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Real estate markets are characterised as spatially dispersed markets comprising thinly traded heterogenous assets bearing large and costly downside risk associated with asset and exchange partner uncertainty. Furthermore, the privacy of market information resulting from the absence of centralised trading renders the necessity of intermediaries in real estate markets to broker risk associated with information asymmetry. As Yinger (1981) states, real estate brokers are uncertain about the market size a listing would attract in each marketing period. Brokers are also uncertain of finding matching buyers because of the many distinguishing attributes of assets and potential buyers. These real estate markets characteristics justify consideration for the application of social networks approaches to resolve uncertainty. The exploration of social relations in commercial real estate transactions is motivated by the potential of social interaction to reduce information asymmetry and opportunism.

Gotham (2006) points out that creating liquidity out of spatially fixity with varying specific market knowledge and familiarity is a problem of creating shared agreements and social relations that allow economic exchange. Hence, understanding the processes which create global capital flows and networks of activity is fundamental to explaining the processes through which market actors comprehend value and risk, interpret the behaviour of buyers and sellers and act to control situations thereby creating liquidity in real estate markets. The study, however, directs attention to how legal and institutional frameworks shape engender liquidity. McAllister et al. (2008) stress the potential opportunistic behaviour when agents develop personal relations and trust to generate repeat business or maintain reputation with a less informed client.

Fixed-sample size sampling tends to be used when the search problem is to establish the optimal amount of search when search costs equated to marginal savings in a search period. Search costs would be determined by the cost per search and the proportion of sellers in a [geographically-defined] population. Sample size matters for this search hence the expectation is that a searcher (vendor) is likely to be inclined to cultivating structural holes. Fixed-sample size sampling tends to be the default search strategy in residential markets, typified by low unit asset values and large unknown potential buyers compared to other real estate markets. Though fixed-sample size sampling tends to emphasise sample size, its relevancy to social network approach is the unconnectedness of the sample. The effect of sample size on reducing price dispersion is synonymous to the range effect on reducing information asymmetry in social network approaches by cultivating structural holes symbolic of search embeddedness and a flow network model.

Sequential sampling tends to be applied when a searcher expects to draw a utility maximising samples from an unknown distribution of likely outcomes. In principle, sequential sampling is

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random, and the distribution is adaptive, but search can be perpetual. When applied to social networks, sequential sampling is non-random and relies on strength of ties for non-perpetual observations that would reduce search costs. Using strength of ties – leverage – to access information requires an actor to demonstrate conformity to expected network behaviour. Hence, sequential sampling is relevant to high-status networks where reliability and predictability is ensured by internalisation, indicative of within-network search and a collaborative network model. It tends to be appropriate for prime real estate markets.

Optimal sampling applies both search embeddedness and within-network search hence combines both flow and collaborative network models. It aims at reducing search costs by using search embeddedness to reduce information asymmetry and within-network search to prevent perpetual search by ensuring predictability of potential buyers. A searcher therefore interacts to gain inside information from wider network clusters by enhancing prominence and demonstrate behaviour.

Drawing from the reviewed literature, the understanding of how social relations probably resolve uncertainty in commercial real estate transaction is presented in Figure 4-11 below. This conceptual perspective on the place of social networks in commercial real estate transactions helps to focus the analysis of data and develop the relevant vocabulary for inference of emerging findings (See Gopaldas, 2016).

Figure 4-11: Social network solvents in commercial real estate markets

Source: Author illustration, 2015

The base layer outlines market uncertainty describing how the combination levels of egocentric and altercentric uncertainty represents uncertainty regarding identity and quality. The top layer indicates network characteristics and associated search strategies. The actor social relations are indicated in bold, and potential search strategies are shown in regular font. The top layer indicates

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how levels of information asymmetry and possible opportunism are associated with search strategies. The emerging network quadrants are explained below.

Quadrant (i) Capitalisation networks

This quadrant represents a sphere of high egocentric and altercentric actors seeking to enhance performance by cultivating structural holes. Ego and alter actors are highly unconnected hence likely to provide non-redundant market information. However, the identity and quality of traded asset and focal actor are uncertain. The focal actor is aims at ensuring transaction success, but is confronted with high information asymmetry, and focal alters’ perception of opportunism is high (a). The preferred search strategy is to exploit structural holes through fixed-sample size sampling (i) such as multiple listing services or on-site advertising. The search strategy reduces market uncertainty by minimising information dispersion through large-size sampling, but it is associated with high search costs. Capitalisation networks can typically be identified with residential markets (See Allen et al., 2015) and peripheral commercial markets (See David and Halbert, 2014)

Quadrant (ii) Contagion networks

The purpose of contagion networks is to use focal actor’s affiliations to indicate potential behaviour. This network type is characterised by high egocentric uncertainty and low altercentric uncertainty. The identities of the trading asset and focal actor are uncertain, but quality is certain.

Focal actor’s information asymmetry is high, but focal alters’ opportunism perception is low.

Focal actor’s purpose for networking is to demonstrate competence though affiliation to professional bodies with prescribed codes of conduct which would lead to visibility for preferential treatment. Affiliation to regulated professional bodies symbolises an actor’s assurance of proper conduct and behaviour in the provision of services. Membership, for instance to the RICS, indicates an actor’s adherence to practices that adequately minimise conflict of interest and ensure providing intermediation and advisory services in the best interest of the client (RICS UK, 2017). Focal actor’s search strategy is optimal search through endogenous social equivalents. David and Halbert (2014) state that transnational financial investors associated with existing professional bodies and participated in business property focused meetings to succeed in incorporating new buildings in those locations into their portfolios. Crowston et al. (2015) also find association between broker ties with other professionals in the industry and success in transaction closing.

Quadrant (c) Convergence networks

Convergent networks tend to be driven by informed clients. As McAllister et al. (2008) state, informed clients reduce the potential for opportunism. Convergence networks are associated with

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low egocentric uncertainty, but high altercentric uncertainty. Clients have high uncertainty about the behaviour of the focal actor (vendor or his/her broker), though the identity of asset and focal actor is certain. The focal actor’s information asymmetry is low, but focal alters’ perception of opportunism is high. The purpose of the network is to access novel and non-redundant information to reduce costs and facilitate quick information flow for risk reduction. Hence, the focal actor exploits exogenous social position which demonstrate observation of tacit norms and use optimal search to avail exchange opportunities to clients for whom their requirements would have been established through novel channels. Halbert and Rouanet (2014) and David and Halbert (2014) show how local-regional coalitions control access to information and participation in urban built environment investments in locations with strong political-economic coalitions.

Quadrant (d) Cooperation networks

Cooperation networks have low egocentric and altercentric uncertainty because of the bond-effect on information access and exchange. Identity and quality of asset and focal actor are certain. Both focal actor’s information asymmetry and focal alters’ opportunism perception are low. Focal actor’s network purpose is to be instrumental by contributing strategic information for lucrative partnership and earn credibility of adding value to the network. Focal actor’s search strategy sequential search influenced by market status. David and Halbert (2014) maintain that the stronger the governing coalitions, the higher the degree of calculative power and autonomy hence the less relevant ‘external’ capital becomes.

4.6 SUMMARY

This chapter explored how networks could be associated with search strategy choices to better understand how private information exchange could be associated with search duration. The chapter reviewed the nature of networks, when networks mattered, and how social network approaches resolved market uncertainty. Empirical studies on social networks were reviewed, and real estate characteristics that made social network approaches relevant to real estate markets were highlighted. The review indicated that though numerous in types, networks could be identified in categories of companionship, emotional and economic exchange. The latter was relevant to this study. Networks could also be identified by network model – network flows as channels, and network coordination as prisms. They could also be identified by research tradition – social capital relating to performance, and social homogeneity relating to behaviour. The chapter further established that social networks were relevant in economic exchange where information was important but problematic such as in real estate markets. Further review of social networks in economic exchange like business alliances and real estate markets identified propensity to network, network scope, strength of ties and network prestige as crucial attributes of addressing information asymmetry and potential opportunism. The chapter ended with

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presenting a conceptual understanding of how social networks through network flows or collaboration resolved uncertainty regarding information asymmetry and opportunism demonstrated by shifts between propensity to network and attaining network prestige. This understanding seems to reveal potential contingencies to searching that could explain the effort required to acquire information, which is search intensity. This framework provides sufficient ground for research design aimed at understanding the contingencies to a search process in commercial real estate markets.

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CHAPTER 5: RESEARCH DESIGN 5. INTRODUCTION

The previous chapters developed a conceptual framework for understanding the operational nature of real estate markets. Chapter 2 established that unique real estate market characteristics constrained efficient information flow, and that the information approach to understanding real estate market liquidity failed to capture the operational nature of the market. Hence, research on real estate liquidity has focused on time-based liquidity. Chapter 3 explored search concepts to understand how search is conducted since it contributes to time on market. It identified three search approaches, fixed-sample size, sequential and optimal sampling which were recognised by their search problems, sampling rules and searching (stopping) rules. Real estate studies on time on market, however, inclined towards analysing the output rather than the process. These studies, nevertheless, highlight that engaging brokers significantly reduced search duration and it did not so much affect the achieved price.

Chapter 4 was then directed at social network approaches to establish a possible explanation of the association between broker interaction and the private nature of real estate information. It was established that social networks were relevant in transactions where information on the identity and quality of trading assets and transaction partners was important, but problematic.

Social networks were relied upon in the absence of market mechanisms to resolve information asymmetry and address opportunism. Networks provided differentiated access to information shaping performance and behaviour which could be associated with search choices. Building on this foundation, this chapter develops a research framework for exploring the ebbs and flows of private information in the search process of commercial office sale transactions to understand the operational nature of real estate markets. The chapter comprises nine sections including a recital of research goals, concept development, research design, data collection, data preparation, data analysis, validity threats, reflexivity, and research ethics.