Price, Place, People, and Local Experience Authors
P e t e r C h i n l oy, Wil l i a m H a r d i n I I I , a n d Z h o n g h u a Wu
Abstract
With property characteristics unobserved or difficult to measure, there are returns to evaluation, due diligence, and performance. Buyers can invest in observable human capital signals including location and experience that lower search costs. Buyers with local experience have lower search costs, a higher probability of closing and can earn a purchase discount from the acquisition of human capital developed from repeatedly transacting in a specific real estate market. For 1,793 apartment transactions in Atlanta over the 1995 to 2007 period, a very small focused group of local, experienced buyers receives acquisition discounts. The comparison is with non-locals and locals with two or fewer purchases. Inexperienced locals receive no or little discount and match non-local results. Non-locals do not pay a premium when compared to the typical buyer.
It is well established that real estate prices can be estimated with hedonic models that capture property attributes and location. A concurrent and growing stream of research highlights that transaction participant characteristics also affect prices and terms. Studies focused on distressed assets, behavioral decision-making, and investor clienteles suggest that both hedonic and participant characteristics are important in price formation.1 The human capital, experience, and expectations of market participants have the potential to impact transactions, along with observable property level attributes. A market institution in commercial real estate is the call for offers that can be either formal or informal. The agent working with the seller establishes a prospective timeline for completion of the transaction. Initial offers are due by a specific date, allowing evaluation. Semifinalists move on to a best and final stage where the buying candidate is selected. The last stage is when the buying candidate carries out formal due diligence and determines whether to perform. The call for offers, evaluation of offers, and closing match actions for evaluation, due diligence, and performance. This process has not emerged by accident. It is a consequence of actions by buyers and sellers and a market institution consistent with the interests of both parties.
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Buyers require ability, experience, and skill in three areas: evaluation, due diligence, and performance. These characteristics are part of a cost function. Buyers lower the marginal cost of search by investing in human capital through being located close to an asset market or by experience in transactions completed within a market. These domicile and experience signals are observable to sellers, agents, and market players. An experienced buyer is one who can perform at the closing table. This participant is highly valued by transaction participants who earn fees from closing transactions and from sellers who want to deal with a knowledgeable buyer who can price a property without extensive additional due diligence and subsequent renegotiations. Sellers are carrying out more limited evaluation and due diligence. While sellers may also have invested in human capital skills by domicile and experience, they typically delegate the management of a transaction to an agent. The selling process involves sorting between buyers based on the buyers’ human capital skills and ability to evaluate a specific property. Buyers able to assess and close may receive a discount. Sellers can offer this discount. It is market-wide, but only captured by those investing in market knowledge including the maintenance of market relations. The call for offers structure is consistent with incentives and institutions. The model offers a testable hypothesis. Buyers with skills have lower costs and an incentive to invest in human capital focused on local knowledge and experience. These buyers obtain better execution through lower acquisition prices. Sellers are offering for liquidity or other reasons that are sometimes unrelated to the market. Buyers not having specific localized human capital include those without experience or from out of market. To jump ahead in the queue of call for offers, less-experienced buyers must offer higher prices. They will have less access to transactions since agents are incentivized to work with local experienced buyers with a ability to close. Local buyers with repeated transaction experience have lower marginal search costs and higher closing probabilities. These buyers have invested their time in building human capital in a specific real estate market. Sellers seeking liquidity or relatively immediate sale match with buyers able to evaluate a property efficiently.2 Sellers and their brokers know that this match involves local buyers with knowledge and previous closing experience. These participants are known in the brokerage and investment communities. Empirical implementation of the structure requires identification of experienced buyers and sellers. Since commercial real estate transactions take place in singleasset or other restricted business entities, these must be traced to the underlying key principals and their domicile.3 Principals are identified by their address, location, and the number of separate buy and sell transactions. The data are 1,793 apartment transactions in the Atlanta market between 1995 and 2007 with data from CoStar Group, Inc. CoStar provides detailed information on transaction price, transaction date, property characteristics, and buyer and seller address. These data allow the assessment of investors’ local presence and experience on property
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transaction prices. The number of transactions of each buyer and seller principal is counted. Buying and selling experience are separately tracked. To preserve degrees of freedom for estimation, buyers and sellers are categorized by experience or number of previous transactions. These groups are segmented into local and non-local buyer and seller cohorts. A focused group of experienced local buyers, comprising about 3% of total buyers, obtains discounts between 7.7% and 11.6% when compared with non-locals and locals without buying experience.4 Inexperienced novice buyers with two or fewer investments do not receive discounts in normal and bad markets. Experienced local buyers obtain larger discounts in bad markets when compared to non-locals and inexperienced locals. Veteran buyers with six or more purchases obtain a discount of 13% to 15% in a bad market. The results suggest that classification of buyers into local and non-local groups is inappropriate. The benefit accrues to a small group of local experienced investors and is not simply associated with the domicile of the buyer. Out-of-market buyers do not overpay when compared to the average market participant. Instead, a very small group of experienced locals earns a purchase discount. When the number of transactions is included to segment the data into experience cohorts, each purchase lowers the price by between 0.2% and 0.5%. This is analogous to the return on human capital. The findings are robust across sampling approaches and multiple delineations. Experienced local buyers obtain discounts in high-volume submarkets, on larger and more expensive properties, when purchasing properties recently constructed, and for those properties with more units. The discounts are at the lower end of the ranges for higher-grade properties, but statistically significant results persist. The results are not sensitive to different ways of classifying experienced versus inexperienced buyers. A very small cohort of locals earns higher returns from acquisitions than nonlocals. Non-locals cannot simply move to earn a return since only this very small group of experienced locals benefit. Non-locals are often located at financial centers. They likely have access to capital markets, but are restricted in information on local markets. These non-local investors have further advantages in diversification across markets. To compensate for the risks of not being as diversified, local investors should obtain higher returns. Since the apartment property type is arguably one of the most transparent property types, it is possible to infer that markets with greater ambiguity and more complex lease and income pricing structures will have potentially larger effects.5 The paper is structured as follows. We first provide the background and place the study in the literature. We then discuss the structure of transaction prices in the property market, followed by a description of the data and a brief outline of the empirical specifications. The results are then discussed. We close with concluding remarks. J R E R
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Background and Literature
There is extensive evidence in real estate markets that people matter and influence price and rent. Controlled for property hedonics and quality, there are predictable price and return differences associated with the type of buyer or seller in a transaction. Even after controlling for property, quality, and other hedonic characteristics, some buyers pay higher or lower prices than others. The characteristics of investors have been shown to affect transaction prices. Sellers with higher loan-to-value ratios have higher reservation prices and hold out for higher prices in a normal market (Genesove and Mayer, 1997, 2001). Sellers who are not occupants do not obtain imputed rents and receive lower prices. These people include lenders, the deceased, and bankrupt sellers (Hardin and Wolverton, 1996; Campbell, Giglio, and Pathak, 2011). There is empirical evidence that some buyers pay higher prices. Hardin and Wolverton (1999), Atkin et al. (2011), and Ling and Petrova (2011) find that real estate investment trusts pay more for properties than other buyers. The premium is attributable to disclosure and reporting requirements on publicly traded entities, pressure to hold stabilized and less risky buildings, and market-specific timing. Other arguments for this premium include lower costs of capital and deeper management. A cohort of market participants acquires properties slightly above market price. Lambson, McQueen, and Slade (2004) find that out-of-state buyers pay more for apartment buildings and posit that the premium is due to higher search costs for non-local buyers and anchoring bias. The anchoring bias comes from buyers using prices in their home market when looking at properties elsewhere. Important and different from the present study, the analysis does not control for market experience and assumes that locals form a homogenous group. For apartment buildings with options for conversion to condos, local buyers pay a premium over out-of-towners (Benjamin, Chinloy, Hardin, and Wu, 2008). Berry, McGreal, Stevenson, Young, and Webb (2003) find that apartment prices are affected by tax benefits associated with owned and rented housing. Buyers facing pressure to close pay higher prices. Ling and Petrova (2008, 2010) find that buyers facing deadlines related to tax-deferred exchanges pay higher prices. Exchange buyers pay a premium of 12.5% on average, in excess of the present value of the tax savings from the deferred sale. Wilhelmsson (2008) shows that in the Swedish residential market, participant bargaining power impacts transaction price. Colwell and Munneke (2006) make a similar argument for commercial office properties using data from Chicago. In residential markets, Harding, Rosenthal, and Sirmans (2003) find that households with children pay 5% to 7% higher prices for houses around the start of the school year. Rutherford, Springer, and Yavas (2005) show that residential sales agents obtain higher prices on houses they own. Ong, Neo, and Spieler
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(2006) find a price premium for properties that subsequently enter foreclosure. Risky buyers pay higher prices. The situation is not confined to buyers. Seller characteristics have been shown to affect price. In the residential market, Shilling, Benjamin, and Sirmans (1990) find that distressed condos transact at a discount of 20%. Sellers with higher loan-tovalue ratios have increased reservation prices. They hold out for higher prices in a normal market (Genesove and Mayer, 1997, 2001). Others investigating residential foreclosures have found similar discounts, including Forgey, Rutherford, and VanBuskirk (1994), Carroll, Clauretie, and Neill (1997), and Daneshvary, Clauretie, and Kader (2011). The sellers of foreclosed property are not benefiting from the imputed rental income over the time on the market. Lippman and McCall (1986) examine how buyers and sellers search for assets with a liquidity adjustment. Liquidity comes with the time to search and close. The price depends on the bearing of liquidity risk. The price is traded off against selling time. Reservation prices are adjusted for normal selling times. These include the liquidity risk for expected time on the market during normal conditions. In an asset class with low volume like direct investment in commercial real estate, the experienced local investor is the likely counterparty to those seeking liquidity. Finally, selling without recall implies that a seller takes the first bid near the reservation price. Selling with recall is where the seller can select the highest bidder among rejected offers. Cheng, Lin, and Liu (2008) have explored this alternative setting. Distressed sellers of commercial real estate take a discount ranging from 9% on retail to 18% on apartments (Ling and Petrova, 2010). The difference between people as opposed to properties is not confined to prices, but applies to rents and net operating income. Benjamin, Chinloy, and Hardin (2007) find that larger local owners obtain higher rents and net income in the apartment market. More experienced operators have lower costs of market search and can price rents accordingly. In all these cases, the characteristics of the market participants, rather than property hedonics alone affect price. People, or market participant, hedonics matter, whether due to tax benefits, domicile, and residence, or being an institutional, large-scale or small investor.
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The Basic Model
In a commercial real estate transaction, a seller makes the decision to sell and works to establish a disposition strategy. In many cases, properties are selectively marketed within the marketplace with subsequent large scale disclosure of the sell intent. Benefits accrue to buyers who are close to the market with the ability to close and also accrue to active buyers who are continuously in the market. When an institutional property is offered to the market, there typically is a specific bid date at which time a group of buying semifinalists is selected. Potential buyers J R E R
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move on to a best and final stage by evidencing an ability to close. In the last stage, the buying candidate carries out more specific and formal due diligence with access to actual operating performance which is typically proprietary. There are three general stages to the transaction, a call for offers (informal and formal options), best and final, and performance.6 Buyers understand the tight timelines and have incentives to invest in human capital. Human capital by location and experience earns a return. Sellers are less likely to evaluate property subsequent to the actual decision to sell. The decision to sell may not be related to property performance, but instead may be based on strategic, fund or investor requirements. Active buyers with close ties to the brokerage community often garner information on a seller’s sale requirements. There are brokers who know that certain buyers will transact if the property meets their requirement. This provides an added incentive for agents and brokers to find acquisitions. These buyers are known to the brokerage community and to transact frequently, which differentiates them from a residential or end-user buyer. With the prescribed timeline through call for informal and formal offers, best and final offers, and performance, there are minimal time-on-market issues. The return is to the buyer able to perform within a prescribed and exogenous timeline. There is also a return to the buyer who is known not to re-trade properties during the closing and due diligence processes.7 A property has a set of hedonic and ownership characteristics, but not all characteristics are easily observable or measured by potential buyers, implying that prospective valuations will differ. Commercial real estate is commonly valued or priced based on some type of discounted cash flow technique. The actual operating results for a property, however, are confidential until movement through the due diligence process. Buyers have different levels of skill, human capital, and knowledge in evaluating property. Knowledge and skill are linked to domicile and experience both in buying and selling. Buyers with domicile (local market) knowledge have expensive fixed (but, sunk) costs associated with a focus on one market. Marginal costs are minimal. Hence, greater experience in a market allows for better assessment of potential rents and market demand. Sellers, even when outsourcing the search for a buyer to agents, retain the ability to assess a prospective buyer’s ability to perform inclusive of the buyer’s potential to re-trade or re-price the asset during due diligence.8 Buyers with high levels of experience signal to sellers and their agents and are known to decide more quickly and with more certainty. These buyers have already done market evaluation and are familiar with environmental, planning, entitlement, legal and tenant matters, and capital, both equity and debt, formation. Sellers can eliminate problematic buyers such as those who have never bought a comparable property or have a track record of re-negotiating or attempting to re-price deals during the due diligence process.
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Implementation of the analysis involves a standard price equation with hedonic descriptors, submarket controls, and buyer and seller characteristics including domicile and experience. Model specification comes from the multifamily literature including Lambson, McQueen, and Slade (2004), Ling and Petrova (2008), and Benjamin, Chinloy, Hardin, and Wu (2008), among others. The operationalized price equation is: P ⫽ X ⫹ H␥ ⫹ .
(1)
Where X are the property characteristics with parameters  including submarket controls. Buyer and seller characteristics including domicile and experience are H with parameters ␥. The focus is on the buyer-oriented characteristics. Human capital skills including experience in the local market on the buy side are expected to earn an acquisition discount while it is anticipated that seller human capital characteristics will only have no or only a minimal effect on price.9
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Sample Construction and Data Summary
The data include multifamily apartment transactions in the Atlanta Metropolitan Statistical Area (MSA) from 1995 to 2007. The market is sufficiently large to include national and local players. The data provider CoStar has information on price, date of sale, and characteristics of the asset, along with basic buyer and seller information.10 The addresses and names of each buyer and seller are used to identify buyers and sellers during the period. Each investor has separate buy and sell counts of transactions. The number of transactions is the measure of experience. Most properties are bought or sold in single-asset entities such as limited liability companies, limited or general partnerships. These entities are traced back to the key principals based on underlying addresses, telephone numbers, and other contact information attributed to ownership. Some names of entities or owners are similar, such as Investments LLC and Investments, LLC. If the address and other contact information of the key principals differ as to these similar names, they are ascribed to separate portfolios, including Investments LLC and Investments, LLC. When this backward tracing algorithm cannot identify the domicile of both the buyer and seller, that transaction is removed. These are transactions where the name and address of the entity is not disclosed. Distressed transactions are removed. These transactions identify foreclosures or defaults, where the seller is not voluntary and are limited in the initial sample. To control for potential outlier effects, very small assets selling for less than $250,000 are not included. The largest and smallest 1% of the transactions by price per unit are deleted as a winsorizing procedure.11 Initially, there are 2,136 multifamily transactions in metro
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Atlanta during the sample period. After the adjustments are made, there are 1,793 observations in the final sample. Local investors are identified as having an address within the state, although nearly all are located in metro Atlanta. Non-local investors are targeting Atlanta as opposed to elsewhere in Georgia.12 The Atlanta market dominates the state, and provides the only area where large local and non-local investors are actively competing for property. The matched pair has one buyer and seller delineated and assigned to a location, type, and portfolio in each case. The portfolios on the buy and sell sides for each investor are summarized as Q⫹ ⫽ 兺t⫽2007 t⫽1995 Qt. Here, time t is identified as the actual transaction year, ranges from 1995 to 2007. By measuring buying and selling experience with transaction volume, the marginal benefits of transacting can be determined. Buyers obtain a discount with experience when their prices decline with overall trading volume. Sellers have a premium to experience when prices received rise with trading volume. To focus on the tests on the buying side, the portfolio activities are further divided into three categories by transaction volume (veteran buyer, Q ⱖ 6, median experience buyer, 1 ⬍ Q ⱕ 5, and rookie or novice buyer, Q ⫽ 1). This classification is made to identify participants with only one transaction.13 Buyers and sellers are classified by whether they are located in the state of Georgia. Georgia participants are local on the buy and sell sides. Exhibit 1 reports the summary statistics. In Panel A, descriptive statistics for the continuous variables are provided. The mean transaction price is $10.4 million. The average number of apartment units per complex is 178. The size variable confirms that the overall sample is composed of large investable assets where national and local investors compete. The mean price per unit is $53,390. The price per unit is a typical metric for the property type used in the literature and is used to standardize across assets and reduce heteroscedasticity. The general characteristics of local and non-local investors are also included in Exhibit 1. For the study period, the local buyer purchased 4.5 properties on average, with a standard deviation of 7.7. The local investor sold 2.46 properties on average, with a standard deviation of 2.61. Locals were adding to their portfolios given the difference in mean buys less sells is 2.04 properties. Nonlocals were in a disposition mode, buying 3.01 assets and selling 3.75, with standard deviations of 2.69 and 3.47 on each side. On average, non-locals reduced their holdings by 0.74 properties. In Panel B of Exhibit 1, the binary variables are provided. Of the sample, 199 transactions or 11.1% of the total transactions have veteran highly experienced local buyers. Non-local experienced investors account for 7.0% of buy-side transactions. Of the total, 488 transactions or 27.2% are from inexperienced local buyers while non-local inexperienced buyers account for 18.6% of the total. More important, the actual number of experienced local buyers as a percentage of total buyers is much lower than 11.1% since the 199 properties identified are the total
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E x h i b i t 1 兩 Summary Statistics of the Atlanta MSA Apartment Sales
Variable
Mean
Std. Dev.
10%
90%
Panel A: Continuous data All properties (N ⫽ 1,793) Sale Price 10,392,435 Price per Unit 53,390 Ln(Price per Unit) 10.74 Age 33.40 Units (100) 1.78 Size 173,672 Land Area (sf) 604,644 Buyer Volume 3.87 Seller Volume 3.00
12,541,541 28,218 0.56 19.25 1.66 166,206 674,809 6.17 3.07
600,000 22,436 10.01 9 0.12 9,400 19,000 1 1
27,325,000 93,333 11.44 53 3.86 394,412 1,410,472 9 8
Non-local Buyers (N ⫽ 749) Sale Price 17,513,514 Price per Unit 61,885 Ln(Price per Unit) 10.91 Age 23.96 Units (100) 2.71 Size 264,530 Land Area (sf) 937,368 Buyer Volume 3.01 Seller Volume 3.75
14,287,166 29,027 0.51 14.91 1.76 169,176 786,948 2.69 3.47
1,850,000 27,528 10.22 7 0.48 32,168 84,070 1 1
34,600,000 101,796 11.53 43 4.55 469,968 1,777,248 7 10
Local buyers (N ⫽ 1,044) Sale Price 5,283,539 Price per Unit 47,297 Ln(Price per Unit) 10.61 Age 40.18 Units (100) 1.16 Size 108,488 Land Area (sf) 365,938 Buyer Volume 4.50 Seller Volume 2.46
7,830,245 25,978 0.56 19.15 1.20 129,444 449,122 7.70 2.61
500,000 20,000 9.90 18 0.10 7,686 12,479 1 1
14,204,000 85,000 11.35 67 2.72 278,002 998,525 10 6
All Transactions
Non-local Buyers
Local Buyers
Mean
Percent
Mean
Percent
Mean
Percent
Local Buyer
1,044
58.22
0.00
1,044
100.00
Local Seller
1,144
63.80
374
0.50
769
73.66
35
1.95
15
2.00
20
1.92
Variable Panel B: Binary Variables
Exchange REIT Buyer
0.00
56
3.12
42
5.60
14
1.34
Portfolio Sale
163
9.09
117
15.62
46
4.41
Good Quality
390
21.75
219
29.27
171
16.37
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E x h i b i t 1 兩 (continued) Summary Statistics of the Atlanta MSA Apartment Sales
All Transactions
Non-local Buyers
Local Buyers
Variable
Mean
Percent
Mean
Percent
Mean
Percent
Average Quality
1,375
76.77
526
70.22
850
81.41
27
1.51
4
0.53
23
2.20
199
11.10
0
0.00
104
9.96
Poor Quality Local Veteran Buyer Local Median Buyer
357
19.91
0
0.00
46
4.41
Local Rookie Buyer
488
27.22
0
0.00
199
19.06
Non-local Vet. Buyer
126
7.03
126
16.82
357
34.20
Non-local Med. Buyer
289
16.12
289
38.58
488
46.74
Non-local Rookie Buyer
334
18.63
334
44.59
0
0.00
1995
61
3.40
24
3.20
37
3.54
1996
105
5.86
48
6.41
57
5.46
1997
131
7.31
42
5.61
89
8.52
1998
87
4.85
32
4.27
55
5.27
1999
145
8.09
42
5.61
103
9.87
2000
134
7.47
45
6.01
89
8.52
2001
94
5.24
30
4.01
64
6.13
2002
109
6.08
23
3.07
86
8.24
2003
118
6.58
28
3.74
90
8.62
2004
150
8.37
68
9.08
82
7.85
2005
229
12.77
128
17.09
101
9.67
2006
271
15.11
151
20.16
120
11.49
2007
159
8.87
88
11.75
71
6.80
Notes: The exhibit reports summary statistics for the sample. The apartment sales data in the Atlanta metropolitan market are from a 12-year period beginning in 1995 and ending 2007. The data are provided by CoStar. Panel A reports the statistics for continuous variables while Panel B reports the statistics for binary variables. Ln(Price per Unit) is the log of Price per Unit. Size is the total square footage of the building area of a property. Land Area is the total square footage of the land occupied by a property. Buyer Volume is the total number of transactions by a buyer. Seller Volume is the total number of transactions by a seller. Local Buyer is an indicator for in-state buyer, which is equal to 1 if the buyer is located in Georgia. Similarly, Local Seller is an indicator for in-state seller. Average Quality is an indicator for a property in ‘‘average’’ condition. Poor Quality is an indicator for a property in ‘‘poor’’ condition. REIT Buyer is equal to 1 if the buyer is a REIT. Exchange ⫽ 1 if the transaction is a 1031 tax exchange sale. Portfolio Sale is an indicator for a property which is part of portfolio sales. Local Veteran Buyer ⫽ 1 if the buyer is an in-state investor and bought at least six properties during this time period, and 0 otherwise. Local Medium Buyer ⫽ 1 if the buyer is an in-state investor and bought at least two but less than six properties, and 0 otherwise. Non-local Vet. Buyer ⫽ 1 if the buyer is an out-of-state investor and bought at least six properties. Non-local Med. Buyer ⫽ 1 if the buyer is out-of-state and bought at least two but less than six properties. Non-local Rookie Buyer ⫽ 1 if the buyer is an out-of-state investor and bought only one property.
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acquisitions of buyers making 6 or more purchases. Very experienced veteran local buyers account for only 3% of total local buyers, but make 11% of the acquisitions. The transactions are segmented into 26 sub-markets.14 Sub-market data are not reported, but this geographic segmentation is used as a modeling control in the empirical results. Exhibit 2 reports selected summary statistics for the five largest submarkets in the sample based on number of transactions during the period of 1995–2007. These five markets are North Clayton/Airport, Decatur, Central Perimeter, Cumberland/Galleria, and West Atlanta. The prices range from $1.8 million to $16.3 million. The average number of units across the five markets ranges from 98 to 283 units. The percentage of all local buyers among the five markets ranges from 45% to 81%. These statistics show that while there are some variations across the submarkets, these markets are largely similar in participants.
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Empirical Results
The operationalized regression models include characteristics shown in the literature to influence price. The hedonic characteristics include age, age squared, number of units, units squared, unit size, unit size squared, land per unit, land per unit squared, and quality measures of poor and average with excellent as the base case. Dummy variables are included for delineation as a portfolio sale, 1,031 exchange and REIT buyers. Twenty-six dummy variables are used to address submarket effects. A battery of variable specifications related to buyer and seller domicile and experience are included.15 As a first test of the role of experience and locality, buyers are segmented into groups based on locality (domicile), local versus non-local, and number of transactions. Rookies or inexperienced buyers have one purchase. Medium-level buyers have two to five purchases. Veteran experienced buyers have at least six acquisitions over the period. Medium experience involves two to five purchases. There are four model specifications in Exhibit 3.16 In Model 1, locality (domicile) by buy or sell position is distinguished. In Model 2, the local buyer group is segmented into three cohorts based on purchase activity. The omitted dummy variable is for non-local buyer. In Model 3, the non-local buyer is further divided into the three categories in addition to the locals, with non-local veteran experienced buyer as the omitted dummy variable. In Model 4, as a robustness check, two interaction terms are added to examine whether the discount to local buyers varies with different market conditions. The results from Model 1 of Exhibit 3 show that local buyers obtain a price discount of 8.7% when compared with non-locals, statistically significant at the 1% level. The initial perception is that this discount occurs simply because of proximity to the asset, without any skills or value enhancement. Model 2 indicates otherwise, where the three categories are distinguished by experience. Being local is not sufficient to earn a large discount. Experience or trading activity matters. J R E R
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E x h i b i t 2 兩 Summary Statistics of the Atlanta MSA Apartment Sales (the Five Largest Submarkets)
Market 2
Mean
Std. Dev.
Market 4
Mean
Std. Dev.
Market 5
Mean
Mean
Std. Dev.
Mean
Std. Dev.
Sale Price
5,164,025.00 5,797,635.00 5,509,240.00 9,151,370.00 2,033,720.00 17,773,519.00 16,313,962.00 16,260,244.00 1,869,345.00 2,178,475.00 15,987.92
58,713.69
28,477.96
72,973.42
29,995.99
53,090.68
23,415.31
29,216.25
15,980.81
10.30
0.46
10.85
0.53
11.11
0.41
10.78
0.48
10.15
0.51
Age
33.69
13.47
44.70
21.53
24.45
12.05
29
9.24
44.73
10.43
1.49
1.32
0.91
1.29
2.67
2.10
2.83
2.09
0.84
0.93
Size
145,732.10
132,613.10
82,416.91
122,896.90
270,789.20
182,707.10
283,502.40
204,891.20
61,761.23
68,451.39
Land Area (sf)
565,425.50
557,471.60
250,628.60
334,556.00
877,200.50
1,048,326.00
888,394.40
651,309.20
184,133.20
247,410.60
Buyer Volume
4.74
0.47
3.36
3.5
4.81
6.89
4.57
6.80
1.98
3.80
Seller Volume
2.55
0.16
2.73
3.34
3.81
3.47
4.00
3.66
1.60
1.18
Units (100)
W u
33,055.34
Ln (Price per unit)
a n d
Panel A:
Price per unit
Std. Dev.
Market 3
H a r d i n ,
Market 1
P r i c e ,
E x h i b i t 2 兩 (continued) Summary Statistics of the Atlanta MSA Apartment Sales (the Five Largest Submarkets)
Mean
Percentage
Mean
Percentage
Mean
Percentage
Mean
Percentage
Mean
Percentage
164
68.33
131
80.86
60
44.78
62
46.62
94
75.81
Local Seller
160
66.67
Exchange
Portfolio Sale
兩
Good Quality
Poor Quality
68
50.75
66
49.62
101
81.45
3
1.85
5
3.73
1
0.75
3
2.42
1
0.42
3
1.85
8
5.97
6
4.51
1
0.81
19
7.92
6
3.7
18
13.43
17
12.78
3
2.42
35
14.58
30
18.52
38
28.35
29
21.8
8
6.45
194
80.83
131
80.86
96
71.64
103
77.44
108
87.10
11
4.58
1
0.62
0
1
0.75
8
6.45
0
and West Atlanta, respectively. The apartment sales data are from a 12-year period beginning 1995 and ending 2007. The data are provided by CoStar. Panel A includes the
兩
continuous variables for the mean and the standard deviation of each variable. Panel B includes the binary variables for the mean and the percentage. The variables are defined
N o .
in the same ways as in Exhibit 1.
兩 4 8 9
4 – 2 0 1 3
E x p e r i e n c e
3 5
Notes: The exhibit reports summary statistics for the five largest submarkets in the sample, which are North Clayton / Airport, Decatur, Central Perimeter, Cumberland / Calleria,
L o c a l
Vo l .
Average Quality
82.10
2.5
a n d
J R E R
REIT Buyer
133
6
P e o p l e ,
Local Buyer
P l a c e ,
Panel B:
4 9 0
E x h i b i t 3 兩 Apartment Pricing: Buyer Discount and Local Presence, Atlanta MSA 1995–2007
兩
Variable
Coeff.
Model 2 T-stat
Coeff.
Model 3 T-stat
Coeff.
Model 4 T-stat
Coeff.
T-stat
10.72***
129.49
10.70***
128.81
10.70***
121.0
10.73***
129.82
Age
⫺0.030***
⫺17.48
⫺0.030***
⫺17.4
⫺0.029***
⫺17.21
⫺0.030***
⫺17.51
0.001***
⫺13.80
0.001***
13.67
0.001***
13.55
0.001***
13.77
⫺0.043***
⫺3.25
⫺0.039***
⫺2.95
⫺0.039***
⫺2.96
⫺0.044***
⫺3.30
Age Squared Units
2.95
0.004***
2.85
0.004***
2.78
0.005***
2.99
0.584***
8.08
0.598***
8.21
0.599***
8.26
0.586***
8.06
Unit Size Sq.
⫺0.100***
⫺4.35
⫺0.104***
⫺4.45
⫺0.104***
⫺4.45
⫺0.101***
⫺4.35
Land Per Unit
⫺0.001
⫺0.46
⫺0.001
⫺0.30
⫺0.001
⫺0.29
⫺0.001
⫺0.39
Land Per Unit Sq.
0.000
0.000
⫺0.31
0.000
Average Quality
⫺0.138***
⫺6.61
⫺0.137***
⫺6.69
⫺0.137***
⫺6.57
⫺0.137***
⫺6.58
Poor Quality
⫺0.474***
⫺4.30
⫺0.475***
⫺4.27
⫺0.473***
⫺4.25
⫺0.061***
⫺4.25
0.82
0.58
Exchange
0.081**
2.01
0.086**
2.23
0.088**
2.28
Portfolio Sale
0.066***
3.25
0.067***
3.30
0.063***
REIT Buyer
0.112***
3.43
0.109***
3.36
0.105***
Local Buyer
⫺0.087***
⫺4.37
Local Seller
0.018
0.012
0.68
0.013
0.67
0.000
0.75
0.078*
1.94
3.11
0.068***
3.35
3.10
⫺0.106***
3.23
0.70
0.016
0.91
Local Veteran Buyer
⫺0.130***
⫺5.13
⫺0.122***
⫺3.73
Local Medium Buyer
⫺0.091***
⫺3.78
⫺0.083***
⫺2.60
Local Rookie Buyer
⫺0.05**
⫺2.00
⫺0.046
⫺1.37
W u
0.005***
Unit Size
a n d
Units Squared
H a r d i n ,
Intercept
C h i n l o y,
Model 1
E x h i b i t 3 兩 (continued) Apartment Pricing: Buyer Discount and Local Presence, Atlanta MSA 1995–2007
Coeff.
T-stat
Coeff.
Non-local Med. Buyer
Model 3 T-stat
Coeff. 0.037 ⫺0.01
Non-local Rookie Buyer Inter-normal Adj. R2
0.635
0.636
0.636
T-stat
Coeff.
T-stat
1.26 ⫺0.33 ⫺0.061**
⫺2.20
⫺0.116**
⫺5.00
0.635
a n d
J R E R
兩
L o c a l
Vo l .
兩 N o .
兩 4 9 1
4 – 2 0 1 3
E x p e r i e n c e
3 5
Notes: The exhibit reports the regression results for apartment pricing based on local presentence. The dependent variable is the logarithm of the transaction price per unit. The variables of interest are dummies for local buyers by experience, i.e., by veteran, medium or rookie, and non-local categories. Local Buyer ⫽ 1 if the buyer is within the state of Georgia, and 0 otherwise. Local Veteran Buyer ⫽ 1 if the buyer is an in-state investor and bought at least six properties during this time period, and 0 otherwise. Local Medium Buyer ⫽ 1 if the buyer is an in-state investor and bought at least two but less than six properties, and 0 otherwise. Local Rookie Buyer ⫽ 1 if the buyer is an in-state investor and bought only one property. Non-local Vet. Buyer ⫽ 1 if the buyer is out-of-state and bought at least six properties. This variable is omitted in Model 3. Non-local Med. Buyer ⫽ 1 if the buyer is out-of-state and bought at least two but less than six properties. Non-local Rookie Buyer ⫽ 1 if the buyer is out-of-state and bought only one property. Inter-good is the interaction term between Local Buyer and the good market dummy. The good market dummy ⫽ 1 if the observation occurs from 2003–2007. Similarly, Inter-normal is the interaction term between Local Buyer and the normal market dummy. The normal market dummy ⫽ 1 if the observation occurs from 1995–2002. Exchange ⫽ 1 if the transaction is a tax-deferred transaction under Section 1031 of the Internal Revenue Code. All transactions are within the time window of 1995 to 2007. Year dummies and submarket dummies are included as control variables. T-statistics based on White-heteroscedasticity consistent standard errors are reported. There are 1,793 observations. * Significant at the 10% level. ** Significant at the 5% level. *** Significant at the 1% level.
P e o p l e ,
Inter-good
Model 4
P l a c e ,
Variable
Model 2
P r i c e ,
Model 1
4 9 2
兩
C h i n l o y,
H a r d i n ,
a n d
W u
In Model 2, experienced veteran local buyers earn the greatest discount of 13%, statistically significant at the 1% level. Those with medium experience obtain a discount of 9.1%, statistically significant at the 1% level. The rookie coefficient is not statistically significant at the 1% level. The results are monotone in experience. More experienced buyers earn a statistically significant discount. In Model 3 of Exhibit 3, there are five buyer groups, with non-local veteran buyer as the omitted dummy variable. For the five buyer groups, only two obtain statistically significant more efficient execution. Veteran local buyers obtain a discount of 12.2%. Medium-experience local buyers receive a discount of 8.3%. Both coefficients are significant at the 1% level. Rookie local buyers obtain no significant discount relative to non-local veteran buyers. Again, just being local is insufficient to earn an acquisition discount. In Model 4 of Exhibit 3, interaction terms are formed as the product of the local buyer and market condition dummies. The two market conditions are defined as: (1) the good or boom market dummy equals 1 if the apartment transactions occur during 2003–2007 and 0 otherwise; and (2) the normal market dummy equals 1 if the apartment transactions occur during 1995–2002 and 0 otherwise. The omitted group is the out-of-state buyers. The discount to local buyers in the good or boom market is smaller at 6.1% and the discount in the normal market is higher at 11.6% than the baseline model results or 8.7%. Both coefficients are statistically significant at the 5% level. These results suggest that the discounts to locals vary with market conditions. The discount in the normal market tends to be greater than that associated with a good or boom market.17 On the seller side, a dummy variable for being local is added to each of the three model specifications. Local sellers obtain no premium as compared with nonlocals. The coefficients range from 0.012 to 0.018, but are not statistically significant at conventional levels across the three specifications. Experienced buyers located close to the asset and known to market participants have acquired a set of specific skills. These skills allow the provision of liquidity where there is a demand by sellers. Sellers demanding immediacy and liquidity through closing the transaction do not differ by location. It is the experienced buyers who become market makers, offering liquidity at times. The benefit to the local buyer is from being active and gaining experience. Being local is, however, not sufficient, as the rookie buyer does not earn a discount at acquisition. Exhibit 4 shows results from the first series of robustness checks. The sample of buyers is divided into separate groups by experience. In Panel A, buyers are grouped based on a different way of classification: (1) 1 or 2, (2) 3–5, and (3) 6 or more. The results show that local buyers with 6 or more purchases obtain a discount of 9.5%. Those in the other two categories receive no statistically significant discount. In Panel B, the buying volumes to classify the three groups are: (1) 1, (2) 2–7 and (3) 8 or more. Again, only the very experienced group receives a statistically significant discount of 8.8%. In the third panel, those
P r i c e ,
P l a c e ,
P e o p l e ,
a n d
L o c a l
E x p e r i e n c e
兩
4 9 3
E x h i b i t 4 兩 Robustness Check: Apartment Pricing and Transaction Volume (Based on Different Classifications by Transaction Volume)
Model 1 Coeff.
Model 2 T-stat.
Coeff.
T-stat.
Panel A: Large (⬎6), Medium (3–5), Small (1–2), N ⫽ 1,793 Local Veteran Buyer
⫺0.095***
⫺4.05
⫺0.102***
⫺3.16
Local Medium Buyer
⫺0.033
⫺1.12
⫺0.040
⫺1.07
Local Rookie Buyer
⫺0.007
⫺0.30
⫺0.013
⫺0.40
Non-local Med. Buyer
⫺0.024
⫺0.80
Non-local Rookie Buyer
⫺0.002
⫺0.08
Panel B: Large (⬎8), Medium (2–7), Small (1), N ⫽ 1,793 Local Veteran Buyer
⫺0.088***
⫺3.06
⫺0.089**
⫺2.51
Local Medium Buyer
⫺0.037
⫺1.49
⫺0.038
⫺1.17
Local Rookie Buyer
⫺0.010
⫺0.43
Non-local Med. Buyer Non-local Rookie Buyer
⫺0.011
⫺0.35
⫺0.006
⫺0.22
0.006
0.20
Panel C: Large (⬎6), Small (1–2), N ⫽ 1,436 Local Veteran Buyer
⫺0.099***
⫺4.01
⫺0.112***
⫺3.34
⫺0.016
⫺0.63
⫺0.029
⫺0.84
⫺0.015
⫺0.51
Local Medium Buyer Local Rookie Buyer Non-local Med. Buyer Non-local Rookie Buyer
Notes: The exhibit reports the robustness results for apartment pricing based on local presence. Panel A is based on three categories: ⬎ 6 transactions for veteran buyers, 3–5 transactions for median buyers, and 1–2 transactions for rookie buyers. Panel B is based on three categories: ⬎ 8 transactions for veteran buyers, 2–7 transactions for median buyers, and 1 transaction for rookie buyers. Panel C is based on only two categories and 1,435 observations: ⬎ 6 transactions for veteran buyers, 1–2 transactions for rookie buyers, while median buyers with 3–5 transactions are deleted from the full sample. The dependent variable is the logarithm of the transaction price per unit. The variables of interest are dummies for local buyers by experience, i.e., by veteran, medium or rookie, and non-local categories. Local Buyer ⫽ 1 if the buyer is within the state of Georgia, and 0 otherwise. Local Veteran Buyer ⫽ 1 if the buyer is an in-state investor and bought at least six properties during this time period, and 0 otherwise. Local Med. Buyer ⫽ 1 if the buyer is an in-state investor and bought at least two but less than six properties, and 0 otherwise. Local Rookie Buyer ⫽ 1 if the buyer is an in-state investor and bought only one property. Non-local Vet. Buyer ⫽ 1 if the buyer is out-of-state and bought at least six properties. This variable is omitted in Model 3. Non-local Med. Buyer ⫽ 1 if the buyer is out-of-state and bought at least two but less
J R E R
兩
Vo l .
3 5
兩
N o .
4 – 2 0 1 3
4 9 4
兩
C h i n l o y,
H a r d i n ,
a n d
W u
E x h i b i t 4 兩 (continued) Robustness Check: Apartment Pricing and Transaction Volume (Based on Different Classifications by Transaction Volume)
than six properties. Non-local Rookie Buyer ⫽ 1 if the buyer is out-of-state and bought only one property. Exchange ⫽ 1 if the transaction is a tax-deferred transaction under Section 1031 of the Internal Revenue Code. All transactions are within the time window of 1995 to 2007. Year dummies and submarket dummies are included as control variables. T-statistics based on Whiteheteroscedasticity consistent standard errors are reported. * Significant at the 10% level. ** Significant at the 5% level. *** Significant at the 1% level.
properties associated with buyers having 3–5 transactions are excluded. The discount for very experienced buyers of 6 or more properties is 9.9%. These results are consistent with those in the baseline estimation, which suggests that the findings are not sensitive to the classification categories and are associated with experience. Overall, the results show that local buyers pay prices that are 9% lower than nonlocals. But, most important, not all locals obtain this discount. The locals have to be experienced. When buyers are disaggregated by experience, only the small cohort purchasing many properties over the sample period 1995–2007 obtains a discount. Inexperienced locals and non-locals earn neither a discount nor pay a premium. Local sellers have no premium or discount. The very experienced local buyers benefit. The results are consistent with the structure of commercial real estate sales. Sellers typically contract with an agent to conduct a call for offers and a sale. Sellers are less likely to invest in due diligence and have likely made a disposition decision based on non-property-specific reasons. Regardless of location or experience, they are selling for financial or strategic reasons not necessarily tied to the property. Buyers invest in due diligence and experience, with these attributes being known to market participants and agents. They are obtaining a discount purchasing from those seeking liquidity and more certain closing. The human capital return to experience comes from an ability to evaluate properties, carry out due diligence, perform and signal to the market this ability via active participation. In a further series of robustness checks, the first three model specifications in Exhibit 3 are used to segment the transactions as shown in Exhibit 5.18 Only the results for the variables of interest are reported. In Panel A, the top 10 of the 26 geographic submarkets by transaction activity of non-local buyers are included. In these submarkets, local buyers are also active. This subsample controls for institutional, out-of-town participants focusing on defined areas. In Panel B, only
P r i c e ,
P l a c e ,
P e o p l e ,
a n d
L o c a l
E x p e r i e n c e
兩
4 9 5
E x h i b i t 5 兩 Robustness Check: Buyer Discount and Local Presence, Atlanta MSA 1995–2007
Model 1 Coeff.
Model 2 T-stat.
Model 3
Coeff.
T-stat.
Coeff.
T-stat.
Panel A: Top 10 markets (N ⫽ 1,019) Local Buyer
⫺0.107***
Local Seller
0.006
⫺4.65 ⫺0.002
⫺0.11
Local Veteran Buyer
⫺0.002 ⫺0.165***
⫺5.45
⫺0.154***
⫺4.15
Local Medium Buyer
⫺0.112***
⫺3.92
⫺0.100***
⫺2.80
Local Rookie Buyer
⫺0.056*
⫺1.67
⫺0.045
⫺1.14
Non-local Med. Buyer
0.019
0.60
Non-local Rookie Buyer
0.008
0.30
0.68
Panel B: Prices over $1M (N ⫽ 1,426) Local Buyer
⫺0.089***
Local Seller
0.003
⫺4.49 0.16
0.001
0.01
0.001
0.03
Local Veteran Buyer
⫺0.109***
⫺4.24
⫺0.102***
⫺3.07
Local Medium Buyer
⫺0.093***
⫺3.73
⫺0.083**
⫺2.59
Local Rookie Buyer
⫺0.066**
⫺2.24
⫺0.059*
⫺1.65
Non-local Med. Buyer Non-local Rookie Buyer
0.033
1.12
⫺0.001
⫺0.33
Panel C: Assets selling more than once (N ⫽ 755) Local Buyer
⫺0.086***
⫺3.80
Local Seller
⫺0.007
⫺0.03
⫺0.009
⫺0.41
⫺0.005
⫺0.21
Local Veteran Buyer
⫺0.132***
⫺3.34
⫺0.157***
⫺3.27
Local Medium Buyer
⫺0.113***
⫺3.48
⫺0.141***
⫺3.29
Local Rookie Buyer
⫺0.047
⫺1.52
⫺0.075*
⫺1.80
Non-local Med. Buyer Non-local Rookie Buyer
0.001
0.01
⫺0.059
⫺1.51
Panel D: Age less than 40 years (N ⫽ 1,328) Local Buyer
⫺0.065***
⫺3.23
Local Seller
⫺0.002
⫺0.12
⫺0.006
⫺0.33
⫺0.006
⫺0.34
Local Veteran Buyer
⫺0.096***
⫺3.77
⫺0.108***
⫺3.31
Local Medium Buyer
⫺0.068***
⫺2.67
⫺0.079**
⫺2.43
Local Rookie Buyer
⫺0.033
⫺1.06
⫺0.046
⫺1.24
0.014
0.48
⫺0.037
⫺1.22
Non-local Med. Buyer Non-local Rookie Buyer
J R E R
兩
Vo l .
3 5
兩
N o .
4 – 2 0 1 3
4 9 6
兩
C h i n l o y,
H a r d i n ,
a n d
W u
E x h i b i t 5 兩 (continued) Robustness Check: Buyer Discount and Local Presence, Atlanta MSA 1995–2007
Model 1 Coeff.
Model 2 T-stat.
Coeff.
Model 3 T-stat.
Coeff.
T-stat.
Panel E: More than 100 units (N ⫽ 1,067) Local Buyer
⫺0.063***
⫺3.16
Local Seller
⫺0.028
⫺1.52
0.030
⫺1.63
0.031*
⫺1.66
Local Veteran Buyer
⫺0.077***
⫺2.91
⫺0.098***
⫺2.94
Local Medium Buyer
⫺0.093***
⫺2.48
⫺0.090**
⫺2.54
Local Rookie Buyer
⫺0.039***
⫺1.03
⫺0.061
⫺1.42
0.002
0.06
⫺0.050
⫺1.64
Non-local Med. Buyer Non-local Rookie Buyer
Notes: This exhibit provides additional robustness checks for Exhibit 3 using various subsamples. The variables are defined the same ways as in Exhibit 3. The dependent variable is the logarithm of the transaction price per unit. The variables of interest are dummies for local buyer by experience. The top 10 markets are those submarkets with the most number of transactions by non-local buyers. Panel C is restricted to the subsample with the assets sold more than once during the period. All transactions are within the time window of 1995 to 2007. Year dummies and submarket dummies are included as control variables. T-statistics based on Whiteheteroscedasticity consistent standard errors are reported. * Significant at the 10% level. ** Significant at the 5% level. *** Significant at the 1% level.
assets selling for at least $1 million are included as these are associated with nonlocal buyers. In Panel C, the subsample includes only properties that sold at least twice. Panels D and E focus on properties less than 40 years of age and with more than 100 units, which is another attribute likely associated with out of market participants. Whether the sample is restricted to more active submarkets, more expensive assets, repeat sales, younger properties, or large properties, the results are similar. In the column for Model 1 in Exhibit 5, more experienced local buyers obtain a discount of at least 6.3%, all statistically significant at the 1% level. The discount for local buyers is 6.3%–10.7% for active submarkets, more expensive assets, repeat sales, as well as newer and larger properties. Local sellers earn no premium over nonlocals in any segmentation. Model 2 of Exhibit 5 divides buyers by number of acquisitions. The results are similar to those in Exhibit 3. The acquisition discount by local buyers improves
P r i c e ,
P l a c e ,
P e o p l e ,
a n d
L o c a l
E x p e r i e n c e
兩
4 9 7
almost monotonically with experience. Experienced veteran local buyers obtain significant price discounts relative to out-of-staters, ranging from 7.7% to 16.5%. These occur even in larger markets where they are competing with institutional non-local buyers. In more active, expensive, and deeper markets, the discounts for those buyers with medium experience range from 6.8% to 11.3%. Local rookies obtain no discount. The coefficients for the local rookie buyer variables range from ⫺0.033 to ⫺0.066, and are either not statistically significant or not statistically significant at the 1% level. The comparison with non-local competitors is indicated in the third column of Exhibit 5 as Model 3. Veteran and medium-experience local buyers earn significant discounts against their non-local veteran competitors with expected marginal effects. These discounts range from 7.9% to 15.7%, all statistically significant at the 5% level. Rookie local buyers obtain no statistically significant discounts or much lower discounts, with marginal significance at the 10% level relative to non-local veteran buyers. Exhibit 5 shows that local sellers obtain no premium as compared with non-locals. The coefficients are not statistically significant at the 10% level across the three specifications. Occasionally sellers require liquidity, exchanging an illiquid asset for cash with immediacy. The asset is offered to the market, and those able to offer the liquidity immediately are locals with experience. With the evidence, the seller requiring immediacy may target those locals with experience, having an ability to close and perform. Exhibit 6 converts experience from a category to a continuous variable on both the buy and sell sides. These are the buyer and seller volume variables. Also included are the domicile of the buyer and seller to identify each participant as local or non-local. In Model 1, local buyers continue to obtain a discount at 7.5%. An asset bought by a local buyer leads to a 0.3% discount, statistically significant at the 1% level. For each additional property a seller sells, the premium is 0.6%, statistically significant at the 5% level. To examine whether the buying discount and the selling premium depend on locality or domicile, two interaction terms between locality and experience are introduced in Model 2 of Exhibit 6.19 Locals with volume on the buy side obtain a discount of 0.3% for each additional asset purchased. The coefficient is statistically significant at the 1% level. On the sell side, there is no difference in selling premium between local sellers and non-local sellers. The coefficient for the interaction term between the local seller dummy and transaction volume is 0.003, but not statistically significant. This suggests that any premium is less dependent on the domicile of the sellers. Both local and non-local sellers are largely market takers when they sell their properties. In Panels A–E in Exhibit 7, the five subsamples based on more active markets, expensive, repeat transactions, age, and number of units are examined separately. In Model 1, local buyers obtain a discount relative to non-local buyers. However, J R E R
兩
Vo l .
3 5
兩
N o .
4 – 2 0 1 3
4 9 8
兩
C h i n l o y,
H a r d i n ,
a n d
W u
E x h i b i t 6 兩 Apartment Pricing and Transaction Volume (Experience), Atlanta MSA 1995–2007
Model 1 Coeff.
Model 2 T-stat.
Coeff.
T-stat.
Intercept
10.715***
128.76
10.66***
134.22
Age
⫺0.030***
⫺17.48
⫺0.030***
⫺17.75
Age Squared
2.5E-04*** ⫺0.045***
Units
13.80 ⫺3.24
2.6E-04*** ⫺0.032***
13.83 ⫺2.62
Units Squared
0.005***
2.87
0.004***
2.71
Unit Size
0.584***
7.90
0.599***
8.05
Unit Size Squared
⫺0.099***
⫺4.18
⫺0.102***
⫺4.27
Land Per Unit
⫺0.001
⫺0.45
⫺0.001
⫺0.36
Land Per Unit Sq. Squared
8.4E-06
0.79
7.2E-06
0.69
Average Quality
⫺0.138***
⫺6.66
⫺0.136***
⫺6.55
Poor Quality
⫺0.479***
⫺4.37
⫺0.482***
⫺4.37
Exchange
0.081**
2.07
0.083**
2.05
Portfolio Sale
0.060***
2.89
0.073***
3.66
REIT Buyer
0.119***
3.65
0.113***
3.51
Local Buyer
⫺0.075***
⫺3.69
Local Seller
0.008
Local Buy*Volume
⫺0.005***
⫺5.17
Local Sell*Volume
0.003
Buyer Volume
⫺0.003***
Seller Volume
0.006**
2
Adj. R
0.636
0.45 ⫺2.86 2.45 1.22
0.636
Notes: The exhibit reports the regression results based on experience. The dependent variable is the log of the per unit sales price. The variables of interest are Buyer Volume, Seller Volume, Local Buyer*Volume, and Local Sell*Volume. Buyer Volume is the number of assets that the buyer bought during the time period. Seller Volume is the number of assets sold during the time period. Local Buyer*Volume is the product of Local Buyer and number of purchases by that buyer. Local Sell*Volume is an interaction term between Local Seller and number of sales associated of that seller. Submarket dummies and year dummies are included as control variables. T-statistics based on White-heteroscedasticity consistent standard errors are reported. There are 1,793 observations. * Significant at the 10% level. ** Significant at the 5% level. *** Significant at the 1% level.
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E x h i b i t 7 兩 Robustness Check: Apartment Pricing and Transaction Volume (Experience)
Model 1
Model 2
Coeff.
T-stat.
Coeff.
T-stat.
⫺0.007***
⫺5.59
Panel A: Top 10 markets (N ⫽ 1,019) Local Buyer
⫺0.090***
⫺3.80
Local Seller
⫺0.006
⫺0.29
Buyer Volume
⫺0.005***
⫺3.65
Seller Volume
0.007**
2.18
Local Buyer*Volume Local Sell*Volume
0.002
0.75
Panel B: Prices over $1M (N ⫽ 1,426) Local Buyer
⫺0.079***
⫺3.88
Local Seller
⫺0.009
⫺0.49
Buyer Volume
⫺0.002**
⫺2.16
Seller Volume
0.009***
3.16 ⫺0.004**
Local Buyer*Volume Local Sell*Volume
⫺4.40
0.004
1.52
Panel C: Assets selling more than once (N ⫽ 755) Local Buyer
⫺0.072***
⫺3.05
Local Seller
⫺0.012
⫺0.53
Buyer Volume
⫺0.004**
⫺2.26
Seller Volume
0.007**
2.30 ⫺0.006***
Local Buyer*Volume Local Sell*Volume
0.004
⫺4.15 1.29
Panel D: Age less than 40 years (N ⫽ 1,238) Local Buyer
⫺0.054***
⫺2.59
Local Seller
⫺0.017
⫺0.93
Buyer Volume
⫺0.003**
⫺2.48
Seller Volume
0.010***
3.81 ⫺0.004***
Local Buyer*Volume Local Sell*Volume
0.003
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E x h i b i t 7 兩 (continued) Robustness Check: Apartment Pricing and Transaction Volume (Experience)
Model 1 Coeff.
Model 2 T-stat.
Coeff.
T-stat.
⫺0.003***
⫺3.43
Panel E: More than 100 units (N ⫽ 1,067) Local Buyer
⫺0.054***
⫺2.60
Local Seller
⫺0.047
⫺2.48
Buyer Volume
⫺0.002**
⫺1.63
Seller Volume Local Buyer*Volume Local Sell*Volume
0.013***
4.44 0.005*
1.78
Notes: The exhibit reports the robustness check for Exhibit 6. The dependent variable is the log of the per unit sales Price. The variables of interest are Buyer Volume, Seller Volume, Local Buyer*Volume, and Local Sell*Volume. Buyer Volume is the number of transactions that the buyer bought during the time period. Seller Volumes is the number of transactions that the seller purchases during the time period. Local Buyer*Volume is an interaction term between Local Buyer and number of transactions associated with the buyer. Local Sell*Volume is an interaction term between Local Seller and number of transactions associated with the seller. Submarket dummies and year dummies are included as control variables. T-statistics based on White-heteroscedasticity consistent standard errors are reported. * Significant at the 10% level. ** Significant at the 5% level. *** Significant at the 1% level.
local sellers do not earn a premium in any specification relative to non-local sellers. Local and non-local buyers obtain a discount of 0.2%–0.5% for each additional property they purchase, as a return to experience. Local and non-local sellers obtain a premium of 0.7%–1.3% for each additional property they sell. In the second column of Exhibit 7 with Model 2, local buyers earn a discount of 0.3%–0.7% for each additional property they purchase, as compared with nonlocal buyers. Local sellers do not earn a premium for each additional property they sell in any specification. The results confirm the finding that the selling premium is less likely related to whether a seller is local or non-local. After completing a battery of robustness checks, a very small cohort of experienced local investors is able to acquire properties at a meaningful discount from the market. The results suggest that local market knowledge gained through experience and an ability to close allow favorable acquisitions. Locals without experience and non-locals are unable to garner purchase discounts. The benefit is not simply from being local, but from experience in the local market.
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Conclusion
While domicile or locality may have a benefit in reducing marginal search costs, what is more important are the information and execution advantages accruing to a very small set of experienced local investors. Price discounts come from experience or human capital and not from mere presence. Being local is insufficient for earning an acquisition discount. Experienced local buyers acquire hard-to-evaluate assets at a discount to market, after controlling for real estate owned, institutional holding, property quality, and amenity set. Profitable transactions for active local players are primarily distinguished at purchase. It is the ability to evaluate deals, complete due diligence, and perform that earn the return on the buy side. Locals without experience and non-locals buy assets at market prices while a very small group of active experienced buyers located near the properties are at a discount to market. Experience pays marginally on selling, but the premium is not dependent on whether a seller is local or nonlocal. These results imply that the industry adage ‘‘you make your money when you buy’’ has some validity. The idea behind the adage is that successful real estate investors make more money by ‘‘buying right’’ rather than ‘‘selling right.’’ This finding is also consistent with a buy-and-hold return for acquisition without resale. Investors with little specific local market experience or specific human capital potentially earn lower returns. The excess return comes primarily from having executed better at purchase. When these experienced locals are sellers, they may earn little or no premium with respect to the market. Non-locals who have general real estate expertise retain an advantage of diversification across markets. Very experienced locals are effectively earning a return that compensates for the lack of diversification. Ability in evaluation, due diligence, and performance has less value on the sell side. Sellers are not evaluating properties. The call for offers is typically an endogenous consequence of market institutions and owner behavior. Buyers differ in ability and are able to exploit this in making their offers. Sellers are not focused on these skills in the sale process. They can accept the discount as an intercept that is market-wide.
兩
Endnotes 1
Distressed assets and forced sales in real estate include Shilling, Benjamin, and Sirmans (1990), Forgey, Rutherford, and VanBuskirk (1994), Hardin and Wolverton (1996), Carroll, Clauretie, and Neil (1997), Pennington-Cross (2006), Clauretie and Daneshvary (2009), Campbell, Giglo, and Pathak (2011), and Daneshvary, Clauretie, and Kader (2011). Behavioral aspects of real estate are considered in Harding, Rosenthal, and Sirmans (2003), Colwell and Munneke (2006), Wilhelmssom (2008), and Anglin and Wiebe (2013). The clientele effect and investment strategy impact is recognized in the literature in papers from Hardin and Wolverton (1999), Lambson, McQueen, and Slade J R E R
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(2004), Elayan, Meyer, and Li (2006), Benjamin, Chinloy, Hardin, and Wu (2008), Ling and Petrova (2008), Sirmans and Slade (2010), and Shilling and Wurtzebach (2013). There is a stream of literature on REO or foreclosure properties that focuses on distressed assets. This is a related and complementary research line. An argument could be made that even with distressed assets there is a tiering based on experience. The term domicile is used to represent the location of the buyer. Submarket and other property-specific locational variables are used in the assessment. More detail is provided in the data section of the paper. The very small group of buyers representing less than 3% of local buyers and a lower percentage of total buyers is involved in about 11.1% of total transactions. The results also imply that the simple use of a control variable for locals and non-locals may not be appropriate. The results suggest that empirical analysis should include controls for both locality and experience since the discount reported in the literature is not that non-locals pay a premium, but that a very small group of experienced locals gets a discount. By comparison, in the housing market offers typically are arriving individually and sequentially. A property is placed on a multiple listing service at which time the clock begins to run. There are specification and measurement issues, such as when a seller removes the property and relists, setting the clock back at zero. The core difference is that the buyers are generally not repeatedly in the market over a short time, and there is a minimal return to human capital through experience. Few buyers have purchased 10 houses over 10 years, other than in distress. Even then, capital market restrictions prohibit buyers from purchasing several houses. Government-sponsored mortgage limits in the housing market restrict loans to business entities. There are limits on the number of loans individuals can have at one time. This same capital market standardizes the financing process, limiting the opportunities for a buyer with experience. In the commercial market, we have players who are continually in the market as buyers and seller. Even when a timetable is not specific to the transaction, the process includes selection of buyers with the knowledge and ability to close. Discussions with agents and brokers in the commercial real estate market and institutional owners indicate that buyers assess the quality of an offer including prospective willingness to close, ability to close, and history of trying to ‘‘re-trade’’ an asset. Anecdotally, they argue that history of closing and not ‘‘re-trading’’ or undue post due diligence negotiation is valued. In the commercial real estate market, value and price are generally assessed on end use and cash flow, which is often proprietary. This is a major deviation from the residential market where hedonic or property characteristics alone are the primary determinants of value or price. Strategic or externally imposed disposition requirements are not investment in human capital. Sellers are expected to largely be price takers. There is little accurate time on market data for commercial real estate. Since the majority of properties are not sold through a MLS, the data differ from residential data. These techniques reduce potential model misspecification. The existing literature focuses on in-state and out-of-state. This definition is substantially subsumed since out-of-Atlanta and out-of-state segmentation are basically the same. The term is used to be consisted with prior research. Alternative classifications are also tested. The results are consistent. Please see Exhibit 4.
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This is a much large number of submarkets than Lambson, Queen, and Slade (2004) use and improves model specification. A substantial number of regression iterations address the domicile (location) of the participants and experience variables, as well as the submarket and physical attributes of the properties. The results are robust across these models. Major findings are presented. Multiple models are used. Additional robustness checks based on model and sample specifications using property size and submarket activity follow. Models including inverse Mills ratio specifications were also run with qualitatively similar results. The models presented follow the literature including Lambson, Queen, and Slade (2004), Benjamin, Chinloy, Hardin, and Wu (2008), Ling and Petrova (2008), among others. The baseline models using the two subsamples by market condition are estimated: the boom market and the normal market. The results are consistent. The discount for the local buyers in the normal market of 8.8% is higher than that in the boom market at 7.9%. More importantly, the discounts for local experienced veteran buyers in the normal market at 13.0% and 15.4% are higher than those in the boom market at 7.9% and 8.5%. The robustness checks are associated with factors and submarket locations related to out-of-market buyers. These factors can be delineated through filtering the data, data inspection, and quantitatively with discriminatory analysis. The goal is to have a subsample that is more in-line with out-of-market buyer preferences to make sure that the assessment covers properties that are traded by local and non-locals. Since the focus is on whether the buying discount and selling premium depend on domicile or locality, we do not include the local buyer dummy and the experience variable in the specification (see Wooldridge, 1995).
References Anglin, P.M. and R. Wiebe. Pricing in an Illiquid Asset Market. Journal of Real Estate Research, 2013, 35, 1, 83–102. Atkin, N.S., V.E. Lambson, G.R. McQueen, B. Platt, B. Slade, and J. Wood. Why Do REITs Overpay and by How Much? Discussion Paper, Brigham Young University, 2011. Berry, J.N., S. McGreal, S. Stevenson, J. Young, and J.R. Webb. Estimation of Apartment Submarkets in Dublin, Ireland. Journal of Real Estate Research, 2003, 25:2, 159–70. Benjamin, J.D., P. Chinloy, and W.G. Hardin III. Institutional-Grade Properties: Performance and Ownership. Journal of Real Estate Research, 2007, 29:3, 219–40. Benjamin, J.D., P. Chinloy, W.G. Hardin III, and Z. Wu. Clientele Effects and Condo Conversions. Real Estate Economics, 2008, 36:3, 611–34. Campbell, J.Y., S. Giglio, and P. Pathak. Forced Sales and House Prices. American Economic Review, 2011, 101:5, 2108–131. Carroll, T.M., T.M. Clauretie, and H.R. Neill. Effect of Foreclosure Status on Residential Selling Price: Comment. Journal of Real Estate Research, 1997, 13:1, 95–102. Cheng, P., Z. Lin, and Y. Liu. A Model of Time-on-Market and Real Estate Price under Sequential Search With Recall. Real Estate Economics, 2008, 36:4, 813–43. Clauretie, T.M. and N. Daneshvary. Estimating the House Foreclosure Discount Corrected for Spatial Price Interdependence and Endogeneity of Marketing Time. Real Estate Economics, 2009, 36:1, 43–67. J R E R
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Wilhelmsson, M. The Evidence of Buyer Bargaining Power in the Stockholm Residential Real Estate Market. Journal of Real Estate Research, 2008, 30:4, 475–500. Wooldridge, J.M. Selection Corrections for Panel Data Models Under Conditional Mean Independence Assumptions. Journal of Econometrics, 1995, 68:1, 115–32.
We are grateful to John Benjamin, Kenneth Rosen, Eli Beracha, and Andrew Weiss for their contributions and insights. We also benefited from presentation and discussion at the University of Mississippi and the University of Wyoming. Three referees of this journal provided helpful and constructive suggestions. We acknowledge research support from the Kogod School of Business at American University, the Tibor and Sheila Hollo School of Real Estate, and Jerome Bain Real Estate Institute at Florida International University.
Peter Chinloy, American University, Washington, DC 20016 or chinloy@ american.edu. William Hardin III, Florida International University, Miami, FL 33199 or hardinw@ fiu.edu. Zhonghua Wu, Florida International University, Miami, FL 33199 or
[email protected]. J R E R
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