Commercial real estate and nonlocal
investors: price disparities on entry and exit
Yu Liu
Georgia State University
Paul Gallimore
University of Reading
Jonathan A. Wiley
Georgia State University
Primary question
• Do nonlocal investors pay more than local investors
for the same real estate assets?
Previous work
o
o
o
o
o
Turnbull and Sirmans (1993)
Watkins (1998)
Lambson, McQueen and Slade (2004)
Clauretie and Thistle (2007)
Ihlanfeldt and Mayock (2012)
Motivation for this study
Prior Research
This Study
Residential (single and
multifamily)
Office
(CoStar COMPs® database)
Single market
138 markets
Buying only
Buying and Selling
Smaller sample size
(generally)
10,971 in purchase sample
11,444 in sales sample
Varied sample horizons
1996 through 2012
No sale conditions
36 individual sale conditions
& combinations
No investor clienteles
24 investor types
No control for selection bias
Propensity-score matching
Nonlocal investors:
22% of purchase sample,
29% of sale sample
Summary Statistics –
Purchase Sample
Variable
Full Sample
(n=10,971)
Mean Std dev
Nonlocal
(n=2,383)
Mean Std dev
Local
(n=8,588)
Mean Std dev
Price per square foot ($)
159
153
167
146
204
147
Land area (SF)
88,284 263,923 164,926 427,004 67,017 190,591
Building size (SF)
53,349 143,531 120,582 209,871 34,693 111,792
Property age (years)
40
32
31
28
43
32
Class A
0.10
0.30
0.26
0.44
0.05
0.23
Class B
0.47
0.50
0.53
0.50
0.46
0.50
Class C
0.43
0.50
0.21
0.41
0.49
0.50
Nonlocal buyer
0.22
0.41
1
0
0
0
Rent difference (%)
3.82
20.09 17.59
40.21
0
0
Buyer distance (miles)
Multi-tenant building
133
0.70
375.95
0.46
597
0.77
612.38
0.42
4
0.69
8.49
0.46
Summary Statistics –
Sales Sample
Variable
Full Sample
(n=11,444)
Mean Std dev
Nonlocal
(n=3,335)
Mean Std dev
Local
(n=8,109)
Mean Std dev
Price per square foot ($)
136
96.90
134 99.60 137 95.76
Land area (SF)
87,400 248,765 119,907 231,627 74,030 254,290
Building size (SF)
48,283 128,806 78,106 155,196 36,018 113,996
Property age (years)
39
31.73
34
27.64
42
33.00
Class A
0.10
0.29
0.18
0.38
0.06
0.24
Class B
0.47
0.50
0.50
0.50
0.45
0.50
Class C
0.44
0.50
0.32
0.47
0.48
0.50
Nonlocal seller
0.29
0.45
1
0
0
0
Rent difference (%)
4.26
22.50 14.63
39.82
0
0
Seller distance (miles)
Multi-tenant building
Marketing duration
186
0.70
368
428.99
0.46
370.31
624
0.73
302
599.76
0.44
326.66
5
0.69
394
10.60
0.46
383.21
Method
OLS Regression Model
ln(Price/SF) = Controls + βN·I{Nonlocal} + ε
Purchases
Sales
Expectation
(+)
(–)
Controls:
Property characteristics, investor types, calendar year, sale
conditions, geographic markets
Propensity-score matching approach
price paid by nonlocals vs.
price paid by local buyers for
exact same properties

price paid by nonlocals vs.
price paid by local buyers for
similar properties

Exclude local transactions that look least like nonlocal transactions
• Perform whole sample probit regression with binary dependent
variable Nonlocal (independent variables same as main model)
Pr{Nonlocal = 1} = Φ{β0 + βXX + βTT + βYY + βCC + βMM}
• Use variable coefficients to produce estimate of probability that
transaction involves nonlocal buyer
Use this to match each nonlocal transaction with closest local
transaction
Local transactions:
some post-match summary stats
Purchases
Full Sample
Nonlocal
Local
(n=10,971)
(n=2,383)
(n=8,588) 2,283
Variable
Mean Std dev
Mean Std dev Mean Std dev
Price per square foot ($)
159
153
204
167
147 169146
117,415
Land area (SF)
88,284 263,923 164,926 427,004 67,017 190,591
78,067
Building size (SF)
53,349 143,531 120,582 209,871 34,693 111,792
Sales
Full Sample
Nonlocal
Local
(n=11,444)
(n=3,335)
(n=8,109) 3,335
Variable
Mean Std dev
Mean Std dev Mean Std dev
Price per square foot ($)
136
96.90
134
99.60
137 142
95.76
112,995
Land area (SF)
87,400 248,765 119,907 231,627 74,030 254,290
Building size (SF)
48,283 128,806 78,106 155,196 36,018 113,996
64,486
Results: ln(Price/SF) = Controls + βN·I{Nonlocal} + ε
Estimated premium – nonlocal buyers
Buyers, propensity-score matched sample
Sellers, propensity-score matched sample
Variable
Constant
ln(Land area)
ln(Building size)
ln(Property age)
Class A
Class B
Multi-tenant building
Coefficient
6.678 ***
-0.046 **
-0.110 ***
-0.168 ***
0.426 ***
0.110 ***
-0.076 ***
(t-stat)
(51.34)
(-1.84)
(-4.33)
(-17.66)
(13.69)
(4.39)
(-3.88)
Variable
Constant
ln(Land area)
ln(Building size)
ln(Property age)
Class A
Class B
Multi-tenant building
Coefficient
6.514 ***
-0.039 **
-0.077 ***
-0.164 ***
0.468 ***
0.095 ***
-0.073 ***
Nonlocal buyer
0.138
(8.00)
Included [22 variables]
Included [11 variables]
Included [105 variables]
Included [119 variables]
56.49%
4,766
Nonlocal seller
Seller type indicators:
Year indicators:
Sale conditions:
Market indicators:
Adjusted R2:
Observations:
-0.070 ***
(-4.75)
Included [21 variables]
Included [11 variables]
Included [123 variables]
Included [123 variables]
53.84%
6,670
Buyer type indicators:
Year indicators:
Sale conditions:
Market indicators:
Adjusted R2:
Observations:
***
(t-stat)
(51.95)
(-1.89)
(-2.97)
(-10.98)
(18.31)
(5.56)
(-6.79)
Results:
Estimated premium/discount – nonlocal buyers
Buyers, propensity-score matched sample
Variable
Constant
ln(Land area)
ln(Building size)
ln(Property age)
Class A
Class B
Multi-tenant building
Coefficient
6.678 ***
-0.046 **
-0.110 ***
-0.168 ***
0.426 ***
0.110 ***
-0.076 ***
Nonlocal buyer
0.138
Buyer type indicators:
Year indicators:
Sale conditions:
Market indicators:
Adjusted R2:
Observations:
***
Sellers, propensity-score matched sample
(t-stat)
Variable
(51.34)
Constant
(-1.84)
ln(Land area)
(-4.33)
ln(Building size)
Base
case
price
effects
(-17.66)
ln(Property age)
(13.69)
Overpay byClass
13.8%
A
(4.39)
Sell at discount
Class Bof 7%
(-3.88)
Multi-tenant building
(8.00)
Included [22 variables]
Included [11 variables]
Included [105 variables]
Included [119 variables]
56.49%
4,766
Nonlocal seller
Seller type indicators:
Year indicators:
Sale conditions:
Market indicators:
Adjusted R2:
Observations:
Coefficient
6.514 ***
-0.039 **
-0.077 ***
-0.164 ***
0.468 ***
0.095 ***
-0.073 ***
-0.070
***
(t-stat)
(51.95)
(-1.89)
(-2.97)
(-10.98)
(18.31)
(5.56)
(-6.79)
(-4.75)
Included [21 variables]
Included [11 variables]
Included [123 variables]
Included [123 variables]
53.84%
6,670
What explains price differences?
• Information Asymmetry – nonlocal investors less well-informed
so get poorer deal when they both buy and sell
Proxy: Distance
• Market Anchoring – investors from higher value markets anchor
valuations on those markets
Means they overbid when they buy but they have to take the market price
when they sell
(unless they sell to another investor from a high-price market)
Proxy: Rent difference
What explains price differences?
• Information Asymmetry – nonlocal investors less well-informed
so get poorer deal when they both buy and sell
Proxy: Distance
• Market Anchoring – investors from higher value markets anchor
valuations on those markets.
Means they overbid when they buy but they have to take the market price
when they sell
(unless they sell to another investor from a high-price market)
Proxy: Rent difference
ln(Price/SF) =Controls + βN·I{Nonlocal}+ βS·Distance + βR·Rent diff + ε.
Purchase sample:
(+)
(+)
(+)
Sales sample:
(–)
(–)
(0)
Results:
Information asymmetry and anchoring effects
Buyers, propensity-score matched sample
Variable
Constant
ln(Land area)
ln(Building size)
ln(Property age)
Class A
Class B
Multi-tenant building
Coefficient
6.704
-0.046
-0.114
-0.166
0.431
0.112
-0.074
Nonlocal buyer
Buyer distance
Rent difference
0.091
0.00007
0.076
Buyer type indicators:
Year indicators:
Sale conditions:
Market indicators:
Adjusted R2:
Observations:
***
**
***
***
***
***
***
***
**
***
(t-stat)
(50.10)
(-2.04)
(-3.63)
(-16.08)
(10.42)
(4.89)
(-4.02)
(5.30)
(2.25)
(2.47)
Included [22 variables]
Included [11 variables]
Included [105 variables]
Included [119 variables]
56.63%
4,766
Sellers, propensity-score matched sample
Variable
Coefficient
(t-stat)
***
Constant
6.501
(50.37)
**
ln(Land area)
-0.038
(-1.86)
***
ln(Building size)
-0.076
(-2.92)
Overpay
by 9.1%
***
ln(Property age)
-0.163
(-10.75)
***
• Overpayment
increases
with
distance
Class A
0.469
(18.56)
***
• Overpayment
increases
with
rent
differential
Class B
0.094
(5.57)
***
Multi-tenant building
-0.073
(-6.72)
Nonlocal seller
Seller distance
Rent difference
Seller type indicators:
Year indicators:
Sale conditions:
Market indicators:
Adjusted R2:
Observations:
-0.046
-0.00004
0.003
***
***
(-2.40)
(-3.95)
(0.06)
Included [21 variables]
Included [11 variables]
Included [123 variables]
Included [123 variables]
53.86%
6,670
e.g. Buyer located 600 miles away pays 600x0.007% = 4.2% more
e.g. Buyer from market with rents 17.5% higher pays 17.5x7.6% = 1.3% more
Results:
Information asymmetry and anchoring effects
Buyers, propensity-score matched sample
Sellers, propensity-score matched sample
Variable
Constant
ln(Land area)
ln(Building size)
ln(Property age)
Class A
Class B
Multi-tenant building
(t-stat)
(50.10)
(-2.04)
(-3.63)
(-16.08)
(10.42)
(4.89)
(-4.02)
Variable
Constant
ln(Land area)
ln(Building size)
ln(Property age)
Class A
Class B
Multi-tenant building
Coefficient
6.501
-0.038
-0.076
-0.163
0.469
0.094
-0.073
(5.30)
Nonlocal seller
Seller distance
Rent difference
-0.046
-0.00004
0.003
Nonlocal buyer
Buyer distance
Coefficient
6.704 ***
-0.046 **
-0.114 ***
-0.166 ***
0.431 ***
0.112 ***
-0.074 ***
0.091
0.00007
***
**
(2.25)
***
Rent difference
0.076
(2.47)
Buyer type indicators: Included [22 variables]
Nonlocal
seller gets Included
1% less[11than
Year indicators:
variables]
locals
for every 250 miles
Sale conditions:
Includedaway
[105 variables]
Market
indicators:
Included [119 variables]
from
market
2
Adjusted R :
56.63%
Observations:
4,766
Sell at discount of 4.6%
• Discounting increases with distance
• Unaffected by nonlocal rent differential
Seller type indicators:
Year indicators:
Sale conditions:
Market indicators:
Adjusted R2:
Observations:
***
**
***
***
***
***
***
***
***
(t-stat)
(50.37)
(-1.86)
(-2.92)
(-10.75)
(18.56)
(5.57)
(-6.72)
(-2.40)
(-3.95)
(0.06)
Included [21 variables]
Included [11 variables]
Included [123 variables]
Included [123 variables]
53.86%
6,670
Information Asymmetry:
Additional test
• Distance may be less than perfect proxy for
information asymmetry
• If nonlocal investors informationally disadvantaged,
prices in transactions between them should:
– reflect smaller premiums than when they buy from locals
– reflect smaller discounts than when they sell to locals
• Test this.......
Information Asymmetry:
Additional test
• Estimate “between nonlocals” effect, using first model
ln(Price/SF) = Controls + βN·I{Nonlocal} + ε
Now describes transaction type
rather than investor
• ......apply to pooled sub-sample
(produced by propensity score matching )
657 “between nonlocals” transactions matched
with most similar “between locals” transactions
Results:
Nonlocal/Nonlocal vs. Local/Local transactions
Variable
Constant
ln(Land area)
ln(Building size)
ln(Property age)
Class A
Class B
Multi-tenant building
Coefficient
7.607 ***
-0.067 **
-0.081 **
-0.160 ***
0.382 ***
0.144 **
-0.119 ***
(t-stat)
(31.98)
(-1.86)
(-1.92)
(-5.74)
(2.80)
(1.88)
(-2.60)
***
Nonlocal investors 0.063
(2.75)
Buyer type indicators: Included [19 variables]
Seller type indicators: Included [20 variables]
Year indicators:
Included [7 variables]
Sale conditions:
Included [62 variables]
Market indicators:
Included [85 variables]
Adjusted R2:
61.21%
Observations:
1,314
Overvalue by 6.3% when
nonlocals buy from nonlocals
(sell to nonlocals)
Overpayment much smaller than when
nonlocals buy from locals (13.8%)
Discount, accepted when nonlocals
sell to locals (7%), disappears
Findings
As compared to local investors, nonlocal investors........
 Overpay on purchase by estimated 13.8%.
 Discount on sale by 7%
 Overpayment positively related to distance (information
asymmetry) and rent differentials (anchoring)
 Discounting also increases with distance (information
asymmetry)
 Pay smaller premiums when buying from other nonlocals and
no discount when selling to other nonlocals