of filial piety, social network, etc. People will, through acquaintances, relatives, memory, or any interaction with the environment, love an area. With growing substantial or emotional bondage, people will not easily give up the environment with their previous experience.
Emotional attachment is defined as affective bond that people develop with their places of residence (Manzo 2005) and it is also named place attachment in Bonaiuto et al. (1999), Lewicka (2008;, and Wall and Reichert (2013). Although the definition of emotional attachment is somewhat different from the definition of place attachment, studies presented in this paper concern emotional attachment to neighborhood (or community) and home. (Note 1) The strength of emotional attachment will be affected by demographic and social factors. According to sociologists, geographers, demographers and psychologists, the relatively significant demographic factors include length of residence (Bonaiuto et al. 1999;Brown et al. 2003;Brown, Perkins, and Brown 2004;Kasarda and Janowitz 1974;Krannich and Greider 1984;Shamai and Ilatov 2005;Lewicka 2008;Shamai and Ilatov 2005), age (Cuba and Hummon 1993;Ng et al. 2005;Shamai and Ilatov 2005), and home ownership (Bolan 1997;Brown et al. 2003;Lewicka 2013;Mesch and Manor 1998;Ringel and Finkelstein 1991). Social ties and sense of security (Brown et al. 2003(Brown et al. , 2004Mesch and Manor 1998) are the two most often studied positive social factors. The following variables can be treated as the elements of social ties: number of friends and relatives in neighborhood; interest in family roots and in the history of residence place (Chrząstowski and Lewicka 2008;Connerly 1986;Dawkins 2006;Landale and Guest 1985;Spilimbergo and Ubeda 2004;Wójcik et al. 2010); trust in neighbors and trust in close persons (Glanville and Paxton 2007;Portes and Landolt 1996).
The relation between emotional attachment and residential stability has bidirectional causalities. The increased mobility of residents would collapse local communities, and further damage the strength of community attachment (Kasarda and Janowitz 1974). On the other hand, residential stability can result in stronger identification with one's community, which was found by Oishi et al. (2007) who preceded a series of experimental studies on the emotional and behavioral consequences of residential stability. However, according to the concept of emotional attachment, residents with stronger identification with their community can also result in higher residential stability. Consequently, if household in high community attachment place has the demand of relocation, he would choose tenure home bias.
Part of the literature on location decisions narrowed the focus on family ties. Several had investigated the correlation between parents' financial transfers to adult children and the effects of such transfers on the attainment of personal care or assistance in the event of disability or poor health (Cigno et al. 1998;Henretta et al. 1997;Schoeni 1995, 1997;Pezzin and Schone 1999;Soldo and Hill 1995). Hedman (2013) emphasized that according to the three hypotheses of affinity, facilitating, and information, household could increase the home bias effect on their relocation decisions. Tomassini et al. (2003) focused on the parent-child proximity and found that parental housing assistance at the time of marriage has a strong effect on the child's choice of location, on the initial and persistent spatial proximity to parents, especially on the proximity to husband's parents in some situations. The strength of familial affective ties is positive correlatively with spatial proximity. Knijn and Liefbroer (2006) emphasized that if the family members lived more than 5 kilometer away, the probability of receiving or giving assistance was actually decreased. Affection, social contacts, and geographic distance among family members are strongly interrelated (Dewit et al. 1988;Knijn and Liefbroer 2006;Lawton et al. 1994).
Households may move near their parents, adult children, siblings or other family members because they are used to favoring associated types of environments (Blaauboer 2011;Feijten et al. 2008, Hedman 2013Mulder 2007).
Although good quality childcare is provided and affordable for every household in Sweden, households are likely to move to neighborhoods where extended family members reside in (Hedman 2013). This home bias phenomena of family ties can be observed in many countries over the world such as Portugal, Spain, and Italy (Tomassini et al. 2003), Turkey and Morocco (Zorlu 2009), Pakistani households in Glasgow (Bowes et al. 1997), and Asian countries especially. Co-residence was prevalent in the Asia-Pacific region, where have the following characteristics: 1.) deeply affected by the traditional Chinese concept pf filial piety; 2.) having strong intergenerational links, living patterns of co-residence; 3.) prevalent economic and noneconomic flows between parents and children. However, with the development of urbanization, modernization, industrialization, and the conflict of co-residence, the length of co-residence with parents has decreased after marriage or further, the intergenerational co-residence life gradually transformed into the nuclear family. (Lowenstein 2002;Ng et al. 2002). Instead of co-residence, the majority households with the traditional Confucian filial piety would choose to live near their parents or family members in Asian societies. No doubt the home bias phenomenon of housing location occurs almost all over the world.
Besides, people move towards their parents, siblings, and other family members, as they suffer special life events or profound changes when the need of support is particularly affective or functional, such as during divorce, marital separation, or the birth of children (Michielin et al. 2008;Wall and Reichert 2013).
Through emotional attachment, social ties, or family ties, People and things living in a specific space can be woven into a complex social network. Chin (2010) found that the impact of social networks on the intentions or preferences of location decisions is not less than the rationality of microeconomics. Economic benefits are visible, computable, and more well-known; however, the power of social network is invisible, insubstantial, and difficult to estimate. Social network can create, cumulate, and solidify social capital in neighborhood through trust, co-operation, collective support and norms (Chin 2010;Bourdieu and Wacquant 1992;Portes and Landolt 1996). In large part social networks are tied to the physical location and relocation would cause greater the transaction costs for better social network (Durlauf and Fafchamps 2005;Glaeser et al. 2002;Jacob and Munasib 2009;Munasib 2009). Dipasquale et al. (1999) find that homeownership gives household an incentive to improve the environment of their neighborhood, encourages household to invest in local amenities, and furthermore, solidifies the social network. Consequently, as the household with deeper social network confront the living disequilibrium or encounter the demand of relocation, ceteris paribus he would have a greater incentive to relocate in the same community where he has previous experience to cost down the transaction cost.

Financial Economics and Home Bias
The information asymmetry is another academic tributary to explain the Home bias phenomena of real estate market. Many literature have proven that Information asymmetry seriously exists in real estate markets (Cooper, et al. 2000;Cutts and Order 2005;Firoozi et al. 2006;Garmaise and Moskowitz 2004;Green 2008;Levitt and Syverson 2008;Rutherford et al. 2007;Wong et al. 2012). The asymmetry of information with respect to property prices comes from two sources. First, sellers have more complete information of their local district and its surrounding environment, including economic and social activities. And, they are more knowledgeable of the local government regulation and the neighborhood facilities which have impact on property prices. Second, sellers have more precise information of the merits and drawbacks of the building materials and structure. The above sources of information asymmetry are both important factors to decide property prices. Consequently, lack of property knowledge is a barrier to becoming a homeowner (Haurin and Morrow-Jones 2006). To improve the asymmetric information of buyers, some countries and local government have regulated to disclose some key items of real estates. The approval of disclosure acts reduces information asymmetry, on the contrary, increases the real estate prices (Nanda and Ross 2012 disclosure of seriously negative spillover effects reduces these real estate prices significantly (Pope 2008). This is a strong evidence that information asymmetry is highly correlated with real estate prices. Garmaise and Moskowitz (2004) prove that buyers will buy properties near their original residence to mitigate information asymmetries, especially in high asymmetric information area, which means that shorter distance between properties and buyers represents more knowledgeable about properties conditions. The Home bias phenomenon indirectly observed on the information asymmetry topic of real estate by Garmaise and Moskowitz. Some literature can indirectly demonstrate the relationship between distance and information via channels of family or social networks. According to the information hypothesis of Hedman (2013), having family in a neighborhood could increase cognitive knowledge about that area. Households always search residences within an awareness and contact space. By way of family or family members, awareness and contact of an area would be encouraged (Brown and Moore 1970; Quigley and Weinberg 1977;van Kempen and Özüekren 1998).
Through the channel of social networks, households can obtain crucial knowledge and information about household finance, wealth management, home maintenance, housing market, etc.; furthermore, homeownership and locational stability can be achieved (Durlauf and Fafchamps 2005;Jacob and Munasib 2009). Throughout the world real estate transactions are full of home bias phenomena.

The Modified Lucas (1978) Asset Price Model and Propositions
Purchasing behavior of real estate was driven by dual motives of self-resided and investment demands (Arrondel and Lefebvr, 2001;Henderson and Ioannides 1987;Hung and So 2012). No matter which motivation triggers purchasing behavior, location selection is very important. Real estate price is determined by two factors. One is the characteristics of the building, including structure, building materials and design. The other is the land, section characteristics, the spillover effects of surrounding environment and facilities and even the future city development roadmap (Dietz 2002;Haurin et al. 2002;Ioannides 2002;Manski 1993Manski , 2000. Spillover effect of surrounding environment and facilities could be side benefits or side effects. Side effects of neighborhood characteristics, such as traffic noise or aircraft noise, will have negative impact on real estate prices (Nelson 1978(Nelson , 1979(Nelson , 1980(Nelson , 1982. High unemployment rate and crime rate of a particular section will reduce buyers' demand of those real estates (Corcoran et al. 1992;Crane 1991). Side benefits of neighborhood characteristics, such as high education standard, high population density, high income, complete medical facilities and complete park plans, will increase the real estate prices (Benabou 1996;Durlanf 1996).
According to the theoretical and empirical literature review above and asset pricing model, I derive some propositions about relationship between Home Bias and real estate prices. By modifying the classical pure trading model Lucas (1978), I demonstrate the effects of Home Bias on real estate prices and its expected return, and further to map the volatilities in Home Bias factor to the volatilities in local property prices.
Proposition 1. The risk attitude factor of Lucas (1978) model can be employed as Home Bias factor in real estate market.
First, recall the article of Garmaise and Moskowitz (2004), they have empirically verified that shorter distances between buyers and properties can mitigate information asymmetry of property buyers. In other words, property buyers can reduce their risk by buying properties nearby. Second, risk aversion buyers will release serious nature of Home Bias in order to diminish bothersome risk and to enhance their investing utility. Accordingly, the degree of severity of home bias can be reflected by the degree of risk aversion.
Asset pricing is always an important topic in finance theory and real estate pricing is also an important research area in urban economics. Therefore, this paper further amends Lucas (1978) model to interpret and present the phenomenon of real estate investment.

Preference Setting
In accordance with Garmaise and Moskowitz (2004) s' argument, Home Bias can reduce the risk of information asymmetry. Thus, a real estate investor with a stronger risk aversion tendency, whose Home Bias phenomenon is stronger, because it can reduce the risk. Using Lucas (1978) model to translate the relationship between Home Bias and a property price, I can use the parameter of relative risk aversion as the proxy variable of Home Bias defined as distance preference. As the real estate investor buys a property near their original residence, which would reduce the risk of information asymmetry. Lower risk makes the risk aversion investor happier, then enhancing the utility of the  The model setting of consumption utility function of representative households is referred to Constantinides (1990), who successfully introduced consumption behaviors into utility function setting. This means the past consumption behaviors can be considered as factors of utility function. When representative households purchase real estates in different districts under different information asymmetry, the over-estimation extents will be different. This will cause representative households have different happiness after their consumption behavior. Considering the above-mentioned feature, this study adds consumption behaviors factors into utility function and introduces Home bias factor of real estate markets into utility function based on our logical inference. Moreover, we limit the utility function as a CRRA (Constant relative risk aversion, CRRA) function. Therefore, the utility function of this representative household at time of s can be formulated as follows.
where  of Eqn.
(2) is subjective time preference and it has another clear economic meanings that a representative buyer is willingness to postpone recent consumption activities to future time periods.  is the level of relative risk aversion.   s c u is a strictly increasing convex consumption function. Thus, Assume at time of t , only real estate goods have productivity to produce consumer goods which may perish. This productivity comes from the incremental value of real estate, t y , during this time period. Assume real estate is trading at a perfectly competitive market and representative household can only trade one unit of real estate goods.
Thus, the return of this representative household represents the return of total market. According to asset pricing theory (Cochrane 2001; Donaldson and Mehra 1984;Duffie 2001;Lucas 1978;Prescott and Mehra 1980), the followings discuss the intertemporal equilibrium consumption decision of the representative household at time t . If the household invests one unit of real estate, the purchasing units of consumer goods will decrease t p . For this representative household, the total utility will be down for From the viewpoint of real estate returns, the Eqn. (4) can be obtained from (3) The Closed-form Solutions of Property Prices and Expected Returns Substitution of (5) into Eqn. (4) yields Accordingly, based on assumption A.3, property return can be rephrased as: Substitution of (7) into Eqn. (6) The detailed deduction process of Eqns. (9) and (10) is as appendix A.

Fluctuations in Property Prices Induced by Home Bias Phenomenon
For measuring the determination of the changes in equilibrium prices that will result from a change in home bias parameters of that equilibrium, the comparative static analysis is carried out by partially differentiating Eqn. (11) with respect to home bias factor. It follows that,

Proposition 2. The trading price of real estate will be lower, as the distances between buyers and properties are relatively shorter.
For the sake of unit-free property, furthermore, the home bias elasticity of real estate price, denoted by   , p is derived. It can measure the percentage change in property prices that result from one percentage change in home bias factor. Hence, the formulas for measuring the coefficient of the home bias elasticity of property price is:

Fluctuations in Property Returns Induced by Home Bias Phenomenon
Advanced analysis with property return is proceeding. The comparative static analysis for expected returns of properties is carried out by partially differentiating Eqn. (12) with respect to home bias factor. Hence, Proposition 4. The expected returns of properties will be higher, as the distances between buyers and properties are relatively shorter.
Proposition 5. The partial derivative of returns to home bias factor is relatively higher for a household with low time preference.
A household with lower time preference would be more willing to sacrifice current consumption to buy closer property, therefore he has more opportunity to get higher returns.
The formulas for measuring the coefficient of the home bias elasticity of property expected return is:   Mehra and Sah (2002) for per capita real consumption of non-durables and services, and then Eqns. (13) and (14) can be calibrate by setting the incremental value of real estate as 200. The parameter  , which measures the curvature of the utility function, the level of relative risk aversion, and Home Bias phenomenon, is an important one in financial economics. Some researchers suggest that the parameter of risk aversion is smaller than or near 1 (the logarithmic risk-aversion). Altug (1985) uses econometric techniques to estimate  to be approximately 0. Arrow (1971) summarizes a number of studies and concludes that  should be near 1, the argument is supported by Kehoe (1984), studying the topic of international trade. Consolidating different advices from experts and scholars, this study calibrates the model result with 1 0    .

Calibrations for Fluctuations in Property Prices
However, others suggest that the parameter of relative risk aversion is larger than 1. Friend and Blume (1975) suggest that  should be set in the range of 2 for the researching the portfolio holdings of individuals. Tobin and Dolde (1971) set a value of 1.5 for  to fit the life cycle savings model. Mehra and Prescott (1985) and Shu (2010) set alpha between 1 and 10, Mehra and Sah (2002) (13) and (14) with  =1, 2, and 3. All results are reported in Figure 5 and Figure 6 of Appendix B, which are consistent with Figures 1 and 2 Figure   1, it is apparent that the partial derivative of property price with respect to  is negative, which means that property prices fall as the household becomes more home-biased. Higher  is associated with lower property prices, which Proves Proposition 2. This is aligned with the arguments from Garmaise and Moskowitz (2004), the household tend to buy closer real estates to mitigate the overvalued situation of property prices caused by highly information asymmetry between buyers and sellers. Figure 1 also shows an interesting appearance. The marginal effects of the home bias factor are heightened in situations of high  and relative low  , revealing that a household buyer with high time preference and located far away from a property is easily affected by home bias behavior when pricing real estate. This result reflects the market segmentation of different administrative regions. As one buyer who originally want to buy a property in other administrative region transfers to buy a property in the same administrative region, this home bias behavior can reduce the considerable information asymmetry, and thus greatly reduce the price overestimated phenomenon. Conversely, as one buyer who originally want to buy a property in the same administrative region transfers to buy a property closer and still in the same administrative region, this kind of home bias effect will be significantly reduced.

Calibrations for Fluctuations in Property Returns
All the calibration results of the comparative static analyses and the coefficient of elasticity of expected returns are displayed Figures 3 and 4. Figure 3 demonstrates that the partial derivative of property return with respect to  is positive, which is consistent with Proposition 4. Property returns increase as the household becomes more home-biased. To catch high expected returns, the more risk aversion household would be willing to sacrifice current consumption to buy closer property with higher expected returns. To sum up the findings of Figure 1 and Figure 3 in comparative static analyses, property prices and expected returns exist the following relationship in real estate market: the property prices in period t fall as  increases, and furthermore this diminished period price will increase the expected returns of the next period. According to these relationships, we can conclude that the real estate price has the mean reverting property so that it will tend to move to the average price over time. Deviations from the average price are expected to revert to the average. Figure 3 also reveals an interesting outcome. The marginal effects of the home bias factor on property returns are larger if the time preference parameter is smaller, which verifies Proposition 5. As a household buyer with high time preference is located far away from a property, his bargaining power is easily affected by home bias behavior when pricing real estate.
The calibration results of the home bias elasticity of expected return are drew on Figure 4, which shows clearly that the coefficient of the home bias elasticity of expected return for property is positive inelastic ( Figure 4. Regardless of the degree of information asymmetry in the real estate market, the percentage change in expected return is positively related to the percentage change in  , as described in Proposition 6. Furthermore, the total variations of property returns induced by variations of Home Bias phenomena are higher if the Home Bias parameter is higher. I redraw the calibrations of Eqns. (15) and (16)

Conclusion
The real estate market is a representative Lemon market. It is full of information asymmetry, which occurs when the seller knows more about a property than the buyer. However, the academic articles discussing the information asymmetry topic in real estate market are relatively few. Above all this article proposes that the real estate market is full of Home Bias phenomenon, which are proved by researches of social psychology perspective, economist, and financial scholars. Furthermore, for echoing with the ecosystem, this article introduces the behavior of biological communities into the real estate pricing model for measuring the variation influence of home bias factor on the property prices and returns. By way of developing the theoretical framework for the home bias phenomena induced by information asymmetry, this article will be helpful to understand the seriousness of information asymmetry in real estate analysis and the impact of investors' purchase behavior on property prices and expected returns.
In order to improve the serious asymmetry of information on the real estate market, people develop a home-biased purchasing behavior and like to buy assets which are easier observed. This purchasing behavior mitigates the information asymmetry risk. According to modified Lucas (1978) asset pricing model, this study finds that the real estate prices follow mean reverting process. The Home Bias behavior can mitigate overvalued situation in lemon market and the marginal negative effects will decrease as the Home Bias phenomenon increases. Furthermore, the article has assessed the variations of property prices induced by slight variations in Home Bias factors through elasticity. The results demonstrate that the Home Bias elasticities of property prices are smaller than 1, which means that variations in household's information cause smaller variations in property prices. Therefore, if a household want to suppress overvalued property prices through shorter distance, the affected rage is relatively limited.
In addition, I account for the effect of Home Bias factors on the property returns. This article shows that property returns increase as the household becomes more Home Bias. The marginal effects of the Home Bias factors on property returns are larger as the time preference parameters are smaller. The Home Bias elasticities of property returns are still inelastic, t.