1 Ekaterina Chernobai California State Polytechnic University Pomona USA College of Business Administration Department of Finance Real Estate and Law ID: 615693
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ERES Conference 2010 (6/26/2010)
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Ekaterina Chernobai
California State Polytechnic University, Pomona, USACollege of Business AdministrationDepartment of Finance, Real Estate, and LawUniversity of Nürtingen, GermanyDepartment of Real Estate Management
Consumption of real assets and the clientele effect
Anna Chernobai
Syracuse University, USA
Whitman School of Management
Department of FinanceSlide2
MotivationPresented by Ekaterina Chernobai
page
2
Financial assets Stocks, bondsMonetary benefits to holders“Clientele effect”:Long-horizon investors buy illiquid assets; bid price down to compensate for future transaction costs; high returns(Vice versa for short-horizon investors)
Long- & short-horizon investors
Liquid & illiquid assets
Real estate assets
Residential real estate
Monetary
& non-monetary benefits
(=utility from consumption)
to holders
“Clientele effect”:
Long- & short-horizon house buyers
Different liquidity houses
Illiquid house: bidding the price down is not the only compensation for illiquidity.
Can also compensate with higher utility
given the right amount of search
Amihud & Mendelson (1986, 1991)
Also: Miller-Modigliani (1961)
ERES Conference 2010 (6/26/2010)Slide3
Motivation
Presented by Ekaterina Chernobai
page
3Does Clientele Effect exist for real assets, which are characterized by heterogeneous valuations, utility from consumption, and have no investment motive ?
Which type of houses is purchased by which type of buyers (by holding period)?
ERES Conference 2010 (6/26/2010)Slide4
The Model
Theoretical model of illiquidity in residential housing markets Krainer & LeRoy (ET
2002) Key features in our model: selling price time on the market proportions of houses by type proportions of households by class
GENERAL EQUILIBRIUM: BUYERS & SELLERS
2 TYPES OF HOUSES
COMPETITION
Presented by Ekaterina Chernobai
page
4
2 CLASSES OF HOUSEHOLDS
UNCERTAINTY
ERES Conference 2010 (6/26/2010)Slide5
The Model
2 TYPES OF HOUSES
2 CLASSES OF HOUSEHOLDS
Presented by Ekaterina Chernobaipage 5Short-tenure (S)e.g., Expect to moveout in 1-5 yearsLong-tenure (L)e.g., Expect to moveout in 20-25 years
Good (HG)Higher potential utility
Bad
(H
B
)
Lower potential utility
?
?
?
?
Search-and-match model
ERES Conference 2010 (6/26/2010)Slide6
Presented by Ekaterina Chernobaipage
6
The Model
Agents differ in their expected housing tenure Short-tenure agents ( S )Long-tenure agents (
L )
Probability (preserve match with housing services during a given period):
π
S
Probability (preserve match with housing services during a given period):
π
L
<
ERES Conference 2010 (6/26/2010)Slide7
Presented by Ekaterina Chernobaipage
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The Model
Houses differ in max amount of services they can provide
Distribution of ε
reflects
heterogeneity
Good houses (
H
G
)
Bad houses (
H
B
)
Prospective buyer’s drawn “fit:” ε
1 ~
Uniform [ 0, 1 ]Prospective buyer’s drawn “fit:”
ε
2 ~
Uniform [ 0, θ ]
0 <
θ
< 1
ERES Conference 2010 (6/26/2010)Slide8
The ModelPresented by Ekaterina Chernobai
page
8
Key assumptions: ● Houses have only consumption value, no investment value ● Can buy or sell only 1 house per period ● Home choice problem, not a homeownership problem
● Buyers ex ante do not observe
level of services of houses
-
Do NOT know if a house is Good or Bad
-
Only know that
in the economy,
P(H
G
) = P(H
B
) = 0.5 ● Sellers do not observe
the type of buyers - Do NOT know if a buyer is Short-tenure or Long-tenure - Only know that in the economy, P(S) = P(L) = 0.5ERES Conference 2010 (6/26/2010)Slide9
The Model
Presented by Ekaterina Chernobai
page
9simultaneously Buyer & Sellersimultaneously Buyer & SellerERES Conference 2010 (6/26/2010)Slide10
Presented by Ekaterina Chernobaipage
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The Model: Buyer’s Side
Visit 2 houses randomly: Good + Bad? Good + Good? Bad + Bad?Buy 1 house
In every period
t
of house-searching process:
Don’t buy either;
Keep searching in next period
t+1
or
Search option has value
!
ERES Conference 2010 (6/26/2010)Slide11
Presented by Ekaterina Chernobaipage
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The Model:
Buyer’s Side Household LIKES a house if: For each class (Short-term, Long-term) and house type (Good , Bad):
● Marginal
Probability (like G ) = (1 –
ε
G
)
Probability (Like G | visit G) =
●
Marginal
Probability (like B ) = (1 –
εB/θ) Probability (Like G | visit G) =
● εG , εB each depends on household class: Short-term or Long-term
● Reservation fit is positively related to sales price
observed fit
≥
reservation fit
ε
ε
ERES Conference 2010 (6/26/2010)Slide12
Presented by Ekaterina Chernobaipage
12
The Model:
Buyer’s Side Household LIKES a house does not guarantee purchase
For each class (Short-term, Long-term) and house type (Good , Bad):
●
Availability factor – negatively related to competition
●
Determined endogenously
P
r(
BUY
a house)
=
P
r(LIKE a house) x
Availability factor μ
l aERES Conference 2010 (6/26/2010)Slide13
Presented by Ekaterina Chernobaipage
13
The Model:
Buyer’s Side Household’s search option value, s : For each class (Short-term , Long-term):
s and s* = search option value during
t
, during
t+1
μ
G
and
μ
B
= per-period probability of house H
G and HB pG and
pB = selling price of house HG and HB β = discount factor v(ε)
= life-time utility given fit ε
●
Life-time Utility v(ε) :
v(ε) =
β ε +
β
π
v(ε) + (1 –
π
) (s + q)
[
]
ERES Conference 2010 (6/26/2010)Slide14
Presented by Ekaterina Chernobaipage
14
The Model:
Buyer’s Side Buyer’s dilemma: For each class (Short-term , Long-term): ● Buyer’s
F.O.C.: Utility(ε) – price = discounted
S
+
value of choice
Net life-time utility
> 0
●
F.O.C. depends on:
House type (Good, Bad) and buyer class (Short, Long)
Choose optimal
ε
1
and ε
2 to maximize search option value
S
ERES Conference 2010 (6/26/2010)Slide15
Seller’s
value of house on the market, q
:
For each house type (Good, Bad): q and q* = value during t, during t+1 M = per-period selling probability p = selling price β = discount factor
Presented by Ekaterina Chernobaipage 15
The Model:
Seller’s Side
q
=
M p +
β
(1
– M
)
q*
Seller sets a take-it-or-leave-it price
Trade-off: High price vs. longer time-on-the-market (liquidity)
Sells in period
t
with some probability
● M is the probability that at least 1 of the visitors wants to buy the house
ERES Conference 2010 (6/26/2010)Slide16
Presented by Ekaterina Chernobaipage
16
The Model:
Seller’s Side Seller’s dilemma:
● Seller’s F.O.C
depends on:
House type (Good, Bad) and buyer class (Short, Long)
Choose optimal
price
to maximize
value of house on the market
p
q
ERES Conference 2010 (6/26/2010)Slide17
Presented by Ekaterina Chernobaipage
17
The Model: Nash Equilibrium
Solve system of equations to compute equilibrium ● 22 equations, 22 unknowns● Compute equilibrium values numerically● Unique solution is attained
ERES Conference 2010 (6/26/2010)Slide18
Presented by Ekaterina Chernobai
page
18
Research Questions Research Questions:
Are
prices
and
liquidity
(time-on-the-market) for Good and Bad houses (H
G
and H
B
) different? How?
Do short-term (S) buyers & long-term (L) buyers buy different house types (
CLIENTELES)? What is the composition of buyers & houses in the market?
Our Hypotheses:
price
G
>
price
B
Bad houses sell faster
(liquid)
Characteristics of buyers L:
Likelihood to buy H
G
Likelihood to buy H
B
>
Characteristic of buyers S:
Likelihood to buy H
G
Likelihood to buy H
B
<
Dominated by Short-term buyers, & Bad houses
ERES Conference 2010 (6/26/2010)Slide19
page 19
Results
Characteristics of Long-term buyers:
Likelihood to buy HGLikelihood to buy HB>
Likelihood to buy HG
Likelihood to buy H
B
<
Characteristics of Short-term buyers:
Presented by Ekaterina Chernobai
Myers and Pitkin (1995): frequently transacted homes are more likely to be “starter” homes owned by higher-mobility young households
McCarthy (1976), Clark and Onaka (1983), and Ermisch, Findlay and Gibb (1996): positive relation b/w housing demand & household age, and a negative relation b/w the two & mobility
ERES Conference 2010 (6/26/2010)Slide20
θ
:
Max level of services from partial-utility house
μ
: Per-period probability to buy this house type– , – – , --- : Expected tenure (S) is 2, 2.5, 3
page
20
Results
θ
= 0.9
θ
= 0.75
(very similar houses) (different houses)
μ
G
/
μ
B
indifferent
indifferent
Long
Short
Long
Short
Long
Short
E[net utility]
G
–
E[net utility]
B
Long
ShortSlide21
page 21
Results
Presented by Ekaterina Chernobai
priceGood > priceBad“Bad” houses sell faster (more liquid)
Past literature: Mixed results on the relationship b/w price & time-on-the-market
Haurin (1998): “house with a value of [the atypicality index] being two standard deviations above the mean is predicted to take 20% longer to sell than would the typical house”.
ERES Conference 2010 (6/26/2010)Slide22
θ
:
Max level of services from partial-utility house p ,TOM
: House price, Expected time on the market – , – – , --- : Expected tenure (S) is 2, 2.5, 3page 22
Results
θ
= 0.9
θ
= 0.75
(very similar houses) (different houses)
p
G
, p
B
TOM
G
, TOM
B
Good
Bad
Good
Bad
Good
Bad
Good
BadSlide23
page 23
Results
Presented by Ekaterina Chernobai
The market is dominated by: - “Bad” houses - Short-term buyersEnglund, Quigley and Redfearn (1999): in Sweden different types of dwellings have different price paths. Bias in repeat sales price index: track smaller, more modest homes that transact more often, rather than the aggregate housing stock.
Jansen, de Vries, Coolen, Lamain and Boelhouwer (2008): in the Netherlands, 30% of the apartments (i.e., low quality) were sold at least twice during the period of study, while the proportion of detached homes (i.e., high quality) sold was at mere 7%.
Case & Shiller (1987), Shiller (1991), Case, Pollakowski & Wachter (1991), Goetzmann (1992), Dreiman & Pennington-Cross (2004)
ERES Conference 2010 (6/26/2010)Slide24
page
24
Results
θ = 0.9 θ
= 0.75 (very similar houses) (different houses)
p
roportion
L
, proportion
S
p
roportion
G
, proportion
B
Long
Short
Good
Bad
Long
Short
Good
Bad
0.5
0.5
0.5
0.5
θ
:
Max level of services from partial-utility house
– , – – , --- : Expected tenure (S) is 2, 2.5, 3Slide25
Presented by Ekaterina Chernobai
page
25
Summary of Main Results - (Theoretical) Clientele effect: Long-term buyers prefer “good” homes Short-term buyers prefer “bad” homes Only consumption incentive
Heterogeneous valuations of houses - Prices and liquidity:
P
G
> P
B
and TOM
G
> TOM
B
Net expected utility compensates for higher price of illiquid (=“good”) houses
As expected tenure(L) PG
, PB and TOMG , TOM
B
- Composition of houses & buyers on the market:
Dominated by “bad” houses & Short-term buyersERES Conference 2010 (6/26/2010)