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Erschienen in: The Journal of Real Estate Finance and Economics 1/2018

30.09.2016

Search Costs, Behavioral Biases, and Information Intermediary Effects

verfasst von: David C. Ling, Andy Naranjo, Milena T. Petrova

Erschienen in: The Journal of Real Estate Finance and Economics | Ausgabe 1/2018

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Abstract

In many markets, buyers, sellers, and their agents have differential information about the quality of heterogeneous assets. We study negotiated transaction prices in the commercial real estate market, which is characterized by heterogeneous assets, illiquidity, and highly segmented local markets, all of which increase the importance of asymmetric information in negotiated pricing outcomes. Using 114,588 industrial, multi-family and office sale transactions that occurred during 1997–2011, we document that distant commercial real estate buyers pay, on average, premiums of 4 % to 15 % relative to local buyers, controlling for individual property characteristics as well as time fixed-effects. We also examine the extent to which the sources of these observed premiums are a product of higher search costs/information asymmetry problems associated with distance (search cost channel) or a result of reference-dependence preference/anchoring based on the price levels in the investors’ local market (behavioral biases channel). Our results suggest the observed price premiums are explained by distant investors who face higher search costs and are at an information disadvantage compared to investors located in closer proximity to the property. In contrast, anchoring plays a more muted role in explaining observed premiums. The use of an intermediary (broker) increases, on average, the acquisition prices of buyers and decreases the disposition prices of sellers by 3 % to 8 %. This result is consistent with the incentive real estate agents have to convince sellers to dispose of their properties too quickly and to convince buyers to search less and therefore pay higher prices.

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Fußnoten
1
The “anchoring” phenomenon is drawn from the behavioral literature. Slovic and Lichtenstein (1971) and Tversky and Kahneman (1974) were among the first to discuss heuristics and biases. The behavioral finance literature is well summarized by Shefrin (2002), Barberis and Thaler (2003), and Baker and Wurgler (2011).
 
2
According to the Federal Reserve’s Financial Accounts of the U.S. (2014q1), the gross real estate holdings of nonfinancial corporations were $10.2 Trillion and the nonresidential real estate assets of nonfinancial, noncorporate businesses totaled $4.3 Trillion. See http://​www.​federalreserve.​gov/​releases/​Z1/​Current/​. Although much of the CRE stock is owned by private or government entities for their own use, the stock of investible (traded) CRE assets is sizable, according to other sources (e.g., Geltner 2015).
 
3
A small percentage of CRE assets are owned by publicly traded real estate investment trusts. However, these public companies buy and sell properties in the illiquid private market.
 
4
In the housing market, a limited literature provides mixed evidence on the effects of distance on transactions prices, in part because of the varying property types examined, sample sizes used, and methodologies employed. Earlier studies using housing markets and small samples suggest that there are no price differences associated with distance (Turnbull and Sirmans 1993). In contrast, Ihlanfeldt and Mayock (2012) use a large number of single-family home sales in Florida and present evidence supporting the hypothesis that buyers with higher search costs pay a premium to acquire their homes. They also provide evidence supporting an anchoring hypothesis whereby buyers coming from high price markets pay more for their homes. Neo et al. (2008) find that foreign buyers in the Singapore housing market pay more than local buyers for low rise houses but not for high-rise condominiums.
 
5
CoStar does provide information on the occupancy, gross and net operating income of some of the buildings in our sample. However, these fields are sparsely populated.
 
6
To control for the potential bias created by unobservable property characteristics that are correlated with buyer and seller characteristics, such as distance, Liu et al. (2015) use propensity score matching on a smaller sample of transactions. They do not, however, show their balancing tests for the propensity score match nor do they consider matching alternatives and techniques. Their post-matched sample is often very different than their pre-matched sample, changing the distribution of the sample in both intended and untended ways. Moreover, they do not include nested anchoring and asymmetric information effects; rather, they simply compare local and non-local transactions using a propensity score match, where nonlocal buyer (seller) is defined as a value of 1 if the property is located in a different market address than the address of the buyer (seller).
 
7
An exception is the work of Liu et al. (2015) who also consider pricing outcomes when the property is sold by distant investors.
 
8
If both the buyer and seller are distant, D B D S  = 0 and D B  + D S  = 2. If both the buyer and seller are local, D B D S  = 0 and D B  + D S  = 0. If the buyer is distant and the seller local, D B D S  = 1 and D B  + D S  = 1. Finally, if the buyer is local and the seller is distant, D B D S  = −1 and D B  + D S  = 1.
 
9
Note that, with respect to distance, there is no omitted intermediate group. Thus, a second set of differences is redundant in the specification since they are perfectly collinear with the first set (e.g., buyers that are not classified as distant buyers (BF) are classified as local buyers, so these two groups sum to 1).
 
10
Buyer distance is calculated based on the following equation: DISTANCE_PROP_BUYER = (7921.6623*arsin(sqrt((sin((0.0174532925199433*latitude_property-0.0174532925199433 *latitude buyer)/2))**2 + cos(0.0174532925199433*latitude_buyer)*cos(0.0174532925199433 *latitude_property)*(sin((0.0174532925199433 *longitude_property-0.0174532925199433* longitude_buyer)/2))**2))). The distance between the seller’s home market and the transacted property is similarly calculated.
 
11
Urban areas are obtained from Demographia World Urban Areas, published annually online http://​www.​demographia.​com/​db-worldua.​pdf
 
12
We only report statistics based on median price differences given the right skewness of the data, which results in biased averages even after winsorizing at the 0.01 % level. Costar is the largest commercial real estate database provider and claims to maintain full market inventory of property and spaces as well as to supply the most comprehensive coverage of historical CRE transactions. CoStar’s coverage of retail, office, industrial, and multifamily transactions included less than 100 CBSAs during the late 1990s and early 2000s and expanded to over 800 CBSAs by 2014. This is one of the reasons we focus on the largest markets to ensure that they have received full coverage since the beginning of the period examined. To assure reliability of the data, CoStar requires agents to physically inspect the site and record and verify a variety of property characteristics and transaction details. While it is likely that some historical transactions may not appear in the dataset, especially due to missing price and other key characteristics data, we are confident that the sample used in our study is representative of the population of historical sales over time, by property type and market, and so are the median price statistics, calculated based on transactions observed in our sample.
 
13
With this semi-log functional form, the percentage price effect with a unit change in a property characteristic is obtained by (exp(coefficient)-1)*100.
 
14
This is because (BF-SF) + (BF + SF) + (BE-SC) + (BE + SC) = 2BF + 2BE. However, the symmetry assumption implies that BF = SF and BE = SC. Thus, 2BF + 2BE = BF + SF + BE + SC.
 
15
We winsorize BPRICEDIF and SPRICEDIF at the 0.1 % level in each tail to eliminate outliers. We test for non-linearity of BDIST and SDIST, but do not find evidence that such a non-linear relationship with LN(PRICE) exists.
 
16
We obtain these inflection points by taking the first derivative of our price function with respect to price difference, setting the first difference equal to zero, and solving for the price difference. For example, in the industrial equation we obtain −0.00036 + 9.43e-07*BPRICEDIF = 0; BPRICEDIF = 381.76.
 
17
We also conduct the analysis based on the specification reported in Table 8, but do not observe any significant out-of-state effects at the market level.
 
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Metadaten
Titel
Search Costs, Behavioral Biases, and Information Intermediary Effects
verfasst von
David C. Ling
Andy Naranjo
Milena T. Petrova
Publikationsdatum
30.09.2016
Verlag
Springer US
Erschienen in
The Journal of Real Estate Finance and Economics / Ausgabe 1/2018
Print ISSN: 0895-5638
Elektronische ISSN: 1573-045X
DOI
https://doi.org/10.1007/s11146-016-9582-z

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